report no. 40934-gh ghana meeting ... - documents & reports

November 28, 2007

Document of the World Bank

Report No. 40934-GH

GhanaMeeting the Challenge of Accelerated and Shared Growth

(In Three Volumes) Volume III: Background Papers

PREM 4Africa Region

Report N

o. 40934-GH

Ghana

Meeting the C

hallenge of Accelerated and Shared G

rowth

Volume III

Country Economic Memorandum

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LDB World Bank’s Live Data Base M or m Million M2 Ratio of Money to quasy-money MAMS A CGE model for MDG Simulations MBB Marginal Budgeting for Bottlenecks MCA Millenium Challenge Account MDBS/PRSC Poverty Reduction Support Credit MDG Millenium Development Goal MDRI Multilateral Debt Relief Initiative MENA Middle East and North Africa MG Mean Group MIC Middle-Income Countries MMYE Ministry of Manpower, Youth and Employment MoE Ministry of Energy MP Members of Parliament MPS Meridian Port Services MRPH Ministry of Railways, Ports and Harbours MRT Ministry of Roads and Transport MTC Ministry of Transport and Communication MW Mega Watt MWH Ministry of Works and Housing MWRWH Ministry of Water Resources, Works and Housing NCA National Communications Authority NDPC National Development Planning Commission NEAP National Environmental Action Plan NED Northern Electricity Department NEF National Electrification Funds NEP National Electrification Project NEPAD New Partnership for Africa’s Development NGOs Non Governmental Organization NITA National Information Technology Agency NTP National Communications Authority O&M Operation and Maintenance ODAs Official Development Assistance PMG Pooled Mean Group Model PPP Public Private Partnership PPRC Producer Price Review Committee PRSC Poverty Reduction Support Credit PSI Presidential Special Initiative PURC Public Utilities Regulatory Commission RCA Revealed Comparative Advantage RSDP Road Sector Development Program REA Rural Electrification Agency REER Real Effective Exchange Rate RELC Research/ Extension Liaison Committees REF Rural Electrification Fund RER Real Exchange Rate RPED Regional Program on Enterprise Development SAM Social Accounting Matrix SAT Submarine Fiber-optic Cable SBI Sustainable Budget Index (Botswana) SHEP Self-Help Electricity Program SIP Strategy Investment Plan SMLE Small, Medium and Large Enterprise

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SMS Short Message Service SNO Second National Operator SOEs State-owned enterprises SPS Stringent Sanitary and Phyto-sanitary SSA Sub-Saharan Africa SWAp Sector-Wide aAproach TFP Total Factor Productivity TMP Telenor Management Partner TMS Tropical Manioc Selection TOT Terms of Trade TUC Trades Union Congress TVET Technical and Vocational Education and Training UEMOA Union économique et monétaire ouest africaine (West African Economic and

Monetary Union) UK United Kingdom UN United Nations UNDP United Nations Development Programme US United States USAID United States Agency for International Development UW Upper West region VALCO Volta Aluminum Company VBTC Volta Basin Technical Committee VoIP Voice Over Internet Protocol VRA Volta River Authority WAGP West African Gas Pipeline WAPGOco West African Gas Pipeline Company WAPP West African Power Pool WATTFP West Africa Transport and Transit Facilitation Project WB World Bank WBES World Business Environment Survey WDI World Development Indicators WDR World Development Report WESTEL Western Telesystems WIAD Women in Agricultural Development WRC Water Resources Commission

Vice President: Country Director:

Sector Director: Sector Manager:

Task Team Leader:

Obiageli K. Ezekwesili (AFRVP) Mats Karlsson (AFCF1) Sudhir Shetty (AFTPM) Antonella Bassani (AFTP4) Zeljko Bogetic (AFTP4)

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ETA Electronic Technology Act FAO Food and Agriculture Organization of the United Nations FASDEP Food and Agriculture Sector Development Policy FBO Farmer-Based Organizations FDI Foreign Direct Investment FEER Fundamental Equilibrium Exchange Rate GASCO Ghana Association of Stevedoring Companies GCC Ghana Co-operatives Council GCNet Customs and Trade facilitation e-government application GDP Gross Domestic Product GHA Ghana Highway Authority GIPC Ghana Investment Promotion Centre GIS Geographic Information System G-JAS Ghana - Joint Assistance Strategy GLSS Ghana Living Standars Survey GMES Ghana Manufacturing Enterprise Survey GMM Generalized Method of Moments GNI Gross National Income GoG Government of Ghana GPHA Ghana Port Harbour Authority GPRS Ghana Poverty Reduction Strategy GSP Generalized System of Preferences GSS Ghana Statistical Service GT Ghana Telecom GWCL Ghana Water Company Ltd GWEP Guinea Worm Eradication Program HD Human Development HHI Herfindahl-Hirschman Index HIPC Heavily Indebted Poor Countries HP Hodrick-Prescott ICA Investment Climate Assessment ICOR Incremental Capital Output Ratio ICT Information and Communication Technology IFC International Finance Corporation IFPRI International Food Policy Research Institute IITA International Institute of Tropical Agriculture IMF International Monetary Fund IOCT Incremental Output-Capital Ratio IPP Independent Power Producer ISP Internet Service Provider ISSER Institute of Statistical, Social and Economic Research (University of Ghana) IT Information Technology ITES IT Enabled Services ITU International Telecommunications Union JTC-IWRM Joint Ghana-Burkina Technical Committee on Integrated Water Resources

Management KWh Kilowatt/hour LBC Licenced Buying Company LCU Local Currency Unit

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TABLE OF CONTENTS

1. POVERTY, LIVELIHOODS, AND ACCESS TO BASIC SERVICES IN GHANA...................... 1

TRENDS IN POVERTY AND INEQUALITY................................................................................................. 4 POVERTY PROFILE AND CORRELATES OF POVERTY ............................................................................ 23 INCOME SOURCES................................................................................................................................. 46

2. LABOR OUTCOMES AND SKILLS IN GHANA.......................................................................... 87

INTRODUCTION AND OBJECTIVES......................................................................................................... 87 TRENDS IN LABOR OUTCOMES: RESULTS FROM THE GLSS SURVEYS ................................................. 89 HOURS WORKED AND HOURLY EARNINGS IN AGE GROUP 25-64......................................................105 EDUCATION, SKILLS, AND LABOR MARKET OUTCOMES ....................................................................107 ECONOMIC RETURNS TO EDUCATION AND TRAINING.........................................................................116 PRIORITIES FOR REFORM.....................................................................................................................118

3. SHARED AND INCLUSIVE GROWTH IN GHANA: FOCUS ON NORTHERN REGIONS AND GENDER.......................................................................................128

SUMMARY ...........................................................................................................................................128 INTRODUCTION ...................................................................................................................................130 THE POTENTIAL FOR INCLUSIVE AGRICULTURAL TRANSFORMATION: THE CASE OF NORTHERN GHANA ................................................................................................................................................132 POTENTIAL FOR INCLUSIVE AGRICULTURAL TRANSFORMATION: INCLUDING WOMEN ....................142 THE LABOUR MARKET AND NORTHERN GHANA................................................................................151 THE LABOUR MARKET AND WOMEN..................................................................................................158 OVERCOMING THE CONSTRAINTS TO ACCELERATED SHARED GROWTH...........................................163 REFERENCES .......................................................................................................................................167 APPENDIX............................................................................................................................................170

4. POLITICAL ECONOMY................................................................................................................171

THE RESILIENCE OF CLIENTELISM AND THE POLITICAL ECONOMY OF GROWTH-SUPPORTING POLICIES IN GHANA ............................................................................................................................171 PREVIOUS ANALYSES OF THE POLITICAL ECONOMY OF ECONOMIC POLICY IN GHANA......................173 POLICIES FOR GROWTH IN GHANA .....................................................................................................174 THE EFFECT OF ELECTIONS ON POLICIES IN GHANA ...........................................................................175 POLITICAL MARKET IMPERFECTIONS AND POLICY MAKING ...............................................................179 THE ABSENCE OF PROGRAMMATIC PARTIES AND THE INABILITY OF POLITICAL COMPETITORS IN GHANA TO MAKE BROADLY CREDIBLE POLICY PROMISES..................................................................181 UNINFORMED VOTERS AND THE DIFFICULTIES OF BUILDING PROGRAMMATIC PARTIES IN GHANA...185 DEMOCRATIC PREFERENCES, SOCIAL POLARIZATION AND THE ABSENCE OF PROGRAMMATIC PARTIES ...............................................................................................................................................189 ARE THE NDC AND NPP BOTH PRO-BUSINESS AND THEREFORE PROGRAMMATICALLY INDISTINGUISHABLE? ..........................................................................................................................193 THE CONSEQUENCES OF CLIENTELIST POLITICAL COMPETITION: CRISIS AND POLICY REFORM IN THE ABSENCE OF CREDIBLY PROGRAMMATIC PARTIES.......................................................................194 WHY IS GHANA PERFORMING WELL?..................................................................................................194 CONCLUSION AND POLICY IMPLICATIONS..........................................................................................197 REFERENCES .......................................................................................................................................200

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LIST OF FIGURES

Figure 1.1: GDP growth per Capita, 1990-2006 .................................................................................. 8 Figure 1.2: GDP Deflator and Consumer Price Index (1999-2006)..................................................... 9 Figure 1.3: Growth Incidence Curve, 1991/92 to 1998/99................................................................. 19 Figure 1.4: Growth Incidence Curve, 1998/99 to 2005/06................................................................. 19 Figure 1.5: Growth Incidence Curve, 1991/92 to 2005/06................................................................. 19 Figure 1.6: Long Term Trend in Per Capita GDP .............................................................................. 20 Figure 1.7: Simulations for Future Poverty Reduction Depending on GDP per Capita Growth ....... 22 Figure 1.8: Ghana Poverty Map ......................................................................................................... 29 Figure 1.9: Gini Decomposition by Income Source, 2005/06............................................................ 48 Figure 1.10: World Prices of Cocoa Beans in Constant 2005/2006 Terms.......................................... 49 Figure 1.11: Worker’s Remittances, Ghana, 1987-2005 ...................................................................... 54 Figure 2.1: Population Pyramids 2000, 2025, 2050 ........................................................................... 90 Figure 2.2: Population by age group in GLSS surveys, ..................................................................... 91 Figure 2.3: Dependency ratios in GLSS surveys, 1991-2006 ............................................................ 91 Figure 2.5: Net Enrolment Rates as a Function of Per Capita GDP in 2000 PPP US$.................... 108 Figure 2.6: Public Expenditure on Primary Education as a –Percent of Per Capita GDP: 2005...... 109 Figure 2.7: Government of Ghana Per Capita Expenditures on Primary Education: 2004-2006..... 110 Figure 2.8: The Capacity of Public TV ET In Institution to Absorb the Pipeline of Junior Secondary School Enrolment by Region: 2006/07 ......................................... 112 Figure 2.9: Unit Cost of Education in Ghana: 2006 ......................................................................... 114 Figure 3.1: Spending on Inputs by Farming Households 2005/6 ..................................................... 143 Figure 3.2: Trends in the Use of Inputs by Farming Households Headed by Women..................... 143 Figure 3.3: Main Trade Learnt during Apprenticeship..................................................................... 162 Figure 4.1: Education and spending patterns in Ghana, 1989-2006................................................. 176 Figure 4.2: Governance in Ghana, 1989-2006 (International Country Risk Guide) ....................... 177 Figure 4.3: Partisan divides on the growth agenda, Ghana and the United States ........................... 183 LIST OF TABLES

Table 1.1: Consumption-Based Poverty measures by locality and urban/rural, 1991-2006 ................ 6 Table 1.2: GDP growth and GDP growth per capita in Ghana, 1991-2006 ......................................... 9 Table 1.3: Contribution to growth in real consumption between 1999 and 2006 .............................. 11 Table 1.4: Asset-based poverty, inequality and growth, Ghana 1997-2003 (percentages) ................ 12 Table 1.5: Sectoral urban-rural decomposition of change in poverty, 1991/92 to 2005/06 ............... 13 Table 1.6: Trends in consumption-based inequality in Ghana, 1991/92 to 2005/06.......................... 14 Table 1.7: Gini index without extreme values, by locality and urban/rural, 1991-2006 .................... 15 Table 1.8: Decomposition by group of selected inequality measures, 1991/92 ................................. 15 Table 1.9: Decomposition by group of selected inequality measures, 1998/99 ................................. 16 Table 1.10: Decomposition by group of selected inequality measures, 2005/06 ................................. 16 Table 1.11: Decomposition of change in poverty headcount, by urban/rural ...................................... 17 Table 1.12: Rate of pro-poor growth, by urban/rural (in %) ................................................................ 18 Table 1.13: Future share of the population in poverty under various growth scenarios....................... 22 Table 1.14: Consumption-Based Share of Population in Poverty (%), 1991-2006.............................. 25 Table 1.15: Consumption-Based Share of the Total Number of Poor (%), 1991-2006 ....................... 26 Table 1.16: Determinants of real consumption per equivalent adult – economic climate.................... 31 Table 1.17: Determinants of logarithm of consumption per equivalent adult, 1991 to 2006............... 32 Table 1.18: Contributions of key factors to growth in household consumption, 1991-2006 ............... 34 Table 1.19: Employment shares and job creation in Ghana by industry, 1991/92 to 2005/06............. 39 Table 1.20: Employment, unemployment, and underemployment rates (%), 1991 to 2006 ................ 40 Table 1.21: Shares of employment by type of employment and geographic location (%), 1991 to 2006...................................................................................................................... 41 Table 1.22: Average Annual Earnings (in ‘000 cedis, Accra January 2006 prices) and Weekly Hours Worked, 1991/2006 ............................................................................ 43

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Table 1.23: Determinants of wage earnings (Heckman regressions) ................................................... 45 Table 1.24: Income Sources Shares and Gini Income Elasticity, 1991-2006 ...................................... 47 Table 1.25: Contribution of the cocoa sector to Agriculture GDP growth, 1980-2006 ....................... 49 Table 1.26: Poverty Status of Cocoa Producers, Ghana 1991-2006..................................................... 51 Table 1.27: Impact of changes in cocoa price on poverty, Ghana 2006............................................... 52 Table 1.28: Cocoa Production and Sales Data by Consumption Decile, Ghana 2006 ......................... 53 Table 1.29: Total Remittances, in million of current dollars, GLSS-based estimates.......................... 55 Table 1.30: Impact of remittances on poverty and inequality .............................................................. 55 Table 1.31: School enrollment, net and gross, primary and secondary (%)......................................... 57 Table 1.32: Youth employment and unemployment, 15-24 age group, 1991 to 2005 (%) .................. 58 Table 1.33: Health professional and facility consulted in case of illness/injury, 1991 to 2006 (%) .... 59 Table 1.34: Access to electricity, 1991 to 2006 (%) ............................................................................ 60 Table 1.35: Access to water, 1991 to 2006 (%).................................................................................... 61 Table 1.36: Access to toilets and sanitation, 1991 to 2006 (%) ........................................................... 62 Table 1.37: Share of students enrolled in public schools by quintile and by cycle, 1991 to 2006....... 63 Table 1.38: Share of visits to public health facilities by quintile and by cycle, 1991 to 2006 ............. 63 Table 1.39: Tariffs structure for residential customers, 1998/99 and 2005/06..................................... 64 Table 1.40: Descriptive Statistics on Electricity Consumption, year 2006 .......................................... 65 Table 2.1: Labor Force Participation in age group 25-64, 1991-2005 ............................................... 92 Table 2.2: Comparison of employment rates by age group, 1991-2005 ............................................ 93 Table 2.3: Composition of the Labor Market, 1991-2005.................................................................. 93 Table 2.4: Employment Distribution by Sector (percentage, for age group 25-64) ........................... 93 Table 2.5: Employment Distribution by Sector (absolute numbers, for age group 25-64) ................ 94 Table 2.6: Employment Status (percentage, for age group 25-64) 1991-2005................................... 95 Table 2.7: Employment Status (Absolute numbers for age group 25-64), 1991-2005....................... 95 Table 2.8: Percentage of Workers Residing in a Poor Household, for Different Categories of Labor Market and Employment Status for age group 25-64, 1991-2005...................... 95 Table 2.9: Percentage of Workers Residing in a Poor Household, for Different Categories of Labor Market and Employment Status for Age Group 25-64, in Rural and Urban Areas 96 Table 2.10: Percentage of People Residing in a Poor Household by Level of Education for Age Group 25-64, 1991-2005............................................................................................ 96 Table 2.11: Unemployment Rates for Age Group 25-64, 1991-2005 .................................................. 97 Table 2.12: Poverty (head count index) among the Unemployed for Age Group 25-64, 1991-2005 ..98 Table 2.13: Distribution of the Population by Education Level in Age Group 25-64, 1991-2005 ...... 98 Table 2.14: Annual Real Earnings across Employment Status in Age group 25-64 (in ‘000 cedis).. 100 Table 2.15: Earnings Ratio’s to private formal sector wage in age group 25-64, 1991-2005............ 100 Table 2.16: Median annual earnings for different categories of workers in age group 25-64, 1991-2005 (in 000 cedi) ..................................................................... 101 Table 2.17: Proportion of workers in age group 25-64 with earnings below the poverty line, 1991-2005.................................................................................................. 103 Table 2.18: Mean Number of Hours Worked per Year in age group 25-64 (1991-2005).................. 105 Table 2.19: Earnings per Actual Hour Worked by Economic Activity, Employment Status and Poverty in age group 25-64, 1991-2005 (in cedi) .................................................... 106 Table 2.20: Enrolment by level of schooling for 2004/05 and 2006/07............................................. 107 Table 2.21: Government of Ghana Education Expenditures By Sub-Sector: 2006............................ 110 Table 2.22: Senior Secondary Enrolments by Program for Ghana: 2006 .......................................... 111 Table 2.23: Median Earnings per Hour Worked in 2005 ................................................................... 116 Table 3.1: Use of All Types of Credit by Population %................................................................... 134 Table 3.2: Regional Distribution of Feeder Roads by Surface Type................................................ 136 Table 3.3: Average sales of fertilizer by region ............................................................................... 137 Table 3.4: Agricultural Households that Spent on Inputs (Percent) ................................................. 138 Table 3.5: Output and Yield per Hectare of Selected Food Crops, 2001-2006................................ 139 Table 3.6: The Owners of Land in the Community.......................................................................... 140 Table 3.7: Percent that Perceive Difficulty with Land Tenure System ............................................ 140

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Table 3.8: Ownership of Plots of Land Owned or Operated by Households (%) ........................... 145 Table 3.9: Success Rate in Trying to Acquire Land for Farming (%).............................................. 145 Table 3.10: Status of Ownership of Plots of Land Farmed (%), 2005 ............................................... 146 Table 3.11: Crops Produced on Various Plots 2005/6........................................................................ 150 Table 3.12: Population aged 15+ who are employed by Region (%) 2005/2006............................... 151 Table 3.13: Population aged 15+ who are employed by Region (%) 1998/1999............................... 152 Table 3.14: Population aged 15+ by Industry and Region (%), 1998/99 ........................................... 153 Table 3.15: Population aged 15+ by Industry and Region (%), 2005/6 ............................................. 153 Table 3.16: Annual Income by Region (%) (for main occupation only), 2005/2006......................... 154 Table 3.17: Annual Income by Region (%) (for main occupation only), 1998/1999......................... 154 Table 3.18: Quality of Employment of Workers 15+ In Main Job, 1998/99 (%) .............................. 155 Table 3.19: Quality of Employment of Workers 15+ In Main Job, 2005/6 (%) ................................ 155 Table 3.20: Distribution of employed who pay for their job-related training .................................... 156 Table 3.21: Distribution of economically active by educational level and region 2005/2006 ........... 156 Table 3.22: Distribution of the economically active by educational level and region (%), 1998/1999............................................................................................................... 156 Table 3.23: A Distribution of the economically active by Region for Adults with University Degree (%), 2005/2006.................................................................................. 157 Table 3.24: Employment by Status in Main Job ................................................................................ 158 Table 3.25: Basic Hourly Earnings of Women and Men ................................................................... 158 Table 3.26: Quality of Employment of Workers Aged 15 Years and Older (%) ............................... 160 Table 3.27: Access to Public Transport by Rural Households (%), 2003 .......................................... 170 Table 3.28: Distribution of Population aged 15+ by main occupation and Region ........................... 170 Table 4.1: Ghanaian policy versus the rest of the world, 1991-1999 and 2000-2004 ...................... 178 Table 4.2: Determinants of newspaper readership, Ghana and South Africa, 2005......................... 187 Table 4.3: Determinants of party preferences: results from Afrobarometer 2005........................... 192 LIST OF BOXES

Box 1.1: Ghana’s electricity sector ...................................................................................................... 65 Box 2.1: Technical and vocational education and training (TVET) institutions and programs ......... 113 Box 2.2: Process of TVET reforms in Ghana..................................................................................... 118

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1. POVERTY, LIVELIHOODS, AND ACCESS TO BASIC SERVICES IN GHANA

INTRODUCTION

Ghana has achieved substantial poverty reduction over the last 15 years and is on track to reduce its poverty rate by half versus the level of 1990 well before the target date of 2015 for the

Millennium development Goals.1 The objective of this study is to document this remarkable

achievement, and more broadly to review the evidence on a range of issues related to poverty reduction using the most recent household survey data available. The structure of the study is as follows. After a brief introduction, we discuss:(a) the trend in poverty and inequality in Ghana (Section 2); (b) the profile of the poor, including the geography of poverty, as well as the determinants or correlates of poverty (Section 3); (c) employment and wage trends, including the issues of youth unemployment, time use and child labour (Section 4); (d) the income sources of the poor, including sections on income inequality, cash crop income (cocoa) and remittances; (e) the access of the poor to basic services in the areas of education, health, and basic infrastructure as well as the benefit incidence analysis of public spending in a few areas including electricity subsidies.

1.1 Ghana has long been considered a star performer in Sub-Saharan Africa. Beginning with the presidency of Rawlings and aided by external support, Ghana embarked on a series of economic reforms in 1983. The focus of the reform package was initially on macroeconomic stabilization through fiscal, monetary and foreign exchange liberalization in the initial phase of reforms (see among others Roe, 1992; Kraus, 1991; IMF, 1990; Ahiakpor, 1991). Following a successful macroeconomic stabilization, the focus of reforms shifted towards structural adjustment measures to accelerate growth with sustained poverty reduction. Ghana during much of the 1990s had one of the strongest growth rates amongst Sub-Saharan countries. While GDP growth rates receded slightly in the late 1990s, they rebounded after 2002, and have reached about 6 percent in recent years (see Bogetic et al., 2007, for an analysis of Ghana growth performance).

1.2 Given these high rates of economic growth, one could expect poverty to have decreased in Ghana since the late 1980s. There is indeed some evidence that substantial poverty reduction took place at the national level in Ghana over the 1988-92 period. This evidence is based on the analysis of the first three rounds of the GLSS Surveys (Ghana Statistical Service, 1995; Coulombe and McKay, 1995; Appiah et al, 2000). However, these studies had to contend with some comparability difficulties due to substantial changes in the survey questionnaire between the second and third rounds of the surveys. Furthermore, results of a participatory poverty assessment conducted in poor communities in 1993 and 1994 (Norton et al., 1995) gave a less than enthusiastic message about the evolution of poverty in the early 1990s. Urban communities considered that the initially beneficial effects of economic reform in the 1980s had not been sustained, and in rural communities vulnerability of livelihoods was widely identified as a key issue, with widespread concern expressed that vulnerability was increasing.

1 This statement is based on the poverty trend in Ghana as computed by the authors in collaboration with the Ghana

Statistical Services. The first target under the MDGs is to reduce by half by 2015 the proportion of the population living with less than one US dollar per day. However, while the poverty line of US$1 per day is appropriate for measuring global trends in poverty, it is not appropriate for measuring poverty trends in any given country, because the US$1 poverty line does not properly take into account the specificity of different countries in terms of cost of living and data issues. At the country level it is better to measure the achievement of the poverty target under the MDGs using the country-specific poverty line which tend to reflect better costs of living in any specific country.

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1.3 More faith has been placed on the results based on the comparison of the third and four rounds of the GLLSS surveys, for respectively the years 1991/92 and 1998/99. Coulombe and McKay (2007) found that the share of the population in poverty had dropped between the two surveys from 51.7 percent to 39.5 percent. This achievement was however not as widespread as one might have hoped. Indeed, the national pattern masked a sharp disparity in performance between geographic areas. Most of the poverty reduction was concentrated in Accra and the Rural Forest area, while poverty fell much more modestly or even rose elsewhere. In the Savannah area, the share of the population in poverty rose in urban areas and other measures of poverty which take into account the distance separating the poor from the poverty line rose as well in rural areas.

1.4 After this brief introduction, in the second section of this study, we analyze how poverty has changed in Ghana over time, with a focus on changes since the late 1990s, but also with older data darting back from the 1960s. The work on recent poverty trends is based on the 2005/2006 nationally representative GLSS (Ghana Living Standards Survey) household survey conducted by Ghana’s Statistical Services. This survey is comparable to previous rounds of the GLSS for 1991/92 and 1998/99. In addition, we also rely on comparable CWIQ (Core Welfare Indicators Surveys) for 1997 and 2003. Finally, we also comment on results obtained with older surveys for part of the country which cover the period 1967 to 1997 in order to have a view on trends in well-being since the independence.

1.5 The main message that emerges from the analysis is that Ghana’s record in terms of poverty reduction since the early 1990s has been very impressive. The estimates presented here, which are based on work done in collaboration with the Ghana Statistical services, suggest that the share of the population living in poverty was reduced from 51.7 percent in 1991/92 to 39.5 percent in 1998/99 and 28.5 percent in 2005/2006. An order of magnitude for the reduction in poverty similar to that observed between the last two GLSS surveys is observed with the CWIQ surveys for the period 1997 to 2003 using asset-based measures of well-being. However, when considering longer periods of time (from independence to today), the results are less positive. Also, concerns exist today about an increase in inequality and about the fact that in the northern regions of the country poverty remains very widespread, even if it has decreased as well in recent years. There is also a concern that poverty may be on the rise in Accra, due in part to migration inflows.

1.6 In the third section of the study, we provide a basic poverty profile using the GLSS data, and comment on a similar profile based on asset poverty using the data from the CWIQ surveys. In addition, results from a poverty map of Ghana based on the combination of census and survey data are presented. Finally, we also conduct an analysis of the correlated or determinants of poverty. The main results of the profile of poverty and of the poverty map confirm the large differences in the incidence of poverty between regions of the country. The analysis also suggests large differences in poverty incidence according to demographic characteristics, education levels, sector of activity (type of industry) and employment status, whether using simple statistical tables or regressions to look at the determinants or correlates of consumption.

1.7 By running the same regressions for the determinants of household consumption using the three GLSS surveys, it is also feasible to decompose changes in the mean level of consumption per equivalent adult of households over time into changes due to differences in household characteristics and changes due to differences in the returns to these characteristics. It turns out that for the full period (1991/92 to 2005/2006), general economic conditions helped improve household consumption by 20.5 percent in urban areas and 38.9 percent in rural areas. Changes in household characteristics also helped for improving standards. First, there was a reduction in household sizes which yielded a gain of 7.9 percent in consumption in urban areas, and 1.4 percent in rural areas. Second, there was an increase in the education level of household heads and spouses, which generated a gain in consumption of 7.8 percent in urban areas, and 2.0 percent in rural areas. The gains from the demographic and education transitions were thus much larger in urban than in rural areas. Finally, households also benefited in some cases from higher returns associated to selected characteristics. In

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urban areas, the gains from changes in the returns associated with different types of employment yielded a 12.2 percent increase in consumption. In rural areas however, the reverse was observed, with a consumption loss of 8.1 percent. This suggests that more attractive jobs became available in urban areas, while this was not the case in rural areas.

1.8 Section four provides a brief and preliminary diagnostic of employment and wage trends in Ghana over the last 15 years. A first interesting question is to assess to what extent the growing economy has been accompanied by a similar growth in the number of jobs. Data from the GLSS surveys suggest that in absolute terms, there has been an increase in employment between 1991 and 2006 of about 2.7 million jobs. When looking at paid employment only, the increase is similar, at 2.2 million jobs, which represents a gain of about 50 percent versus the base year. In terms of areas of work (by industry), there has been a decrease in the share of the population involved in agriculture as well as in community and other services, with a growth in the share of workers in all the other sectors, and especially in manufacturing. The analysis of the labour force participation rates suggests that since the early 1990s, paid employment rates have been fairly stable for the country as a whole but rural areas have experienced a significant decline in labour force participation. While part of this decline may be due to better schooling, it also probably reflects a lack of good rural jobs. By contrast, male individuals from urban areas in general, and in Accra in particular, have seen their employment rates going up considerably. The economic growth experienced by Ghana in the last 15 years has also been accompanied by changes in the structure of the labour market, with an increase in private wage employment, especially in urban areas. Earnings trends and patterns tend to corroborate the findings from the poverty analysis presented earlier. There has been a large increase in earnings since the late 1990s. At the same time, although annual earnings used to be much higher in Accra than elsewhere in the past, results from the latest survey show that workers in other urban areas have now caught up with Accra. The stagnation of earnings in Accra in recent years (associated with an apparent increase in poverty and inequality) might be due to a recent surge in migration, but a more detailed analysis would be required to establish this hypothesis.

1.9 Chapter five present a preliminary analysis of the role played by different income sources in the livelihoods of households, and their contribution to income inequality over time. The section also includes a discussion of two important income sources that have rapidly increased in recent years: revenues from cocoa production, and remittances, both domestic and international. The impact of these income sources on poverty is analyzed using simple techniques. Key results include the fact that income inequality has increased substantially over time, that poverty among cocoa producers has decreased especially rapidly thanks to rapid progress in that sub-sector, and that the impact of international worker’s remittances on poverty may be lower than often expected.

1.10 Finally, Chapter six provides a basic analysis regarding the access to basic services for education, health, and infrastructure (water, electricity and sanitation) for various segments of the population, comparing poor to non-poor households. We also provide trends in access over time. In addition, we provide estimates of the incidence of public spending in various areas. The results suggest that while there has been substantial progress in usage of basic services for health, thanks in part to the extension of pharmacy and chemical stores, less progress has been achieved in education (although our assessment based on the 2005/2006 GLSS predates some important initiatives taken by the government since then). The results also suggest that there has been an increase in access to water, sanitation, and electricity, but that subsidies for utilities implicit in the tariffs structures for residential customers tend to be very poorly targeted.

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SECTION I: POVERTY AND ITS DETERMINANTS

TRENDS IN POVERTY AND INEQUALITY

This section is devoted to an analysis of how poverty has changed in Ghana over time, with a focus on changes since the late 1990s, but also with older data darting back from the 1960s. The work on recent poverty trends is based on the 2005/2006 nationally representative GLSS (Ghana Living Standards Survey) household survey conducted by Ghana’s Statistical Services. This survey is comparable to previous rounds of the GLSS for 1991/92 and 1998/99. In addition, we also rely on comparable CWIQ (Core Welfare Indicators Surveys) for 1997 and 2003. Finally, we comment on results obtained with older surveys for part of the country which cover the period 1967 to 1997 in order to have a view on trends in well-being since the independence. The main message is that Ghana’s poverty reduction since the early 1990s has been very impressive. The estimates presented here, which are based on work done in collaboration with the Ghana Statistical services, suggest that the share of the population living in poverty was reduced from 51.7 percent in 1991/92 to 39.5 percent in 1998/99 and 28.5 percent in 2005/2006. An order of magnitude for the reduction in poverty similar to that observed between the last two GLSS surveys is observed with the CWIQ surveys for the period 1997 to 2003 using asset-based measures of well-being. However, when considering loner periods of time (from independence to today), the results are less positive. Also, concerns exist today about an increase in inequality and about the fact that in the northern regions of the country poverty remains very widespread, even if it has decreased as well in recent years. There is also a concern that poverty may be rising in Accra due to migration inflows.

Trend in Consumption-Based Poverty Measures since the Early 1990s

1.11 This section presents estimates of the trend in poverty in Ghana from 1991/92 to 2005/2006 using repeated rounds of the GLSS surveys. The estimates were obtained in collabouration with staff from the Ghana Statistics Service (see the poverty profile prepared by Ghana Statistics Service, 2006). The details on the methodology used for obtaining the poverty estimates are provided in Annexes 1 and 2. The indicator of well being on which the poverty measures are based is the household’s total consumption per equivalent adult.

1.12 The poverty lines were estimated using the cost of basic needs method in order to pay for a food basket providing 2900 kilocalories per adult equivalent2, while also covering the cost of basic non-foods needs. With the 1998/99 GLSS, the poverty line was estimated at 900,000 Cedis per adult equivalent per year in constant prices of Accra in January 1999, with appropriate deflators for the other regions of the country. The poverty lines for 1991/92 and 2005/2006 have been obtained from the Accra poverty line for 1998/99 by using data on the Consumer Price Index (CPI, hereafter) which has been computed by the Ghana Statistics Services separately for Accra, other urban areas, and rural areas. That is, the poverty lines were computed using various CPI indices going backward in time for the 1991/92

2 The requirement of 2900 kcal per equivalent adult is somewhat higher than the norms adopted in other countries,

both internationally and within West Africa, but we adopted this threshold given the fact that it had been used by the Ghana Statistical Services in the past, as well as for the analysis of the GLLS5. If a lower caloric threshold had been used, the poverty measures would have been lower (both in 2005/2006 and in previous years), but the main messages of the4 analysis would have remained the same.

5

poverty lines, and forward in time for the 2005/06 poverty lines, starting from the Accra line in 1998/99. It should be noted that the official CPI may not reflect as closely as one would like the differences in prices (both regional and over time) that the poor face, given that the CPI reflects the prices of the goods consumed by the population as a whole rather than by the subset of the population that is poor. It would have been better to use a CPI derived from the survey to estimate the price changes faced by the poor over time. Unfortunately, the price data collected in the community module of the survey proved to be problematic, and could not be used with confidence to estimate the poverty trend. For this reason, the official CPI was used instead.

1.13 Table 1.1 provides the shares of the population in poverty as well as higher order measures of poverty for the various strata (there are seven strata or “localities” in the GLSS 3 and GLSS4 surveys, which are the ones listed in Table 1.1). The sample of the surveys does not permit further disaggregation of the data in terms of geographic areas, but we will discuss later the issue of the geography of poverty using a poverty mapped based on the 2001 census. The GLSS5 permits to estimate poverty measures for each of the ten regions, but in order to provide comparisons over time we restrict here the analysis of poverty to the seven areas from the GLSS4 and GLSS5. Standard errors for the poverty estimates provided in Table 1.1 are given in Annex 2. The share of the population in poverty (headcount “P0” in Table 1.1) has fallen between 1991/92 and 1998/99 from 51.7 percent to 39.5 percent, and it has fallen further to 28.5 percent in 2005/06. As shown in Annex 1, which provides standard errors for poverty measures presented in this study, this is a statistically significant decline in poverty. Poverty fell by about 16 points in urban areas, and by 23 points in rural areas. The national pattern though masks disparities in performance by geographic region.

1.14 One concern is the fact that poverty may be rising in Accra, perhaps due to migration inflows. The analysis of employment and earnings in Chapter 4 suggests that earnings have stagnated in real terms in Accra since the late 1990s, while they have increased elsewhere. At the same time, within urban areas, one should be careful in interpreting the results from the poverty estimations too literally. The sharp drop in 1998/99 in the capital was probably due in part to a sampling issue (note that Accra is defined throughout this study as the Greater Accra Metropolitan Area which also covers urban areas in Ga East, Ga West and Tema districts), and the sharp drop for the urban coastal and urban forest areas in 2005/06 is also surprising, and may be due to similar issue. To be more specific, in the case of the capital and the surprising shifts in poverty there, given that the sharp drop in poverty was observed in 1998/99, it was feasible to compare the group of households that were sampled in the GLSS4 to the full set of households living in the Accra area in the census files from 2001. This comparison revealed that the households that had been sampled in 1998/99 were better off on average than the households in the census, which may have explained the very low poverty measures for Accra in 1998/99. This is why we believe that it is best to consider the results for urban areas as a whole rather than by subgroup, and to not infer too much from the changes in poverty estimates between any two surveys which are presented in Table 1.1 separately for Accra and the Coastal and Forest urban areas.

1.15 Perhaps more important than the urban-rural divide, there is also concern that the northern part of the country is being left behind in the growth process. In the case of the urban savannah, it seems well documented that these areas remain very poor. In rural areas, while there was a large drop in poverty in the coastal and forest areas, the drop was again smaller in the rural savannah, so that the gaps between the northern part of the country and other natural regions increased.

6

Table 1.1: Consumption-Based Poverty measures by locality and urban/rural, 1991-2006

Poverty indices Contribution

to national poverty

Population

Share

Average welfare

(thousands) P0 P1 P2 C0 C1 C2 1991/92 Region Urban 33.2 1580 0.277 0.074 0.029 17.8 13.2 10.9 Rural 66.8 909 0.636 0.240 0.117 82.2 86.8 89.1 Locality Accra 8.2 1840 0.231 0.051 0.017 3.7 2.2 1.6 Urban Coastal 8.7 1430 0.283 0.070 0.024 4.7 3.3 2.3 Urban Forest 11.0 1620 0.258 0.064 0.022 5.5 3.8 2.8 Urban Savanah 5.3 1320 0.378 0.136 0.069 3.9 3.9 4.2 Rural Coastal 14.2 1090 0.525 0.161 0.067 14.4 12.3 10.8 Rural Forest 29.6 938 0.616 0.227 0.106 35.3 36.4 35.8 Rural Savanah 23.1 763 0.730 0.305 0.161 32.6 38.1 42.5 National 100.0 1276 0.517 0.185 0.088 100.0 100.0 100.0 1998/99 Region Urban 33.7 1950 0.194 0.053 0.021 16.6 12.9 10.5 Rural 66.3 1140 0.496 0.182 0.089 83.4 87.1 89.5 Locality Accra 11.2 2460 0.044 0.009 0.003 1.3 0.7 0.4 Urban Coastal 5.9 1510 0.310 0.092 0.037 4.6 3.9 3.3 Urban Forest 11.8 2010 0.182 0.051 0.020 5.4 4.3 3.6 Urban Savanah 4.8 1190 0.430 0.114 0.042 5.2 4.0 3.1 Rural Coastal 14.4 1230 0.456 0.142 0.061 16.7 14.8 13.3 Rural Forest 31.3 1300 0.380 0.108 0.044 30.1 24.3 20.7 Rural Savanah 20.6 827 0.700 0.323 0.178 36.6 48.0 55.5 National 100.0 1513 0.395 0.139 0.066 100.0 100.0 100.0 2004/06 Region Urban 37.6 2560 0.108 0.031 0.013 14.3 12.1 10.6 Rural 62.4 1430 0.392 0.135 0.066 85.7 87.9 89.4 Locality Accra 11.8 2720 0.106 0.029 0.011 4.4 3.5 2.8 Urban Coastal 5.8 3030 0.055 0.009 0.002 1.1 0.6 0.3 Urban Forest 14.6 2520 0.069 0.017 0.007 3.5 2.6 2.2 Urban Savanah 5.4 1820 0.276 0.095 0.045 5.2 5.4 5.3 Rural Coastal 11.0 1630 0.240 0.053 0.018 9.2 6.0 4.2 Rural Forest 28.0 1520 0.277 0.068 0.024 27.2 19.8 14.4 Rural Savanah 23.4 1220 0.601 0.254 0.139 49.3 62.1 70.7 National 100.0 2050 0.285 0.096 0.046 100.0 100.0 100.0 Source: Authors using GLSS data. See also Ghana Statistical Service (2007).

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1.16 The evidence shows that the northern savannah area, which is by far the poorest of the ecological zones, has been left behind in the national reduction in poverty, even though poverty was smaller in 2005/06 than in 1991/92. This has resulted in an increase in the share of the poor living in the rural savannah areas (see the variable “C0” in Table 1.1, which stands for contribution to the headcount of poverty “P0”), from 32.6 percent in 1991/92 to 36.6 percent in 1998/99 to 49.3 percent in 2005/06. Hence today, while the rural savannah areas in 2005/2006 accounted for only one fourth of the population, they accounted for half of the poor. Note also that in Table 1.1, the decrease in national poverty between 1991/92 and 2005/2006 (at about 24 points) is larger than that observed in both urban (17 points), but similar to that observed in rural areas (24 points). The fact that the national decrease in poverty is not equal to the weighted average of the decreases in urban and rural areas is due to the fact that the population shares in urban and rural areas do not remain constant over time. There has been an increase in the urban population share (which may be underestimated in the GLSS surveys), which has also contributed to a reduction in poverty. This will be discussed in more details in section 2.4.

1.17 Some caveats are in order about the poverty measures by region. Overall, the headcount in rural areas (39.2 percent in 2005/06) exceeds that of urban areas (10.8 percent), and this is not surprising. Yet there may be some issues with the more detailed estimates by region were given earlier in Table 1.1. In that table, we observe a greater headcount index of poverty in urban Savanah (27.6 percent) than in rural coastal areas (24 percent), a fact that was not observed in the data for 1991/92 and 1998/99 and which underscores the fact that poverty reduction was much weaker in the Savannah than elsewhere. Also, the capital city of Accra was displaying the lowest poverty indicators until 2005, when it ranked second after the urban coastal areas. It is worth mentioning however that the very low poverty headcount for Accra in 1998/99 is likely to have been due to a sampling error, so that the increase in poverty between 1998/99 and 2005/06 in Accra may reflect this error rather than a true worsening of the living conditions of the population there. Also, the very low poverty measures observed in Urban Coastal and Forest areas is somewhat surprising. As was the case for Accra in 1989/99, it could be that the sampling frame was not fully representative of these areas, and that the decrease in poverty may have been overestimated. Therefore, we believe that the trend for urban areas as a whole rather than for each of the sub-areas is probably the most trust-worthy one. In rural areas by contrast, given that the sample size of the survey is larger, the likelihood of similar problems is likely to be less prevalent there.

1.18 As noted by Coudouel et al. (2002; see also Ravallion, 1994), apart from the poverty headcount, higher order measures of poverty provide important information on poverty trends (precise definitions of these poverty measures are given in an annex). The depth of poverty (poverty gap, denoted by “P1” in Table 1.1) provides information regarding how far off households are from the poverty line. It thereby measures the consumption shortfall to eradicate poverty relative to the poverty line across the whole population (i.e., considering a shortfall of zero for non-poor households). Put differently, it gives as a proportion of the poverty line the total resources needed to bring all the poor to the level of the poverty line. In addition, the poverty severity (squared poverty gap, denoted by “P2” in Table 1.1) takes into account not only the distance separating the poor from the poverty line (i.e., the poverty gap), but also the inequality among the poor. That is, a higher weight is placed on those households who are further away from the poverty line. The measures of depth and severity of poverty are important complements of the incidence or headcount of poverty. It might be the case that some groups or regions have a high poverty incidence but low poverty gap (when numerous members are just below the poverty line), while other groups have a low poverty incidence but a high poverty gap for those who are poor (when relatively few members are below the poverty line, but with extremely low levels of consumption or income). In Table 1.1 however, the trends for the poverty gap and squared poverty gap very much mirror those for the headcount. However, when considering the poverty or squared poverty gap, the contribution of rural areas, and especially the rural savannah region, comes out even stronger. For example, the rural savannah region, with less than a quarter of the population concentrates more than 70 percent of the severity of poverty.

8

Testing the Reliability of the Trend in Poverty Using National Accounts Data

1.19 Between 1991/92 and 2005/06, the estimates suggest that the share of the population in poverty decreased by almost half, from 51.7 percent to 28.5 percent. If these estimates are correct, Ghana is on path to reducing poverty by half versus its level of the early 1990s well below the target date of 2015 from the Millennium Development Goals. In proportional terms, the decrease in poverty observed between 1998/99 and 2005/06 is slightly larger than that observed between 1991/92 and 1998/99. The sharp reduction in poverty observed since 1998 may surprise some observers. To assess whether their results make intuitive sense, we can test whether the changes in real consumption that they observe between the GLSS4 and GLSS5 is believable in light of data available from the National Accounts.

1.20 In theory, one could argue that neither growth in real consumption nor growth in GDP as measured from the National Accounts automatically leads to a decline in poverty. One can indeed observe an increase in poverty despite growing GDP per capita, and poverty can fall even if real consumption or GDP per capita is falling. In practice however, the experience in West and Central Africa as elsewhere in the world suggests that over time, economic growth is strongly correlated to poverty reduction. That is, growth in GDP and aggregate consumption per capita tends to be accompanied by a reduction in poverty measures computed using household surveys, especially when the measurement of poverty is conducted over long periods of time during which the economy experienced substantial changes, as is the case in Ghana.

1.21 Therefore, this section summarizes the results of comparisons between GDP and aggregate consumption trends on the one hand, and poverty trend on the other. As shown in Figure 1.1 and Table 1.2, growth in GDP per capita (i.e., after discounting GDP growth for population growth) was positive throughout the 1990s, and increased since 2001 to reach 3.60 percent in 2006 according top preliminary estimates. The average per capita GDP growth between 1991 and 1999 was 2.04 percent, and it increased to 2.36 percent between 1999 and 2006.

Figure 1.1: GDP growth per Capita, 1990-2006

Figure 1: GDP growth per capita, 1990-2006

1.000

1.100

1.200

1.300

1.400

1.500

1990 199119921993 199419951996 199719981999 200020012002 200320042005 2006

Cum

ulat

ive

Gro

wth

Inde

x

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

Ann

ual G

row

th R

ate

(%)Annual GDP growth per capita (right axis)

Cumulative GDP growth (left axis)

Source: Authors, based on IMF data.

9

Table 1.2: GDP growth and GDP growth per capita in Ghana, 1991-2006

1991 to 1999 1999 to 2006 1991 to 2006

Number of years 8 7 15 Average growth rate of GDP (%) 4.65 4.98 4.81 Average growth rate of GDP per capita (%) 2.04 2.36 2.20 Cumulative growth in GDP per capita 16.5 18.4 37.9


1.22 Beyond GDP growth, a second key factor seems to have contributed to the reduction in poverty, at least between 1999 and 2006. Or said differently, one would have expected a smaller reduction in poverty than what was actually observed if there had been a simple one-to-one correlation between GDP growth and the consumption of households. To explain this puzzle, it is important to recall that GDP growth is computed in real terms by taking into account the GDP deflator, which is a measure of changes in the cost of producing the various components of GDP in the economy. By contrast, in Ghana, the poverty lines used to estimate poverty depend in part on the trend in the Consumer Price Index, which measures changes over time in the price of the consumption of the population. Thus, if there is a divergence between the GDP deflator and the CPI, this is one of the factors that could lead to a divergence between the rate of real GDP growth per capita, and the rate of growth in consumption per equivalent adult observed in the surveys. Such discrepancies can occur for example when there is high inflation in the country, whether one considers the inflation in production costs, of the inflation in consumer prices.

1.23 In Ghana, the years between 1999 and 2006 were marked by high rates of inflation, especially from 2000 to 2003. The available data suggest that there was a divergence between 1999 and 2006 between the GDP deflator, which is used to compute real GDP from nominal values by factoring in changes in the prices of the goods produced in the country, and the Consumer Price Index (CPI), which tracks changes in the cost of a basket of goods consumed (rather than produced) in the country. As shown in Figure 1.2, since 1998/99, the annual increase in the GDP deflator has been higher than the annual increase in the CPI, except for the year 2005 where both are virtually equal. In cumulative terms, the GDP deflator has grown much faster than the CPI in recent years. If we assume that nominal consumption as a share of nominal GDP has remained roughly constant over time, the divergence between the GDP deflator and the CPI suggests that real growth in consumption has been higher than real growth in GDP since 1999.

Figure 1.2: GDP Deflator and Consumer Price Index (1999-2006)

Figure 2: GDP Deflator and Consumer Price Index (1999-2006)

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

1999 2000 2001 2002 2003 2004 2005 2006

Annual

incr

ease

in G

DP a

nd C

PI (%

)

0.000

0.500

1.000

1.500

2.000

2.500

3.000

3.500

4.000

4.500

5.000

Cum

ula

tive

Incr

ease

in G

DP a

nd C

PI

(Index

)

GDP

CPI


10

1.24 A number of reasons may have led to the lower increase in the CPI as opposed to the GDP deflator since 1999. First, within the CPI, food prices have increased at a lower pace than other goods, as well as at a lower pace than the GDP deflator. This is due in part to good rainfall, which have led to an increase in the production of cereals, thereby leading to relatively lower prices, as compared to other goods. Second, there has been an improvement in the terms of trade faced by the country, as well as an appreciation of the real exchange rate for the Cedis. The improvement in the terms of trade, in part related to an increase in the world price for cocoa, has led to high values for the GDP deflator without having a similar impact on the CPI. As for the appreciation of the real exchange rate, it has led to a relative decrease in the prices of the goods imported in the country, a large share of which is used for consumption purposes.

1.25 Coming back to poverty measurement, in general any change in poverty can be formally explained by changes in the mean consumption per equivalent adult of household on the one hand, and by changes in inequality or in the distribution of consumption between households on the other hand. In most countries, inequality measures tend to change relatively slowly, so that one would expect growth to play a major role in poverty reduction. Said differently, assuming for the moment that inequality could have remained stable in Ghana (we will come back to that issue below in section 2.5), and given the discrepancy highlighted above between the GDP deflator and the CPI, we would expect a higher decrease in poverty than what would have been suggested according solely to the record on per capita GDP growth. Table 1.3 provides estimates of the contribution to GDP growth and the divergence between the CPI and the GDP deflator on the growth in real consumption. The first line reproduces from Table 1.3 the cumulative GDP per capita growth rate observed between 1999 and 2006, at 18.4 percent. Thereafter, an estimate of the cumulative differential between the GDP deflator and the CPI is provided. This estimate, at 18.7 percent, means that as compared to the year 1999 which is taken as the baseline for the computations, the GDP deflator was 18.7 percent higher than the CPI in 2006. Thus, if we assume that the share of consumption in nominal GDP has remained roughly constant over time, this suggests that household have benefited from an increase in per capita consumption of 37.0 percent. This is actually what we found in the survey data, since the increase in real consumption per equivalent adult between the 1998/99 and 2005/06 surveys was 35.5 percent.

1.26 When inflation rates are high, it is difficult to track costs of living and other similar indicators well. Hence the increase in the GDP deflator may have been overestimated, which would imply that real GDP growth rates would be higher than suggested in Table 1.3. This would reduce the cumulative differential between the GDP deflator and the CPI in table 3b. But the sum of the real GDP growth and the cumulative differential between the GDP deflator and the CPI would remain the same, and this is what matters for our purpose in terms of poverty measurement. Said differently, the observed increase in consumption between the two surveys is quite close to the real growth in consumption from the national accounts (assuming a constant share of nominal consumption to GDP), and this suggests that we can have some confidence in the results on the improvement in welfare measure and the reduction in poverty. Furthermore, the ratio of total consumption in the GLSS surveys (using expansion factors) to the consumption in the National Accounts are relatively close to unity, and, what is more important, do not change over time. This again suggests that the growth in real consumption observed over time is legitimate. Note that in Table 1.3, total consumption in the GLSS is based on 99 percent of households with 0.5 percent of the households deleted from the sample at the two extremes of the distribution to correct for outliers and data errors.

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Table 1.3: Contribution to growth in real consumption between 1999 and 2006

Macroeconomic data

Cumulative growth in GDP per capita 18.4% Cumulative differential between GDP deflator and CPI 18.7% Real growth in consumption assuming stable consumption share (1) +(2) 37.0% Comparison of macro and microeconomic data from the GLSS Increase in real consumption per equivalent adult between 1998/99 and 2005/06 35.5% Ratio of total consumption in 1998/99 GLSS survey to National Accounts 111.8% Ratio of total consumption in 2005/06 GLSS survey to National Accounts 111.3%

Source: Authors.

Testing the Reliability of the Trend in Poverty Using Data from CWIQ Surveys

1.27 Despite the coherence between the data from the GLSS and the National Accounts discussed in the previous section, the very large reduction in consumption-based poverty observed between 1998-99 and 2005-2006 may still surprise some observers. Another test of the reliability of the estimates for the poverty trend obtained from the GLSS4 and GLSS5 surveys can be constructed thanks to the availability of two other nationally representative surveys covering a similar period, namely the 1997 and 2003 CWIQs (Core Welfare Questionnaire Indicators). Using these surveys, it is feasible to check whether the trend in “asset-based poverty” between 1997 and 2003 is similar that for consumption-based poverty in the GLSSs. This is what is done by Diallo and Wodon (2007). The authors examine the trend in asset-based poverty in Ghana between 1997 and 2003 as well as the determinants of determinants of asset-based poverty. They estimate the incidence of poverty based on ownership of a wide range of assets and housing characteristics using factorial analysis and giving the same weights in 1997 and 2003 to each of the assets included in the analysis. This enables the authors to construct an aggregate wealth indicator that is comparable between in both years (subject to caveats discussed below). Separate urban and rural asset-based poverty lines are then chosen so that for the 1997 CWIQ survey, the estimates of asset-based poverty are of the same order of magnitude of those obtained for monetary poverty using the 1998/99 GLSS in urban and rural areas.

1.28 There are limits to the poverty measurement approach based on assets. The idea is that since the asset based poverty lines are defined in terms of relatively comparable asset indices over time, they can be kept constant for the analysis of the poverty trends. Nevertheless, comparisons based on assets could either underestimate or overestimate the actual gains in wealth obtained by households because the comparisons do not actually use data ion the price of the assets. For example, given that the prices of assets (such as consumer electronics, small appliances, etc) have been decreasing globally over the period under review, simply estimating wealth through the number of assets owned by households could lead to an overestimation of wealth, given that this price decrease is not measured properly. On the other hand, it could be that thanks to higher earnings, households have purchased over time better equipments (for example better and more expensive television sets), in which case the increase in wealth over time would be underestimated. There may also be interaction effects between the price of assets and the number of assets owned which are not captured in the analysis. For example, if the price of assets is decreasing over time, households may have been able to increase the number of assets that they own, which would lead to an increase in wealth according to our method of analysis which may not be warranted. In some cases, an increase in the number of assets could happen even if the household living standards have deteriorated. These caveats are mentioned simply to say that asset-based poverty measurement is often not as precise as consumption-based poverty measurement. At the same time, trends in asset wealth do provide additional information, and can be used to assess whether an observed trend in consumption-based poverty appears to be reasonable. This is especially valuable if the trends in asset based poverty are obtained from different surveys than those used for measuring consumption-based poverty (similarly, it is often useful to compare trends in consumption to trends in household income; this is done later).

12

1.29 It should also be noted that when using an asset based poverty measure, it may not be feasible to replicate exactly the consumption-based poverty measures obtained from another survey (for example when there is a larger concentration of households with similar values for the asset index nearby the poverty line; this can happen because an asset index obtained through factorial analysis take a finite number of values). Furthermore, the survey used for measuring asset poverty dates from 1997, while the survey used to measure consumption poverty dates from 1998/1999, with most of the samples interviewed in 1999. In a period of substantial GDP growth, it is therefore normal to observe some differences in poverty estimates between the two surveys due to the passage of time. It turns out that with the 1997 CWIQ survey, Diallo and Wodon (2007) obtain poverty estimates for urban and rural areas at 55.2 percent and 25.0 percent respectively. These estimates are a bit higher than the 1998/99 estimates for urban and rural area at 49.6 percent and 19.4 percent, respectively, but this was considered acceptable given that the GDP per capita growth observed between 1997 and 1999. Finally, note that the national poverty measures may also diverge a bit due between surveys due to the fact that the share of the population in urban and rural areas may also differ between the surveys.

1.30 Table 1.4 provides the results in more details. The national asset-based headcount of poverty is found to have decreased from 45.7 percent in 1997 to 38.9 percent in 2003. This decrease of seven percentage points is roughly in line with the ten points decrease observed using the last two rounds of the GLSS surveys. Indeed, the gap between the two GLSS surveys is a total of seven years, as opposed to six years between the two CWIQ surveys. In addition, economic growth picked up significantly after 2003, so that it is legitimate to expect a larger decrease in poverty in recent years. In addition, as already mentioned, the trend in asset poverty is based on ownership variables that indicate ownership or a lack thereof, without taking into account the value of the assets owned. It is likely that in a period of high growth, households will buy better televisions or radios over time, and this increase in the quality (and price) of the assets owned by households is not captured in an analysis of asset-based poverty.

1.31 Given the above, we would thus argue that it is not too surprising that we find a larger decrease in consumption-based poverty between 1998/99 and 2005/06 than what is found by Diallo and Wodon (2007) using the CWIQ surveys between 1997 and 2003. The convergence of results actually gives confidence in the validity of the consumption-based poverty trend obtained with the last two GLSS surveys. Note that in Table 1.4, the decrease in national poverty is larger than that observed in both urban and rural areas. This is because the share of the population in urban areas increased over time, thereby generating additional poverty reduction which we cam loosely relate to migration (we will come back to the role of urbanization in poverty reduction in the next section).

Table 1.4: Asset-based poverty, inequality and growth, Ghana 1997-2003 (percentages)

Rural Urban National Headcount index Poverty in 1997 55.207 25.002 45.711 Poverty in 2003 51.831 21.072 38.880 Change in poverty -3.376 -3.930 -6.831 Source: Diallo and Wodon (2007).

Sectoral contributions to poverty reduction

1.32 As noted in Annex 1, the poverty measures used here are additive. This means that the poverty measure for the population as a whole is equal to the weighted sum of the poverty measures for the population subgroups, with the weights defined by the population shares of the subgroups. This additive property makes it feasible to analyze the contribution of various population subgroups to changes in overall poverty over time. Assume that households or individuals can be classified according to various sectors in the economy. These may be industrial sectors, geographic sectors (urban versus rural),

13

or any other sectors that the analyst may suggest. The overall change in poverty over time can be decomposed into: (1) changes in poverty within specific sectors, or intra-sectoral changes; (2) changes in poverty due to changes in the population shares of sectors, or inter-sectoral changes; and (3) changes due to the possible correlation between intra-sectoral and inter-sectoral changes, or interaction effect. Details for the decomposition are provided in appendix. Here, we apply the decomposition to the urban-rural issue, and implicitly to a rough measure of the potential impact of migration on poverty.

1.33 Table 5 provides the results. The first column gives the absolute decrease in poverty observed over time (in percentage points) that can be attributed to intra-sectoral effects (i.e., the decrease in poverty in urban and rural areas), population shift effects (the increase in population living in urban areas over time) and interaction effects (this is typically a small reminder in the decomposition). The results are provided only for the headcount index (the share of the population in poverty), but the findings are very similar for the poverty gap and the squared poverty gap. It appears that over the period as a whole, most of the reduction in poverty (specifically, 95 percent of the total gain) was due to a reduction in poverty within urban and rural areas, while the gain that can loosely be associated with migration from rural to urban areas accounted only for 7 percent of the total reduction in poverty. This is a somewhat surprising result that needs to be checked further, and it may be related to an underestimation of the rate of urbanization in the GLSS surveys. Indeed, the share of the population in urban areas according to the GLSS surveys increased only from 33.2 percent in 1991/92 to 37.6 percent in 2005/2006, which seems very low for a period of a total of 15 years.

Table 1.5: Sectoral urban-rural decomposition of change in poverty, 1991/92 to 2005/06

Absolute Change

Percentage Change

1991 to 1999 Total Intra-sectoral effect -12.12 98.77 Population-shift effect -0.18 1.47 Interaction effect 0.03 -0.23 1999 to 2006 Total Intra-sectoral effect -9.80 89.8 Population-shift effect -1.18 10.86 Interaction effect 0.07 -0.65 1991 to 2006 Total Intra-sectoral effect -21.92 94.58 Population-shift effect -1.59 6.86 Interaction effect 0.33 -1.44

Source: Authors using GLSS data.

1.34 The above sectoral decomposition was also applied by Diallo and Wodon (1997) using asset-based poverty measures from the 1997 and 2003 CWIQ surveys. The results were very different. Intra-urban and rural effects generated a reduction in poverty of -3.55 percentage points, but the contribution of migration or urbanization was almost as large, at -3.22 points. The interaction term or residual was negligible, as is the case for consumption-based poverty inn Table 1.5 (decrease in poverty by 0.06 points). The large impact of urbanization in the decomposition was due to the fact that asset-based urban poverty measures were about half those obtained in rural areas, and in addition the share of the population in rural areas had decreased from 68.56 percent in 1997 to 57.89 percent in 2003, which in this case may have been an overestimation of the urbanization rate. Still, even if the decline in the rural population share has been lower than suggested by the CWIQ surveys, it may have been substantial (in many poor countries, the urban share grows by about one point per year), which helps explain the large contribution of the population-shift effect on total asset-based poverty.

Trend in Consumption-Based Inequality Measures since the Early 1990s

1.35 Poverty measures are affected only by changes in consumption for those households below the poverty line (or crossing the line). By contrast, inequality measures take into account the whole

14

distribution of consumption per equivalent adult. While many different inequality measures are available and used in the empirical literature, we focus here on basic statistics of the ratios of consumption levels at various percentiles of the distribution, as well as on the most commonly used measures of inequality (see Annex 3 for a definition of these measures). The results at the national level are presented in Table 1.6. For example, the consumption level per equivalent adult at the 90th percentile of the distribution was in 1991/92 5.2 times higher than at the tenth percentile, but by 2005/2006, this ratio had increased to 6.4. Without exceptions, all of the inequality measures show an increase over time, which in some cases is quite large.

Table 1.6: Trends in consumption-based inequality in Ghana, 1991/92 to 2005/06

1991/92 1998/99 2005/06

p90/p10 5.2 6.2 6.4 p90/p50 2.4 2.5 2.5 p10/p50 0.5 0.4 0.4 p75/p25 2.4 2.7 2.6 p75/p50 1.5 1.7 1.6 p25/p50 0.7 0.6 0.6 Generalized Entropy indices GE(-1) 0.2745 0.3248 0.4149 GE(0) 0.2312 0.2550 0.3153 GE(1) 0.2490 0.2589 0.3730 GE(2) 0.3579 0.3552 0.9928 Gini 0.3728 0.3879 0.4245 Atkinson indices A(0.5) 0.1128 0.1205 0.1543 A(1) 0.2064 0.2251 0.2704 A(2) 0.3544 0.3938 0.4535 Gini index Gini 0.3728 0.3879 0.4245 Source: Authors using GLSS data.

1.36 Of the various measures presented in Table 1.6, the most widely used is probably the Gini index. This is in part because the Gini index is related in a very simple way to the Lorenz curve and takes a value between zero and one. In order to assess the sensitivity of the estimates of the Gini index to outliers or extreme values, we recomputed in Table 1.7 the index on 99 percent of the distribution, after deleting the 0.5 percent most extreme observations at both ends of the distribution. While the increase in inequality is lower after this correction, it remains substantial. The adjusted Gini index for consumption per equivalent adult increased substantially, from 0.353 in 1991/92 to 0.378 in 1998/99 and finally 0.394 in 2005/06. Thus, this confirms that inequality has increased in Ghana. At the same time, it must be mentioned that in comparison to other West African countries, Ghana’s level of inequality is in the middle range, even if within Ghana itself, this increase in inequality is a concern (we discuss to the impact of changes in inequality on poverty in section 2.6; data on the trend in income inequality are provided in chapter 5).

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Table 1.7: Gini index without extreme values, by locality and urban/rural, 1991-2006

1991/92 1998/99 2005/06

Urban/rural Urban 0.321 0.340 0.355 Rural 0.329 0.358 0.361 Locality Accra 0.324 0.283 0.368 Urban Coastal 0.296 0.336 0.362 Urban Forest 0.318 0.342 0.317 Urban Savannah 0.338 0.298 0.379 Rural Coastal 0.316 0.344 0.320 Rural Forest 0.325 0.327 0.327 Rural Savannah 0.326 0.373 0.384 National 0.353 0.378 0.394 Source: Authors using GLSS data.

Table 1.8: Decomposition by group of selected inequality measures, 1991/92

Generalized Entropy indices Atkinson indices GE(-1) GE(0) GE(1) GE(2) Gini A(0.5) A(1) A(2)

Subgroup indices Urban 0.2329 0.1995 0.2127 0.2885 0.3471 0.0977 0.1808 0.3178 Rural 0.2218 0.1937 0.2116 0.3070 0.3417 0.0958 0.1761 0.3073 Within-group inequality 0.2398 0.1956 0.2121 0.3189 0.0967 0.1783 0.3122 Between-group inequality 0.0347 0.0355 0.0369 0.0389 0.0179 0.0342 0.0614 Subgroup indices Western 0.1857 0.1724 0.1903 0.2606 0.3259 0.0865 0.1583 0.2708 Central 0.2282 0.1916 0.2002 0.2591 0.3378 0.0931 0.1744 0.3134 Greater Accra 0.2328 0.2053 0.2233 0.3125 0.3533 0.1014 0.1856 0.3176 Volta 0.1893 0.1741 0.1923 0.2685 0.3272 0.0872 0.1598 0.2746 Eastern 0.2063 0.1853 0.1974 0.2552 0.3383 0.0913 0.1691 0.2921 Ashanti 0.2678 0.2328 0.2564 0.3756 0.3759 0.1149 0.2077 0.3488 Brong Ahafo 0.2153 0.1980 0.2236 0.3224 0.3492 0.0998 0.1797 0.3010 Northern 0.3558 0.2760 0.2854 0.4226 0.3993 0.1305 0.2412 0.4157 Upper East 0.1813 0.1747 0.2032 0.3020 0.3254 0.0896 0.1603 0.2661 Upper West 0.2393 0.1972 0.1950 0.2274 0.3457 0.0937 0.1790 0.3236

Within-group inequality 0.2461 0.2035 0.2212 0.3292 0.1006 0.1844 0.3164 Between-group inequality 0.0284 0.0277 0.0278 0.0286 0.0136 0.0270 0.0556


1.37 As mentioned in Annex 3, it is feasible to decompose inequality measures by groups, so as to better understand whether the increase in inequality observed over time is related to an increase in inequality within groups (such as urban and rural areas, or the various natural regions in the country which serve as strata for the GLSS surveys), or to an increase in the inequality between groups. The results of this exercise as applied to urban areas and key regions are given for the three survey years in tables 1.8 to 1.10. It can be seen that while there was an increase in between group inequality, most of the increase for all the inequality measures was due to higher within group inequality. This suggests that although there are more disparities between various areas of the country (such as between the northern savannah and the rest of the country), there are also changes that are not geographically based which tend to magnify the differences between households in terms of consumption. As will be discussed in more

16

details in chapter 4, some of these changes are related to underlying trends in the labour markets, including changes in the returns to education which have tended to favour better educated workers over time.


Generalized Entropy indices Atkinson indices

GE(-1) GE(0) GE(1) GE(2) Gini A(0.5) A(1) A(2)Subgroup indices Urban 0.2559 0.2064 0.2065 0.2675 0.3493 0.0979 0.1865 0.3386 rural 0.2785 0.2283 0.2383 0.3387 0.3686 0.1098 0.2041 0.3577 Within-group inequality 0.2915 0.2209 0.2235 0.3180 0.1043 0.1959 0.3488 Between-group inequality 0.0333 0.0341 0.0353 0.0371 0.0181 0.0363 0.0691 Subgroup indices Western 0.1908 0.1737 0.1976 0.3384 0.3241 0.0875 0.1594 0.2762 Central 0.1956 0.1770 0.1880 0.2399 0.3314 0.0873 0.1622 0.2812 Greater Accra 0.1681 0.1490 0.1575 0.2163 0.3002 0.0733 0.1384 0.2516 Volta 0.2462 0.2010 0.1985 0.2359 0.3462 0.0952 0.1821 0.3299 Eastern 0.1734 0.1521 0.1597 0.2050 0.3036 0.0747 0.1411 0.2575 Ashanti 0.3155 0.2448 0.2398 0.2918 0.3803 0.1144 0.2172 0.3869 Brong Ahafo 0.2136 0.1826 0.1904 0.2422 0.3318 0.0890 0.1669 0.2993 Northern 0.2695 0.2468 0.2913 0.4888 0.3884 0.1251 0.2187 0.3502 Upper East 0.1926 0.1646 0.1608 0.1797 0.3150 0.0784 0.1517 0.2781 Upper West 0.1717 0.1609 0.1758 0.2301 0.3154 0.0807 0.1486 0.2557 Within-group inequality 0.2525 0.1909 0.1979 0.2935 0.0919 0.1718 0.3044 Between-group inequality 0.0724 0.0641 0.0610 0.0616 0.0316 0.0644 0.1286



Generalized Entropy indices Atkinson indices

GE(-1) GE(0) GE(1) GE(2) Gini A(0.5) A(1) A(2) Subgroup indices Urban 0.3022 0.2403 0.2602 0.4148 0.3738 0.1165 0.2136 0.3767 Rural 0.3599 0.2942 0.4069 1.7583 0.4073 0.1521 0.2549 0.4185 Within-group inequality 0.3738 0.2739 0.3308 0.9489 0.1337 0.2334 0.3968 Between-group inequality 0.0411 0.0414 0.0423 0.0438 0.0238 0.0483 0.0940 Subgroup indices Western 0.2326 0.2069 0.2275 0.3185 0.3552 0.1027 0.1869 0.3175 Central 0.2776 0.2525 0.2981 0.5202 0.3924 0.1277 0.2232 0.3570 Greater Accra 0.3348 0.2810 0.3215 0.5698 0.4092 0.1388 0.2450 0.4010 Volta 0.5591 0.5472 0.9666 4.6356 0.5565 0.3025 0.4214 0.5279 Eastern 0.1974 0.1707 0.1859 0.2750 0.3186 0.0845 0.1569 0.2830 Ashanti 0.2805 0.2359 0.2525 0.3643 0.3765 0.1147 0.2102 0.3594 Brong Ahafo 0.2572 0.2158 0.2252 0.2955 0.3614 0.1044 0.1941 0.3397 Northern 0.3342 0.2698 0.2765 0.3631 0.4022 0.1282 0.2365 0.4006 Upper East 0.3187 0.2654 0.2769 0.3666 0.4016 0.1274 0.2331 0.3893 Upper West 0.3425 0.2914 0.3599 0.7561 0.4125 0.1477 0.2528 0.4065 Within-group inequality 0.3450 0.2609 0.3272 0.9516 0.1338 0.2301 0.3712 Between-group inequality 0.0699 0.0544 0.0459 0.0411 0.0236 0.0524 0.1310


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Contribution of growth and changes in inequality to poverty reduction

1.38 It was mentioned above that inequality has increased over time. The Gini index for consumption per equivalent adult increased from 0.353 in 1991/92 to 0.378 in 1998/99 and finally 0.394 in 2005/06. We provide below a simple decomposition of the contribution to poverty reduction of growth (in consumption per equivalent adult) and changes in inequality (Datt and Ravallion, 1992; the details of the decomposition are given in Annex 1). Note that the growth component in this decomposition refers to the growth in real consumption per equivalent adult as measured in the surveys, as opposed to growth in real GDP per capita as measured from the national Accounts. As discussed earlier, growth in real consumption per equivalent adult can be due in part to GDP per capita growth, but also to changes in relative prices to the extent that there was a divergence between the GDP deflator and the CPI used to estimate trends in real consumption (in addition, there can be demographic effects affecting growth in consumption related to changes in household sizes as measured through the equivalent adult concept).

1.39 Table 1.11 provides the results from the decomposition. Over the full period under review, from 1991 to 2006, the headcount index of poverty was reduced by 23.2 percentage points. If there had been no change in inequality, the reduction in poverty would have reached 27.5 points, so that Ghana would have achieved the MDG target of reducing poverty by half versus its level of 1990. This target has not yet been achieved because the increase in inequality led to an increase in poverty of 4.3 points. Overall, while the increase in inequality was significant, it was still small as compared to the reduction in poverty obtained thanks to growth in real consumption (which takes into account the divergence between the CPI and the GDP deflator alluded to before; note that the effect of the CPI-GDP deflator divergence is different from that analyzed by Grimm and Guenther, 2006, using data from Burkina Faso).

