Spatial analysis of poverty: Combining geospatial data and survey data to study regional inequality in Ghana

Tomomi Tanaka (World Bank)Jia Jun Lee (World Bank)

Abstract

This study combines district level poverty rates, population census data, income data, and geospatial data to investigate how human capital, structural change, infrastructure, and environmental degradation impacted poverty and employment in Ghana. We find that poverty reduction was primarily achieved through increased share of working age population, employment rates and income in the service sector, shift of labor from agriculture to the service sector, expansion of access to electricity, and increased rainfall. Further, the paper investigates the factors that have affected changes in agricultural income and shift of labor from agriculture to industry and services. Soil erosion had a large impact on the changes in agricultural income. Improved access to electricity, road, and market was crucial for creating jobs in non-agricultural sectors. In areas where droughts are severe, more people became engaged in agriculture and less in industry and services. It may be because severe droughts prevent people from investing in non-agricultural sectors. The results of this study suggest that for Ghana to reduce poverty and create jobs in non-agricultural sectors in lagging areas, it needs to invest in infrastructure, and take actions to mitigate damages from soil degradation and droughts.

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1. Introduction

Ghana has achieved significant poverty reduction since the 1990s and accomplished the first Millennium Development Goal (MDG) of reducing the poverty rate by more than half. Between 1991 and 2012, the national poverty rate declined from 52.7 percent to 24.2 percent. The national poverty rate further declined to 23.4 percent by 2016. Starting from higher than the mean for low- and middle-income countries (LMICs), Ghana’s international poverty headcount today is lower than the current LMIC average.

Even though the poverty rate fell at the national level, spatial inequality intensified with some regions lagging. Regional poverty rates had fallen dramatically in the Ashanti, Eastern, Greater Accra, Brong Ahafo, Central, and Western regions (Figure 1), while poverty rates remain above 50 percent in the Northern, Upper West, and Upper East regions. In addition, the pace of poverty reduction has slowed down in the Volta region.

Poverty rates vary not only across regions but also across districts within regions. As Figure 2 shows, poverty rates were generally high in most districts in the three northern regions (Northern, Upper West, and Upper East regions) in 2000. However, the spatial distribution of poverty has changed dramatically between 2000 and 2010. The eastern part of the three Northern regions achieved significant poverty reduction, while several districts in the western side of the country witnessed rising poverty. Clusters of districts with high poverty rates also developed in the inland parts of the country and in the Volta region.

A key question of this study is how some districts were able to reduce poverty, while others were not. We investigate how human capital development, structural change, infrastructure, and environmental degradation affected poverty, income and employment in Ghana.

Human capital development is critical for poverty reduction as educated and healthy labor force is central to wealth creation and productivity growth (Schultz 1961). A higher share of working age population in the economy helps the country achieve economic development and poverty reduction, since there are more people available for productive activities. Investment on education is a plausible way to escape poverty, as education is often a prime means of social mobility. Reduction of fertility helps families to invest more on each of their children. For these reasons we select a share of working age population, educational attainments among working age population, and fertility as key variables for the anlysis.

Even when there is a higher share of working age population, economic development is difficult if there are not enough jobs in productive sectors. McMillan and Harttgen (2014) and Diao, Harttgen, and McMillan (2017) show much of Africa’s recent growth and poverty reduction has been associated with structural change; a substantive decline in the share of the labor force engaged in agriculture. In Ghana, the share of employment in agriculture fell from 50 percent to 42 percent between 2000 and 2010, while the share of employment in services rose from 34

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to 42 percent during that period. Meanwhile, the employment share in industry slightly declined. Ghana’s pattern of structural change is consistent with the general observation in Sub-Saharan Africa, i.e. premature deindustrialization (Rodrik 2016). In Africa, workers mainly relocated from agriculture to trade services, instead of manufacturing (De Vries, Timmer, and De Vries 2015). De Vries, Timmer, and De Vries (2015) and Duarte and Restuccia (2010) point out that even though productivity levels in trade services are often higher than productivity in agriculture in Africa, its productivity growth is sluggish and stagnant. Geiger, Trenczek, and Wacker (2018) confirm that Ghana is following the same trend; productivity in services is stagnant and has been declining in Ghana since 2005. The question arises whether structural change, a shift of labor from agriculture to services, contributed to poverty reduction in Ghana.

Gollin, Parente, and Rogerson (2002) and Christiaensen, Demery, and Kuhl (2011) demonstrate how agriculture can be effective in reducing poverty, especially among the poorest. Breisinger et al. (2008) shows the poverty rate among cocoa farmers declined from 60 percent to 24 percent in Ghana between 1991 and 2005. Beginning in the late 1990s, cocoa production rapidly grew due to favorable prices, and Ghana has become the world’s second-largest cocoa producer. As a result, the poverty rate among cocoa farmers significantly declined. This paper validate that increases in agricultural income contributed to increased consumption not only in cocoa production areas, but also in the whole country.

People in poor areas are often disadvantaged by lack of access to infrastructure such as roads and electricity. Access to infrastructure is necessary for developing household enterprises, raising productivity and increasing incomes. In South Africa, household electrification encouraged the establishments of microenterprises and raised employment (Dinkelman 2011). Improved roads stimulate industrial development (Fernald 1999), and benefits the poor (Jacoby 2000). Growth of road and electricity-generating capacity accounted for approximately half the growth of the productivity residual of India’s manufacturing sector (Hulten, Bennathan, and Srinivasan 2006). In Ghana, there is a substantial difference in access to electricity, market and roads between poor and rich areas. It is conceivable that lack of access to infrastructure inhibits the development of non-agricultural sectors, and misallocate labor across sectors (Gollin, Lagakos, and Waugh 2013).

