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PACIFIC ISLANDS
LABOR MARKET NOTE
East Asia and Pacific Human Development Department
February 2011
Contents
Introduction.................................................................................................................................................................. 1
CHAPTER ONE: Trends in Employment, Unemployment and Inactivity; a low market equilibrium.................................................................................................................................................................... 3
1.1 Participation and Employment Rates............................................................................................3
1.2 Unemployment.........................................................................................................................................8
1.3 Inactivity...................................................................................................................................................16
1.4 Conclusions.............................................................................................................................................20
CHAPTER TWO: Industry, Types of Employment, and Wages............................................................21
2.1 Industry....................................................................................................................................................21
2.2 Types of employment.........................................................................................................................23
2.3 Wages.........................................................................................................................................................27
2.4 Conclusions.............................................................................................................................................32
CHAPTER THREE: Behind the Trends: Labor Supply.............................................................................33
3.1 Education - Providing the Necessary Basic Skills..........................................................................33
3.2 Tertiary and TVET - Managing Expectations...........................................................................38
3.3 Social Norms - Providing the Motivation to Work.................................................................41
3.4 Conclusions.............................................................................................................................................43
CHAPTER FOUR: The Missing Link: Labor Demand...............................................................................44
4.1 Domestic Demand................................................................................................................................44
4.2 International Demand........................................................................................................................47
4.3 Conclusions.............................................................................................................................................49
CHAPTER 5: Conclusions.....................................................................................................................................50
Appendices..................................................................................................................................................................52
Bibliography...............................................................................................................................................................70
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Boxes, Figures and Tables
Box 1: Differing Definitions....................................................................................................................................7Box 2: Measuring unemployment in the Pacific............................................................................................9Box 3: Push-out from the Formal Education Sector in Vanuatu..........................................................36Box 4: Functional literacy vs. reported literacy..........................................................................................38Box 5: Increased dialogue with the private sector – successful initiatives....................................41Box 6: Addressing Cultural Factors in the Labor Market – The Young Farmers Course in Fiji.......................................................................................................................................................................................... 43Box 7: Regional mobility schemes for low-skilled workers..................................................................49Box 8: Selectivity Bias............................................................................................................................................58
Figure 1: Labor participation rate, total (% of total population ages 15+), 2008..........................4Figure 2: Labor force participation rate, by age and sex...........................................................................4Figure 3: Participation Rates, International Comparisons.......................................................................6Figure 4: Share of youth unemployment..........................................................................................................9Figure 5: Activities of Working Age Population, Kiribati, 2006.............................................................9Figure 6: Unemployment by rural/urban location...................................................................................11Figure 7: Unemployment by gender................................................................................................................12Figure 8: Ratio of Youth to Adult Unemployment Rate, 2006..............................................................13Figure 9: Marginal Effects of Education on Unemployment.................................................................15Figure 10: Impact of age on unemployment, unconditional and conditional on individual characteristics, Marginal Effects.......................................................................................................................16Figure 11: Activities of the jobless...................................................................................................................18Figure 12: Truly Inactive, Vanuatu...................................................................................................................18Figure 13: Inactivity by age and gender.........................................................................................................19Figure 14: Youth Inactivity rates in Asia and the Pacific, ILO 2006 (%).........................................20Figure 15: Size of agriculture, forestry,& fisheries sectors, 2000 & 2008 (% GDP)...................22Figure 16: Proportion of population engaged in subsistence activities..........................................23Figure 17: Life cycle – proportion of workers by activity type............................................................24Figure 18: Impact of Education on Probability of being Engaged in Paid Employment (compared to no education), percentage marginal effect......................................................................25Figure 19: Marginal effect of Youth (15-34) on probability of gaining paid employment......26Figure 20: Wage Density by Gender and Age..............................................................................................29Figure 21: Youth (15-24) on wages, Coefficient from Heckman wage regression......................30Figure 22: Impact of Education on Wages, Coefficients from a Heckman Wage Regression. 31Figure 23: Proportion of Young People in Full Time Education..........................................................35Figure 24 Proportion of 15-19 year olds enrolled in Full Time Education....................................35Figure 25: Reason for leaving school, Vanuatu...........................................................................................37Figure 26: Reported Literacy..............................................................................................................................38
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Figure 27: Increasing Focus on Vocational Training - Proportion of age group whose highest qualification is Vocational/Tertiary, Tonga.................................................................................................39Figure 28: Time Spent by Unemployed in Vanuatu Searching for a Job by Age..........................39Figure 29: Searching for work for the first time after leaving school, Samoa (%) by age.......39Figure 30: Proportion at each age still living with parents...................................................................42Figure 31: Proportion of Household Heads by age..................................................................................42Figure 32: Most sought after characteristics in Tongan staff...............................................................46Figure 33: Gross International Migration Rate...........................................................................................47Figure 34: Remittances 2007 (% GDP)..........................................................................................................48Figure 35: Proportion of 15-19 year olds Currently in Education, by gender..............................57Figure 36: Low representation of women in the labor force................................................................58
Table 1: Labor Force Participation Rates.........................................................................................................6Table 2: Employment rate......................................................................................................................................8Table 3: Unemployment rate.................................................................................................................................9Table 4: Profile of the inactive, Tonga............................................................................................................17Table 5: Regional Enrolment levels in pre-primary education............................................................34
Acknowledgements
This report was prepared by Emily Farchy (EASHS), with inputs from Minna Hahn Tong (Consultant) and under the supervision of Philip O’Keefe (HD Sector Coordinator for Pacific, Timor-Leste and PNG) and Xiaoqing Yu (Sector Manger for Social Protection EAP). Wendy Cunningham (HDNVP) also gave useful guidance on report structure and analysis. It also benefited from inputs on education from Stephen Close and review of the initial draft by Christian Bodewig (EASHS). Michelle Lee provided outstanding technical support in finalizing the document. The study would not have been possible without the cooperation and generosity of Gerald Haberkorn, Manager,Statistics for Development Programme of SPC in Noumea, and his staff, who kindly shared data and assisted greatly with understanding the relevant data sets. The team is also grateful to Jimmie Rodgers, Director-General, as well as Bill Parr and Rose Maebiru from SPC for their support in this process.
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Introduction
1. In the Pacific Island Countries (PICs), demographic trends are putting greater pressure on the labor market. High fertility rates, together with low levels of migration (in the case of Melanesia), combine to generate persistently high population growth. With youth populations reaching 40%, the region’s labor markets are under pressure to generate more jobs. In Solomon Islands and Vanuatu, where formal sector employment is already among the lowest in the region, the working-age populations are expected to increase by another 30% within the next decade.1
2. At the same time, numerous factors are making employment opportunities increasingly scarce. In all PICs, small domestic markets, a narrow industrial base, weak private sector, and geographical remoteness which stymies international trade all limit private sector employment opportunities severely. Furthermore, pressure is likely to continue to reduce the heavily aid-dependent public sector, which currently accounts for the majority of formal sector employment.