Table 1.11: Decomposition of change in poverty headcount, by urban/rural

Share of change due to:

Total Change

Growth in real consumption per equivalent adult

in the survey

Redistribution (change in inequality

in consumption in the survey)

1991/92 to 1998/99 National -12.3 -13.1 0.9 Urban -8.3 -10.7 2.4 Rural -14.0 -14.4 0.3 1998/99 to 2005/06 National -10.9 -13.5 2.6 Urban -8.6 -8.6 0.0 Rural -10.4 -13.8 3.4 1991/92 to 2005/06 National -23.2 -27.5 4.3 Urban -16.9 -20.0 3.1 Rural -24.4 -28.7 4.3

Source: Authors using GLSS data. See also Ghana Statistical Services (2007).

1.40 Another way to look at the relationship between growth and inequality is to rely on growth incidence curves (Ravallion and Chen, 2003). These curves graph the growth rates in consumption at various points of the distribution of consumption, starting from the poorest on the left of the horizontal axis to the richest on the right. The growth incidence curve shows the percentage increase in consumption obtain for various groups of the population according to their consumption level. Clearly, as shown in Figures 1.3 to 1.5, the growth rates in consumption have been significantly higher in the upper part of the population, especially in the 1990s. For the period 1999 to 2006, while the upper echelons of

18

the population benefited from very large gains in consumption, and while the very poor had lower gains than the rest of the population (but positive gains nevertheless), the pattern of gains was equitable for a fairly large segment of the population since the growth incidence curve is flat from the second decile to the ninth decile.

1.41 Summing up (or in technical terms integrating) the growth curve up to any given share of the population ranked by increasing level of consumption gives the total growth in consumption of that share of the population. The results are displayed in Table 1.12. Note that because we are dealing with approximations in logarithm and different baselines each year as to whom constitutes the bottom, say, 10 percent of the population, the total growth rate between 1991/92 and 2005/06 need not exactly be the sum of the growth rates between the two sub-periods.

1.42 At the national level, the growth rate in consumption for the period as a whole was 12.1 percent for the bottom decile (the poorest 10 percent of the population). This growth rate in consumption increases to 19.5 percent for the two bottom deciles taken together, and 34.1 percent for the bottom three deciles. The cumulative growth rate in consumption at the poverty line (which in the baseline dataset of 1991/92 is near the median of the distribution since about half the population was poor) is 43.3 percent, which is well below the average growth rate for the population as a whole, at 63.7 percent, but still very large. The same information is provided for comparisons of consumption levels within urban and rural areas. These statistics suggest that growth was smaller in rural than in urban areas, but that the order of magnitude of the differences in growth rates for, say, the bottom 20 percent of the population and the average for the population as a whole was similar in both urban and rural areas, at about 20 percentage points. Thus, while all groups of the population benefited from growth, growth was not strictly speaking pro-poor since better off households gained more.

Table 1.12: Rate of pro-poor growth, by urban/rural (in %)

1991/92 to 1998/99 1998/99 to 2005/06 1991/92 to 2005/06

Urban at 10 percentile 9.7 18.1 29.2 at 20 percentile 11.7 24.9 39.4 at 30 percentile 13.6 27.2 44.5 at poverty line 13.0 24.5 43.3 at mean 23.7 31.0 62.0 Rural at 10 percentile 5.7 6.3 12.4 at 20 percentile 7.0 13.3 21.3 at 30 percentile 9.6 15.4 26.6 at poverty line 16.4 16.8 36.7 at mean 25.3 25.3 56.9 National at 10 percentile 7.4 12.1 20.4 at 20 percentile 9.5 17.3 28.4 at 30 percentile 12.1 19.5 34.1 at poverty line 16.1 20.6 41.6 at mean 24.9 31.1 63.7 Source: Authors using GLSS data.

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Figure 1.3: Growth Incidence Curve, 1991/92 to 1998/99

010

2030

40

Med

ian sp

line

0 20 40 60 80 100Percentiles


020

4060

Med

ian splin

e

0 20 40 60 80 100Percentiles


020

4060

80

Med

ian sp

line

0 20 40 60 80 100Percentiles

Source: Authors, using GLSS data.

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1.43 To summarize, growth since the early 1990s was not strictly pro-poor in the sense that poor gained less then other income groups although the poor incomes increased, too, and inequality increased quite substantially (on the basis of 99 percent of the sample, the Gini index for consumption rose from 0.353 to 0.395, and the increase was larger on the full sample; also, the increase in income inequality was even more pronounced, as will be discussed in chapter 1, volume 3). Yet this should not detract from the country’s achievements in reducing poverty. Ghana’s dramatic reduction in poverty by almost half between 1991 and 2006s is probably the best record in the whole of sub-Saharan Africa over the last 15 years. The share of the population in poverty decreased from 51.7 percent in the early 1990s to 39.5 percent in the late 1990s and 28.5 percent in 2006. Every year on average, the share of the population was thus reduced by about 1.5 percentage point. Given Ghana’s population, some 5 million persons were lifted out of poverty thanks to growth. That is, if there had been no reduction in poverty over the last 15 years, the number of the poor would be 5 million persons higher than it is today, at more than 11 million. Instead, not only the share of the population in poverty, but also the absolute number of the poor decreased, from 7.9 million in 1991/92 to 7.2 million in 1998/99 and 6.2 million in 2005/06.

1.44 At the same time, the extent of the reduction in poverty was lower in the poorest areas of the country (rural Savannah). As a result, the gap between the northern part and the rest of the country has widened. One could argue that future gains in poverty reduction will be more difficult to achieve because these gains will have to take place in more remote and less well endowed areas in terms of physical and human capital as well as agricultural potential. But one could also argue that as the share of the poor in poor areas becomes higher, it is becoming easy to reach a large number of the poor through well targeted policies and programs.

Longer Term Poverty Trend Going Back to the late 1960s

1.45 The record of poverty reduction of Ghana over the last 15 years is remarkable. Yet it may be useful to put this record in perspective with Ghana’s longer term record. It is worth recalling that at the time of its independence in 1957, Ghana had a vibrant economy. The country was a world leader in cocoa which created, along with gold and timber receipts, an enviable external reserve and facilitated the implementation of policies promoting stable price inflation and sustained economic growth. However a deteriorating sectoral, monetary and fiscal policy environment combined with a series of weather and external shocks led to political and economic instability during much of the 1960s and 1970s. During that period, GDP per capita declined continuously (see Figure 1.6) and inflation reached double- and triple-digit figures year after year. This mixture of high inflation, negative external reserves, bad policies, declining cocoa production and price, severe droughts and political instability led to a major social and economic crisis by early 1980s.

Figure 1.6: Long Term Trend in Per Capita GDP

Figure 6: Long Term Trend in Per Capita GDP

0

100

200

300

400

500

600

1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005

Per

Cap

ita

GD

P (

US

$, 2

000)

Ghana

Low Income Countries

Source: Authors based on World Bank Development Indicators Database.

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1.46 Few good estimates of poverty are available for Ghana before the implementation of the series of GLSS surveys. Rough estimates (Green, 1987) have suggested an important increase in poverty during the period of turbulence of the 1970s and 1980s. In particular per capita food production declined by some 30 percent, education attendance and quality also decline significantly. Manufacturing production decreased tremendously. By the early 1980s low food availability and rationing to long queue in order to get any necessity of life. Obviously that miserable environment led to a brain drain of doctors, teachers along with unskilled workers. At that time, estimates suggests that Saudi Arabia had more Ghanaian medical doctors that Accra, and most teachers and many more families headed to Nigeria which was experiencing an oil boom.

1.47 In 1983 the government launched the Economic Recovery Program in an attempt to turn around the economic situation. This program aimed at first stabilizing the economy through a series of wide ranging reforms. Initially the reforms have been a clear suggest as inflation fell from 122 percent in 1983 to around 10 percent in 1985 and GDP growth per capita rose from -7.1 percent to 1.5 percent (Beaudry and Sowa, 1994). Since then growth per capita has been positive, at around 2 percent per capita for most of the period, but with a recent increase to more than 3 percent per capita over the last few years. Unfortunately high inflation has remained a concern. It is only today that, as shown in Figure 1.6, Ghana has reached again the level of per capita GDP that it had enjoyed in the early 1970s (as measured in constant US$ for the year 2000). As compared to the group of low income countries tracked by the World Bank, while the country was above average in the 1960s and 1970s, it has now fallen behind.

1.48 Goldstein and Bhavnani (2007) provide a new look at long-term poverty trends in Ghana by exploiting two household surveys conducted in the Eastern region in 1966-67 and 1997-98. Both surveys were administrated in a single region – Eastern – so that the results are not nationally representative. Furthermore, there are important differences between the two surveys which are bound to create comparability issues. Amongst the more serious statistical issues that had to be handled are the fact that only male-headed households were interviewed in the latter survey while both female- and male-headed were interviewed in the 1960s; in addition the former survey expenditure results are only presented in interval format (grouped data). To take these and other problems into account, the authors undertook a series of meticulous corrections procedures to make the two surveys as comparable as possible. After corrections the authors obtain two relatively comparable datasets, thirty years apart. Bhavnani and Goldstein find that the share of the population in poverty in the Eastern Region increased from 22 to 35 percent between 1967 and 1997. The increase in poverty is confirm for alternative poverty lines using tests based on cumulative density functions of the quarterly and annual expenditures. The fact that poverty increased over that period is not surprising given the reduction in per capita GDP between 1967 and 1997 shown in Figure 1.6 (see the trend in per capita GDP between the two vertical lines that represent the two surveys used by the authors). But in addition, the authors argue that a large redistributive effect was at work, causing inequality to increase (the Gini index went up from 0.23 in 1967 to 0.32 in 1997). This increase in inequality may have been caused in part by high and volatile inflation that probably hit the poorer households harder than the richer ones.

Simulations for the future incidence of poverty

1.49 In this section, we provide simulations for assessing what the level of future poverty could be depending on assumptions for GDP per capita growth. That is, if we assume various levels of growth in GDP per capita, it is straightforward to estimate what poverty would be under the various assumptions provided we are willing to make a number of (rather strong) assumptions. A first assumption is that GDP per capita growth as assumed in the simulations will be essentially perfectly correlated with average growth in the consumption per adult equivalent at the household level as measured in the surveys. That is, we will be using our assumptions for per capita GDP growth as our best bet for the changes over time in per-adult equivalent household consumption. A second assumption is that we can rely on the poverty lines used for measuring poverty in the 2005/06 household survey in order to assess the impact of future growth, once we have determined that there will be a one-to-one

22

relationship between GDP and consumption growth. The fact that we do not change the poverty line for our future poverty measures implies that future growth is assumed not to affect relative prices and consumption patterns in such a way that other poverty lines would have to be used for future poverty measurement. A third assumption is that inequality in per-adult equivalent consumption will not be affected by future growth, so that we only need to incorporate the impact of growth on mean consumption for our poverty simulations. That is, we simply assume that we cannot assess how future inequality will change, so that it is best to assume that inequality will remain unchanged when implementing the simulations.

1.50 If we accept these (strong) assumptions, the procedure for assessing the impact of future growth on poverty is very simple. We compute future poverty measures after scaling up the adult equivalent consumption aggregate for all households in the 2005/2006 survey by a factor equal to the ratio of the estimated per capita GDP in real terms at any future point to the observed per capita GDP at the time of the survey. Table 1.13 and Figure 1.7 give the results of the simulation. For example, with an assumed rate of real GDP growth per capita of 4 percent per year, the share of the population in poverty would decrease from 28.5 percent in 2006 to 14.7 percent in 2015. These simulations are consistent with those of Bogetic et al (2007) who used the MAMS model and project the medium term growth scenario to 2015 based on the current outlook, accelerated growth with closing of infrastructure gaps, and full achievement of MDGs.

Table 1.13: Future share of the population in poverty under various growth scenarios

Assumed rate of growth in real GDP per capita 1% 2% 3% 4% 5% 6% 7% 8% 2006 28.5 28.5 28.5 28.5 28.5 28.5 28.5 28.5 2007 28.2 27.6 27.1 26.8 26.3 25.9 25.6 25.0 2008 27.6 26.8 25.9 24.9 24.1 23.1 22.4 21.7 2009 27.1 25.9 24.4 23.1 21.9 21.1 19.9 18.9 2010 26.8 24.9 23.1 21.6 20.2 18.9 17.5 16.5 2011 26.3 24.1 21.8 20.2 18.4 17.0 15.2 13.7 2012 25.9 23.1 21.0 18.8 17.0 14.9 13.1 11.7 2013 25.5 22.2 19.8 17.4 15.1 13.1 11.5 10.0 2014 24.9 21.6 18.6 16.2 13.5 11.7 9.8 8.6 2015 24.4 21.0 17.7 14.7 12.2 10.2 8.7 7.1 Source: Authors using GLSS survey.

Figure 1.7: Simulations for Future Poverty Reduction Depending on GDP per Capita Growth

0.0

10.0

20.0

30.0

40.0

50.0

60.0

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

3% 4% 5% 6% 7% Source: Authors using GLSS survey.

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POVERTY PROFILE AND CORRELATES OF POVERTY

In this section, we provide a basic poverty profile using the GLSS data, and comment on a similar profile based on asset poverty using the data from the CWIQ surveys. In addition, results from a poverty map of Ghana based on the combination of census and survey data are presented. Finally, we analyse of the correlates or determinants of poverty. The profile of poverty and the poverty map confirm the large differences in the incidence of poverty between regions of the country. The analysis also suggests large differences in poverty incidence according to demographic characteristics, education levels, sector of activity (type of industry) and employment status. Next, by running the same regressions for the determinants of household consumption using the three GLSS surveys, we decompose changes in the mean level of consumption per equivalent adult of households over time into changes due to differences in household characteristics and changes due to differences in the returns to these characteristics. It turns out that from 1991 to 2006, general economic conditions helped improve household consumption by 20.5 percent in urban areas and 38.9 percent in rural areas. Changes in household characteristics also helped for improving standards. First, there was a reduction in household sizes which yielded a gain of 7.9 percent in consumption in urban areas, and 1.4 percent in rural areas. Second, there was an increase in the education level of household heads and spouses, which generated a gain in consumption of 7.8 percent in urban areas, and 2.0 percent in rural areas. The gains from the demographic and education transitions were thus much larger in urban than in rural areas. Finally, households benefited in some cases from higher returns associated to selected characteristics. In urban areas, the gains from changes in the returns associated with different types of employment yielded a 12.2 percent increase in consumption. In rural areas however, the reverse was observed, with a consumption loss of 8.1 percent. This suggests that more attractive jobs became available in urban areas but this was not the case in rural areas, which also explains the lower poverty reduction there.

Consumption-Based Poverty Profile

1.51 A poverty profile is a set of tables giving the probability of being poor according to various characteristics, such as the area in which a household lives or the level of education of the household head. Such a basic profile for the consumption-based poverty measures obtained with the GLSS surveys is provided in Table 1.14. The table provides the share of the population in poverty according characteristics such as the gender of the household head and other demographic characteristics, as well as the education, employment, migration and land ownership of the head. Higher order poverty measures (poverty gap and squared poverty gap) have been computed by the Ghana Statistical Service in a rather detailed profile of poverty.

• Geographic location: Although this is not listed in Table 1.14, as was mentioned earlier, the headcount in rural areas (39.2 percent in 2005/06) exceeds that of urban areas (10.8 percent), with poverty reduced substantially throughout the country since 12991/92. Estimates by region were given earlier in Table 1.1 and discussed in section 2.1.

• Demographic Characteristics (age, sex, marital status, and household size): There is a clear tendency for poverty measures to increase with the age of the household head. The same observation holds in terms of household size, with larger households being much more likely to be poor than smaller ones. By contrast, the likelihood of being poor in urban areas does not vary much between male-headed households and female-headed households, especially at the end of the time period under review. In rural areas, poverty affects more households whose head is male. Individuals who have never been married (and tend to be younger, better educated, and with a

24

smaller number of children if they have any) are less likely to be poor, as are those who are separated or divorced.

• Education Level of the Head and the Spouse: As expected, the probability of being poor decreases with the education level of the household head, from primary, to secondary, and college/post-graduate studies. Those heads for whom the education is not specified in the survey are likely to have no more than primary education. Households’ poverty also decreases with the education level of the spouse, although this is not shown in the table.

• Industrial Classification of the Head: Poverty measures are provided according to the industrial sector of activity of the household head. The highest probability of being poor is among heads working in agriculture, followed by manufacturing and construction, whichever year is considered (1992, 1999 or 2005). However, the poverty headcount decreased substantially for all three groups over the period, from 65 percent to 39 percent in agriculture, from 39 percent to 17 percent in manufacturing, and from 42 percent to 13 percent in construction.

• Employment Status of the Head: Then lowest rates of poverty are observed among public sector workers (8 percent at the national level in 2005/06), followed by wage earners in the private formal sector (10 percent), the self-employed in non-agricultural activities (14 percent), the wage earners in the private informal sector (16 percent), the households with non-working heads (32 percent), and finally the self-employed in agriculture.

• Migration and Land Ownership: In 2005/06, the poverty headcount index has slightly lower among household who have migrated than among those that did not migrate since birth, which represents a reversal of the situation of the early 1990s. Also, land ownership is associated with a lower probability of being poor, as expected.

1.52 Table 1.15 provides the same information in terms of the share of the poor that belong to various household categories. These are the contributions of various categories of households to the total number of the poor, with these contributions depending on the population shares of the various household categories. For example, it can be seen that individuals in households whose head has no education at all account fro 69.2 percent of all the poor in the country as a whole in 2005/2006, which is an increase versus previous years. The share of the poor that live in households where

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Table 1.14: Consumption-Based Share of Population in Poverty (%), 1991-2006

2004/2005 1998/1999 1991/1992

Total Urban Rural Total Urban Rural Total Urban Rural Sex of head Female 19.2 10.5 26.8 35.4 19.8 45.7 43.1 24.5 56.2Male 31.4 10.9 42.4 41.0 19.2 50.7 54.9 29.4 65.9Age of head Less then 30 16.6 6.4 23.2 19.8 7.9 26.5 31.4 12.0 40.430 to 39 25.4 7.9 36.0 35.7 13.2 45.9 47.7 19.9 62.240 to 49 30.3 12.5 42.5 43.1 22.6 54.6 57.9 32.9 72.850 to 59 32.6 13.4 43.9 43.4 21.9 54.0 53.6 32.0 66.360 and over 31.9 11.3 41.5 44.8 26.4 53.1 58.5 37.3 64.9Household size 1 individual 3.6 3.0 4.3 5.6 4.3 6.8 9.4 3.2 13.72 to 3 individuals 10.2 5.5 14.0 18.1 9.0 23.8 25.7 8.2 35.64 to 5 individuals 22.4 9.1 31.5 34.3 14.1 45.0 46.5 22.7 58.06 to 7 individuals 30.4 9.4 42.3 47.9 24.7 58.9 57.8 30.5 72.08 individuals or more 53.5 27.1 62.5 55.9 35.4 63.3 69.3 45.9 79.5Education level of head No education 43.8 22.6 49.7 54.3 33.2 60.8 61.4 40.2 68.0Primary 25.2 11.4 32.0 38.4 22.7 44.8 55.2 30.1 65.1Secondary 1 17.2 7.8 25.2 29.4 14.2 38.5 41.3 21.0 56.5Secondary 2 8.5 4.8 18.3 15.1 5.5 31.0 20.4 11.5 43.9Superior 4.2 2.6 9.0 16.3 9.4 25.1 24.4 14.0 36.9Marital Status Never married 11.0 6.6 18.7 15.5 6.1 25.2 23.0 5.4 36.5Married 30.9 10.9 41.5 40.5 20.1 50.1 53.9 29.3 65.6Divorced/Widowed 21.1 11.8 28.6 38.5 19.2 49.0 44.1 24.4 55.7Industry of head Agriculture 39.3 22.1 41.9 51.7 40.4 53.4 64.9 43.4 68.0Mining/Quarrying 5.2 0.0 9.1 6.9 0.0 11.8 28.4 21.4 34.6Manufacturing 16.8 8.6 29.9 30.4 17.1 42.7 39.3 23.1 58.2Utilities 0.0 0.0 0.0 18.1 5.0 56.0 22.0 28.2 0.0Construction 13.0 7.1 25.1 26.3 16.5 36.1 42.0 42.9 41.0Trading 11.7 7.6 20.3 25.3 13.0 38.6 32.8 24.2 45.8Transport/Communication 13.8 9.3 25.6 9.1 6.9 15.3 24.9 22.7 35.1Financial Services 8.2 6.1 22.4 13.1 0.0 48.1 8.5 7.1 13.3Community & Other Services 10.7 5.8 22.0 24.5 18.8 31.0 36.4 22.9 55.4Employment status of head Public 8.3 5.3 16.0 21.4 12.9 31.5 35.0 22.3 51.6Wage Private Formal 10.6 5.8 20.8 13.5 10.2 18.9 30.2 26.4 38.6Wage Private Informal 15.8 14.0 18.5 23.8 14.7 29.9 35.7 33.1 39.7Self-employment Agriculture 40.3 23.5 42.7 52.4 43.0 53.7 66.1 48.1 68.5Self-employment non-agr. 14.2 7.1 26.4 27.7 15.6 40.7 35.6 22.1 53.9Non Working 32.5 13.6 52.5 42.5 16.8 61.2 41.7 22.8 58.5Migration Yes 28.0 10.6 39.5 37.2 17.5 49.1 54.3 30.2 66.7No 30.5 11.8 38.1 44.1 25.4 50.0 47.1 22.9 58.2Land ownership Yes 21.6 9.9 35.1 37.2 16.4 51.7 - - -No 36.0 13.2 41.8 44.8 37.5 46.2 - - -Total 28.5 10.8 39.2 39.5 19.4 49.5 51.7 27.7 63.6

Source: Authors using GLSS data. See also Ghana Statistical Service (2007).

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Table 1.15: Consumption-Based Share of the Total Number of Poor (%), 1991-2006

2004/2005 1998/1999 1991/1992

Total Urban Rural Total Urban Rural Total Urban Rural Sex of head Female 16.1 28.78 13.96 24.7 33.6 23.0 22.46 29.86 20.87 Male 83.9 71.22 86.04 75.3 66.4 77.0 77.54 70.14 79.13 Age of head Less then 30 5.9 6.27 5.78 4.5 4.0 4.6 6.62 4.46 7.09 30 to 39 20.7 17.01 21.31 23.4 16.8 24.8 23.17 18.69 24.14 40 to 49 29.4 34.93 28.45 31.0 35.5 30.1 28.62 34.16 27.42 50 to 59 24.1 25.86 23.78 20.7 20.8 20.6 19.97 24.81 18.93 60 and over 20.0 15.93 20.67 20.4 22.9 19.9 21.61 17.88 22.42 Household size 1 individual 0.7 2.24 0.45 0.5 1.2 0.4 0.68 0.54 0.71 2 to 3 individuals 5.8 9.97 5.07 6.9 8.0 6.6 6.91 4.46 7.44 4 to 5 individuals 23.7 27.59 23.00 26.8 23.4 27.5 24.28 21.70 24.83 6 to 7 individuals 27.0 21.45 27.90 34.9 35.6 34.8 30.43 30.99 30.31 8 individuals or more 42.9 38.75 43.58 30.9 31.8 30.7 37.70 42.32 36.71 Education level of head No education 69.2 54.26 71.70 59.6 52.2 61.0 62.27 54.88 63.86 Primary 9.3 9.85 9.21 10.3 10.8 10.2 10.07 8.76 10.35 Secondary 1 18.7 27.65 17.15 24.9 27.9 24.3 24.49 29.94 23.31 Secondary 2 1.4 4.09 0.97 2.0 2.8 1.9 1.61 3.71 1.16 Superior 1.3 4.18 0.83 3.0 5.9 2.4 1.58 2.80 1.31 Marital Status Never married 1.4 3.73 1.00 1.1 1.5 1.0 0.94 0.54 1.02 Married 86.2 74.55 88.16 80.7 78.5 81.1 86.09 84.72 86.38 Divorced/Widowed 12.3 21.76 10.70 18.1 19.6 17.8 13.02 14.96 12.59 Industry of head Agriculture 81.9 45.71 87.37 71.9 45.4 77.1 73.99 37.12 81.65 Mining/Quarrying 0.2 0.00 0.26 0.3 0.0 0.3 0.44 0.90 0.34 Manufacturing 5.0 11.90 3.93 7.6 12.7 6.6 5.98 11.01 4.93 Utilities 0.0 0.00 0.00 0.1 0.2 0.1 0.11 0.66 0.00 Construction 1.2 3.50 0.83 1.6 3.2 1.3 1.42 4.69 0.74 Trading 5.6 18.86 3.55 9.2 15.2 8.0 7.02 18.17 4.70 Transport/Communication 1.9 6.86 1.11 1.0 3.3 0.5 1.65 7.16 0.50 Financial Services 0.5 2.34 0.20 0.4 0.0 0.5 0.11 0.42 0.05 Community & Other Services 3.8 10.82 2.76 8.1 20.2 5.7 9.33 19.86 7.14 Employment status of head Public 2.6 8.34 1.59 5.8 11.5 4.7 9.53 19.30 7.42 Wage Private Formal 2.5 6.44 1.83 1.7 4.8 1.1 2.47 8.33 1.20 Wage Private Informal 2.4 9.35 1.29 1.3 2.0 1.2 1.32 4.19 0.70 Self-employment Agriculture 70.7 36.30 76.37 59.1 36.1 63.6 66.33 31.77 73.86 Self-employment non-agr. 9.6 21.01 7.69 16.0 28.9 13.5 12.87 25.75 10.08 Non Working 12.2 18.60 11.09 15.9 16.2 15.9 7.40 10.75 6.68 Migration Yes 76.8 81.84 75.94 63.8 69.3 62.7 67.74 72.29 66.75 No 23.2 18.16 24.06 36.2 30.7 37.3 32.26 27.71 33.25 Land ownership Yes 39.3 67.61 34.55 66.3 72.5 65.1 0.00 0.00 0.00 No 60.7 32.39 65.45 33.7 27.5 34.9 0.00 0.00 0.00 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Source: Authors using GLSS data. See also Ghana Statistical Service (2007).

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Asset-Based Poverty Profile

1.53 A similar profile of poverty based on an assets index constructed from the 1997 and 2003 CWIQ surveys is provided in Diallo and Wodon (2007). The findings are very similar to the findings for consumption-based poverty in the GLSS surveys. Asset-based poverty measures are significantly higher in rural than in urban areas. Given that since most of the population still lives in rural areas, a majority of the poor are thus rural. Yet because the proportion of the population in rural areas decreased from 69 percent to 58 percent according to the CWIQ data, while 83 percent of the poor lived in rural areas in 1997, this proportion has decreased to 77 percent in 2003. By contrast, the share of the poor in rural areas has increase slightly with in the GLSS data. Still, both the CWIQ and the GLSS data suggest that about eight individuals in poverty out of ten live in rural areas. Asset-based poverty is also higher in all the other regions than in Accra and Ashanti, and many of the better off regions had a larger drop in the headcount index of asset-based poverty between 1997 and 2003 than the poorer areas of the country.

1.54 In terms of demographic variables, Diallo and Wodon (2007) also provide a profile according to household size, the sex of the household head, and the age of the head. Small families (1 to 3 members) are better off than larger families (5-10 members or more than 10 members), as expected, but the differences tend to be small. The reason for such small differences is that asset-based poverty is based on a measure of total “wealth” of the household and not the wealth per capita. Hence, a higher household size does not have an automatic negative effect on the wealth measure, as is the case with consumption per equivalent adult. Women headed households are better off than men-headed households, in part because it is more likely to have women headed household living in urban areas. Households with heads under 20 years of age and over 60 are poorer than households in the middle range, probably because younger heads have not had the time yet to accumulate wealth, while older heads are more likely to be rural and a large family. Single and divorced (or separated) heads are less poor than heads in union.

1.55 As with consumption-based poverty, the incidence of asset-based poverty is lower when the head is better educated and when the head is employed either in the public or formal sector. The differences in poverty by education level are very large. Households whose head has no education at all have a probability of being poor at 71.6 percent in rural areas, versus 10.2 percent for rural households with post-secondary or higher education. In urban areas, virtually all heads with a post-secondary or higher education are non-poor (headcount index of 2.3 percent), while the headcount is at 41.4 percent among household whose head has no education.

1.56 Households whose head is an employer or owner tend to be poorer, but this is because most of them work in the agricultural sector as self-employed individuals. This is also why the “private sector” category identified in the CWIQ data shows much higher rates of poverty than the public and unstated/unemployed categories (those household head who can afford to be unemployed for some time are not typically among the poorest). Also, households whose head works in the commerce and services sectors as well as in mining or transportation tend be better off than their counterparts working in the agricultural sector. There is one surprising jump between 1997 and 2003 in the headcount index among rural households whose head is unemployed or did not stated its occupation, but this may due to misclassification in the survey, as it is unlikely that the unemployment rate among household heads doubled between the two years (said differently, a proportion of rural households whose head is classified as unstated/unemployed in 2003 are probably working in the agriculture sector, which would explain the sharp rise in poverty).

1.57 Again as observed with consumption-based poverty, ownership of land also matters for poverty reduction, although apparently more so according to the estimates in urban areas than in rural areas. This is probably because land owners in urban areas are indeed wealthy, while in rural areas, those who do not own land tend to form an heterogeneous group made of both very poor households and wealthier households likely to engaged in the non-farm sector (this heterogeneity among

28

those who do not own land in rural areas would explain why the differences in poverty measures according to land ownership are small there).

Poverty Map

1.58 Geographic poverty profiles based on the GLSS or CWIQ surveys are limited to broad areas, as the sample size of the survey does not enable analysts to construct valid estimates of poverty for example at the district level. However, policy makers may need finely disaggregated information at the level of city neighbourhoods, towns or villages in order to implement anti-poverty programs. A detailed map of poverty in Ghana (Figure 1.8) has been constructed by combining household and survey data, following a methodology developed by Elbers, Lanjouw and Lanjouw (2002, 2003). The idea behind the methodology is rather straightforward. First a regression model of adult equivalent consumption is estimated using GLSS survey data, limiting the set of explanatory variables to those which are common to both that survey and the latest Census. Next, the coefficients from that model are applied to the Census data set to predict the expenditure level of every household in the Census. And finally, these predicted household expenditures are used to construct a series of welfare indicators (e.g. poverty level, depth, severity, inequality) for different geographical subgroups (although the idea behind the methodology is conceptually simple, its proper implementation requires complex computations).

1.59 The latest Housing and Population Census was conducted in spring 2000. The questionnaire is relatively detailed but does not contain information on incomes or consumption. Yet it does contain data on individual characteristics (demography, education and economic activities) as well as on household dwelling characteristics. The Census database turns out more than 18.9 million individuals grouped into 3.7 million households. The Census field work grouped households into around 26,800 enumeration areas (EAs) of 138 households each on average. To construct the poverty map, the fourth round of the GLSS was used instead of the last survey, since the GLSS4 is closer in terms of date of implementation to the census than the GLSS5. The welfare index to be used in the regression models to construct the poverty map (expenditure per equivalent adult in real terms) is the same as the one used for poverty measurement.

1.60 The lowest administrative level for which a formal geographical definition is currently available is the 110 districts (this map could be in the future updated with the new 138 districts). The importance of the District Assemblies in the on-going decentralisation process makes district-level poverty figures fundamental. Those district-level poverty headcount estimates are presented in Figure 8 (at an ulterior stage, the poverty map could be disaggregated by council using the same techniques). These administrative units would be small enough for most decision making while being large enough to enable a statistically robust poverty maps to be computed.

29

Figure 1.8: Ghana Poverty Map

Source: Authors using GLSS and census data.

Determinants of Poverty

1.61 Drawing a profile of poverty is a necessary step to identify the characteristics of the population groups that are poor, but it is not sufficient to measure the impact of various household characteristics on poverty. The problem with a poverty profile lies in the fact that it provides information on who are the poor, or on the probability of being poor among various household categories, but cannot be used to assess the correlates of poverty. For instance, the variation of poverty rates across regions is sometimes better accounted for by the differences in households’ characteristics than by the

30

specificities of each region. To sort out the correlates or determinants of poverty and the impact of various variables on the probability of being poor, regressions are thus needed. Also, when estimating such regressions, it is better to rely on linear regressions for the determinants of consumption per equivalent adult than on categorical regressions for the determinants of poverty. This is because using probits or logits implies throwing away valuable information contained in the household consumption information and runs a higher risk of bias

1.62 In this chapter, as well as in a separate paper by Diallo and Wodon (2007) using the CWIQ surveys, separate regressions are provided for the urban and rural sectors. Apart from a constant, the regressors include (with a few differences depending on the data sets used): (a) geographic location according to key areas or regions; (b) household size variables (number of infants, children, adults and seniors, and their squared value to take into account potential non-linearity in relationships between household size and consumption), whether the household head is a woman, the age of the head, and the marital status of the head; (c) characteristics of the household head, including his/her level of education; his/her employment type and sector of activity; (d) the same set of characteristics for the spouse of the household head; and (e) other variables including migration and land ownership status. The regressions are estimated separately in urban and rural areas, with the logarithm of the consumption per equivalent adult as the dependent variable. The specification of the regression has been kept intentionally simple, so as to permit comparisons over time in the determinants of household consumption and thereby implicitly poverty.

1.63 Table 1.16 provides the results from the regression on the determinants of consumption that pertains to the geographic dummy variables as well as the overall constant. Because these variables are not household characteristics, they essentially represent changes in macroeconomic conditions in the country as a whole, as well as in the different regions, for what could be referred to as a typical poor household3. The value of the coefficients in the table can be interpreted as percentage gains in consumption associated with the various explanatory variables, with the caveat that when a coefficient is not statistically significant, it is replaced by the mention “n\s” in the table.

1.64 Three comments are in order. First, the values of the constants in the regressions are typically increasing over time, suggesting that for poor households (and more generally the population as a whole) there has been an improvement over time in well-being. Second, there has been a reversal within urban areas in the relative positions of Accra as opposed to other urban areas (Accra households were comparatively better off in 1998/99 while other urban households tend to do better than Accra households in 2005/06, controlling for other variables). As noted earlier, this may be a result of the fact that in 1998/99, the households that were interviewed in Accra appeared to be better off than the true overall population for Accra, as appearing in the 2001 Census. If we discount the estimates for 1998/99 in urban areas for this reason, what emerges is the fact that in 1991/02, there were no statistically significant impacts of geographic location in urban areas (after controlling for other variables). Third, for the three years of survey data, households in rural Savannah tend to have levels of consumption lower than rural households in other areas, and the gap has been increasing over time (26.7 percent less consumption in 2005/2006 versus 15.8 percent less in 1991/1992).

3 More exactly, for a household that has as characteristics the excluded reference variables in the regression,

including the fact that the head has no education and works in the agricultural sector.

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Table 1.16: Determinants of real consumption per equivalent adult – economic climate

Urban Rural

2005/2006 1998/1999 1991/1992 2005/2006 1998/1999 1991/1992 Constant Constant 14.873*** 15.120*** 14.813*** 14.664*** 14.209*** 14.412*** Region Accra Ref Ref Ref Urban Coastal 0.271*** -0.430*** n\s Urban Forest 0.160*** -0.164** n\s Urban Savannah n\s -0.508*** n\s Rural Coastal Ref Ref Ref Rural Forest n\s n\s -0.117** Rural Savannah -0.267*** -0.206** -0.158***


1.65 The rest of the coefficient estimates from the regressions are provided in Table 1.17. The messages that emerge are similar to those obtained with the poverty profile presented earlier.

• Demographic Characteristics: An additional person in the household tends to reduce consumption per equivalent adult by up to 13 percent to 17 percent, although the impact is lower for elderly individuals. As in a number of other countries, there are few statistically significant differences between male-headed and female-headed households. In terms of marital structure, households whose head is separated, divorced or widowed tend to be slightly poorer (loss in consumption of 6 percent to 13 percentin 2005/2006).

• Education Level of the Head and the Spouse: As expected, consumption levels increase with the education level of the household head, but the effects are statistically significant only as of secondary schooling. The impact of the spouse’s education is smaller than that of the head, probably because spouses are less likely to work and are likely to earn less. There has however been an increase over time in the gains from education at the upper secondary and tertiary level which has probably contributed to the increase in inequality.

• Other variables: After controlling for other variables, employment and other variables do not appear to have large and systematic impacts on consumption. There is weak evidence that households involved in mining have higher levels of consumption than otherwise comparable households whose head works in other sectors. One rather surprising result (given the evidence provided in the section of this paper on cocoa below) is the fact that cocoa producers are at a disadvantage in 2005/06 versus other self-employed heads in agriculture. It may be that cocoa producers belong to two groups – one group of small land owners with limited production could face substantial deprivation, while the other group, with larger areas cultivated, better equipment and higher production levels would be better off, with their better status picked up by other variables in the regression, but this would have to be confirmed by a more detailed analysis. Finally, households who have not migrated tend to be slightly better off than households who migrated (this does not mean that there were no gains to migration for the households who did migrate).

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Table 1.17: Determinants of logarithm of consumption per equivalent adult, 1991 to 2006

Urban Rural

2005/2006 1998/1999 1991/1992 2005/2006 1998/1999 1991/1992 Age Groups Age 0 to 4 -0.134*** -0.136*** -0.117*** -0.135*** -0.130*** -0.183*** Age 0 to 4 squared n\s 0.013* n\s 0.009*** 0.016*** 0.028*** Age 5 to 14 -0.169*** -0.145*** -0.178*** -0.173*** -0.179*** -0.233*** Age 5 to 14 squared 0.020*** 0.012** 0.015*** 0.023*** 0.017*** 0.034*** Age 15 to 60 -0.134*** -0.203*** -0.182*** -0.145*** -0.202*** -0.151*** Age 15 to 60 squared n\s 0.016*** 0.014*** 0.010*** 0.023*** 0.013*** Age 61 and over n\s n\s n\s n\s n\s -0.181*** Age 61 and over squared n\s -0.104* n\s -0.048** -0.055* 0.064* Sex of head Male Ref Ref Ref Ref Ref Ref Female n\s n\s n\s n\s n\s 0.073** Education level of head No education Ref Ref Ref Ref Ref Ref Primary n\s n\s n\s n\s 0.134*** n\s Secondary 1 0.170*** 0.180*** 0.145*** 0.146*** 0.190*** 0.101*** Secondary 2 0.265*** 0.340*** 0.387*** 0.293*** 0.224*** n\s Superior 0.491*** 0.347*** 0.276*** 0.408*** 0.415*** 0.220** Education level of spouse No education Ref Ref Ref Ref Ref Ref Primary n\s n\s n\s n\s n\s n\s Secondary 1 0.087** n\s 0.117** 0.107*** 0.104*** 0.106** Secondary 2 0.163** 0.295*** 0.413*** 0.426** 0.405** n\s Superior 0.273*** n\s 0.351** n\s 0.421*** n\s Marital Status Married/Informal Ref Ref Ref Ref Ref Ref Never married n\s n\s n\s n\s 0.214*** n\s Separated/Divorced/Widowed -0.134*** n\s n\s -0.065* -0.092* -0.105*** Industry of head Agriculture Ref Ref Ref Ref Ref Ref Mining/Quarrying 0.195* n\s n\s n\s 0.500*** 0.277** Manufacturing n\s n\s n\s -0.163* n\s n\s Utilities n\s n\s n\s n\s n\s 0.704*** Construction n\s n\s n\s n\s n\s n\s Trading n\s n\s n\s n\s 0.266** 0.188* Transport/Communication n\s n\s n\s n\s 0.375** n\s Financial Services n\s n\s n\s n\s n\s 0.618*** Community & Other Services n\s n\s n\s n\s n\s n\s Employment status of head Public n\s n\s -0.145*** n\s n\s n\s Wage/private/formal n\s -0.109** -0.240*** n\s n\s n\s Wage/private/informal -0.265*** -0.198** -0.239*** n\s n\s n\s Self-agriculture-export -0.296*** -0.345*** -0.437*** -0.169* n\s n\s Self-agro-crop Ref Ref Ref Ref Ref Ref Migration and land ownership Migration - Yes Ref Ref Ref Ref Ref Ref Migration - No 0.056 n\s -0.050 0.075** n\s -0.122*** Area of land owned n\s n\s 0.015** n\s 0.001*** 0.002*** Area of land squared n\s n\s n\s n\s -0.000*** -0.000*** Source: Authors using GLSS data.

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1.66 By running the same regressions for the three GLSS surveys, it is also feasible to decompose changes in the mean level of consumption per equivalent adult of households over time into changes due to differences in household characteristics and changes due to differences in the returns to these characteristics (using the Oaxaca decomposition). The results are provided in Table 1.18. For brevity, we focus on the discussion of the results obtained for the whole period under review (the changes in levels of consumption observed in Table 1.18 are different from those reported in section 2 because of the logarithmic transformation used in the regressions).

• Impact of general economic conditions: The first line in the table captures the changes in the constant of the regression as well as the geographic dummy variables. These changes do not reflect changes in household characteristics, but rather changes in the general economic conditions in the country, and how these play out in the various parts of the country. In urban areas, for the full period, general economic conditions helped improve household consumption by 20.5 percent in urban areas and 38.9 percent in rural areas.

• Changes in household characteristics: Household characteristics improved in two major ways. First, there was a reduction in household sizes, which accounts for most of the positive impact of the change in demographic characteristics on consumption (gain of 7.9 percent in consumption in urban areas, and 1.4 percent in rural areas). Second, there was an increase in the education level of household heads and spouses, which generated a gain in consumption of 7.8 percent in urban areas, and 2.0 percent in rural areas. However, the fact that the gains from the demographic and education transitions were much larger in urban than in rural areas suggest that additional efforts must be made in rural areas on these issues.

• Changes in the returns to household characteristics: In urban areas, the gains from changes in the returns associated with different types of employment yielded a 12.2 percent increase in consumption over time for the full period. In rural areas, the reverse was observed, with a consumption loss of 8.1 percent. Given that various household characteristics (on the industrial sector of activity as well as the employment status of the head) are combined in this category, one has to be careful about interpretation. But the basic findings that more attractive jobs became available in urban areas, while this was not the case in rural areas, is coherent with the general poverty trend and the fact that at least some categories of rural household are lagging further behind the rest of the country.

• Overall changes in consumption levels: In urban areas, the improvement in general economic conditions accounted for about half of the total gains in consumption, while in rural areas basically all of the gains were due to the improvement in general economic conditions. The fact that in urban areas there were also gains associated with improvements in household characteristics and the returns to these characteristics helps explains why we observe a substantial difference in the total gains in urban as opposed to rural areas. The increase in the average consumption of households between 1991 and 2005 was 46.1 percent in urban areas (21.3 percent between 1991 and 1999 and 24.8 percent between 1999 and 2006). In rural areas, the increase was about eight percentage points lower, at 37.8 percent (22.3 percent between 1991 and 1999 and 15.5 percent between 1999 and 2006). One key message from this analysis is that in rural areas, more efforts could be placed on helping households move faster through the education and demographic transitions.

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Table 1.18: Contributions of key factors to growth in household consumption, 1991-2006

1991 to 1999 1999 to 2006 1991 to 2006

Change in

returns Change in

characteristics Change in

returns Change in

characteristics Change in

returns Change in

characteristics Urban Geography/overall 5.6% 4.7% 8.8% 0.5% 20.5% -1.0% Demographic -3.5% 4.7% 6.6% 2.6% 2.5% 7.9% Education -1.6% 3.9% 1.7% 2.9% -0.9% 7.8% Employment 9.5% 0.9% 2.8% -1.3% 12.2% -0.3% Others -2.4% -0.4% -0.3% 0.4% -3.1% 0.4% Column Total 7.6% 13.7% 19.6% 5.2% 31.3% 14.8% General total 21.3% 24.8% 46.1% Rural Geography/overall -2.0% 1.3% 40.7% -1.0% 38.8% 0.2% Demographic 1.3% 2.5% 2.1% -1.0% 2.8% 2.0% Education 4.1% 3.9% -2.8% -1.9% 2.0% 1.4% Employment 10.5% 1.7% -19.6% -1.0% -8.1% -0.3% Others -1.1% 0.0% -0.4% 0.4% -1.1% 0.0% Column Total 12.8% 9.4% 20.0% -4.5% 34.4% 3.3% General total 22.2% 15.5% 37.8% Source: Authors using GLSS data.

1.67 The discussion of the determinants or correlates of poverty has focused above on consumption indicators from the GLSS surveys. Before moving to the next section, it is worth mentioning some additional findings from the analysis of the correlates of household wealth carried by Diallo and Wodon (2007). We focus here on differences in findings rather than on similarities.

1.68 In terms of demographic variables, apart from information on the number of infants, children, adults, and seniors (and their squared values), on whether the head is female, on the age of the head (and its squared value), and on the marital status of the head, the regressors in the wealth analysis also include whether the head is mentally or physically disabled. One key difference in the wealth as opposed to the consumption analysis is that most household size variables have no or fairly small impacts on assets, for the reasons already explained earlier (the authors do not divide assets by household size when measuring well-being). Furthermore, in 1997, but not in 2003, a handicap reduces the assets owned by households by about 6 percent in rural areas and at the national level (in 2003, the coefficient is still negative, but smaller and not statistically significant). This suggests a mild negative impact of handicap on asset-based well-being. In rural areas and at the national level, female heads have slightly higher levels of assets, with gains ranging from 2 percent to 7 percent, but this is not the case in urban areas. Controlling for other characteristics, the age of the head does not have a statistically significant impact on assets in most cases. Finally, heads in a union have slightly higher levels of wealth, probably related to the need for higher accumulation in order to support their wife and children (this is again a finding that differs from the consumption-based regressions, and the difference is essentially again due to the difference in the treatment of household size).

1.69 The impact of education on asset wealth is confirmed. Literacy brings in a gain of about 5 percent to 7 percent versus having a head illiterate, and primary education brings in a bit more (gain of 2 percent to 4 percent in most cases). Completing junior secondary school adds 6 percent to 8 percent in terms of assets versus no education (on top of the gain associated to literacy), while secondary/technical education brings in a larger gain of 13 to 18 percent. At the post-secondary and higher level, the gain in asset wealth versus no education at all varies from 29 to 37 percent (to which one must also add the gain linked to literacy). The impact of employment is lower, and actually in most case not statistically

35

significant once education is controlled for. For example, whether the head is employed or not does not make a large difference, and there is no systematic gain or loss associated to the private or parastatal sector as compared to the public sector, except for rural areas in 1997. What does matter, however, is the sector of activity of the head, with households whose head is not in agriculture doing better, with assets gains of 15 percent to 22 percent versus households whose head is in agriculture. Finally, as is the case for the analysis of consumption, even after controlling for all the above variables, geographic location still maters. In the national regressions, living in urban areas brings in a gain in assets of 31 percent to 37 percent. As for the regional gains or losses, they are also large, which helps explain the relatively high levels of migration observed within the country.

36

SECTION II: LABOUR MARKETS AND INCOME SOURCES

EMPLOYMENT AND WAGES

This section provides a brief and preliminary diagnostic of employment and wage trends in Ghana over the last 15 years. A first interesting question is to assess to what extent the growing economy has been accompanied by a similar growth in the number of jobs. Data from the GLSS surveys suggest that in absolute terms, there has been an increase in employment between 1991 and 2006 of about 2.7 million jobs. When looking at paid employment only, the increase is similar, at 2.2 million jobs, which represents a gain of about 50 percent versus the base year. In terms of areas of work (by industry), there has been a decrease in the share of the population involved in agriculture as well as in community and other services, with a growth in the share of workers in all the other sectors, and especially in manufacturing. The analysis of the labour force participation rates suggests that since the early 1990s, paid employment rates have been fairly stable for the country as a whole but rural areas have experienced a significant decline in labour force participation. While part of this decline may be due to better schooling, it also probably reflects a lack of good rural jobs. By contrast, male individuals from urban areas in general, and in Accra in particular, have seen their employment rates going up considerably. The economic growth experienced by Ghana in the last 15 years has also been accompanied by changes in the structure of the labour market, with an increase in private wage employment, especially in urban areas. Earnings trends and patterns tend to corroborate the findings from the poverty analysis presented earlier. There has been a large increase in earnings since the late 1990s. At the same time, although annual earnings used to be much higher in Accra than elsewhere in the past, results from the latest survey show that workers in other urban areas have now caught up with Accra. The stagnation of earnings in Accra in recent years (associated with an apparent increase in poverty and inequality) might be due to a recent surge in migration, but a more detailed analysis would be required to establish this hypothesis.

Basic Statistics on Employment and Job Creation

1.70 Ghana’s recent growth has been associated with a number of factors, including improvements in traditional exports such as cocoa, gold and timber. The economy has become more open. There has been significant growth not only in service activities, but also in agriculture (especially among cash crops). Thanks to high growth, unemployment rates have remained low (see Canagarajah et al., 1998, on Ghanaian labour market in the late 1980s and early 1990s). There have been episodes of public sector retrenchment, but the reduction in the size of the civil service as a proportion of the number of jobs has not been detrimental, thanks to an increase in employment in the private sector, both formal and informal. This chapter provides a brief diagnostic of employment and wage trends in Ghana over the last 15 years.

1.71 At the outset, it is important to signal that the labour sections in the GLSS 3, 4 and 5 questionnaires have seen significant changes over time. One important difference in the questionnaires affecting the statistics presented here concerns whether an individual is working or not. Indeed, in GLSS4, all individuals already at school were automatically disqualified as potential workers. Since almost all working students (based on GLSS3 and 5 evidences) work unpaid for family enterprises, we do not present the “all jobs” employment figures for 1998/99 and concentrate our analysis on workers receiving some kind of earnings for that year, whether as wage payment or as compensation in self-employment. Apart that single - but important - comparability issue, we believe our GLSS-based labour figures are consistent enough to feel confident about any conclusions based on them.

37

1.72 Another important information to provide relates to the differences in analysis between this paper and the next paper on labour markets in this volume. In this paper, we provide data on all workers in the population aged 15 years and above. In the labor market paper that follows, data are provided only for the population aged 25 to 64, but with much more details in the analysis. This difference in the universe on which the estimations are based implies that the data presented in this paper are different from those presented in the labor market paper. In subsequent work, we will provide in one place all statistics, for the population as a whole as well as for young workers (aged 15-24) and older worker (aged 25-64), together with a detailed explanations as to how data are made comparables between samples and between household survey years.

1.73 A first interesting question is to assess to what extent the growing economy has been accompanied by a similar growth in the number of jobs. Table 1.19 provides the answer to that question. In absolute terms, there has been an increase in employment between 1991 and 2006 of about 3 million jobs. When looking at paid employment only, the increase is similar, at 2.9 million jobs. In terms of areas of work (by industry), there has been a decrease in the share of the population involved in agriculture as well as in community and other services, with a growth in the share of workers in all the other sectors, and especially in manufacturing.

Employment, Unemployment, and Underemployment

1.74 The impressive poverty reduction enjoyed by Ghana during the last 15 years has been associated with good labour market outcomes in terms of job growth. As was the case for poverty, in order to document in more details the functioning of the labour market during that period we have access to two separate sets of household surveys. On one hand we have the three rounds of the Ghana Living Standards Survey conducted in 1991/92, 1998/99 and more recently in 2005/06, and on the other hand, the CWIQ surveys which have only been conducted twice, the last time in 2003. Given that the GLSS data cover a longer time span and that the latest round of the survey is the most recent one available, we will concentrate here our analysis on those surveys. The GLSS data are also more comprehensive since they cover levels of earnings, unlike CWIQ surveys. However we will also use the CWIQ surveys in some cases to validate our results.

1.75 Table 1.20 presents a series of basic employment indicators covering the period from 1991 to 2006. The figures are broken down by region, sex and quintile of consumption per equivalent adult. During those 15 years, the labour force participation or “employment rate” for the country as a whole has declined from 75.9 to 70.5 percent. A large part of this decline is due to better schooling outcomes, but the decline may also reflect a lack of job opportunities in rural areas. Indeed, the decline is wholly due to a drop in rural areas; by contrast urban areas (particularly Accra) have experienced a small increase in employment rates. In rural areas, the large decline from 83.6 percent to only 76.8 percent has been experienced by both male and female individuals. Since most individuals from poor households are to be found in rural areas, it is not surprising to find out that this rural decline in employment mainly affected individuals from the poorer expenditure quintiles, particularly the first (poorest) quintile. The main group that witnessed an increase in employment rates was that of urbanite males. Particularly in Accra4, males have seen a large increase in their employment rate, from 54.9 percent in the early 1990s to 61.4 percent in 2005/06.

1.76 If we exclude unpaid workers from our definition of employment rate, we have a somewhat similar but smoother pattern. Overall paid employment rates are more steady going from 53.5 percent in 1991/92 to 52.5 percent in 1998/99 and 51.5 percent in 2005/06, but a trend breakdown by sex and locality yields a pattern over time similar to that observed for all employment (whether paid or unpaid): there has been a decline in the paid rural employment rate and a significant increase in the rate for males 4 As a reminder, Accra is defined throughout this study as the Greater Accra Metropolitan Area which also covers

urban areas in Ga East, Ga West and Tema districts.

38

living in urban areas. It is worth noting that excluding unpaid labour from our analysis gives Accra a much higher employment rates than in rural areas while the reverse was true when unpaid work was taken into account. The predominance of unpaid workers (and in all likelihood low productivity workers) in some specific groups is clearly apparent when we examine the employment rates per quintile. If we concentrate on the latest round of data, the paid employment rate increases rapidly between the lowest and the highest quintiles (from 35.0 percent to 62.1 percent in 2005/06) while the “all workers” employment rate profile is rather flat (around 70 percent). Those unpaid worker are therefore disproportionably (and more and more over time) found in the first quintile. If job search is a cause of rural-to-urban migration, those changes in employment patterns would be consistent with the recent surge in migration toward southern towns.

1.77 Apparently the important decline in poverty experienced in both urban and rural areas was not explained by a higher labour supply, but by higher returns of education, physical capital or land, as well as by an improvement in the dependency ratio of households thanks to the demographic transition. That could also help to explain the change in behaviour in the labour market when we examine the employment figures according to the level of expenditure (quintile-based statistics). While the employment rate steadily declined as household were getting better off 15 years ago, no significantly differences in employment rates between quintiles could be found in 2005/06.

1.78 In Table 1.20, we are presenting statistics on two definitions of unemployment. Broad unemployment takes into account all non working individuals available for work, while the narrow definition is limited to those whom are actively looking for a job. At between two and five percent, the unemployment rate does not seems to be a major problem in Ghana although Coulombe et al. (2005) using the CWIQ 2003 survey found that unemployment was concentrated in the younger segment of the population (15 to 24 years old). Even if no specific study examining labour market flexibility have been published lately, it is likely that the high economic growth and a fairly flexible labour market (Beaudry and Sowa, 1994; Canagarajah et al., 1998) has kept unemployment rate under control. However, Accra is having a more worrying unemployment problem as close to 10 percent of the male population are available for work (broad unemployment) even if only slightly more than half of them are actively looking for a job. Underemployment seems also rather low and declining. In 2005/06, less than 6 percent of workers were looking for more work, mainly in rural areas.

1.79 Economic structure of Ghana in terms of jobs is changing (Table 1.21 complements Table 1.20). As was mentioned earlier, although agriculture remains the most important economic activity in terms of the share of total jobs that it provides, it has declined since the early 1990s when more than 47 percent of the labour force was concentrated in this sector (if we include also unpaid workers, the proportion in agriculture decreased to 56.7 percent in 2005/06 from 60.5 percent in 1991/92). Almost all those agriculture workers are self-employed, and Table 1.21 shows that 40.3 percent of the working population has that status.

1.80 The changing structure of the Ghanaian economy is best revealed by an examination of the different wage sectors. In the last 15 years, the percentage of public sector workers has continuously declined from around 13 percent in 1991/92 to 8.4 percent in the late 1990s and only 8.0 percent in 2005/06. That relative decline in public employment has been compensated by an increase in the private sector, both formal and informal. The percentage of individuals working in the private sector as wage employees went up and private formal sector work reaches now more than a quarter of the working population in the capital. In the nation as a whole however, even if the economic structure is changing, around 87 percent of the working population is still occupied in the informal sector. While the formal sector (public and private) represents more than 40 percent of the working population in Accra, it represents less than 8 percent of the workers in rural areas, and even less if unpaid workers are taken into account. Similar findings for the increase in private formal sector jobs are found with the CWIQ surveys which suggest that nationally, the share of private formal jobs increased from 4.3 percent in 1997 to 6.7 percent in 2003.

39

Tab

le 1

.19:

Em

ploy

men

t sha

res

and

job

crea

tion

in G

hana

by

indu

stry

, 199

1/92

to 2

005/

06

Acc

ra

Oth

er U

rban

R

ural

A

ll

1991

/92

1998

/99

2005

/06

1991

/92

1998

/99

2005

/06

1991

/92

1998

/99

2005

/06

1991

/92

1998

/99

2005

/06

A

ll jo

bs

Agr

icul

ture

(%

) 1.

1 …

1.

2 27

.4

…

22.3

76

.0

…

76.2

59

.8

…

55.8

M

inin

g/Q

uarr

ying

(%

) 0.

2 …

0.

3 0.

9 …

1.

3 0.

4 …

0.

5 0.

5 …

0.

7 M

anuf

actu

ring

(%

) 24

.3

…

20.8

11

.7

…

16.3

6.

5 …

8.

6 8.

9 …

11

.6

Util

ities

(%

) 1.

1 …

0.

3 0.

2 …

0.

7 0.

0 …

0.

0 0.

1 …

0.

2 C

onst

ruct

ion

(%)

2.9

…

3.7

1.9

…

3.2

0.8

…

0.9

1.2

…

1.7

Tra

ding

(%

) 32

.1

…

38.6

34

.4

…

32.9

10

.3

…

8.9

17.2

…

17

.6

Tra

nspo

rt/C

omm

unic

atio

n (%

) 5.

4 …

7.

7 5.

1 …

4.

9 0.

6 …

1.

1 1.

9 …

2.

7 Fi

nanc

ial S

ervi

ces

(%)

4.1

…

5.1

0.7

…

2.0

0.1

…

0.2

0.5

…

1.1

Com

mun

ity &

Oth

er S

erv.

(%

) 28

.8

…

22.2

17

.8

…

16.3

5.

3 …

3.

6 9.

8 …

8.

5 A

ll (%

) 10

0.0

…

100.

0 10

0.0

…

100.

0 10

0.0

…

100.

0 10

0.0

…

100.

0 N

umbe

r of

wor

kers

(in

‘00

0)

405.

2 …

97

2.7

1224

.9

…

2157

.8

3915

.7

…

5409

.3

5545

.8

…

8539

.8

P

aid

jobs

onl

y A

gric

ultu

re (

%)

1.1

3.5

1.1

18.3

21

.8

17.2

65

.6

63.0

65

.4

46.9

45

.7

42.0

M

inin

g/Q

uarr

ying

(%

) 0.

2 0.

1 0.

3 1.

1 1.

6 1.

4 0.

7 0.

7 0.

8 0.

7 0.

8 0.

9 M

anuf

actu

ring

(%

) 22

.0

18.5

20

.4

12.9

18

.4

15.9

9.

0 11

.7

11.1

11

.3

14.2

13

.9

Util

ities

(%

) 1.

1 0.

6 0.

3 0.

2 0.

6 0.

8 0.

1 0.

1 0.

1 0.

2 0.

3 0.

3 C

onst

ruct

ion

(%)

3.1

2.3

3.8

2.3

2.5

3.6

1.3

1.3

1.2

1.7

1.7

2.3

Tra

ding

(%

) 32

.4

41.8

38

.5

38.1

31

.3

35.2

14

.9

14.7

13

.8

22.7

22

.0

23.7

T

rans

port

/Com

mun

icat

ion

(%)

5.7

7.5

8.0

6.1

4.4

5.6

0.8

1.1

1.8

2.7

2.7

3.8

Fina

ncia

l Ser

vice

s (%

) 4.

4 4.

3 5.

2 0.

8 1.

2 2.

3 0.

2 0.

3 0.

3 0.

7 1.

0 1.

6 C

omm

unity

& O

ther

Ser

v. (

%)

29.9

21

.5

22.4

20

.2

18.2

18

.0

7.6

7.2

5.5

13.1

11

.6

11.6

A

ll (%

) 10

0.0

100.

0 10

0.0

100.

0 10

0.0

100.

0 10

0.0

100.

0 10

0.0

100.

0 10

0.0

100.

0 N

umbe

r of

wor

kers

(in

‘00

0)

395.

2 64

6.9

1186

.5

1065

.9

1134

.6

2313

.0

2594

.2

3139

.8

4449

.3

4055

.2

4921

.3

7948

.8

Sour

ce: A

utho

rs b

ased

on

GL

SS s

urve

ys.

40

Tab

le 1

.20:

Em

ploy

men

t, un

empl

oym

ent,

and

unde

rem

ploy

men

t rat

es (%

), 19

91 to

200

6

Em

ploy

men

t (pa

id o

r no

t)

Em

ploy

men

t (pa

id o

nly)

U

nem

ploy

men

t (na

rrow

) U

nem

ploy

men

t (br

oad)

U

nder

empl

oym

ent

91

/92

98/9

9 05

/06

91/9

2 98

/99

05/0

6 91

/92

98/9

9 05

/06

91/9

2 98

/99

05/0

6 91

/92

98/9

9 05

/06

Gha

na

75.9

…

70

.5

53.5

52

.5

51.5

2.

7 1.

9 2.

3 4.

1 5.

7 5.

0 7.

9 …

5.

7 Se

x

Mal

e 76

.2

…

71.5

59

.7

56.1

57

.1

2.7

2.3

2.4

3.8

5.2

4.6

8.5

…

5.8

F

emal

e 75

.6

…

69.6

48

.3

49.3

46

.5

2.6

1.5

2.3

4.3

6.1

5.3

7.3

…

5.7

Loc

ality

Mal

e 54

.9

…

61.4

53

.3

53.4

59

.7

8.9

6.8

5.5

11.0

13

.6

9.7

6.1

…

1.8

Fem

ale

59.3

…

53

.4

54.5

52

.3

50.2

5.

0 3.

1 4.

2 8.

1 12

.3

8.2

5.7

…

2.3

Acc

ra

All

57.4

…

57

.3

54.0

52

.8

54.8

6.

7 4.

9 4.

8 9.

4 12

.9

8.9

5.9

…

2.1

M

ale

63.3

…

66

.3

54.2

52

.6

57.8

5.

1 3.

3 3.

6 5.

8 6.

3 5.

3 5.

7 …

5.

3 Fe

mal

e 64

.4

…

62.1

53

.1

49.9

50

.5

6.2

2.5

3.7

7.8

6.6

7.1

6.9

…

4.7

Oth

er

Urb

an

All

63.9

…

64

.0

53.6

51

.1

53.8

5.

7 2.

9 3.

6 6.

9 6.

5 6.

3 6.

3 …

5.

0

Mal

e 84

.4

…

76.5

62

.8

57.9

56

.2

0.9

1.0

1.0

1.9

3.1

3.0

10

…

6.9

Fem

ale

82.8

…

77

.1

45.4

48

.5

43.7

0.

7 0.

8 1.

1 2.

3 4.

6 3.

8 7.

7 …

7.

0

Rur

al

All

83.6

…

76

.8

53.4

52

.9

49.5

0.

8 0.

9 1.

1 2.

1 3.

9 3.

4 8.

8 …

7.

0 Q

uint

ile

L

owes

t 81

.0

…

70.3

44

.5

40.6

35

.0

1.0

0.8

1.8

2.6

4.8

6.6

6.9

…

5.6

S

econ

d 77

.3

…

71.9

49

.3

50.9

47

.0

1.1

0.2

1.9

2.5

3.8

4.2

7.9

…

5.4

T

hird

75

.6

…

69.5

51

.8

50.9

51

.1

3.1

1.6

2.4

4.2

4.7

4.3

8.4

…

6.0

F

ourt

h 73

.5

…

70.3

55

.0

55.1

57

.6

3.2

3.1

2.3

4.9

6.1

4.2

8.4

…

6.5

H

ighe

st

73.3

…

70

.6

63.7

60

.5

62.1

4.

3 2.

9 2.

9 5.

7 7.

9 5.

6 7.

6 …

5.

1 P

over

ty S

tatu

s

Ver

y po

or

79.4

…

70

.7

46.7

43

.6

33.9

1.

1 0.

6 1.

7 2.

7 4.

9 6.

7 7.

5 …

5.

8

Poo

r 76

.4

…

71.4

51

.2

49.7

45

.3

2.3

0.5

2.5

3.2

3.3

5.5

8.2

…

5.4

N

on p

oor

73.5

…

70

.3

58.5

56

.3

56.0

3.

8 2.

6 2.

4 5.

2 6.

4 4.

6 8.

0 …

5.

7 So

urce

: Aut

hors

usi

ng G

LSS

dat

a.

Not

es:

Em

ploy

men

t ra

te i

s de

fine

d as

the

per

cent

age

of i

ndiv

idua

ls a

ged

betw

een

15 a

nd 6

4 de

clar

ing

a jo

b in

the

las

t 7

days

. T

wo

stat

istic

s ar

e pr

esen

ted,

the

fir

st

incl

udin

g al

l job

s, p

aid

or n

ot w

hile

the

seco

nd o

ne li

mit

itsel

f to

pai

d em

ploy

men

t; na

rrow

une

mpl

oym

ent r

ate

is th

e pe

rcen

tage

of

indi

vidu

als

avai

labl

e an

d lo

okin

g fo

r a

job

in r

elat

ion

to t

he l

abou

r fo

rce;

bro

ad u

nem

ploy

men

t ra

te i

s on

ly c

once

rned

with

ind

ivid

uals

ava

ilabl

e fo

r w

ork,

not

nec

essa

rily

loo

king

; an

d fi

nally

un

dere

mpl

oym

ent r

ate

is th

e pe

rcen

tage

of

wor

king

indi

vidu

als

will

ing

to w

ork

mor

e ho

urs.

41

Tab

le 1

.21:

Sha

res

of e

mpl

oym

ent b

y ty

pe o

f em

ploy

men

t and

geo

grap

hic

loca

tion

(%),

1991

to 2

006

Acc

ra

Oth

er U

rban

R

ural

G

hana

1991

/92

1998

/99

2005

/06

1991

/92

1998

/99

2005

/06

1991

/92

1998

/99

2005

/06

1991

/92

1998

/99

2005

/06

Stat

us in

Em

ploy

men

t

Wag

e Pu

blic

25

.5

14.6

15

.1

21.1

14

.1

13.3

7.

6 5.

0 3.

5 12

.9

8.4

8.0

W

age

Priv

ate

Form

al

16.7

14

.2

26.3

6.

7 7.

1 11

.2

2.5

3.0

3.9

4.9

5.4

9.2

W

age

Priv

ate

Info

rmal

6.

7 8.

9 12

.8

4.7

2.4

8.3

1.8

2.5

4.1

3.0

3.3

6.6

S

elf-

empl

oym

ent A

gric

ultu

re

0.2

2.6

0.5

14.8

20

.2

15.1

63

.5

61.0

62

.5

44.5

44

.0

39.6

Sel

f-em

ploy

men

t Non

Agr

. 51

.0

59.7

45

.3

52.8

56

.2

52.1

24

.7

28.5

25

.9

34.6

39

.0

36.5

All

100.

0 10

0.0

100.

0 10

0.0

100.

0 10

0.0

100.

0 10

0.0

100.

0 10

0.0

100.

0 10

0.0

Sour

ce: A

utho

rs u

sing

GL

SS d

ata.

42

Earning trends

1.81 Investigating earnings in the context of a mainly agrarian developing economy could be a daunting task since the concept is rather ambiguous then. In the case of wage employment, earnings can be easily defined as salary received in cash plus the value of any other payments in kind. However defining earnings in the case of the self-employed is rather more difficult as the declared earnings are likely to include return on both human capital and physical capital, and much of the activity of the self-employed is not monetized. Still, in the case of Ghana, the details available in the GLSS surveys and the overall quality of the survey make it feasible to assess with reasonable confidence earnings trends, as well as the determinants of earnings.

1.82 For Ghana as a whole, the average annual individual earnings in real terms stood at 8.8 millions cedis per worker in 2005/065, a substantial increase from the 5.4 millions cedis figure in the early 1990s (Table 1.22). The existing gender gap in earnings found in early 1990s has been increasing considerably as the increase in earnings was much larger for male workers (from 5.8 to 10.6 millions cedis) than for female workers (from 4.9 to 7.2 millions cedis). The “relative” stagnation in consumption level in Accra between 1998/99 and 2005/06 is confirmed by the earning statistics that shows no increase (actually, a small decline) in average earnings in the capital. By contrast, the large increase in expenditure (and the decline in poverty) found previously in non-Accra areas over the last seven years goes hand-to-hand with the very large increase in employment earnings. Also, the increase in earnings since 1998/99 has benefited all quintiles defined in terms of consumption, and even slightly more the poorest quintiles.

1.83 In 2005/06, public sector wage earners were still enjoying, on average, the best salaries followed by workers from the private formal sector and the self-employed in non-agricultural activities. Self-employed farmers are still at the bottom of the scale in terms of earnings. If we analyse the figures by industry, workers in agriculture are by far the lowest earners (5.4 millions cedis in 2005/06) followed by workers in manufacturing (9.7 million cedis). At the other extreme, white collar workers in the financial sector enjoy the best pay check, followed closely by individuals in the utility industry (water and electricity, among others).

5 All earning figures found in this section do not take into account unpaid – usually family based - work. All figures are in Accra, January 2006 constant cedis.

43

Table 1.22: Average Annual Earnings (in ‘000 cedis, Accra January 2006 prices) and Weekly Hours Worked, 1991/2006

Earnings Hours Worked 1991/92 1998/99 2005/06 1991/92 1998/99 2005/06

Ghana 5,358 5,818 8,961 37.3 40.6 42.5 Sex Male 5,772 7,016 10,608 39.8 43.2 44.1 Female 4,926 4,635 7,173 34.8 38.0 40.8 Locality

Male 11,018 14,318 14,783 44.5 55.1 54.1 Female 8,310 9,880 8,989 42.4 57.6 52.0

Accra

All 9,488 12,055 12,072 43.3 56.4 53.1 Male 8,408 8,687 14,651 47.4 48.5 47.6 Female 6,732 6,321 9,811 41.2 42.4 42.2

Other Urban

All 7,506 7,395 12,199 44.1 45.2 44.9 Male 4,207 5,047 7,544 36.5 39.2 39.7 Female 3,418 2,817 5,204 30.2 32.0 37.0

Rural

All 3,846 3,963 6,449 33.6 35.7 38.4 Quintile Lowest 2,578 2,586 4,914 33.6 34.8 44.2 Second 3,884 3,633 6,089 34.9 34.5 38.4 Third 4,985 4,655 7,191 36.4 38.4 39.8 Fourth 5,278 5,755 8,988 37.4 41.8 43.1 Highest 8,042 9,277 13,454 41.4 47.2 45.4 Poverty Status Very poor 3,249 2,794 4,829 34.0 34.3 44.8 Poor 4,173 4,118 5,625 36.2 35.2 38.3 Non poor 6,714 6,968 9,838 39.3 43.3 42.6 Status in employment Wage Public 8,975 11,574 17,691 40.5 43.2 43.0 Wage Private Formal 7,664 9,515 12,530 48.2 52.4 52.0 Wage Private Informal 5,280 4,997 7,716 44.5 52.2 47.7 Self-employment Agriculture 2,696 2,730 5,187 31.8 33.9 36.3 Self-employment Non Agr. 7,112 7,159 10,336 41.1 43.9 45.5 Industry Agriculture 2,870 2,850 5,371 32.3 34.4 36.4 Mining/Quarrying 12,088 16,310 18,604 47.5 59.6 51.5 Manufacturing 6,478 6,177 9,711 38.1 40.1 42.8 Utilities 7,806 10,138 20,370 46.1 46.1 48.4 Construction 6,743 7,335 11,830 45.2 37.6 41.9 Trading 6,872 7,401 10,202 42.4 46.2 48.0 Transport/Communication 9,505 14,914 13,733 56.0 60.1 63.3 Financial Services 14,147 17,409 21,434 44.4 49.6 48.7 Community & Other Services 8,728 7,946 13,217 39.8 45.0 43.5 Source: Authors using GLSS data.