The spatial inequality may not only reflect disparities in infrastructure but also ecological differences. Even though agriculture remains the dominant sector in the three Northern regions, the climate is not suitable for cocoa and other cash crops. Farmers in these regions are mainly engaged in rain-fed, traditional subsistence agriculture, as they have limited access to irrigation. In addition, the northern regions are frequently affected by floods and droughts, accompanied by high temperatures and intense heat (Figure 4). The northern floods of 2007, for example, affected 317,000 people, destroyed 1,000 km of roads, 210 schools, and 45 health clinics, and damaged or contaminated 630 water facilities. Furthermore, it was followed immediately by drought. Crop losses are higher in districts with no irrigation, and when floods and droughts occur one after another (Yiran and Stringer 2016). Poverty in the northern regions may have been also influenced by land degradation, as land degradation is concentrated in the

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poor northern regions (Figure 4). land degradation leads to lower agricultural production, which in turn leads to food insecurity, poverty and vulnerability (Lal 2004, West et al. 2014).

This study combines district level poverty rates, population census data, income data, and geospatial data to investigate how human capital, structural transformation, access to infrastructure, and environmental factors affected poverty, income and employment. We find poverty reduction was achieved through increased employment rates, especially women, increased income in the service sector, shifts of labor from agriculture to services, expansion of access to electricity, and increased rainfall. In addition, education, access to electricity, shift of labor from agriculture to services are important determining factors of the level of poverty.

The paper further investigates what determines agricultural income and structural change. Better access to electricity is the most critical factor facilitating the shift of labor from agriculture to industry and services. Soil erosion had a large impact on the changes in agricultural income. Severe drought inhibits people from moving from agriculture to non-agricultural sectors. It may be because a large economic loss from drought prevents people from accumulating capital for investment.

The results of this study suggest that for Ghana to reduce poverty and create jobs in non-agricultural sectors in lagging regions and districts, it needs to invest on infrastructure, and mitigate economic losses from soil degradation and drought.

This paper relates to a large literature on income and welfare mobility (Fields et al. 2003, Jäntti and Jenkins 2015), and spatial dynamics (Higgins, Bird, and Harris 2010, Jalan and Ravallion 2002, De Vreyer, Herrera, and Mesplé-Somps 2009). We make a significant contribution to the existing literature by combining household survey data with geospatial information. Geospatial information allows us to examine the impacts of environmental degradation on poverty and structural transformation. This paper shows climate change and environmental degradation are disproportionally affecting the poor areas.

2. Data

This study combines poverty rates, population census data, mean income by sector estimated from household survey data (Ghana Living Standards Survey [GLSS]), and the Hidden Dimensions Dataset (HDD) at the district level to identify determining factors of the level and change of poverty rates.

2.1 District poverty rate

We use poverty rates estimated at two time periods, 2000 and 2010. Coulombe (2005) applies the small-area estimation technique developed by Elbers, Lanjouw, and Lanjouw (2003), and estimates the poverty rates of 110 districts, using the 2000 Population and Housing Census and GLSS 4. The data collection of GLSS 4 was carried out from April 1998 to March 1999. We call them ‘2000 poverty rates’ throughout the paper, as the 2000 Population and Housing Census

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was used for the estimation. The district with the highest poverty rate was East Mamprusi in the Northern region, with a poverty rate of 86.1 percent. The district with the lowest poverty rate was Accra Metropolitan in Greater Accra, with a poverty rate of 5.2 percent.

Using the 2010 Population and Housing Census and GLSS 6 conducted from October 2012 to October 2013, Ghana Statistical Service (2015) estimates poverty rates of 216 districts, using the same small-area estimation technique (Elbers, Lanjouw, and Lanjouw 2003) as Coulombe (2005). We call them ‘2010 poverty rates’ throughout the paper, as the 2010 Population and Housing Census was used for the estimation. The district with the lowest poverty rate was La Dade Kotopon Municipal in Greater Accra, with a poverty rate of 1.3 percent. The district with the highest poverty rate was Wa West in the Upper West region, with a poverty rate of 92.4 percent. The 2010 poverty rates are the latest district level data on poverty at moment. This project will be extended and new district poverty rates will be estimated using the survey data from GLSS 7, which was collected from October 2016 to October 2017.

Between 2000 and 2010, the number of districts increased from 110 to 216. It was done by dividing the original districts into smaller districts. To analyze the changes of district poverty rates between the two-time periods, we need to create a panel data of district poverty rates. The paper estimated the poverty rates of the 110 old district units for 2010, by merging the data of new 216 districts into 110 old districts using district population as weights.1

2.2 Population census

The 2000 and 2010 Population and Housing Census gathered information on household members in various aspects including educational attainment, migration, demographic characteristics, employment, and fertility.

2.3 Mean income by sector

Mean income per worker is estimated for three major sectors, i.e. agriculture, industry and service, using the Rural Income Generating Activities Labor (RIGA-L) database, constructed by the Food and Agriculture Organization of the United Nations (FAO). RIGA-L is an individual employment dataset created from household survey data. This study uses the RIGA-L created from GLSS 4 (1998/99) and GLSS 6 (2012/13). The RIGA income data is constructed using GLSS data with the idea of including all sources household income that are often neglected in the computation of income. Such sources include income from self-employment and income from own-farm production of crops. The paper calculates the mean income of workers who hold jobs in a specific sector (either as primary jobs or secondary jobs) in each of the 110 original districts.2 It is not possible to obtain a reliable mean income of a sector if there are not

1 Suppose District A was divided into Districts B and C, and 40 percent and 60 percent of the population of District A are now living in Districts B and C, respectively. Let us assume the poverty rates of District B and C are 10 percent and 20 percent, respectively, then the poverty rate of the original District A is estimated to be 16 percent. 2 We use household weights when taking average.