3. Yet despite high unemployment, employers complain of difficulties recruiting appropriately skilled workers, and vacancies remain unfilled. In the Solomon Islands, 17% of public sector positions are vacant, three-quarters of these in critical skill areas (teachers, nurses, and public administrators) which indicates a significant mismatch of skills. While the available opportunities and skill capabilities vary widely across the region, the need to increase the supply of appropriately skilled labor and promote entrepreneurialism cuts across countries.
4. Given the lack of information and analysis on these issues to date, this report attempts to use existing country data to improve understanding of the key features of the PIC labor markets. This note aims to develop a clearer picture of labor market outcomes and their determinants. It begins in Chapter by describing the aggregate labor market situation in the selected Pacific countries, focusing on trends in employment, unemployment, and inactivity. Chapter 2 takes a closer look at this equilibrium, the sectors of employment, and wages received by existing employees. The subsequent chapters further investigate the labor supply and labor demand factors that may be driving the sub-optimal market equilibrium.
5. The analysis focuses on the countries of Kiribati, Samoa, Solomon Islands, Tonga, and Vanuatu. The analysis draws upon five quantitative data sets: the Kiribati Household Income and Expenditure Survey 2006, the Solomon Islands Income and Expenditure Survey 2005/2006, the Vanuatu Household Income and Expenditure Survey 2006, and the Samoa Population and Housing Census 2006. It also makes some use of the Tonga Population and Housing Census 2006. The quantitative analysis is
1 Home and Away, 2006.1
informed and underpinned by qualitative information gathered by World Bank staff from consultations and informal focus groups in Tonga, Solomon Islands, and Vanuatu in early 2010.2
6. The focus countries were selected based on data availability as well as an attempt to capture the diversity of the region. The countries examined in this analysis differ in terms of their economic development, geography, ethnic compositions, and migration patterns. A large degree of cultural and economic homogeneity exists among the areas of Melanesia, Micronesia, and Polynesia. Of the countries chosen for further study, Samoa and Tonga belong to Polynesia, Vanuatu and the Solomon Islands are part of Melanesia, and Kiribati, with its large number of atolls and reef islands, is part of Micronesia. While the Melanesian countries tend to be richer in arable land and resources, Polynesia, with its links to Australia and New Zealand, tends to be more developed economically. Tonga and Samoa3 have relatively liberal access to New Zealand under a quota arrangement4, while remittances have played a much smaller role in the development of Vanuatu and the Solomon Islands.
7. The analysis suggests that three factors are critical in explaining employment and unemployment patterns in the region. First, the limitations of the labor supply: the skills output of the education system, the unrealistic expectations engendered by a culture of qualification inflation, and the cultural norms of mutual support that can, in some instances, lessen the urgency of seeking work. The second factor is the limited demand for labor in the underdeveloped private sectors of the small and remote Pacific Island economies. The final factor is the poor equilibrium that results from this mismatch of supply and demand.
8. However, it should be noted that given the heterogeneity of countries and the limitations of the data, it is difficult to draw general conclusions about the regional labor market or compare across countries. Although progress is being made in improving the coverage, relevance, and comparability of data in the region, some limitations still persist. As a result, cross-country comparisons should be made with caution. This analysis therefore focuses on within-country trends.
9. It should also be emphasized that while the findings suggest a number of policy implications, the principal focus of this report is diagnostic rather than policy-oriented. Further information and analysis is needed as a basis for developing specific policy recommendations. However, it is hoped that the empirical analysis presented here will be useful for national policy makers and donors as they assess the outcomes of current labor market policies and consider future directions.
2 The consultations were conducted with government, non-governmental organizations, the donor community, church and community leaders, private sector employers and employer placement agencies, and youth (see Appendix 2: List of consultations undertaken in support of quantitative findings).3 1,100 per year are entitled to enter New Zealand, provided they have secured a job.4 Gibson et al, 2008.
2
CHAPTER ONE: Trends in Employment, Unemployment and Inactivity; a low market equilibrium
10. Although data limitations make cross-country comparisons difficult, country-level analysis reveals disparities in employment and unemployment among various subgroups that are similar to those found in other countries. As described in this chapter, gender and locational disparities are evident in many PIC labor markets. Striking differences can also be found in employment and unemployment rates between different age groups, similar to the experience elsewhere. This chapter begins by examining participation, employment, and unemployment rates within the PICs, focusing in particular on comparisons among major population subgroups. However, the findings are highly dependent on disparate methodologies and definitions. Those who are not in formal employment—who are part of the subsistence economy, engaged in non-formal employment, or dependent on their families for support—may be un- or under- employed without appearing as such in the labor force statistics. Thus, section 1.3 delves deeper into underemployment by looking at inactivity.
1.1 Participation and Employment Rates
11. While Pacific labor force participation rates do not, on the whole, appear out of sync with the regional average and international comparators, much of this may result from data measurement issues. Labor force participation rates in the PICs, as measured in this paper, range from just over 30% in Kiribati to 70% in the Solomon Islands. Compared to the regional average of 74.6%5 this appears low (Figure 1). Indeed, even this low measurement of labor force participation represents an upper bound since, due to data deficiencies, the labor force measure in the Solomon Islands does not exclude those that are inactive. Due to these and other methodological inconsistencies outlined below, comparisons made within countries—across location, sex, and age—may be more informative than cross-country comparisons. The remainder of this section therefore focuses on within-country comparisons for a selection of PICs.6
5 ILO, KILM, 2009.6These methodological differences also lie behind the disparities in figures drawn from different sources,--for example, Figure 1 (drawn from the WDI database) and Table 1 (drawn directly from the HIES) are not directly comparable.
3
Figure 1: Labor participation rate, total (% of total population ages 15+), 2008
0 20 40 60 80 100
BarbadosTrinidad and Tobago
SurinameVanuatu
CambodiaNew Zealand
ThailandPapua New Guinea
JapanTonga
Fiji
Note: The participation rate consists of those who are either employed or actively seeking employment. It excludes those who are sick, disabled, engaged in household duties, or enrolled in full-time education.