44

1.84 To have a better understanding of the determinants of labour earnings in Ghana, multivariate analysis has been conducted using standard Heckman models. Preliminary results are presented in Table 1.23. It is likely that these results will change a bit as more detailed work is done with the survey, but the key findings are likely to remain valid. The labour force participation and wage regressions are estimated using three mutually exclusive and exhaustive sub-samples: Accra, Other Urban and Rural (only the wage regressions are shown in Table 1.23). The results suggest that annual earnings by male individuals are much higher than for female counterparts. Male earnings are 50 percent higher than female earnings (51 percent in Accra, 47 percent in Other Urban and 62 percent in rural areas). In particular the “male premium” has been increasing steadily in Accra, going from 22 percent in the early 1990s, to 36 percent in 1998/99 to more than 50 percent in 2005/06. Male-female differences in the number of hours worked explain part of the differences but this supply-effect is limited as the differences in hours worked is fairly small (see Table 1.22).

1.85 The returns to education confirm the lack of reward of having only completed the primary level. That is, in most sample, having a primary education as your highest grade completed does not lead to a statistically significant gain in earnings as compared to having no education at all. The education premium for having some secondary education is more significant, particularly for the second tier secondary. Obviously, tertiary education is having the highest reward on the labour market. Compared to non-farm self-employed workers, public sector workers benefit from the larger employment status premium in annual earnings, at around 40 percent in Accra and rural areas, and 21 percent in other urban areas. That public sector premium is much larger than before in recent years, reflecting the recent pay increases for civil servants. In all localities (but particularly in Accra), changes over time in earning premium associated with employment status (as well as to some extent education) have led to an increase in inequality in earnings.

1.86 Among the different industries only mining and financial services stand out. Compared to the trade industry, mining workers enjoy a large earnings premium reflecting in part longer hours of work, as shown in Table 1.22 and probably also harder physical work. Workers from the financial service industry also enjoy a large premium, particularly in Accra.

1.87 Finally, after having controlled for differences in education and economic structure or employment type, the northern Savannah zone does not seem to be that different in terms of earnings potential. In urban areas, the regression results suggest few statistically significant differences in earnings between ecological zones. In rural areas, the workers from the Forest zone are still better off although that location premium has diminished continuously over the last 15 years, from a premium close to 40 percent in 1991/92 to only 19 percent lately.

45

Tab

le 1

.23:

Det

erm

inan

ts o

f wag

e ea

rnin

gs (H

eckm

an r

egre

ssio

ns)

A

ccra

Oth

er U

rban

Rur

al

19

91/9

2 19

98/9

9 20

05/0

6

1991

/92

1998

/99

2005

/06

19

91/9

2 19

98/9

9 20

05/0

6 M

ale

***

0.21

7 **

* 0.

363

***0

.505

***

0.40

4 **

* 0.

473

***

0.46

6

***

0.71

6 **

* 0.

597

***

0.62

4 A

ge

* 0.

050

***

0.06

2 **

*0.0

71

**

* 0.

078

***

0.06

8 **

* 0.

088

**

* 0.

056

***

0.05

1 **

* 0.

053

Age

squ

ared

0.

000

***-

0.00

1 **

*-0.

001

**

*-0.

001

***-

0.00

1 **

*-0.

001

**

*-0.

001

***

0.00

0 **

*-0.

001

Edu

catio

n L

evel

No

Edu

catio

n (o

mitt

ed)

Pr

imar

y 0.

020

0.19

0 0.

071

**

0.2

79

* 0.

233

0.00

3

0.09

0 **

* 0.

169

0.07

7 Se

cond

ary

(low

er)

***

0.46

0 0.

065

0.21

7

***

0.30

8 **

* 0.

479

***

0.23

2

* 0.

116

***

0.28

1 0.

091

Seco

ndar

y (H

ighe

r)

***

0.91

7 **

* 0.

527

***

0.41

4

***

0.53

0 **

* 0.

901

***

0.33

2

** 0

.271

**

* 0.

484

0.12

5 Po

st S

econ

d.

***

1.16

4 **

* 0.

414

***

0.70

4

***

0.57

3 **

* 0.

882

***

0.76

1

***

0.57

6 **

* 0.

768

***

0.44

2 E

mpl

oym

ent S

tatu

s

Non

-Far

m S

elf-

Em

ploy

ed (

omitt

ed)

Wag

e Pu

blic

*0

.278

0.

064

***0

.398

-0.0

03

**0.

252

**0.

207

0.

199

***0

.326

**

*0.4

48

Wag

e Pr

ivat

e Fo

rmal

0.

006

-0.1

57

*0.1

63

**

-0.3

56

0.07

8 -0

.027

-0.1

57

0.13

1 0.

019

Wag

e Pr

ivat

e In

form

al

-0.0

41

***-

0.54

4 **

*-0.

346

**

*-0.

682

-0.1

56

***-

0.23

2

**-0

.379

0.

129

-0.0

49

Farm

Sel

f-E

mpl

oyed

**

*-1.

152

-0.9

07

-0.5

19

**

*-0.

913

***-

1.15

6 **

*-0.

726

**

*-0.

740

***-

0.65

1 **

*-0.

525

Indu

stry

Tra

de (

omitt

ed)

A

gric

ultu

re

-0.0

57

-0.3

65

-0.1

32

0.

283

-0.0

21

-0.0

63

-0

.326

-0

.220

-0

.201

M

inin

g **

*-0.

578

***

0.94

9 **

* 0.

268

**

* 1.

487

***

0.68

2 **

* 0.

463

-0

.111

**

0.5

22

* 0.

432

Man

ufac

turi

ng

** 0

.369

-0

.146

0.

053

-0

.037

-0

.172

-0

.016

***-

0.58

9 **

*-0.

240

0.02

4 U

tiliti

es

0.15

5 **

* 0.

611

0.18

5

-0.2

12

-0.3

16

0.27

2

-0.1

94

***

0.77

5 0.

119

Con

stru

ctio

n 0.

268

0.18

0 0.

101

0.

136

-0.4

54

-0.1

29

-0

.130

-0

.252

0.

183

Tra

nspo

rt &

Com

mun

icat

ion

** 0

.493

0.

233

0.09

9

0.23

4 0.

070

0.08

0

0.14

4 0.

265

0.08

7 Fi

nanc

ial S

ervi

ces

***

0.56

8 0.

234

***

0.30

5

* 0.

460

0.21

8 0.

033

0.

333

***

0.65

4 -0

.193

C

omm

unity

& O

ther

Ser

vice

s 0.

262

0.00

0 0.

068

**

0.2

98

*-0.

205

-0.0

33

-0

.187

-0

.144

*-

0.20

3 E

colo

gica

l Zon

e

Sava

nnah

(om

itted

)

Coa

stal

..

.. ..

**

*0.4

75

-0.1

99

0.08

6

***0

.278

-0

.108

0.

089

Fore

st

.. ..

..

0.15

0 -0

.066

*0

.169

***0

.385

**

*0.2

96

**0.

186

Mar

ital S

tatu

s

Mar

ried

(om

itted

)

Sing

le

***-

0.39

2 -0

.211

-0

.169

***-

0.25

0 **

-0.2

22

***-

0.22

1

***-

0.34

4 **

*-0.

397

***-

0.26

7 D

ivor

ced

0.18

6 -0

.143

0.

016

0.

010

-0.1

01

-0.0

81

0.

006

***-

0.21

8 **

*-0.

218

Wid

owed

0.

348

-0.0

54

-0.2

22

*-

0.33

1 **

-0.3

87

-0.1

37

-0

.120

**

-0.2

33

*-0.

166

Inte

rcep

t **

* 13

.432

**

* 14

.167

**

* 13

.503

***

12.7

86

***

13.2

12

***

13.4

36

**

* 13

.323

**

* 13

.002

**

* 13

.812

So

urce

: Aut

hors

usi

ng G

LSS

dat

a.

46

INCOME SOURCES

This chapter presents a preliminary analysis of the role played by different income sources in the livelihoods of households, and their contribution to income inequality over time. The chapter also includes a discussion of two important income sources that have rapidly increased in recent years: revenues from cocoa production, and remittances, both domestic and international. The impact of these income sources on poverty is analyzed using simple techniques. Key results include the fact that income inequality has increased substantially over time, that poverty among cocoa producers has decreased especially rapidly thanks to rapid progress in that sub-sector, and that the impact of international worker’s remittances on poverty may be lower than often expected.

Income Sources and Income Inequality

1.88 The analysis of disaggregated income data is valuable for analytic purposes because it provides a good indication of the main sources of livelihoods of households, from which they can finance their consumption or save. This remains the case even if total household income is often under-recorded in household surveys, as is also the case in Ghana. The GLSS surveys provide detailed information on incomes that households earn from different sources, including wage employment, agriculture, non-farm businesses, rent and transfers of different types. A total of 16 different sources are presented in Table 1.24. The table gives the share of income from different sources as well as the marginal impact on income inequality of each source for all three years of data. We discuss here a few general findings and their relationship to the increase in income inequality over time.

1.89 The three dominant components of household income are earnings from employment, other agricultural income (with incomes from cash crops as well as from roots, fruits and vegetables accounted for separately), and profit from non-farm enterprises or business. Together these three income sources account for two thirds (65.4 percent) of total reported income in 2005/06. Although this is not shown in Table 1.24, in that year, still nearly two thirds of Ghanaian households earned some income from agriculture (all agricultural income sources combined), a proportion substantially higher than that for the other two largest income sources, non-farm businesses and wages (44 percent and 28 percent of households respectively reporting income from these sources. A large majority of households also report having received, or sent, remittances, and a significant minority earn income from rent, but in both cases the average amounts and shares in total income are much smaller. Fewer households reported earning income from the public transfers and other elements included in the other income aggregate. In broad terms the income patterns for 2005/2006 are similar to those for earlier surveys, with however an increase in the share of income that comes from wage employment over time. However, there has been a sharp increase in income inequality, with the Gini index for per capita income increasing from 0.526 in 1991/92 to 0.573 in 1998/99 and finally 0.657 in 2005/06. Thus the increase in income inequality has been larger than that observed for consumption (see the discussion in Chapter 2).

1.90 As discussed in Wodon and Yitzhaki (2002), source decompositions of the Gini index have been used extensively to analyze how various sources of income affect the inequality in total income (see Annex 3 for a methodological explanation of those decompositions). Results from the decomposition by source of the Gini index are also presented in Table 1.24. For policy simulations, it is the marginal contribution of an income source that matters, and this marginal impact depends on the so-called source’s Gini Income Elasticity (GIE), as well as the share of total income from the source. When an income source has a GIE of one, it means that it moves perfectly in sync with total income, so that a change in the source does not affect the inequality in total income. A source with a GIE larger than one is affecting the richer part of the population more, while a source with a GIE smaller than one is affecting the poorer part more. Thus if an income source has a GIE larger than one, a marginal increase in the income from that source results in higher inequality. The larger the GIE is, the larger the increase in overall inequality will be. A source with a GIE equal to zero is not correlated with total income or consumption. For example, a universal allocation identical for all individuals would have a GIE of zero.

47

1.91 The results from source decompositions of the Gini index of inequality can be visualized graphically. In Figure 1.9, the share of income of a source is represented on the vertical axis. The GIE is represented on the horizontal axis. All sources on the left of an hypothetical vertical line that would cross the horizontal axis at a value of the GIE of one are inequality decreasing at the margin, while sources on the right side of the vertical line are inequality increasing. The more a source is on the left (right) of the vertical axis, the more it is inequality reducing (increasing) at the margin. A few important findings emerge from the analysis.

• Inequality neutral sources: Among the large income sources which represent a high share of total income, several have a GIE close to one and are therefore inequality neutral. This is the case for income from employment and income from roots/fruit/vegetables.

• Inequality increasing sources: the most inequality increasing source at the margin is net remittances (the difference between remittances received and sent). This means that a large share of remittances are received by comparatively richer households, so that the impact of remittances on poverty is likely to be limited (a more detailed discussion of remittances is given in paras. 1.100 – 1.103.

• Inequality decreasing sources: Two large income sources are inequality decreasing at the margin: income from cocoa production, and income from non-farm enterprises, many of which are informal and located in the service sector. A more detailed discussion of income from cocoa production is provided in the next section. This section confirms that even though the poor have comparatively less income from cocoa than the non-poor, cocoa is important to them, and their share of income from cocoa is higher than their share of other income sources, many of which are concentrated in richer urban areas.

Table 1.24: Income Sources Shares and Gini Income Elasticity, 1991-2006

1991/92 1998/99 2005/06

Income Share

Gini Income

Elasticity

Income Share

Gini Income

Elasticity

Income Share

Gini Income

Elasticity Income from employment 21,9% 1,12 22,8% 1,08 26,0% 1,03 Income from cash crop 6,3% 0,87 7,1% 0,77 6,6% 0,63 Income from roots/fruit/vegetables 8,8% 1,23 6,3% 1,30 8,6% 1,02 Other agric income 19,6% 0,54 23,5% 0,90 18,4% 0,86 Income from renting out land 0,1% 0,50 0,1% 0,69 0,0% 0,45 Income from sharecropping 0,4% 1,19 0,4% 0,95 0,2% 0,72 Income from renting out livestock 0,0% 0,40 0,0% -0,45 0,0% 0,18 Income from renting out agric. equipment 0,1% 1,48 0,1% 0,72 0,1% 1,18 Non-farm rent income 0,0% 1,17 0,1% 1,38 12,1% 1,52 Imputed rent - household owner 1,0% -0,01 1,3% 0,22 1,2% 0,20 Value of non-farm products consumed 4,0% 0,90 2,9% 0,77 1,9% 0,64 Profit from non-farm enterprises 31,4% 1,15 28,3% 0,96 21,0% 0,85 Net remittances 3,2% 1,08 4,8% 1,66 2,3% 2,04 Scholarship 0,1% 1,27 0,1% 0,69 0,0% 0,65 Income from water sold 0,1% 1,41 0,2% 1,20 0,2% 0,75 Miscellaneous income 3,1% 1,47 1,9% 0,98 1,5% 1,14 Gini index for total income per equivalent adult 0,526 0,573 0,657 Source: Authors using GLSS data.

48

Figure 1.9: Gini Decomposition by Income Source, 2005/06


Agriculture and Cocoa Producers

1.92 The poverty profiles presented in Chapter 3 for consumption-based poverty and the results available in Diallo and Wodon (2007) for assets-based poverty suggest that at about eight of ten poor individuals live in rural areas in Ghana. According to the consumption-based profile, 9.2 percent of the poor live in the rural coastal areas in 2005/2006, 27.2 percent in the rural forest areas, and 49.3 percent in the rural savannah areas. While the proportion of the poor living in rural areas was similar in 1998/99, the repartition of the poor changed, with a decrease of the share of the poor living in the rural coastal and forest areas, and a corresponding increase in the savannah areas. This differentiated geographical pattern can be linked in large part to the cocoa sector, which is concentrated in the rural forest areas and also benefits the coastal areas. Indeed, the rebound in the cocoa sector has contributed significantly to growth and poverty reduction.

1.93 As shown in Figure 1.10, the world cocoa market experienced a sharp decline in prices during the 1980s, before stabilizing in the 1990s. A second decline in prices took place from 1998 to 2001, but there has been a rebound since 2002. According to the International Cocoa Organization (2007), the decline in prices in the 1980s was due to excessive production, while for most of the 1990s, there was an overall balance between demand for and supply of cocoa.

Renting livestock

Imputed rent Renting land

Cash crop

Non-farm Autoconsumption

Scholarship

SharecroppingWater sale

Profit from non farm

Other Agric

Roots/fruits/veg

Employment

MiscellaneousRenting agric eq.

Non farm renting

Net remittances

0

.05

.1

.15

.2

.25

Share of Per Capita Income

0 .5 1 1.5 2 Gini Elasticity

49

Figure 1.10: World Prices of Cocoa Beans in Constant 2005/2006 Terms

World Prices in the Cocoa Marketin constant 2005/06 terms

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

US $ / tonne

-40.0

-30.0

-20.0

-10.0

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

US $ / tonne Annual rate %

Data source: ICCO’s Market Committee (February 2007, p. 27)

1.94 The rebound in world prices after 2001 has probably helped for the recovery and expansion of the cocoa sector by giving better incentives for cocoa production to farmers. In addition, there has been a sharp increase in cocoa yields since 2001, as well as an increase in the areas under cocoa cultivation (at a rate of about 5 percent per year). These factors have contributed to an increase in total cocoa production from 367,000 tons in 2002 to 665,000 tons in 2004, after which year production leveled off. This increase in production has generated higher revenues not only for producers and merchants, but also for the government thanks to higher export duties. Cocoa production accounts today for about a third of total export revenue. As noted by Bogetic et al. (2007), while cocoa accounted for only 10 percent of total crop and livestock production values during 2001-2005, it generated 28 percent of total agricultural growth (see Table 1.25).

Table 1.25: Contribution of the cocoa sector to Agriculture GDP growth, 1980-2006

1980-2005 1991-1995 1996-2000 2001-2005 2006

Annul Real Growth (%) 2.7 2.0 3.9 5.5 5.6

Cultivations other than cocoa and Livestock 2.6 1.5 3.4 4.5 5.8

Cocoa production and marketing 4.0 7.0 6.0 14.8 8.3

Forestry and logging 4.3 1.9 10.8 5.1 2.5

Fishing 1.8 1.8 0.6 3.0 3.6

Sectoral Share of Agricultural GDP (%) Cultivations other than cocoa and Livestock 69 69 68 68 66

Cocoa production and marketing 9 8 9 10 13

Forestry and logging 8 7 9 10 10

Fishing 14 15 14 12 11

Contribution to Agriculture GDP Growth (%) Cultivations other than cocoa and Livestock 65 51 60 55 69 Cocoa production and marketing 13 28 14 28 19 Forestry and logging 13 7 24 9 4 Fishing 10 14 2 7 7 Source: Bogetic et al. (2007), based on Ghana Statistic Service data and authors’ calculation.

50

1.95 At least four key factors are at the source of the doubling of cocoa production between 2002 and 2004: an increase in the labour supply of producers, the government’s assistance to the sector through a variety of policies, the increased competition among licensed buying companies in a context of gradual liberalization that started in the mid-1980s, and a surplus of Ivory Coast’s exports since the beginning of civil war. We turn to each of the four explanations below:

• The increase in labour supply appears clearly in data analyzed by Zeitlin (2005) from the Centre for the Study of African Economies. Evidence comes from two surveys, the first one conducted in 2002 and the other in 2004, each with about 450 cocoa farmers. The survey suggests an output increase of 34 percent which is lower than the doubling of production, but this is not surprising since part of the increase in production may be due to new farmers producing cocoa. The total number of working days spent by households on cocoa-related activities per year doubled from 328 to 640. This expansion of labour came from extra time devoted to cocoa rather than an increase in the number of persons working on the crop (household size among producers decreased over the time-period).

• The use of fertilizers increased dramatically, from 0.5 kg to 5.1 kg per household in the data gathered by Zeitlin (2005). Brooks et al. (2007) suggest that free mass spraying of insecticides on cocoa crops reduced the incidence of pests and diseases. The plantation of new tree varieties, especially in the context of old-farm rehabilitation also boosted output. Efforts put on transportation infrastructures in cocoa-growing areas reduced shipping costs (USDA 2005, ISSER 2005) and information campaigns on higher-productivity and faster-maturing tree varieties also accounted for some gains (Edwin and Masters 2005).

• Competition among licensed buying companies (LBCs) is considered another key determinant of the gains in production. Established in 1947, the Cocoa Marketing Board (CMB) monopolized the internal and external marketing of the cocoa production. Renamed the “Ghana Cocoa Board” or Cocobod in 1979, the government extended its control over the purchase of inputs, internal prices, quality standards, exports, among other things. With the gradual liberalization of the sector since the mid-1980s, LBCs were allowed to purchase domestically and export directly, buying and selling at prices fixed by the Ghana Cocoa Board. Zeitlin (2005) argues that “in spite of the fixed purchasing price, competition for producers’ output by LBCs remains an important institutional feature - indeed, a driver of growth in the cocoa sector”. Varangis and Schreiber (2001) highlight the stimulating effect of competition on producers’ efficiency as well as the positive impact it induces on profits. Likewise, Tiffin et al. (2004) tell the success story of the Kuapa Kokoo Ltd. (‘Good Cocoa Farmers’ in Twi, the local language), a company involving more than 2,000 farmers from 22 villages to volunteer and organize the seasonal delivery of 100 tons of cocoa beans per village.

• Cocoa smuggled from Côte d’Ivoire due to the civil war there has also contributed to the increase in production and exports. Brooks et al. (2007) estimate this inflow at between 120,000 to 150,000 tons in 2004, which is indeed very high (representing half of the increase in production between 2002 and 2004; the question of prices giving incentives to smuggle was raised by Bulir (2002) to explain low levels of output prior to its doubling.)

1.96 Even after discounting quantities smuggled in from Côte d’Ivoire, given the increase in cocoa prices, yields and production, one would expect poverty among cocoa farmers to have been reduced substantially over time, and indeed faster than for other population groups. Table 1.26 provides estimates of poverty among cocoa producers from the GLSS surveys. In 2005/2006, according to the survey, the cocoa industry provided a livelihood then mobilized 249,336 households, thereby contributing to the livelihood of 1.9 million people (6.3 percent of the population). Of these, 23.9 percent were poor, a lower rate than for the population as a whole. By contrast in 1991/92 cocoa producers were poorer than the population as a whole. Note also that in 2005/2006, the differences between cocoa producers and the population as a whole is even larger in terms of the poverty gap and squared poverty gap than for the headcount index.

51

Table 1.26: Poverty Status of Cocoa Producers, Ghana 1991-2006

1991/1992 1998/1999 2006

Poverty, population as a whole Headcount index of poverty 51,7 39,5 28,5 Poverty gap 18,5 13,9 9,6 Squared poverty gap 8,8 6,6 4,6

Poverty, cocoa producers Headcount index of poverty 60,1 36,7 23,9 Poverty gap 21,3 9,4 6,0 Squared poverty gap 10,0 3,4 2,1


1.97 While poverty reduction among cocoa producers has been spectacular, this does not mean that producers are still not vulnerable to changes in world cocoa prices. Indeed, income from the sale of cocoa represents a large share of total household income among producers. What could be the impact on poverty of changes in producer prices? The answer to this question is provided in Table 1.27 using fairly strong, but also straightforward assumptions: we measure the income obtained from cocoa production by households, assess the difference in income that would follow alternative producer prices, and assume that this difference in income translates into an equivalent difference in the consumption per capita of households used to measure poverty. More sophisticated methods could be used to measure the “general equilibrium” effect of a drop or increase in cocoa producer prices, but such simulations require a much larger number of assumptions which are a subject of debate. The estimations given below provide “first round” likely poverty effects from lower or higher producer prices paid to households due to a drop or increase in world cocoa prices. In the case of a drop in prices, we assume that households cannot compensate their cocoa income loss through other activities, at least in the short run (work on other African countries on cash crops suggests that this is the case). In the case of a price increase, we assume that all of the increase in revenues is used for household consumption.

1.98 As shown in Table 1.27, a decrease of 100 cedis in the producer price of cocoa per kilo would increase poverty among producers by about three points, which is relatively small. Similarly, an increase in the producer price of 100 cedis would reduce poverty by about two percentage points. Thus, not only are cocoa producers less poor on average today than the population as a whole, and certainly less poor than the typical rural household, they seem not to be too vulnerable to changes in producer prices. Probably, at least some cocoa producers remain vulnerable to external price shocks. But overall, the reduction in poverty observed in this group would probably not be reversed by adverse price shocks of reasonable magnitude. Also, because cocoa producers represent only a small portion of the overall population, national poverty measures are even significantly less sensitive to changes in cocoa producer prices.

52

Tab

le 1

.27:

Im

pact

of c

hang

es in

coc

oa p

rice

on

pove

rty,

Gha

na 2

006

P

erce

ntag

e ch

ange

s in

indi

vidu

al p

rodu

cer

pric

es

-2

0%

-15%

-1

0%

-5%

N

o ch

ange

+5

%

+10%

+1

5%

+20%

A

vera

ge e

quiv

alen

t red

uctio

n in

Ced

is

-180

-1

35

-90

-45

- 45

90

13

5 18

0 P

over

ty, p

opul

atio

n as

a w

hole

Hea

dcou

nt in

dex

of p

over

ty

29,1

29

,0

28,8

28

,8

28,5

28

,4

28,3

28

,2

28,2

Po

vert

y ga

p 9,

8 9,

8 9,

7 9,

6 9,

6 9,

6 9,

5 9,

5 9,

5 Sq

uare

d po

vert

y ga

p 4,

7 4,

7 4,

6 4,

6 4,

6 4,

6 4,

6 4,

5 4,

5 P

over

ty, c

ocoa

pro

duce

rs

H

eadc

ount

inde

x of

pov

erty

27

,9

27,4

26

,1

25,6

23

,9

22,9

22

,3

21,7

21

,4

Pove

rty

gap

7,8

7,2

6,7

6,3

6,0

5,7

5,4

5,2

5,0

Squa

red

pove

rty

gap

3,1

2,7

2,5

2,3

2,1

2,0

1,9

1,7

1,6

A

bsol

ute

chan

ges

in m

edia

n pr

oduc

er p

rice

s

80

0

Ced

is/K

g 82

5

Ced

is/K

g 85

0

Ced

is/K

g 87

5

Ced

is/K

g 90

0

Ced

is/K

g 92

5

Ced

is/K

g 95

0

Ced

is/K

g 97

5

Ced

is/K

g 10

00

Ced

is/K

g P

over

ty, p

opul

atio

n as

a w

hole

Hea

dcou

nt in

dex

of p

over

ty

28,9

28

,8

28,8

28

,7

28,5

28

,5

28,4

28

,4

28,3

Po

vert

y ga

p 9,

7 9,

7 9,

6 9,

6 9,

6 9,

6 9,

5 9,

5 9,

5 Sq

uare

d po

vert

y ga

p 4,

6 4,

6 4,

6 4,

6 4,

6 4,

6 4,

6 4,

6 4,

6 P

over

ty, c

ocoa

pro

duce

rs

H

eadc

ount

inde

x of

pov

erty

26

,8

26,0

25

,6

25,1

23

,9

23,2

22

,8

22,5

22

,1

Pove

rty

gap

6,8

6,6

6,3

6,1

6,0

5,8

5,7

5,5

5,4

Squa

red

pove

rty

gap

2,5

2,4

2,3

2,2

2,1

2,0

2,0

1,9

1,8

Sour

ce: A

utho

rs u

sing

GL

SS d

ata.

53

1.99 Table 1.28 shows that today about a quarter of cocoa producers are poor. The share of cocoa production that is produced by the poor is similar, as shown in Table 1.26, even though some of the richer cocoa producers in the upper brackets of welfare tend to have larger quantities produced. The poorest 20 percent of the producers earn only 8 percent of cocoa revenues from producer sales, while the richest 20 percent earn 32 percent of cocoa revenues. This also suggests that across-the-board subsidies or support for all producers, while potentially beneficial for the growth of the sector, would not necessarily be well targeted to the poor (even if they would help in reducing overall income inequality, as mentioned in the previous section).

Table 1.28: Cocoa Production and Sales Data by Consumption Decile, Ghana 2006

Consumption decile

Total Income

from cocoa

(billions of cedis)

Mean household

income from cocoa

(cedis)

Share in total cocoa

income

Cumulative share (%) in total cocoa

income

Population with

positive cocoa sales

Population share in

population with

positive sales (%)

Mean Income

for those with

positive sales

(cedis) Poorest 10% 78,3 260 312 2,6 2,6 113 350 4,2 5 337 770 D2 113,0 338 808 3,8 6,4 270 958 10,1 3 000 431 D3 250,2 678 127 8,4 14,7 318 126 11,9 4 945 041 D4 225,0 550 061 7,5 22,3 323 426 12,1 3 601 356 D5 312,8 726 005 10,4 32,7 397 638 14,8 4 105 582 D6 370,5 759 357 12,4 45,1 385 779 14,4 4 752 120 D7 384,4 766 116 12,8 57,9 263 858 9,8 6 085 019 D8 301,6 518 084 10,1 68,0 259 699 9,7 4 335 762 D9 451,9 660 620 15,1 83,1 204 359 7,6 6 321 341 Richest 10% 506,9 555 693 16,9 100,0 142 508 5,3 7 311 617 National 2 994,7 597 576 100,0 2 679 701 100,0 5 049 004


International Worker’s Remittances and Domestic Private Transfers

1.100 This section provides a brief summary on trends in remittances and their impact on poverty. We consider first international remittances. Importantly, when discussing the impact of international remittances on poverty, it is important to clarify the concepts that are used. Total “remittances” or private net transfers from abroad were estimated in 2005 at US$1.55 billion by the International Monetary Fund and the Bank of Ghana. This is also the definition of “remittances” used in a Bank of Ghana working paper by Addison (2004) who estimated total remittances to have reached at US$1.017 billion in 2003, with the following definition remittances: “Current transfers between other sectors of the economy and non-residents comprise those occurring between individuals, between non-governmental institutions or organizations (or between the two groups) or between non-resident government institutions and individuals or non-governmental institutions. The same basic items described above are generally applicable. In addition, there is the category of workers remittances. Workers remittances covers current transfers by migrant who are employed in other economies and

considered resident there. This category of transfers often involves related persons.”6 By contrast,

following standard practice in the economics literature based on household surveys, we focus on a subset of total foreign remittances that directly benefit households at home, namely on international worker’s remittances. Or even more precisely, the authors focus on the measurement of net private transfers

6 In a recent press release of March 27, 2006, the Bank of Ghana estimated that “total private inward transfers”

received from NGOs, religious groups, individuals etc. through financial intermediaries reached $4.76 billion in 2005, of which $1.39 billion (29.2 percent) represented remittances from individuals. This is a different concept form the net current private transfers used when analyzing Balance of Payment accounts as done by Addison (2004).

54

received by households from friends and relatives living abroad, and these typically consist of international workers’ remittances.

1.101 The data from the Ghana Living Standards Surveys suggest that the net private transfers or international worker remittances received from abroad reached US$253 millions in 2005/06, of which US$232.5 million were received in cash7. While these estimates of workers’ remittances may appear to be low as compared to the total net foreign transfers received in the country (both by households and other entities such as churches, NGOs, etc.), they are actually higher than the official estimates of worker’s remittances provided by the IMF. Indeed, according to the IMF Balance of Payment yearbook (which is based itself on data from the Central Bank pf Ghana), international worker’s remittances reached US$99 million in 2005 (the Balance of Payment yearbook states that “other current transfers” amounted to a much larger US$1.451 billion, so that total current private net transfers reached US$1.55 billion in the IMF data, the figure quoted above). In terms of trends over time, the statistics from the International Monetary Fund suggest that international workers remittances increased from a low base of US$7 million in 1987 to US$99 million in 2005 (see Figure 1.11). While limited as compared to total net transfers, these amounts are large. At the same time, Ghana is not in the top 15 African recipient countries of international workers’ remittances, even if most of these leading recipient countries have a smaller population than Ghana. The main reason for GLSS5-based figures of international workers’ remittances to be higher than the IMF estimates is probably that survey-based estimates take into account all transfers received by households whether through formal or informal channels, while National Account estimates refer typically primarily to transactions going through official channels such as banks.

Figure 1.11: Worker’s Remittances, Ghana, 1987-2005

0

20

40

60

80

100

120

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Year

Rem

itta

nces

(in

US$

mil

lion

)

Sources: Authors, based on IMF Balance of Payments, various issues

1.102 The trend over time in international workers remittances as it emerges from the GLSS data is given in Table 1.29 which suggests that households received US$49 million of international remittances in 1991/92, US$143 million in 1998/99, and US$271 millions in 2005/06. The majority of those international remittances are coming from outside Africa and the increase over the period is almost exclusively explained by remittances received from outside Africa. Thus, estimates of the volume of international remittances are about twice as large in the household surveys than in official statistics. Domestic remittances are even slightly larger than international remittances in 2005/06, although they have not increased at the same rate as international remittances. While the level of domestic and international remittances has increased over time, the proportion of households receiving income from 7 These estimates come from Section 11B of the GLSS5 questionnaire. We summed up cash transfers (question 9)

received from abroad (question 14) that do not need to be repaid (question 8). The cedis figures were converted in dollars using an exchange rate of 9072 cedis per dollar (average rate during the period of the survey). The preliminary GLSS5 sampling fraction (594) was used to compute the nationwide estimates

55

domestic remittances has remained relatively stable over the last 15 years, varying from 24 percent to 34 percent, with actually a fall in 2005/2006. Similarly, the proportion of households receiving international remittances has remained around 6 percent to 8 percent.

1.103 Apart the overall size of domestic and international remittances, a crucial question concerns the distributional effects of those remittances. Are domestic and international remittances benefiting mainly the well-off Ghanaians or the most destitute? The data from the GLSS surveys indicate that richer households living in urban areas receive most of the international remittances, and even for domestic remittances, non-poor households benefit the most. As a result, the impact of remittances on poverty is not very high, as was conjectured when looking at the contribution of remittances to inequality. If households did not benefit from any remittances art all, the share of the population in poverty would increase by about two percentage points, as shown in Table 1.30.

Table 1.29: Total Remittances, in million of current dollars, GLSS-based estimates

From Abroad Domestic Africa Non Africa Total 1991/92 In cash 66.4 6.0 31.6 104.1 In food 18.5 0.4 1.0 19.8 In non-food 15.3 2.0 7.8 25.1 Total 100.2 8.5 40.3 149.0 1998/99 In cash 141.9 7.8 108.5 258.2 In food 36.9 0.8 1.4 39.1 In non-food 32.8 3.6 21.0 57.4 Total 211.7 12.1 130.9 354.8 2005/06 In cash 184.0 11.3 221.2 416.5 In food 28.0 0.8 1.5 30.4 In non-food 59.1 1.6 34.5 95.2 Total 271.1 13.7 257.2 542.0


Table 1.30: Impact of remittances on poverty and inequality

1991/92 1998/99 2005/06 Poverty Gini Poverty Gini Poverty Gini

With remittances 0.517 0.360 0.395 0.378 0.285 0.394Without remittances Foreign 0.523 0.359 0.402 0.376 0.291 0.390 Domestic 0.530 0.363 0.412 0.378 0.301 0.397 All remittances 0.536 0.361 0.418 0.377 0.306 0.393


56

SECTION III: BASIC SERVICES AND PUBLIC SPENDING

ACCESS TO AND USAGE OF BASIC SERVICES

This section provides a basic analysis regarding the access to basic services for education, health, and infrastructure (water, electricity and sanitation) for various segments of the population, comparing poor to non-poor households. We also provide trends in access over time. In addition, we provide estimates of the incidence of public spending in various areas. The results suggest that while there has been substantial progress in usage of basic services for health, thanks in part to the extension of pharmacy and chemical stores, less progress has been achieved in education (although our assessment based on the 2005/2006 GLSS predates some important initiatives taken by the government since then). The results also suggest that there has been an increase in access to water, sanitation, and electricity, but that subsidies for utilities implicit in the tariffs structures for residential customers tend to be very poorly targeted.

Education

1.104 Both school attendance and the quality of education received are long term causal factors of poverty, growth, and more generally a household’s quality of life, if not immediately at least in the future. Especially in Ghana, the quality of education has been a recurrent issue that is difficult to tackle and measured. Unfortunately this is still true in this study as GLSS data can help to measure the quantity of education received by children and young adults (through enrolment ratios), but not its quality. This section focuses therefore on school attendance and school enrolment at two levels-primary and secondary. As school enrolment increases over time, literacy rates and educational attainment for the whole population is also likely to rise as well.

1.105 School attendance of children at the primary and secondary school levels is examined in terms of net enrolment rates which are the proportion of those in the relevant age range attending primary or secondary school; and in terms of gross enrolment rates where the age of the student is not taken into account. When some students delay their schooling, repeat grades or return to school after an absence, gross enrolment rates can easily reach over 100 percent. At the primary level net enrolment rates at the national level increased from around 74 percent in 1991-1992 to 83 percent in 1998-1999, with a small additional increase to 85 percent in 2005-2006 (see Table 1.31). The increase in gross enrolment has been rather larger as it went from 107 percent in the early 1990s to 146 percent in 2005/06. The much larger increase of the gross enrolment suggests than many children either start school too late or repeat grades, suggesting quality issues in the education received.

1.106 During the last 15 years the urban/rural gap in net enrolment rates in primary school went from 10 percent to 12 percent in favour of the urban areas, while it went from 18 percent to 25 percent when we use gross enrolment rates. A large part of the urban/rural differences is due to much lower enrolment rates in the savannah area. Although the enrolments rates for poor children have increase appreciably during the 15 year period they did not manage to close the gap between them and the children coming from richer households. By contrast, it can be shown that enrolment rates for girls were slightly below that for boys in previous years, but were almost at parity in 2005-2006.

57

Table 1.31: School enrollment, net and gross, primary and secondary (%)

1991/92 1998/99 2005/06 Poor Non

Poor All Poor Non

Poor All Poor Non

Poor All

Urban Primary Net 75.9 88.8 84.6 88.4 91.0 90.4 85.5 96.5 95.0 Primary Gross 111.1 125.6 120.8 116.4 127.4 124.3 152.6 168.0 165.8 Secondary Net 38.7 45.6 43.4 44.7 51.2 49.9 36.1 57.7 55.0 Secondary Gross 46.4 54.2 51.7 48.7 56.0 54.3 44.2 72.6 68.8 Rural Primary Net 65.7 77.7 69.3 74.6 87.2 80.2 70.8 87.2 79.9 Primary Gross 94.7 113.0 100.2 112.6 126.5 118.7 125.3 154.0 139.0 Secondary Net 33.0 36.8 34.2 30.6 40.9 35.4 22.3 39.1 32.0 Secondary Gross 36.9 42.7 38.7 38.5 47.4 42.5 25.6 45.0 35.3 Total Primary Net 67.5 83.4 74.1 76.9 88.9 83.4 72.6 91.2 84.8 Primary Gross 97.6 119.4 106.7 113.3 126.9 120.5 128.2 160.1 147.1 Secondary Net 34.2 41.7 37.5 33.3 46.1 40.7 24.5 48.2 40.9 Secondary Gross 38.9 49.1 43.4 40.6 51.6 46.9 28.1 58.8 47.6


1.107 Both the net and gross enrolment rates in secondary school are much lower than those for primary school across all groups. For the country as a whole, gross enrolment rates at secondary school increased from around 43 percent in 1991-1992 to almost 48 percent in 2005-2006. However those national figures hide large discrepancies between the urban and the rural areas. Over that period both gross and net enrolment rates went slightly down in rural areas (38.7 to 35.3 for gross rates) while the increase for the gross rates was substantial at 17 percent. This widening urban-rural differential again suggests that specific policies need to target the poorest rural areas. Although further study would be needed to establish this fact, it is likely that a lack of availability of nearby secondary school institutions is one of the key culprits for this widening gap.

1.108 While secondary enrollment rates do not seem to pick up significantly in rural areas, the uptake in primary school enrollment seems to have contributed to a reduction in the proportion of youths working. More generally, there are links between education and employment trends. As discussed earlier in section 4 devoted to employment and wages, the evidence for the period 1998-2006 indicates that poverty continues to decline in all localities, although progress has been slower in Accra. Similarly earnings have been rising very fast outside Accra while the earnings trend in Accra has been rather flat. Although earnings in Accra remain higher than in other urban areas and rural ones, it is likely that pressure from migrants has restrained earnings growth. At the national level, the urbanization of the economy and the move toward higher productivity sectors has led to a rise in wage employment and a decrease in non-farm self-employment, at least in the less efficient segments such as petty trade. The younger population, aged between 15 and 24, has been responding rapidly to those changes by staying at (and even returning to) school, particularly in the rural sector. In urban areas, the youth employment rate has remained relatively low and stable at around 28 percent (due to high school enrollment rates) while in rural areas there has been a sharp decline in youth employment from around 72 percent to only 50 percent.

1.109 In the absence of high unemployment (see Table 1.32), it is likely that rural youth are remaining in school for longer periods, or perhaps chose to return to school to acquire the basic skills necessary to a higher productivity economy (this could explain the drop in employment rates observed over time; note that for 1998-99, changes in the questionnaire do not enable us to measure properly employment and unemployment rates in that age group). While rural areas are still experiencing a lower school attendance rate than urban areas, the gross enrolment rate in primary schools is slowly

58

catching up on the also rising urban rate, and this should eventually have a spillover effects on higher levels of schooling. However the flatter trend in rural secondary school enrolment lets us believe that the children whom would want to continue from primary to secondary education would be constrained by a lack nearby school availability, as already mentioned.

Table 1.32: Youth employment and unemployment, 15-24 age group, 1991 to 2005 (%)

1991/92 1998/99 2005/06 Change Urban Employment 31.7 .. 24.7 -7.0 Unemployment 8.3 .. 7.3 -1.0 Rural Employment 68.7 .. 46.1 -22.6 Unemployment 2.0 .. 3.4 +1.4


Health

1.110 The information presented here concerns the use of health facilities by individuals who considered themselves to have been ill or injured in the two weeks preceding the interview in the GLSS survey. Respondents report themselves whether or not they have been ill or injured, and those who consider that they have are asked about their use of health facilities. Self-diagnosis of illness or injury is inevitably subjective; therefore it is rather unwise to focus on prevalence of illness or injury defined in this way. Indeed there is likely to be a systematic bias. Different people may have different perceptions of what it means to be ill or injured. In particular a richer individual might be more likely to report him- or her- self as ill or injured in circumstances that a poorer person would not. This matters less though for examining the use of health facilities.

1.111 The survey inquired into the extent to which the ill or injured persons consult various types of health practitioners. During the 15 year span under study, the recent introduction of a new player has changed considerably people’s behaviour (Table 1.33). In early 1990s, around half of ill/injured individuals did not consult anyone while 50 percent of the consultations were with a doctor, a nurse, a medical assistant, a pharmacist etc. The introduction a few years ago of private sector and licensed chemical stores generated a new convenient and cheap alternative health care provider. Those Chemical Stores are operated by Chemicals who usually have training in nursing or some other related profession. They provide advice and sell a wide range of drugs that could only be bought at a pharmacy in the past. From 1991/92 to 2005/06, the proportion of individual going to a pharmacy or chemical store when ill went from a negligible 3.2 percent to 20.8 percent. The popularity of the chemical stores has probably helped in reducing the share of individuals not consulting health professionals when ill or injured. Indeed, the proportion of ill/injured people not consulting decreased from more than 50 percent in 1991/92 to only 40 percent in 2005/06.

1.112 While chemicals are used indiscriminately by poor and non poor households in rural areas, chemicals are particularly popular in urban areas with individuals from the poorest households. Again, this seems to indicate that chemicals provide a source of consultation not available in the past. Richer urbanites still prefer to see a doctor when ill while the use of chemicals is slightly ahead of doctors in rural areas. The popularity of the pharmacist and chemical is also reflected in the type of facility visited. The proportion of visits taken at the hospital has been reduced slightly in rural areas but nevertheless increased significantly in urban areas. Other less expensive venues such as dispensaries and health clinics have loss part of their clientele. Based on those findings a crucial question concerns the efficiency of those shifts in people behaviour when facing an illness or injury. Is seeing a chemical instead of a doctor at the hospital more efficient, a more rational allocation of resources? Unfortunately no information is provided in GLSS on the quality of care received. A more detailed database would be needed to investigate whether the surge of pharmacists and chemicals has contributed to improving health outcomes.

59

Table 1.33: Health professional and facility consulted in case of illness/injury, 1991 to 2006 (%)

1991/92 1998/99 2005/06 Poor Non

Poor All Poor Non

Poor All Poor Non

Poor All

Type of professional consulted Urban Doctor 28.8 41.4 38.9 19.2 37.4 34.1 21.9 37.5 36.4 Nurse, midwife 3.9 3.4 3.5 8.7 4.8 5.5 7.1 5.4 5.5 Medical Assistant 11.2 4.4 5.7 5.0 3.1 3.4 3.1 2.7 2.7 Pharmacist/Chem. 1.8 3.8 3.4 2.3 6.8 6.0 32.4 20.2 21.1 Other 3.9 6.6 6.1 6.7 3.9 4.4 3.3 1.6 1.7 Did not consult 50.5 40.5 42.5 58.1 43.9 46.5 32.1 32.6 32.5 All 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Rural Doctor 14.7 23.2 18.4 9.0 17.6 13.8 12.5 18.7 16.9 Nurse, midwife 7.8 10.1 8.8 9.1 8.8 9.0 11.1 9.3 9.8 Medical Assistant 9.3 8.5 8.9 11.1 7.9 9.3 5.5 5.8 5.7 Pharmacist/Chem. 1.9 4.8 3.2 0.8 1.4 1.1 20.5 20.7 20.6 Other 7.4 4.3 6.1 5.9 7.0 6.5 3.0 2.7 2.8 Did not consult 59.0 49.1 54.7 64.1 57.3 60.3 47.4 42.7 44.1 All 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Total Doctor 16.7 31.7 25.0 10.5 25.2 19.8 13.6 26.4 23.6 Nurse, midwife 7.2 7.0 7.1 9.1 7.3 8.0 10.6 7.7 8.3 Medical Assistant 9.6 6.5 7.9 10.2 6.1 7.6 5.2 4.5 4.7 Pharmacist/Chem. 1.9 4.3 3.2 1.0 3.5 2.6 21.9 20.5 20.8 Other 6.9 5.4 6.1 6.0 5.8 5.9 3.1 2.3 2.4 Did not consult 57.8 45.1 50.7 63.2 52.2 56.2 45.6 38.6 40.1 All 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Type of facility consulted Urban Hospital 19.6 25.7 24.5 17.3 26.9 25.2 18.8 30.1 29.3 Pharmacy, Chem.Store 2.1 4.6 4.1 2.2 7.6 6.6 18.6 17.2 17.3 Other 27.7 29.0 28.8 22.5 21.5 21.7 30.5 20.1 20.9 Did not consult 50.5 40.6 42.6 58.1 43.9 46.5 32.1 32.6 32.5 All 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Rural Hospital 12,7 19.8 15.8 7.1 13.6 10.7 11.3 15.6 14.4 Pharmacy, Chem.Store 1.6 5.4 3.2 1.2 1.6 1.4 23.2 22.4 22.6 Other 26.6 25.8 26.2 27.7 27.5 27.6 18.1 19.3 18.9 Did not consult 59.1 49.1 54.7 64.1 57.3 60.3 47.4 42.7 44.1 All 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Total Hospital 13.7 22.6 18.6 8.6 18.7 15.0 12.2 21.5 19.5 Pharmacy, Chem.Store 1.7 5.0 3.5 1.4 3.9 3.0 22.7 20.3 20.8 Other 26.8 27.3 27.1 26.9 25.2 25.8 19.6 19.6 19.6 Did not consult 57.8 45.1 50.8 63.2 52.2 56.2 45.6 38.6 40.1 All 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Source: Authors using GLSS data.

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Basic Infrastructure Services

1.113 Poverty is multidimensional and access to infrastructure is a crucial dimension. In this section we examine access to electricity as well as types of toilet and source of water. The tables bellow cover these three dimensions with a breakdown of data according to urban and rural location, poor and non-poor status, for the three GLSS surveys spanning the period 1991 to 2006.

1.114 Access to electricity has increased substantially during the last 15 years, from less than 30 percent in 1991/92 to around 50 percent in 2005/06 (Table 1.34). Although impressive for the country as a whole, most of the improvement in access has come from the rural areas. From a very low level of access of less than 10 percent in the early 1990s, access rate to electricity has tripled over the period under study. The urban areas have not faired as well over time although the use of electricity is still much higher there than in rural areas. After a 10 percentage points increase during the 1990s, access to electricity has stayed rather stable in urban areas recently. Thus, the increased access to electricity in rural areas reflects the sustained rural electrification carried out over the period. Although the upward trend has benefited more the rural areas than the richer urban ones, the poorest households still have a much lower access rate to electricity than better off households, especially in rural areas where non-poor households are twice more likely to have access.

Table 1.34: Access to electricity, 1991 to 2006 (%)

1991/92 1998/99 2005/06 Poor Non

Poor All Poor Non

Poor All Poor Non

Poor All

Urban 52.5 72.7 68.9 39.9 84.5 78.3 49.3 80.9 78.6 Rural 5.3 12.2 8.7 8.6 27.1 19.7 14.4 31.8 27.0 Total 13.1 40.8 29.8 13.9 53.3 41.4 20.3 56.0 49.2 Source: Authors using GLSS data.

1.115 As shown in Table 1.35, a large majority of households in urban areas, poor or not, have access to potable water (defined as reliance on any water source except unprotected wells or natural sources). Incidentally that higher access rate to potable water has left little space for improvement. From 1991/92 to 2005/05, the use of unsafe water diminished progressively from 20 percent to 12 percent. By contrast, rural areas – which started from a much lower base – have experienced a large increase in the proportion of households having access to potable water. In 1991/92, 65 percent of households were not using safe water while only 35 percent were in that situation 15 years later. Although there is still a lot space for further improvement, the access gap between poor and non poor households has to a large extent been close by 2005/06 (even if access to piped water is much lower in rural areas). Most of the improvement in access to safe water has been due to borehole (forage), particularly in rural areas. These trends are consistent with Government interventions which are focused mainly on improving access for rural areas while encouraging the need to ensure private partnerships in water provision for urban areas.

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Table 1.35: Access to water, 1991 to 2006 (%)

1991/92 1998/99 2005/06 Poor Non

Poor All Poor Non

Poor All Poor Non

Poor All

Urban Inside pipe 23.4 41.9 38.4 10.7 37.9 34.1 16.2 35.9 34.4 Water vendor 3.4 3.7 3.6 2.1 7.5 6.8 3.4 4.4 4.4 Neighbour/Private 25.1 21.0 21.7 29.8 28.5 28.7 29.8 26.9 27.1 Public standpipe 13.9 13.2 13.4 20.0 13.3 14.3 16.0 15.5 15.5 Borehole 3.7 2.2 2.5 2.5 1.3 1.5 14.7 5.4 6.1 Well 14.2 9.6 10.5 15.3 6.4 7.7 15.7 9.5 9.9 Natural sources 16.3 8.5 9.9 19.5 5.0 7.0 4.2 2.4 2.5 All 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Rural Inside pipe 1.5 3.7 2.6 0.7 4.9 3.2 0.5 3.3 2.6 Water vendor 0.3 0.5 0.4 0.2 2.6 1.7 0.1 0.7 0.6 Neighbour/Private 1.9 3.0 2.4 2.1 7.7 5.5 1.3 5.9 4.6 Public standpipe 6.1 11.1 8.5 9.2 12.5 11.2 3.3 8.4 7.0 Borehole 22.8 19.5 21.2 33.3 25.3 28.5 57.0 45.8 48.9 Well 14.6 17.6 16.1 18.8 11.7 14.5 11.9 10.1 10.6 Natural sources 52.8 44.8 48.9 35.6 35.4 35.4 25.9 25.8 25.9 All 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Total Inside pipe 5.1 21.7 15.1 2.4 20.0 14.7 3.2 19.4 16.3 Water vendor 0.8 2.0 1.5 0.6 4.9 3.6 0.6 2.6 2.2 Neighbour/Private 5.7 11.5 9.2 6.9 17.2 14.1 6.1 16.2 14.3 Public standpipe 7.4 12.1 10.2 11.1 12.9 12.3 5.5 11.9 10.7 Borehole 19.6 11.3 14.6 28.0 14.3 18.5 49.9 25.9 30.4 Well 14.6 13.8 14.1 18.2 9.3 12.0 12.5 9.8 10.3 Natural sources 46.8 27.6 35.2 32.8 21.5 24.9 22.3 14.3 15.8 All 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Source: Authors using GLSS data.

1.116 The data suggest that although all groups have benefited from recent increases in the provision of KVIPs, wealthier groups are still much more likely to have access to adequate sanitation. The information on sanitation is provided in Table 1.36. The proportion of households having access to adequate toilet facilities (a flush toilet or the KVIP toilet) has increased sharply in urban areas between 1991/1992 and 1998/1999, and further to the years leading to 2005-2006. However the changes observed in rural areas have been rather small. Further analysis reveals that the increase in access is predominantly due to large increases in the use of KVIP toilets in urban areas over the fifteen year period.

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Table 1.36: Access to toilets and sanitation, 1991 to 2006 (%)

1991/92 1998/99 2005/06 Poor Non

Poor All Poor Non

Poor All Poor Non

Poor All

Urban Flust toilet 7.1 20.0 17.6 1.4 17.8 15.5 3.2 23.8 22.3 Pit latrine 35.3 28.3 29.6 23.6 17.5 18.4 18.2 15.5 15.7 Pan/Bucket 23.7 25.2 24.9 5.5 13.6 12.5 1.6 2.7 2.6 KVIP 12.9 12.5 12.6 50.0 44.1 44.9 59.8 53.6 54.1 Other 21.0 13.9 15.3 19.5 7.0 8.8 17.2 4.4 5.3 All 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Rural Flust toilet 0.7 2.2 1.4 0.4 2.0 1.3 0.2 1.4 1.1 Pit latrine 60.9 61.6 61.2 40.3 48.7 45.4 33.0 47.4 43.4 Pan/Bucket 2.9 5.1 4.0 2.6 4.0 3.4 0.0 0.3 0.3 KVIP 3.0 4.4 3.7 12.4 24.8 19.9 14.0 28.9 24.8 Other 32.5 26.7 29.7 44.4 20.4 30.0 52.6 22.0 30.5 All 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Total Flust toilet 1.7 10.6 7.1 0.6 9.2 6.6 0.7 12.4 10.2 Pit latrine 56.7 45.9 50.2 37.4 34.5 35.4 30.5 31.7 31.5 Pan/Bucket 6.4 14.6 11.3 3.1 8.4 6.8 0.3 1.5 1.3 KVIP 4.6 8.3 6.8 18.8 33.6 29.1 21.8 41.1 37.4 Other 30.6 20.7 24.6 40.1 14.3 22.1 46.7 13.3 19.6 All 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Source: Authors using GLSS data.

Benefit incidence analysis for public spending for education and health

1.117 Benefit incidence analysis has become a standard tool of analysis in policy-oriented development economics. As noted by Demery (2003), benefit incidence analysis is typically obtained by combining data on the use of government services from household surveys with data on the cost of providing those services from government budgets. The technique essentially involves three steps. First, the unit cost of providing a particular service is estimated using government budget data. Second, household survey data are used to allocate the benefits of public spending for specific services to the households using the services. Third, the data at the household level are aggregated into benefit incidence statistics for sub-groups of the population in order to compare how the subsidy is distributed across those groups. The most common way of grouping households is on the basis of indicators such as income or consumption per equivalent adult.

1.118 There are a range of potentially difficult issues to consider when conducting benefit incidence analysis. For example, there is an issue as to how the benefits of publicly-provided goods should be measured. For market-based goods and services, prices can reasonably be considered as reflecting the values assigned by households to those goods and services. But when goods and services are publicly provided, as is the case for public education services, price data are not available, and the analyst must instead base the analysis on cost data. Even when governments subsidize private goods, the prices paid by households need not measure underlying values if the supply of the goods and services is rationed. Yet trying to estimate the value for households of benefits in kind provided by governments is difficult. This is why most studies on benefit incidence analysis simply combine the cost of providing public services with information on their use in order to generate distributions of the benefit of government spending.

1.119 We provide below estimates of the incidence of public spending for education and health using the standard, simplified method used by most analysts. Table 1.37 is based on data from the

63

GLSS surveys, with the aim to assess who actually uses public services in Ghana for the social sectors in both 1991/92 and 2005/06. We do not use the data for 1998/99 because that surveys does not identify whether children attend public or private schools. The table provides information on the share of students enrolled in school which belong to the various quintiles of the distribution of consumption by equivalent adult. If we are willing to assume that the unit cost of providing public schooling benefits is constant across all regions of the country within each cycle, these shares measures the share of the benefits from public schooling that accrue to the various quintiles of the population.

1.120 For primary schooling, the first four quintiles benefited in 1991/92 in roughly equal ways from public spending for primary education, while the richest quintile benefits less, simply because households in that quintile tend to have fewer children, and are also more likely to send their children to private schools. In 2005/2006, the distribution of benefits from public primary schooling has become more pro-poor. Yet for secondary schooling, the pattern is very different in both years, as the benefits are much more skewed to the upper quintiles of the distribution, with an actual worsening of the distribution over time, as richer and urban children enrolled more in secondary school, while poorer and rural children did not, probably in part due to a lack of access. As for tertiary education, as expected, for both years the benefits accrue almost exclusively to the richest segments of the population. Overall, taking into account the budget allocations for the various levels of schooling, including for tertiary education, public spending in education remains tilted towards those households who are non-poor.

Table 1.37: Share of students enrolled in public schools by quintile and by cycle, 1991 to 2006

Primary Secondary Tertiary Quintile 1991/92 2005/06 1991/92 2005/06 1991/92 2005/06 Poorest quintile 21.3 24.1 15.7 12.1 0.0 1.8 2nd quintile 23.1 24.7 18.6 17.2 5.3 7.8 3er quintile 21.7 22.9 22.8 23.7 0.0 9.4 4tth quintile 19.4 17.6 21.4 23.3 21.1 14.1 Richest quintile 14.5 10.7 21.6 23.8 73.7 66.9


1.121 Table 1.38 is based on the health section from the GLSS surveys. The table provides information on the share of visits to public health facilities by the various quintiles of the distribution of consumption. For ease of interpretation, the clinics include all public facilities but hospitals. Again, assuming that the unit cost of providing health benefits in public facilities is constant across all regions of the country by type of facility, the shares in Table 1.38 measure the share of the total benefits from health public spending that accrue to the various quintiles of the population. As was observed with education, public spending for health care is again tilted towards the richer segments of the population, especially in the case of hospitals. In the case of clinics, there has been an improvement in benefit incidence over time for the lower quintiles, but in the case of hospitals, the distribution of benefits has not changed much over time.

Table 1.38: Share of visits to public health facilities by quintile and by cycle, 1991 to 2006

Hospitals Clinics Quintile 1991/92 2005/06 1991/92 2005/06 Poorest quintile 9.4 9.1 15.7 18.3 2nd quintile 14.2 15.0 17.1 20.0 3er quintile 17.7 19.3 20.8 19.0 4tth quintile 24.3 23.6 19.2 22.0 Richest quintile 34.4 33.1 27.2 20.8 Source: Authors using GLSS data.

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Benefit incidence analysis for electricity subsidies

1.122 Ghana is facing a severe power crisis which has resulted in widespread load-shedding and could have significant macroeconomic repercussions. First, power shortages are disruptive for economic activities and could negatively affect GDP growth, and thereby future poverty reduction (see Bogetic et al., 2007, and Estache and Vagliasindi, 2007). The industry and services sectors, which together account for nearly 75 percent of Ghana’s GDP, rely critically on electricity. Secondly, the financial distress of the electricity sector will require large budgetary support by the government in 2007, with amounts that could be in excess of 2 percent of GDP. While these subsidies may not be explicit, there are certainly in existence and very large.

1.123 Several other SSA countries currently find themselves in comparable situations of generation capacity deficit and financial crisis. But the crisis in Ghana is both severe and paradoxical, because the power sector has for several decades been considered as a relative strength of the Ghanaian economy. Indeed, about half of Ghana’s population has access to electricity, one of the highest rates in West Africa. This high level of electrification is in part based on the availability of low-cost hydroelectric power. In 2006, hydroelectricity represented more than two thirds of generation. The impact of rising oil prices in Ghana should therefore have been modest compared to the shock experienced by countries such as Senegal, Burkina Faso, Guinea, Benin, and Togo that rely primarily on imported oil for power generation.

1.124 One underlying cause behind Ghana power crisis is that “cheap” hydroelectricity is a scarce resource that is no longer sufficient to cover entirely national consumption. A long standing policy of maintaining low electricity prices has sent inadequate economic signals and fueled an increase in demand. This policy has also deteriorated the financial viability of the power sector, making the financing of new investments more difficult as a result. Yet while tariffs are set today at too low a level in order for the electricity sector to be sustainable, there are concerns about raising tariffs, both for the competitiveness of some of Ghana’s key industries and the ability to pay of residential customers, some of whom are poor. Prevailing residential tariffs include subsidies for households who consume small amounts of electricity. It is feared that an increase in tariffs could exacerbate poverty. Yet while prevailing subsidies are supposed to be targeted to the poor, this may not necessarily be the case, because many among the poor simply do not have access to electricity. In this section, we assess the relationship between electricity tariffs and poverty reduction, and we measure the targeting performance of implicit electricity subsidies received by residential households due to the fact that current tariffs do not cover costs.

1.125 The tariff structure for electricity and changes thereof over time is given in Table 1.39. Prices per Kwh are lower for the lower brackets of consumption (assuming that customers in the lower brackets have consumption levels close to the upper threshold of consumption of that bracket). The objective is to try to make electricity more affordable for the poor, under the assumption that the quantity consumed by a poor household is typically lower than that consumed by a richer household. However the prices follow an Inverted Block Tariff (IBT) structure whereby even those who consume large amounts of electricity benefit from subsidies for part of their consumption. A second important feature of the tariff structure is that prices increased faster than the Consumer Price Index. The CPI was multiplied by approximately 4.5 between 1998/99 and 2005/06. By contrast, electricity prices were multiplied by 6.5 in the lowest bracket, 12.2 in the 50-300 kWh bracket, 14.2 in the 300-600 kWh bracket, and 5.9 in the top bracket. Thus, the middle brackets saw the largest increases in prices, but these increases have not been sufficient to enable the sector to operate without large losses.

Table 1.39: Tariffs structure for residential customers, 1998/99 and 2005/06

1998/99 2005/06 Prime fixe 2000 Cedis 13000 Cedis 0-50 Kwh - - 50-300 Kwh 50 Cedis 610 Cedis 300-600 Kwh 75 Cedis 1065 Cedis > 600 Kwh 180 Cedis 1065 Cedis

Source: Ghana PURC.

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1.126 According to estimates by Wodon et al. (2007), only about 10 percent of the prevailing electricity subsidies reach the poor. There are a number of reasons for this poor targeting, including the fact that poor households tend to have lower access rates to electricity in their neighborhood or village than other households, have lower take up or connection rates than other households even when there is access in their geographic areas, and have lower consumption levels than other households. As a result, the amount of consumption of electricity by the poor is much lower than in the overall population, as shown in Table 1.40. The table provides data by decile of total consumption per capita on household expenditure for electricity in Ghana. Total expenditure and expenditure on electricity increase from the bottom to the top deciles. Using the tariff structure, we can compute the quantity of electricity consumed. While the differences in quantity consumed are relatively small between the various deciles, the fact that the proportion of households connected to the network is lower among the poor implies that they spend much less for electricity than other groups, and thereby receive much fewer subsidies.

Table 1.40: Descriptive Statistics on Electricity Consumption, year 2006

Decile expenditure

per eq. adult.

Total expenditure per eq. adult per month

Electricity expenditure

in per eq. adult per

month

Consumption In Kwh per month per household

(Q>0)

Household Access to electricity

(%)

Access to electricity

at PSU level (%)

Take up

rate (%)

% of households paying for electricity

Subsidy (Cedis

per month)

1 32283.38 1842.15 88.44 22.6 41.6 54.3 20.3 1103.5 2 63053.96 2282.36 92.53 26.1 47.6 54.7 23.3 3203.8 3 86766.75 3457.78 91.11 34.2 57.6 59.4 31.0 4807.8 4 109384.84 4614.47 93.14 38.4 63.4 60.7 36.2 6055.0 5 134412.17 6353.69 100.28 43.0 66.9 64.3 40.9 8403.3 6 163128.7 8678.54 111.83 50.4 72.4 69.5 46.8 10926.9 7 200855.06 12511.00 114.75 58.1 77.0 75.4 54.8 13801.2 8 250125.84 15886.65 115.66 66.7 82.8 80.6 62.6 18126.7 9 337264.78 26414.92 137.25 71.2 85.3 83.4 67.7 24194.4

10 897868.75 37711.3 146.63 80.9 89.8 90.1 77.5 35542.6 Total 172415.5 9019.68 116.39 49.2 68.4 71.8 46.1 9694.9


Box 1.1: Ghana’s electricity sector

Ghana has initiated opening its electricity sector to competition and private participation, but the has so far essentially remained public, and is still organized around two state-owned entities, ECG and VRA. The Electricity Company of Ghana (ECG) is in charge of electricity distribution in most of the country, including the capital city Accra, southern and central Ghana. ECG develops, operates and maintains the distribution grid, and is also in charge of commercial operations including metering, billing, and revenue collection. The Volta River Authority (VRA) is a state-owned entity whose primary responsibility is to operate Ghana’s hydro-electric generation capacity and its transmission system. The Ministry of Energy exerts the overall responsibility for energy policy formulation and implementation, while the Energy Commission is responsible for national energy planning, licensing, and technical regulations. In 1997, a specialized regulatory body, the Public Utilities Regulatory Commission (PURC), was set up to regulate electricity tariffs and customer services (as well as electricity tariffs and services). Retail electricity tariffs applied to end-users have two components. The Bulk Supply Tariff represents the cost of generation and transmission. It is the primary source of revenue for VRA. The Distribution Service Charge represents the main source of revenue for ECG. Due to the rise in oil prices, VRA generation costs have increased significantly while regulated tariffs which are denominated in local currency have tended to decrease in real terms. This situation should have required frequent and significant tariff adjustments. While the regulatory process to adjust tariffs seems to have been slow, a major reason for the failure to adjust tariffs has been political intervention. In 2006, when PURC eventually decided to adjust electricity tariffs, with a 16 percent increase of the Bulk Supply Tariffs, the government decided to postpone indefinitely the application of the new tariffs.

An adequate measure of the opportunity cost of electricity in Ghana would be the cost of developing new sources of generation. Estimating for the Long Run Marginal Cost (LRMC) of electricity generation in Ghana is a matter that is open to debate because in the near future, Ghana should be able to import natural gas from Nigeria thereby reducing considerably the cost of thermal generation. It is however clear, that electricity tariffs in Ghana have been insufficient for several years. For instance, Low-Voltage retail tariffs do not even cover the short-run variable costs of power generation (i.e. the cost of oil). Therefore, improved targeting of electricity tariff subsidies appears to be an essential step to restore the financial viability of the electricity sector in Ghana. The government has recently taken a modest step in this direction by announcing a temporary surcharge on regulated tariffs to compensate for the high cost oil. However this surcharge would be applied only to commercial users. This measure is unlikely to be sufficient to restore the financial viability of the power sector. It will also have the effect of creating an indiscriminate subsidy for consumption of electricity by households regardless of their level of income or of their volume of electricity consumption. Source: Wodon et al. (2007b). See also Estache and Vagliasindi (2007).

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TECHNICAL ANNEXES

67

Annex 1: Poverty Measures and Decompositions

This annex is reproduced from Coudouel et al. (2002). It provides mathematical expressions for the most commonly used poverty measures and for their decomposition by sector or, more generally, by group. The note focuses on the first three poverty measures of the so-called FGT class (Foster, Greer, and Thorbecke, 1984), namely the headcount, the poverty gap, and the squared poverty gap.

Poverty measures

Poverty Headcount: This is the share of the population which is poor, i.e. the proportion of the population for whom consumption or income y is less than the poverty line z. Suppose we have a population of size n in which q people are poor. Then the headcount index is defined as:

n

qH =

Poverty Gap: The poverty gap, which is often considered as representing the depth of poverty, is the mean distance separating the population from the poverty line, with the non-poor being given a distance of zero. The poverty gap is a measure of the poverty deficit of the entire population, where the notion of “poverty deficit” captures the resources that would be needed to lift all the poor out of poverty through perfectly targeted cash transfers. It is defined as follows:

∑ ⎥⎦

⎤⎢⎣

⎡ −=

=

q

i

i

z

yz

nPG

1

1

where yi is the income of individual i, and the sum is taken only on those individuals who are poor (in practice, we often work with household rather than individual income, but individual income can still be defined as being equal, say, to the per capita income of the household). The poverty gap can be written as being equal to the product of the income gap ratio and the headcount index of poverty, where the income gap ratio is itself defined as:

PG=I*H, with

∑=

=−

=q

iiq

q poortheofincomeaveragetheisyq

ywherez

yzI

1

.1

It must be emphasized that the income gap ratio I in itself is not a good measure of poverty. Assume that some households or individuals who are poor but close to the poverty line are improving their standards of living over time, and thereby become non-poor. The Income gap ratio will increase because the mean distance separating the poor from the poverty line will increase (this happens because some of those who were less poor have emerged from poverty – so that those still in poverty are on average further away from the poverty line), suggesting a deterioration in welfare, while nobody is worst off and some people are actually better off. Although the income gap ratio I will increase, the poverty gap itself PG will decrease, because the headcount index of poverty will decrease, suggesting an improvement towards poverty reduction. The problem with the income gap ratio is that it is defined only on the population that is poor, while the poverty gap is defined over the population as a whole. As mentioned above, the poverty gap is a useful statistics to assess how much resources would be needed to eradicate poverty through cash transfers perfectly targeted to the poor. Assume for example that the poverty gap is equal to 0.20. This means that the cash transfer needed to lift the poor out of poverty each poor person represents 20 percent of the poverty line. If the mean income in the country is equal to twice

68

the poverty line, the cash transfer would represent 10 percent of the country’s mean income. Now, if it is the mean income of the non-poor which is equal to twice the poverty line, and if half the population is poor, it can be shown that the tax rate that would have to be imposed on the non-poor to lift the poor out of poverty with perfectly targeted transfers would be 20 percent again. If the mean income of the non-poor is equal to four times the poverty line, under the same assumption the necessary tax rate would be 10 percent. Such simple simulations can be used to communicate in an intuitive manner the meaning of the poverty gap. In practice however, given that perfectly targeted cash transfers to eradicate poverty are neither feasible nor necessarily a good thing (high tax rates could stifle economic growth and thereby future poverty reduction), one must be careful in their use. Squared Poverty Gap: This is often described as a measure of the severity of poverty. While the poverty gap takes into account the distance separating the poor from the poverty line, the squared poverty gap takes the square of that distance into account. When using the squared poverty gap, the poverty gap is weighted by itself, so as to give more weight to the very poor. Said differently, the squared poverty gap takes into account the inequality among the poor. It is obtained as follows:

∑ ⎥⎦

⎤⎢⎣

⎡ −==

q

i

i

z

yz

nP

1

21

2

The headcount, the poverty gap, and the squared poverty gap are the first three measures of the Foster-Greer-Thorbecke class of poverty measures. The general formula for this class of poverty measures depends on a parameter α which takes a value of zero for the headcount, one for the poverty gap, and two for the squared poverty gap in the following expression:

α

α ∑ ⎥⎦

⎤⎢⎣

⎡ −==

q

i

i

z

yz

nP

1

1

It is important to use the poverty gap or the squared poverty gap in addition to the headcount for evaluation purposes, since these measure different aspects of income poverty. Indeed, the basing evaluation on the headcount ratio would consider as more effective policies which lift the richest of the poor (those close to the line) out of poverty. On the basis of the poverty gap PG and the squared poverty gap P2, on the other hand, puts the emphasis on helping those who are further away from the line, the poorest of the poor. Decompositions for changes in poverty over time Two main decompositions have been used in the literature to analyze changes in poverty over time. The first decomposition deals with shifts in poverty between sectors or groups (Ravallion and Huppi, 1991). The second decomposition deals with the contribution of income growth and changes in inequality to changes in poverty (Datt and Ravallion, 1992; Kakwani, 1997). Sectoral decomposition The poverty measures of the FGT class are additive. This means that the poverty measure for the population as a whole is equal to the weighted sum of the poverty measures for the population subgroups, with the weights defined by the population shares of the subgroups. This additive property makes it feasible to analyze the contribution of various population subgroups to changes in overall poverty over time. Assume that households or individuals can be classified according to various sectors in the economy. These may be industrial sectors, geographic sectors (urban versus rural), or any other sectors that the analyst may suggest. The overall change in poverty over time can be decomposed into: 1) changes in poverty within specific sectors, or intra-sectoral changes, 2) changes in poverty due to changes in the population shares of sectors, or inter-sectoral changes, and 3) changes due to the possible correlation

69

between intra-sectoral and inter-sectoral changes, or interaction effect. Denote by Pit the poverty measure in sector i at time t; there are m sectors (i=1,…, m), with population share ni in sector i, and two periods (1 and 2). Then, the overall change in poverty is equal to:

)()()()( 121

121

1211

121 ii

m

iii

m

iiii

m

iiii nnPPnnPPPnP −∑ −+∑ −+∑ −=Δ

===

| | | Intra-sectoral Inter-sectoral Interaction effect Growth and inequality decomposition Changes in poverty rates can also be decomposed into changes due to economic growth (or mean income) in the absence of changes in inequality (or income distribution), and changes in inequality in the absence of growth. Denoting by P(µt , Lt) the poverty measure corresponding to a mean income in period t of µt and a Lorenz curve Lt , the decomposition is:

rrrrir RLPLPLPLPP +−+−=Δ )],(),([)],(),([ 122 μμμμ | | | Growth impact Inequality impact Residual The first component is the change in poverty that would have been observed if the Lorenz curve had remained unchanged, while the second component is the change that would have been observed if mean income had not changed. The last component is a residual.