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sufficient number of workers in a sector. There are fewer workers in the industry and service sectors than in agriculture, particularly in poor districts in earlier survey data (GLSS 4). Reliable data of mean income in the industry and service sectors was obtained only for 88 and 93 out of 110 districts in 1998/99, respectively. Because of the reliability concern, the paper uses the mean income data of industry and services only in the regressions presented in Tables 2 and 3, in which we investigate the impacts of changes in per capita income and share of employment on the changes on poverty rates and consumption.

2.4 Hidden Dimensions Dataset (HHD)

HDD is a global geospatial dataset created by the World Bank, linking environment and natural resource measures to poverty and other human development indicators. This paper uses the following variables from the dataset: normalized difference vegetation index (NDVI), net primary productivity (NPP), night time lights (NTL), soil degradation, less favored agricultural land (LFAL), road density, distance to market, flood frequency, drought severity, and air pollution. The dataset was created for the 110-district boundary for this study. Precipitation, NDVI, NPP, NTL, and air pollution data are available for multiple years including 2000 and 2010, but data on soil erosion, LFAL, road density, distance to market, flood frequency, and drought severity are not available for multiple years.

Normalized Difference Vegetation index (NDVI)

Normalized Difference Vegetation Index (NDVI) is a satellite imagery-derived measure of “greenness”, or the relative density and health of vegetation, of the earth’s surface. It specifies where and how much green vegetation is growing at a certain time.

The equation combining the bands of visible red (VIS) and near-infrared (NIR) that produces the NDVI is given by:

NDVI=NIR−VISNIR+VIS

The data is produced and collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA’s terra satellite. The resolution image used as an input is 10km 2, and the values range from -1 and +1. Values greater than 0.1 generally indicate increasing degrees in the greenness and intensity of vegetation. Values between 0 and 0.1 are normally representative of rocks and bare soil, and values less than 0 often imply clouds, rain, and snow. The NDVI for each pixel is averaged across all grid cells within the boundary of districts to obtain the mean NDVI of each district. This paper uses the mean NDVI estimated at the 110-district level in 2000 and 2010.

NDVI is used to predict crop prices (Brown, Pinzon, and Prince 2008, Higgins, Hintermann, and Brown 2015) and as an index for drought insurance (Bacchini and Miguez 2015, Makaudze and Miranda 2010). NDVI is correlated with the probability of child malnourishment (Bauer and Mburu 2017).

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Net Primary Productivity (NPP)

NPP is the total amount of carbon dioxide taken in by plants. It is measured as the difference between how much carbon dioxide is taken in by plants and how much carbon dioxide is given out by plants. Carbon dioxide contributes to warming of the planet and it is therefore important to track plant productivity to understand where carbon dioxide comes from and where it goes. The change in NPP over time is often used as a measure of land degradation. The losses of NPP can be caused by human-induced dryland degradation (Zika and Erb 2009). Urban areas tend to have considerably higher NPP (O'Neill and Abson 2009), as humans often build settlements in areas of high biological productivity. In general, NPP is similar to NDVI in that they are both measures of vegetation density, however, NPP is usually a better measure of biomass productivity (Xu et al. 2012).

The unit of NPP is grams of carbon per m2 per day. The data is captured with Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA’s Terra and Aqua satellites. This paper uses the mean NPP at the 110-district level in 2000 and 2010. The NPP for each pixel is averaged across all grid cells within each district to obtain the mean NPP.

Night Time Lights (NTL)

NTL is often used to study the distribution of economic activities (Henderson et al. 2017, Michalopoulos and Papaioannou 2013, Chen and Nordhaus 2011), income growth (Henderson, Storeygard, and Weil 2012), and poverty (Jean et al. 2016).

The data is collected using polar orbiting satellites, which have an Operation Linescan system that can detect low levels of visible-near infrared radiance at night. NTL data was compiled from the Defense Meteorological Satellite Program (DMSP) for the years 2000 and 2010 allowing for cross-comparability. NTL is averaged across all grid cells within each district to obtain the mean NTL.

Soil Erosion

Soil erosion can lead to land degradation in the form of nutrient loss, a decrease in the effective root depth, water imbalance in the root zone, productivity reduction (Yang et al. 2003). Further, soil degradation can trigger food insecurity and contribute to vulnerability and poverty (Lal 2004, West et al. 2014). This paper uses the soil erosion dataset calculated using the adjusted Revised Universal Soil Loss Equation (RUSLE) model by Naipal et al. (2015). The RUSLE model predicts erosion rates and takes into account rainfall erosivity, slope steepness, slope length, soil erodibility, land cover, and support practice. The soil erosion rate for each pixel is averaged across all grid cells within each district to obtain the mean soil erosion at the 110-district level. Mean soil erosion is shown in Figure 3. Soil erosion is particularly serious in poor Upper West region.

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Less Favored Agricultural Land

Less favored agricultural land (LFAL) is susceptible to low productivity and degradation, because its agricultural potential is constrained biophysically by terrain, poor soil quality, or limited rainfall (Barbier and Hochard 2014). This paper uses the LFAL variable defined by Pender and Hazell (2000), World Bank (2008), and Barbier and Hochard (2014). LFAL consists of irrigated land on terrain greater than 8 percent median slope or poor soil quality, rainfed land with a length of growing period of more than 120 days but either on terrain greater than 8 percent median slope or with poor soil quality, semi-arid land (land with length of growing period (LGP) of 60–119 days), and arid land (land with LGP of less than 60 days). The global extent of agriculture is defined as all areas with 10 percent or greater cropland, grazing land or irrigated area net of areas with a growing period of zero days. The data source is from various years.

Road Density

The Netherlands Environmental Assessment Agency maintains the Global Roads Inventory Project (GRIP). GRIP combines data collected from about 60 public sources including the United Nations, national spatial data sources, topographic agencies, non-governmental and international organizations, and volunteered geographic information. HDD defines road density as the mean length of road in kilometers per cell, re-scaled from GRIP’s original 10km resolution to the boundaries of 110 districts. Values are for the year 2013, and include total road density, as well as disaggregated densities of highways, primary roads, secondary roads, tertiary roads, and urban/residential roads. Road density by district is shown in Figure 4. Road density is high along the coast and in major cities.