Source: World Development Indicators from World Databank online
12. Overall rural and urban participation rates are not markedly different, although a comparison between age groups in rural and urban areas reveals greater disparities. Rural participation is marginally higher than urban participation in Kiribati, Vanuatu, and the Solomon Islands and slightly lower in Samoa. However, a more discernable difference can be seen in the age at which individuals join the labor market in rural and urban areas, with rural youth participating at a substantially higher rate than their urban counterparts. A possible explanation is that youth in rural areas leave school earlier since productive activities in family agriculture can provide an immediate source of employment, an opportunity which is unavailable to urban youths. The participation trend is reversed in adulthood, with urban participation rates for those between the ages of 35 and 55 being higher than for those in rural areas.
13. In terms of gender, participation rates tend to be higher for males than for females across the four countries studied (see Figure 2). In Solomon Islands and Vanuatu, female participation hovers between 60-70%, around 10-20% lower than male participation). Female participation hovers around 40% in Samoa and 25% in Kiribati, while male participation reaches 80% in the former and over 45% in the latter. Furthermore, while males are more likely to participate in the labor force as they age through their 20s and 30s, the participation rate of women remains fairly constant after the age of 20. As a result, the gap in gender participation rates increases with age. However, these gender disparities remain less marked than in other parts of the world such as South Asia and the Middle East and North Africa. In the region, Indonesia, the Philippines, and Thailand have male participation rates of over 80%, compared to female participation rates of around 50%, 50%, and 65%, respectively.
4
Figure 2: Labor force participation rate, by age and sexVanuatu
15-19 20-24 25-29 30-34 35-650%
10%
20%
30%
40%
50%
60%
70%
80%
90%
MaleFemaleEducation
Kiribati
15-1
9
20-2
4
25-2
9
30-3
4
35-6
50%
10%
20%
30%
40%
50%
60%
70%
MaleFemaleEducation
Samoa
15
-19
20
-24
25
-29
30
-34
35
-650%
10%
20%
30%
40%
50%
60%
70%
80%
90%
MaleFemaleEducation
Solomon Islands*
15-1
9
20-2
4
25-2
9
30-3
4
35-6
5
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
MaleFemaleEducation
Source: HIES and Census data. * Solomon Island HIES has no labour market component and no information on inactivity, these estimates therefore represent an upper bound on participation rates
5
14. Significant disparities are also evident between youth and the older adult population, which tends to have higher participation rates (Table 1). The magnitude of this disparity varies from country to country. Youth in Kiribati are over 8% less likely to participate in the labor force than those over 25 years of age. In Tonga, youth are nearly 25% less likely to join the labor force, and in Solomon Islands, the youth employment rate is over 30% lower than adult participation (Table 1). This phenomenon of lower participation among youth can be seen in labor markets elsewhere, as well (Figure 3) particularly in other small islands in the Caribbean. It should be noted that many of the youth currently outside the labor force are enrolled in full-time education.
Table 1: Labor Force Participation Rates15-24 25-64 Total (15-64)
Tonga** 47.9% 72.4% 58.0%Samoa 37.0% 61.1% 52.9%
Vanuatu* 52.3 72.3% 65.6%Kiribati* 24.5% 33.0% 30.3%
Solomon Islands* 48.5% 83.2% 69.3%Note: Participation rate is defined as the ratio between the labor force and the working age population (ILO), where separation of inactivity from the labor force in Solomon Islands and Samoa is not possible due to data
deficiencies.Source: HIES, Census.* Estimates from Household survey samples. **15-30
Figure 3: Participation Rates, International Comparisons
Austra
lia
New Z
ealand
Cam
bodia
Thaila
nd
Indonesia
Philippin
es
Barbados
Trinid
ad and T
obago
Jam
aica
Vanuatu
Tonga
Sam
oa
Solo
mon Is
lands0
102030405060708090
Labour force particip-ation rate (%) 15-24
Labour force particip-ation rate (%) 15-64
Note: Figures may vary from those calculated directly from HIES (Table 1), particularly in the case of the Solomon Islands where HIES data gives no information on inactivity which is therefore inseparable from the
labor force.Source: ILO, KILM (2009).
6
Box 1: Differing Definitions
Defining employment and unemploymentAn individual is classified as unemployed if they are not in employment but are actively seeking employment (Box 2). All PICs adhere to this definition, although until 2006, Tonga made no explicit reference to ‘actively looking for work,’ and Samoa differentiates between ‘previously employed’ and those ‘looking for a job for the first time.’ Four different measures emerge from across the region: (i) listing specific economic activities including a separate category for ‘unemployed’—this is used in Samoa and Tonga as well as the Cook Islands, French Polynesia, New Caledonia, and Wallis and Futuna; (ii) ‘Not working’ AND ‘actively looking for employment’ (includes subsistence activities)—this is used in Kiribati, the Solomon Islands, and Vanuatu as well as Fiji, Niue, PNG, Tokelau, Tuvalu; (iii) in American Samoa, CNMI, FSM, Guam, Nauru, and Palau, individuals must, in addition to the requirements of the second definition, be ‘able to take a job if offered’; (iv) used only in the Marshall Islands is the second definition but excluding subsistence activities.7
In addition to these differing definitions of unemployment, there are further differences in the questions regarding the reasons for inactivity: Vanuatu, Tonga, and Kiribati offer ‘do not want to work’ alongside education, household duties, retired and disabled, and ‘subsistence’ activities which create further difficulties for cross-country comparisons. Kiribati offers the additional alternatives of ‘seasonal worker,’ ‘temporary layoff,’ ‘job to start in future,’ and finally ‘no work available’ (which captures discouraged workers). In addition, Kiribati distinguishes between ‘reason for not working’ and ‘reason for not working in the last 7 days,’ which additionally includes, illness, weather, layoff, strike and ‘family member sick.’ The Samoan Census, as mentioned above, does not distinguish between unemployment and inactivity and thus has no follow-up questions. Finally, the Solomon Islands HIES contained no labor force component. Thus, similar to Samoa, for the purposes of this study, unemployment in the Solomon Islands includes the inactive as well as those actively seeking employment.
Defining youth
In the Pacific, the category ‘youth’ is more usually defined in terms of progression through life. It is the acquisition of responsibilities—such as marriage or becoming the head of a household—that is cited as the moment of progression from youth rather than a strict definition in terms of years. Thus, while national definitions vary across the region (e.g., 15-30 years in Kiribati, 12-25 in PNG,12-29 in Samoa, 14-29 in Solomon Islands, and 15-30 in Vanuatu), the social conception of youth tends to cover the period from the age of 15 to 35 (the age at which many become head of a household- see Figure 31). Indeed, as can be seen from Figure 30, by the age of 40, some 23% of Samoans are still living as youth in the households of their parents.