70

Annex 2: Consumption Aggregate, Poverty Lines, and Standard Errors of Poverty Measures

As noted by Coudouel et al. (2002; see also Ravallion, 1994), to measure poverty, one needs: a) an indicator of well-being or welfare such as per capita caloric intake or per capita expenditure; b) a threshold (the poverty line) to which each individual or household’s welfare can be compared; and c) a poverty measure. Differences in poverty estimates can result from differences in the choice of the indicator, the threshold, or the poverty measure. The main poverty measures used in empirical work have been presented in Annex 1. This annex provides information on the construction of the consumption aggregate and the poverty lines used for poverty measurement in Ghana. The annex is adapted and expanded from the material included in the poverty profile produced by the Ghana Statistical Service (2007). In addition, the annex provides estimates of poverty together with their standard errors. Data sources

The consumption-based poverty measures presented in this study are estimated using the third, fourth and fifth rounds of the Ghana Living Standards Survey (GLSS). The GLSS is a nationally representative multi-purpose survey of households in Ghana, which collects information on many different dimensions of well-being including education, health and employment. Five rounds of data have been collected, starting in 1987/88. In this study we focus on the three most recent rounds—those conducted in 1991/92, 1998/99 and 2005/06. The questionnaires used for these three rounds were almost identical. Hence, the consumption aggregates are comparable. These total consumption of each household includes both food and non-food items (including housing). Food and non-food consumption commodities may be explicitly purchased by households, or acquired through other means (e.g. as output of own production activities, payment for work done in the form of commodities, or from transfers from other households). The household consumption takes account of all of these sources. Construction of the consumption aggregate

The indicator of well-being used to measure poverty is the total household consumption per equivalent adult expressed in constant prices of Accra in January 2006. The first step in constructing this measure is to estimate total household consumption expenditure. Table A2.1 sets out in detail how this is done, covering the components of this, their composition and sources within the different GLSS questionnaires. This consumption measure covers food, housing and other non-food items, and includes imputations for consumption from sources other than market purchases. These imputations include consumption from the output of own production (mostly agriculture, but also from non-farm enterprises), wage payments and transfers received in kind, and imputed rent from owner-occupied dwellings. An imputation is also made for consumption services derived from durable consumer goods owned by the household, rather than including expenditure on the acquisition of such goods (these are lumpy expenditures, e.g. purchasing a car, more like investment rather than consumption). Total consumption expenditure is estimated for a twelve-month period based on information collected with the questionnaire. In the case of frequent purchases (e.g. food purchases, consumption of own produced food, frequently purchased non-food items such as soap, tobacco) this is estimated by grossing up responses relating to a shorter recall period. Households received multiple visits at regular intervals of a few days in the course of the survey (in GLSS 3 eight visits at two-day intervals in rural areas and eleven visits at three-day intervals in urban areas; seven visits at 5-day interval in the case of GLSS 4; and 11 visits at three days interval in GLSS 5). In each case, in all but the first two visits, they were asked about their purchases of each item since the last visit, and the answers to these “bounded recall” questions (recall relative to a fixed reference point) was used as the basis for estimating annual expenditure or consumption. Similar principles were used to estimate annual expenditure on frequently purchased non-food items and on consumption of own produced food (valuing items at the price at which they could have been sold). In the case of consumption of own produced food, allowance was made for the number of months in which an item was normally consumed.

71

The recall period for frequently purchased or consumed items did change between GLSS 3, GLSS 4 and GLSS 5, and experimental evidence for Ghana and elsewhere suggest that lengthening the recall period causes respondents to progressively forget more items of expenditure. A study for Ghana by Scott and Amenuvegbe (1990) found that, on average, respondents forgot 2.9 percent of expenditure for each day by which the recall period was lengthened (up to seven days). Given this evidence, this figure was used to estimate what each household’s expenditure on frequent purchases in GLSS 3 would have been had the same recall period been used as for GLSS 4 and GLSS 5. A longer recall period, generally three or twelve months, was used in collecting information on less frequently purchased consumption items (e.g. clothing and footwear); this again is grossed up as necessary. As noted above, purchases of durable goods were not included in this, and some other expenditure items deemed not to be associated with increases in welfare were also excluded such as expenditure on hospital stays. This is also a lumpy item, and it would not be reasonable to regard a household as being significantly better off because it had to make a large expenditure on an emergency operation, say. Everyday medical expenses were though included in the consumption measure. In the case of owner occupied dwellings, imputed rents were estimated based on a hedonic equation, which related rents of rented housing to characteristics, and uses this to estimate rental values for owner-occupied dwellings based on their characteristics and amenities. Consumption flows (use values) for durable goods were estimated based on assumed depreciation rates. In both cases the procedures used for GLSS 3, GLSS 4 and GLSS 5 were identical. The remaining items in the estimate of household consumption relate to the value of wage payments received in kind, and consumption of the output of non-farm enterprises owned and operated by the household. The sum of all the items in Table A2.1 gives the estimate of total household consumption expenditure, which is expressed in nominal values (current prices).

72

Tab

le A

2.1:

Est

imat

ion

of to

tal h

ouse

hold

con

sum

ptio

n ex

pend

itur

e fr

om th

e G

LSS

3, G

LSS

4, a

nd G

LSS

5 su

rvey

s

Ele

men

t of t

otal

hou

seho

ld

cons

umpt

ion

Com

posi

tion

So

urce

of d

ata

in

GL

SS q

uest

ionn

aire

N

otes

Exp

endi

ture

on

food

, be

vera

ges

and

toba

cco

Exp

endi

ture

on

abou

t 12

0 co

mm

oditi

es (

base

d on

pat

tern

in

seve

ral

shor

t re

call

peri

ods

in th

e pa

st m

onth

) Se

ctio

n 9B

Con

sum

ptio

n of

fo

od

com

mod

ities

fr

om

own

prod

uctio

n,

valu

ed

by

resp

onde

nts

at p

rice

s at

whi

ch th

ey c

ould

be

sold

Se

ctio

n 8H

Con

sum

ptio

n of

ow

n pr

oduc

ed

food

W

age

inco

me

rece

ived

in

fo

rm

of

food

(b

ased

on

pa

ymen

t in

terv

al

repo

rted

by

resp

onde

nts)

Se

ctio

n 4

Exp

endi

ture

on

freq

uent

ly p

urch

ased

non

-foo

d ite

ms

(bas

ed o

n pa

ttern

in

seve

ral s

hort

rec

all p

erio

ds in

the

past

mon

th)

Sect

ion

9A2

Se

ctio

n 9B

in G

LSS

5

Exp

endi

ture

on

less

-fre

quen

tly p

urch

ased

non

-foo

d go

ods

and

serv

ices

(b

ased

on

patte

rn o

ver

last

3 o

r la

st 1

2 m

onth

s)

Sect

ion

9A1

Exc

ludi

ng

purc

hase

s of

du

rabl

e go

ods

and

expe

nditu

re

on

hosp

ital

stay

s E

xpen

ditu

re o

n ed

ucat

ion

(bas

ed o

n pa

ttern

for

eac

h ch

ild i

n pa

st 1

2 m

onth

s)

Sect

ion

2

Exp

endi

ture

on

non-

food

ite

ms

Exp

endi

ture

on

hous

ehol

d ut

ilitie

s: w

ater

, el

ectr

icity

, ga

rbag

e di

spos

al

(bas

ed o

n pa

ymen

t int

erva

l rep

orte

d by

res

pond

ents

) Se

ctio

n 7

Act

ual

rent

al

expe

nditu

re

(bas

ed

on

paym

ent

inte

rval

re

port

ed

by

resp

onde

nts)

Se

ctio

n 7

Impu

ted

rent

of

owne

r oc

cupi

ed d

wel

lings

Se

ctio

n 7

Est

imat

ed

base

d on

he

doni

c re

gres

sion

eq

uatio

n

Exp

endi

ture

on

hous

ing

Wag

e in

com

e re

ceiv

ed a

s su

bsid

ized

hou

sing

(ba

sed

on p

aym

ent

inte

rval

re

port

ed b

y re

spon

dent

s)

Sect

ion

4

Dur

able

goo

ds u

ser

valu

es

Sect

ion

12B

Con

sum

ptio

n fr

om o

utpu

t of

non

-far

m e

nter

pris

es (

base

d on

tw

o w

eek

peri

od)

Sect

ion

10D

Impu

ted

expe

ndit

ure

on n

on-f

ood

item

s

Wag

e in

com

e in

kin

d in

for

ms

othe

r th

an f

ood

and

hous

ing

(bas

ed o

n pa

ymen

t int

erva

l rep

orte

d by

res

pond

ents

) Se

ctio

n 4

73

Allowing for differences in the size and composition of households Adjustments are needed to construct a standard of living measure that takes into account differences in the size and composition of households. A simple way of doing this would be to divide total consumption by household size to obtain consumption expenditure per capita. But this would not allow for the fact that different members (e.g. young children and adults) are likely to have different consumption needs. To account for differences in needs, the idea is to measure household size in equivalent adults, using an appropriate adult equivalence scale that reflects the relative consumption needs of different household members (e.g. based on age, gender). The equivalence scale used is based on calorie requirements commonly used in nutritional studies in Ghana, as provided in Table A2.2. Calorie requirements are distinguished by age category and gender, information which is also reported in the household questionnaire. This information is used to estimate household size in number of adult equivalents.

Table A2.2: Recommended energy intakes per person according to gender and age

Category Age (years) Average energy allowance per day (kcal)

Equivalence scale

Infants 0 - 0.5 650 0.22

0.5 - 1.0 850 0.29

Children 1 – 3 1300 0.45 4 – 6 1800 0.62

7 – 10 2000 0.69

Males 11 – 14 2500 0.86 15 – 18 3000 1.03 19 – 25 2900 1.00 25 - 50 2900 1.00

51+ 2300 0.79

Females 11 - 14 2200 0.76 15 - 18 2200 0.76 19 - 25 2200 0.76 25 - 50 2200 0.76 51+ 1900 0.66 Source: Recommended Dietary Allowances, 10th edition, (Washington D.C.: National Academy Press, 1989).

The standard of living measure is then measured by dividing the estimate of total household consumption expenditure in constant prices by household size measured in number of equivalent adults. The poverty analysis is based on the distribution of this standard of living measure over all households in the sample, weighting each household by its size in number of persons. This household size weight means that for example a poor household of six members is given twice the weight of an equally poor household of three persons. Each individual (rather than each household) in the sample is given equal weight. Note that this equal weighting of all individuals when estimating poverty measures violates the assumption that individuals differ in needs, but this is still what is done in practice in empirical studies on poverty. Allowing for cost of living variations Having estimated total household consumption expenditure, further steps are needed before it is possible to compare standards of living across households. Because the standard of living is expressed in nominal terms, it must be adjusted to allow for variations in prices faced by households. Three sources of variation are relevant for purposes of this study: (i) differences in the cost of living between different localities at a point in time; (ii) variations in prices within the time periods covered by the surveys, which can occur due to inflation, seasonality and other reasons; (iii) most importantly (in comparing trends between the three GLSS rounds) inflation between the GLSS 3, GLSS 4 and GLSS 5 (substantial in this case).

74

A cost of living index was constructed capturing these different dimensions of variation. Geographic differences in the cost of living were estimated based on the GLSS 4 price questionnaire, in conjunction with expenditure data from the GLSS 4 household questionnaire. Based on five localities, Paasche cost of living indices were constructed for food and non-food separately. The hedonic regression equation was used to estimate a housing cost of living index by comparing rental values for a dwelling with the same characteristics and amenities in each locality. These procedures give the geographic cost of living indices reported in Table A2.3. The regional cost of living index based on GLSS 4 presented in Table A2.3 indicates that there are significant differences in the prices of food and housing, with urban areas in general and Accra in particular being more expensive for these items than rural areas. The prices of other non-food items are much more uniform. The regional cost of living index is a weighted average of these three regional sub-indices.

Table A2.3: Regional cost of living indices

Food index Non food index Housing index Accra 1.0000 1.0000 1.0000 Other Urban 0.9183 0.9086 0.6442 Rural Coastal 0.8832 0.9753 0.6149 Rural Forest 0.8212 0.9839 0.5296 Rural Savannah 0.7310 1.0484 0.4491 Source: Computed from the Ghana Living Standards Survey, 1998/99.

Variations in prices within and between the sample years were then allowed by using the Consumer Price Index, using separate series for food and non-food, as well as for Accra, other urban and rural areas. A single overall cost of living index was constructed combining the geographic and over time variations. This was used to deflate the estimate of total household consumption expenditure, so that it was now expressed in the constant prices of a reference locality and time period (Accra in January 2006). Construction of the poverty lines The approach taken was to anchor the poverty lines in calorie requirements. The method involves examining the average consumption basket of the bottom x percent (say 50 percent) of the population ranked by the standard of living measure, and computing how many calories this basket provides per adult equivalent. The quantities of each item consumed in the basket can then be scaled up (or down) in the appropriate proportion to compute the basket with this composition, which would provide the minimum calorie requirements (2900 kilocalories per equivalent adult based on the scale used in Ghana). This provides an estimate of the food expenditure required to attain 2900 kilocalories, based on the consumption basket of the poorest x percent of the distribution. Obviously, one of the issues is the choice of x. It is worth noting that some observers find 2900 Kcal too high given that most poverty profiles in other developing countries use between 2100 and 2400 Kcal for their poverty lines. Yet those countries usually construct a per capita welfare measure while ours is based on equivalent adult. It would be easy to show that our level of kilocalories on a per capita basis would be 2202 kcal per day. Taking account of non-food needs is more difficult. Following common practice in other developing countries (Ravallion, 1994), the non-food poverty line is based on the expenditure devoted to non-food items of those households whose total consumption expenditure is at the level of (or close to) the food poverty line. This is based on the principle that these non-food consumption items are essential for households, so that they will even forgo meeting their calorie requirements (or consume an “inferior” basket) in order to purchase them. This poverty line methodology had been used in the previous poverty profile based on GLSS 3 and 4 (GSS, 2000). The methodology used suggests food poverty line of, in round figures, 700,000 when x=50 percent (slightly lower for lower values of x), while allowing for non-food requirements suggests an overall poverty line of approximately 900,000 cedis per equivalent adult per year in Accra, January 1999 prices. As shown in World Bank (1995), this line represents roughly $1 a day. This latter line would be

75

used as the overall poverty line for Ghana. The lower poverty line of 700,000 is used as an extreme poverty line; people whose standard of living measure lies below this would not be able to meet their calorie requirements even if they spent their entire budget on food. These same poverty lines of 700,000 and 900,000 cedis were used for the analysis of the 1991/92 and 2005/2006 surveys but they were inflated using locality specific Consumer Price Index (CPI) provided by GSS, backward for the 1991/92 survey and forward to January 2006 prices for the 2005/2006 survey, yielding extreme and overall poverty lines of 2,884,700 cedis and 3,708,900 cedis in 2006. Those lines take into account price differentials between the different localities. In 2006 in local prices the higher line can be translated to 3,708,900 (Accra); 2,773,170 (Other Urban); 3,146,220 (Rural Coastal); 3,034,800 (Rural Forest) and 2,850,120 (Rural Savannah). Standard errors of poverty measures As any other statistics computed from survey data, poverty measures have standard errors, and these standard errors must be considered when assessing whether changes in poverty over time, or differences in poverty between groups can be considered as statistically significant. To complement the estimates provided in the main text, this section provides the standard errors of the main poverty measures estimated with the three GLSS surveys (Tables A2.4 and A2.5).

76

T

able

A2.

4: P

over

ty m

easu

res

by u

rban

/rur

al lo

cati

on w

ith

stan

dard

err

ors

and

conf

iden

ce in

terv

als

20

06

1998

/99

1991

/92

P

over

ty

Std.

Err

. [9

5% C

onf.

Int.]

P

over

ty

Std.

Err

. [9

5% C

onf.

Inte

rval

] P

over

ty

Std.

Err

. [9

5% C

onf.

Inte

rval

]

Hea

dcou

nt in

dex

Urb

an

10.8

1.

4 8.

1 13

.6

19.4

2.

6 14

.2

24.6

27

.7

2.3

23.2

32

.3

Rur

al

39.2

2.

0 35

.3

43.1

49

.6

2.6

44.4

54

.8

63.6

1.

6 60

.4

66.8

G

hana

28

.5

1.5

25.6

31

.5

39.5

2.

3 35

.0

43.9

51

.7

1.7

48.4

55

.0

P

over

ty g

ap

Urb

an

3.1

0.5

2.0

4.1

5.3

0.8

3.8

6.9

7.4

1.0

5.4

9.4

Rur

al

13.5

0.

9 11

.7

15.3

18

.2

1.6

15.1

21

.3

24.0

1.

0 22

.0

26.0

G

hana

9.

6 0.

6 8.

3 10

.9

13.9

1.

2 11

.6

16.2

18

.5

0.9

16.8

20

.2

Sq

uare

d po

vert

y ga

p U

rban

1.

3 0.

3 0.

8 1.

8 2.

1 0.

3 1.

4 2.

7 2.

9 0.

6 1.

7 4.

1 R

ural

6.

6 0.

6 5.

5 7.

7 8.

9 1.

0 7.

0 10

.9

11.7

0.

7 10

.4

13.0

G

hana

4.

6 0.

4 3.

8 5.

3 6.

6 0.

7 5.

2 8.

0 8.

8 0.

5 7.

7 9.

8 So

urce

: Aut

hors

.

77

Tab

le A

2.5:

Pov

erty

mea

sure

s by

reg

ion

wit

h st

anda

rd e

rror

s an

d co

nfid

ence

inte

rval

s

2006

19

98/9

9 19

91/9

2

Pov

erty

St

d. E

rr.

[95%

Con

f. In

t.]

Pov

erty

St

d. E

rr.

[95%

Con

f. In

terv

al]

Pov

erty

St

d. E

rr.

[95%

Con

f. In

terv

al]

H

eadc

ount

inde

x W

este

rn

18.4

3.

3 11

.9

24.9

27

.3

3.6

20.1

34

.4

59.6

3.

7 52

.3

66.8

C

entr

al

19.9

3.

5 13

.1

26.8

48

.4

3.6

41.3

55

.6

44.3

4.

0 36

.5

52.0

G

reat

er A

ccra

11

.8

2.5

6.9

16.7

5.

2 1.

6 2.

1 8.

3 25

.8

3.4

19.2

32

.4

Vol

ta

31.4

4.

4 22

.8

40.1

43

.7

5.2

33.5

53

.8

48.0

4.

1 39

.9

56.1

E

aste

rn

15.1

2.

7 9.

7 20

.4

37.7

2.

9 32

.0

43.5

57

.0

4.6

48.0

66

.0

Ash

anti

20.3

2.

5 15

.3

25.3

27

.7

4.8

18.2

37

.3

41.2

3.

7 33

.9

48.5

B

rong

Aha

fo

29.5

4.

0 21

.6

37.3

35

.8

5.8

24.5

47

.2

65.0

4.

9 55

.3

74.7

N

orth

ern

52.3

5.

9 40

.8

63.9

69

.2

6.4

56.7

81

.7

63.4

6.

6 50

.5

76.4

U

pper

Eas

t 70

.4

4.8

61.0

79

.7

83.9

9.

2 65

.8

102.

0 88

.4

3.2

82.2

94

.6

Upp

er W

est

87.9

3.

3 81

.3

94.4

88

.2

5.6

77.1

99

.2

66.9

5.

0 57

.1

76.7

G

hana

28

.5

1.5

25.6

31

.5

39.5

2.

3 35

.0

43.9

51

.7

1.7

48.4

55

.0

P

over

ty g

ap

Wes

tern

4.

2 1.

0 2.

2 6.

3 7.

0 1.

0 5.

0 8.

9 20

.5

1.9

16.7

24

.3

Cen

tral

4.

3 1.

0 2.

4 6.

2 14

.8

1.6

11.7

17

.9

12.9

2.

2 8.

6 17

.2

Gre

ater

Acc

ra

3.1

0.8

1.5

4.6

1.1

0.3

0.4

1.7

6.3

1.1

4.1

8.5

Vol

ta

7.3

1.2

4.9

9.6

15.6

2.

4 11

.0

20.3

15

.9

1.6

12.7

19

.1

Eas

tern

3.

3 0.

8 1.

7 4.

9 9.

9 1.

3 7.

3 12

.4

20.1

2.

3 15

.5

24.6

A

shan

ti 5.

2 0.

8 3.

7 6.

7 8.

5 1.

9 4.

8 12

.1

12.9

1.

5 9.

9 15

.9

Bro

ng A

hafo

7.

8 1.

4 5.

1 10

.5

9.8

2.0

5.8

13.7

22

.8

2.4

18.1

27

.4

Nor

ther

n 20

.7

2.7

15.4

26

.1

29.9

4.

1 21

.8

38.1

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78

Annex 3: inequality measures and their decomposition

This annex is reproduced from Coudouel et al. (2002) and Wodon and Yitzhaki (2002). It provides mathematical expressions for the most commonly used inequality measures: the Gini, Theil and Atkinson indices. Each index can be generalized in order to put more weight on selected parts of the distribution of consumption. As is the case for poverty measures, most inequality measures can be decomposed by group or by source. The annex presents decomposition by group formulas for the general entropy class of inequality measures which includes the Theil index (used in Chapter 2 of this study), and decomposition by source formulas for the extended Gini index (used in Chapter 5 of this study). Inequality measures The standard Gini index measures twice the surface between the Lorenz curve, which maps the cumulative income share on the vertical axis against the distribution of the population on the vertical axis, and the line of equal distribution. A large number of mathematical expressions have been proposed for the Gini index, but the easiest to manipulate is based on the covariance between the income Y of an individual or household and the F rank that the individual or household occupies in the distribution of income (this rank takes a value between zero for the poorest and one for the richest). Denoting by y the mean income, the standard Gini index is defined as:

Gini = 2 cov (Y, F) / y The Gini has attractive theoretical and statistical properties which other inequality measures do not have, which explains why it is used by most researchers. The extended Gini uses a parameter ν to emphasize various parts of the distribution. The higher the weight, the more emphasis is placed on the bottom part of the distribution (ν=2 for the standard Gini index):

y

FyGini

)]1[,cov()(

1−−−=

ννν

Another family of inequality measures is the General Entropy measure, defined as:

⎥⎥

⎦

⎤

⎢⎢

⎣

⎡∑ −⎟

⎟⎠

⎞⎜⎜⎝

⎛

−=

=

n

i

i

y

y

nGE

12

111

)(α

ααα

With ∑==

n

i iy

y

nGE

1log

1)0( ,

y

y

y

y

nGE i

n

i

i∑==1

log1

)1( and ∑ −==

n

ii yy

ynGE

1

22

)(2

1)2(

Measures from the GE class are sensitive to changes at the lower end of the distribution for α close to zero, equally sensitive to changes across the distribution for α equal to one (which is the Theil index), and sensitive to changes at the higher end of the distribution for higher values. A third class of inequality measures was proposed by Atkinson. This class also has a weighting parameter ε (which measures aversion to inequality) and some of its theoretical properties are similar to those of the extended Gini index. The Atkinson class is defined as follows:

)1(1

1

11

1εε

ε

−

=

−

⎥⎥

⎦

⎤

⎢⎢

⎣

⎡∑ ⎟

⎟⎠

⎞⎜⎜⎝

⎛−=

n

i

i

y

y

nA

79

Decomposition of inequality measures by group: Illustrations for the GE class Inequality is often decomposed by population groups to assess the contribution to total inequality of inequality within and between groups, for instance within and between individuals in urban and rural areas. Inequality measures can also be decomposed according to consumption or income sources in order to identify which component contributes most to overall inequality. Finally, decompositions can be used to analyze changes in income inequality over time. Below, decompositions are provided for the GE class. Consider first decompositions at one point in time. Total inequality I can be decomposed into a component of inequality between the population groups Ib and the remaining within-group inequality Iw. The decomposition by population subgroups of the GE class is defined as:

⎥⎥

⎦

⎤

⎢⎢

⎣

⎡−⎟⎟

⎠

⎞⎜⎜⎝

⎛∑

−+∑=+=

==

− 11

)(1

21

1α

αα

ααα

y

yfGEfvIII n

k

jj

k

jjjjbw

where fj is the population share of group j (j=1,2,..k); vj is the income share of group j ; and yj is the average income in groups j. Inequality measures can also be decomposed by source of consumption or income. The decomposition for the GE measure with α=2 is as follows:

∑=∑=f

ff

ff

f GEGESI )2().2(μ

μρ

where Sf is the contribution of income source f; ρf is the correlation between component f and total income; and μf / μ is the share of component f in total income. If Sf is large, then component f is an important source of inequality. Consider next decompositions for changes in inequality over time. Using sub-group decompositions, changes in inequality can be decomposed into: 1) changes in the numbers of people in various groups or “allocation” effects; 2) changes in the relative incomes of various groups or “income” effects; and 3) changes in inequality within groups or “pure inequality” effects. Because the arithmetic can be complex for some inequality measures, this decomposition is usually applied only to Generalized Entropy index GE(0) as follows:

[ ] ( )∑ Δ−+∑ Δ−+∑ Δ+∑ Δ=Δ====

k

jjij

k

jijj

k

jj

k

jjj yfvffGEGEfGE

1111))(log()log()0()0()0( μλλ

| \ / | Pure inequality effects Allocation effects Income effects where Δ is the difference operator, λj is the mean income of group j relative to the overall mean (i.e., λj = μ(yj)/μ(y)) and the over-bar represents averages. The first term captures the pure inequality effects, the second and third terms, the allocation effects, and the fourth term, the income effects. Using source decompositions, changes can be decomposed by income source. This allows to see whether an income source f has a large influence on changes in total inequality over time. For the General Entropy index with α=2, defining St as above, the decomposition is:

∑Δ=Δf

fSGE )2(

80

Decomposition of inequality measures by source: Illustrations for the extended Gini To analyze the impact of various sources of income on inequality in per capita income, we use in Chapter 5 of the study a source decomposition of the Gini index proposed by Lerman and Yitzhaki (1985; see also Garner, 1993 for an application to inequality in consumption rather than income). As before, denote total per capita income by y, the cumulative distribution function for total per capita income by F(y) (this takes a value of zero for the poorest household and one for the richest), and the mean total per capita income across all households by y . The Gini index can be decomposed by source as follows:

Gy = 2 cov [y, F(y)]/ y = Σi SiRiGi

where Gy is the Gini index for total income, Gi is the Gini index for income yi from source i, Si is the share of total income obtained from source i, and Ri is the Gini correlation between income from source i and total income. The Gini correlation is defined as Ri = cov [yi, F(y)] / cov[(yi, F(yi)], where F(yi) is the cumulative distribution function of per capita income from source i. The Gini correlation Ri can take values between –1 and 1. Income from sources such as income from capital which tend to be strongly and positively correlated with total income will have large positive Gini correlations. Income from sources such as transfers tend to have smaller, and possibly negative Gini correlations. The overall (absolute) contribution of a source of income i to the inequality in total per capita income is thus SiRiGi. This decomposition provides a simple way to assess the impact on the inequality in total income of a marginal percentage change equal for all households in the income from a particular source. As shown in Stark, Taylor, and Yitzhaki, (1986), the impact of increasing for all households the income from source i in such a way that yi is multiplied by (1 + ei) where ei tends to zero, is:

)( yiiii

y GGRSe

G−=

∂∂

This equation can be rewritten to show that the percentage change in inequality due to a marginal percentage change in the income from source i is equal to that source’s contribution to the Gini minus its contribution to total income. In other words, at the margin, what matters for evaluating the redistributive impact of income sources is not their Gini, but rather the product RiGi which is called the pseudo Gini. Alternatively, denoting by ηi = RiGi/Gy the so-called Gini income elasticity (GIE) for source i, the marginal impact of a percentage change in income from source i identical for all households on the Gini for total income in percentage terms is:

)1(/

−=−= iiiy

iii

y

iy SSG

GRS

G

eGη

∂∂

Thus a percentage increase in the income from a source with a GIE ηi smaller (larger) than one will decrease (increase) the inequality in per capita income. The lower the GIE is, the larger the redistributive impact will be. The GIE of income source i can be written as:

i

ii S

1*

))y(F,ycov(

))y(F,xcov(=η ,

where xi.is income source (or expenditure item) i per capita, y is income per capita, and Si is the share of source i in income. The ratio of the covariances is an instrumental variable estimator of the slope of the Engel curve of source i with respect to income y, with F(y) being the instrument. Hence the ratio of the covariances can be interpreted as the slope (or the marginal propensity) of the Engel of X with respect to

81

Y. Si is the average propensity so that the ratio of the two yield the income elasticity of the Engel curve. Note, that at the same time, the GIE is the income elasticity of the Gini with respect to an increase in income source i. The same decomposition can be applied to per capita consumption and its sources. The same decomposition can also be applied to the extended Gini which uses a parameter ν to emphasize various parts of the distribution. The higher the weight is, the more emphasis will be placed on the bottom part of the distribution (ν=2 for the standard Gini index):

y

FyGy

)]1[,cov()(

1−−−=ννν

82

Annex 4: Regression Analysis

Regression analysis is used in various chapters of this report. The annex provides background on the various types of regressions used and the rationale for doing so, with a focus on a) the correlated or determinants of the logarithm of household consumption; and b) the determinants of labour force participation and wages. This annex is reproduced from Coudouel et al. (2002). Correlates or Determinants of Poverty/Consumption It has become a standard practice to analyze the determinants of poverty through categorical regressions such as probits and logits. When using such categorical regressions, it is assumed that the actual (per capita) income or consumption of households is not observed. We act as if we only know whether a household is poor or not, which is denoted by a categorical variable which takes the value one if the household is poor, and zero if the household is not poor. Under the hypothesis of a normal standard distribution for the error term, the model is estimated as a probit. If the error term is assumed to have a logistic distribution, the model is estimated as a logit. The main problem with categorical regressions is that the estimates are sensitive to specification errors. With probits, the parameters will be biased if the underlying distribution is not normal. More generally, the model does not make use of all the information available, because it collapses income or expenditure into a binary variable. This does not mean that probit or logit regressions should never be used. Categorical regressions will typically have better predictive power for targeting, that is for classifying households as poor or non-poor. The alternative is to use the full information available for the dependant variable (indicator of well-being), and to run a regression of the log on the indicator (if the distribution is log normal.) Assume that wi is the normalized indicator divided by the poverty line, so that wi = yi/z, where z is the poverty line and yi is (per capita) income or consumption. A unitary value for wi signifies that the household has its level of income or consumption exactly at the level of the poverty line. Denoting by Xi the vector of independent variables, the following regression can be estimated:

Log wi = γ’Xi + εi From this regression, the probability of being poor can then be estimated as follows:

Prob[log wi <0 | Xi] = F[-(γ’Xi)/σ] where σ is the standard deviation of the error terms and F is the cumulative density of the standard normal distribution. Once regressions have been estimated to analyze the determinants of poverty, the coefficients on the variables (γ) can thus inform on the various correlates of poverty and be used to simulate the impact of various policies. Determinants of labour force participation and wages Similar to the analysis of correlates of poverty, regressions can be used to analyze the determinants of individual labour income. To analyze the impact of individual characteristics on labour income, and to measure among other things the impact of a better education on earnings, other types of regressions must be used. The standard approach consists in running a so-called Heckman model. Denote by log wi the logarithm of the wage (or earnings) observed for individual i in the sample. The wage wi is non zero only if it is larger than the individual’s reservation wage (otherwise, the individual chooses not to work.) The difference between the individual’s wage and reservation wage is denoted by Δ*i. The individual’s wage on the market is determined by geographic location (separate regressions are run for the urban and rural sectors), years of experience E, and years of schooling S. There may be other determinants of wages but these are not observed. The difference between the individual’s wage and his reservation wage is

83

determined by the same characteristics, plus the number of babies B, children C, and adult family members A of the individual (and their square.) The Heckman model is written as:

wi = w*i if Δ*i > 0, and 0 if Δ*i < 0 Log w*i = αw + β w1Ei + β w2Ei

2 + β w3Si + β w4Si2 + ε w i

Δ*i=αΔ+βΔ1Ei+βΔ2Ei2+βΔ3Si+βΔ4Si

2+βΔ5Bi+βΔ6Bi2+βΔ7Ci+βΔ8Ci

2+βΔ9Ai+βΔ10Ai2+εΔi = mΔi + εΔi

The expected value of εwi is not zero. Denoting by ϕ and Φ the standard normal density and cumulative density, and noting that σΔ, the standard error of εΔi, is normalized to one, we have:

E[Log w*i |Δ*i>0] =αw+βw1Ei+β w2Ei2+βw3Si+βw4Si

2+λϕ(mΔi)/Φ(mΔi) E[Log w*i |Δ*i<0] =αw+βw1Ei+βw2Ei

2+βw3Si+βw4Si2-λϕ(mΔi)/[1-Φ(mΔi)]

If λ is statistically different from zero, the returns to education will differ between the employed and the unemployed, although the difference will typically be small. Simple approximations of the private returns to education (or more precisely, of the marginal impact of a better education on individual earnings) can be computed from the above wage regressions by taking the first derivative of the expected wage with respect to the number of years of schooling. Thus the “return” to education for year of schooling S is ∂E[Log w*i]/∂S = βw3+2βw4S when λ is zero. The returns are increasing (decreasing) with the number of years of schooling if the coefficient βw4 is positive (negative.) These returns do not take into account the positive impact on the probability of working of education (i.e., the fact that βΔ3Si+βΔ4Si

2 is typically positive.) The returns also do not include estimates of the costs of schooling for parents and society (which reduce the returns) and of the indirect effects and externalities associated with education (which typically increase the returns, from the point of view of both the society and the household.)

84

References Addison, E.K.Y, (2004), The Macroeconomic Impact of Remittances, Bank of Ghana, Paper presented at

the Conference on Migration and Development in Ghana, September 14-16, 2004 Ahiakpor, James C.W. (1991) Rawlings, Economic Policy Reform, and the Poor: Consistency or

Betrayal?, The Journal of Modern African Studies, Vol. 29(4): 583-600. Appiah, K, S.G. Laryea-Adjei and L. Demery (2000), Poverty in a Changing Environment, in E.

Aryeetey, J. Harrigan and M. Nissanke (eds), Economic Reforms in Ghana: The Miracle and the Mirage, Oxford: James Currey.

Beaudry, P. and N.K. Sowa (1994), Ghana, in S. Horton, R. Kanbur and D. Mazumdar (eds), Labour

Markets in an Era of Adjustment, Volume 2: Case Studies, Washington D.C.: The World Bank. Bogetic, Z., et al. (2007). Ghana's Growth Story, Ghana CEM, Volume 1: Meeting the Challenge of

Accelerated and Shared Growth, The World Bank, Washington, DC.

Bulir Aleš (2002), Can Price Incentive to Smuggle Explain the Contraction of the Cocoa Supply in Ghana, Journal of African Economics, vol. 11(3): 413-439.

Brooks J., A. Croppenstedt, and E. Aggrey-Fynn, (2007), Distortions to Agricultural Incentives in Ghana,

Agricultural Distortions Working Paper, The World Bank, Washington DC. Canagarajah, S., D. Mazumdar and X. Ye (1998), The structure and Determinants of Inequality and

Poverty in Ghana, 1988-92, The World Bank Policy Research Working Paper No. 1998, The World Bank

Coudouel, A., J. Hentschel, and Q. Wodon (2002), Poverty Measurement and Analysis, in J, Klugman

(ed.), A Sourcebook for Poverty Reduction Strategies, Washington, DC: The World Bank. Coulombe, H., and A. McKay (1995), An Assessment of Trends in Poverty in Ghana, 1988-92, PSP

Discussion Paper No. 81, Poverty and Social Policy Department, The World Bank, Washington D.C.

Coulombe, H., and A. McKay, (2007) Growth with Selective Poverty Reduction – Ghana in the 1990s, in

Wodon, Q., editor, Growth and Poverty Reduction: Case Studies from West Africa, The World Bank Working Paper No. 79, Washington, DC.

Datt, G. and M. Ravallion (1992), Growth and redistribution components of changes in poverty measures:

A decomposition with applications to Brazil and India in the 1980s, Journal of Development Economics, 38(2): 275-295.

Demery, L. (2003), Analyzing the Incidence of Public Spending, in F. Bourguignon and L.A. Pereira da

Silva (eds.), Evaluating the Poverty and Distributional Impact of Economic Policies: Techniques and Tools, Washington D.C.: The World Bank.

Diallo, and Q. Wodon, (2007), Asset-Based Poverty: trends and Determinants in Ghana (1997-2003),

mimeo, The World Bank, Wasahington, DC. Elbers, C., J. O. Lanjouw, and P. Lanjouw, (2002), Welfare in Villages and Towns: Micro level

Estimation of Poverty and Inequality, Policy Research Working Paper No. 2911, DECRG-The World Bank, Washington DC

Elbers, C., J. O. Lanjouw, and P. Lanjouw, (2003), Micro-Level Estimation of Poverty and Inequality,

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Econometrica, 71(1): 355-364. Foster, J., J. Greer, and E. Thorbecke, (1984), A class of decomposable poverty measures, Econometrica,

52: 761-765. Garner, T. I., (1993), Consumer Expenditures and Inequality: An Analysis Based on Decomposition of

the Gini Coefficient, The Review of Economics and Statistics, 75(1): 134-138. Ghana Statistical Service (1995), The Pattern of Poverty in Ghana, 1988-1992, GSS, Accra, Ghana Ghana Statistical Service (2000a), Poverty Trends in Ghana in the 1990s, GSS, Accra, Ghana Ghana Statistical Service (2000b), The Estimation Of Components of Household Incomes and

Expenditures: A Methodological Guide Based on the Ghana Living Standards Survey, 1991/92 and 1998/99, GSS, Accra, Ghana.

Ghana Statistical Service (2007), Pattern and Trends of Poverty in Ghana 1991-2006, Accra: Ghana

Statistical Service Goldstein, M. and R. Bhavnani (2007) From Independence to Economic Reform: Rural Poverty in Ghana

from 1967-1997, mimeo, The World Bank, Washington, DC. Green, R. H, (1987), Ghana, Helsinki: World Institute for Development Economics Research, 23 Kakwani, N., 1997, Growth Rates of Per-Capita Income and Aggregate Welfare: An International

Comparison, Review of Economics and Statistics, 79: 202-211. Kraus, J., (1991), The Political Economy of Stabilization and Structural Adjustment in Ghana, in D.S.

Rothchild (ed.), Ghana: the political economy of recovery, London: Lynne Rienner Lerman, R. and S. Yitzhaki, (1985), Income Inequality Effects by Income Source: A New Approach and

Application to the U.S., The Review of Economics and Statistics, 67(1): 151-56. Norton, A., E. B.-D. Aryeetey, D. Korboe and D.K.T. Dogbe (1995), Poverty Assessment in Ghana using

Qualitative and Participatory Research Methods, PSP Discussion Paper No. 83, The World Bank, Washington D.C.

Ravallion, M., (1994), Poverty Comparisons Fundamentals of Pure and Applied Economics, Volume 56,

Chur, Switzerland: Harwood Academic Publishers. Ravallion, M., and S. Chen, (2003), Measuring Pro-Poor Growth, Economics Letters, 78: 93-99. Ravallion, M. and M. Huppi (1991), Measuring Changes In Poverty: A Methodological Case Study Of

Indonesia During An Adjustment Period, The World Bank Economic Review, 5(1): 57-82. Roe, A., (1992), Adjustment and Equity in Ghana, OECD, Paris. Scott, C. and B. Amenuvegbe (1990), Effect of Recall Duration on Reporting of Household Expenditures:

An Experimental Study in Ghana, Social Dimensions of Adjustment Working Paper No. 6, The World Bank, Washington D.C.

Tiffin P., J. MacDonald, H. Maamah, and F. Osei-Opare, (2004), From Tree-minders to Global Players:

Cocoa Farmers in Ghana, in Chains of Fortune: Linking Women Producers and Workers with Global Markets, Commonwealth Secretariat.

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Varangis P. and G. Schreiber, (2001), Cocoa Market Reforms in West Africa, T. Akayima, J. Baffes J., D.

Larson, and P. Varangis. (eds.), Commodity Market Reforms: Lessons of Two Decades, The World Bank, Washington, D.C.

Wodon, Q., F. Bertholet and C. Tsimpo, (2007), Assessing Changes over Time in the Targeting

Performance of Electricity Subsidies in Ghana, mimeo, The World Bank, Washington, DC. Wodon, Q. and S. Yitzhaki, (2002), Inequality and Social Welfare, in J. Klugman (ed.), Poverty Reduction

Strategy Papers Sourcebook, The World Bank: Washington, DC. Zeitlin, A. (2005), Market Structure and Productivity Growth in Ghanaian Cocoa Production, mimeo,

Center for the Study of African Economies.

87

2. LABOR OUTCOMES AND SKILLS IN GHANA

8

INTRODUCTION AND OBJECTIVES

2.1 During the last 15 years, Ghana had one of the strongest growth rates amongst Sub-Saharan countries and an outstanding performance in terms of poverty reduction. The Government of Ghana (GOG) has put substantial effort in achieving macro economic stability and this has clearly paid off. With Tanzania, Uganda, and some other West African countries, Ghana is among the strongest policy performers among low-income African countries (see Bogetic et al., 2007, first volume of this CEM, for an analysis of Ghana’s growth performance). Since 1990s, the average annual growth has averaged around 5 percent and has reached more than 6 percent in 2005–06. With a population growth rate of slightly less than 2.5%, this translates in GDP per capita growth of up to 4%. This strong economic growth has led to massive poverty reduction (see chapter 1 of this Volume 3 of the CEM by Coulombe and Wodon, 2007). The share of the population in poverty fell from 51.7 percent in 1991–02 to 39.5 percent in 1998–09, and 28.5 percent in 2005–06. It is expected that Ghana will meet the Millennium Development Goals of reducing poverty by half versus its level around 1990 (to 25.8 percent) well ahead of the target date of 2015. At the same time, there was an increase in inequality and the pace of poverty reduction has been weaker in the northern regions, which were already poorer in the 1990s.

2.2 The first objective of this chapter is to document the trends in labor market outcomes in Ghana over the last 15 years. Since the vast majority of people depend on their labor as the primary source of income, the quantity and quality of employment play a central role for the translation of growth into poverty reduction. This chapter first documents labor market trends in Ghana, using a simple framework to make the link between labor market outcomes and the well-being of households. Thereafter, the chapter discusses how the skills of workers could be improved so that they benefit from higher wages, and what this may entail for Government authorities as they consider employment policies. Overall, the analysis focuses on data for the last 15 years to answer the following questions: How has growth been translated in terms of job creation? How have changes in the demographic structure of the population affected the labor market? How has the distribution of employment across various sectors changed? Who works and what type of skills do workers possess? What is the quality of the jobs created in both the public and private sectors? What share of workers in various sectors belongs to poor households? Which groups among the working population are relatively disadvantaged as measured by labor market status, earnings, education, skills and other job attributes? Answers to these questions could guide the Government in making informed policy choices in order to further accelerate economic growth and adapt employment policies to the needs of the Ghanaian labor market.

2.3 In order to facilitate the analysis of labor market outcomes, we rely on a simple conceptual framework. The detailed framework is available in Nouve and Wodon (2007). While the framework is not fully applied here, its basic idea is used to consider the fact the well-being of a household and its members depend in part on its level of consumption per capita. Consumption is itself related to total income, including labor income. Thus, labor income per capita (or per equivalent adult) is a key determinant of the expected consumption level of households, and thus their probability of being poor. goal of the framework is to examine the contribution of each of these ratios to national and regional per Labor market per capita is itself a function of a few simple variables: (i) the dependency ratio of the household (number of persons in the household divided by number of working age adults); (ii) the labor force participation rate of the working age population; (iii) the employment rate among the active working age population; (iv) the hourly wages for the employed workers; and (v) the number of hours worked by the employed workers. Differences between household groups in labor income per capita, and thereby to a large extent in consumption per capita, depend on differences in the above five parameters or 8 This Chapter is based on a preliminary draft of the ongoing sector work on “Job creation and skills development in

Ghana”. The complete report is expected to be reviewed in the Bank in November 2007 and discussed with the Government in December 2007.

88

variables. Given this conceptual framework, we look in some details at the above five variables in the next section of the chapter, in order to document and explain trends in labor market outcomes.

2.4 There is however one important caveat to the analysis related to the comparability of the surveys that needs to be emphasized – we do not consider here the issue of youth employment. Most of the statistics presented in the next section of this chapter are based on GLSS surveys for 1991/92, 1988/99, and 2005/06, but there are comparability issues between surveys. The surveys are comparable for most questions, but there is one major difference in the design of the 1998/99 questionnaire that needs to be taken into account. In the 1998/99 questionnaire, once a child or youth was enrolled in school, no questions were asked about his labor activities, while this was not the case in the 1991/92 and 2005/06 surveys. This means that when considering the whole adult population aged 15 to 64, only the data from the 1991/92 and 2005/2006 surveys are comparable, as estimates based on the 1998/99 surveys do not account properly for the labor activities of youths aged 15 to 24 (or children below the age of 15). By contrast, when considering the population aged 25 to 64, the three surveys are to a large extent comparable, although in some cases, we still find some divergence in the results obtained with the 1998/99 survey as compared to the other two surveys. In this paper, we report on the trends for the 25-64 age group for all three surveys. In a separate upcoming study on “Job creation and skills development”, we will also analyze the issue of youth employment for the 15-24 age group using the first and third surveys only, but this work has not been completed yet. However, in the second part of the chapter, we do provide an analysis related to youth employment by discussing policies related to education and skills development.

2.5 Beyond a descriptive analysis of labor market outcomes, the second part of the chapter is devoted to a discussion of education and skills as they relate to labor market outcomes. The context for this analysis is the fact that trade, rapid advances in science and technology, and intensified economic competition have shaped the demand for skills in countries worldwide. These changes are part of globalization and have increased the attention given to education and training systems and how well these systems are preparing youth for entry to the world of work and supporting more seasoned workers in adjusting to structural changes taking place in labor markets. Ghana is part of this trend with its adoption of the Free Compulsory Universal Basic Education Program in 1996 that set out to ensure nine years of basic education for all young people and a more extended set of reforms in its Education Strategic Plan (ESP) for 2003-2015 that would meet the Millennium Development Goals for education and prepare youth with the skills needed for overcoming poverty and raising living standards. Concern exists whether skills have become or may become a constraint to Ghana’s further growth and capacity for reducing poverty. Noting the limited opportunities for skills development beyond basic education, a White Paper was prepared in 2004 building on the ESP and calling for increased emphasis on technical, vocational, and agricultural education and apprenticeship. The Ministry of Education, Science and Sports (MOESS) in a sector review refers to evidence of a widespread disparity between what education institutions produce and what the labor market wants. In the second part of this Chapter, we first provide an overview of the landscape for skills development starting with the foundation of basic education through nine years of schooling and the progress made toward providing access to good quality basic education for all. At the completion of basic education, the focus turns to options for further education and skills development, comparing school-based and post-school options for training and highlighting issues of access, quality, efficiency and financing in the various programs. Next, using again the GLSS surveys, we provide preliminary results regarding the analysis of the returns to education and training, looking for evidence of growing skills gaps. Finally, we review the ESP recommendations for sector reforms involving skills and benchmarks recommendations against regional and international experience.

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TRENDS IN LABOR OUTCOMES: RESULTS FROM THE GLSS SURVEYS

Demographic trends and dependency ratios

2.6 Ghana is experiencing a shift in demographic structure leading to falling dependency ratio. This creates a window of opportunity as there will be more working people relative to dependents. Annual population growth has fallen from about 4% in the early eighties to slightly less than 2.5% in 2005. With this population growth rate Ghana is below the Sub-Sahara African average, but the country is still above the average for low income countries as a whole. Apart from a reduction in its population growth rate, Ghana is experiencing a demographic transition with a falling dependency ratio (this is the ratio of the total population to the working age population). The working age population is indeed increasing as a share of the total population (from 52 percent in 1983 to 57 percent in 2005), while the share of children (0-14 age cohort) has been decreased from 45% to 39%. The proportion of people above 65 is increasing rapidly, but from a low base, and thereby remains low at about 4% in 2005. Overall, since the proportion of people that are too young or too old to work is falling and there are more working individuals relative to dependents, most households are benefiting from a drop in their dependency ratio. This potential gain should continue over time, as suggested by projected population pyramids in Figure 2.1. Of course, for households to benefit from the falling dependency ratio the economy must create sufficient jobs so that both the existing active population and the new cohorts who are entering the labor force are able to find work. Yet as will be documented in this chapter, this has largely been the experience in Ghana over the last 15 years, with the growth in employment closely matching the growth in the supply of labor among the adult population, especially in the 25-64 age group.

2.7 The fall in dependency ratios is already observed in the various rounds of the GLSS surveys. As shown in Figures 2.2 and 2.3, while the population has grown for all age groups between 1991 and 2006, the largest increase has been observed among the working age population, and especially among youths. This has resulted in a substantial decrease in the dependency rations, which have been estimated in two different ways as the total population divided by the population either between 15 and 64, or between 25 and 64. For example, when using as the denominator the population between 25 and 64, the dependency ratio has decreased from 2.72 in 1991/92 to 2.38 in 2005/06. This is likely to have been a key factor in the improvements in the consumption per capita indicators used to measure poverty.

2.8 However, while Ghana is benefiting from a demographic transition, the urban population is increasing rapidly, which implies a rapid rise in the urban labor supply, especially among youth. While the youngest age cohort (0-14) is growing at the slowest rate (about 1% per year), the working age population (15-64 years) is growing at a much higher 3% per year, and the rate is higher for youth. Another important trend is the rapid rate of urbanization, with the share of the urban population increasing from 32% in 1983 to 48% in 2005. Together, the high rate of population growth and the rapid urbanization have yielded a large increase in new job seekers, especially in cities, and especially among youth. We will document in this chapter the fact that poverty has decreased much less among the unemployed than among other groups over time. This suggests that interventions to help the unemployed, and especially young workers with limited experience who may have difficulties in finding good jobs, are important to respond to the aspirations of the new cohorts entering the labor force.

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Figure 2.1: Population Pyramids 2000, 2025, 2050

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Figure 2.2: Population by age group in GLSS surveys,

Figure 2.2: Population by age group in GLSS surveys, 1991-2006

8.7

8.0

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2006 1998/99 1991/92

Pop

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roup

0 to 14 15 to 24 25 to 59 60 and over

Source: Based on Coulombe and Wodon (2007), using GLSS surveys.

Figure 2.3: Dependency ratios in GLSS surveys, 1991-2006

Figure 2.3: Dependency Ratios in GLSS surveys, 1991-2006

1.88

2.02

2.13

2.38

2.56

2.72

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Dependency ratio (adults 15-59) Dependency ratio (adults 20-59)


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Labor outcomes among workers aged 25-64

2.9 Although labor force participation rates are high in Ghana, the data need to be interpreted with caution. There is substantial debate whether labor force participation is being measured accurately in a Sub-Sahara African context, especially for work in rural areas and particularly for women. In Ghana, the data suggests that labor force participation is very high among the adult population aged 25-64 (more than 90% for men, and more than 85% for women) and has remained stable over time. Stylized facts (based on a preliminary probit analysis) suggest that (i) participation is higher in rural areas; (ii) better educated adults are less likely to participate in the labor force, both in urban and rural areas – after controlling for age and family situation; (iii) being married increases the probability of participating in the labor force for both men and women at least in rural areas. However, in order to better appreciate the extent to which different individuals work, an analysis of hours worked (given later below) is required.

Table 2.1: Labor Force Participation in age group 25-64, 1991-2005

1991/92 1998/99 2005/06 change Sex Male 92.2 87.4 92.2 0.0 Female 86.4 80.5 86.2 -0.2 Age 24-54 89.5 84.0 89.7 0.2 55-64 85.1 80.4 83.6 -1.5 Education Level No education 88.5 81.7 88.5 0.0 Primary 88.8 86.5 90.0 1.2 Secondary lower 90.3 84.5 90.1 -0.2 Secondary Higher 86.3 82.3 84.5 -1.8 Post Secondary 87.8 87.2 88.9 1.1 Urban/Rural Urban 86.5 81.3 85.0 -1.5 Rural 90.2 84.8 91.7 1.5 Region Western 93.4 86.3 89.1 -4.3 Central 92.9 93.1 94.8 1.9 Greater Accra 85.4 78.6 81.6 -3.8 Volta 90.5 89.1 91.1 0.6 Eastern 87.6 82.7 91.6 4.0 Ashanti 88.0 86.2 92.0 4.0 Brong Ahafo 94.4 88.6 93.9 -0.5 Northern 89.8 73.2 89.7 -0.1 Upper East 90.0 58.6 76.8 -13.2 Upper West 70.6 81.7 79.8 9.2 All 88.9 83.5 88.9 0.0


2.10 The employment rate has remained high among the adult population over 25 years of age, and has decreased among children and youths in part due to better school enrollment rates. The employment rate is defined as the percentage of the adult population that is working. The unemployment rate is defined as the share of the adult population in the labor force that is currently looking for work. According to the GLSS surveys, the employment rate of the working age population (25-64) has remained at about 86 percent over time (with a slight drop in 1998/99). The unemployment rate has also remained stable. This implies that the labor force rate has remained stable as well. By contrast, the labor force participation has dropped among younger individuals, in part thanks to better enrollment in schools (see Coulombe and Wodon, 2007). The child labor rate has been reduced by almost half over the last 15 years, and the youth employment rate has also decreased. Finally, the employment rate among the elderly has also fallen consistently, which probably suggests an improvement in their living conditions as at least some elderly persons may not need to work anymore to maintain a decent standard of living.

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Table 2.2: Comparison of employment rates by age group, 1991-2005

1991/92 1998/99 2005/06 Employment rate (15-64) 75.9 - 70.2 Employment rate (25-65) 86.5 82.0 85.9 Child labor rate (7-14) 27.9 - 15.9 Elderly Employment rate (65+) 63.0 55.0 52.4 Unemployment rate (25-64) 2.7 2.4 2.3


2.11 Growth has been associated with substantial job creation, with the increase in the number of workers matching the increase in the number of adult population. Between 1991 and 2005, as shown in table 2.3, the labor force among the population aged 25 to 64 grew by about two thirds (from 4.2 to 7.1 million people), which is comparable to the growth rate in the population itself for that age group. In terms of job creation, some 2.8 million jobs were created for the population aged 25-64 over the last fifteen years.

Table 2.3: Composition of the Labor Market, 1991-2005

1991/92 1998/99 2005/06 Working age population (25-64) 4,764,710 6,038,507 7,941,919 Inactive (last 7 days) 527,060 994,123 878,437 Active (last 7 days) 4,237,650 5,044,384 7,063,482 Unemployed 114,610 120,715 163,668 Employed 4,123,040 4,923,669 6,899,814 Child Population (7-14) 3,506,190 4,074,978 4,552,153 Child Laborers 979,660 - 592,308 Elderly Population (65+) 580,350 806,838 1,015,964 Employed Elderly 365,730 420,228 547,062


2.12 Agriculture remains by far the most important sector of employment, but its importance has slightly decreased over time. As shown in tables 2.4 and 2.5, agriculture still accounts for more than half of total employment in 2005/06. The services sector (trading, transport and communication, financial services, and other services) comes in second place, employing roughly one third of the labor force, while the industry sector employed about 15% of workers. While the share of agriculture sector workers has fallen in total employment, the share of workers employed in industrial sectors has risen. As for the services sector, its share in total employment has decreased over time due to a drop in the shares of workers employed in community and other services. In terms of new job creation during 1991-2005 (2.8 million new jobs for the age group 25-64), approximately 44.5% of the new jobs have been created in agriculture (1.24 million), with close to one million new jobs in services (including Government), and more than half a million in industry.

Table 2.4: Employment Distribution by Sector (percentage, for age group 25-64)

1991/92 1998/99 2005/06 changes Agriculture 55.6 52.9 54.1 -1.5 Mining/Quarrying 0.6 0.8 0.7 0.1 Manufacturing 9.1 11.9 11.6 2.5 Utilities 0.2 0.2 0.3 0.1 Construction 1.4 1.4 1.8 0.4 Trading 18.4 19.3 18.8 0.4 Transport/Communication 2.4 2.3 2.7 0.3 Financial Services 0.7 0.9 1.3 0.6 Community & Other Services 11.6 10.3 8.8 -2.8 All 100.0 100.0 100.0 0.0


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Table 2.5: Employment Distribution by Sector (absolute numbers, for age group 25-64)

1991/92 1998/99 2005/06 change distribution Agriculture 2,294,390 2,419,690 3,533,997 1,239,607 44.6 Mining/Quarrying 23,360 49,794 54,436 31,076 1.1 Manufacturing 375,950 619,075 835,085 459,135 16.5 Utilities 8,030 9,188 17,475 9,445 0.3 Construction 59,130 85,022 134,635 75,505 2.7 Trading 757,010 1,027,445 1,387,163 630,153 22.7 Transport/Communication 99,280 134,920 206,843 107,563 3.9 Financial Services 27,010 47,304 92,098 65,088 2.3 Community & Other Services 478,880 531,231 638,082 159,202 5.7 All 4,123,040 4,923,670 6,899,813 2,776,773 100.0


2.13 A large share of labor force remains employed in the informal sector, but there has also been a shift from public to private formal sector employment. In tables 2.6 and 2.7, workers are classified into six groups as follows: salaried workers in the public sector, salaried workers in the private formal sector, salaried workers in the private formal sector, self-employed workers in agriculture who are paid, self-employed workers in agriculture who are unpaid (these may for example be family workers), and self-employed workers working in outside of the agriculture sector. Following this categorization, more than 80 percent of total employment remains in the informal sector, and this share has remained increased slightly. However, the small increase in the share of informal workers is due mostly to a rise in the share of unpaid self-employed agriculture workers. Since these workers are most likely to be at the margin of the labor market (many are women who combine domestic and other work), it is quite possible that among similar persons, some may declare working and others not working, which may make statistics on these workers more volatile. If we had computed employment trends by excluding all or some of these workers (for example based on the amount of time worked), the rate of formalization would probably have increased over time. It has been argued by Aryeetey et al (2004) that the decline in public sector employment over time has been compensated mainly by employment growth in informal sector employment. This is not necessarily the case according to the data in tables 2.6 and 2.7, since there has been an increase in private sector formal employment over time. It is also worth noting than in urban areas over 50 percent of employment is salaried, while in rural areas, most workers are self-employed. Note also that the distinction between paid and unpaid agriculture workers is an imperfect way to try to identify those workers who tend to belong to larger farms or small enterprises, versus subsistence farmers working on their own; unfortunately, differences in definitions between surveys make it difficult to measure over time more precisely the share of informal workers working in firms or farms with multiple employees as opposed to by themselves.

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Table 2.6: Employment Status (percentage, for age group 25-64) 1991-2005

1991/92 1998/99 2005/06 change Wage Public sector 12.3 8.1 6.6 -5.7 Wage Private sector Formal 4.1 4.5 6.7 2.6 Wage Private sector Informal 2.2 2.2 4.3 2.1 Self-employed Agro - Paid 36.9 38.0 30.7 -6.2 Self-employed Agro - Unpaid 16.4 13.6 21.8 5.4 Self-employed Non Agro 28.0 33.6 29.9 1.9 All 100.0 100.0 100.0 0.0

Source: Based on Coulombe and Wodon (2007), using GLSS surveys; Changes are expressed in percentage points.

Table 2.7: Employment Status (Absolute numbers for age group 25-64), 1991-2005

1991/92 1998/99 2005/06 change distribution Wage Public sector 508,080 417,258 487,019 21,061 -0.8 Wage Private sector Formal 167,900 246,771 497,876 329,976 11.9 Wage Private sector Informal 92,710 128,162 335,948 243,238 8.8 Self-employed Agro - Paid 1,519,860 1,734,566 2,214,710 694,850 25.0 Self-employed Agro - Unpaid 678,170 622,588 1,193,344 515,174 18.6 Self-employed Non Agro 1,156,320 1,774,324 2,170,917 1,014,597 36.5 All 4,123,040 4,923,670 6,899,813 2,776,773 100.0 Source: Based on Coulombe and Wodon (2007), using GLSS surveys; Changes are expressed in percentage points.

2.14 The share of workers of various status in poverty has decreased for all groups, but the decrease has been largest for workers with better jobs. Tables 2.8 to 2.11 provide data on the share of individuals who are poor in the various years, by employment category. There has been a decrease over time in poverty for all categories of individuals, but the decrease has been largest in proportional terms for the categories of workers with the smallest rates of poverty in the initial year. For example, poverty among formal sector workers has virtually been eliminated, while it still; remained substantial among the self-employed in 2005/06. This suggests that improvements in wages and standards of living have been larger for better off individuals, which confirms the findings by Coulombe and Wodon (2007) that inequality in consumption per capita has increased over time in Ghana. Also, in 2005/06, the probability of being poor remains much higher for agricultural workers than for any other group of workers.

Table 2.8: Percentage of Workers Residing in a Poor Household, for Different Categories of Labor Market and Employment Status for age group 25-64, 1991-2005

1991/92 1998/99 2005/06 change Wage Public sector 26.4 15.9 5.9 -20.5 Wage Private sector Formal 22.6 9.0 5.7 -16.9 Wage Private sector Informal 29.1 15.3 13.1 -16.0 Self-employed Agro – Paid 59.5 47.7 32.4 -27.1 Self-employed Agro – Unpaid 67.8 54.4 45.3 -22.5 Self-employed Non Agro 35.3 22.6 13.1 -22.2 Unemployed 21.7 8.0 18.9 -2.8 Inactive 42.7 40.1 29.3 -13.4 All 46.6 34.5 24.3 -22.3


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Table 2.9: Percentage of Workers Residing in a Poor Household, for Different Categories of Labor Market and Employment Status for Age Group 25-64, in Rural and Urban Areas

1991/92 1998/99 2005/06 Change Urban Rural Urban Rural Urban Rural Urban Rural

Wage Public sector 15.5 43.0 10.1 23.7 3.5 13.5 -12.0 -29.5 Wage Private sector Formal 20.4 27.4 5.0 16.7 3.4 12.8 -17.0 -14.6 Wage Private sector Informal 27.4 32.6 2.9 26.7 11.9 15.1 -15.5 -17.5 Self-employed Agro - Paid 37.4 61.7 40.0 48.7 17.1 34.6 -20.3 -27.1 Self-employed Agro - Unpaid 51.7 70.1 41.4 56.6 30.6 46.9 -21.1 -23.2 Self-employed Non Agro 21.3 52.5 12.6 32.9 5.3 24.4 -16.0 -28.1 Unemployed 16.7 41.9 3.9 20.8 12.4 42.5 -4.3 0.6 Inactive 24.5 56.0 17.6 55.2 14.0 48.6 -10.5 -7.4 All 23.5 59.0 15.7 44.8 8.8 35.1 -14.7 -23.9


1991/92 1998/99 2005/06 Agriculture 61.0 48.9 36.1 Mining/Quarrying 28.1 7.7 3.4 Manufacturing 37.9 25.3 16.6 Utilities 18.2 10.0 0.0 Construction 27.2 21.8 10.5 Trading 34.5 21.2 10.6 Transport/Communication 18.4 5.4 10.0 Financial Services 10.8 6.3 5.3 Community & Other Services 25.8 16.0 6.2 All 47.8 34.0 23.8


Table 2.10: Percentage of People Residing in a Poor Household by Level of Education for Age Group 25-64, 1991-2005

1991/92 1998/99 2005/06 No education 58.0 48.5 39.3 Primary 46.6 32.7 20.1 Secondary lower 33.5 23.2 12.8 Secondary Higher 14.0 12.3 6.9 Post Secondary 17.4 12.9 3.8 All 46.6 34.5 24.3


2.15 Who are the unemployed? Unemployment, as in other Low Income Countries (LICs), is predominantly an urban phenomenon, and heavily concentrated in the capital, with an unemployment rate in Greater Accra more than twice higher than in the rest of the country. It is also concentrated among the young and relatively well-educated – especially those with secondary education (Table 2.12). This is because unemployment in low income countries has often been described as a luxury, in the sense that only the non-poor can afford not to be working in some capacity. However, the reality is more nuanced for Ghana: in 2005 about one out of five unemployed workers resided in a poor household and the rate of decrease in poverty among the unemployed was much smaller than for the population as a whole. In fact, while poverty among the unemployed decreased in most regions, it increased in Accra, the Volta, the Northern, and the Upper West regions. Clearly, unemployment is progressively becoming a serious issue for at least part of Accra’s population, probably in part due to migration to the city by relatively young workers, some of whom have a hard time finding work. At the same time, in some areas, sample sizes for the unemployed are small, which may lead to large variations over time in poverty rates which need not indicate a dramatic deterioration in employment patterns. Also, the proportion of the unemployed remains very small (at least when using a “narrow” definition of unemployment), and has actually

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decreased over time at the national level, so that underemployment is probably a more serious issue for many Ghanaians than unemployment per se.

Table 2.11: Unemployment Rates for Age Group 25-64, 1991-2005

1991/92 1998/99 2005/06 change Sex Male 2.8 3.0 2.2 -0.6 Female 2.6 1.8 2.4 -0.2 Age 24-54 2.9 2.5 2.5 -0.4 55-64 1.1 1.5 1.2 0.1 Education Level No education 1.3 0.9 1.4 0.1 Primary 2.1 2.5 2.3 0.2 Secondary lower 4.6 4.0 2.7 -1.9 Secondary Higher 7.9 4.2 6.1 -1.8 Post Secondary 4.0 2.3 3.4 -0.6 Urban/Rural Urban 6.4 5.3 4.6 -1.8 Rural 0.8 0.9 0.8 0.0 Region Western 1.2 2.5 2.0 0.8 Central 1.2 0.6 3.0 1.8 Greater Accra 7.5 8.1 4.9 -2.6 Volta 3.2 0.6 1.4 -1.8 Eastern 0.8 1.4 1.2 0.4 Ashanti 4.7 3.1 2.8 -1.9 Brong Ahafo 0.8 0.7 1.1 0.3 Northern 1.6 1.0 0.7 -0.9 Upper East 1.0 3.3 2.4 1.4 Upper West 0.7 0.4 3.6 2.9 All 2.7 2.4 2.3 -0.4


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Table 2.12: Poverty (head count index) among the Unemployed for Age Group 25-64, 1991-2005

1991/92 1998/99 2005/06 change Sex Male 23.3 7.8 22.1 -1.2 Female 20.2 8.2 16.2 -4.0 Age 24-54 21.5 5.9 19.3 -2.2 55-64 25.0 34.0 11.4 -13.6 Education Level No education 30.8 13.3 35.0 4.2 Primary 33.3 19.3 24.5 -8.8 Secondary lower 17.5 4.8 13.5 -4.0 Secondary Higher 20.0 0.0 9.7 -10.3 Post Secondary 0.0 11.5 3.5 3.5 Urban/Rural Urban 16.7 3.9 12.4 -4.3 Rural 41.9 20.8 42.5 0.6 Region Western 37.5 20.5 0.0 -37.5 Central 42.9 17.5 3.0 -39.9 Greater Accra 16.4 0.3 23.9 7.5 Volta 8.0 25.9 14.4 6.4 Eastern 75.0 13.7 10.2 -64.8 Ashanti 25.0 5.2 5.6 -19.4 Brong Ahafo 20.0 0.0 0.0 -20.0 Northern 11.1 51.2 34.6 23.5 Upper East 100.0 6.4 71.8 -28.2 Upper West 0.0 0.0 92.5 92.5 All 21.7 8.0 18.9 -2.8 Source: Based on Coulombe and Wodon (2007), using GLSS surveys; Changes are expressed in percentage points. Note: The drop in the poverty statistics for then unemployed in 1998/99 is probably due in part to a sampling issue in the greater Accra area where poverty was observed to fall much more than expected that year (see Coulombe and Wodon, 2007). 2.16 The population is becoming more educated and is increasing its skills. The percentage of people (not workers) with no formal education has dropped by 4.4 percentage points over the last 15 years, while the share of population with tertiary education increased from 2.6% in 1991 to 7.6% in 2005 (Table 2.13). As suggested by other authors (e.g., Aryeetey et al 2004; Dabalen et al 2003), it is likely that part of this improvement is due to a number of factors, including the fact that : (i) workers are upgrading their skills in order to be better able to “take advantage of growing opportunities arising from increased globalization”; (ii) in the manufacturing sector, which is growing, training is more easily available and paid for and provided by the employer; (iii) foreign or large firms also provide more training than domestic and small one; and (iv) already educated workers receive more training.

Table 2.13: Distribution of the Population by Education Level in Age Group 25-64, 1991-2005

1991/92 1998/99 2005/06 change No education 53.6 45.1 49.2 -4.4 Primary 9.9 11.8 10.7 0.8 Secondary lower 29.4 32.9 27.0 -2.4 Secondary Higher 4.5 5.3 5.4 0.9 Post Secondary 2.6 4.8 7.6 5.0 All 100.0 100.0 100.0 0.0 Source: Based on Coulombe and Wodon (2007), using GLSS surveys; Changes are expressed in percentage points. Note: The drop in the share of the population with no education in 1998/99 is probably overestimated.

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Income from Work in Age Group 25-64

2.17 It has long been a source of debate as to whether earnings data are of good quality in Low Income Countries (LICs) with large rural population shares. Many LICS collect wage data only among and salaries workers. This approach has the merit of being straightforward and using uncontroversial data; but its draw back is that it is limited to only a small section of the labor force – often as small 10% - which also represent better off workers. It is therefore difficult to carry out a credible analysis of labor markets more generally by restricting oneself to wage and salary workers. The Ghana Living Standard Surveys (GLSS) collects wage data on a much larger group of individuals. It includes a separate module for agricultural and non-agricultural business, and the Ghana Statistical Services provide data on business profits using a standard method across the years, as explained in GSS (2000). Based on this information, income for the self-employed can be calculated as the profit of the business, divided by the number of household members working in the business. For agricultural businesses, a similar approach can be followed. The GSS computes household agricultural income by deducting total input costs (seeds, fertilizer, etc), inclusive wages paid to non-household members, and an allowance for depreciation from the value of gross output, which is either sold to the market or self-consumed. This divided by the number of working household members can be used to get income per worker. Income from rent and remittances however should not be incorporated to obtain earnings per worker.

2.18 In Ghana, the labor market appears to remain polarized with large differences in earnings between different types of workers and with public sector employment paying the highest wages on average. In Figure 2.4, the frequency distribution of income from work for various categories of workers shows that there is a clear hierarchy in wages, with salaried workers in the public sector faring best, followed by salaried workers in the private formal sector. The next group is composed of salaried workers in the private formal sector and self-employed workers who are not in agriculture. The self-employed workers in agriculture who are paid have the lowest wages of all paid workers. In addition, the groups of wage and salary workers who have the highest earnings also tend to have a distribution of earnings that has a limited spread (less variance in earnings between individuals). Finally, formal sector workers not only have better paid jobs, but in addition they usually bring home other valuable job attributes (such as job security, worker protection, access to social security, etc.).