Distance to Market

The European Commission’s Joint Research Centre created a global map of travel time to cities from the data in World Development Report (World Bank 2009). Accessibility is defined as the travel time to a city of 50,000 or more people using road or water-based travel. Factors that influence travel time include available transport networks, environmental factors like land cover and slope, and political boundaries and borders. A cost-distance algorithm is used to determine the ‘cost’ of travelling between raster cells3, measured in time units. The resulting raster grid represents a ‘friction-surface’ of these costs. Mean distant to market by district is illustrated in Figure 4. It shows the distance to market is longer in poor rural areas.

Flood Occurrence and Drought Severity

HDD uses the flood occurrence and drought severity data that is made available by the World Resources Institute’s Aquaduct platform. Flood occurrence is defined as the number of flood events recorded from 1985 to 2011. Flood events from news, governmental, instrumental, and remote sensing sources are aggregated by the Global Active Archive of Major Flood, which is then used to estimate the extent of flooding in the affected regions. The remote sensing data is

3 A raster is composed of an array of equally sized cells arranged in rows and columns.

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taken from Brackenridge, the Dartmouth Flood Observatory and the University of Colorado (2011)4 and is in flood extent polygons. Drought severity measures the average length of droughts times the dryness of the droughts from 1901 to 2008. Drought is defined as a continuous period during which the soil moisture level is below the 20 th percentile for a given location. Length of drought is the number of months, while dryness is the average number of percentage points soil moisture drops below the 20th percentile. The number of floods and drought severity are shown in Figure 3. The fire show how disproportionally floods and drought are affecting poor northern districts.

Air pollution

Global fine particulate matter (PM2.5) concentrations were estimated using information from a combination of satellite-, simulation- and monitor-based sources. A Geographically Weighted Regression (GWR) is applied to global satellite-derived PM2.5 estimates. Aerosol optical depth from multiple satellite products was combined based on their relative uncertainties as determined using ground-based sun photometer (Aerosol Robotic Network (AERONET)) observations. The GWR predictors included simulated aerosol composition and land use information. The resultant PM2.5 estimates were highly consistent (R2 = 0.81) with out-of-sample cross-validated PM2.5 concentrations from monitors. The data is produced by and follow a similar methodology to the Global Burden of Disease report. This paper uses the mean PM2.5 air pollution levels at the 110-district level in 2000 and 2010 as an air pollution variable.

3. Trends of poverty, economic structure, human capital, infrastructure and environmental degradation

3.1 Trends of poverty

Regional inequality widened over years. Figure 1 shows the changes in poverty rates at the national and regional levels. Between 1991 and 2016, the national poverty rate declined from 52.7 percent to 23.4 percent. Ashanti, Eastern, Greater Accra, Brong Ahafo, Central, and Western regions experienced significant poverty reduction, as the poverty rates dropped by more than 20 percent in these regions. However, the poverty rates dropped by less than 20 percent points in the Volta, Upper East, Northern, and Upper West regions. In 2016, the poverty rates remain above 50 percent in the Northern, Upper West, and Upper East regions.

Poverty rates vary not only across regions but also across districts within regions. Figure 2 shows the district level poverty rates estimated by Coulombe (2005) and Ghana Statistical Service (2015) for 2000 and 2010, respectively. In 2000, poverty was widely spread in the northern parts of the country. However, the spatial distribution of poverty has changed

4 http://floodobservatory.colorado.edu/

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dramatically in 2010. The eastern part of the three Northern regions achieved significant poverty reduction, while several districts in the western side of the country witnessed increased poverty. Clusters of districts with high poverty rates also developed in the inland parts of the country and in Volta region.

Table 2 shows that the average poverty rates at the 110-district level declined from 46.9 to 29.3 percent between 2000 and 2010. However, the poverty rate of the poorest district remains high (81 percent).

Some of the poor districts in 2000 were able to drastically reduce poverty rates by 2010 while others were not. For example, Gushiegu-Karaga district in the Northern region reduced the poverty rate from 85.7 percent to 34.3 percent between 2000 and 2010, while the poverty rate dropped from 85.5 percent to only 70.3 percent in Nadowli district in Upper West region during the same period.

3.2 Economic structure

Table 1 shows the average employment rate at the 110-district level increased from 64.8 to 70.7 percent between 2000 and 2010. The employment rate increased for both men and women during the period.

Ghana went through a significant structural transformation between 2000 and 2010. The average share of employment in agriculture declined from 63.3 percent to 53.1 percent, while the average share of employment in services increase from 23.5 percent to 33 percent. The share of labor force in industry increased from 13.3 percent to 13.9 percent. In addition, the average income in the industry and service sector rose dramatically. Figure 5 shows the correlations of variables with the district poverty rates in 2010. The shares of employment in service and agriculture is highly correlated with poverty. The share of migrants within districts is also highly correlated with poverty rates. The total employment rates, as well as the employment rates of both men and women, are moderately correlated with the district poverty rates. The mean income in non-agricultural sectors is not strongly correlated with the district poverty rates.

Night time lights are not strongly correlated with poverty (Figure 5), however, they are highly correlated with agricultural income. As discussed in Section 2, night time lights often reflect the distribution of economic activities. Night time lights may be reflecting opportunities for farmers to get linked to market.