15. Likewise, employment rates are substantially higher for older adults than for young people (Table 2). In addition to the lower labor force participation rates among youth, even those who do enter the labor force are less likely to be found in
7
Error: Reference source not found.7
employment. The employment rate among young people in the Solomon Islands is just 43% compared with 67% among adults. This disparity is even starker in Kiribati with 52% of those young people in the labor force finding employment compared to 88% of adults—over 35% more. While the disparities in Samoa and Vanuatu are smaller in magnitude, much of this stems from the way unemployment has been measured, and the relative rates are still striking (Table 2)8.
Table 2: Employment rate15-24 25-64 Total (15-64)
Samoa 96.2% 99.45% 98.68%Vanuatu* 97.55% 99.32% 98.81%Kiribati* 52.12% 87.84% 78.54%
Solomon Islands* 43.43% 66.76% 60.23%
Estimates from Household survey samples. Note: Employment rate is defined as the proportion of the labor force
that is employed (ILO).Source: HIES, Census.
1.2 Unemployment
16. Due to major disparities in the definition of unemployment - and its measurement - across countries, analysis must focus on within-country comparisons. As with employment rates, due to differing survey methodologies, unemployment rates in the PICs are not easily comparable (Box 2). For example, countries such as the Solomon Islands and Vanuatu, very similar in their economic structure and in the large share of the labor force made up of informal employment, experience vast differences in unemployment rates. One issue is that inactivity is significant across many of the PICs, and much of the cross-country disparities revealed by the unemployment figures stem from differences in the way survey instruments approach - or fail to approach - this question.9 Given these data issues, within-country analysis may again be more informative than cross-country comparisons.
Box 2: Measuring unemployment in the Pacific
The standard international definitions of unemployment may not capture the full extent
8 The disparity between participation rate and employment rate in Solomon Islands is 30%. However, due to the lack of a labor force component, this figure includes all unemployment (including the inactive, who are not participants of the labor force) and may therefore be misleadingly large.9 The labor force question on the Samoan census does not probe into the question of inactivity, instead assuming that, if not working, an individual is either unemployed and actively seeking employment or ‘unable to do any activity due to old age or disability.’ The Solomon Islands HIES (2006) has no labor force component, thus unemployment again refers to those not working (and not in education or domestic duties) irrespective of whether they are actively seeking employment. Therefore, unemployment is somewhat higher. Only the Kiribati HIES, includes an option for ‘seasonal worker’ and ‘job to start in future.’
8
of unemployment in the Pacific Island Countries. Standard international census-based definitions of unemployment capture all people 15 years of age and older who, in the reference week prior to the census, (i) did no work (or undertake any productive activity, which includes subsistence farming and fishing for own consumption), (ii) actively looked for work, and (iii) were available and willing to start work if a job was offered to them. Only when people satisfy all three criteria are they classified as unemployed. This definition, while widely used internationally, underestimates the ‘true’ extent of unemployment in the Pacific where a large proportion of those not working are inactive in the labor market—not in school, not working, and not looking for work.
While discouraged worker effects are evident in many labor markets internationally, the problem is thought to be particularly acute in the Pacific. In the Tonga 2006 census, for example, only 388 people fulfill the criteria for the standard definition of unemployment–this represents just 1.1% of the total labor force. However, there was a group of 195 people that did not work because of bad weather conditions or because they could not afford the transportation costs to work. In addition, a group of 1,241 people did not work and did not look for work because they believed that no work was available. If all these people were included in the unemployed category, the unemployment rate would increase to 5.1%.10 Furthermore, if the 1,449 people who classified their activity as subsistence but who live in urban areas were included in the unemployment figures, the rate would rise to 9.2%.
Table 3: Unemployment rate Figure 4: Share of youth unemployment
15-24 25-64Total (15-64)
Tonga 1.7% 0.7% 1.1%
Samoa* 3.8% 0.6% 1.3%
Vanuatu 4.3% 1.6% 2.3%
Kiribati 47.9% 12.2% 22.4%Solomon Islands* 56.6% 33.2% 39.8%
Solomon Islands
Kiribati
Vanuatu
Samoa
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
38.8
58.1
52.4
68.6
60.2
41.9
47.6
31.4
15-2425-65
Source: Census, HIES.Notes: Unemployment rate is the proportion of the labor force (not in full-time education, engaged in domestic duties, or inactive where inactivity separable) that is unemployed. The * symbol indicates that the numbers do not separate those actively seeking employment from the inactive due to lack of data
Figure 5: Activities of Working Age Population, Kiribati, 2006
10 It has been argued that most (if not all) residents of Kolomotu’a, Kolofo’ou, and Neiafu reporting to work primarily as subsistence farmers are more likely to be unemployed, given widely recognized land shortages for agricultural production within urban boundaries (Haberkorn, 2006). Accepting this premise could put urban unemployment as high as 23.9%, raising the national average to 9.2%.
9
Working age population0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Discouraged
Inactive (don't want to work)
Disabled
Retired*
Students
Household duties
Unemployed (seeking em-ployment)
Working
Source: Kiribati HIES, 2006.* 247 individuals list themselves as retired despite being of working age, including 67 individuals under the age of 25.
17. In terms of location, as in the case of participation rates, disparities in unemployment in urban and rural areas vary by age group. In general, unemployment is more prevalent among the young in both rural and urban areas. In Samoa, where census data provides a larger and more accurate sample, the urban unemployment rate begins at a higher rate than that in rural areas but decreases with age at a faster rate. By their mid to late 20s, workers in rural and urban areas in Solomon Islands, Kiribati, and Samoa face the same unemployment rate (Figure 6). This pattern may stem from the tendency of rural families who own land to need family members to work on it.
18. Analysis of gender disparities suggests that higher unemployment among women is driven largely by characteristics (such as educational attainment) shared by women, rather than resulting directly from their gender. The descriptive statistics presented in Figure 7 show a mixed picture of relative gender employment rates across countries. However, once factors such as education level, age, marital status, number of dependents, and remittances are taken into account, gender itself does not appear to be a significant determinant of unemployment (see Appendix 8).
10
Figure 6: Unemployment by rural/urban location
Solomon Islands Kiribati
15 -19 20 -24 25 - 30 30 - 34 35 - 650
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Rural
Urban
15 -19 20 -24 25 - 30 30 - 34 35 - 650%
10%
20%
30%
40%
50%
60%
70%
Rural Urban
Samoa Vanuatu
15 -19 20 -24 25 - 30 30 - 34 35 - 650%
2%
4%
6%
8%
10%
12%
Rural Urban
15 -19 20 -24 25 - 30 30 - 34 35 - 650%
1%
2%
3%
4%
5%
6%
7%
Rural Urban
Note: Figures not directly comparable across countries (see Box 2).Source: HIES.