Figure 2.4: Income from work for 2005 in Age group 25-64

0.2

.4.6

Den

sity

10 12 14 16 18 20lne

Wage Public sector Wage Private sector FormalWage Private sector Informal Self-employed Agro, paidSelf-employed NonAgro


2.19 For all categories, earnings have substantially increased during 1991-2005, but the gap between formal public and private sector workers has increased. As shown in table 2.14, using the above approaches to capture earnings for many workers suggests that earnings of wage and salary workers are more than double those from individual self-employed. Also, public sector employment pays the highest wages on average. Table 2.15 suggests that the largest earnings gain is observed for the self-employed in agriculture who are paid, but this increase is from a low base, and subject to some extent to measurement error (the earnings of this group are essentially constructed as profits of the household farm

100

business divided by the number of family workers, which is very sensitive to movements in and out of the household, and probably leads to an underestimation of true earnings). The second highest increase is for wage workers in the public sector (89%). Other groups have seen earning gains between 40% and 50% in real terms over the 15 year period under review. Overall, the wage gaps between different employment categories remain high, as expected. Yet what is striking is that in 2005, public sector workers earn 47% more than private formal sector workers, with an increasing differential between both groups over time. Private informal sector wage workers earn about 63% of the private formal sector earnings, while individual self-employed earn less than a third of what private sector workers are paid.

Table 2.14: Annual Real Earnings across Employment Status in Age group 25-64 (in ‘000 cedis)

1991/92 1998/99 2005/06 Change Wage Public sector 7,470 8,992 14,120 89.0 Wage Private sector Formal 6,480 8,054 9,574 47.7 Wage Private sector Informal 4,205 3,915 6,025 43.3 Self-employed Agro-Paid 1,597 1,385 3,064 91.9 Self-employed Agro-Unpaid - - - Self-employed Non Agro 3,455 3,733 5,180 49.9 All 1,442 1,293 2,551 76.9

Source: Based on Coulombe and Wodon (2007), using GLSS surveys; Changes are expressed in percentage points. Note: Median annual earnings expressed in local currency in 1991 prices. Some divergences in trends observed with the 1998/99 data may be due to sampling issues and/or lower level of comparability.

Table 2.15: Earnings Ratio’s to private formal sector wage in age group 25-64, 1991-2005

1991/92 1998/99 2005/06 Wage Public sector 1.15 1.12 1.47 Wage Private sector Formal 1.00 1.00 1.00 Wage Private sector Informal 0.65 0.49 0.63 Self-employed Agro-Paid 0.25 0.17 0.32 Self-employed Agro-Unpaid - - - Self-employed Non Agro 0.53 0.46 0.54 All 0.22 0.16 0.27

Source: Based on Coulombe and Wodon (2007), using GLSS surveys; Some divergences in trends observed with the 1998/99 data may be due to sampling issues and/or lower level of comparability.

2.20 The relative high level of wage setting in the public sector and its increase over time is a potential concern for the rest of the economy. Analysis in other countries (including in Africa region) has shown that high public sector wages induce high wages in the formal private sector since they have to keep up with the public sector to attract good people. As shown in Ethiopia, it is also likely that high public sector earnings will induce queuing for public sector jobs, given their relatively high pay (and associated benefits). Further work is needed to look at the effect of public sector wage setting. Based on earlier works on Ghana, there is evidence that both firm size and unionization are important explanatory factors for this wage premium (Soderbom and Teal; Kingdon, Sandefur and Teal and others, 2005) Although the size effect is likely to reflect a difference in productivity (it may also be due to a higher cost to monitor), the union effect indicates the importance of institutions and labor regulation.

2.21 There are also disparities in earnings by region, socio-economic groups, sector of activities, gender, age and skills. More data on earnings according to selected characteristics are given in Table 2.16. Stylized facts on the earnings of wage and salary workers and individual self-employed show among others that: Women earn consistently less than men, independently of their employment status, and for both wage and salary workers and individual self-employed; The earnings-age profile follows an inverse U-shape pattern; In general earnings increase with education; Earnings in the urban sector are higher than those in the rural sector; Agriculture workers have consistently the lowest earnings; and Earnings in the manufacturing and trading sectors are at the tail end of the earnings distribution. As was already implicit in Table 2.8, the data in Table 2.17 suggests that the share of low earners has consistently decreased for virtually all groups, but the pace of progress has been uneven between groups.

10

1

Tab

le 2

.16:

Med

ian

annu

al e

arni

ngs

for

diff

eren

t cat

egor

ies

of w

orke

rs in

age

gro

up 2

5-64

, 199

1-20

05 (i

n 00

0 ce

di)

Wag

e P

ublic

Sec

tor

Wag

e P

riva

te S

ecto

r F

orm

al

Wag

e P

riva

te S

ecto

r In

form

al

1991

/92

1998

/99

2005

/06

chan

ge

1991

/92

1998

/99

2005

/06

chan

ge

1991

/92

1998

/99

2005

/06

chan

ge

Sex

Mal

e 7,

522

9,50

8 14

,215

89

.0

6,55

6 8,

367

10,4

23

59.0

5,

042

3,91

5 7,

155

41.9

Fe

mal

e 7,

098

7,11

2 13

,734

93

.5

5,86

2 5,

669

6,24

3 6.

5 1,

673

3,98

0 3,

381

102.

1 A

ge

24-5

4 7,

477

9,04

2 13

,691

83

.1

6,55

5 8,

256

9,50

0 44

.9

4,16

3 4,

048

5,98

4 43

.7

55-6

4 7,

455

8,97

6 17

,802

13

8.8

4,95

0 6,

840

10,8

71

119.

6 5,

980

2,98

0 9,

643

61.3

E

duca

tionL

evel

N

o ed

ucat

ion

5,03

5 5,

896

5,61

7 11

.6

4,64

9 3,

314

6,48

4 39

.5

4,40

0 3,

980

4,87

7 10

.8

Prim

ary

6,14

3 5,

623

7,48

2 21

.8

5,16

1 8,

011

7,93

0 53

.7

3,07

6 3,

821

5,97

1 94

.1

Seco

ndar

y lo

wer

6,

973

7,05

6 9,

622

38.0

6,

318

6,62

9 8,

258

30.7

4,

163

3,86

7 6,

745

62.0

Se

cond

ary

Hig

her

10,0

96

11,9

36

12,4

60

23.4

7,

817

11,9

52

10,1

88

30.3

7,

172

3,82

9 5,

064

29.4

Po

st S

econ

dary

10

,752

10

,315

19

,072

77

.4

10,3

87

12,0

51

15,5

57

49.8

15

,592

4,

674

7,91

8 49

.2

Urb

an/R

ural

U

rban

8,

602

9,55

8 14

,554

69

.2

6,70

6 8,

781

9,69

0 44

.5

4,09

4 4,

826

6,02

5 47

.2

Rur

al

6,11

9 8,

400

11,6

82

90.9

5,

332

5,22

3 8,

775

64.6

4,

384

3,50

7 6,

428

46.6

Se

ctor

A

gric

ultu

re

4,96

7 7,

733

8,53

7 71

.9

6,35

8 3,

592

7,42

9 16

.8

5,46

7 2,

914

5,51

6 0.

9 M

inin

g/Q

uarr

ying

12

,739

16

,685

12

,255

3.

8 18

,919

16

,104

22

,112

16

.9

1,46

5 11

,989

2,

263

54.5

M

anuf

actu

ring

5,

932

11,7

41

13,3

81

125.

6 6,

177

8,25

6 11

,237

81

.9

2,39

3 2,

240

5,23

6 11

8.8

Util

ities

6,

929

12,2

15

34,3

36

95.5

4,

345

. 11

,425

16

2.9

. 6,

622

5,23

8

Con

stru

ctio

n 6,

050

12,0

99

12,6

17

108.

5 8,

797

6,66

4 11

,439

30

.0

4,38

4 3,

915

11,4

65

161.

5 T

radi

ng

8,03

4 10

,672

6,

769

15.7

4,

756

8,27

7 8,

931

87.8

4,

171

3,98

0 6,

005

44.0

T

rans

port

/Com

mun

icat

ion

8,50

6 11

,036

17

,789

10

9.1

3,62

6 7,

684

7,77

0 11

4.3

5,63

6 4,

905

7,05

2 25

.1

Fina

ncia

l Ser

vice

s 9,

780

16,4

91

18,0

77

84.8

8,

242

15,4

45

15,5

28

88.4

27

,688

3,

115

8,14

9 70

.6

Com

mun

ity &

Oth

er S

ervi

ces

,792

8,

030

4,12

0 81

.2

6,28

7 5,

518

7,20

5 14

.6

4,82

0 4,

119

5,40

5 12

.1

All

7,47

0 8,

992

14,1

20

89.0

6,

480

8,05

4 9,

574

47.7

4,

205

3,91

5 6,

025

43.3

Sour

ce: B

ased

on

Cou

lom

be a

nd W

odon

(20

07),

usi

ng G

LSS

sur

veys

; Cha

nges

are

exp

ress

ed in

per

cent

age

poin

ts. S

ome

dive

rgen

ces

in tr

ends

obs

erve

d w

ith th

e 19

98/9

9 da

ta m

ay b

e du

e to

sam

plin

g is

sues

and

/or

low

er le

vel o

f co

mpa

rabi

lity.

10

2

Tab

le 2

.17:

(con

tinu

ed):

Med

ian

annu

al e

arni

ngs

for

diff

eren

t cat

egor

ies

of w

orke

rs in

age

gro

up 2

5-64

, 199

1-20

05 (i

n 00

0 ce

di)

Se

lf-e

mpl

oyed

Agr

o-P

aid

Self

-em

ploy

ed N

on A

gro

19

91/9

2 19

98/9

9 20

05/0

6 ch

ange

19

91/9

2 19

98/9

9 20

05/0

6 ch

ange

Sex

Mal

e 2,

168

1,87

9 3,

750

73.0

5,

869

6,11

1 7,

664

30.6

Fe

mal

e 1,

065

892

1,85

4 74

.1

3,05

6 2,

778

4,17

9 36

.7

Age

24

-54

1,57

6 1,

400

3,07

3 95

.0

3,46

4 3,

857

5,20

5 50

.3

55-6

4 1,

747

1,19

4 3,

013

72.5

3,

407

2,94

4 4,

345

27.5

E

duca

tion

Lev

el

No

educ

atio

n 1,

466

1,09

2 2,

941

100.

6 2,

900

2,57

2 3,

605

24.3

Pr

imar

y 1,

806

1,40

0 2,

705

49.8

3,

298

2,91

2 4,

845

46.9

Se

cond

ary

low

er

1,96

2 1,

904

3,46

4 76

.6

4,30

9 4,

800

6,00

0 39

.2

Seco

ndar

y H

ighe

r 2,

511

1,64

2 4,

290

70.8

7,

491

8,53

6 9,

269

23.7

Po

st S

econ

dary

2,

093

2,03

0 4,

894

133.

8 13

,849

9,

239

7,74

8 44

.1

Urb

an/R

ural

U

rban

2,

465

1,33

3 3,

887

57.7

4,

062

5,54

9 6,

072

49.5

R

ural

1,

576

1,38

5 2,

950

87.2

2,

747

2,45

7 3,

690

34.3

Se

ctor

A

gric

ultu

re

1,59

7 1,

385

3,06

4 91

.9

.

Min

ing/

Qua

rryi

ng

.

.

3,18

0 2,

575

14,2

26

347.

4 M

anuf

actu

ring

.

. .

2,

883

2,66

1 4,

584

59.0

U

tiliti

es

. .

.

14,4

62

14,1

73

8,43

2 41

.7

Con

stru

ctio

n .

. .

6,

052

4,36

1 7,

374

21.8

T

radi

ng

. .

.

3,38

0 4,

001

5,20

0 53

.8

Tra

nspo

rt/C

omm

unic

atio

n .

. .

14

,437

16

,329

11

,065

23

.4

Fina

ncia

l Ser

vice

s .

. .

3,

480

6,57

5 6,

408

84.1

C

omm

unity

& O

ther

Ser

v.

. .

.

6,10

7 5,

395

4,18

6 31

.5

A

ll 1,

597

1,38

5 3,

064

91.9

3,

455

3,73

3 5,

180

49.9

Sour

ce:

Bas

ed o

n C

oulo

mbe

and

Wod

on (

2007

), u

sing

GL

SS s

urve

ys;

Cha

nges

are

exp

ress

ed i

n pe

rcen

tage

poi

nts.

Som

e di

verg

ence

s in

tre

nds

obse

rved

with

the

199

8/99

da

ta m

ay b

e du

e to

sam

plin

g is

sues

and

/or

low

er le

vel o

f co

mpa

rabi

lity.

10

3

Tab

le 2

.17:

Pro

port

ion

of w

orke

rs in

age

gro

up 2

5-64

wit

h ea

rnin

gs b

elow

the

pove

rty

line,

199

1-20

05

Wag

e P

ublic

Sec

tor

Wag

e P

riva

te S

ecto

r F

orm

al

Wag

e P

riva

te S

ecto

r In

form

al

1991

/92

1998

/99

2005

/06

chan

ge

1991

/92

1998

/99

2005

/06

chan

ge

1991

/92

1998

/99

2005

/06

chan

ge

Sex

Mal

e 11

.9

7.1

6.5

-5.4

24

.9

19.5

11

.7

-13.

2 36

.8

40.8

25

.4

-11.

4 Fe

mal

e 19

.0

20.9

5.

4 -1

3.6

37.8

30

.4

22.9

-1

4.9

71.9

48

.9

54.1

-1

7.8

Age

24

-54

13.8

11

.2

5.8

-8.0

26

.4

21.4

13

.4

-13.

0 46

.4

40.3

32

.3

-14.

1 55

-64

16.9

6.

8 9.

2 -7

.7

35.7

22

.9

19.9

-1

5.8

40.0

69

.8

19.0

-2

1.0

Edu

catio

n L

evel

N

o ed

ucat

ion

31.9

18

.3

18.5

-1

3.4

42.1

53

.2

23.4

-1

8.7

45.7

37

.4

39.7

-6

.0

Prim

ary

28.1

28

.0

6.5

-21.

6 38

.5

21.8

21

.0

-17.

5 53

.3

49.6

36

.8

-16.

5 Se

cond

ary

low

er

10.5

16

.2

8.0

-2.5

25

.4

24.5

14

.9

-10.

5 44

.4

45.0

28

.5

-15.

9 Se

cond

ary

Hig

her

5.4

4.9

3.5

-1.9

16

.7

8.8

8.3

-8.4

40

.0

37.2

30

.9

-9.1

Po

st S

econ

dary

5.

2 4.

9 5.

2 0.

0 11

.1

3.7

9.3

-1.8

50

.0

33.7

11

.7

-38.

3 U

rban

/Rur

al

Urb

an

10.0

8.

4 5.

7 -4

.3

23.6

16

.0

12.0

-1

1.6

47.6

32

.9

32.4

-1

5.2

Rur

al

20.2

13

.8

7.9

-12.

3 34

.2

32.0

19

.7

-14.

5 41

.9

51.1

30

.4

-11.

5 Se

ctor

A

gric

ultu

re

36.1

21

.5

18.5

-1

7.6

25.0

52

.5

16.9

-8

.1

38.1

54

.0

35.4

-2

.7

Min

ing/

Qua

rryi

ng

0.0

0.0

7.2

7.2

0.0

2.4

1.6

1.6

100.

0 0.

0 63

.1

-36.

9 M

anuf

actu

ring

10

.7

6.4

14.2

3.

5 21

.3

18.4

10

.0

-11.

3 64

.7

66.8

35

.4

-29.

3 U

tiliti

es

0.0

0.0

0.0

0.0

0.0

. 28

.6

28.6

.

0.0

0.0

C

onst

ruct

ion

5.6

0.0

0.0

-5.6

21

.1

19.4

7.

4 -1

3.7

41.2

31

.8

13.5

-2

7.7

Tra

ding

26

.1

11.3

29

.2

3.1

43.8

20

.5

17.8

-2

6.0

47.8

47

.5

30.5

-1

7.3

Tra

nspo

rt/C

omm

unic

atio

n 3.

8 0.

0 10

.0

6.2

51.4

25

.0

15.4

-3

6.0

42.1

13

.4

27.4

-1

4.7

Fina

ncia

l Ser

vice

s 0.

0 0.

0 5.

0 5.

0 8.

3 0.

0 4.

3 -4

.0

0.0

61.4

21

.0

21.0

C

omm

unity

& O

ther

Se

rvic

es

12.2

13

.3

4.9

-7.3

26

.9

24.8

18

.9

-8.0

40

.7

42.0

42

.1

1.4

All

14.1

10

.7

6.2

-7.9

27

.0

21.5

13

.9

-13.

1 45

.7

42.4

31

.7

-14.

0 So

urce

: Bas

ed o

n C

oulo

mbe

and

Wod

on (

2007

), u

sing

GL

SS s

urve

ys; C

hang

es a

re e

xpre

ssed

in p

erce

ntag

e po

ints

. Som

e di

verg

ence

s in

tren

ds o

bser

ved

with

the

1998

/99

data

may

be

due

to s

ampl

ing

issu

es a

nd/o

r lo

wer

leve

l of

com

para

bilit

y.

10

4

Tab

le 2

.18:

(con

tinu

ed):

Pro

port

ion

of w

orke

rs in

age

gro

up 2

5-64

wit

h ea

rnin

gs b

elow

the

pove

rty

line,

199

1-20

05

Se

lf-e

mpl

oyed

Agr

o - P

aid

Self

-em

ploy

ed N

on A

gro

19

91/9

2 19

98/9

9 20

05/0

6 ch

ange

19

91/9

2 19

98/9

9 20

05/0

6 ch

ange

Se

x

M

ale

72.1

71

.4

49.8

-2

2.3

36.9

31

.6

26.5

-1

0.4

Fem

ale

90.9

89

.2

71.5

-1

9.4

56.9

57

.3

46.8

-1

0.1

Age

24

-54

80.6

78

.7

57.9

-2

2.7

52.4

49

.1

41.2

-1

1.2

55-6

4 74

.3

79.5

59

.2

-15.

1 50

.8

59.3

43

.5

-7.3

E

duca

tion

Lev

el

No

educ

atio

n 82

.6

83.2

59

.6

-23.

0 58

.1

61.4

51

.4

-6.7

Pr

imar

y 79

.2

80.0

63

.4

-15.

8 54

.4

54.9

41

.9

-12.

5 Se

cond

ary

low

er

71.9

70

.6

53.4

-1

8.5

45.3

41

.9

34.9

-1

0.4

Seco

ndar

y H

ighe

r 72

.7

71.5

46

.0

-26.

7 25

.5

25.3

27

.1

1.6

Post

Sec

onda

ry

60.0

71

.7

47.8

-1

2.2

25.0

17

.3

29.4

4.

4 U

rban

/Rur

al

Urb

an

67.4

74

.8

48.6

-1

8.8

46.7

38

.0

35.3

-1

1.4

Rur

al

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105

Hours Worked and Hourly Earnings in Age Group 25-64

2.22 Labor supply expressed in hours worked per year is lowest in agriculture sector and highest for wage workers in the private sector. Based on average work load reported in hours per year, the hours worked are the highest in transport and communication, and lowest in agriculture where seasonality plays a larger role (see Table 2.18). Wage workers in the private formal sector work the highest number of hours, followed by wage workers in the private informal sector, and non agriculture self-employed workers and public sector wage workers. The number of hours worked has increased for all categories of workers over time, which has been one of the elements leading to an increase in standards of living. However, there are differences in work pressure between categories of workers. For example, public sector workers, who earn more, are working fewer hours than private sector workers. For every eight hours worked by a wage worker in the private formal sector, the public sector workers work 7 hours. Over time, the gap between public sector and formal private sector workers has widened, despite the faster increase in the former’s wages documented earlier. It is also worth noting that the poor supply less labor than the non-poor (they work about 10% less hours), so that underemployment, defined as working limited number of hours, is probably one of the contributing factors to poverty (in rural areas mainly).

Table 2.18: Mean Number of Hours Worked per Year in age group 25-64 (1991-2005)

1991/92 1998/99 2005/06 change Sector Agriculture 1,400 1,356 1,541 10.1 Mining/Quarrying 2,067 2,254 2,662 28.8 Manufacturing 1,653 1,658 1,953 18.1 Utilities 2,332 2,202 2,380 2.1 Construction 1,868 1,403 1,894 1.4 Trading 1,893 1,943 2,230 17.8 Transport/Communication 2,707 2,687 2,885 6.6 Financial Services 2,160 2,312 2,452 13.5 Community & Other Services 1,904 2,086 2,064 8.4 Employment Status Wage Public sector 1,974 2,091 2,074 5.1 Wage Private sector Formal 2,237 2,187 2,516 12.5 Wage Private sector Informal 1,902 2,230 2,217 16.6 Self-employed Agro-Paid 1,451 1,389 1,579 8.8 Self-employed Agro-Unpaid 1,211 1,210 1,429 18.0 Self-employed Non Agro 1,817 1,846 2,091 15.1 Poverty Status Very poor 1,418 1,286 1,520 7.2 Poor 1,558 1,359 1,727 10.8 Non poor 1,765 1,827 1,924 9.0 All 1,620 1,651 1,846 14.0


2.23 There are large gaps in hourly earnings for different categories of employment. Overall, the hourly earnings in industry and services are broadly similar (with differences according to selected sub-sectors), while the agriculture sector has significantly lower hourly earnings (although it is likely that these are underestimated). During the last 15 years, hourly earnings in the agriculture sector have increased by about 24%, those in the industry by 34%, which is lower than the average for all workers. Among employment status categories, public sector workers have benefited from the largest increase in hourly earnings, at about twice the rate of the national average. The increase in hourly earnings of poor workers has been much lower (and in some cases negative) than for non-poor workers (Table 2.19).

106

Table 2.19: Earnings per Actual Hour Worked by Economic Activity, Employment Status and Poverty in age group 25-64, 1991-2005 (in cedi)

1991/92 1998/99 2005/06 change Sector Agriculture 757 724 937 23.8 Mining/Quarrying 5,341 6,685 5,297 -0.8 Manufacturing 2,255 2,384 3,021 34.0 Utilities 3,437 4,473 6,988 103.3 Construction 3,539 3,925 4,992 41.1 Trading 2,269 2,271 2,598 14.5 Transport/Communication 3,004 3,843 3,078 2.5 Financial Services 4,779 7,829 5,793 21.2 Community & Other Services 4,123 3,563 5,183 25.7 Employment Status Wage Public sector 3,954 4,869 6,925 75.1 Wage Private sector Formal 3,232 3,806 3,930 21.6 Wage Private sector Informal 2,306 2,189 2,782 20.6 Self-employed Agro-Paid 1,277 1,211 2,033 59.2 Self-employed Agro-Unpaid - - Self-employed Non Agro 2,388 2,309 2,713 13.6 Poverty status Very poor 912 829 552 -39.5 Poor 1,270 1,261 1,346 6.0 Non poor 2,157 2,117 2,483 15.1 All 1,521 1,669 2,060 35.4 Source: Based on Coulombe and Wodon (2007), using GLSS surveys; Changes are expressed in percentage points.

2.24 Differences in earnings across different types of employment suggest several conclusions. Wage and salary workers earnings are up to three times higher (in the case of public sector workers) than the hourly earnings of the individual self-employed. Further preliminary analysis based on regression techniques also suggest that men are more likely to be in wage and salary work than women. Young people are by contrast less likely to be wage and salary workers (the following section looks in a bit more details at the issues related to education and shows that for example that education has strong effects on employment types and functions as a sorting mechanism to get access to better paid and higher productivity jobs). Overall, workers (men and women) with secondary education, TVET or Tertiary education have the highest probability to be wage and salary workers, while those with lower education have higher probabilities to be self-employed, and many agriculture workers have no education

9

2.25 From this analysis of labor market trends and characteristics, a few conclusions stand out:

• Earnings from work have increased for all groups but less so for the poor. This rise in earnings has been an important factor for poverty reduction in Ghana. However, a majority of the labor force remains in low productivity, agricultural activities, which yield low earnings. There have been limited gains in formal jobs as a share of total employment, but the decline in the share of public sector jobs has been compensated by a larger number of private formal jobs.

• Looking forward, a key question is what labor policies are needed to further reduce poverty, and stimulate economic growth in order to further improve welfare? In the short and medium term, the first objective needs to focus on generating more jobs, including jobs with higher earnings and productivity. This means generating even more jobs in the private formal sector, particularly in the industry and services sectors. Earlier work of Francis Teal, has shown that one way forward to create more and higher paying jobs is to attract private firms that export. Higher paying jobs are often found in larger firms, but domestic demand is weak to support firm growth. Exporting

9 Detailed regression analysis will be provided in an upcoming report on “Job creation and skills development in

Ghana”.

107

firms may be able to expand quicker and offer good jobs, accelerating spending and growth within the economy. The second objective is to raise labor productivity in agricultural activities in order to better help the poor (see chapter 2 in volume 2).

• The public sector’s attractive pay policies are likely to affect labor supply decisions. They contribute to earnings polarization of the labor market and also affect wage setting in the private sector. In addition, they encourages queuing for higher paying public sector jobs rather than less attractive opportunities in the private sector. These effects can limit the potential dynamism of the private sector in attracting the needed labor with the appropriate skills.

• A large amount of the working population remains low skilled, lacking even primary education. Based on the preliminary analysis the returns to education are positive, but low at low levels of schooling. The analysis also confirms that education plays a role in the allocation to high paying jobs, suggesting that the poor require more education to get access to such jobs. Apprenticeship may be one way to go here. The next section provides more analysis of links between education and labor market outcomes.

10

EDUCATION, SKILLS, AND LABOR MARKET OUTCOMES

The landscape for skills development: Basic education 11

2.26 Ghana has made progress increasing the access of young people to primary education as part of its efforts to meet the Millennium Development Goals for education. A sound basic education providing literacy, numeracy, and good communication skills is essential for youth to acquire the more advanced skills demanded by employers or that may be needed to create one’s own employment. Capitation grants were first piloted in 2004/05 and mainstreamed in 2005/06.

12 Net enrolment rates of

youth 6-11 years of age increased nationally as a consequence from 59 percent in 2004/05 to nearly 79 percent in 2006/07 with net admission rates for 6 year olds more than doubling to 69.3 percent from the 2004/05 figure of 26.2 percent. Gross enrolment rates, including over-age and under-age youth, rose to nearly 91 percent in 2006/07 (see Table 2.20).

Table 2.20: Enrolment by level of schooling for 2004/05 and 2006/07

Primary Junior secondary Senior secondary 2004/05 2006/07 2004/05 2006/07 2004/05 2006/07 Enrolment 2935611 3365762 1012258 1132318 338519 414491 Gross enrolment 83.4 90.7 70.3 74.8 25.5 30.2 Net Enrolment 59.1 78.6 31.6 50.7 n.a. 10.6

Source: Ministry of Education and Sports EMIS (2007)

2.27 The increase in the net enrolment rate for primary education to 78.6 percent pushed Ghana above the Sub-Saharan Africa average of 66.3 percent average for this rate. Figure 2.3 below plots net enrolment rates for 23 low, middle, and upper income countries in 2004. The figure shows substantial variance in net enrolment rates among countries with less than US$5,000 per capita income. The increase in Ghana’s enrolment rate in 2006/07 moves it closer to the top of the range for this group of countries, approaching rates achieved by middle-income countries. As per capita income rises, the variance in net enrolment rates is reduced with most middle and upper income countries achieving net enrolment rates for primary education above 80 percent.

10

See the upcoming report on “Job creation and skills development in Ghana” for as more detailed analysis. 11

This section is based on data drawn from Ghana’s Education Management Information System (EMIS) and the Preliminary Education Sector Performance Report 2006 using EMIS data prepared by the Ministry of Education, Science and Sports. 12

For every child enrolled in basic education, a school receives 30,000 Cedis, about US$3.10 to offset a reduction in school levies.

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2.28 National primary enrolment rates, however, hide disparities across and within regions and between sexes and efficiency remains low. Within the country’s 10 regions, the Northern and Upper West regions show the lowest net enrolment rates with 68 and 70 percent respectively in 2006/07. The net enrolment rate of young girls in primary education trailed the rate for young boys in 2006/07, 77.3 percent to 79.8. As a percent of total enrolment, however, girls increased their share from 47.9 percent in 2004/05 to 48.5 percent in 2006/07. The share ranged from 46.3 percent in the Northern Region to 51.2 percent in Greater Accra. The MOESS estimates it will not meet its target for universal primary completion by 2012, preventing achievement of universal basic completion by 2015, and as an indicator of the system’s inefficiencies, for every 100 students enrolled in the first grade, approximately 56 remain to complete the sixth grade.

Figure 2.4: Net Enrolment Rates as a Function of Per Capita GDP in 2000 PPP US$

2.29 The growth in enrolment in public primary schools has put pressure on teacher training and assignments. The introduction of capitation grants system-wide in 2005/06 eliminated public school levies and led to a sharp increase in primary school enrolments (see Table 2.20). Enrolment in private schools initially declined in 2005/06 as fees were reduced in public schools, but stabilized in 2006/07 with 16.1 percent of primary students enrolled in private schools. The expansion in public schools led to new teachers being hired. Many, however, lacked appropriate training as the percentage of trained teachers fell from 67 percent in 2005/06 to 62 percent in 2006/07. In private schools, less than 20 percent of teachers were properly trained. Pupil-teacher ratios in public primary schools were uneven within and across regions in 2005/06 ranging from 29.4 pupils per teacher in the Volta Region to 42.8 in the Upper East Region with the range by District even wider.

2.30 Students in public primary schools face crowded conditions and infrastructure need repairs. Nationally, the seats available per 100 students fell from 90 to 80 in 2006/007, indicating increased crowding. Twenty-seven percent of public primary classrooms were in need of major repairs with conditions in private schools somewhat better with 12 percent requiring major repairs. In the larger picture, Ghana has succeeded in opening its doors to primary education for many previously under-served youth, but extending these gains and improving the quality of their schooling poses further challenges and will require additional investment to improve primary education as a building block for basic education.

2.31 Enrolments in junior secondary school (JSS) have expanded like those for primary education, although at a slower rate. Basic education in Ghana includes primary and JSS, together representing 9 years of schooling. JSS enrolments grew between 2004/05 and 2006/07 (see Table 2.20). Much of the expansion came from increased numbers completing primary school and continuing to JSS. Nearly 93 percent of those completing primary school in 2005/06 continued to the next level. Net admission rates for 12 year old first-year JSS students in 2004/05 surged from 12.2 to 44.4 percent in 2006/07. Public schools were the beneficiaries of growth as enrolments fell in private schools.

2.32 The same challenges to access, quality, and efficiency observed for primary schools are observed at the JSS level. The accessibility and condition of schools varies across regions and districts. Gender parity continues to be an issue. The enrolment rates of young girls trail those of young boys.

Net Enrolment Rates as a Function of Per Capita GDP in 2000PPP US$

020 40 60 80

100120

0 5000 10000 15000 20000 25000 30000 35000

GDP

NER

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Teachers lack appropriate credentials with the problem being more severe for private schools (accounting for 15.9 percent of all schools at the JSS level). In public schools, nearly eight of every ten teachers have completed Teacher Training College, but only 27 percent meet this standard among private JSS. Facilities are also in need of repairs and the growth of JSS enrollments has produced crowding. For every 100 students entering JSS, 65 are expected to complete JSS.

2.33 These conditions are reflected in low pass rate for those taking the Basic Education Certificate Examination (BECE), which determines whether or not a student is able to continue to the second cycle of education. The BECE is currently the only indicator measuring achievement and thus the quality of basic education. Sixty-two percent passed the BECE in 2005/06, a figure that has been relatively stable since 2002/03. The variation in pass rates across regions ranged from 47 percent in the Northern Region to 70 percent in the Greater Accra Region. Boys performed better than girls. Those failing the BECE join others who drop out or simply never enroll and who now must enter the labor market with limited education and skills. A 2003 survey by the Ghana Statistical Service found a significant number of children on the street engaged in labor who were not attending school. The proportion increased from 8 percent among 5 to 9 year olds to 54 percent for those ages 15 to 17.

2.34 Even with increased spending on education, Ghana continues to face challenges to reducing regional disparities in access and improving quality. Ghana has matched international norms in spending on education with actual expenditures in 2006/07 reaching 6 percent of GDP. It spent 12.6 percent of its per capita GDP on primary education roughly at the mid-point of such spending by a list of middle-income countries in Figure 2.5. Spending from its own budget on primary education has shown consistent double-digit growth over the last three years, increasing in 2006/07 by 30 percent. Against a background of rising enrolments, Figure 2.5 shows the additional expenditure has increased spending per student at the primary level. But while spending for primary education has increased in absolute terms, it has fallen as a share of the overall education budget from 41 percent in 2003/04 to 35 percent in 2006/07 as spending for secondary and tertiary education has grown.

Figure 2.5: Public Expenditure on Primary Education as a Percent of Per Capita GDP: 2005

Public Expenditure on Primary Education as a Percent of PerCapita GDP: 2005

0

5

10

15

20

Botswana Brazil* Chile Ghana Mauritius Mexico*Mozambique*Philippines* Swaziland*

Source: World Bank, EDSTATS

Percent

110

2.35 Ghana’s investment budget is focused on meeting the rising demand for senior secondary education. Nearly half of its investment budget in 2006/07 was spent on senior secondary schools with no allocation to pre-school, primary, or JSS levels. Investment in these three sub-sectors was covered by development partners and under decentralization by District Assemblies. The investment budget of the partners in 2006/07, nearly US$ 6.6 million, more than doubled that of the Government of Ghana. Thirty-four percent of donor investment in education is spent on primary education. Pre-school and JSS bring the share to 58 percent with only 1.3 percent spent by development partners on senior secondary education. Faced with a growing number of youth emerging from basic education, the Government of Ghana has elected to focus its investment on senior secondary education and draw on the resources of donors and local governments to meet the still growing demand for investment in basic education.

2.36 Ghana faces a continued need for investment in basic education, but also it faces growing demands for investment in post-basic education. The Government of Ghana is already mobilizing its own resources for education at internationally accepted levels. It is spending on primary education what other middle-income countries are spending on average, but it still has need for more, and at the same time, it has to respond to the growing numbers of youth progressing through basic education and expecting to enter a senior secondary school. Donors are playing an important role in helping Ghana fill these gaps. While seeking to mobilize additional resources, the evidence above shows less than two of every three students successfully completing primary education and JSS. This emphasizes the importance of policies to improve the efficiency with which current resources are being spent.

Figure 2.6: Government of Ghana Per Capita Expenditures on Primary Education: 2004-2006

Table 2.21: Government of Ghana Education Expenditures By Sub-Sector: 2006

Sub-Sector Recurrent Investment % % Pre-School 5.1 Primary 35.5 Junior Secondary 22.3 Senior Secondary 13.1 47.3 Non-formal Education 1.0 Special Education 0.6 3.4 Teacher Education 4.7 2.6 TVET 1.0 2.7 Tertiary 16.7 7.8 Subvented Agencies 3.0 Mgmt 33.2 HIV/AIDS Total 100.0 100.0 Source: Government of Ghana

0

200000

400000

600000

800000

1000000

2004 2005 2006

Source: Government of Ghana

Ced

is (

mill

ion

s)

111

Senior Secondary Schools

2.37 Secondary enrolments are expanding, but conditions are similar to basic education. The increase in senior secondary schools (SSS) enrollments actually began in 1999/00 and by 2006/07 had doubled for boys and girls, but net enrolment rates remained low at 10.6 percent. Similar to basic education, opportunities for access are uneven across Ghana’s 10 regions as net enrolment rates range from a low of 5.3 percent in the Upper West Region to a high of 18.5 percent in the Central Region. Gender parity remains a problem: nationally, young women accounted for 44 percent of SSS enrolments in 2006/07, lower than that of basic education, with variation among regions. The percentage of girls enrolled ranged from 32.1 percent in the Northern Region to 47.5 percent in the Central Region. As observed, the percentage of girls enrolled declines as they progress through school.

2.38 Slightly over nine of ten secondary students pursue a general secondary education with less than one out of ten following a technical or vocational curriculum. Of those enrolled in general secondary education, more than one-third are enrolled in a business curriculum and about the same share in general arts (see Table 2.22). Eleven percent pursue a science education and just over 9 percent enroll in agriculture. Curriculum choices vary by gender. Girls are more likely than boys to enroll in secretarial studies, general arts, and home economics, while boys are found more frequently in agriculture, accounting, technical and visual arts programs.

2.39 With a net enrolment rate of 10.6 percent, nine out of ten youth 15 to 17 years of age are not enrolled in a secondary school. These out-of-school youth are either employed, unemployed, or out of the labor force. When under and over age youth in secondary schools are counted, the gross enrolment rate for secondary schools reaches 30.2 percent. For the out-of-school population, the access to skills is through apprenticeships, informal learning on the job, or non-formal training programs.

Table 2.22: Senior Secondary Enrolments by Program for Ghana: 2006

Program Boys Girls Total Boys Girls Total Percent Number Public Agriculture 12.4% 4.9% 9.2% 26,698 7,846 34,544 Business Accounting 23.6% 16.9% 20.8% 50,873 27,176 78,049 Secretarial 1.8% 3.0% 2.3% 3,844 4,868 8,712 General Science 13.4% 8.5% 11.3% 28,900 13,729 42,629 Arts 32.1% 38.8% 35.0% 69,103 62,329 131,432 Technical 6.6% 1.5% 4.4% 14,115 2,367 16,482 Vocational H. Economics 2.1% 20.8% 10.1% 4,588 33,354 37,942 Vis. Arts. 8.0% 5.6% 7.0% 17,191 9,068 26,259 Total 100.0% 100.0% 100.0% 215,312 160,737 376,049 Private Agriculture 4.1% 2.1% 3.0% 722 445 1,167 Business Accounting 33.4% 25.4% 29.0% 5,836 5,327 11,163 Secretarial 2.5% 12.3% 7.8% 439 2,572 3,011 General Science 12.0% 7.6% 9.6% 2,096 1,598 3,694 Arts 33.7% 38.5% 36.4% 5,891 8,083 13,974 Technical 5.6% 0.9% 3.1% 980 194 1,174 Vocational H. Economics 2.3% 9.5% 6.2% 395 1,993 2,388 Vis. Arts. 6.3% 3.6% 4.9% 1,106 765 1,871 Total 100.0% 100.0% 100.0% 17,465 20,977 38,442

Source: Ministry of Education, Science and Sports EMIS 2007

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2.40 For those enrolled in secondary education, quality is an issue. In public schools (accounting for 91 percent of SSS enrolments) in 2006/07, 15 percent of teachers were not fully trained and this figure rises to over 40 percent in private schools. One out of five public secondary schools required major repairs in 2006/07 compared with only three percent of private schools. Crowding exists in public secondary schools with 0.78 seats per student available in 2005/06 and 0.63 writing places available per student. The number of text books for each student ranges from one for every two students of science and social studies to just over one text book for each student of mathematics and the English language.

2.41 Like basic education, pass rates for secondary students are low. The pass rates on the Senior Secondary School Certificate Examination (SSSCE), the main instrument for assessment of learning and access to further formal education at the tertiary level, stood at 59 percent in 2005/06. Boys uniformly score higher than girls on mathematics, science, social studies, and English language exams. However, the picture for the SSSCE is much like that for the BECE with large numbers of students not passing the exam. Adding these students to the numbers who withdraw from education or who never enter the system creates a weak foundation for building a skilled workforce.

Figure 2.7: The Capacity of Public TV ET In Institution to Absorb the Pipeline of Junior Secondary School Enrolment by Region: 2006/07

Technical and vocational education and training (TVET)

2.42 Government provides skills training through technical and vocational education and training (TVET) institutions. These institutions serve the need for intermediate, advanced, and technical skills with entry requirements varying by institution from no prior education to passing of the BECE and even the SSSCE. There are two main public systems: (i) one is handled by the Ghana Education Service (GES) in Technical Training Institutes under the MOESS; and (ii) the other is the National Vocational Training Institutes (NTVI) run by the Ministry of Manpower, Youth and Employment (MMYE) (see Box 2.1). In addition, other technical ministries offer sector-specific training programs as do community (for-profit and non-profit) institutions. Public capacity accounted for 47,935 training places in 2006/07.

13

Formal and traditional apprenticeships are also an important source of training on-the-job.

2.43 Public TVET institutions provide places for a small share of the potential demand for training. The enrolment in junior secondary schools in 2006/07 is a measure of the potential pipeline of students who could advance to a senior secondary education and attempt to enroll in a TVET program. Figure 2.8 expresses public enrolment in TVET as a percent of this pipeline for each of the 10 Regions in Ghana. Nationally, 5 percent of junior secondary students can expect to find a place for study in a public TVET institution with variation across the 10 Regions from 1.7 percent in Brong Ahafo to 10.6 percent in the Upper East. If private TVET capacity is included, the percentage rises to 7.2 percent. Of those students who actually advance beyond a junior secondary education, 12.7 percent enroll in a TVET institution.

13

Ministry of Education, Science and Sports, EMIS 2006, Table 3.2

0 2 4 6 8 10 12

Ashanti

Brong Ahafo Central

Eastern Greater Accra Northern Upper East Upper West

Volta Western Total

Percent

113

Box 2.1: Technical and vocational education and training (TVET) institutions and programs

Technical training institutes under the MOESS: Technical Training Institutes (TTI’s): There are 24 TTI’s under the Ghana Education Service (GES) that enrolled 20,214 trainees in 2005/06 with enrolment falling in 2006/07 to 18,xxx. TTI’s offer three-year programs that produce craft-persons at intermediate and advanced levels and also technician levels. About 80 percent of graduates obtain intermediate level training. Graduates who acquire the prerequisites may proceed to a Polytechnic at the tertiary level for more advanced training.

Polytechnics offer advanced training at the tertiary level with graduates employed as technicians, technologists, and professionals. Ten Polytechnics enrolled 24,664 students in 2005/06. Enrolments in technical and science fields, however, fell from 40 percent in 2003/04 to 35 percent in 2005/06 with an increase in enrollments in the humanities and arts. Those choosing the latter usually sought training in business fields. Unlike universities whose enrolments increased 32 percent to 84,078 between 2003/04 and 2005/06, enrollments in Polytechnics have remained relatively stable over the past three years. Young women accounted for nearly 30 percent of total enrolments

Number of Public and Private TVET Institutions Type of TVET Institutions Public 188 GES Technical Training Inst. 24 National Vocational Training Inst. 38 Integrated Community Centers for Employable skills (ICCES) 61 Social Welfare Centers 15 Leadership Training Inst. 9 Opportunities Industrialisation Centers 3 Community Development Centers 24 Agriculture Training Inst. 3 Road & Transport Training Centers 1 Gratis Foundation 9 Private 252 Total 440

Source: EMIS 2006, Table 1

Technical training institutes under the Ministry of Manpower, Youth and Employment (MMYE):

Vocational Training Institutes (VTI’s): There are 38 VTI’s under the NVTI, enrolling 8,395 trainees. These Institutes offer courses with a high practical content leading to certificates enabling top graduates to continue studies at TTI’s. Integrated Community Centers for Employable Skills (ICCES): There are 61 ICCES training sites in 2006/07, down from 68 in 2005/06. These Centers, enrolling 11,800 trainees (most recent figure for 2002), provide skills training in mostly rural communities for JSS graduates, dropouts from JSS and SSS, and illiterate youth and adults. Fees are inexpensive compared with TTI s and VTIs. The program provides skills for self-support and self-employment. The non-formal nature of the program also allows the study of other subjects like family life education, maternal and child care, drug abuse and rights and responsibilities of young people to their communities. ICCES students can take exams offered by the NVTI, but if not, they receive certificates acknowledging their participation. No current data are available on enrolments.

Other public institutions: They provide non-formal training options, ranging from Community Development Centers with 24 sites to Social Welfare Centers with 15 sites along with a variety of smaller public institutions.

Private sector: The private sector and especially the non-governmental organizations, like the churches play a formidable role in the provision of TVET in Ghana.1 In total, the EMIS identifies 252 private training sites.2

-----------------------------------------------------------------------------------

1/ UNESCO (2003) reports that the majority of private TVET institutions are owned by religious bodies and individual proprietors. Each accounts for about one-third of private TVET institutions. The proportion between profit and non-profit is about 50:50. 2/ This estimate of private training institutions is conservative as other sources suggest a much larger number exceeding 450 (Palmer 2007a, Chapter 5).

114

2.44 The upgrading of instructors is important to public and private TVET institutions. Public training centers are larger on average than private training centers with 372 trainees per public institution in 2006/07 compared with 162 trainees per private institution. Only about half of TVET instructors hold the Teacher’s Certificate A with a slightly higher percentage in public than private centers, 57 versus 43 percent. About 84 percent of teachers in public and private centers have obtained the minimum technical qualification of a Technician II Certificate or above. Of the public centers responding, 46 percent of TVET instructors report they hardly ever have in-service training and another 23 percent say they have this training only once a year. The figures for private TVET institutions are 43 and 23 percent respectively.

Figure 2.8: Unit Cost of Education in Ghana: 2006

2.45 Workshops also require upgrading. In 2005/06, only 12 percent of public training centers described themselves as “well-equipped,” while 29 percent of private training centers did so. In contrast, thirty-seven percent of public training institutions described their facilities as “poorly equipped” or with “no equipment.” The comparable figure for private institutions was 12 percent. There are, however, public and private institutions that offer high quality training, such as the private Don Bosco training institutes in Tema/Ashiaman and Sunyani and some public training centers equipped by donors like the Netherlands, but these are exceptions (Palmer, 2007a)

Public training sites in 2006/07 report that 18

percent of their classrooms need major repairs compared with 37 percent of private institutions.

2.46 Programs available for helping youth make the transition to employment are not carefully evaluated. Rigorous evaluations using appropriate control and treatment groups are not available for training programs. Moreover, only a few programs have introduced tracer studies to track placement and earnings of graduates. Tracer studies that have been launched lack adequate coverage. What evidence is available is largely anecdotal and is generally unfavorable as reported in the White Paper. Furthermore, public training capacity in Ghana, like that in many other countries, is heavily focused on the skill needs of the small formal sector, and with few exceptions does not address the need for entrepreneurial skills in the much larger informal economy (Haan and Serriere, 2002).

2.47 Financing good quality TVET is costly. With the concern for quality above, the unit cost of TVET in Ghana is already 4.5 times that of a primary education and 2.8 times that of a general secondary education. The MOESS with slightly over 14 percent of secondary students enrolled in TVET allocates one percent of its education expenditure to these students. MMYE spends about 12 percent of its budget on VTIs and smaller sums are spent by other technical ministries like Roads and Local Government. Only seven percent of donor spending on services went to TVET in 2006 and there was no allocation for

Unit Cost of Education in Ghana: 2006

0

500000

1000000

1500000

2000000

2500000

3000000

3500000

4000000

Primary JSS SSS TVET

Source: Ministry of Education, Science and Sports, EMIS 2007

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investment in TVET.14

JICA is currently upgrading five workshops and plans to continue with another five workshops in 2007.

2.48 With the diversity of TVET providers, it is surprising to find little coordination in the use of scarce public resources. The National Coordinating Committee for Technical and Vocational Education and Training (NACVET) under the MOESS is charged with coordinating public and private providers, but there is little evidence of its success with many different bodies and vested interests involved (UNESCO, 2003). The legal framework surrounding the TVET sector is itself fragmented and UNESCO reports a “dizzying array” of examinations. The lack of a comprehensive policy for TVET development is apparent and represents one of the key issues addressed in the ESP with the introduction in 2006 of the Council for Technical and Vocational Education and Training Act with a new coordinating body.

Formal and Traditional Apprenticeships

2.49 Enterprises in Ghana, as in most countries throughout West Africa, are active trainers offering formal and traditional apprenticeships and training on-the-job. Formal apprenticeships are found in the wage sector of the economy, which in Ghana is small accounting for about 16 percent of employment based on the 2006/07 Ghana Living Standards Survey (Coulombe and Wodon 2007).

These

apprenticeships are coordinated by the NVTI. Traditional apprenticeships are found in the informal sector of the economy and consist of a private contractual arrangement between a parent or apprentice and a master craftsperson who agrees to provide practical training in the workplace, ranging from several months to three or four years, and subsequently certify the training in return for a fee or reduced earnings while learning.

2.50 Enterprise-based training reaches more persons than public training institutions. While numbers are difficult to come by, Atchoarena and Delluc (2001) report 80 to 90 percent of all basic skills training comes from traditional or informal apprenticeships in Ghana, compared with 5 to 10 percent from public training institutions. Almost all of the training programs taken at VTI’s, can be mastered through traditional apprenticeship.

Enterprises in the formal sector also provide short-term training to workers

beyond formal apprenticeships, helping upgrade skills and introduce new technology. Enterprise surveys in other African countries like Kenya, Zambia, and Zimbabwe and worldwide show large enterprises, many engaged in exporting and use of technology, as active trainers. The training offered by these enterprises favors those with higher levels of general education.

2.51 Almost all apprentices and masters lack formal vocational or technical training. Recent labor force data for Ghana show that in 2000 there were 207,047 economically active persons (15 years and older) in apprenticeship training. Over three-quarters of the apprentices were 15 to 29 years of age and 57 percent were males. The males were mainly in auto mechanics, carpentry, tailoring and driving, while females were primarily in dressmaking, hairdressing, and catering. Out of the total, only 5.3 percent had formal vocational or technical training.

2.52 Since traditional apprenticeships form a major source of skills for youth in Ghana, their quality is an issue.

In many countries and business environments, traditional apprenticeships serve the

informal sector well, but are proving too narrowly focused to cope with the increasing pace of technological change, skills upgrading, and expanding markets. The strengths of traditional apprenticeship need to be weighted against the disadvantages. Although the traditional apprenticeships have not been carefully evaluated in cost-benefit terms, their main strength lies in their: practical orientation, self-regulation, and self-financing. They cater to individuals who lack credentials for formal training, serve important target groups (rural populations and urban poor), and are generally cost-effective. However, traditional apprenticeships have many weaknesses: (i) they are gender-biased; (ii) they exclude applicants from the very poorest households unable to finance modest fees; (iii) they are based on traditional technologies as master craftsmen fail to keep up with technological change; and (iv) they depend on the master craftsman for their standards and quality assurance which varies widely.

14

Ibid. Japan is the only major development partner currently engaged in TVET with its focus on helping develop the policy framework for TVET.

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Economic returns to Education and Training15

2.53 Education influences wages and earnings directly by raising the productivity of the worker and indirectly by promoting entry into more lucrative forms of employment. This section provides preliminary estimates of the economic returns to education and training based on Ghana household living standards surveys.

16 As a guide to future

investment, we highlight variations in returns across levels of education and for different modes of training (covering institution-based TVET, short-term enterprise-based training, and apprenticeships.

17), looking for indications of a

tightening labor market for skills as reflected through returns to investments in education and training in the survey periods. In this context, first the focus in this section is on the marginal effect of the different levels of education on the likelihood of entry into each of the employment categories. Is higher education, for example, a factor influencing entry to wage employment? What role does TVET play in this choice? Second the focus is to estimate the direct effects of education on wages and earnings, controlling for individual and family characteristics. To consider interactions of these effects, separate regressions are estimated for gender, age cluster, and rural-urban residence. A final set of earnings functions allows for the interaction of education, short-term training, and apprenticeship in influencing wages and earnings in each of the employment categories. Controls for selection bias affecting the choice of wage and self-employment are introduced in all earnings functions. All results are preliminary, as additional work needs to be done with revised GLSS data sets that became available after this preliminary analysis was conducted. Because of the preliminary nature of the estimates, we do not provide the detailed regression results, but rather indicate in a more qualitative way the main findings.

2.54 Education influences earnings indirectly through the type of jobs held. Preliminary multinomial logit functions estimated for 1998 and 2005 suggest important marginal effects of education and other personal and family characteristics on the likelihood of being in various employment status categories. The results reveal the strong influence of education on the type of job held. In survey years, education lowers the likelihood of becoming an informal sector worker and increases the chances of becoming a wage worker. Beyond basic and secondary education, TVET and tertiary education also affect the likelihood of wage employment. The previous section on labor supply identified the weak capacity of TVET in institutional settings, including that offered by the MOESS and MMYE, but the results in terms of the determinants of various types of employment suggest the importance of this training to finding wage employment.

15

This section provides a preliminary analysis that will be extended in the upcoming report on “Job creation and skills development in Ghana”. 16 The specification of the earnings function is a standard Mincer equation including education in categorical form, age, and marital status. Measures of training and apprenticeship are subsequently included in binomial form to assess their effect on earnings independent of education. Regression estimates are based on ordinary least squares methods controlling for selection bias. The regression coefficients for each education category reflect the percentage difference in log earnings from the omitted category of no education. 17

For a historical review of the economic returns to education in Ghana, see Palmer (2007b). Recent evidence points to private economic returns for post-basic education that now exceed those for primary education.

Table 2.23: Median Earnings per Hour Worked in 2005

Wage Public sector 6,925 Wage Private sector Formal 3,930 Wage Private sector Informal 2,782 Self-employed Agro – Paid 2,033 Self-employed Agro - Unpaid - Self-employed Non Agro 2,713 All 2,060

Source: Based on Coulome and Wodon (2007)

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2.55 Education also influences earnings directly.18

Preliminary findings also suggest a positive influence of education on log-earnings for all sub-populations. In the regression analysis used to obtain these findings, the regression coefficient for each education category represents the percentage difference in earnings for that category compared with the omitted category of no education. The results suggest that completion of a secondary education leads to earnings that are significantly higher than those of men with no education and men with a tertiary education earn on average even more. The results also suggest that men tend to have moderately higher earnings than women for most levels of education.

2.56 Gains in earnings with TVET match those of general secondary education. Institution-based TVET fares well in comparison with general secondary education. Quality issues notwithstanding, the incremental gain in earnings for those with TVET largely often match the gains for males and females with a general secondary education. This is an important finding when it comes to choices for further investment in post-basic education. The regressions in the upcoming study on “Job creation and skills development” show vintage effects for education favoring youth 15 to 30 years of age in all survey years which would be consistent with rising demand for education. The general consistency of findings adds to the confidence of conclusions about the importance of education to the earnings and well-being of individuals.

2.57 Basic education improves earnings for the self-employed. Based on preliminary analysis, basic education (nine years) is important to the self-employed but it seems that post-basic education exhibits no or only limited impact on the earnings of these groups, with the exception of TVET’s positive impact on the earnings of the self-employed. The picture is different for wage workers, as completion of education at secondary and higher levels tends to translate into significantly higher earnings.

2.58 Apprenticeships are found in all types of employment, but short-term training is mainly found in wage employment. Whereas completion of an apprenticeship is found in all categories of employment with the rate higher for wage workers, short-term training is largely associated with working for wages. One out of ten wage workers have had short-term training as compared with one out of a 100 among the self-employed.

19 Apprenticeship has also become important to earnings for the self-employed.

Having been an apprentice is associated with incrementally higher earnings for various sub-populations except those residing in an urban area that apprenticeship is a positive factor influencing earnings in the so-called informal sector, more so than in wage employment.

2.59 Short term training has only a limited impact on earnings for those in wage employment. Having attended a short training course of less than 6 months has a positive and statistically significant impact on the earnings of those in wage employment, but not those who are self-employed. Again, these are preliminary results that need additional validation, but they suggest that wage workers with short-term training earn on average 15 percent more than those without this training (there may be self-selection at work, however), while by contrast the impact is not statistically significant for other sub-populations. For wage workers, much of this training is likely provided by the enterprise in which they are employed. Overall, one may conclude that apprenticeship and short-term training contribute to higher earnings, but the effect is influenced by the type of employment held.

18

The direct impact of education on log-earnings is estimated using ordinary least squares with the population stratified by gender, age, and rural-urban status allowing for differences in the effect of education for each sub-population. This relationship is estimated for the three employment categories to assess differences in the role played by education on earnings. A simple specification is used with the log of earnings per hour worked regressed on education, measured in categorical form, and controlling for personal and family characteristics. Age is retained as a continuous variable in the equation with a squared term to allow for non-linearity within two broad age groups: 15 to 30 years and 31 to 64 years to examine vintage effects for education. Controls for selection bias are included. 19

The likelihood of enterprise-based training is correlated with the size of the enterprise, the use of technology and an educated workforce, foreign direct investment, and being an exporter. Large enterprises are more likely than smaller ones to provide training for their workforce. See Johanson and Adams (2004) and Tan (2005).

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Priorities for Reform

2.60 Anticipating the positive returns, Ghana early in the decade launched the reform of its education and training systems. The White Paper, introduced in 2004, refers to the mismatch of skills and jobs with an education and training system that is poorly connected with the skills that employers need. This section examines the strategy Ghana has adopted to address the skills problem, first in the ESP and then in the White Paper. This section places the strategy alongside other country reformers in the Region and globally and, in the context faced by Ghana, reflects on priorities for the reform program that would prepare youth with the skills needed for overcoming poverty and raising living standards.

2.61 The White Paper calls for accelerating the ESP goals for education and placing increased emphasis on the expansion of TVET. The TVET reforms in Ghana were launched a decade ago (see Box 2.2). The objective was to improve the impact and image of TVET, which was generally considered second-class education, and to align TVET more closely with the needs of industry and trainees for skills. To implement the reform, the TVET sub-committee identified strategies that included: (i) a structure for implementation and coordination of the reforms; (ii) the development of a National Apprenticeship System; and (iii) a sustainable funding base for TVET. The White Paper laid out plans to: (i) elevate TVET at the secondary level as a credible alternative to general secondary education; and (ii) open access to apprenticeship for junior secondary school leavers who do not continue to senior secondary schools.

20

The reform strategy calls for a ten-fold increase by 2015 in technical and agricultural training in secondary schools from a gross enrolment rate of 1.5 percent to 15 percent, and an increase in apprenticeships, beginning in 2008, from a gross enrolment rate of 2 percent to 20 percent. In this context, traditional apprenticeships are to be brought into the formal system.

Box 2.2: Process of TVET reforms in Ghana

TVET reforms began in Ghana even before adoption of the ESP with a series of workshops and consultations in April 1997 leading to the first Draft TVET Policy Document in July 1999. A task force further refined this document with input from development partners. JICA, for example, began in April 2000 at the invitation of the Government to develop a Master Plan to Strengthen Technical Education in Ghana. Other work was done on agriculture, information and communication technology, the informal sector and gender issues. The products were incorporated in the draft policy framework with the goal of streamlining the technical and vocational education and training system, including the indigenous systems of informal apprenticeship, to enhance Ghana’s human resource capacity in the global market and make it demand-driven. A National Forum was organized in May 2004 to present the Draft TVET Policy Document to stakeholders.

A TVET sub-committee was formed as a follow-up to the White Paper to identify strategies and propose implementation activities for delivery of the improved TVET system. It issued a report in September 2006 that provided a logical framework for TVET implementation activities. A new institution was identified to replace NACVET as an instrument for implementation and coordination of TVET reforms. The National Council for Technical and Vocational Education and Training (COTVET) was to be established along with other regulating bodies to oversee development of TVET. In addition to improving the operation of TTI’s and VTI’s, the goals of COTVET were to include improving the operation of public agricultural training institutes and facilitating the operation of private TVET providers. COTVET was thus expected to cover public and private training institutions. In addition to classroom and workshop-based sources of training, a National Apprenticeship Training Board was to be established to formulate policies for apprenticeship and identify and train those in industry associations and enterprises who would provide apprenticeship training.

------------------------------------------------------------------------------ Source: Ministry of Education and Sports and Ministry of Manpower and Youth and Employment (2004); and National Education Reform Implementation Committee (2006)

2.62 The TVET strategy of the White Paper contains a holistic view. To achieve the goal of a more market responsive training system, the strategy calls for: (i) improving linkages of providers with

20

The White Paper identifies four curriculum streams for senior secondary students: general, vocational, technical, and agricultural.

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industry; (ii) shifting to an outcomes focus with competency-based training; (iii) improving infrastructure and instructor training; and (iv) strengthening sector management and leadership. Plans were announced for development of a national qualifications framework that would grant academic credit for past learning and open pathways to further education and training options as part of a lifelong learning system. The framework would be extended to traditional apprenticeships. With TVET receiving only one percent of the public education budget, the strategy calls for an increase in public expenditure on TVET and the introduction of additional sustainable sources of financing.

2.63 Ghana’s awareness of the education and training system’s weaknesses and the potential adverse consequences for growth and poverty reduction place it on a familiar path traveled by other countries. Attention to TVET reforms is observed throughout Sub-Saharan Africa, and also worldwide from South Korea to Chile and from South Africa to Northern Europe. Nations are seeking to improve the performance of training systems to smooth the transition of young people from school to work and create a more competitive workforce for the global economy. While these reforms vary from country to country, they share many features and can be divided into two categories: (i) reform of technical and vocational education (TVE) at secondary and tertiary levels of education, which is applied mainly to ministries of education and influences when TVE is first offered and the design of curricula for vocational and general education; and (ii) reform of non-formal training, which includes a more diverse set of providers and objectives outside ministries of education including technical ministries (e.g. agriculture, health, labor), for-profit and non-profit institutions, and employers.

2.64 There are two important lessons learned from experience. Overall there is a rich record of experience in the Africa region with reforms that cover formal and non-formal training but also their integration with education as part of a lifelong learning framework. This record can serve as a benchmark and guide for the reform of training in Ghana:

• The first lesson is the importance of building on a solid foundation of basic education. Good training requires young people who have the requisite general education for acquiring skills for the workplace. Knowledge of basic math, sciences, and communication skills is essential to acquiring job-specific skills and encouraging employers to invest in workers.

• The second lesson is the importance of maintaining sound macroeconomic policies and sustaining a favorable investment climate for job creation.

Training itself does not create jobs

and is dependent on investment to produce these jobs. Government and donors can only provide so much of the investment and the remainder has to come from the private sector. Creating an investment friendly climate is important alongside policies that provide access to good quality skills training and encourage youth to invest in themselves. In Ghana, this includes improving the business climate in the informal economy.

Reforming TVE

2.65 Timing of the TVE introduction is important. While there is pressure from some educators to start vocational instruction as early as junior secondary education, the pattern emerging worldwide is to defer vocational specialization until senior secondary education, with advanced countries moving vocational content even later to the first two to three years of tertiary education. Ghana in its White Paper finds efforts to vocationalize its JSS programs unsuccessful. Pushing vocational content later in the secondary curriculum provides room for a solid basic education foundation to support later vocational choices and avoids pressure to make these choices before the student is ready. Ghana is part of this trend with its focus on TTIs for junior secondary school leavers and the availability of polytechnics for those seeking more advanced vocational content.

2.66 However, by not yet having reached the goal of universal basic education nor providing for universal secondary education, there are large numbers of youth in Ghana (nearly 90 percent of the 15-17 year age group), who are not enrolled in secondary education and who cannot follow this pattern of skills development. The option for these youth is: (i) a VTI which provides a pathway back to the formal system in TTI’s; or (ii) one of the other non-formal sources of TVET available from public and private providers, including traditional apprenticeship.

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2.67 Expanding choices for youth is key to successful education. Globally, the movement is toward expanding choices for youth enabling them to recover from situations where there have been no choices for further education or where bad choices may have been made and education was abandoned (World Bank 2007). Within the current TVET strategy in Ghana, VTI’s appear to be the main instrument for providing this choice, but one that is severely limited in capacity. While it is good that other options like traditional apprenticeships are supported for helping youth acquire skills, these options do not offer youth second chances for returning to formal education and this places a ceiling on the future for these youth and the human resources capacity of the economy.

2.68 There is also the need to improve articulation. Based on global trends, steps are being taken in many countries to create horizontal and vertical pathways allowing vocational students to transfer to other programs at the same level and continue to higher levels of education (Adams 2007). Usually, TVE has been subject to criticism where it has been used to track students who are considered to be less able to pursue academic pathways leading to higher education. Where secondary TVE is treated as a terminal education and students are not allowed to pursue further formal education, it has gained the reputation of being a dead-end. Parents and students and even employers under these conditions view it as “second-class” education. Many countries are taking steps to dispel this image by improving articulation within the system by opening pathways that allow students to move horizontally to other vocational programs or even back to general education and vice-versa without losing time in studies. These reforms are observed in Latin America and other regions. South Africa has upgraded previously terminal TVET to qualify for higher education. Similar steps have been taken in countries as diverse as Tunisia, South Korea, and Denmark.

2.69 Overall, the trend is toward blending curricula so that vocational students receive more academic content to broaden their occupational focus and general students are given more opportunity to apply academic principles to practical problems. Botswana, for example, has re-oriented its pre-vocational content toward research, investigation, creative thinking, and problem solving (Weeks 2005). Wilson (2005:84) asserts that one of the effects of globalization has been the increasing convergence between academic and vocational education. This is reflected in policy, practice and curriculum development. This reform is observed in a number of OECD countries (Bowers, Sonnet, Bardone (1999:27)). However, Ghana’s TVET strategy, while allowing for movement from VTI’s to TTI’s and from TTI’s to polytechnics, does not offer the wider range of horizontal choices. There is no discussion of this in the Ghana TVET strategy, although there is recognition of the need for more academic subjects in the TTI curriculum to enable greater vertical integration with polytechnics.

2.70 Ghana’s strategy for all its effort to expand access and improve quality of TVET continues to sustain the division between academic and vocational tracks. Students at the age of 15, leaving junior secondary school, are given a choice of senior secondary school or a TTI, or alternatively, to leave formal schooling and pursue skills through non-formal means of training, including apprenticeship. There are no options to reverse the choice of a TTI. As students will discover new interests and even capacities for learning as they mature, providing horizontal pathways is important to enabling students to pursue these choices. Singapore has built a secondary school system around enabling these choices by opening pathways across the system. Ghana does not yet have these options for students as part of its larger ESP or within its TVET strategy. The partition between general and vocational education remains largely in place.

2.71 Bringing school and work closer together can help complementarity of the system. The building of greater complementarity between general and vocationally-oriented education occurs through reforms that combine schooling with apprenticeship and work experience. On this point, Ghana is moving forcefully with plans for a more active role in apprenticeship. Sweden is introducing mandatory work experience in upper secondary education to account for 15 percent of the curriculum. Australia has offered a similar program with promising results for student motivation, confidence and satisfaction. The long-standing example of this combination of schooling and work is found in the “dual system” of Germany where students split time between school and work. These programs can work well in advanced countries with large formal sectors that are creating jobs, but may not work as well in developing

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countries like Ghana where the formal sector is much smaller. Ghana’s focus on the informal sector and traditional apprenticeships is more consistent with the character of its economy.

2.72 Formalizing traditional apprenticeships is likely to be difficult. Few countries, if any, have been successful in formalizing traditional apprenticeships as Ghana proposes. The White Paper calls for Government assuming the cost of the first year of the apprenticeship program (See White Paper (2004):13). Figures as high as US$500 have been mentioned that are well beyond current estimated costs for apprenticeship and that threaten distortions in the program and raise sustainability issues for future support. The plan aims at encouraging the formation of trade associations in the informal sector to deliver training assistance to members, integrate traditional apprenticeships into the NQF, and provide guidelines for non-governmental organization (NGO) activities in informal sector training. The use of trade associations to organize training, as found elsewhere, is an effective means to support the informal sector with skills as has the use of NGO’s in delivering this training.

21 However, no country has so far

successfully integrated traditional apprenticeships into a NQF. South Africa has failed to do so as part of the South African Qualifications Authority.

2.73 As suggested below, there are other ways to certify skills and promote quality than the NQF. Government will want to proceed carefully to avoid destroying what works well in traditional apprenticeships. A review in West Africa points to actions that could improve traditional apprenticeships. These include providing literacy and basic education for master craftsmen and apprentices, improved access to technology for master craftsmen, and further technical and pedagogical training for master craftsmen. The ILO will soon be producing the proceedings of its May 2007 workshop on informal apprenticeship training in West Africa, which contains many examples of how traditional apprenticeships could be improved.

Reforming non-formal training, (the second “T” in TVET)

2.74 Non-formal training is offered by technical ministries (labor, agriculture, health); private for-profit and non-profit institutions; and employers. Non-formal training is frequently of short duration, competency-based rather than time-based as found in schools, and delivered by skilled craftsmen rather than instructors with teaching degrees. Non-formal training does not necessarily open pathways to further formal education, although NQFs are beginning to open this door. The goals of non-formal training go beyond preparation of youth for first employment to preparation of youth and adults for coping with the changes found in a market economy. The objectives may include skills upgrading for the employed as new technologies are introduced, equipping the unemployed with marketable skills to search for employment, facilitating occupational mobility, and providing at-risk youth and young adults with “second chance” opportunities for skills missed in early education.

2.75 Focusing on governance could improve links to the market. The issues of access, quality, relevance, and financing in the TVET system of Ghana are not unusual in the experience of other countries in the region and worldwide. Faced with a diverse public and private provider community for TVET, countries have focused on governance frameworks to integrate the system and improve links to the market and efficiency. The creation of COTVET is therefore a familiar tool for this and can be found in countries like Cote d’Ivoire, Mozambique, South Africa, Tanzania, and Zambia, but also in other countries world-wide. These coordination bodies, sometimes embedded in ministries and in other cases as autonomous bodies, bring employers and government together in decision-making and allocation of public expenditure on TVET. Both parties can be joined by representatives of workers and civil society. Different models are found with powers ranging from advisory to decision-making and responsibilities covering different activities supporting a market-led training system. Each country designs its governance framework to fit its circumstances and stakeholders’ needs.

2.76 Ghana is adopting best practice in its approach to governance of the TVET system as a means to coordinate, guide, and learn from this diverse provider community. To improve the relevance and outcomes of training, countries are moving away from centralized TVET systems and

21

Johanson and Adams (2004)

122

bringing decisions on instructor hiring and firing, curriculum and course offerings, choice of pedagogy, and management of locally generated resources closer to the market at the training institution level. Instead of ministry officials far from local markets making these decisions, managers of training institutions are taking responsibility for these decisions with the expectation they will be held accountable for performance. Decentralization of management has become an important tool for reform and improving market outcomes. At the same time, it requires building capacity of local managers of training institutions to make decisions that they have not been called on before to make. They must become skilled in reading local market demand for skills and organizing resources to meet this demand. Ghana’s plans for strengthening educational management are consistent with this, but could usefully be extended to private providers improving their performance and reducing pressures on public provision.

2.77 Improving the quality of training is important. Other than financing, the most important issue in TVET reforms is quality assurance. Quality can vary widely in training systems and efforts to promote the integration of public and private provision, placing them on the same level of performance, require introducing quality assurance frameworks.

• Providing certification of skills. In developed countries and some developing countries, National Qualification Frameworks (NQFs), like that proposed in Ghana, have been created that give individuals credit for past learning from different modes of delivery and give the trainee a credential that is accepted in the market place and the education system as a measure of learning and performance. Whether the skill is acquired in the workplace or in a school setting, the learning can be measured and credit given, placing all forms of training on a level playing field. However, NQFs require considerable capacity to develop and manage and may not be suitable for all countries.

22

• Involving employers in setting standards. A different but related TVET reform in terms of improving quality involves engaging employers in setting standards to guide curriculum development. This approach may well be a better fit for improving quality and standards for traditional apprenticeships than a NQF. In Ghana, engaging employers in setting standards is not emphasized in the ESP, but is expected to be part of the reform. Education and training institutions frequently set standards for skills development without engaging employers. Reforms that bring employers into this process can improve the relevance and quality of the training offered. Independent testing and certification of trainees can then determine if the standard has been met.

• Shifting attention to outcomes. Building a standards-based training system shifts attention to outcomes of the training, balancing attention given to the input side. TVET reforms focus attention not just on what goes into skills development, but what comes out and the impact of the training on employment, earnings, and further education and training. Ghana can proceed with development of a NQF and should link this with other regional efforts to promote the portability of skills. However, giving higher priority to developing a standards-based training system is likely to provide a faster payoff to quality improvements.

• Building a quality assurance framework. This is essential and a priority for Ghana with its large non-government provider base. Other instruments such as licensing can help improve quality by setting minimum standards for training providers, while accreditation can set even higher standards. One of the most glaring omissions from the TVET strategy in Ghana is its monitoring and evaluation plan. The TVET sub-committee’s logical framework identifies expected results for each activity in the strategy and timetables for achieving results along with responsible agencies, but little is said about the monitoring of key performance indicators or the conduct of impact evaluations. The latter require careful formulation of treatment and control groups to measure the net impact of training offered. While steps were taken to cost the ESP and later the White Paper, there was little information available on training costs of different providers, much less the benefits in employment and higher earnings. Effective TVET systems are developing

22

Young (2005)

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“learning cultures,” whereby performance is monitored carefully for lessons to guide ongoing policy development and investment.