3.3 Human capital

Between 2000 and 2010, the mean fertility (average number of children per women) declined from 3.2 to 2.7 children. In addition, the share of working age adults who completed primary

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and secondary school increased from 50.6 to 67.7 percent, and 45.1 to 54.7 percent, respectively. Figure 5 demonstrates the share of working age adults who completed primary and secondary school completion is the most highly correlated variables with poverty among all variables considered in this study. The share of working age population as well as fertility are correlated with the district poverty rate, suggesting the human capital variables selected for this study are substantially important factors on poverty reduction. The human capital variables do not only correlate with poverty but also strongly relate to agricultural income and the share of labor by sector. Lower fertility and a higher share of working age population are associated with higher agricultural income, and a higher share of working age population and the education achievement are strongly correlated with a higher share of labor force in non-agricultural sector, and a lower share of labor in the agricultural sector (Figures 6 and 7).

3.4 Access to infrastructure

Access to electricity significantly improved between 2000 and 2010 (Table 1). The mean percentage of households with access to electricity increased from 29.2 percent to 48,8 percent between 2000 and 2010. The correlation coefficients of access to electricity, roads, and markets and poverty rates are reported in Figure 5. Access to electricity is exceptionally highly correlated with poverty. Figures 6 and 7 suggest access to electricity is the key factor facilitating the shift of labor from agriculture to industry and services.

3.5 Climate change and environmental degradation

Table 1 shows NPP (biological productivity) declined dramatically between 2000 and 2010. Figure 5 indicates NPP is higher in rich districts than poor districts. This suggests rich districts are endowed with biological productivity, while poor districts are poorly endowed with biological productivity. Figure 5 suggests poor districts also suffer from insufficient rainfall, severe droughts, higher flood frequency and serious soil erosion.

The mean poverty rate of the 10 districts with the most frequent floods is 41 percent, while the mean poverty rate of the 10 districts with the least flood frequency is 20.7 percent. Flood frequency is highest in Saboba, Chereponi, Zabzugu, and Tatale districts in the Northern region. In 2018 over 2,000 residents of Zabzugu district were evacuated due to floods .5 Zabzugu district often get cut off from the Northern region, as the main road gets flooded. In 2016, over 1,000 people were displaced from their homes in Chereponi district.6

The mean poverty rate of the 10 districts with the highest score of drought severity is 45.3 percent, while the mean poverty rate of the 10 districts with the least severe drought is 16.4 percent. Drought severity is highest in Bawku West and Bawku East districts in Upper The

5 http://hisz.rsoe.hu/alertmap/database/?pageid=event_desc&edis_id=FF-20180317-62281-GHA6 https://www.pulse.com.gh/news/northern-regional-floods-1000-displaced-in-floods-in-chereponi-district-id5008342.html

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poverty rates of Bawku West and Bawku East districts are 47.1 percent and 68.1 percent, respectively.

Many poor districts suffer from both floods and droughts. The old East Gonja district in Northern Region (which currently includes East Gonja and Kpandai districts under the 216-district system) had the highest poverty rates of 80.9 percent in 2012.7 It experienced 13.4 major floods between 1985 and 2011 on average, and its drought severity score is 24.1. These numbers are much higher than the average of 9.9 and 20.9 in the country, respectively.

In districts with low agricultural income, drought severity and soil erosion are significantly higher (Figure 6).

Districts with high shares of agricultural labor have less rainfall and biological productivity and suffer from more frequent floods and severe droughts as well as more intense soil erosion (Figure 6).

4. Panel data analysis

In the previous section, we have demonstrated how selected variables are strongly correlated with poverty rates. The fundamental setback of such simple statistical analysis is causal inferences. In this section, we conduct panel data regressions to investigate causal effects of the changes in the key variables on the changes in poverty. Panel data analysis is useful in controlling for the effects of other variables that are not included as independent variables in the regression model.

First, we estimate three alternative regression models and conduct several tests to find out which regression model is most appropriate for each estimation.

If district specific effect does not exist, ordinary least squares (OLS) produces efficient and consistent estimates.

Y ¿=α+ β X ¿+ε¿ui=0 (district specific effect=0)

If there are individual differences in intercepts, then a fixed effect model is suitable.

Y ¿=(α+ui)+β X¿+v¿

A random effect model assumes individual specific errors.

Y ¿=α+ β X ¿+(u i+v¿¿¿)¿

7 East Gonja is the poorest district when the poverty rates are estimated for the old 110 districts.

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Fixed effects are tested by the F-test, and random effects are tested by the Lagrange multiplier test (Breusch and Pagan 1980). If the null hypothesis is not rejected by the F-test, then the pooled OLD regression is favored to fixed effect regression. If the null hypothesis is not rejected by the Lagrange multiplier test, then the pooled OLD regression is favored to random effect regression. In addition, we conduct the Hausman specification test to compare a random effect model and a fixed effect model. The results of all tests, and a selected regression model for each estimation are summarized in Table A1 in the Appendix. In all cases, the Hausman specification test was not rejected. Thus, we use the random effect model (in Tables 2 and 3) unless it is rejected by the Lagrange multiplier test. When the random effect model is rejected by the Lagrange multiplier test but the fixed effect model was not rejected by the F-test, we use the fixed effect model (Table 4).

Table 2 contains the results of random effect regression results. The left column contains the regression result for poverty rates, and the right column contains the regression result for consumption. The regression results demonstrate poverty rates significantly declined and consumption increased in districts where income per worker in the service sector increased. Between 2000 and 2010, increases in the service sector income by GHC 1 million led to 1.2 percent point reduction in district poverty rates. Consumption also increased in districts where agricultural income per worker increased. Increased shares of working age population within districts decreased poverty and increased consumption. It is probably because the share of dependent household members declined in districts where poverty reduced, and consumption increased. An increase in employment rates (the percentage of working population who are economically active) also lead to poverty reduction and consumption growth. An increased employment rate by 1 percent led to a reduction in poverty rates by 1 percent point. Increased shares of workers in the service sector contribute to both poverty reduction and consumption increases. One percent shift of labor from agriculture to the service sector led to 0.6 percentage point poverty reduction.