11
Figure 7: Unemployment by gender
Samoa Vanuatu
15 -19 20 -24 25 - 30 30 - 34 35 - 650%
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
Male Female
15 -19 20 -24 25 - 30 30 - 34 35 - 650%
1%
2%
3%
4%
5%
6%
Male Female
Kiribati Solomon Islands
15 -19 20 -24 25 - 30 30 - 34 35 - 650%
10%
20%
30%
40%
50%
60%
70%
80%
Male Female
15 -19 20 -24 25 - 30 30 - 34 35 - 650%
10%
20%
30%
40%
50%
60%
70%
Male Female
Note: Figures not directly comparable across countries (see Box 2).Source: HIES.
19. Consistent with the age-related findings on participation and employment, the unemployment rate is higher among young people compared to older adults (Figure 8). This pattern holds across the four countries studied. In Tonga, Vanuatu, and Samoa, the unemployment rate among youth is 1%, 2.2%, and 2.7% higher, respectively, than the rate among adults. In Kiribati and the Solomon Islands, the unemployment rate among young adults is 20%-30% higher than among older adults.
12
Figure 8: Ratio of Youth to Adult Unemployment Rate, 2006
World
South Asia
South-East Asia & the Pacific
East Asia
Latin America & the Caribbean
Middle East
North Africa
Sub-Saharan Africa
Solomon Islands*
Kiribati*
Vanuatu*
Samoa*
Tonga*
0 1 2 3 4 5 6 7 8
Source: ILO, 2010 *HIES and Census data, 2006
20. Notably, young people account for the majority of unemployment in the Pacific region. In the Solomon Islands, youth make up approximately 23% of the labor force, but their share in unemployment is nearly 39%. In Vanuatu, where youth comprise 24% of the labor force, their share in unemployment is over 53% (Figure 4). Youth in Kiribati make up 21% of the labor force and contribute over 58% to unemployment, while in Samoa, youth comprise 20% of the labor force but make up nearly 69% of unemployment. The shares of youth unemployment in total employment elsewhere in the region tend to be lower or around the same levels (excluding the Solomon Islands)—for example, 48.9% in the Philippines, 60.8% in Indonesia, and 52.5% in Thailand11. In developed countries such as Australia and New Zealand, the share is 40.8% and 45.3%, and in Japan, this figure lies at just 18%.
21. Beyond its correlation with experience, age should have little effect on the productivity of workers and hence the demand for labor, suggesting that other factors must be behind these dynamics. Even after taking job tenure into account, analysis of the country data finds that youth are significantly more likely to be found in unemployment.12 It is likely that age is proxying for some other attribute of the adult workforce that is systematically more conducive to employment. If unemployment in the region is to be addressed, it will be important to understand whether this attribute is driving the adult workforce to supply their labor in larger numbers or whether this attribute is leading employers to demand older workers in larger numbers. The
11 ILO, 2008a.12 Tenure does not perfectly capture experience since it only captures experience in current position, however it is likely to pick up much of the effect, since experience within a firm is likely to be more valued than experience elsewhere.
13
remainder of this section explores two possible explanations for the disparities among age groups.
22. One possible characteristic demanded by employers that may be correlated with age is cognitive skills, but the effect of education on unemployment runs counter to expectations. Primary education is associated with increased probability of unemployment compared to having no education in both Vanuatu (by 1.2%) the Solomon Islands (by 6%) and Kiribati (by 6%) (Figure 9).13 Similarly, those with secondary education are conditionally more likely to be found in unemployment, by 8% in the Solomon Islands and by 0.4% in Samoa. While those with a certificate or diploma are less likely (8% in the Solomon Islands14) to be found in unemployment, tertiary education in both Vanuatu and Samoa is associated with a negative effect on employment. These results may be suggestive of the higher reservation status demanded by individuals with education, leading to queuing behavior for formal, public sector, and wage jobs. The results also suggest a possible excess in supply of highly educated labor and/or a quality of education that employers do not deem substantial enough to justify higher reservation wages.15 While such effects of education on unemployment may seem counterintuitive, they mirror patterns seen in some other developing countries.
13 It is important to bear in mind that unobservable characteristics (such as wealth, connections, motivation) may be affecting selection into these courses.14 Though 1% more likely in Samoa.15 A second explanation for this seemingly undesirable impact of education may be definitional. Unemployment refers only to those who are actively seeking employment and does not include the inactive (those who are not searching). Thus, the results outlined above reflect not only how the employability of individuals varies with education but also how these characteristics are associated with their tendency to search for employment. As Appendix 9 shows, the young, the rural, and those belonging to households in which more members are already employed are less likely to search for employment when they are jobless. The more educated, on the other hand, are more likely to search for employment, which may be partially driving the finding that lower levels of education are associated with unemployment (Appendix 8).
14
Figure 9: Marginal Effects of Education on Unemployment
Vanuatu Solomon Islands Samoa Kiribati
-0.04
-0.02
0
0.02
0.04
0.06
0.08
PrimarySecondaryCertificateTertiary
Source: Regression Analysis on Census and HIES data (see Appendix 7).Note: Results in white denote statistically insignificant coefficients.
23. A second possible explanatory factor behind the disparities among age groups is the presence of behavioral attributes in workers which are not captured in the data. Even after controlling for education levels, youth remains significantly associated with increased unemployment (see Figure 10), suggesting that other factors may be influencing outcomes. One possibility may be that youth is correlated with immaturity and a concomitant lack of behavioral skills. In the absence of the quantitative data on the types and levels of behavioral skills that has been highly informative elsewhere measures of responsibilities such as marriage and household head status are likely to be correlated with maturity and as such can provide some indication of the importance of these behavioral skills. Individuals who head a household have significantly lower probability of being unemployed in all countries (see Appendix 7). In Vanuatu, heading a household reduces the probability of being in unemployment by 5%, in the Solomon Islands by a massive 28%, and in Kiribati by 2.6%. Marriage, which is frequently used in the Pacific as a signal of the end of youth as described in Box 1, has a similar effect in reducing the probability of unemployment.16
16 While it could be argued that the effect of marriage in reducing unemployment is due to the fact that women leave the labor force after getting married, this argument is not supported by the finding that marriage has no effect in increasing inactivity levels (relative to unemployment) even when domestic duties are included in inactivity (see Appendix 9).
15
Figure 10: Impact of age on unemployment, unconditional and conditional on individual characteristics, Marginal Effects
Vanuatu Solomon Islands Samoa Kiribati
-0.06
-0.04
-0.02
0
0.02
0.04
0.06
0.08
0.1
0.12
UnconditionalConditional on educationConditional on education and ma-turity signals*
Source: Regression Analysis on Census and HIES data (see Appendix 7)
Error: Reference source not foundNote: Results in white denote statistically insignificant coefficients. All results include controls for gender, education, rural-urban location.