2.78 Financing mechanisms for TVET. How a country finances TVET and the incentives this financing creates for improving the performance of the system can be especially important to the outcomes observed. Ghana’s public expenditure on TVET (slightly over one percent of the education budget) is well below spending in similar country settings. Nearby Sierra Leone, struggling to recover from a long internal conflict spends over 4 percent of its education budget on TVET with other low-income countries like Ethiopia spending up to 10 percent. The education budget will be increased to 7.5 percent for TVET, while the labor budget will rise to 20 percent. Private financing already plays an important role in the provision of TVET in Ghana. In view of the low level of public expenditure, private financing from households and enterprises will continue to play an important role in spending on TVET. Other sources of financing can be sought from sale of contract training services, sale of products produced by trainees, community contributions, and external donor assistance. Payroll and turnover taxes are used in some countries where there is a stable, formal economy as well as tax expenditures where there is an effective tax collection system.

2.79 Creating incentives for good performance. Beyond the mobilization of resources for the system, a more important issue is how these resources are spent and the incentives created for good performance. Countries like Chile have separated financing from provision. Its national training agency, SENCE, no longer maintains its own capacity for delivering training and instead buys training services on the open market from public and private providers using competition to reduce costs and get the best training for the money spent. In Sub-Saharan Africa, Mauritius and its Industrial Vocational Training Board have recently adopted this approach. Performance-based budgeting, competitive procurement of training, and vouchers are some of the demand-side financing reforms that can improve the incentives for performance of training institutions that are not now part of the Ghana TVET reform.

2.80 It is not necessary for either the MOESS or MMYE to divest, respectively, their TTI’s and TVI’s and subject them to competition. The same incentives for performance can be achieved by changes in the budget procedure for these institutions moving away from buying inputs (class taught, instructors hired, curricula developed, etc.) to buying outcomes (trainees able to find jobs, increases in numbers passing certification exams, reduction in dropouts, etc.). The TVET strategy in Ghana is presently focused on resource mobilization and has little to say about changing the incentives for performance and how mobilized resources are spent. Improving management capacity for TTIs and TVIs and holding managers accountable for results would be necessary to make a performance-based system work.

Policy Priorities based on Lessons from International Experience

2.81 Overall, Ghana has taken important steps toward opening access to education and introducing TVET reforms related to governance, management, quality assurance, and financing, but as suggested above, there are other actions that could be taken to improve the performance of the system.

2.82 Improve the quality of formal education. There has been good progress in expanding access at all levels of formal education as a foundation for training, but quality remains a serious issue. As reflected in completion rates and scores on basic and secondary completion exams, a large majority of youth leave the formal education system without a good education foundation.

2.83 Lower the walls that separate education and training and open pathways horizontally and vertically. This will improve the choices available to youth in acquiring skills and accommodating different learning needs. Providing second chances to those who were left without opportunities for education and training or who simply made bad choices as youth when those opportunities were available is a priority to reduce the long-term social costs that come from a lack of skills. Currently, this is missing from the ESP. Steps are needed to improve articulation within the education system to open pathways between general and vocational education and promote choices.

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2.84 Strengthen the capacity of the private sector, especially for traditional apprenticeships. Given the weak capacity of public TVET, there is a need to strengthen this capacity and that of the private sector, especially for traditional apprenticeship which is the primary means for skills acquisition for youth leaving the formal education system.

2.85 Caution in formalizing traditional apprenticeships. Ghana’s movement to improve traditional apprenticeship as part of the TVET system is a sensible part of the strategy. It is probably the largest source of skills for youth in Ghana today and is closely connected with actual employment. However, reforms should not destroy what works well, but should strengthen the system with a set of well-defined interventions. Actions to regulate the informal sector and traditional apprenticeships could easily destroy the conditions that make it work in providing employment and income for thousands of youth and adults unable to find employment in the wage sector. With this caution considered, there are a number of ways in which traditional apprenticeships could be expanded and improved with Government’s role being carefully defined as a facilitator of the process.

2.86 Improve performance of public and private training institutions by strengthening employers’ engagement. Improving traditional apprenticeships should be a priority within the TVET strategy along with increased emphasis on quality assurance for guiding training markets. There is reason to doubt from regional and international experience that Ghana will be able to mount a National Qualification Framework (NQF) quickly, but it can begin to build the elements of a NQF starting with engaging employers in setting skill standards and building capacity to test and certify the training available, including that in traditional apprenticeships.

2.87 Link funding of the training system to outcomes and performance. The condition of public and private training institutions and the inequality of access across the country beg further investment and resource mobilization. There is little to be gained, however, in mobilizing additional resources if the incentives for performance remain unchanged. By moving to funding formulas that focus as much on the outcomes and performance of the providers as on inputs and then holding managers accountable for results, the incentive can be changed in a meaningful way to alter the outcomes. Currently, the TVET strategy is silent on this point. There is an urgency to go beyond mobilizing additional resources for skills development and focus on how resources are spent and the importance of shifting the focus on outcomes from inputs and creating incentives for better performance by providers through demand-side financing.

2.88 Long-term political commitment. Ghana adopted the Council for Technical and Vocational Education and Training Act in 2006, establishing COTVET. It has laid the groundwork for TVET reform and introduced COTVET to lead the effort. It has been slow to implement COTVET. There are numerous challenges ahead. Experience shows that TVET reform is a journey of 10 to 15 years, not a 5-year donor project. It requires a strong champion in government to lead the way and a good strategy. The ESP and the follow-up White Paper provide this strategy. This study highlights gaps that if filled could strengthen the strategy.

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3. SHARED AND INCLUSIVE GROWTH IN GHANA: FOCUS ON NORTHERN REGIONS AND GENDER

SUMMARY

3.1 Even though Ghana has seen considerable improvements in its growth and poverty reduction records in the last few years, this has been accompanied by growing inequality. The growing inequality is seen among different socio-economic groups throughout the country. The continuing inequality is probably best reflected by the development disparities between northern and southern Ghana. It is also seen by the differences in income-earning capacities of men and women.

3.2 This study has examined the possibility of differential access to various resources as an explanation for the wide disparity in productivity and incomes between the north and south. It has also questioned the role of policy in such a differential. In doing this the study has documented the differences in input use, access to road networks, credit, land, irrigation, etc.

3.3 The study documents the relatively greater use of modern inputs in northern Ghana than in the south but shows that it does not lead to any higher output for many crops. It suggests that the lower access to credit for agriculture in the north is the result of a riskier environment with poorer climatic conditions and far less infrastructure to counter the difficult terrain. The paper argues that while it might appear that farmers in the north cannot borrow simply because they are poor and do not have collateral, the reality is that low infrastructural investments make the returns on individual investments low. Without irrigation, banks know that they are dealing not only with the huge idiosyncratic risks associated with individual borrowers but significant covariate risks as well.

3.4 But there may also be socio-cultural practices that affect access to credit. It is observed that there are traditional practices that do not attach much significance to commercial relations but reduce every transaction to a social one, and these are likely to affect the ability of farmers to engage with a more modern economic system. It is important that institutions are developed to provide incentives for engaging in commercial transactions as households trade risks as opposed to being extremely risk-averse.

3.5 On land, the report observes that changing climatic and soil conditions make it difficult for farmers to continue the practice of leaving land to fallow for long periods. While there are no significant differences in access to land, it is obvious that the need for more land is stronger in the north. The need for more land is in response to the farming practice of fallowing land that may be costly in the long term. Reforms have to consider options that allow more intensive use of land. Efforts at irrigation have not had the desired impact on land use and agriculture generally.

3.6 It is acknowledged that government has worked with donors over the last forty years to address the issue of an imbalance between northern and southern agriculture. Aside from the several initiatives to address agricultural problems nationwide, there have been a number of very specific large initiatives for northern Ghana, namely the Upper Regional Agricultural Development Programme sponsored by IBRD and ODA, and the Northern Regional Rural Integrated Development Programme sponsored by CIDA. These failed to adequately address the problem as a result of inappropriate institutional arrangements. They left in their wake several interventions that only provided local elite groups considerable access to subsidized facilities. This experience does not necessarily suggest that such interventions should not be considered. What it means is that careful targeting of sound infrastructural and other capital investments that remove the obstacles to private investment and create competitive advantage are essential. Private investment should be one that promotes expanded trade between the north and other regions of Ghana and the world.

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3.7 The paper has indicated that there is need to study closely the relationships between the productivity of women farmers and their access to various inputs. While there is ample evidence that the productivity of farms run by women is often less than those run by men, a fact that affects relative incomes significantly, the role played by differential access to resources is not very clear and varies from place to place, as a result of considerable heterogeneity.

3.8 Indeed, the use of large survey data sets like the GLSS and the ISSER land survey suggest little difference in the use of inputs and access to credit and land. On the other hand, smaller, mainly qualitative studies show some differences in the use of various inputs, use of new technologies, access to land and credit. But these vary from place to place and do not easily lend themselves to generalization. In discussing land for example, the study indicates that the differences in productivity do not lie in differential access to land, but can be explained partly by differential security of tenure, and security varies significantly among the various communities. In addition, it is pointed out that that intra-household arrangements that are driven largely by social norms, promote women engaging in less competitive activities. Indeed the discussion revealed that women face a time constraint that is sometimes made even more burdensome because they cannot negotiate not providing labour on their partners’ farms. This time constraint has implications for productivity-improving investments. If the investments are labour demanding, women may be less inclined to participate especially if it takes time away from activities that generate income that is within their control.

3.9 The study provides a lot of evidence that there are significant differences in employment between the northern regions and the south. In terms of the types of employment, it is observed that the three northern regions have the lowest proportion of people working in paid employment in their main job. Of those working, only 4.9%, 3.3% and 4.9% respectively for the Northern, Upper East and Upper West are in paid employment in their main job. On the other hand, in the Greater Accra and Ashanti regions about 46.3% and 17.5% respectively are in paid employment. A larger proportion of the employed in the north is engaged in agriculture than in the south. There are also distinct differences in the quality of employment measured by the availability of a contract, which is also associated with limited opportunities for skills development. The study finds the marked differences in employment to be explained by the differences in levels of education.

3.10 The study also finds from survey data evidence of lower representation of women among wage workers. It is suggested that a lower level of educational attainment of women may partly explain their low representation among wage workers. It may also be explained by women’s dual role of operating in the market and in the household economy. Their concentration in agriculture and trading may be explained by these factors, as well as the relatively lower entry requirements in terms of education, skills and capital. Without adequate education women will not be employed in white collar jobs or else will be relegated to the lower echelons.

3.11 Evidence from a study using the 2000 population and housing census to determine the presence of gender segregation in occupation found low levels of segregation. This was done by estimating segregation indices using five different measures. Evidence on gender segregation by industry of employment is, however, not conclusive. There are quite distinct patterns in industry of employment by men and women and the report indicates that they are influenced by gender patterns in the provision of skills, particularly in the traditional apprenticeship setting.

3.12 This study also shows that the average earnings of women in wage employment are lower than that of men. It is estimated that the ratio of the earnings of men to women is about 1.4. Again, this is explained by the lower educational attainment and skills acquisition. Women tend to be found at the lower end of the job ladder compared to men. There is hardly any evidence of women in same positions as men being paid less. This is attributable to the collective bargaining arrangements that characterise most formal employment in Ghana.

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3.13 This study advocates considerable investment in women’s education as well as support for farming and other households to consider a realignment of household responsibilities. Indeed if farming households saw their efforts as being directed to support one enterprise, such realignment of responsibilities could be more easily negotiated. In the absence of such agreed realignment of responsibilities within households more and more women would devote time and resources to non-farm activity, which the GLSS portrays as a growing trend. Civil society advocacy work on gender needs to promote intra-household re-allocation of resources and changing negotiated responsibilities. This will best be done through educational campaigns.

INTRODUCTION

3.14 There are growing indications that Ghana’s attainment of some of the Millennium Development Goals (MDGs) is progressing steadily. The latest data on poverty incidence shows that poverty incidence declined from 39.5% in 1998/99 to 28.5% in 2005/06. While this was happening significant improvements in economic growth performance also occurred. Economic growth was 6.2% in 2006 after rising from 5.8% in 2004 and 2005 and 5.2% in 2003. Even though there is considerable reason to be optimistic about future trends in relation to growth and poverty reduction, one cannot afford to be overly so after taking into account the still high inequalities that are associated with the economy. Recent figures on living standards suggest a worsening distribution with a Gini coefficient of 0.394 for 2006, compared to the 0.378 and 0.352 measured for 1998/99 and 1991/92. Clearly, the observed growth has not been more equally distributed.

3.15 The CEM analysis in volume 1 shows considerable progress in growth and poverty reduction in Ghana in recent years. Also, having looked at data on growth and poverty reduction in Ghana over a twenty-year period (1980-2000), Aryeetey and McKay (2007) in a study of “pro-poor growth” argued that Ghana had made significant progress in reducing poverty in income and some non-income dimensions at the same time that growth was steady, and therefore, “in that aggregate sense Ghana has achieved pro-poor growth in absolute terms over this period”. But the CEM also shows (See Volume 1 and chapter 1 of Volume 3) and Aryeetey and Kay also point out the fact that “the picture is more complex once disaggregated (for example by location), and there has been less progress in other, especially health, indicators”.

3.16 For many Ghanaians, the fact that growth could be more equally shared is brought home in very clear terms when they see the growing numbers of people on the streets without employment. This is seen as a reflection of a growing rural-urban divide. Many of these mainly young persons have no skills that could easily make them employable. They have travelled from far away rural places to Accra and Kumasi and other urban centres in the pursuit of employment and better living conditions. Even though they are not likely to find employment, their failure is not likely to deter others from following suit, a trend that will continue to deplete rural areas of human capital.

3.17 An equally visible representation of the inequalities in Ghanaian society is the sharp division between northern and southern Ghana. While this may be an old problem, it takes different dimensions with time as its scope changes with institutional developments in Ghana without affecting significantly the core elements of the absence of significant economic opportunity in the north. The north has not seen any major changes in the sources of livelihoods for people, and whatever changes may be observed have occurred far more slowly than in many other parts of Ghana.

3.18 Inequality is generally also seen in the differential income-earning opportunities for men and women. There is ample evidence that men and women generally earn their incomes from different sources and activities, and that the activities that engage many more women often attract lower remunerations than those for men. Explanations for the differences may largely be drawn from differential access to the factors that enhance labour productivity and other institutional factors.

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3.19 It is important to observe that governments in Ghana have over the years expressed a strong commitment to addressing the problems of exclusion associated with the above forms of inequality. Many have gone as far as initiating specific programmes for addressing them but these have often failed to make meaningful progress. Several such initiatives have been limited to expending greater public resources on bringing excluded groups into the ‘development net’ but they have often not made the desired impact. Good examples of this are the Upper Regional Agricultural Development Programme (URADEP) and the Northern Region Rural Integrated Programme (NORRIP) which are acknowledged to have had much limited impact than anticipated (Aryeetey 1995). The conclusion that one may draw from such experiences is that countering exclusion requires a lot more than expending public resources. Resources need to be mobilised in a sustainable manner, properly targeted to have impact, and their management avoids elite capture. Aryeetey and McKay (2006) have attributed the difficulties encountered with earlier efforts to the use of inappropriate institutional arrangements to deal with the problems.

3.20 The national gender and children policy indicates that “the overall goal of this policy framework is to mainstream gender concerns in the national development process in order to improve the social, legal/civic, political, economic and cultural conditions of the people of Ghana, particularly women and children”. The policy attributes failure to adequately deal with gender imbalances to poor policies and weak institutional arrangements.

3.21 It is obvious that the problem facing the economy and society of Ghana is not just a simple “growth versus distribution” dichotomy. Clearly, without growth there is no prospect of many of the MDGs being met. But even with growth, at current levels of inequality many of the goals will not be met. There is a growing understanding that what is needed is shared growth. The concept is similar in interpretation to the relative pro-poor growth concept which focuses on the distributional profile over time. It is however potentially broader than the concept of pro-poor growth (Nissanke and Sindzingre 2005). Shared growth is important not only because for a given rate of growth more sharing leads to more poverty reduction. It is also important because, given Ghana’s current inequalities, ethnic divisions, geographic-climatic disadvantages, and the epidemic of HIV/AIDS, without shared growth, growth will slow down.

3.22 There is no clear consensus yet on how to achieve shared growth. The mix of macroeconomic policies and policies for micro-sectoral transformation in agriculture, education, health and in gender relations, etc as well as the desired institutional transformation, including property rights and improvements to the public sector requires great attention. It is still not certain what roles may be played by local level entities and community-based organizations.

3.23 In Ghana, as indeed in many Sub-Saharan African economies, institutions that prevent emergence out of a low-equilibrium poverty trap to achieve a growth path that is equitably shared among individuals may not be easily found. But institutions play an important part in ensuring shared growth, and their patterns of emergence and establishment of norms may constitute an important obstacle for shared growth. Thus, the focus in this paper is the institutions and policies that affect the participation of women and northern Ghana in the economy.

3.24 This paper discusses the potential for rapid economic growth and development in Ghana in the face of the imbalances that are associated with gender and northern Ghana. The paper investigates how exclusion is being experienced by the two groups, particularly in the agricultural sector and the non-agricultural labour markets, and the systems and processes (both formal and informal) that create and reproduce such exclusion. The paper also provides some recommendations on what needs to change to enable residents in the three northern regions/residents of the savannah zone, and women in Ghana to fully participate in a ‘shared growth’ process.

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THE POTENTIAL FOR INCLUSIVE AGRICULTURAL TRANSFORMATION: THE CASE OF NORTHERN GHANA

3.25 One of the most striking things about agriculture in northern Ghana is that it has always been seen to have far more potential for productivity improvements resulting from mechanization than there is in the southern parts of the country. This is due to the relative flatness of the land surface in most parts and the presence of relatively large water bodies that could be used for irrigation purposes. In other words, there could be man-made solutions to the relative dryness of the area, but mechanized agriculture and irrigation require a lot more capital investment than other forms of agriculture. In the absence of mechanized agriculture, traditional practices have not been adapted to deal with changing environmental and climatic conditions as well as changes in the availability of land. The result is that farmers in northern Ghana tend to be poorer than other farmers who often have a more diversified portfolio of assets and are therefore a little less dependent on nature.

3.26 As suggested earlier, past efforts at dealing with the above problems have not been successful and we discuss some of the reasons for that here. We must point out that there is always the danger that in transforming or modernizing agriculture, elite groups might capture the public resources that could be made available and ensure that any transformation is not inclusive. In this section, we present various elements in the development and transformation of agriculture, and show how northern Ghana is different from the rest. We also discuss the political economy of these developments.

Overview of Agriculture in Ghana

3.27 Agriculture is the mainstay of Ghana’s economy and about 56% of the population is rural. The country has a land area of 23.8 million hectares of which 57% is agricultural land (MOFA, 2003). Ghana’s agricultural sector is divided into four sub-sectors, namely, the Crops and Livestock, the Cocoa Production and Marketing, the Forestry and Logging, and the Fisheries sub-sectors. The agricultural sector is the largest sector with a current share of 36% of Gross Domestic Product (GDP) compared with 25% for industry. The sector also accounts or over 40% of the growth in national output compared with 32% by services and 27% by industry. Agriculture employs the bulk of the country’s labour force and contributes to government revenue, mainly through exports duties of commodities such as cocoa. Considering however that agriculture yields low incomes for most of its stakeholders, there have been several suggestions for improvements in productivity through transformation of the sector. Inclusive agricultural transformation is expected to ensure higher productivity, incomes and equitable growth.

3.28 Agricultural transformation is “the process by which individual farms shift from highly diversified, subsistence-oriented production towards more specialised production oriented towards the market and other systems of exchange” Staatz (1998). This process of agricultural transformation implies an increase in productivity and a change in farming practices, institutions and infrastructure. It is expected with agricultural transformation that there will be a greater use of inputs, irrigation and the intensification of linkages with the market as a source of inputs and an outlet for production. With agricultural transformation, the absolute numbers involved in agricultural production can decline. Ideally, agricultural transformation not only makes possible the release of labour to other sectors but also allows for an increase in food production as well as the supply of agricultural raw materials. An increase in the supply of food in an economy such as Ghana’s reduces inflationary pressures and can support a slower rate in the growth in real wages than would otherwise be the case. Actual yields for several crops are below their potential and there is evidence that increased use of irrigation, the appropriate application of fertiliser and other inputs will improve yields. Agricultural transformation also involves the shift to the production of higher valued crops. For this to happen, a coordinated approach to the provision of services and access to necessary inputs is required (Jackson and Acharya, 2007).

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3.29 In both of Ghana’s poverty reduction strategy papers, i.e. GPRS I (2003-2005) and GPRS II (2006-2009), strategies to modernise agriculture were prioritised. In the Government’s medium term priorities of GPRS I the strategy for agriculture modernisation was based on reforms of the land acquisition process, provision of extension services, irrigation and affordable credit. In GPRS II there is a carry over of priorities from GPRS I and there is an expansion of inputs earmarked for improved provision.

3.30 Despite the role played by the agricultural sector in the country’s growth process, the sector faces several challenges. Constraints facing the agricultural sector can be classified under human resource and managerial skills, natural resource management, technology development and dissemination, infrastructure, and market access, food security and irrigation development. MOFA (2007) summarizes the constraints to agriculture as follows: (i) there is an aging farmer population yet the sector is unable to attract the youth; (ii) high illiteracy among farmers which affects their ability to adopt and use new technology; (iii) high incidence of poverty among producers, (iv) about 70% of the total land surface is prone to severe erosion (v) limited knowledge in post-harvest management, particularly of perishable produce which have resulted in post-harvest losses of about 20-50% for fruits, vegetables, roots and tubers, and about 20-30% for cereals and legumes; (vi) poor road and transport infrastructure; (vii) lack of marketing skills, inadequate product development and generally weak commodity value chains; (viii) less than 1% of arable land is under irrigation and the management of existing systems further limits their effectiveness; and (ix) lack of credit.

3.31 Although the constraints to agricultural transformation are common to all the agro-ecological zones, there are indications that the savannah regions (Northern Region, Upper East Region and Upper West Region) tend to be the most affected and this accounts for the higher incidence of poverty in that area. The extent to which this is true remains to be established. Using existing data sources such as the GLSS, CWIQ, ISSER Land Policy Reform Survey Data and other secondary sources, this section of the report discusses how access to irrigation, storage facilities, production inputs, modern knowledge systems and extension systems is more limited in northern Ghana compared to other parts of the country and how the limitation constrains a more effective participation of northern Ghana in modern agricultural systems. It is mentioned that northern Ghana like southern Burkina Faso has great potential to transform agriculture in order to attain sustainable growth but for this to occur, there is the need for a much larger investment in irrigation, storage and better transport networks linking production and distribution areas.

Differential Access to Credit

3.32 Access to credit remains a major challenge to many businesses but more so within the agricultural sector and this is due to the higher lending risk associated with farming. The risks may be idiosyncratic i.e. peculiar to each farming household or more generalized for communities. While lending institutions generally specialize in dealing with such idiosyncratic risks by gathering relevant information on potential borrowers, it becomes extremely difficult to do so when relevant information is extremely scanty and unreliable. That has been the problem with financing agriculture in poor countries, including Ghana. Information about household assets and income is extremely unreliable, and this varies with the level of education of farmers.

3.33 A number of studies have already highlighted the problem of limited access to credit by entrepreneurs within the agricultural sector. Parker et al (1995) and Aryeetey et al (1994) have suggested that while over a half of entrepreneurs in Ghana often mention credit as a major constraint to expansion, less than a tenth of them will find credit. Estache and Vagliasindi (2007a) also report that access to finance came out as a serious concern of firms in all the surveys conducted in the late 1990s and even more recently; more than 40% of firms in various surveys rated access to finance as one of the three biggest problems that they faced in 1996, 1998 and 2002. Although the percentage declined between 1998 and 2002, credit was ranked as the second most significant constraint in the WBES survey in 1999/2000 and the WDR survey in 1996.

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3.34 Despite the general lack of credit, particularly in the agricultural sector, some regional variations have been observed in Ghana that put the north at a greater disadvantage. For instance, the third Ghana Living Standards Survey data (GLSS 3) showed that only 28% of all sampled households obtained credit in the twelve months preceding the survey of which the Ashanti Region had the highest proportion followed by the Central Region, Eastern Region and the Greater Accra Region in that order (Table 3.1). The Northern, Upper East and Upper West Regions recorded the lowest receipts of credit (1.5%, 0.3% and 1.2% respectively). Out of the total number of households who obtained credit in GLSS 3, 12.6% was for agricultural purposes (purchase agricultural equipment or inputs). Of this figure, except for the Greater Accra Region (which is not agricultural), Upper East and Upper West Regions recorded the lowest shares (5.7% each respectively) with the Northern Region recording the highest proportion of respondents who obtained credit for agricultural purposes. It must be added that from GLSS 3, a greater proportion of credit was from informal sources (89.5 %). A chi-square test for significant differences among the regional shares was positive.

3.35 The trend in use of credit in northern Ghana did not change significantly between GLSS 3 and 4 (1991 and 1999) although the use of credit in the country improved somewhat over the two periods. In GLSS 4, about 35% of households obtained credit in the twelve months prior to the survey, of which again, the Ashanti Region had the highest proportion followed by Greater Accra, Volta and Central Region in that order. Again, the Northern, Upper East and Upper West Regions recorded the lowest (1.7%, 0.4% and 0.6% respectively) The GLSS 5 data shows that Northern Region recorded an increased use of credit (8.7%) compared to the other two regions in the Savannah zone. It must be added that from GLSS 4, although there has been a marginal shift from informal to formal sources of credit over the two periods, it still remains that most available credit was from informal sources (87% in GLSS 4 and 77.2% in GLSS 5). But this is a widespread phenomenon in Ghana. Indeed it is also reflected by Bogetić et al (2007) who show that firms in Ghana financed slightly more investment through trade credit than firms in five of the other six economies (3.2% in Ghana compared to 2.0% in the other five). Also, firms in Ghana used the formal financial sector to finance less investment than in any of the other six economies23 except Uganda.

3.36 Access to formal credit, particularly for agriculture, remains lowest in Upper East and Upper West. This has led to the reliance on informal credit especially in rural areas where local traders lend inputs to be paid back in grain after the harvest. IFAD (2007) reports that there are other forms of informal credit within small farmer populations. For instance, if a family is short of food, it may borrow grain from other families in the community, repaying it after the harvest with interest rates of up to 50%. Some NGOs have also introduced small-farmer group credit schemes to support the introduction of inputs, irrigation or diversification into new crops. However, these schemes still reach only a very small portion of the farming population.

23

The countries surveyed are Ghana, Uganda, Mauritius, Madagascar, South Africa, Tanzania, Mauritius and Kenya

Table 3.1: Use of All Types of Credit by Population %

Source: GLSS 3, 4 & 5

Region GLSS 3 GLSS 4 GLSS 5

Western 3.3 7.4 11.7Central 4.2 6.9 9.9Gt. Accra 4 5.9 14.7Eastern 4 3.9 7.9

Volta 2.2 6.9 13.9

Ashanti 7.5 10.7 17.7

Brong Ahafo 3.3 3.5 10.9

Northern 1.5 1.7 8.7

Upper West 0.3 0.4 2.5Upper East 1.2 0.6 2

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3.37 The lower access to credit for agriculture in the north, particularly, Upper East and Upper West is not surprising. For lenders it is easily a riskier environment with poorer climatic conditions and far less infrastructure to counter the difficult terrain. On the surface it might appear that farmers here cannot borrow simply because they are poor and do not have collateral. The fact of the matter is that low infrastructural investments make the returns on individual investments low. For example, without irrigation, banks know very well that they are dealing not only with the huge idiosyncratic risks associated with individual borrowers, but also significant covariate risks associated with the dry terrain. The likelihood of poor harvests remains very high, and there is no insurance against these. The low incomes in the region mean that the households themselves cannot undertake those large capital investments. Clearly there is a huge vicious circle that has to be addressed.

3.38 In addressing the problems of high risk, it is important to note that there is a sense in which economic policies of the last two decades have either not solved the problem or indeed worsened the situation. Some have attributed low incomes in northern Ghana to premature trade liberalization. Indeed Al-hassan and Diao (2007) attribute the low incomes to the slow-down in the staple crops sub-sector (groundnuts, cassava and cowpeas) as a result of trade liberalization because farmers have difficulty competing with cheaper food imports. Poor access to credit in northern Ghana has also been partly attributed to poor structuring of financial sector liberalization which effectively meant high bank lending rates for many years that poor farmers could not afford (Aryeetey 1997).

3.39 While it is important to acknowledge the contribution of poorly sequenced and structured liberalization, it is also necessary to point out that earlier interventionist policies had not been of much helpful either. Putting pressure on banks such as the Social Security Bank and the Agricultural Development Bank to lend to specific groups of farmers in government sponsored programmes contributed significantly to their poor portfolios without necessarily improving farmer investments (Aryeetey 1997). The conclusion that has been drawn from studies of the financial system over the years to explain the non-availability of formal credit to poor farmers is that there were both policy constraints and structural difficulties. The reforms dealt with many of the policy constraints without addressing the structural ones early enough. For example the non-presence of banks in most of northern Ghana makes it difficult for farmers to access their services. But acute illiteracy among potential borrowers also makes it necessary for banks to invest more in the way they obtain information and provide services, a cost which they often seek to avoid.

3.40 There are other socio-cultural practices that affect access to credit. For instance, traditional practices that do not attach much significance to commercial relations but reduce every transaction to a social one are likely to affect the ability of farmers to engage with a more modern economic system. For many, asset holding in non-financial forms is an important part of the culture. While cattle is largely used to store value that could be transferred to others after leaving this world, reluctance to turn this asset into a financial one for investment purposes requires attention. The sale of cattle in order to smooth consumption is appreciated, but the scope of such transactions is rather limiting. In effect, attitudes towards a modern economy must change in order to make lending a major part of that economy. It is important that we develop institutions that provide incentives for engaging in commercial transactions as households trade risks as opposed to being extremely risk-averse.

Road Networks

3.41 Ghana’s infrastructure development for long was not the best in the sub-region and this has had serious repercussions on the production, marketing and development of agricultural goods. Road transport which forms the major means of distributing agricultural products within Ghana and also for international trade remains to be adequately developed. Estache and Vagliasindi (2007b) report that in 2003, the proportion of roads paved in Ghana was 17.9% which compares favourably with 14.7% in other low income countries and 12.7% in Sub-Saharan Africa. Also, according to the 2005-2006 Road Sector Development Programme, a total 11,723 km of paved and gravelled roads surveyed could be classified as

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falling into good, fair or poor conditions; 46.1% were in good condition, 29% in fair condition and 24.9% in poor condition. This compares quite well with only 30% in good condition in 2006.

3.42 However, the feeder road network in 2006 showed considerable regional variation. For instance, an inventory by the Department of Feeder Roads indicated a total of 41,000 km of feeder roads in the country in that year, of which the proportion paved in the Central Region was 19.3%, compared to 12.3% in Ashanti Region, 0.69% in the Northern Region, 0.65% in Upper West and 1.72% in the Upper East Region. In terms of feeder roads gravelled, 7.74% were in the Central Region, 14% were in the Northern Region, 6.3% and 4.8% were in the Upper West and Upper East Regions respectively. Clearly, less feeder roads have been paved in Northern Ghana compared to the rest of the country (Table 3.2)

Table 3.2: Regional Distribution of Feeder Roads by Surface Type

Paved Gravel EarthLength (Km) Length (Km) Length (Km)

Gt. Accra 1336 140.2 706.34 489.46Eastern 3994 384.1 2451.3 1158.6Volta 3213 158.13 2096.69 958.18Ashanti 5446 162.56 2853.13 2430.31Central 3098 255.6 1706.8 1135.6Western 5461 94.8 3140.9 2225.3Brong Ahafo 7200 87.2 3544 3568.8Northern 6162 9.2 3090.8 3062Upper West 3006 8.65 1393.25 1604.8Upper East 2076 22.7 1063.8 989.5Total 40992 1323.14 22047.01 17622.55

Region Total Network

Source: Ministry of Transport, 2005-2006 Road Sector Review Report, p. 94

3.43 The disadvantage households in the three northern regions face with respect to the low road network density and poor quality of the road network is revealed in the access to public transportation of rural households. In 2003, less than 50% of the households in the three northern regions could access public transport in less than 30 minutes (Appendix Table 1). On the other hand the situation was much better in the Greater Accra, Central and Western regions where between 87% and 91% of households could access public transport in less than 30 minutes.

3.44 A number of explanations have often been put forward for the low network of roads in the north. The usual explanation is that the current network is basically an expansion of the network inherited from colonial times. The colonial network had been structured to facilitate the export of raw materials from the colony. Thus, since northern Ghana had no items of export interest at the time, no attention was paid to the transport infrastructure. In the post-colonial period, it is observed that since the structure of the economy has not changed and the exports have remained the same, the supporting infrastructure has also remained the same basically. The additional argument has been that the lower population densities of northern Ghana and the greater distances between settlements, which are often relatively small, make transport infrastructure far more expensive in relative terms.

3.45 While both arguments may make sense in a static economic model, the arguments could easily be turned around if the issues were approached within a more dynamic setting. So long as northern Ghana can develop a significant trade potential, (focusing on its competitive advantage in a number of areas), opening up the region by developing transport infrastructure carefully will be essential. Developing transport infrastructure will be best done if the intention is to facilitate trade between northern Ghana and the rest of the world, including the south of Ghana. And the most obvious markets for products of the region are Burkina Faso, northern Togo and northern Cote d’Ivoire. Again, if the rest of Ghana were to expand trade with these other markets, northern Ghana could be seen as the central point

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for organizing the trade. In our view the strongest argument for developing the road and other transport infrastructure will be the creation of trade links to the sub-region. Northern Ghana has to be at the core of a diversified trading relationship within West Africa.

24 It is interesting that sheanuts (from northern

Ghana) were the leading non-traditional agricultural export in 2005 and second in 2006, and yet their cultivation is hardly organized commercially. Developing a proper value chain for the crop is critical, as indeed for others, including mango. It is important that new interest in the export market will lead to the development of appropriate infrastructure.

Differential Access to Inputs

3.46 The limited number of studies on input use report the rudimentary nature of farm equipment throughout Ghana, and they seldom focus on the variation by region. What the analysis in this sub-section show is that the application of a number of modern inputs is higher in northern Ghana than in the south, and this is related to the fact that a number of state-sponsored interventions are behind this. What is worrisome about this observation is the fact that they have not necessarily led to steady improvements in output across board.

3.47 The availability of operational tractors and draught oxen is low in most parts of the northern regions. This causes many farmers to plant their crops late. According to IFAD (2002), the problem was especially evident in the 2001 farming season when crops that were sown late failed to reach maturity before the end of the rains. Fertiliser use for food crops is low in Ghana, and has declined very significantly since subsidies were removed in the era of economic reforms. For instance, 45,000 tonnes of fertiliser were imported into the country in 1990 (for both cash and food crops), but this figure had fallen to less than 12,000 tonnes by 1994 (IFAD, 2002). Even in 1990, when fertilisers were still subsidised, MOFA estimated a national application rate of as little as 4.2 kg/ha for both annual and perennial crops. Recent increases in the price of many agricultural inputs including fertilizer meant that most farmers cannot afford to use it or may consider its use un-economical.

Table 3.3: Average sales of fertilizer by region

Source: FAO, 2005, Fertilizer Use by Crop in Ghana

3.48 However, in terms of regional variations, Upper East and Upper West recorded the highest fertilizer sale in the country between 1997 and 2001 (Table 3.3). This was due partly to the production of vegetables such as tomatoes and onions under irrigation during the dry season in the Upper East Region. The Upper East Region has two large irrigation schemes at Tono and Vea and due to the high economic

24

We acknowledge that this suggestion is reflected in the DFID-CEPA study on Economic Growth in Northern Ghana (2006). It was also shared by participants at the ISSER-EGN workshop at the “Northern Road Show” held at Tamale in September 2006.

Regions 1997 1998 1999 2000 2001

Ashanti 5 167 3 893 2 023 4 046 7 438

Brong Ahafo 7 582 5 712 2 969 5 937 10 914

Central 1 629 1 229 638 1 275 2 345

Eastern 1 011 762 396 792 1 455

Greater Accra 1 236 931 484 967 1 779

Northern 15 220 11 467 5 960 11 917 21 910

Upper Regions 15 501 11 679 6 070 12 137 22 314

Volta 8 481 6 390 3 321 6 640 12 208

Western 337 254 132 264 483

Total 56 164 42 317 16 593 43 975 80 846

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value of tomatoes and onions during the dry season, farmers are willing to purchase and apply fertilizers to these crops. The Western Region has the lowest consumption of fertilizers since the major farming activity is cocoa. The use of fertilizers on cocoa is minimal and it is only recently that the use of fertilizers on cocoa has attracted the attention of farmers.

Table 3.4: Agricultural Households that Spent on Inputs (Percent)

1991/92 Western CentralGreater Accra Volta Eastern Ashanti

Brong-Ahafo Northern Upper East

Upper West All

Inorganic Fertiliser 5.62 4.08 14.23 5.63 18.22 15.29 12.34 32.82 22.41 26.15 26.15Organic Fertiliser 0.58 1.56 30.13 3.72 9.58 7.81 6.01 9.55 6.62 0.35 0.35Insecticides 21.66 8.52 22.18 8.68 8.10 27.13 17.68 2.31 0.00 4.26 4.26Herbicides 5.56 0.06 0.00 0.72 0.60 3.18 3.35 0.59 0.00 0.00 0.00Storage 0.83 6.78 5.44 1.67 7.10 0.64 3.89 8.49 4.24 22.61 22.61Purchased Seeds 23.07 33.85 32.22 20.98 25.18 33.89 33.68 42.26 44.14 77.48 77.48Irrigation 0.00 0.00 1.67 0.00 1.41 0.00 0.18 0.71 0.00 4.08 4.08Bags 4.86 37.58 25.52 18.69 21.97 12.02 42.59 45.76 32.60 41.84 41.84Petrol 18.02 2.16 2.93 1.05 0.87 10.96 1.85 0.30 1.36 0.44 0.44Hired Labour 63.77 68.37 52.30 72.91 66.11 74.95 80.47 43.32 18.00 34.84 34.84Rent Equipment 0.32 0.30 12.97 0.76 2.81 3.31 3.25 3.86 0.00 0.00 0.00Hand tools 71.31 82.59 53.97 61.28 83.79 72.91 62.43 68.66 78.27 92.29 92.29

1998/99Inorganic Fertiliser 18.82 9.32 29.60 13.89 16.86 11.66 22.53 25.34 20.72 22.85 17.59Organic Fertiliser 4.22 2.44 3.29 6.70 2.94 7.44 6.76 20.18 3.46 28.95 8.47Insecticides 43.31 23.18 11.53 6.10 21.32 22.29 21.64 9.10 4.26 0.00 18.58Herbicides 4.58 3.46 2.02 1.31 11.76 6.54 4.04 0.18 0.28 0.00 4.24Storage 6.58 0.35 8.35 4.49 4.63 4.19 8.16 9.58 4.65 24.05 6.60Purchased Seeds 28.13 39.87 30.62 35.51 31.87 55.93 52.89 25.02 17.27 23.69 36.33Irrigation 0.00 0.00 0.00 0.33 0.45 0.30 0.67 0.00 0.00 0.00 0.23Bags 26.34 10.27 23.16 35.05 31.32 14.78 35.60 60.39 16.09 52.67 31.72Petrol 32.08 15.69 0.00 0.51 3.12 11.36 12.43 0.59 0.00 0.00 9.05Hired Labour 68.70 70.54 48.36 69.02 74.02 83.20 84.64 64.65 32.06 57.16 70.53Rent Equipment 15.26 9.62 35.60 9.17 4.84 8.46 2.08 18.80 0.00 3.96 9.22Hand tools 80.37 72.14 41.15 70.56 87.61 78.83 44.05 77.54 64.74 78.54 73.65

2005/2006infertiliser 21.0 14.7 26.0 8.9 13.4 18.4 27.2 33.3 38.8 19.7 20.6organfer 5.6 7.8 1.8 0.3 6.2 3.1 5.7 12.8 6.1 22.9 6.4insecticides 32.6 12.9 13.7 9.7 18.7 19.5 27.2 7.1 10.4 12.6 17.6herbicides 20.9 4.2 15.5 18.3 26.3 35.2 14.6 3.5 0.8 1.3 17.2storage 1.6 5.0 1.8 2.6 3.5 3.0 3.8 1.0 2.4 1.3 2.9purchaseed 21.9 19.5 17.5 24.7 21.3 31.7 25.6 25.8 41.2 8.4 24.8irrigation 0.3 0.0 2.4 0.3 0.9 0.3 0.8 0.2 0.2 0.0 0.4bags 4.4 12.1 4.8 21.9 13.2 8.5 15.7 30.4 35.5 21.1 16.0petrol 23.2 3.6 0.0 0.9 4.4 4.8 6.3 1.2 1.1 1.9 5.6hiredlabour 54.3 55.9 25.2 43.1 45.5 65.3 67.1 42.7 30.8 31.9 51.1rentequip 10.1 2.1 4.7 2.1 3.3 8.7 7.9 5.4 2.5 6.8 5.5handtools 64.9 45.2 39.0 33.7 49.9 60.8 68.6 61.7 65.1 47.0 55.2

Source: Computed from GLSS 3, 4 & 5

3.49 Again, in northern Ghana there are two locally important exceptions to the general rule of negligible fertilizer use on food crops. For instance, between 1991/92 and 2005/6, the proportion of households in the three northern regions that spent on fertilizers was higher than the national average (Table 3.4). IFAD (2002) reports that where cotton is grown, as in parts of Upper West Region, a large proportion of farmers tend to use at least some of their subsidized cotton fertilizer allocation and their enhanced yields testify to its efficacy. Where irrigation is available and fertilizer is subsidized, as under the Tono irrigation scheme, most farmers apply fertilizer to their rice which results in enhanced yields. It is important to add that even under such circumstances there are still a significant number of farmers who do not use fertilizer. This view point is corroborated in Table 3.4 above.

3.50 The fact that the greater use of modern inputs in northern Ghana does not lead to superior performance in output is borne out by the figures for different crop performance in Table 3.5 below. The figures suggest that cereals like millet and sorghum which are grown mainly in the north do not see better increases in yields than other crops, and this could be due to many factors associated with the environment for farming in the north.

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Table 3.5: Output and Yield per Hectare of Selected Food Crops, 2001-2006

Food Crop

% Change2005/06

Cassava 8,966 9,731 10,239 9,738 9,567 9,638 0.74Yam 3,547 3,900 3,813 3,892 3,922 4,288 9.33Cocoyam 1,688 1,860 1,805 1,715 1,685 1,660 -1.48Plantain 2,074 2,279 2,329 2,380 2,791 2,900 3.91

Maize 938 1,400 1,289 1,157 1,171 1,188 1.45Sorghum 280 257 338 287 305 315 3.28Millet 134 218 176 143 185 165 -10.81Rice (Paddy)

296 280 241 241 236 2505.93

Roots, Tubers, PlantainCassava 12.3 12.3 12.7 12.4 12.8 12.2 -4.69Yam 12.3 13 11.9 12.5 13 13.2 1.54Cocoyam 6.4 6.6 6.5 6.4 6.6 6.4 -3.03Plantain 7.8 8.3 8.1 8.5 9.6 9.7 1.04

Maize 1.3 1.5 1.6 1.6 1.6 1.5 -6.25Sorghum 0.9 0.8 1.0 1.0 1.0 1.0 0Millet 0.7 1.1 0.9 0.8 1.0 0.8 -20Rice (Paddy)

2.2 2.3 2.0 2.01.9 2.0 5.26

Cereal Crops

Yield (tonnes per hectare)

Cereal Crops

Output (000 tonnes)Roots, Tubers &Plantain 2001 2002 2003 2004 2005 2006

Source: ISSER, State of the Ghanaian Economy Report 2006, 2007

3.51 The most significant increase in yield per hectare for a crop between 2005 and 2006 was for rice, grown in different parts of Ghana with significant attention from the authorities. The northern cereals of sorghum and millet did not see improvements in yield.

Access to Land

3.52 Is access to agricultural land a problem in Ghana? And is it worse in northern Ghana? Access to land has often been cited as a major constraint to the development of agriculture (Nyanteng and Seini, 2000). Land tenure is often based on the community’s social organization, and the basic unit of ownership is the family or clan (Table 3.6).

3.53 What recent research shows is that while individual clan or family numbers may have no great difficulty in accessing small parcels of land for farming, their security is often threatened by the lack of clarity in their right to transfer use rights to others (ISSER 2007). This may sometimes be associated with the absence of clear titles.

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3.54 In the northern regions there is a complex system of communally owned land, with many local variations and this affects the potential of farmers to participate in modern agricultural systems to achieve higher income, as they limit their productivity. Control over land ownership is in the hands of the land priest, variously called the Tendana, Tigatu or Totem (the land priest is traditionally the community’s spiritual leader). He distributes land to members of the group, mediates land disputes and acts as a link between the community and the spirits of their ancestors. Membership in the community is expected to guarantee access to natural resources such as ponds, groves and open lands, considered part of the common heritage. Such access routes may however be changing through the development of markets or commercialized access rights that give greater access to people willing to pay for land. This is observed everywhere with the ISSER survey data. Thus, for farmers in the north, low incomes become the main obstacle in their effort to access land. The ISSER survey (2005) probed into difficulties in accessing land and the data suggests that in Upper East and Upper West there was a strong perception that some groups had greater difficulty than others (Table 3.7). This was very different from the perception in all other regions. The groups that were seen to have difficult access to land under the prevailing tenure arrangements may often be migrants or women.

3.55 It is important to emphasize the need for caution about land tenure reforms in order to create greater access for farmers. Taking into account the growing incidence of commercialized land transfers, reform needs to focus on how best to create access for commercial farmers without endangering the livelihoods of small peasant farmers. In the past this was done by the state using its authority over vested lands to create access for larger farmers. There is ample evidence of abuse of the system as elite groups from both the north and south captured these, a development which led to general dissatisfaction with the capacity of the state to allocate land.

3.56 Changing climatic and soil conditions make it difficult for farmers to leave land to fallow for long periods, as has traditionally been done. Their need for more land is in response to this farming practice that may be costly in the long term. Reforms have to consider options that allow more intensive use of land.

Irrigation

3.57 Upper East Region and Upper West Region fall in the sahelian savannah and are therefore covered by dry grassland. There is one short rainy season, followed by a long period of dry weather influenced by the dry harmattan wind from the Sahara Desert. Farmers live generally at the subsistence level, and farming is confined mainly to the short rainy season. In the dry season farmers can cultivate land only under irrigation which is very limited and out of the reach of many peasant farmers. Most farmers are idle during this period, and many able-bodied young people migrate to other parts of the country to earn an income. A negligible number of total farmland in

Table 3.6: The Owners of Land in the Community

PercentChiefs/traditional council 64.2Family/lineage 63.6Individuals 42.3Government 2.7Tendana 8.9

Source: ISSER (2005) Land Tenure and Land Policy Research Project, Legon.

Table 3.7: Percent that Perceive Difficulty with Land Tenure System

Region Freq. %Western 130 46Central 52 18Greater Accra 80 27Volta 47 16Eastern 17 7Ashanti 44 18Brong Ahafo 51 20Northern 85 38Upper East 186 91Upper West 191 81Total 883 34

Source: ISSER Survey (2005)

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Ghana, about 6,000 ha out of several thousands have access to irrigation (IFAD, 2002). Indeed, for Ghana as a whole there has been very limited expenditure on irrigation (Table 3.4).

3.58 The most significant irrigation infrastructure developments have been the Tono and Vea dams. The construction of the Vea dam was indeed a part of the Upper Regional Agricultural Development Programme (URADEP) started in 1975. The introduction of large irrigation dams has not been without its problems. In the early days of the Vea dam there were reports of reluctance of displaced farmers to move to the irrigated lands since it meant longer distances to get to new farms allocated to them. At the same time they were reluctant to grant access to other farmers, and this often led to conflicts and violence. Unfortunately all evaluations of these efforts have suggested extremely limited outcomes (IBRD 1980, CIDA 1992). Aryeetey (1985) provided considerable evidence of institutional difficulties which often led to elite capture of such development programmes and hence denied poor farming households adequate access to limited infrastructure and other facilities. The distribution of subsidized agricultural equipment and inputs under URADEP, including tractors and fertilizer was largely made to elite groups of senior public servants and the local business communities that had political connections. They also had advantage in access to other publicly irrigated lands as local communities could not readily relate to these.

Summary: Constraints to Agricultural Development in the North

3.59 This section of the report has explored the constraints to more effective participation of northern Ghana residents in a more modern agricultural system. Using existing data sets and other secondary information the report draws the following conclusions: First, access to finance remains a major bottleneck to farmers in northern Ghana and this significantly reduces the potential to transform agriculture. The limited access to finance is due to unusually high idiosyncratic and covariate risks that arise from a difficult physical environment. This explains why these regions have the lowest presence of banks; the banks know they are not dealing with the peculiar risks associated with individuals and households only. Unfortunately capital investments to counter the problem have been poorly planned and have had limited impact. The situation leads to a high cost of credit, which is mainly informal.

3.60 The poor planning of earlier interventions has meant that despite the fairly significant investment made by government and donors, it has not been possible to improve significantly access to different types of infrastructure. The road network remains inadequate as its planning is not related to the future trading possibilities of the regions. Donor and government support for the use of various agricultural inputs has not led to significant increases in yields as other requirements for productive agriculture remain lacking. Access to land may not be any more or any less difficult than in other parts of Ghana, but as in other parts of the country, competing uses for land in a poorly structured market place are changing the land owning structure, and this creates difficulty for smaller poorer farmers.

3.61 In effect, while economic relationships may be changing as a result of increased market influences from national economic policy changes, the socio-political and cultural context of change has not been properly developed to cope with the economic change. The structures and institutions that should support market transactions in the area of credit and land are not present and have to be developed to make agricultural investments more meaningful. A more supportive institutional arrangement could reduce the incidence of conflicts over land and natural resources, for example, if properly designed.

3.62 There is no doubt about the fact that a transformation of agriculture to make it more productive will have significant effects on economic growth and poverty reduction. There is ample evidence of how concentrated and targeted investment with proper support services can lead to improved production and productivity in the sahelian zone as suggested by the Burkina Faso experience (Al-hassan and Diao 2006). Once government concentrates infrastructure development in northern Ghana on supporting an economy that trades with others, both in and outside Ghana, it should be fairly easy to develop the competitive advantage of various exportable commodities and begin to link that with other economic sectors. Ghana has to make strategic choices in which agricultural commodities can be supported in this regard.

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POTENTIAL FOR INCLUSIVE AGRICULTURAL TRANSFORMATION: INCLUDING WOMEN

3.63 The implementation of policies and programmes to increase the quality and quantity of service provision, infrastructure and inputs must be explicitly designed to take into account the heterogeneity amongst farmers, especially in the short run when it is not anticipated that there will be 100% coverage. This is important considering that there is some ambiguity in the relationships between the productivity of women farmers and their access to various inputs. While there is ample evidence that the productivity of farms run by women are often less productive than those run by men, a fact that affects relative incomes, the role played by differential access to resources is not very clear and varies from place to place. Some studies from Africa in general suggest that “gender affects farmers’ access to labour, land, and other agricultural inputs. Gender may also affect farmers’ preferences concerning outputs” (Doss, 1999). But the review of twenty five years of research into women farmers in Africa shows that gender roles are dynamic and change with new circumstances (Doss, 1999). This section of the paper presents a broad picture on the use of inputs and access to and control over land by men and women farmers in Ghana. This information is needed to inform the design of effective strategies to ensure that agricultural transformation will maximise growth and reduce poverty. It will show that men and women may have differential access to credit. Use will be made of the third, fourth and fifth waves of the Ghana Living Standards Survey, the Core Welfare Indicators Questionnaire Survey of 2003 and the 2005 survey conducted by ISSER for the Land Tenure and Land Policy Research Project.

Access to Inputs, Land and Credit amongst Women Farmers in Ghana

3.64 A lot has been written on differential access and utilization of various resources and inputs in agriculture by men and women. Data from the various surveys under GLSS and other sources do not however show significant variations in how such resources are accessed and used. Observed differences in how inputs are used can often be linked to preferences made in the production of particular crops. And some of these choices may have been influenced by various cultural practices. A shortcoming of the following discussion is that the various surveys provide information on the household instead of the individual. This is a weakness because the range of choices available and constraints to women farmers may differ between those living in female headed households and those living in households headed by men.

Inputs

3.65 Evidence from Ghana shows that the adoption of new technologies differs between male and female farmers. An evaluation of the Ghana Grains Development Project found that a significantly lower proportion of women farmers adopted the modern grain variety compared to their male counterparts (Morris, Tripp and Dankyi, 1999). The reasons for the difference in uptake between the two groups of farmers was attributed to the greater contact between male farmers and extension workers compared to the latter’s contact with female farmers, greater use of credit by male farmers compared to female farmers and more years of schooling by male farmers. Education has been found to be important in increasing the likelihood of adoption of new technologies. The adoption of the new maize variety involved fixed start-up costs. The existence of these fixed costs would make the adoption of the new maize variety more attractive for large scale farmers who tend to be men than for small-scale farmers who tend to be women. Doss and Morris (1998) found that after controlling for age, the level of education, contact with extension workers and proximity to the market, the sex of the farmer is not significant in explaining the likelihood of adopting a new technology. However, the insignificance of the coefficient of the sex variable is not surprising since the factors that can explain the differing rate of adoption of new varieties by men and women farmers are already included in the equation.

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Figure 3.1: Spending on Inputs by Farming Households 2005/6

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Fertiliser Insecticides Herbicides Storage PurchasedSeeds

Irrigation Bags Petrol HiredLabour

RentEquipment

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Inputs

Spending on Inputs by Farming Households, 2005/6

Female Headed Households

Male Headed Households

Source: Ghana Statistical Service Fifth Ghana Living Standards Surveys, Accra.

3.66 With the exception of hired labour, a lower proportion of farming households headed by women spend on inputs (Figure 3.1). The use of fertiliser, either organic or inorganic amongst farming households is generally low, anyway. The removal of the subsidy on fertilisers in 1990 can partly explain this low usage. Less than a third of farming households utilise either inorganic or organic fertiliser. The proportion of farming households headed by women that spent on fertiliser has increased since the early 1990s. However, there has been no appreciable change in the proportion that spent on fertilisers between 1998/99 and 2005/6 (Figure 3.2). Fertiliser use was higher amongst farming households headed by women in the Upper East region, i.e. 35.6% compared to 2.7% of farming households headed by women in the Volta region in 2005/6.

Figure 3.2: Trends in the Use of Inputs by Farming Households Headed by Women

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Fertiliser Insecticides Herbicides Storage PurchasedSeeds

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Spending on Inputs by Farming Households Headed by Women

1991/92

1998/99

2005/6

Source: Ghana Statistical Service, Third, Fourth and Fifth Ghana Living Standards Surveys, Accra.

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3.67 Less than 1% of farming households use irrigation facilities. This is not unexpected because less than 1% of the arable land in Ghana is irrigated. There was no appreciable change in the proportion of farming households headed by women that spent on irrigation between 1991/92 and 1998/9. However, in 2005/6 less than 1 percent of farming households headed by women had incurred any costs on irrigation (Figure 3.2).

3.68 The use of insecticides has increased over time amongst agricultural households headed by women (Figure 3.2). There is wide regional variation in the use of insecticides amongst farming households headed by women. About 24% of farming households headed by women in the Western region spent on insecticides compared to less than 3% in the Central region. The proportion of farming households headed by women in the Western region that spent on insecticide was not significantly different than the proportion of households headed by men. The high incidence of cocoa farming in the region may explain the patterns.

3.69 There was a sharp increase in the proportion of farming households headed by women that purchased seed between 1991/92 and 1998/99. This however, was not sustained in 2005/6 (Figure 3.1).

3.70 The use of hired labour is widespread amongst farming households in the country. More households headed by women use hired labour than do households headed by men (Figure 3.1). This may be because households headed by women tend to be smaller than those headed by men.

3.71 A study of women’s land rights in western Ghana finds that even though women were just as likely as men to invest in cocoa tree planting, productivity on cocoa farms owned by women was lower than that on farms owned by men (Quisumbing et al, 1999). The lower productivity of women’s cocoa farms was explained by the unequal access of women to inputs that would increase productivity. But it was not clear what unequal access here meant and how it came about. There is greater specialization among men engaged in the cash economy than among women, and this is tied to the additional responsibility of women of feeding the household throughout the year. Clearly they are not going to be able to specialize to the same extent without considerable re-arrangement of household responsibilities. Re-allocation of resources and responsibilities within households in order to create incentives for greater risk-taking among women is essential.

3.72 The discussion on access to inputs has been conducted using information on the heads of households because of insufficient data on spending on inputs by individual farmers. However, the trends suggest that where the woman is the decision-maker (because she is the head of the household) she is less likely to spend on inputs. The relatively lower use of inputs such as insecticides, herbicides and fertilisers may be because of the small size of women’s farms and the time constraint that women face because of their multiple responsibilities. Norms and practices impose demands on women that can reduce the amount of time they spend on their own farms. The link of some women farmers with the market economy is tenuous. Women farmers living under these conditions will therefore not generate enough cash income to purchase inputs and more likely than not, will be perceived as risky creditors when they apply for loans. Gender relations as a result of perspectives and attitudes that restrict women’s use of modern farming inputs have been found in some locations to affect women’s demand for inputs such as insecticides (Padmanabahn, 2007).

Land

3.73 The focus of this section is to assess the extent to which current land tenure arrangements facilitate the participation of woman in a process of agricultural transformation. Two questions are pertinent here. The first is whether women have access to land and the second is whether women have security of tenure that will encourage investment in modern agricultural practices that will increase productivity. Before a discussion under these headings is begun it is important to highlight some constraints of the available data sets. In the GLSS survey pertinent questions linked to land ownership are with respect to the household rather than the individual. This makes it difficult to obtain a clear picture of

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the situation of women in households headed by men. However, case study evidence – limited though it might be in making inferences to the entire population – will provide some more information on the situation of individual women.

3.74 Evidence from both the GLSS 5 and the ISSER study – both conducted in 2005-6 period suggest that almost all the plots of land farmed by households are owned by the household, although the proportion of households that have deeds to support the claim to land ownership is still in the minority (Table 3.8). The pattern of land ownership does not differ very much between households headed by men and those headed by women. A case study using information on individuals and not households throws further light on the situation of land holding. In a study of three cocoa communities in Western, Eastern and Ashanti region between 1995 and 1997 it was found that in two of the villages a higher proportion of women in the study owned land compared to men (Takane, 2002).

Table 3.8: Ownership of Plots of Land Owned or Operated by Households (%)

Women Men Women Men Women MenOwned by household with deed 5.7 7.8 8.3 10.5 16.5 13.6Owned but no deed 41.3 36.9 23.7 19.2 63.9 64.8Not owned by household 53.8 55.3 68.0 70.3 19.4 21.4

Can Sell land 18.7 22.2 6.5 5.1 18.0 17.3Can use as Collateral 4.1 4.9 9.5 11.5 7.7 8.4Can Sell and use as Collateral 43.1 43.6 62.3 58.7 48.0 45.8No Rights to land 34.1 29.3 21.7 24.7 26.3 28.5

How Land was ObtainedBought 2.5 4.5Rented for cash or kind 8.1 9.2 10.1 11.1 4.8 6.5Sharecropped 9.1 14.3 11.2 13.3 7.8 13.2Use free of charge 56.0 27.9 34.8 34.7 30.0 27.4Distributed by village/family 26.8 48.6 43.9 40.9 54.8 48.5

1991/92 1998/99 2005/6

Source: Ghana Statistical Service, Third, Fourth and Fifth Ghana Living Standards Surveys, Accra.

3.75 The findings of GLSS 5 with respect to land ownership and supporting documentation are confirmed by the ISSER survey of 2005. In this study 14% of households headed by women and 17% of households headed by men had plots of land that had been registered.

3.76 There is wide regional variation. About a third of the plots farmed by households headed by women in the Western Region in 2005/6 had supporting documentation compared to 12.3% in the Volta region. In the Volta and Brong-Ahafo regions, the proportion of farms owned by women that had supporting documentation was higher than the proportion of farms owned by men.

3.77 Are women less able than men to acquire land if they want to? The answer appears to be in the negative. Women do not appear to be disadvantaged with respect to men if they make an attempt to acquire more land for farming. Almost all the women (94%) and men (93%) in the ISSER survey who attempted to acquire land were successful. The region with the lowest success rate in acquiring land for farming for women was the Greater Accra region, i.e. 73.7%.

Table 3.9: Success Rate in Trying to Acquire Land for Farming (%)

Region Women MenWestern 97.0 86.9Central 100.0 99.4Greater Accra 73.7 94.3Volta 91.7 94.9Eastern 87.5 84.2Ashanti 97.4 97.9Brong Ahafo 96.8 95.6Northern 100.0 100.0Upper East 85.7 81.3Upper West 100.0 82.4All 94.0 93.4


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None of the women in Central Region or Upper Region who tried to acquire land for farming were unsuccessful (Table 3.9).

3.78 The ISSER survey (2005) reveals the different permutations in the status of land ownership of plots that are farmed by a household or individual (Table 3.9). About 42% of men farmed land that they owned (in the different permutations) compared to 35% of women in the ISSER Survey of 2005. A lower proportion of women are tenant farmers. A significantly higher proportion of women compared to men farm on land distributed by the family or village (Table 3.8 and 3.10). Ownership to land can be claimed by right of lineage. Land can also be gifted from a man to his wife or children. This can therefore explain the large proportion of households headed by women and men that consider themselves owners of land even though they may not have supporting documentary evidence.

Table 3.10: Status of Ownership of Plots of Land Farmed (%), 2005

Men WomenLand owner 36.5 33.3Tenant 41.8 31.9Using lineage land 12.7 30.4Both tenant and landowner 4.1 0.0Using lineage land and tenant 2.5 2.9Land owner, tenant and using lineage land 0.4 0.0Land owner and using lineage land 1.2 1.5Others 0.8 0.0


3.79 The evidence thus seems to suggest that women may not be more constrained than men in terms of accessing land. Cultural norms and practices in the country are not designed to constrain access of women to land.

3.80 The next question of pertinence therefore is whether women have as much security of tenure as do men. In southern Ghana some share contracts can result in the transfer in the ownership of land from the original landlord to the tenant. This is the yemayenkye system. The acquisition process however, is a lengthy one.

25 The evidence from cocoa farming communities in Ashanti, Western and Eastern regions

reveals that the process can take any thing between 6 to 20 years. In southern Ghana long-term rights to land can be established by the planting of perennials and by maintaining the farms in good condition. Women were found to be less likely to be involved in the yemayenkye contracts that could result in transfer of ownership from the tenant to the landlord. This is because they do not have the strength to clear land for the planting of cocoa trees (Takane, 2002). Another possible reason is the time constraint women face. Women’s time is divided between providing labour on their husband’s farms, their own farms, non-farm income earning activities, responsibilities in the care economy and leisure. The time constraint that they face means that they may not be able to devote as much time as they should to maintain the farm. These reasons may explain why fewer households headed by women compared to men are in tenancy arrangements.

3.81 One indicator of security of tenure and control over land is whether the land can be sold or and/or used as collateral. Women headed households do not appear to be disadvantaged compared to men in terms of whether the land they own can be sold and/or used as collateral. The proportion of women farmers that can neither sell the land or use it as collateral is not significantly different from the

25

The tenant establishes the farm with his/her own labour and other expenses. Landlords can indicate what variety of cocoa to plant and how often the farm should be sprayed. Tenants are responsible for all the farm tasks and receive half of the cocoa harvest. In some instances the landlord and tenant can agree to share the land when the cocoa trees are mature. In this instance the landlord loses his/her claim to the land.

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proportion of male farmers (Table 3.10). A similar pattern exists in the ISSER Land Survey where 40% of male respondents and 35% of female respondents reported that they could sell the land they claimed ownership to outright if they wanted to. A higher proportion of male and female respondents indicated that they could lease out the land if they desired to – i.e. 59% and 57% respectively.

3.82 Takane (2002) classified control over land and security of tenure by married women in southern Ghana under five headings. A married woman may have full control over the land she farms and the income from the products, and therefore be completely independent of her husband. This is because she probably obtained land as a gift from her father. The second group of women are those who have usufruct rights to land and control over the product. These are women who may have tenancy arrangements. The third category of women is those who have usufruct rights to their husband’s land for cocoa farming and have control over the income generated from the farm. The fourth category of women have usufruct rights over land but can only farm food crops and the final category of women have usufruct rights but are not in control of the disposal and income of the crops grown on the farm. Women who fall into the first two categories have the most independence and control over the use of their labour and any investments they may have made on the farm. The third category of women has a less secure tenure because the rights to the land and its output are determined by the relationship with the husband. However, that the husband has agreed to the planting of cocoa is a signal that he might hand over the land to the wife in the future. In the sample of 3 cocoa farming communities it was found that 36% of the married women surveyed could be classified under the first two headings. Forty-four percent of married women were classified in the fifth category where their rights to land and the output were the most restricted. This classification of married woman in southern Ghana brings to the fore the heterogeneity of the circumstances of women.

3.83 An important issue that arises therefore is whether insecurity of access will adversely affect the decision to invest in productivity improving activities. The evidence on this is mixed. The ISSER survey finds that less than 20% of the farming respondents had undertaken any major capital investments in the last five years. There was no significant difference on the basis of the sex of the respondents. For the majority of the respondents, i.e. 78% of women and 72% of men, investments were not undertaken because they could not afford to. Almost the same proportion of men and women identified poor security of tenure as a constraint on investment. Place and Hazell (1993) did not find that land rights affected investment or productivity. The most recent analytical piece on how gender may affect women’s participation in agriculture is provided by Goldstein and Udry (2006). They examine the impact of what they refer to as “ambiguous and contested land rights” on investment and productivity in agriculture in the Akwapim area. They then argue that “individuals who hold powerful positions in a local political hierarchy have more secure tenure rights, and that as a consequence they invest more in land fertility and have substantially higher output. The intensity of investments on different plots cultivated by a given individual corresponds to that individual’s security of tenure over those specific plots and in turn to the individual’s position in the political hierarchy relevant to those specific plots”. They infer directly that men are more likely to have these political positions and this explains most of their larger investments than women. They “interpret these results in the context of a simple model of the political allocation of land rights in local matrilineages” (Goldstein and Udry 2006). Because women’s security of tenure is weaker than men, they do not allow land to fallow long enough, afraid of losing that parcel. Their study does not, however, address the question of why the women do not then turn to increased use of fertilizer or other inputs if they cannot let land lie fallow for long.

3.84 The evidence suggests that land ownership has implications for the time use of women and access to labour. Married women who have their own land are not always obliged to provide labour on their husband’s farms. Where women do not have rights over land “the uneven distribution of land rights within the household also affects the labour exchange between spouses. The situation allows husbands with land rights to exercise greater control over their wives’ labour through their prerogative over such matters as the distribution of income from land and future gifts of land. If the wife has land rights from other sources, such as bequests from her father, the decision about whether or not to supply labour to her

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husband becomes only one of several choices that she can make after weighing various opportunity costs” (Takane 2002, p. 77-78). Husbands do not normally provide their labour for food production on their wife’s farm.

3.85 Married women who do not own their own land are in a weaker position to negotiate how they spend their time compared to women who have their own land independent of their husbands. Unmarried women face a labour constraint particularly if they do not have adult sons. They then have to resort to paying for hired labour. The labour constraint that women face may also explain why the size of their landholding tends to be smaller than that of men (Takane, 2002); Whitehead 2000).

3.86 It is our position that while there is ample evidence from various studies of lower productivity from farms owned by women and hence lower incomes (e.g. Goldstein and Udry 2006), the reasons do not lie in differential access to land, but can be explained partly by differential security of tenure. Women who want to acquire land from the market and family land in most parts of Ghana have not experienced unusual difficulties, as clearly shown by the ISSER studies. It is likely that intra-household arrangements driven largely by social norms, support women engaging in less competitive activities.

3.87 Both men and women are constrained in general by traditional norms and rules to gain ownership of land with minimal conditions attached. However, that a greater proportion of women than men farm on lineage land, suggests that even though access to land may not differ significantly on the basis of sex, norms and rules can constrain the access of women to controlling rights over land devoid of conditions from other parties. These conditions can constrain production and investment decisions and can adversely affect the ability of women to access credit and could also impact on their access to irrigated land. Thus the evidence would suggest that some women are more likely to have insecure rights to the land they farm and some may even be more severely constrained in terms of decisions on how their labour is allocated and control over the disposal of the output on the farms they work on.

3.88 Further research needs to be conducted to determine if security of tenure will affect investment decisions. What this discussion reveals though is that women face a time constraint that is sometimes made even more burdensome because they cannot negotiate not providing labour on their husband’s farms. The time constraint women face has implications for productivity improving investments. If these investments are labour demanding women may be less inclined to participate especially if it takes time away from activities that generate income that is within their control.

Credit

3.89 The use of credit among Ghanaian small enterprises is very limited (Aryeetey et.al. 1994). But women are just as likely, if not more so, than men to want to borrow to support their business. From GLSS data, of the households in which a member had borrowed about 42% of those who had taken loans were women in 2005/6. About 76% of women and 74% of men in the ISSER survey of 2005 had attempted to borrow. The success rate of the application was higher for men, but this was not significant. A significantly higher proportion of women reported that collateral was demanded. This was probably because more women approached banks than did men.

3.90 In general insufficient income or difficulty to raise the needed collateral is a reason why most loan applications are rejected by various institutions. However, whilst in 1998/99 insufficient income was a reason why almost 60% of the loan applications of women were denied it was less of a problem in 2005/6. In 2005/6 the rejection of loan applications due to insufficient income applied to only 31% of the unsuccessful women and 32% of unsuccessful men. Insufficient collateral emerged as a more frequently cited reason for the rejection of loan applications, rising from 16% to 27% of unsuccessful loan applications made by women in 1998/99 and 2005/6 respectively. This was a reason for the failure of 25% of men whose loans were denied in 2005/6. About 40% (38%) of women (men) whose loan applications were rejected in the ISSER survey was because the lender was not satisfied with the proposal or because the borrower did not have collateral. Land and labour are the two assets that farmers have.

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Land however, is the only asset that can be traded or used as collateral. The low proportions of both men and women who own land with supporting documentation and therefore have control over land as a privately owned asset is a problem that needs to be addressed adequately. Dealing with the problem of land acquisition and land ownership will go a long way towards addressing the problem of access to credit.

3.91 About 10% and 16% of the loan applications in 1998/99 and 2005/6 respectively were for the purchase of agricultural land, equipment or inputs. In 2005/6, 8% of women who took loans did so to finance agriculture related expenditures. In 1998/99, the proportion of women who took loans for agriculture purposes was 4.2%. This contrasts with 16% and 22% of men in 1998/99 and 2005/6 respectively. The increase in the proportion of women that took loans for agricultural purposes may be a reflection of the increase in microfinance packages that are available to women. It may also be indicative of an increase in the numbers of women who are able and willing to successfully apply for loans. Considering that more than half the working population was employed in agriculture the proportion of loan applications from the sector may be considered low. This low demand for credit may be because of the relatively lower incomes of farming households, difficulty to raise the necessary collateral, the transactions costs associated with applying for a loan and the perception of a likelihood of failure.

3.92 The failure rate of loan applications may signal an unsatisfied demand for credit. The low proportion of loans for the purposes of spending on inputs or investing in agriculture land or equipment also signals the existence of a credit constraint facing operators in the sector. The low incomes and lack of collateral are both important in determining whether a loan application will be made and the success of the application. Women farmers are more likely to be faced with these kinds of constraints. The combination of the constraints of relatively lower land ownership, incomes and access to credit suggest that women are relatively disadvantaged than men in participating successfully in the process of agricultural transformation if strategies are not addressed to reduce the constraints.