We further investigate the difference in the impacts of labor participation by gender. In the regressions presented in Table 3, we disaggregate a share of working age population and employment rate into male and female. The regression results suggest an increase share of working population among male increases consumption and reduces poverty. On the other hand, increased employment rates of women weakly reduce poverty and increase consumption.

In the regressions presented in Table 4, we examine how human capital development, infrastructure improvement, and environmental factors have affected poverty reduction.8 In the first regression, we used the completion rate of primary school among working age adults as in independent variable, and in the second regression, we used the completion rate of secondary school among working age adults as in independent variable. the Poverty reduction 8 The regression does not include sector mean income as independent variables as there are too many missing observations, especially for industry and service sectors in poor districts.

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is associated with increased employment rates, improved access to electricity, and increased precipitation. One percent increase in the share of households with access to electricity led to 0.4 percent point drop in poverty rates. In addition, an increase in rainfall by 1 meter led to 0.2 percent reduction in poverty rates. The regression results indicate improved human capital indicators, such as fertility, educational achievement, and demographic change had no impacts on poverty reduction. It may be because ten years is not long enough to benefit from human capital development.

Next, the paper investigates factors that have affected changes in agricultural income and structural change. Since we do not have panel data for soil erosion, distance to market, road density, LFAL, flood frequency and drought severity, we cannot run the same model of panel data analysis. We estimate the following regression model instead.

∆Y ¿=α+β1∆ X1¿+ β2 X 2i+ε¿

where ∆Y ¿ is the annual change in income per worker in agriculture, annual change in the shares of agriculture, industry and services, respectively. ∆ X1¿¿ includes demographic and other variables that are panel data. X 2i includes other variables that are not a part of the panel data, i.e., soil erosion, distance to market, road density, LFAL, flood frequency and drought severity.

Table 5 shows soil erosion had a large impact on the changes in agricultural income. Agricultural income declined in districts with higher soil erosion. Improved access to electricity, roads, and markets were crucial for creating jobs in non-agricultural sectors. Increased shares of households with access to electricity led to a reduction in the share of workers in agriculture and an increase of the share of workers in industry. Between 2000 and 2010, a 1 percent improvement in the share of households with access to electricity increased the share of workers in industry and decreased the share of workers in agriculture both by 0.06 percent. Better access to markets and roads increased the share of employment in the service sector. In areas where droughts are severe, more people became engaged in agriculture and less in industry and services. It may be because severe droughts prevented people from accumulating capital to invest in non-agricultural sectors.

5. Conclusion and Policy Recommendation

This study combines district level poverty rates, population census data, income data, and geospatial data to investigate how human capital development, economic transformation, improved access to infrastructure and environmental degradation impacted poverty, agricultural income and allocations of labor in Ghana. We find poverty reduction was achieved through increased employment rates and income in the service sector, shifts of labor from agriculture to the service sector, expansion of access to electricity, and increased rainfall . In

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addition, educational achievement, access to electricity, shift of labor from agriculture to services are important determining factors of the level of poverty.

The paper further investigates the factors that have affected changes in agricultural income and structural change. Soil erosion had a huge impact on the changes in agricultural income. Districts with high shares of agricultural labor have less rainfall and biological productivity and suffer from more frequent floods and severe droughts as well as more intense soil erosion. In addition, they are far from markets, have lower road density, and lower economic activities. Improved access to electricity, roads, and markets were crucial for creating jobs in non-agricultural sectors. In areas where droughts are severe, more people became engaged in agriculture and less in industry and services. It may be because severe droughts prevent people from investing in non-agricultural sectors.

The results of this study have two key implications in terms of policy recommendations for Ghana to reduce poverty and create jobs in non-agricultural sectors in lagging areas. Firstly, Ghana needs to invest on infrastructure, which will improve access to markets, and value chains on agricultural commodities, which will enable structural change and the creation of jobs in agricultural and non-agricultural sectors. Such investments in infrastructure will reduce poverty whilst improving sustainable natural resource management.

In addition, the Government of Ghana needs to invest on a transformative landscape, and sustainable land and water management initiatives in districts affected by soil erosion and land degradation in the northern regions to boost agricultural production and reduce rural poverty. Such initiatives will also build the resilience of communities and ecosystems to climate change and natural shocks such as floods and droughts. In particular, soil erosion can be mitigated by introducing sustainable farming system and regulation of reservoirs for proper water utilization. Improved water infrastructure can provide water supply for agriculture, as well as prevent flooding.

The results of panel data analysis do not support the hypothesis that human capital development is a significant force behind poverty reduction. However, ten years may not be long enough to realize the benefit of human capital development. The analytical results suggest increase labor participation by women is effective in poverty reduction. Reducing fertility by family planning is a practicable way to encourage women to stay active in the labor force.

15

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Table 1: Summary table 2000 2010

Sample Mean Min Max Sample Mean Min Max

Poverty rate 110 0.469 0.052 0.861 110 0.293 0.025 0.809Economic structureEmployment rate (%) 110 0.648 0.430 0.852 110 0.707 0.515 0.848of men 110 0.661 0.455 0.874 110 0.716 0.492 0.865of women 110 0.634 0.410 0.833 110 0.698 0.537 0.829-Sector of employment (%)- Agriculture 110 0.633 0.058 0.890 110 0.531 0.023 0.912 Industry 110 0.133 0.039 0.368 110 0.139 0.021 0.305 Service 110 0.235 0.070 0.696 110 0.330 0.067 0.786-Income (million)- Agriculture 101 0.003 0.001 0.040 110 0.002 0.001 0.023 Industry 88 0.002 0.000 0.008 107 0.029 0.000 0.142 Service 93 0.003 0.000 0.042 110 0.025 0.003 0.160