24. The age-related disparities in unemployment are also likely to be explained by differences in experience levels, but data to explore this hypothesis is not available. Unfortunately, the existing household surveys do not adequately capture experience amongst the unemployed. However, it may be fair to assume that experience is driving some of the remaining impact of age on unemployment, once education and signals of maturity have been taken into account.
1.3 Inactivity
25. In addition to unemployment, inactivity—particularly among youth—is increasingly being recognized as a concern in the PICs. As described earlier in the chapter, unemployment figures do not capture the full extent of underemployment of labor in the Pacific region. Given the inter-dependence of unemployment and inactivity—youth focus groups in Tonga and Solomon Islands17 reveal that much of the inactivity stems from frustration with the lack of labor market opportunities—forgone productive potential is likely to be much higher than unemployment statistics alone suggest. This section therefore takes a closer look at inactivity in the Pacific region, with a focus on youth in light of growing concerns for this particular group.
26. While ‘inactive’ youth, especially girls, may be busy in productive non-market (home) work, many inactive youth are not engaged in any meaningful activity. In
17 Conducted by World Bank staff in January 2010 in Solomon Islands and Tonga. 16
Samoa, for example, 37% of women of working age are jobless as are 14% of men, but these figures increase to 43% and 15%, respectively, for only the population between the ages of 15 and 35. However, only 1.2% of these jobless females and 4.6% of the jobless males are actually unemployed (that is, actively seeking work).
27. To analyze the causes of inactivity, it is necessary to break joblessness down into its component parts. These parts include:
- Jobless: the person is not in school and not working. People who are jobless are either looking for a job (unemployed) or inactive in the labor market.
- Unemployed: the person is not in school or working but is available for work and searching for a job. All unemployed people are classified as participating in the labor force.
- Inactive in the labor market: the person is not in school, not working, and not looking for a job. People in this position can be divided into four further categories:
a. Truly inactive: the person is not in school, not working, and has no intention of finding work. Truly inactive workers can be further divided into two subgroups: discouraged workers (those who do not look for work because they believe no work is available) and seasonal workers who have no intention of working in the off-season. In the PICs, these two subcategories can be supplemented by a third: those that do not work and do not look for work because they do not have the inclination to work.
b. Active in non-market activities: the person is dedicating his or her time to home duties.
c. Other reasons: the person is not working or studying due to illness, old age, disability, or other reasons.
Table 4: Profile of the inactive, Tonga Male Female 15 - 19 20 - 24 25 - 29 30 - 34 15 - 19 20 - 24 25 - 29 30 - 34Not interested in finding work 67.6% 66.6% 58.5% 50.3% 58.3% 52.1% 43.9% 36.1%Weather condition 2.5% 1.0% 1.7% 1.8% 1.5% 1.3% 0.7% 0.6%Believes no work available 15.9% 15.2% 14.0% 13.4% 13.5% 11.1% 7.1% 6.0%Retired disabled family responsibilities 13.8% 16.8% 25.0% 34.3% 26.3% 35.3% 48.0% 57.0%Cannot afford transportation cost 0.3% 0.4% 0.7% 0.2% 0.4% 0.2% 0.3% 0.3%
Source: Tonga, 2006
17
28. Youth, both male and female, tend to be more likely to be truly inactive in the labor market than adults (Figure 13). Indeed, three-quarters of those who claim to have ‘no interest in finding work’ in Vanuatu are below the age of 35 (Figure 12). This result continues to hold when factors such as gender, urban/rural location, education, position in household, and number of employed members in household are taken into account (Appendix 9). Conversely, in both Vanuatu and Kiribati, adults between the age of 35 and 50 tend to be more likely to be active.
Figure 11: Activities of the joblessKiribati Tonga
Active in nonmarket activities, household duties
Other legitimate nonmarket activities (disabled, retired)
Series1 134 13%
Unemployed
Somewhat unemployed
Active in nonmarket activities, household duties
Other legitimate nonmarket activities (disabled, retired)
Truly Inac-tive
Weather condition
Retired disabled family responsibilities
Do not want to work
Believes no work available
Truly Inactive
Source: Tonga Census 2006, Kiribati HIES 2006
18
Figure 12: Truly Inactive, Vanuatu
Source: Vanuatu HIES, 2006
Figure 13: Inactivity by age and genderTonga Vanuatu Kiribati
15 - 1
9
20 - 2
4
25 - 2
9
30 - 3
4
35 - 3
9
40 - 4
4
45 - 4
9
50 - 5
4
55 - 5
9
60 - 6
4
0%
5%
10%
15%
20%
25%
30%
35%
40%
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%male female
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
0%
5%
10%
15%
20%25%
30%
35%
40%
45%
Source: Tonga census, 2006 Vanuatu HIES, 2006. Kiribati HIES, 2006
29. In terms of international comparisons, while inactivity rates in Samoa and Tonga are high relative to the Asia Pacific region, inactivity rates in the remaining PICs are not regional outliers.18 Variations in youth inactivity rates are quite large across the Asia Pacific region. Countries such as the Republic of Korea have youth inactivity rates as high as 60 per cent, while others such as Australia and China have rates around 30 per cent (Figure 14).19
18 This may be partly attributable in Samoa and Tonga to higher outmigration and selection bias among young people staying in-country.19 These figures are derived by the ILO Trends Team, using econometric modeling techniques and are not entirely consistent with the inactivity rates derived from the household survey and census data.
19
Figure 14: Youth Inactivity rates in Asia and the Pacific, ILO 2006 (%)
AustraliaJapan
New ZealandChina
MongoliaFiji
FrenchGuam
PNGSamoa
Solomon IslandsTonga
VanuatuCambodiaEast TimorIndonesia
Lao PDRMalaysia
MyanmarPhilippinesSingapore
ThailandViet Nam
East A
siaSou
th Eas
t Asia
0 10 20 30 40 50 60 70
Source: ILO 2008a, youth 15-24
1.4 Conclusions
30. While the labor market situation in the PICs is generally consistent with the experience of other developing countries, the factors underlying the age-related disparities in employment merit further attention. As described above, locational and gender disparities are evident in many PIC labor markets but are not staggering compared to regional averages. However, as in many parts of the world, age-related disparities stand out more starkly, with a clear pattern of lower participation, higher unemployment, and higher inactivity among youth compared to older adults, even after accounting for educational disparities and tenure. The finding that youth are not only less likely to be employed but are also less likely to actively search for employment indicates that both supply and demand factors may be at work. These factors are examined in later chapters.