Policy Issues

3.93 Modernising agriculture is an important component of Ghana’s second poverty reduction strategy. This is to be achieved through the pursuit of a number of strategies. These include reform of the land acquisition and property rights, the provision of irrigation infrastructure, access to credit and inputs, access to extension services, agriculture mechanisation and selective crop development. The need to design strategies to ensure access of women to inputs, credit and land features prominently in the document. On the strategy of selective crop development a pertinent question to be asked is whether crops women grow are different from that of men? If there is a major difference this will have significant implications for whether women will be included in the modernisation process. A study using the third GLSS found that “many crops are disproportionately grown by men or women, depending on the ecological zone and the method of defining the farmer. Female headed households are more likely to be directly affected by policies towards staple crops than are women farming their own land in male-headed households” (Doss 2002, p.1999).

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Table 3.11: Crops Produced on Various Plots 2005/6

Women Men Women Men Women Men Women Men Women's share of: Avocado 0.6 0.9 0.6 0.6 0.8 1.6 0.3 0.2 21.4Bananas 1.3 2.2 1.0 1.9 1.9 4.0 0.1 0.3 18.7Beans 3.4 6.1 1.3 2.1 1.3 1.7 9.5 12.8 18.2Cashew 1.5 1.4 0.2 0.3 0.1 0.3 5.3 3.1 29.5Cassava 52.1 36.9 68.5 60.4 60.4 46.2 22.3 17.6 35.9Cocoa 13.7 17.3 5.1 10.0 22.5 32.6 0.5 1.3 24.0Coconut 13.7 16.5 5.9 10.2 22.1 30.7 0.5 1.2 38.19*Cocoyam 16.1 10.4 4.7 8.3 25.0 18.3 4.6 1.4 13.5Cotton 0.2 0.6 0.0 0.1 0.2 0.0 0.5 1.5 24.7Groundnuts 7.3 8.8 2.0 0.6 1.4 1.5 23.9 20.4 27.3Maize 35.5 37.6 50.3 49.4 37.0 36.5 21.7 34.8 27.2Mango 0.9 1.0 2.6 1.2 0.6 0.7 0.3 1.1 27.2Millet 1.7 6.4 0.0 0.0 0.1 0.1 6.4 16.3 9.6Oil Palm 9.1 11.8 15.0 18.2 11.2 19.2 0.3 0.5 23.4Onions 0.9 0.4 0.5 0.8 0.8 0.2 1.3 0.4 48.05*Oranges 2.3 2.9 3.5 6.8 2.9 4.2 0.0 0.1 23.5Okro 6.8 4.0 4.0 3.6 2.8 3.0 17.6 5.3 40.24*Pawpaw 0.9 0.8 2.0 1.7 0.7 1.0 0.5 0.2 31.3Pineapple 14.8 7.9 21.9 16.2 9.9 7.2 20.5 5.8 42.71*Plantain 0.3 0.2 0.2 0.3 0.1 0.1 0.9 0.4 31.6Rice 2.3 3.8 0.0 0.2 0.3 1.0 8.2 8.5 19.0Rubber 0.0 0.1 0.0 0.3 0.0 0.0 0.2 0.1 15.6Sheanut 0.9 3.8 0.0 0.0 0.0 0.0 3.6 9.7 8.9Tomatoes 8.3 5.6 10.6 11.0 5.1 5.5 13.8 3.8 37.2Watermelon 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.4 0.0Yam 10.5 13.7 5.0 4.1 9.6 10.0 16.3 21.5 23.3

Coastal Zone Forest SavannahGhana

*Share of plots growing these crops that have women landholders is more than 10 percentage points greater than share of plots that have women landholders. Source: GLSS 5

3.94 Agricultural development has involved not only measures to increase productivity but also involves the promotion of non-traditional crops, particularly for export. Preliminary analysis using data from GLSS 5 suggests that women have not been left out of the cultivation of non-traditional exports such as pineapples and pawpaw. They account for a not insignificant proportion of the total number of plots cultivated with these crops (Table 3.11). With the exception of watermelon, there are no crops that may be described as men or women’s crops on the basis of the crop being the sole preserve of women or men farmers. Cassava may be described as a woman’s crop because more than half of plots with women landholders are cultivated with the crop. However, an analysis on the basis of ecological zones finds that cassava dominates the plots of both men and women landholders in the coastal zone. Thus its description as a woman’s crop may be more apt amongst farmers in the forest zone. What the data suggests is that women are not excluded from the production of various crops. The data provided however, does not provide information on whether women have controlling rights over the income from the sale of the crops. This needs to be established to inform the gender implications of selective crop policies as outlined in the Growth and Poverty Reduction Strategy for 2006-2009.

3.95 Gender relations are dynamic and change as the status of crops change from traditional subsistence to cash crops and as new ones are introduced. What are considered as women’s or men’s crops at a point in time may no longer benefit from that description as support to the crop increases and potential income opportunities for its production expands. Thus as new crops are selected for support careful consideration must be provided to take into account the extent their introduction influences women’s participation in their production and sale, and the impact of the introduction of these crops on the time of women. There is evidence to show that with the emergence of cocoa as a cash crop in the Volta region, men reduced their cultivation of yam, a crop that was traditionally cropped by men. Women, however, were not able to take over production of yam because its cultivation is labour

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demanding. As a result women concentrated on the cultivation of cassava. This is a less labour demanding crop but is lower in nutritional value compared to yam even though it has more calories (Bukh, 1979).

THE LABOUR MARKET AND NORTHERN GHANA

3.96 The labour market in Ghana has undergone considerable change over the last two decades, but has been steady in its slow creation of new jobs. The change is demonstrated in part by the changing importance of the different sectors in absorbing labour. The GLSS III and IV reports show the proportion of the labour force in agriculture decreased from about 61% to about 58% over the 1990s.

3.97 Baah-Boateng and Turkson (2005) attribute the change in the labour market in Ghana to globalisation and the reduction of government’s participation in direct productive activities. In particular they argue that the changes were partly a result of the rapid exchange rate depreciation and trade liberalisation that were pursued in the 1980s. These policies led to a collapse of many inefficient firms and resulted in labour moving from the manufacturing sector to agriculture and also to the informal services sector.

3.98 This section of the report shows that employment trends in northern Ghana are clearly different from the situation in the south. The explanation for this is in part due to the fact that the economy in northern Ghana has been influenced far less by global developments in the last two decades than is the case for the south. Participation in world trade has been limited largely to the consumption of imported consumption items. Another explanation for the differences is the low levels of education and poorer skills development.

Trends in Employment

3.99 The GLSS V data show that about 68% of the adult population (i.e. above 15 years) are economically active (Table 3.12). Across the regions of Ghana, the Northern region has the highest labour participation rates, whilst Greater Accra has the lowest. This pattern is consistent with the GLSS IV data in which we observe that the three northern regions have the highest participation rates – the rates were about 87%, 86%, 80% for Upper East, Upper West and Northern regions respectively whilst the rate was about 56% in Greater Accra for 1998/1999. The importance of the significant differences in employment across regions is underlined by the test of association (Pearsons Chi-squared test). This test shows that at a 1% significance level, the null hypothesis that there is no association between the region and the percent of working adults is rejected.

Table 3.12: Population aged 15+ who are employed by Region (%) 2005/2006

% Working Paid employee

Self employed Unpaid family worker

Other

Western 65.3 21.5 59.6 17.0 1.9Central 67.0 24.0 63.8 7.6 4.6Greater Accra 57.0 46.3 47.4 3.1 3.2Volta 72.4 10.2 68.7 20.1 0.9Eastern 74.5 12.6 67.7 17.0 2.7Ashanti 68.9 17.5 60.3 16.9 5.3Brong Ahafo 72.6 11.2 60.9 26.7 1.3Northern 77.9 4.9 61.4 32.7 1.0Upper East 63.0 3.3 60.4 35.2 1.0Upper West 68.7 4.9 38.2 56.5 0.4National 68.5 16.3 59.1 22.2 2.4Chi-Squared (p-value) 0.0 0.0 Source: Ghana Statistical Service, Fifth Ghana Living Standards Survey, Accra.

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3.100 An important factor that explains these differences has to do with the poverty levels. Given the non-existence of a formal social safety net, the poor cannot afford to remain unemployed for long periods. The poor are less likely to have social networks that can support them during long periods of economic inactivity. In addition, the poor are less likely to stay in school for as long as the non-poor. This point is also made by Baah-Boateng and Turkson (2005, p-105) who argue that poverty is “the overriding factor that pushes children into active employment”. We note however that the labour force participation rate has reduced for Ghana as a whole – the percent of the labour force working was about 68% in 2005/2006 compared to the level of about 75% in the late 1990s (Table 3.12 and 3.13).

3.101 In terms of the types of employment, we note that the three northern regions have the lowest proportion of people working in paid employment in their main job (Table 3.12). As shown in Table 3.12, of those working, only 4.9%, 3.3% and 4.9% respectively for the Northern, Upper East and Upper West are in paid employment in their main job. This contrast with the rates for say Greater Accra and Ashanti regions where about 46.3% and 17.5% respectively are in paid employment. Here also we find that Pearson’s Chi-squared test of the hypothesis that regions do not make a difference in terms of the types of employment is rejected at the 1% level. This trend has not changed over the last 5 or so years. Estimates from the GLSS IV data suggest that the trend has persisted over the last 7 years. In the 1998/1999 survey, the proportions of adult labour in the Northern, Upper East and Upper West regions have been respectively 3.3%, 7.1% and 4.6%.

3.102 In Ghana as a whole the proportion of the adult working population who are unpaid family workers in their main job is about 22.2% based on data from 2005/2006 survey (GSS 2007). This is not too different from the 1998/1999 level of 22.4. The proportion of unpaid family workers is significantly higher in the three northern regions (Table 3.13). Indeed one notes that the proportion of unpaid family workers in the upper West is about 56.5%. This represents an increase over the 1998/1999 level. The majority of those working in the northern regions are farm workers – the proportion of those working who are farm workers are about 79%, 75% and 74% respectively in Northern, Upper West and Upper East. Again compared to the other regions (or the national average), this is very high.

Table 3.13: Population aged 15+ who are employed by Region (%) 1998/1999

Region % Working Paid Employee Self-EmployedUnpaid Family

Worker

Western 76.6 16.3 68.0 15.6Central 79.2 11.4 79.2 9.4Greater Accra 56.8 34.7 60.2 5.0Volta 82.1 9.4 60.2 30.1Eastern 71.7 10.4 70.6 19.0Ashanti 71.0 14.1 70.7 15.2Brong-Ahafo 73.4 9.8 76.7 13.4Northern 80.2 3.3 54.1 42.7Upper East 85.9 7.1 48.4 44.5Upper West 87.3 4.6 53.1 42.3Total 74.8 12.5 65.0 22.4

Source: Ghana Statistical Service, Fourth Ghana Living Standards Survey, Accra.

3.103 Looking at the distribution of the working population by their main occupation, we note that agriculture absorbs the most number with over 58% working in this sector26. The other important sectors

26

Here, the data is based on the GLSS IV as there seems to be a problem with the data for this particular question in the GLSS V. Admittedly, the GLSS V data is still being cleaned and so considerable caution is needed in its interpretation.

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are sales and commerce (17.4%) and production27 (14.9%). Looking at the three northern regions we observe that the patterns, in terms of a ranking of these sectors, are the same as that of the country as a whole. However the dominance of agriculture is more pronounced for the three northern regions. In the Upper West region sales and commerce does not seem to be an important source of employment with only about 1% of the adult working population in the sector (Appendix Table 2). A distribution of employment by industry of employment of the adult working population also confirms the dominance of agriculture as the most important source of employment (Table 3.14) – about 59% of the adult working population are engaged in agriculture. This is followed by wholesale and retail trade which absorbs about 19%. Across the regions, we find that the importance of agriculture is most pronounced in the three northern regions. The proportion of the adult population employed in agriculture in their main job has declined in the economy as a whole and in the three northern regions, although the sector still dominates. Employment in manufacturing increased in the Northern and Upper East regions.

Table 3.14: Population aged 15+ by Industry and Region (%), 1998/99

Agriculture Mining Manufacturing Utilities ConstructionWholesale/Retail Trade

Transport/Communication

Financial Services

Community/Social Services

Western 59.2 1.5 8.7 0 1.4 17.5 1.4 1.1 9.1Central 59.4 0.2 9.9 0.1 1.8 21.2 1.6 1.7 4Greater Accra 5.9 0 16 0.2 4 44 6.1 11.2 12.6Eastern 59.6 0 11.2 0.1 1.2 19 1.1 1.9 5.8Volta 65.1 0.1 4.4 0 1.6 16 0.9 4 7.8Ashanti 48.2 1.5 10.2 0.1 3.6 25.6 1.8 3 6Brong Ahafo 61.4 0 4.8 0 1.5 21.2 1.2 2.3 7.7Northern 84.4 0 4.4 0 0 7 0.9 0.5 2.8Upper West 79.3 0 10.6 0 0.9 1 1.1 0.8 6.2Upper East 86.5 0 1.4 0.4 0 6.6 0.5 0.4 4.2Total 58.8 0.4 8.4 0.1 1.8 19.3 1.7 2.8 6.7 Source: Ghana Statistical Service, Fourth Ghana Living Standards Survey, Accra.

Table 3.15: Population aged 15+ by Industry and Region (%), 2005/6 Agric/For/Fishi

ng Mining Manufacturing UtilitiesConstruction/R

eal EstateWholesale/Ret

ailHotels/Restaur

ants Transport FinanceCommunity/Social Services Other

Western 58.9 3.2 8.6 0.1 1.7 13.7 2.9 2.6 0.1 7.8 0.1Central 55.9 0.4 10.3 0.7 2.7 14.8 1.8 3.2 0.0 9.8 0.4Greater Accra 7.9 0.3 19.5 0.2 7.4 32.7 4.2 7.6 1.4 17.1 1.4Volta 63.0 0.0 12.6 0.4 1.6 11.5 2.2 1.6 0.2 6.5 0.1Eastern 54.9 1.3 15.4 0.2 1.7 14.6 0.7 2.4 0.4 7.9 0.2Ashanti 49.7 1.0 9.7 0.2 3.8 19.4 2.5 3.9 0.4 8.7 0.5Brong Ahafo 68.7 0.1 7.4 0.2 1.4 11.6 1.0 1.4 0.2 7.2 0.3Northern 75.1 0.0 7.2 0.0 1.1 10.0 1.1 1.2 0.0 3.5 0.0Upper East 78.0 0.0 6.5 0.0 1.7 7.1 1.7 0.8 0.1 2.4 0.4Upper West 80.6 0.1 10.0 0.0 0.8 3.8 0.6 0.1 0.0 3.6 0.0Total 54.7 0.7 11.4 0.2 2.7 15.9 2.0 3.0 0.3 8.3 0.4 Source: Ghana Statistical Service, Fifth Ghana Living Standards Survey, Accra.

3.104 A distribution of the annual income from the main jobs of adults in the labour market shows that about 44% earned less than ¢1 million annually from their main jobs (Table 3.16). Less than 1% of the population earned more than ¢20 million annually from their main jobs. The data show that for the three northern regions, the highest proportion of the employed earned ¢5 million or less from their main jobs. However relatively more people seemed to have earned more than ¢20 million in the northern regions than in say Brong Ahafo or Ashanti. This suggests an improvement over the 1998/1999 levels where no one earned more than ¢20 million (Table 3.17). But the observation that very many people are earning less than ¢5 million annually whilst some proportion of the working population are earning more than ¢20 million might suggest that growing intra-regional inequality may be found in the northern regions.

27

The types of occupation that is classified under production include miners, quarrymen, paper makers, shoemakers, food processing, tobacco processing etc.

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3.105 The low incomes earned by those employed in the three northern regions reflect the high proportion of people who engage in agriculture. As the 2007 poverty report shows, households engaged in food crop farming (which is largely the case for the three northern regions) remain the poorest in the country in spite of a 13 percentage point decrease in the incidence of poverty between 1999 and 2006.

Table 3.16: Annual Income by Region (%) (for main occupation only), 2005/2006

Source: Ghana Statistical Service, Fifth Ghana Living Standards Survey, Accra.

Table 3.17: Annual Income by Region (%) (for main occupation only), 1998/1999

Source: Generated from the GLSS IV

Quality of Employment

3.106 We employ some conditions of service that prevail in the work place as a basis for assessing the quality of employment in the different parts of the country. Some of these conditions include, the signing of a written contract at the start of work, the presence of a trade union at the place of work, entitlement to paid holiday, entitlement to paid sick leave, payment of pension, receiving training on the job. A distribution of the employed that benefit from these conditions of service across regions is shown in Table 3.16. We note that generally the quality of employment for the average economically active adult is low in Ghana. Only about 8% of the economically active population signed a written contract when they started work and have trade unions in their work place (Table 3.19). About 9% are entitled to paid holidays. These low proportions run through the other indicators presented.

3.107 The regions with the highest proportions of workers employed in quality conditions are the Greater Accra and Western regions. When these regions are excluded from the sample, we observe that workers in the three northern regions do not always fare significantly worse than the others. For example

¢1mil or less ¢1mil – ¢5mil ¢5mil-¢10mil ¢10-¢20mil ¢20mil +Western 35.90% 44.50% 13.40% 4.50% 1.70%Central 50.70% 38.80% 7.70% 1.90% 1.00%Greater Accra 50.80% 37.70% 6.60% 1.60% 3.30%Volta 42.30% 50.90% 5.20% 1.70% ---Eastern 49.50% 42.50% 5.90% 1.80% 0.40%Ashanti 35.00% 51.70% 10.50% 2.80% ---Brong Ahafo 42.50% 50.00% 6.20% 1.40% ---Northern 38.80% 43.90% 10.20% 5.30% 1.80%Upper East 59.10% 33.80% 5.40% 1.00% 0.70%Upper West 59.70% 36.10% 2.80% 1.40%Total 43.80% 44.20% 8.30% 2.80% 0.90%Chi-Squared (p-value) 0

¢1 mil or less ¢1 – ¢5 mil ¢5 – ¢10 mil ¢10- ¢ 20 mil ¢ 20 mil +

Western 41.0 20.1 15.9 20.6 2.3Central 59.1 17.0 14.7 9.1 0.0Greater Accra 61.2 23.9 10.4 4.5 0.0Eastern 55.4 22.9 11.9 9.2 0.5Volta 52.5 18.4 18.2 11.0 0.0Ashanti 49.5 25.5 15.0 9.9 0.1Brong Ahafo 26.8 29.4 24.2 18.1 1.5Northern 72.2 13.4 7.3 7.0 0.0Upper West 66.7 16.7 16.7 0.0 0.0Upper East 81.5 17.3 1.2 0.0 0.0Total 54.1 20.5 13.7 11.2 0.6

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the proportion of workers who have signed contracts in the Upper East region is higher than the proportion of workers in Central and Eastern Region. However, by 2005/6 the situation is slightly different. In none of the indicators of quality of employment is the proportion of workers in any one of the single northern regions higher or equal to the national average. This suggests that quality jobs are not being created in these regions. These regions are characterised by a lower supply of physical and socio-economic infrastructure and utilities and are not endowed with a skilled and educated workforce. It is therefore not unexpected that jobs that provide security of tenure and some form of insurance to their workers are not found here.

Table 3.18: Quality of Employment of Workers 15+ In Main Job, 1998/99 (%)

RegionSign Written

Contract

Trade Union In Work

PlaceEntitled to

Paid Holidays

Entitled to Paid Sick

LeaveReceive Pension

Entitled to Free Medical

Care

Social Security Benefit

Receive Training

Western 11.4 11.0 11.3 12.4 4.5 9.6 11.3 4.9Central 4.5 2.2 4.8 5.7 3.2 5.3 3.5 2.1Greater Accra 15.8 12.4 23.9 23.2 12.2 20.0 18.9 9.2Volta 6.5 5.5 6.2 6.6 4.3 5.6 5.7 2.5Eastern 5.1 6.3 7.4 7.7 3.9 4.0 6.4 4.5Ashanti 6.6 6.3 8.3 8.6 4.4 6.7 5.6 3.1Brong-Ahafo 6.9 6.1 7.4 7.1 3.5 2.9 6.5 3.7Northern 2.5 2.6 2.8 2.8 1.4 2.2 2.5 1.9Upper East 7.1 5.7 7.1 7.1 7.1 6.3 6.2 3.2Upper West 4.0 4.0 4.2 4.2 3.5 3.6 3.9 2.3Total 7.1 6.4 8.4 8.6 4.6 6.7 7.1 3.8 Source: Ghana Statistical Service, Fourth Ghana Living Standards Survey, Accra.

Table 3.19: Quality of Employment of Workers 15+ In Main Job, 2005/6 (%)

RegionWritten Contract Trade Union

Paid Holdidays

Entitled to Sick Leave

Entitled to Maternity Leave

Sick & Maternity Leave

Will receive Retirement Pension

Subsidised Medical Care

Other Social Security Benefits

Received training in last six months

Western 8.5 9.1 8.3 6.0 0.3 2.1 6.6 6.6 4.5 3.2Central 8.6 10.0 8.2 6.0 0.8 3.3 7.3 6.7 3.6 4.0Greater Accra 23.2 15.0 24.6 20.8 2.2 4.4 18.8 20.2 16.4 3.7Volta 5.9 4.7 4.6 3.8 0.3 1.3 4.1 3.2 2.8 1.9Eastern 6.4 5.5 7.5 6.5 0.4 1.1 6.8 4.8 5.2 2.7Ashanti 5.9 6.7 8.5 7.1 0.4 2.3 5.5 5.7 4.9 4.0Brong Ahafo 6.8 6.0 7.0 5.8 0.5 2.0 5.2 4.4 4.8 3.4Northern 4.0 2.3 3.4 2.6 0.5 0.7 3.1 2.1 2.3 0.9Upper East 2.3 1.8 1.9 1.4 0.0 0.5 1.9 0.8 1.0 0.6Upper West 2.7 2.5 2.8 2.2 0.4 0.7 2.5 1.9 2.4 1.4Total 8.3 7.0 8.9 7.2 0.7 2.0 7.0 6.5 5.6 2.9


Skills Development

3.108 The GLSS IV data suggests that only a small proportion (about 4%) of the economically active population receive on-the-job training related to their work. Across Ghana, the proportion is lowest for the Northern and Central regions (Table 3.20). For the majority of those who receive training related to their jobs, the employer pays for the training – about 82% (Table 3.20). The probability that an employee will pay for the training themselves is highest in the Central (12.6%), Western (10.9%) and Ashanti (8.4%) regions. For the economically active population who received training on the jobs in the three northern regions, the training is mostly paid by the employer, an international agency or it is free.

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Table 3.20: Distribution of employed who pay for their job-related training

Myself Employer Shared FreeInternational Agency Other

Western 10.90% 74.60% 3.30% 11.20%Central 12.60% 67.80% 19.50%Greater Accra 5.60% 82.80% 9.20% 2.30%Eastern 88.20% 4.90% 6.90%Volta 3.90% 89.80% 2.60% 2.00% 1.60%Ashanti 8.40% 69.20% 17.10% 3.80% 1.50%Brong Ahafo 82.50% 7.80% 9.70%Northern 0.80% 99.20%Upper West 100.00%Upper East 82.10% 6.00% 11.90%Total 5.10% 82.40% 1.60% 8.80% 1.70% 0.40%Chi-Squared (p-value) 0

Who paid for training


Table 3.21: Distribution of economically active by educational level and region 2005/2006

Never NoneKindergarten/

Primary Middle/JSSSSS/O' &

A'levelVocational/T

echnicalUniversity/

TertiaryWestern 22.50% 0.00% 19.40% 42.30% 9.70% 4.50% 1.50%Central 22.90% 0.20% 21.20% 38.90% 9.10% 4.10% 3.40%Greater Accra 13.00% 0.20% 12.00% 41.00% 19.70% 7.30% 6.50%Volta 28.80% 0.10% 26.10% 33.00% 8.40% 2.60% 1.00%Eastern 23.50% 0.30% 20.30% 44.10% 6.90% 3.90% 0.90%Ashanti 19.90% 0.10% 17.30% 46.00% 11.90% 2.80% 2.00%Brong Ahafo 34.80% 0.10% 20.10% 33.60% 7.70% 2.40% 1.20%Northern 70.20% 0.40% 12.90% 8.90% 5.20% 1.50% 0.80%Upper East 67.60% 0.00% 15.60% 9.80% 5.20% 1.10% 0.70%Upper West 70.40% 0.10% 12.30% 9.90% 4.90% 1.60% 0.70%National 35.10% 0.20% 17.40% 32.30% 9.50% 3.40% 2.10%Chi-Squared (p-value) 0 Source: Ghana Statistical Service, Fifth Ghana Living Standards Survey, Accra.

Table 3.22: Distribution of the economically active by educational level and region (%), 1998/1999

Never attended School

Did not complete any

level

Kindergarten/Primary

Middle/JSS SSS/O' & A' Level

Vocational and Technical

University/Polytechnic

Other

Western 23.4 2.1 25.5 37.6 6.4 1.9 0.8 2.2Central 29.4 5 31 28.3 2.9 1.8 0.6 1Greater Accra 14.5 1.2 17.2 39.1 14.5 7.8 3.1 2.6Eastern 30.2 2.6 27 29.3 5.1 1.4 0.7 3.8Volta 23.5 7.3 22.4 37 5.2 1.5 0.6 2.5Ashanti 20.3 2.9 25.5 39.9 6.5 1.6 0.4 2.9Brong Ahafo 25.7 7.1 23.6 36.5 3.3 0.7 0.6 2.6Northern 66.2 4.3 12.1 8.2 4.5 0.3 0.5 4Upper West 54.7 7.8 14.8 16.8 3 0.1 0.8 2Upper East 68.9 4.3 9.9 11.7 3.1 1 0.1 1All 31.6 4 22 30.8 6 2 0.9 2.7 Source: Ghana Statistical Service, Fourth Ghana Living Standards Survey, Accra.

3.109 Looking at the educational background of the economically active population, we note that almost 35% of the adult population have never attended school using the GLSS V data (Table 3.21). This remains high, and is an increase over the 1998/1999 level of about 32%. In fact for about 85% of the economically active population the highest educational level they have reached is the middle school/JSS level or lower. In other words, less than 15% of the economically active population have gone beyond the

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middle school or JSS level. Even though this represents an improvement over the 1998/1999 level (of about 12%), it is still low and in part explains why we have a large proportion of the economically active earning less than ¢5 million annually from their main jobs.

3.110 Across the regions we note that the proportion of the economically active that has never attended school is highest in the three northern regions – the rates are 70%, 67.6% and 70.4% for the Northern, Upper West and Upper East regions respectively. This contrasts with a proportion of 13% for Greater Accra Region. What this shows clearly, is that the capital content of labour may be much lower on average, for the economically active in the three northern regions. This undoubtedly explains why the differences in the types of employment across the regions. Also the low levels of earnings from employment in these regions compared to the others will be partly accounted for by the differences in the educational levels of the average Ghanaian living in the Northern part of the country compared to everybody else.

3.111 To further investigate this point we test to see whether there are any differences in employment for those who have been to the university. This is shown in Table 3.23. We note here that no adult with a university degree is unemployed. A test of the hypothesis that there are no differences in the employment rates across regions yields a p-value of about 24% - i.e. we cannot reject this hypothesis. This supports the argument that education is important in explaining differences in employment across the country.

Table 3.23: A Distribution of the economically active by Region for Adults with University Degree (%), 2005/2006

yes noWestern 63.60% 36.40%Central 88.90% 11.10%Greater Accra 79.60% 20.40%Volta 75.00% 25.00%Eastern 80.00% 20.00%Ashanti 90.90% 9.10%Brong Ahafo 93.80% 6.30%Northern 100.00%Upper East 100.00%Upper West 100.00%Total 84.10% 15.90%Pearson Chi-Squared (p-value) 0.244

Proportion who have done work


Policy Issues

3.112 Opening up northern Ghana and getting it hooked more and more into the expanding world economy as a producer will be the fastest way to create productive employment in the area. Developing the tradable goods sector by modernizing agriculture around a number of key export commodities and developing a value chain around these will not only lead to employment creation but also skills development for higher income earning opportunities.

3.113 Current policies on education and skills development are not linked to the generation of employment. It is important that the pursuit of high-value agriculture in the north, as for example under the Millennium Challenge Account, is linked with training programmes for young persons. They could be

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trained in the processing of agricultural products, storage and handling, provision of support services, management, etc.

THE LABOUR MARKET AND WOMEN

3.114 Data from the fourth and fifth Ghana Living Standards Survey of 1998/99 and the Core Welfare Indicators Questionnaire Survey of 2003 –conducted by the Ghana Statistical Service – suggest that there is a decline in the percentage of women aged 15 years and above who are in paid or unpaid employment in their main job. This decline in the proportion of women in employment may be explained by the increase over time in the proportion of persons aged 15-24 years in school. In 1998/99, about 34% of the population aged 15-24 years was in school compared to 47% in 2003 and 48% in 2005/6. 28% of women aged 15-24 years were in school in 1998/99. The proportion rose to 42% in 2003 and 43% in 2005/6. Thus an increase in years of schooling will lengthen the period it takes for both men and women to enter the labour market.

3.115 The agricultural sector is the single largest sector of employment. The proportion of women aged 15 years and above employed in this sector in their main job remained fairly constant at 51% in 1998/99 and 2005/6 (Table 3.24). The majority of workers are self-employed and the incidence of self-employment amongst women declined between 1998/99 and 2005/6 from about 69% to 61% %. The majority of self-employed women and men do not employ others. Less than 10% of women are in regular paid employment in their main job. The incidence of unpaid family labour amongst women has been high and accounted for almost 30% of the employment of women in their main job.

Table 3.24: Employment by Status in Main Job

Men Women Men WomenPaid Employee 23.1 6.7 26.9 8.6Non-Agriculture Self-Employed 18.1 38.9 14.2 35.9Non-Agriculture unpaid family worker 1.2 2.5 0.6 2.0Agriculture Self-employed 48.1 29.8 44.2 24.7Agriculture Unpaid Family Worker 9.2 22.0 11.1 26.5Domestic Employees 0.1 0.3Apprentice 2.7 2.0Other 0.3 0.0 0.2 0.1

1998/99 2005/6

Source: Ghana Statistical Service, Ghana Living Standards Surveys, 1998/99 and 2005/6.

Table 3.25: Basic Hourly Earnings of Women and Men

Notes: 1. Population aged 15 years and above. 2. Population aged 15-64 years. Source: Ghana Statistical Service, Ghana Living Standards Surveys, 1991/92 and 1998/99.

1991/921 1998/992

Ratio of mean earnings of men to women

Mean Earnings (nominal)

Ratio of mean earnings of men to women

Mean Earnings (nominal)

Professional/Technical 0.97 782 1.11 1339Administrative/Managerial . 775 0.75 2694Clerical 0.87 256 2.85 2859Sales or Commercial 1.75 178 2.56 1427Service 0.73 168 1.45 805Agricultural 1.67 100 1.61 519Production 0.96 167 1.33 889All workers 1.14 176 1.43 918

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3.116 The lower level of educational attainment of women may partly explain their low representation amongst wage workers. It may also be explained by women’s dual role of operating in the market and the household economy. Their concentration in agriculture and trading may be explained by these factors as well as the relatively lower barrier entry requirements in terms of education, skills and capital. Without adequate education women will not be employed in white collar jobs or else will be relegated to the lower echelons.

3.117 Evidence on gender segregation in occupations using data from the 2000 population and housing census finds low levels of segregation. This conclusion is arrived at after estimating segregation indices using five different measures. However, the evidence on gender segregation by industry of employment is not conclusive. Some measures suggest high levels of segregation whilst others suggest low levels (Baah-Boateng, 2006). The discussion on the skills acquisition below suggests that gender patterns in the provision of skills particularly in the traditional apprentice setting may explain some of the gender patterns in industry of employment and occupations.

3.118 The average earnings of women in wage employment tend to be lower than that of men. Based on data from the GLSS 4 it is estimated that the ratio of the earnings of men to women is about 1.4 (Table 3.25). The lower educational attainment and skills acquisition may explain this differential. Women tend to be found at the lower end of the job ladder compared to men.

The Quality of Women’s Employment

3.119 The quality of employment is defined here in terms of the conditions of service, i.e. whether employment is covered by a signed contract, there is a pension and other social security benefits, a trade union in the work place, whether there are medical benefits and paid holidays. Since the majority of Ghanaians are self-employed and work in the informal sector, the conditions of service for many lacks these provisions. These provisions are available largely to workers in paid employment. Even then it may be observed that not all workers who are in paid employment benefit from these provisions (Table 3.26).

3.120 Women in general are particularly disadvantaged. Only 3% of working women aged 15 years and above in 2005/6 can expect to receive a pension when they retire compared to about 11% of men. Focusing on the subset of workers who are paid employees reveals that the differences between men and women are not always significant (Table 3.26). However, less than a third of women are entitled to sick leave and/or maternity leave.

3.121 More than 80 percent of women are employed in micro-enterprises – that is businesses that employ less than 5 people. These micro-enterprises, found mainly in the informal sector are unlikely to provide social security or other benefits. Less than 2% of women are employed in businesses employing more than 30 or more people.

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Table 3.26: Quality of Employment of Workers Aged 15 Years and Older (%)

Entire Work Force Men Women Men WomenSigned Contract 12.4 3.6 12.6 4.3Trades Union 11.2 3.0 10.9 3.4Paid Holidays 14.6 4.4 13.3 4.7Sick Leave 15.6 4.3 12.8 2.0Maternity Leave 0.8 0.5Sick and Maternity Leave 1.2 2.8Pension 8.4 2.1 10.7 3.5Subsidised Medical Care 12.6 2.8 10.2 3.1Other Social Security Benefits 12.1 3.5 8.4 2.9Training since started job 6.4 1.7 3.9 1.9

Persons in Paid Employment Men Women Men WomenSigned Contract 52.9 52.2 45.3 47.7Trades Union 47.8 44.1 39.3 38.1Paid Holidays 62.0 64.6 49.1 54.0Sick Leave 66.6 62.1 47.1 21.8Maternity Leave 2.6 4.3Sick and Maternity Leave 4.4 32.1Pension 36.2 31.5 39.7 40.9Subsidised Medical Care 53.9 40.2 37.8 34.3Other Social Security Benefits 51.9 51.9 31.3 33.0Training in the last six months 27.4 25.2 9.4 11.2

1998/99 2005/6

1998/99 2005/6

Source: Ghana Statistical Service, Fourth Ghana Living Standards Survey, 1989/99, Accra

Skills Development

3.122 If Ghana is to achieve middle-income country status then it must transform itself from a low-skilled economy to a high-skilled one and it must be able to shift into the production of products with high value addition. Although enrolment rates for both girls and boys are on the increase, the proportion of the workforce that has undergone vocational and technical training is very low. In 1998/99 only 2.2% of men and 1% of women aged 15 years and above had completed vocational or technical education. The proportion increased to about 3.3% of men and 2.4% of women in 2005/6. The opportunities for training in the work place appear limited. To begin with most of the working population is found in the agriculture sector. The sector as it is presently organised – small-scale farming with a large subsistence element and a large farmer to extension worker ratio- provides limited opportunity for training in modern farm practices. The small size of most firms in the other sectors of the economy can make the provision of training in the work place prohibitive. Almost 11% of workers employed in large firms, i.e. firms employing a 100 or more people had undergone some training. This compares with 10% of workers employed in medium firms (i.e. employing 30-99 people) and 7.5% of workers employed in small firms (i.e. employing 5-30 people). Only 1.2% of workers employed in micro-enterprises received any training.

3.123 The likelihood of receiving training also depends on the occupation and particular job requirements. It is therefore not surprising that women are less likely than men to receive any training. Women are to be found in the micro firms that are unlikely to provide training and are concentrated in activities and occupations that are unlikely to have training components. In 1998/99 less than 2% of women and about 7% of men had received any kind of training in their main job. It is difficult to compare trends in training amongst the working population between GLSS 4 and 5. This is because of a difference in the question asked in the two surveys. In 2005/6 workers were asked about training received in the last six months. A lower proportion of working women had received any training in the six months prior to

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the survey in 2005/6. However, limiting the sample to those in paid employment finds that a higher proportion of women had received training compared to men. This trend could be suggestive of more women being in positions that provide training opportunities. It might also be reflective of a reduction in the bias against women in terms of opportunities for training.

3.124 More than half of women aged 15 years and above cannot read or write in any language. Attendance at literacy classes is low with less than 10 percent of the adult men or women ever attending a class. About 40% of women and men who cannot read or write in any language have never attended literacy classes because they are not available. About 30% of women do not think they need the adult literacy classes, 7% of women who cannot read and write do not think the classes are useful and 16% do not attend because they consider the classes take too much time. The ability to read and write is critical for the acquisition of knowledge independent of direct contact with another person and can be an important platform to support innovation and creativity. Women are handicapped compared to men because a substantial proportion of them cannot read and write. The reasons given for why women who cannot read and write do not attend adult literacy classes suggests that the likelihood of many women in the working population ever being able to read and write may be quite low unless innovative measures are introduced to change these perceptions.

3.125 There are 23 public technical and 29 public vocational schools. The private sector also provides a large number of technical and vocational schools. The National Vocational Training Institute, Opportunities and Industrialisation Centres and the Integrated Community Centres for Employable Skills provide vocational and technical training. The ICCES are community based centres developed and owned by the community. The low incidence of technical and vocational training amongst the working population suggests that there is a shortage in training facilities and/or possibly a shortage in the demand for training. These are issues that must be investigated thoroughly.

3.126 In addition to formal training provided by public and private vocational and technical skills there is apprentice-based training. Apprentice based training provides for about 90% of the technical and vocational training in Ghana. Apprenticeship training takes place in the numerous small and micro-scale enterprises in the informal manufacturing and services sector. Apprenticeship training also takes place within firms in the formal sector. There is no standard curriculum and there is no recognized certification for this training. Women are less likely to have undergone some apprenticeship training compared to men. Less than 30% of women employed in the manufacturing sector, for example, had received or were currently in an apprenticeship programme compared to over 50% of men. Women also tend to receive apprentice training in a limited number of trades (Figure 3.3). Indeed about three quarters of the women who have either undergone apprentice training or are currently apprentices received training in three trades. In addition, there appears to be segregation by gender. The trades learnt by women do not have a large male presence. This segregation in training can explain the industry segregation that is observed. What gives rise to this segregation? Evidence from a survey of manufacturing firms conducted in the 1990s found a high correlation between the sex of the firm owner providing apprenticeship and the apprentices. Thus women seamstresses tended to have predominantly female apprentices whilst male carpenters tended to have predominantly male apprentices. This can explain to some extent the difficulties that members of both sexes may find in breaking into non-traditional areas. Why would a female seamstress only train girls and why would a tailor only train men? Further research is needed to understand this phenomenon as a means of widening employment opportunities for women.

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Figure 3.3: Main Trade Learnt during Apprenticeship

Trade that was learnt

0.0

0.1

0.2

0.3

0.4

0.5

0.6

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75

Sector

male

female

Source: Ghana Statistical Service, Fourth Ghana Living Standards Survey, 1989/99, Accra [It is not possible to discuss the particular trades because the researchers currently do not have the Sector/Trade Skills code book].

3.127 The amount of skills training in the country is low. However, women are less likely to undertake training programmes either through the informal apprenticeship system or through on the job or other training programmes. With about half of working women in agriculture and a significant proportion of women employed in the non-agriculture sector working in micro firms the opportunities for training for women are limited compared to the opportunities for men.

3.128 The experience of countries such as Singapore, Taiwan and Malaysia show that an increase in vocational and technical training both within the formal schooling system and on the job is required if the transition is to be made from production of goods of low value added. The high incidence of illiteracy amongst workers, even in the manufacturing sector is a constraint to accelerated growth. Adult literacy programmes that improve the literacy and numeracy of the working population are critical to ensure that they can read, understand and implement written instructions. If women are to be included in a growth strategy that will transform Ghana to a middle-income economy then they have to be effectively targeted in training programmes. The content of the programmes must be re-designed to improve upon their relevance. The timing of the programmes must be reassessed to ensure that they are provided at convenient times for women and men.

3.129 Incentive programmes must be designed to encourage on the job training. Public private partnerships will have to be initiated to encourage firms provide training to their workers for their skills to be upgraded. Adult education programmes that target the working population must be designed. Targets must be incorporated within all projects and programmes to ensure the participation of women. Strategies must be designed to ensure the effective participation of women in training programmes.

3.130 The continued concentration of about half of the working population in agriculture will limit the extent of skills development in the economy. For there to be skills development, agricultural transformation that reduces the demand for labour in agriculture and an overall economic strategy that increases the demand for labour in the non-agriculture sector is required. If women remain within subsistence agriculture the opportunities for skills development for them will remain limited.

3.131 It is important to emphasize the point that Ghanaian culture is not static and cannot be used as a major explanation for the absence of change in the choices that individuals make. It is influenced, as with all cultures, through its engagement with other cultures. More entrepreneurial women will come out of

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economic systems that support enterprise and reward less social arrangements for managing production and distribution. The sum of what we have observed is that there is need for institutional arrangements that support the development of markets.

OVERCOMING THE CONSTRAINTS TO ACCELERATED SHARED GROWTH

3.132 This paper recognizes the need for a much stronger focus on the distributional pattern of growth and poverty reduction, and on inequality. The discussion of poverty reduction in Ghana usually focuses on the national level poverty headcount figures (where considerable progress has been achieved), as shown here. But the discussion seldom moves from there to consider the disaggregation that will show that sizeable groups of the population have been excluded from poverty reduction. It is acknowledged here that inequality has not been an important focus of discussion in Ghana, possibly because levels of inequality are lower than in some other Sub-Saharan African countries. However, increases in inequality carry important political risks. This issue did not feature much in the GPRS and in previous World Bank CEM. But there are two important reasons why Ghana must take the issue of reducing gaps a lot more seriously, as is indeed reflected by the current. The first is political and the second is economic.

3.133 The main political argument that may be made is that spatial inequality breeds resentment, tension and often violence. It leads to grievance in a manner that may lead to open conflict. Conflict retards growth and development in many ways, apart from holding back social cohesion and peace. Considering that Ghana finds itself in a volatile political region, West Africa, the odds that opportunists may seek to take advantage of the situation to advance personal political ambitions are high. Ghana therefore needs to aspire towards shared growth in a serious manner. There is ample evidence that many ‘ethnic’ conflicts in northern Ghana are as much about resource control as they may be over local politics. Today many northern civil society organizations advocate quite passionately on radio stations for greater equity, often using arguments based on perceived injustice.

3.134 The main economic reason for tackling the problem of spatial inequality is that overall accelerated growth and development in Ghana would be impaired by inadequate attention to northern Ghana. The three northern regions cover more than a third of the total surface area of Ghana. The area has the potential of contributing immensely towards aggregate long-term growth through its natural resources and human capital. With the appropriate infrastructure and investments, it has the potential for offering markets to several of the products of the south while supplying many of the agricultural and other needs of the south. Total output will be significantly enhanced by greater trade between north and south Ghana, and this has to be the first step in expanding the economy of Ghana. The big question becomes ‘how?’

3.135 Enhancing investment in northern Ghana calls for different policies that focus on excluded regions and groups. The north-south differential is a major issue that requires a more sophisticated and nuanced discussion. Solving it requires that the discussion must reflect the diversity of experiences within southern and northern Ghana. It must move beyond acknowledging the existence of disparities to one of discussing the harder political questions of negotiating inter-regional transfers through public expenditures and private investments.

3.136 On the gender front, the current inequality affects different socio-economic groups differently. As a result the interests of the different groups are not easy to negotiate without bringing in other factors. While there are clear areas where policy interventions may be called for in order to create a level playing field for all, such as in education, other interventions are a lot more complex to conceive common policies for.

3.137 A first step to ensuring that there is a more equitable investment programme is for the regional and gender distribution of public investments and other expenditures to be made easily available in the public domain. Greater effort must be made in budget statements to present gender disaggregated data. In many instances targets are set for numbers of persons who are expected to benefit from employment

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schemes, microfinance schemes or the supply of inputs without sex disaggregated targets being presented. Statements also sometimes report the number of actual beneficiaries of such schemes without providing a break down by sex. Ghana does not have the tradition of providing a regional break down of expenditures or revenues in budget statements either in the main text, appendices or annexes to the budget statement. Current trends towards greater fiscal transparency must also include the disaggregation of fiscal data by region and gender, where relevant. The provision of regional disaggregated data and where relevant, gender disaggregated data in the public domain is one way of ensuring that regional and gender equity issues are always a parameter in the allocation of resources and the design of policies and programmes.

Mobilizing Resources for Northern Development

3.138 What this study has documented is not a lack of interest in developing northern Ghana but the absence of strategies that allow more resources, including those from the private sector, to be made available for investment. The study identifies poor use of inputs, weak infrastructure, especially roads and irrigation, poor functioning of credit markets and poorly organized land markets that offer little incentive for market transactions. In all of these areas, earlier state and donor interventions

28 have not had the

desired impact. Indeed the evidence of both the state and donors having intervened unsatisfactorily in the past is quite well documented (Aryeetey and Cox 1997).

3.139 Northern Ghana can best be developed through targeted injection of large amounts of capital to address all of the challenges identified. It is acknowledged that there are still many state and donor sponsored projects and programs to address many of these challenges. We tend to be of the view that they remain poorly coordinated and not designed to achieve as large an impact as is required. It is essential that the different initiatives are properly coordinated as efforts to crowd in private investment. We have argued in this paper that the investment must be directed at supporting the development of trade between the north and other places. This requires considerable private participation. The state’s approach should be to bring northern Ghana closer in trading terms to the economies of neighbouring countries. Putting in place the required infrastructure becomes the key issue in public expenditures. Incentivizing the private sector to respond with complementary investments in production and distribution will be the way to generate productive employment and shared growth that reduces inequality.

3.140 The faster development of various poor districts in the north requires that greater coordination of resource mobilization and utilization has to be made. This calls for more emphasis on coordination at the regional level. In particular, incentive schemes can be designed to encourage investment in targeted locations. More effective use of the structures created through the decentralisation process may be used for this purpose. The functions of the Regional Coordinating Councils must be re-assessed and re-designed to perform the role of articulating the broader needs of the regions, designing strategies and programmes to encourage investment in the regions and acting as a pressure point on central government to be more sensitive to the regional dimensions of polices and strategies. The capacities of these councils will have to be assessed and gaps filled and capacities strengthened to perform the new functions that may be assigned to them. The experience in the past of regional based development projects in Ghana has not been very promising. However, the experience of other countries shows that regional bodies can play an important part in closing regional gaps and reducing the marginalisation of some regions. The design of new regional based strategies in Ghana must learn from the mistakes of the past and the positive international experiences that exist.

3.141 Current policies on education and skills development are not linked to the generation of employment. It is important that the pursuit of high-value agriculture in the north, as for example under the Millennium Challenge Account, is linked with training programmes for young persons. They could be 28

The Upper Regional Agricultural Development Programme (URADEP) and the Northern Regional Rural Integrated Development Programme (NORRIP) were both major donor assisted interventions intended to quicken the development of northern Ghana.

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trained in the processing of agricultural products, storage and handling, provision of support services, management, etc.

Including Women in the Growth Process

3.142 The discussion on women in agriculture in particular has revealed the heterogeneity amongst women. Whether or not a woman can have controlling rights to land depends on the norms and customs of her ethnic group, her natal family conditions, whether she is married, her age and her ability to generate income from non-farm sources. The heterogeneity amongst women must increasingly be recognised in the design of strategies to include women in the growth process. A “one size fits all” paradigm in the design of strategies will run into difficulties.

3.143 The discussion on land, suggests that where women do not have controlling rights to land continued participation in agriculture may not necessarily translate into independence or an increase in their income. Women would therefore prefer to participate in non-farm activities insofar as the incomes generated from that activity are not controlled by another person. This suggests then that the strategy of agricultural transformation should have as an integral part the promotion of non-farm activities.

3.144 Women face a more severe time burden than do men. This is because some women must provide labour on their husband’s farms in addition to their responsibilities in the care economy. The migration of young men and women to the urban areas suggests that there may be an emerging labour shortage in several farming communities. Active participation of women in agricultural transformation will require that modern practices, methods and technologies are labour saving-rather than labour absorbing. Re-allocation of resources and responsibilities within households in order to create incentives for greater risk-taking among women is essential.

3.145 The agricultural transformation process may not carry women along with it if they are not able to have controlling rights over the income generated from their labour. Evidence suggests that in some communities, for example in cocoa communities, “land rights were initially concentrated amongst men but are gradually being redistributed to women” (Takane 2002, p. 69). Does external policy have a role to play in hastening this trend as a means of securing women’s participation in a process of agricultural transformation? The evidence on security of tenure and its impact on investments is mixed. Further research needs to be conducted, beyond case study evidence, to determine if security of tenure will affect investment decisions. The review conducted in this paper suggests that further study is required on the relationship between labour availability on the one hand and output and productivity of women and men.

3.146 Current evidence reveals that there is an emerging land market, as indicated by the increasing proportion of plots that have been acquired through outright purchase. However, women will only be able to participate in this changing, even if slow, trend to private ownership of land if they have the resources to support the purchase. Again the linkage between credit and land comes into play. What financial instruments can be developed to increase the proportion of women who can purchase or rent land for commercial agricultural activity?

3.147 There is some debate as to whether land registration will improve women’s control over land. It is possible that land registration could weaken rather than strengthen the landholding rights of women. This is because “there is little possibility that a land registrar will take full account of the rights of tenants and female farmers who have been endeavouring to strengthen their land rights through continuous labour and capital investment in the farmland to win landholding rights in the future” (Takane, 2002). Land tenure reforms that separate ownership rights from use rights, e.g. through land banks as proposed by Aryeetey (2006) are likely to give women agricultural entrepreneurs greater security of tenure.

3.148 The use of modern inputs in Ghana’s agriculture is low amongst households headed by men and women. Input usage must be increased if agricultural transformation is to occur. Available evidence does not suggest a significantly lower use of inputs amongst households headed by women. More individual

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level data is required on a nationally representative basis to determine if women farmers are “excluded” in the use of inputs. However, if women are less likely to have contact with extension workers, if are less likely to be able to read and write in the language the instructions are written in and if they do not have the time or cannot afford the labour required to administer the inputs effectively they are unlikely to increase their demand for inputs. Without adequate returns to make the extra expenditure profitable neither male nor female farmers will increase the use of inputs such as fertiliser and insecticides. A conscious effort must be made to ensure that women farmers have as much contact with extension officers as do the male farmers.

3.149 A constant concern that is repeatedly mentioned is the time burden of women. This means that the terms of reference of research related to the introduction of new crops and/farming practices must extend beyond the domain of technical considerations to include an assessment of the impact of these innovations on the socio-economic context into which they are being introduced. The conditions governing the production environment and circumstances of women must be taken into account to ensure that strategies to increase agricultural production do not leave them out because of the conflicts and stress that these new crops and farming practices introduce for them.

3.150 Gender relations are dynamic and change as the status of crops change from traditional subsistence to cash crops and as new ones are introduced. What are considered as women’s or men’s crops at a point in time may no longer benefit from that description as support to the crop increases and potential income opportunities for its production expands. Thus as new crops are selected for support, as suggested in GPRS II, careful consideration must be provided to take into account the extent their introduction influences women’s participation in their production and sale, and the impact of the introduction of these crops on the time of women. In addition, the research component

3.151 The current thinking in terms of the transformation of the economy is not only the modernisation of agriculture but also the development of a vibrant ICT sector. Women’s skills are limited in range and programmes that improve upon the education and skills’ of both the school-age and adult female population is important if women are to participate in the emergence of the “new economy” in Ghana. A second look must be taken of the apprenticeship and training systems in both the formal and informal sectors to ensure that they are designed in a manner that both sexes have an opportunity to obtain the required skills.

3.152 Incentive programmes must be designed to encourage on the job training. Public private partnerships will have to be initiated to encourage firms provide training to their workers for their skills to be upgraded. Adult education programmes that target the working population must be designed. Targets must be incorporated within all projects and programmes to ensure the participation of women. Strategies must be designed to ensure the effective participation of women in training programmes.

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Aryeetey, E. and A. Cox (1997) “Aid Effectiveness in Ghana” in J. Carlsson et.al, (ed). Foreign Aid in Africa, Nordiska Afrikainstituet, Uppsala Aryeetey, E. (2006) “Toward Equitable and Efficient Land Tenure Reforms in Ghana” Paper presented at

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APPENDIX

Table 3.27: Access to Public Transport by Rural Households (%), 2003

Source: Ghana Statistical Service, Core Welfare Indicators Questionnaire 2003, Accra

Table 3.28: Distribution of Population aged 15+ by main occupation and Region

Western CentralGreater Accra Eastern Volta Ashanti

Brong Ahafo Northern Upper West Upper East Total

Professional/Technical 5.2 2.5 8.6 3.3 4.0 3.8 6.5 1.8 2.6 1.3 4.0

Administration/Managerial 0.4 0.0 2.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3Clerical 1.8 0.7 5.5 1.5 1.7 1.1 0.3 0.3 3.4 0.7 1.6Sales/Commercial 14.0 19.4 42.9 14.5 14.9 23.4 20.0 7.2 1.1 5.7 17.4Service 2.6 2.6 10.7 3.1 3.7 4.5 2.3 1.3 0.8 2.2 3.6Agriculture 58.2 58.4 5.8 59.1 63.0 48.0 60.4 84.3 80.0 86.6 58.2Production 17.9 16.5 24.1 18.6 12.6 19.3 10.5 5.0 12.0 3.5 14.9Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Source: Ghana Statistical Service, Fourth Ghana Living Standards Survey, Accra.

Public transport less than 30 mins

Western 82.76

Central 87.53

Greater Accra 91.99

Volta 71.24

Eastern 77.89

Ashanti 87.46Brong-Ahafo 71.26

Northern 44.08

Upper East 34.42Upper West 42.69

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4. POLITICAL ECONOMY

THE RESILIENCE OF CLIENTELISM AND THE POLITICAL ECONOMY OF GROWTH-SUPPORTING POLICIES IN GHANA

4.1 Ghana’s successful transition to democracy in 2000 and its accelerated growth and sound macroeconomic management have attracted significant and justifiable praise. The Africa Peer Review Mechanism (2005) report for Ghana praises Ghana as “an oasis of peace and tranquility in a sub-region perpetually in turmoil” (p. xii). However, in light of the experiences of India and, especially, China as examples of how growth can be harnessed to reduce poverty, Ghanaians are looking to accelerate growth and achieve middle-income status in the foreseeable future.

4.2 Ghanaian recent growth record is strong within the African region but falls considerably short of the experience of fast-growing Asian countries. Over the period 2000 – 2004, the annual growth of real per capita income was approximately 4 percent in India, 7.5 percent in China, but 2.7 percent in Ghana. It accelerated somewhat in 2005 to 3.2 percent and is projected to increase to 3.7 percent in 2007.

29 As the African Peer Review report also indicates, accelerating growth may require significant changes in Ghana’s policy stance across a range of issues. This paper investigates the political economy of those changes and, in particular, the electoral incentives of Ghanaian policy makers to pursue them.

4.3 One rationale for looking closely at policy reform is that past growth in Ghana may not have created an entirely robust platform for future growth. Fickle natural resource markets, for example, have played an important role in recent growth surges. Agricultural growth was 1.7 percent per year over the period 1991 to 1995, 3.9 percent from 1996 – 2000 and 5.7 percent from 2001-2005 (Bogetic, et al. 2007). Cocoa production, which grew approximately 1.1 percent in the early period, jumped to approximately 10 percent over the later period.

30 Faster, and sustained, future growth would therefore be facilitated by a variety of reforms to enhance productivity and the efficiency of investments.

4.4 Improvements to infrastructure quantity and quality are high on the list of reforms discussed in this context. Education is another. President Kufuor’s recent announcement of the Education Reform Programme, lengthening basic (and free) education from nine to 11 years, launching a large school building and model school program, and decentralizing to District Assemblies of pre-tertiary teacher hiring and firing are two indications of this. In the legal realm, insecure secure land markets make long run investments in land less attractive and raise the costs of credit for making those investments. Udry and Goldstein (2006), for example, show that agricultural productivity and investment varies significantly depending on the relationship of landholders to the traditional leaders who control land allocation and losses from the property rights attached to the cultivation of some stool lands could be as much as one percent of GDP.

4.5 Legal uncertainty also appears to stifle manufacturing investment. Of firms surveyed in 2002, 43 percent (20 percent of small firms and almost 50 percent of medium sized firms) cited credit access as one of the top three problems that they confronted. Unpredictable public sector decision making, especially in areas of tax and regulation, and uncompetitive and shallow credit markets may also deter investment: the fraction of surveyed firms that noted taxes as one of the three most important problems that they confront rose from around 4 percent in 1996 and 1998 surveys to 11.7 percent in 2002 (Teal, et al. 2006 p. 47). Reforms in these areas embrace the broad set of governance concerns (democratic and political, economic and corporate) described in the Africa Peer Review Mechanism report (2005).

29

IMF Key Economic Indicators, February 11, 2007. 30 Table 2.3 p. 10, The World Bank (2005a). Ghana: Natural Resources Management and Growth Sustainability. Country Department 10, Africa Region.

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4.6 The evolution of Ghanaian policies over time has exhibited notable improvements in some dimensions, but stagnation in others. As the first section of this paper documents, Ghanaian policy performance on a range of policy issues falls short of what one might expect, when comparing it to other countries. The pattern of policy performance, relative to other countries and over time, is not random, however. It appears to respond to systematic electoral incentives that confront Ghanaian policy makers. The analysis of these incentives is the focus of this paper.

4.7 In particular, the analysis here describes the strength of political incentives in Ghana to pursue policies that benefit narrow groups in society (clientelist policies) at the expense of policies in the broad public interest. Clientelist policies are usually inconsistent with the broad reforms of public policy required to sustain accelerated growth. As the incentives of politicians to use clientelist appeals to gather votes increase, they prefer policies that increase local infrastructure to policies that improve education; they prefer policies that increase infrastructure quantity for targeted groups over those that improve quality (such as maintenance) for all groups; and among policies that improve education, they prefer those that increase the number and compensation of teachers rather than those that improve teaching quality (such as adjusting teacher compensation to the educational progress of their students).

4.8 The point of departure of this analysis is that clientelism is a political strategy that politicians choose when they have no other cost-effective way to mobilize electoral support. This paper documents several reasons for the political preference for clientelist strategies in Ghanaian politics. These are all political market imperfections such as those described by Keefer and Khemani (2005). One is the inability of Ghanaian political competitors to make credible programmatic promises to voters. Another, related imperfection in Ghana is low levels of voter information about government decision making and its effects. A third is that voters appear to feel strongly about issues unrelated to growth. Evidence below suggests that voter preferences over democracy and voter opinions about the position of their social group in the country have a significant impact on their partisan preferences. To the extent that this is the case, party performance on the economy is less important electorally, once again diluting incentives to pursue a pro-growth agenda. Each of these could have “deep” roots in history or society. Colonial powers might have suppressed political parties. Patron-client relations could be particularly embedded in society, making clientelist appeals by politicians more cost-effective. Ethnic grievances could be rooted in events long past. These “deep” causes of distortion in political competition are not the focus of attention here. However, the analysis provides insight on where to take future investigations that aim to identify which deep causes might matter more.

4.9 Observers note that some East Asian countries have grown quite rapidly despite significant clientelism. In fact, however, the most successful East Asian countries have strong institutional counterweights to the unchecked exercise of clientelism. China, for example, has a heavily institutionalized ruling party that imposes discipline on party members and constrains top leaders from acting arbitrarily; both are key to attracting investment (see Gehlbach and Keefer). In democracies, elections are supposed to provide the discipline that constrains clientelism. This paper focuses on reasons why elections can fail to do this.

4.10 After documenting the role of political market imperfections in undermining incentives to pursue better policies in Ghana, this paper turns to the question of why Ghana performs far better than many other young democracies that also exhibit predominantly clientelist political incentives. Four distinctions between Ghana and other young democracies seem particularly important in this regard.

• First, compared to other young democracies, there is no dominant single party in Ghana; the existence of at least two parties that are capable of winning office is essential to electoral accountability.

• Second, although parties are not programmatic, they are more institutionalized – exhibit greater party discipline – than is the case in many other countries that lack programmatic parties.

• Third, relative to influential “patrons” in other countries, traditional leaders in Ghana are more constrained from below, by their “clients”, and from above, by bodies such as the House of Chiefs, from acting arbitrarily or pursuing their self-interest.

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• Finally, fourth, the prospects of extra-institutional intervention (e.g., from the military), to cut government tenures short, which have a destructive effect on the policy incentives of governments, seem to be attenuated in Ghana relative to other countries where clientelist political incentives predominate.

PREVIOUS ANALYSES OF THE POLITICAL ECONOMY OF ECONOMIC POLICY IN GHANA

4.11 The analysis in this chapter is related to and advances a large literature on the political economy of policy making in Africa. Nicholas van de Walle (2001) argues that policy outcomes in Africa result “from the interaction between the clientelistic needs of neopatrimonial states, the extremely low capacity of these state structures, and the dominant economic ideas among policy elites in the 1960s and 1970s” (p. 16). This paper carefully summarizes Ghanaian policies across a wide range of issues, compares them to those of other countries and other democracies, and argues that the best explanation for them is, indeed, clientelism.

4.12 In addition, the reasons why politics in Ghana are clientelist are explored here. The analysis gives particular emphasis to the inability of politicians to make credible commitments to citizens regarding broad, programmatic policies, compelling them to make credible, specific promises to small groups of citizens for whom that is possible, resulting in clientelist policies. Van de Walle (2003) makes a related point about African politics generally, that “programmatic and ideological cleavages have not shaped political competition [in Africa] nearly as much as ethnic and regional factors” (p. 298).

31 This

paper uses microdata from the Afrobarometer survey to categorically demonstrate the inability of political parties in Ghana to make credible, programmatic appeals regarding economic policy.

4.13 For reasons similar to those identified by van de Walle, in a Drivers of Change Policy Brief on Ghana commissioned by DFID (UK Department for International Development), Booth, et al. (2005) argue that the vigorous competition for votes in democratic Ghana has not reduced, and may have accentuated, the attraction to politicians of making clientelist appeals for political support (p. 3). They conclude, therefore, that “Under current conditions [of neopatrimonialism], there are serious doubts about the willingness of politicians to change their approach to [reform of the public service and to the pervasiveness of clientelism in business-government relations]” (p. 1). This paper offers the first systematic evidence of these claims, and links them to the underlying nature of political competition in Ghana.

4.14 Another key contribution of this paper is to link issues of information and citizen education to citizen electoral behavior and, ultimately, to politician incentives. A recent World Bank report on social accountability (World Bank, forthcoming) underlines the imperfect information of average Ghanaians with regard to such important government decisions as those related to spending, and with regard to their civil and political rights in general is a key theme of a recent report on social accountability in Ghana (World Bank, forthcoming). The analysis here supports the conclusions of that report, with more, and more direct, evidence on the impact of information on both intermediate political indicators (the extent to which parties make programmatic promises) and on final policies.

4.15 The distortionary role of special interests, cronies or insiders in policy making is also a frequent focus of political economy analyses of Africa, and Ghana specifically. Booth, et al. (2005) point to the significant influence of (unidentified) special interests opposed to reform, despite the increased pressures on politicians to gather support from all citizens. Their influence, deriving from their personal relationship with policy makers, their financial support for political campaigns, or their ability to mobilize voters to support one party or the other, has given special interests continuing ability to block reform.

31 The reason that programmatic parties are missing is not well-understood. Most observers attribute their absence to the peculiarities of colonial rule and the manner in which colonial authorities obliged domestic political competitors to organize. It is worth noting that programmatic parties are, in democracies with and without a colonial past, relatively rare.

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4.16 The analysis here explains why special interests are so influential by underlining that clientelism and special interest influence are two sides of the same coin: in environments where politicians are unable to attract large fractions of the electorate with broad policy promises, they must rely instead on the promises they can make to narrow slices of the electorate. These may be special interests as they are traditionally-defined (organized groups of workers and firms, cronies of political officials), but are just as likely to be residents of a particular village or members of a certain clan.

4.17 The question considered in this paper, therefore, is why politicians do not see more electoral benefit in pursuing those policies that are most likely to increase voter welfare (e.g., improved basic services (water and sanitation, health), governance and education, for example)? Why is it that special interests and patron-based appeals remain strongly influential in Ghana, even as the advent of competitive elections endow Ghanaian citizens with better tools to hold governments accountable for the policies they enact?

4.18 To answer these questions, the next section of the paper documents the pattern of policy adoption in Ghana relative to policies in other countries. The paper goes on to describe the political incentives of politicians, using the Afrobarometer survey to identify what citizens look for in politicians and evidence of the claims that politicians actually make when they campaign. This analysis concludes that electoral competition in Ghana is less effective than it could be in deterring politicians from catering to narrow interests and encouraging them to be more attentive to broad-based, growth-promoting reforms.

POLICIES FOR GROWTH IN GHANA

4.19 It is well-known that Ghana’s policy record is superior to the sub-Saharan African average. However, markets for capital are global. Despite its fine performance relative to neighboring countries, therefore, relative to all countries or all countries with competitive elections, Ghanaian policies are at best average and frequently below average. Much debate surrounds the question of which policies are key to growth and development. Few disagree, however, that legal security, education, a pro-active and high quality bureaucracy, and a business environment free of barriers to entry are important.

4.20 More importantly, policies still reflect an emphasis on easily measured and/or easily targeted quantity improvements, consistent with clientelist political incentives, and less emphasis on quality improvements. The irony is that, as Bratton (2007) points out, citizens across Africa are at least as dissatisfied with the quality as with the quantity of health and education services; political market imperfections, though, turn out to impose significant obstacles to quality improvement.

4.21 The Drivers of Change (2004) report on Ghana finds little evidence that political incentives favor improved public sector financial management or better public sector performance generally. A more quantitative comparison across countries supports this conclusion. Ghana performs significantly worse on governance (corruption, bureaucratic quality or the rule of law) than in other countries with competitive elections. Using data from the International Country Risk Guide and compared to other countries with competitive elections, corruption in Ghana is moderately (.31) worse on a six point scale where the average score is 2.5; bureaucratic quality is .48 worse (almost one-half of a standard deviation) on a 6 point scale where the average is 2.2; and the rule of law is substantially worse (1.48, more than one standard deviation) on, again, a six point scale, where the average score is 3.8.

4.22 A more mixed picture emerges from comparisons of the legal and regulatory environment for business. Booth, et al. (2005) argue that “Both organized business associations, such as the Association of Ghana Industries, and individual entrepreneurs generally prefer to cultivate politicians with a view to protection or other special treatment, not to exercise pressure for better general conditions for investment” (p. 6).32 Consistent with this, the World Bank’s Doing Business Indicators in 2004 suggest that the business environment is friendlier to incumbents than to potential entrants: the costs of 32

Hart and Gyimah-Boadi (2000) are somewhat more optimistic about business associations, but still emphasize that of all the associations, only two – recently founded and smaller than the others – are notably identified with efforts to liberalize the economy, the Ghana Union Traders Association (GUTA) and the Ghana Association of Women Entrepreneurs (GAWE) (p. 13).

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firing a worker and enforcing a contract are lower in Ghana; the costs of starting a business are higher. Start-up costs have dropped, from 106 percent of per capita GDP in 2003 to 79 percent in 2005, but they remain high.

4.23 This policy record is consistent with clientelist political incentives. The rule of law and bureaucratic quality are non-targeted goods that benefit all citizens and are under-provided. Similarly, though less clearly, narrow well-defined groups – incumbent firms – appear to enjoy a friendlier regulatory environment: they can easily adjust the size of their workforce in response to negative shocks, and they can easily enforce contracts.

4.24 Several aspects of fiscal policy are relevant for economic development generally, even if not directly related to private investment incentives. In simple comparisons to the average of all other countries or all other countries with competitive elections, Ghana exhibits significantly less secondary school enrollment and tax collection, and significantly greater spending on public sector wages and public investment. Gross secondary school enrollment is 22 percentage points less in Ghana than in other democracies; public sector wages (a rough measure of patronage employment) is four percent of GDP higher in Ghana than in the average democracy. The schooling and wage comparisons remain large and significant even when controlling for other country characteristics.

4.25 Again, this record is symptomatic of a political system in which electoral incentives with respect to targeted and visible government services, such as jobs in the public sector or targeted infrastructure projects, are high, whereas incentives to provide quality public goods and services are relatively weak. Such political systems are frequently described as clientelist and, indeed, are the policy consequences of pervasive clientelism that other analysts (e.g., DFID Drivers of Change report on Ghana, 2004).

4.26 These comparisons underline two phenomena. First, political incentives in Ghana to provide public goods that benefit all citizens, and particularly a regulatory and governance environment conducive to faster economic growth, have been relatively weak. Second, though, incentives to provide targeted government services (those benefiting narrow groups in the society) appear to be robust and strong relative to comparator countries. Explaining this pattern of policy performance in Ghana is the subject of the remainder of this paper.

THE EFFECT OF ELECTIONS ON POLICIES IN GHANA

4.27 Two questions are paramount in investigating the underlying political incentives that drive Ghana’s policy record. The first, examined in this section, is whether the introduction of competitive elections has affected public policies. The second, investigated in the next section, is the extent to which differences in political market imperfections explain divergences between Ghanaian and other democracies.

4.28 The 2000 elections were a watershed for Ghana, in which electoral competitiveness and the credibility of elections were cemented by the constitutional replacement of a sitting government by the opposition.

33 Competitive elections might be expected to increase political accountability to citizens,

but whether to all citizens or to targeted groups of citizens depends on the role of clientelist political strategies in electoral competition. Van de Walle (2001) argues that in many African countries “democratization has had little impact on economic decision making, because the new democratic regimes remain governed by neopatrimonial logic (p. 18).” The evidence of policy movement in Ghana after 2000 provides some support for the argument that clientelist pressures remain significant.

4.29 This is immediately evident if one simply looks at the evolution of policy outcomes in Ghana before and after the 2000 elections, as in figures 4.1 and 4.2. Ghanaian governments have become markedly more sensitive to distributional issues: tax revenues doubled from 1990 to 2005, to 23.8 percent in 2005, though they are projected to have fallen to 21.5 percent in 2006. However, the evidence indicates as well that pressures to spend these resources on targeted groups of voters have remained the

33

Political liberalization began in 1992. Presidential elections took place in 1996; but fully competitive elections did not occur until 2000.

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same or increased relative to the pre-democratic period. Figure 1 shows that secondary school enrollment has increased over the period, but the percentage increase is small.

34 In contrast, since dropping

dramatically in 2002, public investment has steadily risen to the high levels that are historically characteristic of public investment in Ghana. Meanwhile, the public sector wage bill increased significantly as a percentage of national income, from 4.3 percent of GDP in 1990 to 6.7 percent in 2001, and to a projected 9.3 percent in 2006. This leaves little room in current expenditures either for operations and maintenance, increasing the risk that substantial infrastructure investments will have a much shorter lifespan than expected, or for adequate supplies to provide quality basic services (e.g., education, health etc).

4.30 Figure 4.2 depicts the evolution of governance indicators (those from International Country Risk Guide, the only ones available over the period). These show no change or, indeed, a deterioration, following the introduction of competitive elections. The decline reported in the ICRG and recorded in Figure 2 has several possible explanations. One is that electoral competition exacerbated the violence of conflicts regarding chiefly succession.

Figure 4.1: Education and spending patterns in Ghana, 1989-2006 (World Development Indicators, IMF Key Economic Indicators)

0%

5%10%

15%20%

25%30%

35%40%

45%

1989

1991

1993

1995

1997

1999

2001

2003

2005

gross secondary enrollment

public investment/GDP

central government wage bill/GDP

tax revenues/GDP

34

Enrollment data are sparse; education spending data are sparser still.