NTL 110 2.656 0.00058.56

6 110 3.102 0.000 57.019Human capitalWorking age pop (%) 110 0.521 0.434 0.647 110 0.552 0.468 0.679Fertility 110 3.235 1.912 4.044 110 2.731 1.630 3.464Completed primary school (%) 110 0.506 0.103 0.838 110 0.677 0.192 0.928Completed secondary school (%) 110 0.451 0.094 0.770 110 0.547 0.116 0.833Infrastructure Access to electricity (%) 110 0.297 0.039 0.896 110 0.488 0.112 0.910

Road density 110 12.959 7.21534.59

0Distance to market 110 1.813 0.085 5.571Environmental factorsPrecipitation 110 1.002 0.802 1.255 110 1.292 0.932 2.013NDVI 110 0.293 0.093 0.548 110 0.334 0.080 0.548NPP 110 0.691 0.004 1.130 110 0.396 0.114 0.687

Air pollution 110 30.458 22.91135.37

7 110 32.86619.35

1 40.392

Soil erosion 110 4.587 0.12331.76

9LFAL 110 0.463 0.013 1.000

Flood frequency 110 9.857 2.00020.00

0

Drought severity 110 20.922 6.80627.59

6

19

Table 2: The impacts of changes in economic structure on the changes in poverty rates and consumption (panel data analysis): Random effect regressions

(1) (2) Poverty rate ConsumptionIncome per worker in agriculture -2.190 12,297.2***

(2.512) (3,559.6)Income per worker in industry 0.428 -288.2

(0.458) (665.7)Income per worker in service -1.199** 1,853.1**

(0.537) (788.4)Share of working age population (%) -0.950** 2,254.2***

(0.452) (633.2)Employment rate (%) -0.966*** 1,195.3***

(0.138) (207.7)% employed in industry -0.378* -180.4

(0.210) (300.9)% employed in service -0.635*** 964.8***

(0.126) (177.1)Constant 1.785*** -1,693.0***

(0.228) (327.1)Observations 193 192R-squared 0.55 0.61Number of districts 110 110

Standard errors in parentheses.*** p<0.01, ** p<0.05, * p<0.1.

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Table 3: The impacts of changes in economic structure and gender disaggregated labor participation on the changes in poverty rates and consumption (panel data analysis): Random effect regressions

(1) (2)Poverty rate Consumption

Income per worker in agriculture -1.107 12,856.6***(2.514) (3,531.6)

Income per worker in industry 0.189 150.1(0.464) (666.1)

Income per worker in service -1.302** 2,007.4***(0.534) (774.1)

Share of working age population among male (%) -2.077*** 3,462.5***(0.582) (801.5)

Share of working age population among female (%) 0.836 386.7(0.749) (1,045.6)

Employment rate of men (%) -0.267 -1,011.5**(0.353) (499.8)

Employment rate of women (%) -0.609* 2,041.0***(0.324) (464.3)

% employed in industry -0.327 -130.6(0.208) (295.1)

% employed in service -0.741*** 998.2***(0.130) (179.9)

Constant 1.540*** -1,368.7***(0.241) (341.5)

Observations 193 192R-squared 0.57 0.63Number of districts 110 110

Standard errors in parentheses.*** p<0.01, ** p<0.05, * p<0.1.

21

Table 4: The impacts of changes in human capital, infrastructure and environments on the changes in poverty rates (panel data analysis): Fixed effect regressions

(1) (2)Poverty rate Poverty rate

Share of working age population (%) -0.097 0.232(0.823) (0.873)

Employment rate (%) -0.509** -0.451**(0.203) (0.188)

% employed in industry 0.236 0.207(0.294) (0.294)

% employed in service 0.069 0.176(0.237) (0.258)

Fertility 0.048 0.029(0.063) (0.061)

% adults who completed primary school -0.037(0.269)

% adults who completed secondary school -0.451(0.467)

Access to electricity (%) -0.423** -0.401**(0.164) (0.162)

NDVI -0.035 0.006(0.140) (0.143)

NPP -0.060 -0.066(0.091) (0.088)

Precipitation -0.195*** -0.212***(0.069) (0.070)

Constant 1.038* 1.058*(0.563) (0.560)

Observations 220 220R-squared 0.705 0.708Number of districts 110 110 Standard errors in parentheses.*** p<0.01, ** p<0.05, * p<0.1.

22

Table 5: The impacts of changes in infrastructure and environments on the changes in agricultural income per worker and the shares of employment

Annual change in Income per worker in

agriculture

Annual change in % employed in agriculture

Annual change in % employed in

industry

Annual change in % employed in

serviceAnnual change in the share of working age population

0.007 -0.389** 0.033 0.356***(0.015) (0.165) (0.108) (0.135)

Annual change in the share of employed-0.009 0.991*** 0.001 0.009(0.008) (0.070) (0.047) (0.051)

Annual change in the share of migrants-0.006 -0.185*** 0.056** 0.129***(0.006) (0.037) (0.027) (0.039)

Annual change in the share of adults completed primary education

-0.022* -0.116* 0.056 0.060(0.011) (0.064) (0.042) (0.051)

Annual change in the share of households with access to electricity

-0.206 -6.503*** 5.529*** 0.974(0.298) (2.374) (1.875) (2.464)

Annual change of NDVI -0.000 -0.016* 0.002 0.015***(0.001) (0.009) (0.006) (0.005)

Annual change of NPP 0.001 0.007** -0.001 -0.006**(0.001) (0.003) (0.002) (0.003)

Annual change of precipitation -0.002 -0.028 0.023* 0.005(0.002) (0.020) (0.012) (0.018)

Soil erosion -0.000*** -0.000 0.000 0.000(0.000) (0.000) (0.000) (0.000)

Distance to market 3.709 47.685* 22.584 -70.269**(4.978) (27.399) (22.475) (28.704)

Road density 1.634 -5.962 -7.135* 13.097***(1.066) (5.039) (3.596) (4.905)