20
CHAPTER TWO: Industry, Types of Employment, and Wages
31. The unemployment rates described in the previous chapter, while low, do not capture the full extent of forgone productivity. In Tonga, where 57% of the working age population is economically active, only 37% receive a regular paid income. Informal subsistence work is likely to represent an important sector for many, particularly those in Melanesia. However, it has been argued20 that much of what is currently ascribed to subsistence—particularly among young people and among residents of urban areas—merely represents quasi jobless, unproductive labor or ‘helping round the house’ (focus group participant) while waiting for productive employment.
32. An analysis of the characteristics of workers across industries and types of employment can help shed light on the factors that drive supply and demand in the labor market. This chapter examines the characteristics of workers across industries and types of employment in an effort to infer whether education, age, gender or something else is driving supply or demand to a given industry or type of employment. The chapter then turns to the correlates of wages to delve deeper into the causes of unemployment and investigate the reservation status hypothesis. Four broad labor market categories—wage employment, self-employment, subsistence and public sector employment (since this is the most prestigious and sought-after employment)—are considered.
2.1 Industry
33. Agriculture is a critical component of private sector activity in the Pacific Islands. Subsistence agriculture accounts for 38% of employment in Samoa— over 50% for males but less than 10% for women21, a pattern which holds irrespective of age. In Papua New Guinea, over 80% of the population lives in rural areas and engages in subsistence or smallholder agriculture.22 For the majority of the population in the PICs who live in rural areas, agriculture represents the mainstay of their income, employment, and livelihood. However, it should be noted that despite accounting for a large proportion of the workforce, the agriculture and forestry sectors generally account for only 10-30% of GDP in the PICs (Figure 15).
20 Haberkorn, 2007.21 Census 2006.22 Pacific Economic Review.
21
Figure 15: Size of agriculture, forestry,& fisheries sectors, 2000 & 2008 (% GDP)
Source: ADB (2009)
34. Agriculture remains an unpopular occupational choice for young people. Young people tend view agriculture as a demeaning occupation and as something to do if necessary while waiting for more suitable employment. The reluctance of youth to enter a career in the agricultural field, along with limited access to credit, markets, and new production techniques, can stymie the agricultural entrepreneurialism necessary to enter the production of niche agricultural products.
35. Although natural resources—including fisheries, logging and mining—are vitally important as a source of employment, these resources are fast diminishing. In 2008, logging accounted for 13 per cent of government revenue and almost two-thirds of exports in the Solomon Islands, but logging rates are well beyond the sustainable yield. If this pace continues, natural forests will be exhausted by 2015. PNG’s mining and petroleum sector continues to be a major export and a significant revenue earner for the government, contributing 36 per cent to total revenues in 2008.While the PNG Liquefied Natural Gas Project, expected to commence in 2013, has an expected life of 30 years, the Government of PNG faces considerable challenges in managing the project, including providing a skilled workforce.
36. The industrial sectors within the PICs remain small, and the development of microenterprises is inhibited by lack of capital. The industrial sectors that do exist in the PICs are often sustained by import restrictions, and given their size and isolation, these fledgling industries are often limited to internal markets and provide little employment. Lack of capital is frequently highlighted as a key factor behind the inhibition of microenterprise within the industrial sectors23, and 70% of Tongans felt that there is not sufficient venture capitalist funding available for new and growing
23 Youth focus group, Cunningham, Solomon Islands; youth focus group, Farchy, Tonga; Labour Division, MCIE, Solomon Islands.
22
firms.24 While micro loan and grant initiatives have been introduced in the Solomon Islands to encourage entrepreneurialism in the industrial sector25, the results remain mixed, and a lack of entrepreneurialism and financial management has often impeded such efforts.26
37. Public sector employment accounts for a large proportion of wage employment and is the most coveted and competitive sector. The public sector encompasses nearly 60% of those engaged in wage employment in Kiribati and over 26% of those engaged in paid employment in Vanuatu. In the Solomon Islands, public sector employment accounts for over 41% of wage employment.
2.2 Types of employment
38. In many Pacific countries, a substantial part of the labor force is engaged in subsistence activities. In Kiribati, 16% of the labor force is engaged in subsistence activities, compared to 25% in Samoa and the Solomon Islands and 43% in Vanuatu (Figure 16). In Tonga, the proportion of subsistence workers (21%) was much higher in rural (outer islands) areas than in urban areas, where only 9% of the population was engaged in subsistence work.
39. In general, labor in the Pacific region appears to be mobile across different types of employment (Figure 17). As age increases, more young adults are able to gain wage work and move out of subsistence activities. This pattern is indicative of the pragmatic approach outlined by youths in informal focus group discussions in Tonga and the Solomon Islands, which suggested that young people work in subsistence activities (often on small family-owned plots) to help the
household while waiting to find employment.
24 GEM, 2009.25 Labour Division, MCIE.26 Labour Division, MCIE.
23
Figure 16: Proportion of population engaged in subsistence activities
15-1
9
20-2
4
25-2
9
30-3
4
35-3
9
40-4
4
45-4
9
50-5
4
55-5
9
60-6
4
0%
10%
20%
30%
40%
50%
60%
70%
Paid work
Own Business
Subsistence Work
Source: HIES
Figure 17: Life cycle – proportion of workers by activity typeVanuatu: Male Vanuatu: Female
15-1
9
20-2
4
25-2
9
30-3
4
35-3
9
40-4
4
45-4
9
50-5
4
55-5
9
60-6
4
0%
10%
20%
30%
40%
50%
60%
70%
Paid work
Own Business
Subsistence Work <15 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65+0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Solomon Islands: Male Solomon Islands: Female
15-1
9
20-2
4
25-2
9
30-3
4
35-3
9
40-4
4
45-4
9
50-5
4
55-5
9
60-6
4
65+
0%
10%
20%
30%
40%
50%
60%
70%
80%
15-1
9
20-2
4
25-2
9
30-3
4
35-3
9
40-4
4
45-4
9
50-5
4
55-5
9
60-6
4
65+
0%
10%
20%
30%
40%
50%
60%
70%
80%
Tonga: Male Tonga: Female
15 -
19
20 -
24
25 -
29
30 -
34
35 -
39
40 -
44
45 -
49
50 -
54
55 -
59
60 -
640%
10%20%30%40%50%60%70%80%90%
15 -
19
20 -
24
25 -
29
30 -
34
35 -
39
40 -
44
45 -
49
50 -
54
55 -
59
60 -
64
0%
10%
20%
30%
40%
50%
60%
70%
80%
Kiribati: Male Kiribati: Female
15-1
9
20-2
4
25-2
9
30-3
4
35-3
9
40-4
4
45-4
9
50-5
4
55-5
9
60-6
4
0%
10%
20%
30%
40%
50%
60%
70%
<15 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65+0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Samoa: Male Samoa: Female
<15 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65+0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
<15
15-1
9
20-2
4
25-2
9
30-3
4
35-3
9
40-4
4
45-4
9
50-5
4
55-5
9
60-6
4
65+
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Source: HIES, Census 2006
40. Engagement in subsistence activities is high among young people, while paid employment follows an inverted U shape across the lifecycle. Despite their
24
reluctance to work in agriculture, engagement in subsistence activities is high among young people, then declines starkly with age. The proportion of young male workers engaged in subsistence activities falls roughly by half - from 60% in Vanuatu, the Solomon Islands, Kiribati, and Samoa (50% in Tonga) at age 15-19 to about 30% by age 30. At the same time, the proportion of workers engaged in paid work appears low for youth across all countries studied but rises continuously between the ages of 15-25 and falls away after the age of 50. While this inverted U applies to both male and female workers, female employment peaks at lower levels and earlier than male employment in four of the five countries.