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Figure 4.2: Governance in Ghana, 1989-2006 (International Country Risk Guide)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

1989

1991

1993

1995

1997

1999

2001

2003

2005

corruption rule of law bureaucratic quality

4.31 As Nugent (2001c) notes, succession is an intensely political issue and contenders seek support for their “candidacy” from higher level government officials. Any within-clan negotiated settlement regarding chiefly succession is not credible as long as one group believes that it can get a better deal should a different government come to power. Violence is more likely when negotiated settlements are less credible. When high level officials change little, chieftaincy disputes, once resolved, are more likely to remain settled. Competitive elections may have introduced uncertainty into the permanency of these agreements to resolve these disputes, creating incentives for participants in chiefly disputes to persist in their claims and, in some instances, to engage in violence. Some evidence of this emerged following the 2000 elections, in a serious chieftaincy dispute in the North (Nugent 2001c).

35

35

Though such disputes are regular occurrences, and were a focus of political campaigns in the 1996 elections, the level of violence associated with the most recent northern disputes was unusually high.

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Table 4.1: Ghanaian policy versus the rest of the world, 1991-1999 and 2000-2004

No controls Controls

Dependent variable: Ghana_Pre Ghana_Post Ghana_Pre Ghana_Post

Corruption (ICRG: higher less corrupt) (0-6)

-.50 (.00)

-.28 (.00)

.20 (.13)

.55 (.00)

Bureaucratic Quality (ICRG) (0-6)

.48 (.00)

-.35 (.00)

1.37 (.00)

.66 (.00)

Rule of law (ICRG) (0-6) -.87 (.00)

-1.96 (.00)

.03 (.84)

-.92 (.00)

Gross secondary school enrollment

-27.28 (.00)

-33.14 (.00)

-8.27 (.01)

-15.42 (.00)

Public sector wages/GDP -.02 (.00)

.02 (.00)

-.03 (.00)

.01 (.49)

Tax revenues/GDP -2.89 (.00)

2.07 (.01)

-1.19 (.28)

5.20 (.00)

Public investment/GDP .04 (.00)

.03 (.00)

.03 (.00)

.02 (.00)

Note: Each row presents two regressions. The first, “No controls”, is the simple regression of the variable in the left column (e.g., corruption) on two dummy variables, Ghana_Pre (equals one if the observation is from Ghana prior to 2000), and Ghana_Post (equals one if the observation is from Ghana after 1999), controlling only for the year of the observation. “Year” is included because, among other reasons, subjective governance indicators are known to exhibit secular trends. The second, “Controls” repeats the exercise, controlling in addition for an Africa dummy (excluding Ghana); total population; total land area; ppp-adjusted income per capita; the percent of the population that is young; and the percent that is rural. All comparisons include more than 100 countries and 1000 total observations. p-values based on cluster-adjusted standard errors are reported.

4.32 The other possible explanation for the ICRG decline reported in Figure 4.2 is the transition to democracy itself, a pattern that has been evident in other transition environments. Both Mexico following the 2000 elections and Indonesia after 1997 and the fall of Suharto exhibited similar declines in their governance indicators. The regularity of these declines hints that they may be transitional phenomena. Bäck and Hadenius (2007) argue that a decline in administrative capacity is a regularity of transitions, brought about by the weakening of top-down control before bottom-up democratic accountability mechanisms are consolidated.

36

4.33 The evolution of Ghana’s relative position before and after the competitive elections of 2000 also provides evidence of a significant shift towards more “responsive” politics, but responsive especially to the particular demands of narrow or targetable groups of citizens. This can be seen in Table 1, where the evolution from 1991-2004 of policy variables ranging from corruption to taxation are explained as a function of a number of control variables and of two Ghana variables: Ghana_pre takes a value of one for all Ghana observations from before 2000 and zero otherwise; Ghana_post is one for all Ghana observations after 1999 and zero otherwise. The comparison made in Table 4.1, therefore, is between Ghana’s position, relative to that of all other countries, prior to the competitive elections of 2000 and subsequent to them.

4.34 Regardless of whether one controls for numerous other determinants of these policy outcomes, prior to 2000 Ghana was a significant negative outlier with respect to secondary school

36

The declines may also reflect governance progress. For example, the success of large incumbent firms may have depended on special arrangements reached with earlier governments and which were not available to all firms. With the advent of competitive elections, these arrangements may not be politically sustainable. The uncertainty surrounding these arrangements may be reflected in reduced governance scores, though in the longer-run, their disappearance is strictly good for governance.

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enrollment and the public sector wage bill. It was a significant positive outlier with respect to bureaucratic quality and public investment. In simple comparisons with the rest of the world (i.e., not controlling for income per capita), Ghana also collected significantly lower tax revenues (nearly three percent of GDP below the average of all other countries), and exhibited significantly higher corruption and worse rule of law.

4.35 During the period 2000-2004, whether or not one controls for other determinants of policy outcomes, Ghana went from being a negative to a positive outlier with regard to tax collection, supporting the idea that competitive elections have driven policy makers to increase their efforts on behalf of citizens. However, Ghana remained a negative outlier with regard to secondary school enrollment and the rule of law, and it moved from being a positive to a negative outlier in the measure of bureaucratic quality, indicating that incentives to provide public goods and services have not necessarily increased.

4.36 On the other hand, government spending on public investment, often used to benefit targeted constituencies in the implementation of clientelist political strategies, continues to be significantly above average. Similarly consistent with persistent clientelist pressures, in simple comparisons (not controlling for factors such as incomes per capita), Ghana went from being a significantly negative to significantly positive outlier with respect to public sector wages. In 2006, the comparatively large size of the wage bill benefits less than 10% of the total labor force (Ghana CEM Aide Memoire, December 16, 2006).

POLITICAL MARKET IMPERFECTIONS AND POLICY MAKING

4.37 It is apparent from Figures 4.1 and 4.2 and Table 4.1 that many policies did not improve as dramatically as one might have expected with the advent of competitively-elected governments. One possible reason is that the pre-democratic government exhibited relatively strong incentives to serve the broad public interest. An additional explanation, however, is that distortions in the competition for votes weaken political incentives to provide broad public goods. Keefer and Khemani (2005) emphasize three such distortions, or political market imperfections: the credibility of political competitors, voter information, and social polarization.

4.38 All of these affect the ability of voters to hold politicians accountable for broad public policies, such as secure land, better education, the rule of law, or deep credit markets. In particular, all of these affect the ability and incentives of political competitors to make credible promises about broad public policies. In other words, if the election of 2000 had been conducted between political competitors able to make credible promises to all voters about broad public policies, if all voters had been fully informed, and if social polarization had had no effect on voter assessments of their political alternatives, we might expect public policies following the election to have demonstrated a marked increase in responsiveness to all citizens and a marked decline in clientelist policies appealing to narrow constituencies.

Political market imperfections: Credibility and policy

4.39 Credibility is a rare but important political commodity. The credibility of electoral promisees demands that political competitors bear a reputational or other loss if they do not fulfill them. Politicians who have a limited horizon (e.g., because of impending military coup), or who have no reputation for pursuing particular policy directions, have less to lose if they fail to fulfill costly promises. Individual politicians who cannot implement policy promises without securing the collective agreement of multiple other politicians are also unable, individually, to make credible promises about those policies. 62 percent of all 25,000 respondents to the 2005 wave of the Afrobarometer survey across 18 African countries said that their politicians always make promises to get elected; 82 percent of respondents said that they never or rarely keep their promises. Ghana was one of these countries and its responses were almost exactly equal to the average response.

4.40 The lack of political credibility has a significant impact on public policy. When voters cannot infer credible candidate positions on issues of broad public concern, they have strong incentives to base

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their electoral choices on two criteria: what is the past performance of the candidate? and does the voter have a personal connection with the candidate that makes credible the promise of narrow government benefits, for example, in the form of jobs or public works? Both weaken political incentives to pursue policies in the broad public interest.

4.41 When voters rely on the past performance of incumbents, voters can threaten incumbents with expulsion, but not if challengers credibly promise to do better (challengers are not credible, either), but rather only if incumbents fail to achieve quite modest performance targets. Ferejohn (1986) was the first to demonstrate that using past performance as a guide to future voting in an environment where political challengers are not credible offers voters weak leverage to secure improved performance.

4.42 The other response to the lack of broadly credible policy promises is to rely on narrow promises between politicians and small groups of constituents who can believe their promises (as between patrons and clients). Even when political parties cannot convey credible policy stances to voters, individual candidates sometimes can. Keefer and Vlaicu (2006) argue that this results in strong incentives to provide targeted goods (to those few voters to whom individual candidates can make credible promises) and weak incentives to provide public goods (which benefit all voters, including those who do not believe the candidates’ promises). These effects are particularly strong when national political success requires the cooperation of local “big men” or patrons, who are able to make credible promises to local groups of voters.

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4.43 Electoral promises are not credible unless political competitors bear a reputational or other loss if they do not fulfill them. Politicians who have a limited horizon (e.g., because of impending military coup), or who have no reputation for pursuing particular policy directions, have less to lose if they fail to fulfill costly promises. Individual politicians who cannot implement policy promises without securing the collective agreement of multiple other politicians are also unable, individually, to make credible promises about those policies.

4.44 Programmatic political parties – those with broad policy reputations – can solve these problems. However, as Keefer and Vlaicu (2006) argue, despite the electoral payoffs to programmatic reputations, parties may not have incentives to make the investments necessary to build them. There may be no programmatic legacy (e.g., because of the practices of colonial powers). The electoral payoffs to dealing with patrons in society (e.g., traditional leaders) may be greater than building up a reputation directly with voters. Moreover, investments in reputation-building may require foregoing clientelist payoffs to narrow constituencies. Disciplining intra-party demands for pork and clientelist payoffs in order to develop a reputation for regulatory probity, service delivery efficacy and macroeconomic prudence creates strains within the party and raises the threat of defection. Defection potentially weakens the party faster than party leaders can develop a reputation for broad policy accomplishment.

4.45 Actual policies in Ghana reflect the tendencies that Keefer and Vlaicu (2006) predict will emerge in democracies in which political competitors are less able to make broad credible policy promises. As the earlier discussion and Annex 1 make clear, Ghana spends far more than average on targeted expenditures, such as government employment and public investment, but is average or below average in the provision of public goods, such as the rule of law, the regulatory environment for new investments and firms, and the quality of education, and in the lengths it goes to push back corruption.

38

37

Charismatic national candidates may also have a large role to play in settings where political parties are non-programmatic and non-credible. However, because charismatic appeal is usually not based on a record of development-related policy achievements, electoral competition between charismatic leaders also provides a weak basis for increasing political incentives to provide public goods. 38

The increase in public and the overall investment rate since 2000 has been consistent with the accelerated economic growth. This suggests either that investments have had positive returns, or that they have provided simply a strong fiscal stimulus to the economy. In either case, there are indications that efficiency and maintenance of public investments and their choice as well as geographical distribution can be improved.

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The discussion below offers other, more direct evidence that Ghanaian parties are not able to make credible policy promises.

Political market imperfections: Information and the political salience of non-economic issues

4.46 The reputational loss that politicians bear if they renege on their promises is irrelevant if citizens are uninformed about political promises and about whether incumbents fulfill those promises. It is easy to see that if voters are uninformed on either count, elections provide a weak vehicle of accountability. If voters do not know what politicians are promising, they cannot express an electoral preference for the candidate that promises to improve their welfare the most. If they cannot monitor whether promises are fulfilled, the promises are themselves irrelevant: incumbents have no reason to exert effort to fulfill them. The information difficulties of citizens explain in part why political competitors in many democracies prefer to make concrete, easily monitored promises, for example regarding infrastructure, rather than less tangible and less easily monitored promises, such as those to improve the quality of education.

4.47 Competitive elections also do less to persuade politicians that they should pursue broad public goods that benefit all citizens when citizens care deeply about issues unrelated to broad public goods (or economic policy) or when society is polarized. For example, as the Ghana case shows, parties may differ in their association with democratization (or independence); if voters feel strongly about either of these, they will be less demanding with regard to the economic performance of governments. Social polarization is more complicated. It may mean that groups of citizens would rather suffer a welfare loss than see some other groups in society benefit. In these cases, citizens vote for parties depending on which side of the social cleavage they stand, not based on their economic performance.

4.48 Polarization can be both a cause and a consequence of the inability of politicians to make broadly credible promises. When society is polarized, the level of trust among social groups is low. In particular, political competitors from one group cannot make credible promises to members of other groups. In addition, non-credible political competitors may find that appeals to members of their social groups, at the expense of other social groups, are the best way to make credible appeals to any voters at all. In this case, polarization is a byproduct of a lack of political credibility; in the former case, it is a potential cause of it.

4.49 In all three areas, one finds an explanation for the particular pattern of Ghanaian policy performance documented above, and for the slow evolution of policy following the introduction of competitive elections in 2000. Ghanaian voters are not well informed about political promises or policy contributions to their welfare. Political parties are weakly institutionalized and enjoy limited programmatic reputations among voters. And social polarization is a continuing concern. As a consequence, citizens are able to hold elected governments in Ghana accountable for pork and private, targeted goods. They can also hold them accountable for overall performance, albeit only loosely (Ferejohn 1986). They cannot, however, rely on credible promises by challengers as a way to leverage better incumbent performance on broad policy issues.

THE ABSENCE OF PROGRAMMATIC PARTIES AND THE INABILITY OF POLITICAL

COMPETITORS IN GHANA TO MAKE BROADLY CREDIBLE POLICY PROMISES

4.50 The findings reported below conclude that political parties in Ghana are not programmatic and political competitors are not able to make broadly credible promises to citizens. This conclusion contrasts with the weight of scholarly opinion, which argues that Ghanaian parties are programmatic with regard to economic policy. Based on the historical record, observers conclude that the major parties in Ghana have clearly divergent stands on broad economic policy issues. The NPP, they argue, is a part of the anti-Nkrumah tradition of J.B. Danquah and President Kofi Busia, typically viewed as the party of business (or at least, of successful businessmen, or “big men”) (Nugent 1999, 290). The NDC, on the other hand, began as a revolutionary, socialist party that rapidly became more populist and

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“developmental” (oriented towards public works).39

The Economist Intelligence Unit (2006, p. 10-12) describes the NPP as the party of the market and of business, opposed to interventionist policies of Ghana’s post-independence leader, Kwame Nkrumah and virulently opposed to the aggressive anti-business policies followed by the Rawlings government immediately after taking power in 1981. The NDC, in contrast, traditionally favored more significant government intervention in the economy, but lost a definite policy identity as a result of major liberalizations that began under the Economic Recovery Program (ERP) around 1983.

4.51 While these descriptions may accurately depict the policy preferences of top party leaders over time, they do not offer information about specific programmatic stances of parties themselves, nor about whether citizens actually believe them. Based on their survey of 690 voters in 2003, Lindberg and Morrison (2005) argue that partisan preferences differ by education, the rural-urban divide, income and occupation, the same dividing lines that one observes in mature democracies. They also conclude that Ghanaian parties do convey programmatic messages to voters.

4.52 There are several reasons to question the conclusion that Ghanaian parties are programmatic, however. First, the policy choices described above are inconsistent with the broad credibility of political competitors. On the contrary, the pattern of policy choices in Ghana documented earlier, giving emphasis to the provision of private or targeted goods and to rent-seeking at the expense of public or non-targeted goods, is uniquely consistent with the inability of political competitors to make broadly credible policy promises, as Keefer and Vlaicu (2006) argue. Second, previous analyses have not specifically linked the policy preferences (as opposed to socio-economic characteristics) of respondents explicitly to their partisan preferences. The information presented below does this. In particular, information from various sources supports the conclusions that the supporters of different parties do not hold different views on key programmatic issues; political campaigns are almost devoid of programmatic content; and money is enormously important in Ghanaian politics.

Supporters of different parties advocate similar views on programmatic issues

4.53 If parties are programmatic in a country, we expect party supporters to exhibit different preferences regarding “programmatic” issues. If supporters do not, parties have wasted resources in developing a programmatic reputation regarding policies that voters do not care about; or they have not invested in a programmatic reputation. In mature democracies, for example, where political parties tend to be strongly programmatic, it is usually the case that voter support for parties can be predicted by voter preferences on those issues. In countries where parties do not offer credible policy differences on economic policy – or where such policy differences are not salient or relevant for voters – an association between voters’ economic policy preferences and their partisan inclinations is less evident.

4.54 For example, in the United States, in a survey of 2,369 voters in February 2002, Republican and Democratic voters expressed significantly different preferences regarding tax cuts that were consistent with their party affiliation. About 55 percent of Republican-leaning respondents said tax cuts should be made permanent and 12 percent said they should be repealed. In contrast, 12 percent of Democratic-leaning respondents said they should be made permanent and 31 percent said they should be repealed. Similarly, 70 percent of Republican-leaning voters described themselves as pro-business and only 30 percent as not being pro-business. Democratic-leaning voters split 50-50.

40

4.55 The Afrobarometer 2002 survey of Ghana also provides no support for the argument that Ghanaian parties are able to make credible programmatic promises to voters. The survey probed 1200 respondents about their party preferences and also a about their opinions about a market-based economy. A large plurality favored a free market. About 10 percent of respondents (126 out of 1200) agreed with the statement, “For someone like me, it does not matter what kind of economic system we

39 Parties in the tradition of Kwame Nkrumah, also with a more left-leaning ideology, persist as well, but none approaches the two main parties as a political force; in the 2000 elections, the two main parties won 93 percent of the presidential votes and all but eight parliamentary seats (Nugent 2001, 422). 40

Pew Research Center for the People and the Press. Survey of February 2002. http://people-press.org/dataarchive/

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have.” Thirty percent (372) agreed with the statement, “A government run economy is preferable to a free market economy” and half (612) agreed with the reverse statement, “A free market economy is preferable to a government run economy.”

41

4.56 These responses were nearly the same across supporters of different parties, contrary to what one would expect if the parties made credible programmatic promises regarding economic policy. Of the respondents who felt closer to the National Democratic Congress (NDC), 46 percent said that a free market was preferable, nearly the same as the 48 percent of respondents who favored the more business-oriented New Patriotic Party (NPP). The large number (393) of respondents who expressed no party preference likely includes some supporters of the NDC who were reluctant to admit their preference for the party with weaker democratic credentials. 60 percent of these respondents expressed support for the market.

4.57 The 2005 Afrobarometer survey did not ask this question. It did ask whether respondents believed that people should be responsible for their own success, or whether the government should bear the main responsibility. 56 percent of all Ghanaian respondents, 61 percent of NPP supporters, 57 percent of the many (403) respondents who did not express a party preference, and 44 percent of NDC supporters favored the self-reliance response, versus 47 percent of respondents in all other countries. But for two caveats, this would lead one to believe that a programmatic gap opened between the NDC and NPP in 2005 (61 percent of the NPP versus only 44 percent of the NDC favoring self-reliance). The first is that an unusually large fraction of respondents did not express a party preference in either 2002 or 2005. Survey organizers believe that they lean towards the NDC, but their answers to the self-reliance question are close to those of the NPP. Second, even the gap between 44 and 61 is small relative to other countries. Figure 4.3 summarizes the stronger divide over economic issues that seems to separate Republicans and Democrats relative to the 2002 gap between NPP and NDC supporters.

Figure 4.3: Partisan divides on the growth agenda, Ghana and the United States

0%

10%

20%

30%

40%

50%

60%

NPP NDC Republican Democratic

percent favoring markets percent favoring permanent tax cuts

Note: The figure depicts the percent of 2002 survey respondents who expressed support for the indicated party and who agreed either that the market was a preferable form of economic organization (Ghana, NPP and NDC), or that tax cuts should be made permanent (the US, Republicans and Democrats). See text.

Broad public policy issues are absent from campaign debates

4.58 The issues that are actually discussed during electoral campaigns in Ghana provide further evidence of the absence of credibly programmatic parties. Campaigns emphasize past performance and clientelist promises, and make very little reference to the programmatic stance of the party. For example, in 1996, the NDC based its presidential campaign on two pillars: the personality and probity of

41

The sympathy of Ghanaians to a market economy is noteworthy. By way of comparison, in South Africa more than 20 percent or respondents (563 out of 2400) responded that the type of economic system was irrelevant, fewer than 40 percent (910) thought that a market system was preferable, and more than a quarter (657) thought that a government run system was preferable. The market question is number 12 in the survey; answers are highly correlated with answers to questions about respondent opinions regarding government control of production and distribution of all goods and services (13A) and of individuals deciding for themselves what to produce, buy and sell (13B).

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President Rawlings; and its success in implementing public works projects (Nugent 2001a, p. 297). The NPP, lacking an incumbency track record and unable to make credible promises of its future actions, instead attempted to shape the public’s image of the performance of the NDC, highlighting (urban) public opposition to the introduction of the value-added tax; allegations of corruption; and ethnic conflict in the North (Nugent 2001a, p. 297). In 2000, the NPP again focused on the incumbent government’s performance, emphasizing the national economic “crisis”, the probity of the NDC government and continued high youth unemployment (Nugent 2001a, 418-420).

4.59 Campaigns at the individual parliamentary level demonstrate a similar emphasis on past performance and local public works. They illustrate, more importantly, that individual candidates seem to derive little or no electoral benefit from the policy reputations of their parties, as we would expect when those policy reputations are fragile or non-existent. The Center for the Development of Democracy-Ghana (CDD-Ghana) held debates for parliamentary candidates in 24 constituencies across the country in 2004 and recorded the proceedings of 15 of them in detail (CDD, no date). Of 14 NPP candidates who appeared in the 15 debates, eight made no mention at all of the national party, either its program or its past performance, despite the generally recognized success of the NPP’s first four years in government. Only five of all the NPP candidates mentioned policy issues that extended beyond the constituency; the remainder confined themselves exclusively to constituency-level policy interventions. Candidates focused instead on their individual past performance, generally their contribution to local public works projects and to targeted transfers, such as student scholarships, many funded out of their own pockets or out of the MPs’ Common Fund.

4.60 It is not unusual for local issues to loom large in parliamentary elections, even in mature democracies. What is unusual in mature democracies, but much less so in younger democracies, is for the positions of the candidates’ parties on key national issues to be of practically no relevance to electoral decision making. It is especially unusual for non-incumbents, with no track record of personal accomplishment, to ignore the party. However, of 12 non-incumbents from the NPP, only half mentioned the accomplishments of the NPP government. The NDC candidates were even more emphatic in underplaying the national party, with only three of 12 candidates mentioning the national party’s accomplishments, and only two of 12 mentioning policy issues that transcended constituency borders.

4.61 It is perhaps not surprising, then, that when programmatic promises are made, they are not viewed as binding. In 2000, the NPP did promise a leaner public administration and more focus on the private sector (Nugent 2001a, 418), as did the NDC in its 2004 manifesto.

42 Despite this, the public

sector wage burden rose substantially following the NPP victory, as Figure 4.1 illustrates. The growth of the government wage bill, despite these promises, illustrates the struggle that parties face in attempting to build a programmatic reputation for the long term in the face of short term pressures to satisfy targeted constituencies.

Money plays an inordinately large role in political campaigns

4.62 The substantial role of money in elections is a final indicator of the lack of programmatic content in electoral competition. Again, the point is not that money is absent in political competition in mature democracies, but that in Ghana, a large share of electoral expenditures appear to be handouts from the candidates to voters rather than investments in burnishing a candidate’s policy achievements or improving the party’s reputation. Lindberg (2003) provides one indication of the demand for funds for elections, based on extensive interaction with Ghanaian MPs around the 2000 elections. “All interviewees expressed great concern that the amounts they were forced to spend on personal patronage to constituents had increased dramatically as compared to previous election campaigns” (p. 130). The sums devoted to this are also relatively large. Of 73 MP candidates interviewed, 46 percent reported spending at least 2 years salary on their campaigns. Only 10 percent of those candidates who also ran in the 1996 elections reported spending as much. 57 percent reported that they devoted at least 25 percent of their total spending to personal patronage in 2000, compared to 50 percent in 1996. This is consistent with the Afrobarometer 2005 responses to the question, how often do politicians offer gifts to voters during

42

http://www.ghanaweb.com/GhanaHomePage/election2004/ndc_manifesto.pdf

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campaigns? 72 percent of Ghanaian respondents said always or often, about the same as the non-Ghanaian average.

4.63 These efforts are in addition to political efforts to provide “pork.” Constituents are aware of new school buildings, etc., but judge candidates not only on their success or likely success in providing pork, but also according to their likely efficacy in providing individual benefits. In fact, consistent with the substance of electoral debate among MP candidates, reported earlier, Lindberg’s interlocutors described the job of an MP as being entirely taken up with individual constituent concerns: every morning they confront a queue of 10-20 constituents; in their constituencies, they will spend days solving individual constituent problems and handing out small gifts. These activities crowd out legislative activity: attendance in the chamber is poor; attendance at committee meetings irregular (p. 129). This allocation of time, electoral effort and personal resources, and the government tendency to emphasize client list policies over broad public good provision are all natural consequences of an electoral environment in which broad national policy promises are not credible and have little electoral consequence.

4.64 The lack of credibility (or absence) of party programs explains the conspicuous absence of any discussion of candidate and party positions on broader funding initiatives or of distinctions among the parties with respect to broad policy issues. Instead, rational voters, and rational candidates, focus on those issues for which credible commitments are feasible. As the record of these candidate debates indicates, and consistent with the earlier discussion, those issues are precisely related to subsidies for local constituents (in this case, usually student scholarships) and the construction of local public works.

4.65 Why, though, given the electoral advantages of making credible programmatic promises, have parties failed to develop programmatic reputations? There are several possible explanations. The most important, evaluated at length below, appears to be that voters are still poorly informed about the broad national policies that governments undertake. If voters do not understand the connection between broad policies and their own welfare, or if they cannot monitor the fulfillment of promises, they cannot hold politicians accountable for broad policy performance, and politicians have no incentive to invest in a programmatic reputation.

4.66 Of course, it is possible that broad national policies simply have no effect on many Ghanaian voters (e.g., because they are subsistence farmers with scant connection either to national politicians or to markets). If voters believed national policies to be unimportant for their welfare, neither voters nor politicians would have any incentive to pay attention to national policies in their electoral decisions. This is not a plausible conjecture, however. The welfare of nearly all Ghanaians is affected by inflation, education policy, or any of the other policies depicted in Table 4.1. Even if it were the case that, for example, subsistence farmers were not affected by broad policies, the evidence reported below indicates that the substance of political debate does not seem to change across poor or rich, or rural or urban constituencies. This is inconsistent with the claim that citizen indifference explains the near irrelevance of broad-based policy promises to electoral decision making.

4.67 However, as the discussion below indicates, two other factors are at play that also affect voter behavior and political incentives to invest in programmatic reputations. First, social cleavages appear to be important; voters partisan preferences are influenced by their feelings regarding social polarization. Second, voter partisan preferences seem to be related to their attitudes regarding democracy and the democratization process in Ghana.

UNINFORMED VOTERS AND THE DIFFICULTIES OF BUILDING PROGRAMMATIC PARTIES IN GHANA

4.68 Politicians cannot easily be held accountable for performance on issues where citizens are uninformed: when citizens cannot observe government decisions or the effects of decisions on their welfare, they cannot verify whether candidates who make promises regarding these issues also fulfill those promises. The relationship to the earlier credibility discussion is immediate. For example, in the face of imperfectly informed voters, parties are less likely to invest in broad programmatic reputations.

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4.69 We cannot observe information effects directly, since we have no data that measures the level of citizen information directly. There are, however, two strategies for inferring indirectly the effects of voter information. One is to examine the effects of citizen education on voter behavior and government policy choices. Uneducated citizens are less likely to know the lines of accountability from citizens to politicians (e.g., basic civics); they are less to be able to draw implications for their own welfare from broad government policy decisions; and they are less likely to have access to information sources that are most likely to convey the information needed to hold government accountable for broad policy performance. The other strategy is to examine constraints on information markets: the characteristics of media markets and government policies towards the collection and dissemination of information.

Education, voter information and party incentives to invest in programmatic (broad policy) reputations

4.70 Ghanaian voters are less exposed to the usual sources of information about government policy and are less educated. Only 41 percent of Ghanaian respondents to the 2005 Afrobarometer surveys (2002 and 2005) had more than primary education (about the same as all Afrobarometer respondents, but much less than more than about 75 percent of South African respondents). Uneducated voters are less likely to receive information that would enable them to hold governments accountable. For example, among Ghanaian respondents to the 1999 Afrobarometer survey, Bratton, et al. (2001) find that only 19 percent of voters with no education could identify the Economic Recovery Programme (ERP), which had guided the government’s policies for more than 16 years. Among those with up to 10 years of education, 36 percent could identify it; among those with more than ten years of education, 66 percent could. Across all countries surveyed by Afrobarometer in 2005, more educated respondents were much more likely to answer correctly when they were asked whether the government had a policy of offering free primary education or free health care.

43

4.71 Similarly, Ghanaians are less likely to read the newspaper. Newspaper circulation is a variable that is frequently used to gauge citizen information and less-educated citizens are far less likely to get their news from newspapers. However, according to the 2005 Afrobarometer surveys in the two countries, 64 percent of Ghanaian respondents reported never getting news from newspapers, compared to 26 percent of South African respondents. Instead, 67 percent of Ghanaians rely on the radio, compared to 78 percent of all Afrobarometer respondents and 57 percent of South African respondents. This matters because media (radio, television) differ in what and how much they report. Phone-in programs, for example, dominate the Ghanaian airwaves, offering (arguably) less information to listeners about government performance than they might get from reading the newspaper.

4.72 Not surprisingly, education appears to have a substantial effect on how citizens receive information: less educated voters are less likely to read the newspaper. The role of education is crucial to disentangle. To the extent that education drives the acquisition of information, for example, interventions to strengthen newspaper markets will have less effect on government accountability when education is low. In fact, it does not seem to be the case that poorer citizens are both less likely to be educated and to be able to afford a newspaper. If one controls for a measure of assets (a count of respondents’ answers to the questions: do they have a radio, television, bicycle, motorcycle and/or car?) and the frequency with which respondents receive news from the radio (to control for possible substitution effects between radio and newspapers), education is still by far the most significant determinant of whether respondents read newspapers. This is true in both Ghana and South Africa. Table 4.2 summarizes these results.

44

43

The Ghana responses were similar to the overall responses on education. However, more educated Ghanaian respondents were less likely to identify government health policy correctly. This is difficult to explain, but almost surely relates to recent large reforms in health policy in Ghana. 44

The same results emerge using 2002 Afrobarometer data, where a measure of household income is available. They are also robust to controlling for respondent interest in public affairs, whether in 2002 or in 2005.

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Table 4.2: Determinants of newspaper readership, Ghana and South Africa, 2005

Dependent variable: How often do you get news from the newspaper? (categorical, 0-4)

Ghana South Africa

Education (categorical, 1-9) .29 (.00)

.32 (.00)

Household assets (sum of 5 assets) .18 (.00)

.31 (.00)

Radio news (categorical, 0-4) .06 (.03)

.24 (.00)

R2 .25 .27

N 1178 2375

Note: Least squares estimates, robust standard errors. p-values based on cluster-adjusted standard errors are reported. Constant not reported.

4.73 Even if the general level of information and education in a country were low, politicians could still find it advantageous to invest in a programmatic reputation to appeal to educated and informed voters if a sufficiently large fraction of citizens were educated. This would entail costs – it would, for example, curtail their ability to make clientelist appeals to uneducated voters when those appeals undermined their ability to carry out their programmatic promises. These costs would be worthwhile only if the fraction of educated voters were sufficiently high. This does not seem to be the case in Ghana, however.

4.74 One way to see that political parties in Ghana have not invested significantly in reaching educated voters with credible programmatic appeals is ask whether the partisan preferences of more educated Ghanaian voters are more affected by their beliefs regarding the role of the state. If parties are investing in appeals to more educated voters, we would expect those voters to differ in their partisan preferences depending on whether they are more or less sympathetic to state intervention in the economy. In fact, the contrary is true. Sympathy for a market-based economy (in the 2002 Afrobarometer survey) and agreement with the statement that people (as opposed to the government) are responsible for their own welfare is just as unlikely to affect the partisan preferences of educated voters as it is those of less-educated voters, whether or not one controls for household income or assets.45 That is, even among educated voters, who are in principle better able to distinguish whether programmatic promises are carried out, those who support the market are no more likely to favor the supposedly more market-oriented NPP.

Media markets and voter information

4.75 When media access is distorted by government policy or by market imperfections in private media markets, even educated voters find it difficult to gather the information needed to hold government accountable. Historically, Ghanaian governments have subjected media markets to strict controls. However, since 1991 the legal regime governing media has seen tremendous liberalization. The 1992 Constitution stipulates that, “there shall be no impediments to the establishment of private press or media; and in particular, there shall be no law requiring any person to obtain a license as a prerequisite to the establishment or operation of a newspaper, journal or other media for mass communication or information” (162: 3). Through the 1990s, after liberalization, the government continued to exert some 45

These conclusions emerge from a probit regression that looks at respondents who indicate that they prefer the NPP or the NDC (excluding respondents who express no preference), regressing whether a respondent favors the NPP or the NDC on whether they favor the market (or self-reliance), their level of education, the interaction of these two variables, and their income (or assets). If parties were programmatic and educated voters were more receptive to programmatic messages, the coefficient on the interaction of market preferences and education would be significant. Instead, it is highly insignificant.

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pressure on the media after liberalization, especially through the Criminal Libel and Seditious Laws. However, in 2001 President Kufuor oversaw this law’s repeal (Hasty 2005, p. 166).

4.76 Liberalization has brought dramatic change to the media in Ghana. The BBC reported that “The west African state of Ghana remains one of the few countries on the continent where the media seemingly operates without fear of systematic harassment by the authorities. The authorities observe a rare tolerance, media laws remain flexible, and the constitution is vocal in defending media freedoms” (BBC 2006). Tettey (2003) quotes the Commonwealth Press Union as describing Ghana’s media in 2001 as one of the most unfettered in Africa (p. 83) and agrees with observers who attribute the transparency of the 2000 election results to the relatively large number of private FM radio stations around the country that monitored local polling booths and announced preliminary local results before the central government did (p. 99). The Freedom House indicators of press freedom place Ghana at the same level as Greece and Israel, and fourth out of 26 African countries.

4.77 The media marketplace is also significantly more robust than in 1991, prior to liberalization, with many more private outlets and fewer state outlets. According to the BBC, Ghana had registered some 350 publications as at 31 May 2006. Meanwhile, the Evening News, one of three state-owned dailies, suspended publication in 2006. For its part, the broadcasting regulator, the National Communications Authority (NCA), had by March 2005 licensed some 84 FM stations (most of them privately-owned); prior to 1995, there were none. The NCA had also issued 27 television licenses though only four of the TV stations were in operation by July 2006.

4.78 Oversight of media is still far from ideal and exhibits notable contrasts with more mature democracies. By any standard the state presence in media remains large. State television, radio and newspapers continue to command by far the largest audience. The state-owned Daily Graphic has daily circulation of 150,000. In contrast, no private newspaper has circulation greater than 20,000, with the vast majority hovering around 2,000. Private newspapers are also heavily dependent on government support in the form of advertising. Government advertising constitutes 40 percent of private newspaper revenues, compared to only three percent for the Daily Graphic. Because commercial advertisers prefer the market reach of the Daily Graphic, 90 per cent of the Graphic’s revenue comes from the corporate world.

4.79 Despite liberalization, media regulation remains significant. The National Media Commission registers and regulates the operations of newspapers; the National Communications Authority (NCA) governs the broadcasting sector plus the rest of the country's telecommunications industry. The latter is typical of mature democracies. The 15-member NMC was set up in July 1993 following the adoption by parliament of the National Media Commission Act of 1993. According to the Act, the NMC's mandate includes the promotion of press freedom, the maintenance of journalistic standards, the settlement of media complaints, insulation of state-owned press from government control, and the registration of newspapers. The establishment of the NMC is also sanctioned by Section 166 (1) of the country's 1992 Constitution.

4.80 The NCA, on the other hand, regulates the broadcast industry more aggressively and with more overt political objectives than is common in mature democracies. In April 2004, for example, the NCA shut down Light FM, alleging that the station was broadcasting illegally from southeastern Ghana. However, the BBC reports an NCA official as explaining its decision by saying that the organization would "not relent in its responsibility to bring sanity to the airwaves" (BBC 2006).

4.81 Finally, the BBC report notes that Ghanaian journalists still exercise some self-censorship, particularly in dealing with reports relating to state security. Government officials have not refrained from threats to curb an unruly media, though nothing has come of these. For example, in July 2006, one government minister reacted to phone-in programs in which callers were, in the minister’s view, persistently insulting the president, by saying that authorities might curb such programs (BBC 2006). The BBC reports that on 14 August 2006, President Kufuor urged the National Media Commission to “streamline” the media to ensure that it was not destructive, but rather contributed to the development of the nation (BBC 2006). In July 2005, the former director of the state radio and acting director-general of the Ghana Broadcasting Corporation told the private Accra Daily Mail newspaper that the country's broadcasting scene was "unfortunate" and "confused", without clear ground rules (BBC 2006).

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4.82 There is disagreement among Ghana observers regarding the implications of current regulation of media markets. Some argue that government continues to obstruct the flow of information through media to citizens. However, given the dominant effects of education on newspaper readership documented earlier, it seems more likely that the low level of education in the country has had a greater impact on media markets and citizen information than government regulation of these markets. The BBC reports observer concerns that quality is a principal obstacle to a more influential media. Consistent with the limited market for newspapers, media salaries remain low, particularly in the private sector; salaries with the public media outlets, meanwhile, are between 100 and 300 US dollars per month. Consequently, absent substantial improvements in education, further liberalization of media markets is unlikely to have a notable effect either on the markets themselves or on citizen information.

Government policies towards information and transparency

4.83 In contrast to government regulation of media markets, government policies and practices towards transparency, the disclosure of information and the monitoring of results of government programs are a clearer obstacle to citizen information. Problems in transparency have been widely discussed in numerous recent reports. This is nowhere clearer than with respect to budgets and spending. The Joint Government of Ghana and Development Partner Decentralization Policy Review (January 2007) observed that expenditures and revenues of Metropolitan, Municipal and District Assemblies (MMDAs) are entirely opaque, with data that is neither current nor accurate. Expenditure assignments – that is, lines of accountability – are equally blurred (p. vi).

4.84 At the national level, Ghanaian performance is significantly better than that of neighboring countries. Nevertheless, information on central government spending and lines of accountability is also incomplete. Approved budgets of the central government appear to have only a limited relationship with actual spending patterns. The Budget Deviation Index, measuring the average percent deviation of actual spending at the level of budget headings compared to budgeted spending, was 33 percent in 2005, continuing an upward trend (World Bank 2006, p. ix). The Public Expenditure and Financial Accountability (PEFA) review of Ghana public sector financial management rated its systems only as average, overall. It revealed considerable progress with regard to budget transparency, particularly on the revenue side, for example, but continued opacity on expenditures and in-year reporting (p. xi). Summarizing the obstacles to citizen information

4.85 The trend in media markets is clearly towards increased diversity of opinion, expanded access and substantial relaxation of government controls on content. Not only do summary evaluations by BBC observers and Freedom House support this conclusion. So also do Ghanaians themselves: although the state has a considerable presence in the market, private media appear not to be held in higher regard by the public. Among Afrobarometer 2005 respondents, trust in state broadcasters and print media is actually slightly higher than trust in private media.

4.86 It is unlikely, therefore, that media regulations and state intervention in the media are significant independent determinants of limited voter information in Ghana, of the slow development of programmatic parties and, ultimately, of the policy choices documented earlier. Instead, it is most likely that to the extent that incomplete voter information is a determinant of policy outcomes, this incompleteness is a function of voter education and government policies and practices towards the dissemination of state information.

DEMOCRATIC PREFERENCES, SOCIAL POLARIZATION AND THE ABSENCE OF PROGRAMMATIC PARTIES

4.87 Voters often care much more about non-economic issues and party stances on those non-economic issues than they do about economic issues. When this is the case, the investments that parties make in a programmatic reputation with respect to economic policies have more modest effects on their electoral chances. Two such possible non-economic issues in Ghana are democratization and social polarization. Both appear to have systematic effects on citizens’ partisan preferences in Ghana, which would further attenuate electoral accountability for broad policy performance.

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Attitudes towards democracy and party preferences

4.88 After a long period in which they lacked political rights, it is not surprising that Ghanaians would care about the democratic credentials of parties and their commitment to democratic rule. To the extent that this is a powerful effect, we would expect that Ghanaians who value democracy highly might therefore support the NPP, with its stronger democracy credentials. Afrobarometer data supports the contention that attitudes about democracy are statistically significant determinants of Ghanaians’ partisan preferences. In the 2005 survey, 85 percent of the 1070 respondents who answered the question (and 75 percent of all respondents) answered that democracy was preferable to any other form of government. This ratio was 12 percentage points higher for NPP supporters than NDC supporters; democracy supporters were 11 percent more likely to express a preference for the NPP.

4.89 The democracy effect is relatively large, and constitutes a disincentive to party investments in a programmatic reputation with regard to economic policy (the payoff to such an investment is low if voters care most about the democratic credentials of parties). However, the effects are driven by a small number of voters: the 14 percent of respondents who were less supportive of democracy. This effect has already begun to dissipate: whereas 75 percent of all respondents unambiguously supported democracy in the 2005 survey, the fraction was only 52 percent in 2002.

Social and ethnic cleavages and party preferences

4.90 A more important non-economic difference between parties that seems to drive voter preferences is parties’ ethnic character. One can use the Afrobarometer surveys to assess respondents’ sense of ethnic grievance. This sense seems to have increased slightly between 2002 and 2005. In 2002, 30 percent respondents indicated that the group with which they identified (ethnic or religious) was economically worse off than others; 15 percent indicated that their group was treated unfairly by the government. Seven percent of respondents thought that both were true. In 2005, only ethnic/tribal identification was recorded; each of the corresponding percentages was nevertheless slightly higher: 38 percent indicated that their group was economically worse off; 18 percent believed the government treated their group unfairly; and 12 percent reported both of these grievances.

4.91 A sense of ethnic grievance becomes important when it comes to dominate the electoral choices of citizens. It is difficult to judge the extent to which political competitors in Ghana have used ethnic appeals to electoral advantage, however. If one looks at voting behavior, it appears that ethnic divisions are large. Gyimah-Boadi (2003) reports that support for President Kufour in the 2000 elections was far stronger in regions where the Akan are concentrated (his vote share was 80 percent in Ashanti, 62 percent in Central, and 58 percent in Brong Ahafo, for example). The vote share of the NDC’s John Mills, in contrast, was much stronger among the non-Akan and especially among the Ewe: 88 percent in Volta, 51 percent in Northern, 57 percent in Upper East, and 62 percent in Upper West. Outcomes of the parliamentary elections were similar. The 2005 Afrobarometer data suggests that Akan self-identity is significantly associated with electoral behavior. Akan voters were 23 percent more likely to prefer the NPP and 24 percent less likely to prefer the NDC. Ewe and Northern voters, though, showed no significant tendencies towards one or the other.

4.92 Others contast supporters of the two parties and find significant, non-ethnic differences. Based on their survey of 690 voters in 2003, Lindberg and Morrison (2005) argue that partisan preferences can be explained by education, the rural-urban divide, income and occupation; they do not directly examine ethnic polarization among voters, however. Nugent also argues that such voting patterns are consistent with an urban-rural divide in the Ghanaian electorate or a divide between the marginalized and non-marginalized (Nugent 2001b), but again he was not able to appeal to direct measures of ethnically-based partisan preferences in his analysis.

4.93 One can also look at actual government actions to get a sense of ethnicity in politics. Gyimah-Boadi (2003) argues that, early in his tenure, in the midst of social tension and coup attempts resulting from structural adjustment, Jerry Rawlings relied more than ever on allies from the Ewe-Volta

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Region, at the expense of his ideological base, urban workers, students, radical intelligentsia, and Northern representatives of these groups, all of which abandoned the regime (p. 128). On the other hand, he also observes that the Rawlings victory in 1996 required much more than his Volta votes, just as the Kufuor victory required support from non-Akans. Reflecting this, the Rawlings government maintained ethno-regional balance on the cabinet and in such constitutionally designated bodies as Council of State, National Media Commission, Commission on Human Rights and Administrative Justice, and National Commission on Civic Education. For example, the first Rawlings Cabinet following the 1996 elections had 16 Akans, out of 27 cabinet members (p. 13).

4.94 On the other hand, however, the ethnic composition of cabinets differs significantly between the Kufuor and Rawlings governments. The cabinets of May 1999 and in May 2000 (the last year of the Rawlings administration) were approximately 50 percent Akan and 25 percent Volta or Ewe (out of the 16 cabinet members for which information is available). At a similar point in the Kufuor administration (February 2006), out of 21 seats for which information is available, approximately 75 percent were held by Akans and 14 percent (three seats) were held by Volta or Ewe.

4.95 Similarly, there has been little systematic study, and therefore there is limited direct evidence, of whether public policy differentially affects ethnic groups. In a report on economic growth in Northern Ghana prepared for DFID, the Overseas Development Institute (ODI) points to two possible examples: the decision to make Kumasi a second international airport rather than move to implement the 1996 decision to develop an airport in Tamale; reluctance to promote shea nut exports (a northern product) because of concern that this would jeopardize cocoa exports (a southern product) (p. 22).

4.96 No existing research allows us to compare ethnic grievances with other determinants of partisan preferences. We can fill this gap with Afrobarometer data, with which we can examine preferences for the NPP, the NDC, or for no party at all, as a function of ethnic resentments and ethnic identification (to get at social polarization); preferences for democracy (to get at ideological polarization); religiosity (to capture religious factors that may affect party preferences); preferences regarding education policy (to capture whether voters view parties differently with respect to social service policies); beliefs regarding the proper contribution of individuals and government to individual welfare (to get at preferences for market versus government intervention); and years of education and personal assets.

4.97 Table 4.3 reports these results. As should be expected in an environment in which narrow promises to select constituencies – clientelism – are the dominant political strategy, the explanatory power of these regressions (given by the R2 statistics) is low. However, the chi-squared (Wald) statistics strongly reject the hypothesis that the right-hand side variables that are controlled for are collectively insignificant. Several more specific conclusions also emerge immediately from the table.

4.98 First, results indicate that the two main parties in Ghana are, indeed, slowly developing credible programmatic appeals to voters. Consistent with the predictions of Nugent and Lindberg and Morrison, partisans of the two parties do exhibit a statistically significant difference with regard to their “market” orientation: those who believe in self-reliance were five or six percent more likely to prefer the NPP and six or seven percent less likely to prefer the NDC. These results are in marked contrast to those from a similar exercise using 2002 data and a different market question (which is preferable, a free market or a government-run economy?). Using that data, those who preferred the free market were, if anything, less likely to support the NPP.

4.99 Other results are more striking and less encouraging, however. Even controlling for income, education and other variables that might be conflated with ethnically-motivated partisan preferences, two different measures of ethnic resentment nevertheless have a significant impact on partisan preferences. Those bearing ethnic grievances were 8 – 13 percent more likely to prefer the NDC and 8-13 percent less likely to refer the NPP, both substantially larger effects than those associated self-reliance. The same variables are also strongly significant using earlier 2002 data.

4.100 The 2005 results given in Table 4.3 indicate grievance is not the primary basis for ethnically-motivated partisan preferences. Akan voters are more than 20 percent more likely to prefer the NPP and 20 percent less likely to prefer the NDC regardless of a sense of grievance. This, however,

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is consistent with the essential argument in this paper: in the absence of other mechanisms for conveying the effective credibility of political promises, politicians and voters are left to rely on ascriptive characteristics (ethnic identity) as a way to establish the credibility of their promises relative to those of political competitors.

4.101 Preferences for democracy are also significant determinants of partisan preference, even controlling for other respondent characteristics. Those who believe that democracy is more favorable than any other system (democracy equals three) are 14 percent more likely to vote for the NPP and 10 percent less likely to vote for the NDC than those who believe non-democracy could be better than democracy in some circumstances (democracy equals one).

Table 4.3: Determinants of party preferences: results from Afrobarometer 2005

Dependent variable: Favors NPP? Favors NDC? Favors no party?

Respondent’s group is. . . Respondent’s group is. . . Respondent’s group is. . .

Resentment variable: Worse off econom-

ically

Treated worse by

gov.

Worse off econom-

ically

Treated worse by

gov.

Worse off econom-

ically

Treated worse by

gov.

Resentment (1,0) -.08 (.02)

-.13 (.002)

.08 (.002)

.13 (.00)

-.02 (.57)

-.03 (.41)

Democracy preferable (1 – 3)

.07 (.01)

.07 (.01)

-.05 (.01)

-.05 (.02)

-.01 (.62)

-.01 (.58)

Frequent attendance at religious services (1,0)

-.002 (.95)

-.01 (.81)

-.02 (.48)

-.01 (.55)

.02 (.54)

.016 (.58)

Akan? .21 (.001)

.20 (.003)

-.23 (.00)

-.23 (.00)

.03 (.62)

.03 (.64)

Ewe? -.02 (.81)

-.03 (.71)

.04 (.44)

.05 (.34)

-.04 (.61)

-.04 (.58)

Northern? .06 (.42)

.05 (.47)

-.07 (.11)

-.07 (.12)

.003 (.96)

-.003 (.97)

Prefers better to cheaper education (0,1)

.002 (.93)

.01 (.18)

-.008 (.65)

-.006 (.71)

.014 (.43)

.014 (.44)

Individuals, not gov’t., responsible for personal well-being. (0,1)

.05 (.10)

.06 (.08)

-.06 (.013)

-.07 (.01)

.02 (.59)

.02 (.57)

Years of education (9 categories)

.01 (.20)

.01 (.18)

-.007 (.28)

-.007 (.27)

-.01 (.22)

-.01 (.23)

Personal assets(0-5) .02 (.22)

.02 (.16)

.0003 (.97)

-.003 (.80)

-.02 (.13)

-.02 (.14)

R2 .05 .05 .12 .12 .01 .01

Chi-squared (Wald) 72.06 73.49 118.11 115.25 9.8 10.17

N 1060 1060 1060 1060 1060 1060

Note: Table 6 presents the estimate of probit regressions; the p-statistics reported in parentheses are based on robust standard errors. The dependent variable “Favors NPP?” equals 1 if respondent feels closer to the NPP, zero otherwise; similarly with “Favors NDC?”. The three resentment variables equal 1 if respondent feels the group with which respondent identifies is worse off economically, treated worse by government, or both. The variables “ethnic/linguistic” and “religious” equal 1 if the respondent identifies most closely with the corresponding group in society.

4.102 The democracy and ethnic variables have a far larger impact on citizen preferences than do the “policy” variables (price and quality of education and self-reliance). The results therefore strongly

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support the argument that political incentives to undertake broad-based economic reforms in the public interest are weak. Because political competition cannot be about programmatic promises, which are not credible, but must instead revolve around clientelist concerns, social polarization, and attitudes towards democracy, the electoral incentives to undertake these broad reforms are weak. Second, the hazards of undertaking such reforms are significant. It is easier for party members who oppose wide-ranging economic reforms to defect from the government party and set up their own party when the government party enjoys no programmatic advantage. Individuals with a high profile among disaffected ethnic groups or with a substantial clientelist following potentially lose little when they defect from non-programmatic parties.

4.103 The earlier discussion indicates that voters in Ghana also rely disproportionately on their ex post evaluation of incumbent performance in making electoral decisions. It therefore bears asking, if one controls for these ex post evaluations, do the results in Table 4.3 still hold? The Afrobarometer (2005) survey asks for some ex post evaluations by respondents, for example regarding how well or badly they think the government is managing the economy or creating jobs. These variables are highly significant when added to the NPP and NDC regressions (those who believe the government is doing a good job are far more likely to support the NPP and less likely to support the NDC). However, their inclusion has little effect on the key parameters of concern here: resentment, Akan self-identification, and belief in self-reliance.

ARE THE NDC AND NPP BOTH PRO-BUSINESS AND THEREFORE PROGRAMMATICALLY INDISTINGUISHABLE?

4.104 Some observers argue that the NDC and NPP credibly espouse the same policies, as opposed to the contention here that they cannot credibly promise any broad policy program. Boafo-Arthur (2003, 211), for example, argues that the NDC has shed its original Marxist-Leninist roots, such that the NDC and NPP are not fundamentally different. This is an alternative explanation of why voters and candidates would also emphasize local and individual constituent issues in their work.

4.105 That each of the parties is closer to the other than either is to the policies of the Nkrumah or PNDC eras is beyond dispute. However, the absence of well-defined and well-publicized party positions on major issues makes it difficult to argue that they have credible programs between which voters are indifferent. It is more likely that the policy programs are not credible or that the voters are not informed about them, and therefore not electorally salient.

4.106 Indeed, a reading of the NDC 2004 Manifesto yields two immediate conclusions. First, the NDC leadership made a significant investment in attempting to craft a well-defined programmatic identity (a natural reaction to being out of power and of lacking the fiscal resources to provide the targeted payouts that are the bread and butter of non-programmatic electoral competition). Second, it went to considerable effort to distinguish itself from the NPP, for example with regard to taxation and the size of government (the NDC argues for less), resources for social services and the poor (the NDC argues for more, though the manifesto is silent on how these and tax cuts can be simultaneously pursued), and economic growth (the NDC claims to place more emphasis on public-private partnerships).

4.107 The policy environment in Ghana is also inconsistent with the hypothesis that the NDC and NPP both have credible programmatic agendas reflective of a pro-growth, pro-market agenda. If they did, then they would both have strong electoral incentives to pursue this agenda more aggressively, since failure to pursue the agenda would invite a strong and credible challenge from the other party. Instead, it appears that relatively slow progress in easing business regulations and human capital constraints to business growth, or in improving governance, does not put incumbents at a significant electoral disadvantage to challengers. The pattern of policies discussed earlier is consistent with the lack of credibility of programmatic promises, not the equivalence of credible programs across the two parties.

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THE CONSEQUENCES OF CLIENTELIST POLITICAL COMPETITION: CRISIS AND POLICY REFORM IN THE ABSENCE OF CREDIBLY PROGRAMMATIC PARTIES

4.108 The earlier discussion illustrates the real-world policy consequences in Ghana of political competition that is not rooted in programmatic competition between parties, but rather in clientelist appeals, ideological differences related to democracy, and ethnic grievance. A more detailed look at three specific examples, the elections of 2000, land policy and infrastructure development, provides further evidence of the consequences of clientelist political strategies.

4.109 The first example is that a relatively minor economic crisis led to a large shift in electoral support across the two main parties in 2000 compared to 1996. Growth of constant, purchasing power parity adjusted GDP dropped slightly from 4.6 percent in 1998 to 4.3 percent in 1999 to 3.6 percent in 2000 (Unified Database, Ghana). In addition, though, Ghana’s external debt position deteriorated. Net transfers turned negative in 1999, with outflows of 50.9 million dollars, and deteriorated further in 2000, with outflows of 163.8 million dollars, a reversal of nearly 400 million dollars in three years. As a consequence, government purchases of goods and services, including compensation of government employees, dropped to eight percent of GDP in 1999 and 7.8 percent in the election year of 2000, substantially lower than in 1995 and the election year of 1996, when they were 13.7 and 12 percent. Public investment expenditures were 8.7 and 9.3 percent of GDP in 1999 and 2000, compared to 14 and 13.3 percent in 1995-96.

4.110 Heightened debt finance obligations led to significant retrenchment in expenditures on goods and services. These, however, are precisely the line items from which governments finance clientelist promises to narrow groups of supporters. The inability to fulfill these promises ensured that a relatively minor crisis had major electoral consequences. This perhaps explains, for example, why the Ewe, originally very strong Rawlings supporters, are not strongly identified with the NDC in the Afrobarometer 2005 data.

4.111 The second example is the persistence of apparently inefficient land allocation rules, despite many years of struggle to reform them. A large fraction of land in Ghana is controlled by traditional leaders, who can allocate and re-allocate this land to satisfy personal and clan priorities. There is some evidence that they exercise this control in a way that has large negative effects on land productivity and investment (although, perhaps, positive effects on equity). The efficiency costs of current land management systems are widely recognized in Ghana, but reforms are strongly resisted by traditional leaders. What allows traditional leaders to block reforms? One reason is likely the deference that is traditionally granted to these leaders. Another, however, is the reliance of competitors for national political office on traditional leaders to mobilize electoral support. Since political competitors cannot easily make credible promises to voters themselves, they rely instead on traditional leaders. This endows these leaders with substantial influence over national level policy making.

4.112 Finally, Ghanaian governments have experienced chronic difficulties in allocating large infrastructure expenditures to maximize growth. As the earlier analysis demonstrates, Ghana spends far more than the average country on infrastructure, but the effects of this spending on infrastructure quality have, in the past, been disappointing (Aryeetey and Fosu, 2001). Infrastructure spending, however, presents substantial opportunities to satisfy the narrow interests to whom politicians have made clientelist promises. The growth effects of this spending have been attenuated by its diversion to satisfying the demands of narrow interests.

WHY IS GHANA PERFORMING WELL?

4.113 Despite the persistence of clientelist political incentives, it is still the case that Ghana is performing notably better than other countries where clientelist incentives are also powerful and where the ability of politicians to make programmatic policy commitments to voters is weak. One explanation is that Ghanaian parties, in contrast to those in other young democracies, are indeed becoming more programmatic and better able to make broadly credible promises to the electorate. Table 3 suggests this possibility by demonstrating an association between “self-reliant” voters and support for

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the NPP and NDC. This effect is new, however (it did not exist in 2002), and it is too early to judge its strength or durability.

4.114 Four other explanations, reviewed below, also help to account for the difference in Ghanaian economic performance. Ghana benefits from having two competitive political parties; from the extent to which the parties are institutionalized and not reliant on particular personalities; from the incentives of key societal “patrons” – traditional leaders – to take account of the broad social consequences of their actions; and from falling extra-institutional threats to political authority.

A multi-party democracy

4.115 Many young democracies exhibit a single large party confronting a number of splinter parties. This is not the case in Ghana. When there are many splinter parties, voters can credibly threaten to expel under-performing incumbents only if they agree on the splinter party to which they will give their votes; if they cannot agree, incumbents know that even with a small plurality of support, they are likely to remain in office, since the opposition is scattered. Electoral accountability is substantially weakened.

4.116 The problem of splinter parties is acute in young democracies. Every additional year that countries are governed by competitively elected officials is associated with a greater than one percent decline in the ratio of government to opposition seats in the legislature (Database of Political Institutions, Beck, et al. 2001). Ghana, though, is strikingly different from other young democracies. Among the 15 democracies that in 2004 had enjoyed competitively elected governments for fewer than 10 years, the ratio of seats held by the main government and main opposition parties was 1.85. In Ghana, it was 1.09.

Party institutionalization

4.117 Most of the discussion in this paper focuses on the incentives that programmatic parties give to politicians to pursue broad social interests, since they allow politicians to make credible promises about broad public policies. Programmatic parties are also more disciplined and better able to carry out broad promises, since dissidents who leave the party to pursue narrower interests lose the votes of citizens who support the party independent of the individual characteristics of its candidates. Countries lacking programmatic parties can nevertheless replicate some of their benefits if their non-programmatic parties exhibit greater institutionalization.

4.118 Institutionalization can take a variety of forms. Parties may have party machines, an administrative apparatus that makes rewards to party activists more transparent and less dependent on the whims of individual party leaders. They may have formal procedures for selecting candidates, including the top party leaders. The key is that in both cases, institutionalization allows party leaders to make credible promises to party members regarding their position in the party and the benefits they can expect from party activism. Dissidents cannot easily replicate these promises should they leave the party to start their own. As a consequence, party leaders can more easily undertake policy reforms in the broad public interest, even at the cost of the private interests of some party members. The Communist Party in China is one example of the success of this strategy (Gehlbach and Keefer).

4.119 Though the evidence is largely indirect, Ghanaian parties appear to be more institutionalized than is common among young democracies. Gyimah-Boadi (2003) observes that in 1996, the NDC victory relied on a political machine created out of entities with a national presence, especially the Committees for the Defence of the Revolution and the 31st December Women’s Movement (p. 134). Nugent (2001c) also notes that NDC candidates in the north had a well-endowed party apparatus with which to campaign (campaign staff and vehicles) (p. 102). Presumably, NPP candidates in their strongholds benefited from a similar apparatus.

4.120 In addition, postulants for party nominations incur substantial costs to obtain the nomination and do not defect from the party in the event that they lose it. Nugent (2001c) claims that, prior to the 1996 elections, it is more than likely that one parliamentary candidate for the NDC obtained his nomination “. . . by outbidding his rivals in a highly corrupt primary election” (p. 97). The role of money is not surprising in a non-programmatic party. More interesting is that these candidates

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expended money to secure the NDC nomination rather than simply standing as independents, setting up their own party, or moving to a different party, any of which might have been a cheaper option. Their willingness to do so is linked to the benefits that the party offered them in terms of mobilizing voters (of course, this early in the transition to democracy, the NDC nomination may have been valuable because the NDC was sure to win the election).

Restraints on arbitrary decision making by traditional leaders

4.121 In most democracies where politics is patron-driven, patrons have substantial discretion to extract rents from society. They are not bound together institutionally in a way that would give them incentives to pay attention to broader social interests. Nor, in their relations with “clients”, are they bound by cultural and institutional constraints from exploiting clients. In Ghana, in contrast, traditional leaders are enmeshed in a web of rules, obligations and institutional constraints that limit their ability to extract high rents for themselves at the expense of society generally.

4.122 Though clans differ significantly in the nature of these institutional restraints (the Ga, for example, had no custom of destoolment prior to the colonial period according to Li 1995, 350), there is ample evidence of Asante chiefs being destooled for abuse of power from the 18th century onwards (Li 1995, 338-342, documents many cases in the Asante area, starting with the destoolment of King Osei Kwame in the late 1700s). These pressures increased during the colonial period, according to Li, even among peoples with no tradition of destoolment, as the British systematically weakened the institution of the chief without imposing corresponding constraints on the asafo (groups of commoners which could challenge the authority of the chief). None of this need imply that the constraints confronting chiefs are equivalent to those confronting any elected official, only that they are more significant than is typically found in countries where patron influence on politics is great.

4.123 Chiefs also confront constraints “from above” on their ability to act arbitrarily. Chieftaincy disputes – and many other disputes over traditional matters, including those regarding the allocation of stool land – have long been decided by the corresponding Regional House of Chiefs, with appeal to the National House of Chiefs. The constitution of the Fourth Republic formalized this practice in the case of chieftaincies, allowing appeals to the Supreme Court. Of course, these constraints did not always operate in the interests of clan members (for example, colonial governments in Ghana were more interested in controlling chiefly behavior to assure the governability of the colony than they were in maximizing the welfare of Ghanaians).

4.124 Chiefs sit together in corporate bodies, and have had persistent incentives, at least since colonial times, to act collectively to persuade Accra-based governments to protect or increase their authority. Nugent (2001c, 87-88) reviews the debates over the constitution that was drafted during the Fourth Republic and notes that the chiefs jointly argued that the House of Chiefs should be the upper house of the central legislature. Other delegates to the Consultative Assembly defeated this initiative, seeking instead to depoliticize the chieftaincy with paragraph 270: “Parliament shall have no power to enact any law which (a) confers on any person or authority the right to accord or withdraw recognition to or from a chief for any purpose whatsoever; or (b) in any way detracts or derogates from the honor and dignity of the institution of chieftaincy.”

4.125 The experience of acting collectively also imposes unusual discipline on chiefs in their role as patrons. Individual chiefs who act in ways that undermine the positions of chiefs generally or the clans of other chiefs are less likely to see their interests protected in the House of Chiefs. Such behavior could range from abusive treatment of their subjects, undermining popular support for chiefly authority generally, to lobbying the central government for clan privileges that come at the expense of other clans. Although there is a long-running debate about the extent to which chiefs are accountable to their clans, the do seem to be constrained from acting in their own interests in ways that are not characteristic of patrons in clientelist systems elsewhere.

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Extra-institutional threats to government stability

4.126 In many democracies, from the Philippines to (in their democratic periods) Bangladesh and Pakistan, elected governments often labor under the shadow of military intervention. The threat of overthrow creates a variety of perverse incentives that undermine public policy. Three stand out. First, of course, it leads to public policies that favor military interests, which are frequently at odds with broader social interests. Second, the threat shortens the horizons of political actors. Political competitors cannot make promises even to narrow interests, since narrow interests are likely to believe the chances that a government will be allowed to stay in power to carry out the promises are small. Electoral accountability is severely attenuated. Political development is also stifled by shortened horizons: incentives of politicians to build programmatic reputations, or even institutionalized parties, are limited when those investments may be swept away by military intervention. Third, military intervention may not be institutional (the institution of the army taking over the government), but rather anarchic: segments of the military, probably competing with each other, may battle for control of the state.

4.127 It is difficult to observe the shadow of military intervention. We have only a limited understanding of when it is more likely, and military threats to civilian governments are rarely overt and easily observed. Nevertheless, and regardless of the reason, the shadow of military intervention in Ghanaian politics seems to have substantially diminished over the last 20 years, culminating in the conduct of the 2000 elections.

CONCLUSION AND POLICY IMPLICATIONS

4.128 The recent macroeconomic stability and growth record of Ghana are notable achievements. Ghanaians are nevertheless dissatisfied with the rate of poverty reduction. The first part of this paper provides quantitative evidence that justifies this dissatisfaction: in areas ranging from education to the business environment, despite important gains in recent years, Ghana falls below the average of other countries. Reforms in these areas can provide a foundation for sustained growth. However, previous reports and many observers argue that clientelism and related phenomena hobble the pursuit of these reforms and, therefore, the achievement of faster growth. This was the theme of the DFID Drivers of Change report on Ghana, for example.

A reform agenda for attacking political market imperfections in Ghana

4.129 The analysis here adds to the insights of earlier work in several ways. The Drivers of Change (2004) report on Ghana concludes that deepened democratization is essential to promote the policy reforms needed to ensure the broad-based acceleration of economic growth. This paper asks why democratic accountability falls short and points to specific reasons why political incentives in Ghana lead to an under-provision of public goods and a greater emphasis on clientelist policies. These include the inability of political competitors to make promises that are broadly credible to most Ghanaians; the lack of voter information; and social, particularly ethnic, polarization. These political market imperfections point to a particular strategy of reform, which includes improving education, increasing government transparency, and more explicitly (and transparently) ensuring that the costs and benefits of policy fall equally across ethnic groups.

4.130 Education reform is not a new recommendation. This paper argues that there are two reasons for urgency. The usual and current arguments are that improved access and quality are necessary in order to increase the human capital input into economic production and to build skills for new economic opportunities. The message of this paper is that they are crucial as well to enhance the electoral accountability of politicians for their performance on the broad policy issues that are crucial to sustained, fast growth.

4.131 The collection of and access to government information needs to be significantly improved as well. The Freedom of Information Act has not yet been approved, despite a lengthy gestation, but is itself only a first step. As the experience of countries far richer than Ghana demonstrate, implementation of FOIAs is difficult and often falls short. More important than FOIAs, however, is a culture of transparency, in which the basic information of governments – how much is spent, on what, and to what

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effect? – is routinely collected and made available to citizens. Here, reviews of public expenditure and financial management in Ghana reveal substantial progress but also continuing substantial gaps in the information government collects and makes easily available.

4.132 The significant, and possibly increasing role of ethnic identity in political competition in Ghana, weakens political accountability for public sector performance. Even where no bias exists or is intended, in an environment where ethnicity plays a large role and where information about government decision making is sparse and poorly disseminated, the perception of bias can be significant. To alleviate this, public policy should be self-consciously and more transparently free of biases that exacerbate social cleavages.

4.133 Other reforms that are frequently discussed in donor circles may have less of an effect, given the discussion here. For example, campaign finance reform might seem an obvious response to the significant role that money plays in Ghanaian politics. However, it is unlikely to have a notable effect on political incentives, for two reasons. First, campaign finance reform does not change the underlying political market imperfections (uninformed voters and non-credible politicians) that drive both the demand for money and policy distortions. Second, campaign finance regulation is notoriously difficult to implement and enforce, particularly when the demand for money in politics is high.

How can reform be catalyzed?

4.134 As is the case with all reforms, the three areas of reform targeted here are not necessarily a high priority of government. Education and the lack of government transparency appear to be the biggest obstacles to voter information, but both are public goods that non-credible politicians are reluctant to provide. Voting is along ethnic lines, but this again is likely to be a natural consequence of efforts by politicians to overcome their general lack of credibility. Finally, politicians have fewer incentives to invest in broadly credible, programmatic reputations when voters are poorly informed and, in any case, nurture ethnic grievances that reduce the salience of broad-based economic and social policies. These reforms, in other words, are vulnerable to the very political market imperfections that they are intended to ameliorate. Nevertheless, they advance our ability to construct a reform strategy in several ways.

4.135 First, reforms in any one of these areas should trigger a virtuous cycle. As information increases, or as education increases, or as ethnic appeals diminish, political actors will find it increasingly advantageous to invest in programmatic policy reputations. As they make these investments, they will have correspondingly greater incentives to pursue programmatic policy reforms – including those related to information, education and the pursuit of policies that are ethnically-neutral – which in turn triggers further reforms. The product of all this should be a policy environment that accelerates investment, growth and poverty reduction.

4.136 Second, even if steps to de-emphasize clientelist promises is difficult for politicians in the short run, it is also true that political competitors who can make broadly credible promises have an electoral advantage over those whose credibility extends only to a narrow segment of the electorate. The reforms under discussion here can be in the self-interest of the politicians who are asked to undertake them.

4.137 Third, the reforms are useful complements to others that are currently under discussion. Some observers point to the need to strengthen civil society. The Drivers of Change (2004) report on Ghana, for example, argues that reform logjams can be broken when domestic constituencies for reform can be activated. The strategy here points to education, transparency and ethnic-neutrality as the three key policy areas in which such constituencies should be mobilized.

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4.138 The main message here, however, is that although obstacles to reform could be substantial, reform, once started, should be self-reinforcing. When voters are better informed, politicians will have a greater incentive to invest in programmatic reputations, political competition will then begin to revolve around accountability for broad policy performance, and those policies – including those regarding education and information dissemination – will continue to improve. A virtuous cycle emerges as well in efforts to ensure a perception of even-handedness, since the most effective way to do this is to approve policies that apply equally to all Ghanaians. It is both difficult and costly to achieve the reality and perception of such even-handedness through pork barrel infrastructure or other clientelist policies. However, once parties develop a greater reputation for delivering broad policies in an even-handed way, political incentives to continue to do so will increase.

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Nugent, Paul (1999). “Living in the Past: Urban, Rural and Ethnic Themes in the 1992 and 1996 Elections in Ghana.” The Journal of Modern African Studies 37:2, 287-319.

_____ (2001a). “Winners, Losers and Also Rans: Money, Moral Authority and Voting Patterns in the Ghana 2000 Election.” African Affairs 100: 405-428.

_____ (2001b). “Ethnicity as an Explanatory Factor in the Ghana 2000 Elections.” African Issues 29:1/2, 2-7.

_____ (2001c). “The Things that Money Can Buy: Chieftaincy, the Media and the 1996 elections in Hohoe-North Constituency.” Ghana Studies 4: 85-106.

Overseas Development Institute (2005). Economic Growth in Northern Ghana: Revised Report for DFID Ghana. Accra, London (October).

Teal, Francis, James Habyarimana, Papa Thiam, Ginger Turner (2006). “Ghana: An Analysis of Firm Productivity.” Regional Program for Enterprise Development (RPED), Africa Private Sector Group (AFTPS), The World Bank (June).

Tettey, Wisdom J. (2003). “The Mass Media, Political Expression and Democratic Transition.” Chapter 3 in Wisdom J. Tettey, Korbla P. Puplampu, and Bruce J. Berman, Critical Perspectives on Politics and Socio-Economic Development in Ghana. Leiden: Brill, p. 83-106.

Van de Walle, Nicolas (2001). African Economies and the Politics of Permanent Crisis, 1979-1999. New York: Cambridge University Press.

_____ (2003). “Presidentialism and clientelism in Africa’s emerging market systems.” Journal of Modern African Studies 41:2, 297-321.

The World Bank (forthcoming). Building the Demand-side of Good Governance: Enhancing Conditions for Social Accountability in Ghana.

The World Bank (2006). Ghana – 2006 external review of public financial management (Vol. 1 of 2).

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