LFAL -0.000 -0.001 0.000 0.001(0.000) (0.001) (0.001) (0.001)

Flood 0.000 -0.000 0.000 0.000(0.000) (0.000) (0.000) (0.000)

Drought -0.000 0.000*** -0.000* -0.000***(0.000) (0.000) (0.000) (0.000)

Constant 0.000 -0.004** 0.000 0.004***

23

(0.001) (0.002) (0.002) (0.001)Observations 101 110 110 110R-squared 0.449 0.876 0.335 0.654Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

24

Figure 1: Poverty Rates by Region, Percentage

Ghana G. Accra Ashanti Eastern Central Western Brong Ahafo

Volta Upper East

Northern Upper West

0102030405060708090

100

52.7

26.0

41.048.0 44.0

60.065.0

57.067.0 63.0

88.0

31.9

13.524.0

17.823.4 22.9

34.0 37.3

72.9

55.7

89.1

23.4

2.511.6 12.6 13.8

21.126.8

37.3

54.861.1

70.9

1991 2005 2016%

Source: GLSS3, GLSS5, and GLSS7; GSS and World Bank staff calculations.

25

Figure 2: Poverty Maps in 2000 and 2010

2000 2010

Sources: Coulombe 2005; GSS 2015.

26

Figure 3: Flood, Droughts, and Soil Erosion

Number of floods from 1985 to 2011 Drought severity between 1901 and 2008

Soil erosion rate (various years)

Source: HDD

27

Figure 4: Access to Roads, Markets, and Electricity

Road density Distance to market (km)

Source: HDD Source: HDD

Share of households using electricity

2010 Population and Housing Census

28

Figure 5: Correlation with poverty rates (2010)

Income in industry

Income in service

Air pollution

NDVI

Income in agriculture

LFAL

Road density

Soil erosion

NTL

Flood frequency

Employment rate of men (%)

Employment rate of women (%)

Share of employment in industry (%)

Employment rate (%)

Drought severity

Fertility

Precipitation

Distance to market

Working age pop (%)

Share of migrants (%)

NPP

Share of employment in agriculture (%)

Sahre of employment in service (%)

Access to electricity (%)

Completed primary school (%)

Completed secondary school (%)

0.080

0.088

0.176

0.246

0.262

0.303

0.305

0.354

0.387

0.392

0.425

0.429

0.430

0.440

0.447

0.456

0.463

0.468

0.573

0.582

0.583

0.594

0.601

0.697

0.700

0.720

29

Significant at 1 percent

level.

Figure 6: Correlation with agricultural income and the percentage of employment in agriculture (2010)

Air pollution

NPP

Precipitation

Flood frequency

Employment rate of men (%)

Employment rate (%)

LFAL

Employment rate of women (%)

Completed primary school (%)

Distance to market

NDVI

Road density

Completed secondary school (%)

Access to electricity (%)

Soil erosion

% of migrants (%)

Drought severity

Working age pop (%)

Fertility

NTL

0.032

0.057

0.058

0.068

0.123

0.163

0.188

0.193

0.209

0.227

0.237

0.245

0.252

0.342

0.352

0.352

0.453

0.518

0.533

0.768

Agricultural income

Air pollution

NDVI

Soil erosion

Precipitation

Flood frequency

LFAL

NPP

Drought severity

Road density

NTL

Distance to market

% of migrants (%)

Fertility

Completed primary school (%)

Employment rate of men (%)

Employment rate of women (%)

Completed secondary school (%)

Employment rate (%)

Working age pop (%)

Access to electricity (%)

0.049

0.068

0.173

0.264

0.278

0.345

0.398

0.546

0.580

0.635

0.642

0.644

0.663

0.765

0.774

0.777

0.787

0.801

0.836

0.851

% of employment in agricul-ture

30

Figure 7: Correlation with the percentage of employment in industry and services (2010)

NDVI

Air pollution

Soil erosion

LFAL

Flood frequency

NTL

Road density

Precipitation

Fertility

NPP

Distance to market

% of migrants (%)

Drought severity

Completed secondary school (%)

Working age pop (%)

Completed primary school (%)

Employment rate of women (%)

Employment rate of men (%)

Employment rate (%)

Access to electricity (%)

0.002

0.104

0.161

0.271

0.323

0.363

0.373

0.410

0.417

0.425

0.454

0.471

0.568

0.600

0.607

0.617

0.622

0.635

0.648

0.649

% of employment in industry

Air pollution

NDVI

Soil erosion

Precipitation

Flood frequency

LFAL

NPP

Drought severity

Road density

% of migrants (%)

Distance to market

NTL

Fertility

Completed primary school (%)

Employment rate of men (%)

Employment rate of women (%)

Completed secondary school (%)

Employment rate (%)

Working age pop (%)

Access to electricity (%)

0.024

0.086

0.162

0.186

0.237

0.342

0.353

0.488

0.604

0.650

0.654

0.677

0.694

0.750

0.755

0.763

0.785

0.786

0.844

0.848

% of employment in service

31

Appendix:

Table A1: Post estimation test results

Fixed effect (F test) Random effect (Breusch-Pagan Lagrange Multiplier test)

Hausman test Selected model

Table 2 (1) Prob > F = 0.000 Prob > chibar2 = 0.000 Not conclusive Random effect

Table 2 (2) Prob > F = 0.000 Prob > chibar2 = 0.036 Not conclusive Random effect

Table 3 (1) Prob > F = 0.000 Prob > chibar2 = 0.001 Not conclusive Random effect

Table 3 (2) Prob > F = 0.000 Prob > chibar2 = 0.041 Not conclusive Random effect

Table 4 (1) Prob > F = 0.083 Prob > chibar2 = 0.154 Not conclusive Fixed effect

Table 4 (2) Prob > F = 0.106 Prob > chibar2 = 0.148 Not conclusive Fixed effect

32