41. The probability of paid employment increases with higher levels of educational qualifications, at all levels starting from secondary school(see Appendix 12).The largest marginal impacts can be seen with certificates and tertiary education, which raise the expected probability of being in paid employment by about 30%, 15%, 30%, 65% in Vanuatu, Solomon Islands, Kiribati, and Samoa respectively. This finding indicates that at higher levels, education is valued by employers. In contrast, the finding that primary education reduces the probability of paid employment compared to having no education suggests that the skills acquired in primary school are not deemed by employers as sufficient to justify the increase in reservation wage.
Figure 18: Impact of Education on Probability of being Engaged in Paid Employment (compared to no education), percentage marginal effect.
Vanuatu Solomon Islands Kiribati Samoa
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
PrimarySecondaryCertificate/diplomaTertiary
Source: Regression Analysis on Census and HIES data (see Appendix 12).Note: Results in white denote statistically insignificant coefficients.
42. Education at lower levels appears to increase the likelihood of self-employment. In contrast, higher qualifications—including certificates and diplomas—have the tendency to decrease the likelihood of self-employment in both Vanuatu and the
25
Solomon Islands (see Appendix 11).27 This finding may result from the fact that those in self-employment do not require higher education as a signal of their skills.
43. Aside from age and education, signals of maturity also appear to increase the probability of wage employment. (Figure 18) shows that even after controlling for education levels, young workers are 5% less likely to be engaged in wage work in Vanuatu, 8% less likely in the Solomon Islands and Samoa, and 10% less likely in Kiribati. As in the case of unemployment rates as discussed in Chapter 1, it appears that other factors may be at work. When signals of maturity such as marriage and status as a household head are taken into consideration, the significance of chronological age systematically reduces in Vanuatu and Solomon Islands (see Appendix 10).Marriage increases the probability of working for pay across all countries, with the magnitude of this effect ranging from 2% in Vanuatu and the Solomon Islands to over 7% in Samoa. Being a household head has a similarly positive effect on the probability of paid employment. These findings suggest that maturity may be one of the true characteristics that wage employers are seeking.
Error: Reference source not foundFigure 19: Marginal effect of Youth (15-34) on probability of gaining paid employment
Vanuat
Solomon Islands
Kiribati
Samoa
-10% -9% -8% -7% -6% -5% -4% -3% -2% -1% 0%
Source: Regression Analysis on Census and HIES data (see Appendix 13)
Error: Reference source not foundNote: Omitted Category is unpaid employment, includes controls for education, gender and rural/urban.
27 A small sample of self-employed in Kiribati renders little significant results.26
2.3 Wages
44. One question emerging from the analysis of employment in the Pacific region is whether high labor costs or high reservation wages are contributing to unemployment. This section attempts to address this question by examining wage levels in the PICs—including the factors that contribute to higher reservation wages—and the characteristics of workers that may be associated with higher or lower wages. It is important to note that due to the lack of a labor force component in the Tonga Census, Samoa Census, and Kiribati HIES, the analysis for this section is drawn solely from the Solomon Islands HIES 1995/96 and the Vanuatu HIES 1996.
45. Unit labor costs, defined as the ratio of wages to productivity (value added per employee) appear to be relatively high in the PICs, driven in part by high public sector wages.28Not only can high labor costs undermine international competitiveness, but they can also exacerbate unemployment problems when the private sector chooses to hire foreign workers.29 Notably, public sector wages (including government business enterprises), which are supported by the inflow of aid, are high and exert substantial pressure on private sector salaries. In addition, high public sector wages attract the most able employees, lowering the productivity of the pool available to the private sector and further augmenting the unit labor costs they face.
46. Substantial remittances in those PICs with access to overseas labor markets also raise the reservation wage and thereby increase the unit cost of labor even further. Tonga, where an average of 1,800 people (net) migrate overseas every year30, received $100 million in remittances both in 2007 and again in 2008.31 Per capita, this amounts to $827, or $1537 per member of the working age population. To put this in perspective, the bottom tier of government workers—meaning those in the most prestigious, highly paid, and competitive sector—are paid just $47 per week ($2444 pa) on average.
47. Within a given sector, female workers do not earn significantly less than their male counterparts (Figure 20), although there is some evidence of a glass ceiling for women. Women earn more when they are young but less than their male counterparts when they are old,32which is indicative of a glass ceiling. In government
28 Unit labour costs in manufacturing in Fiji and Papua New Guinea have been found to be considerably higher than in the rapidly growing countries of East Asia such as Indonesia, Malaysia, and Thailand (Duncan (1996)). More work is needed on this subject.29 Tonga Chamber of Commerce.30 Tonga Census, 2006. Estimated indirectly by applying the demographic balancing equation to the known 1996-2006 intercensal population growth rate and estimated crude birth rate and crude death rate (SPC).31 World Bank 2010.32 This pattern is statistically significant in government employment in both Vanuatu and the Solomon Islands, and while the trend is evidenced in all wage work more generally, the statistical significance falls below 5%.
27
employment in Vanuatu and the Solomon Islands, females tend to earn more when the sample is restricted to youth, but no effect is discernable among adults. During informal consultations with the private sector in both Tonga and the Solomon Islands, employers noted their preference for hiring women over men and attributed this to the perceived enhanced behavioral skills of women. According to this hypothesis, men are likely to leave the formal education system with few behavioral skills, so when hiring youth, employers exhibit a preference for women. As individuals mature and gain responsibilities and skills, this advantage erodes and even reverses.
28
Figure 20: Wage Density by Gender and Age0
.000
1.0
002
.000
3.0
004
kden
sity
wag
e_1j
ob0 10000 20000 30000 40000 50000
x
Youth (15-29) Adults (30-64)
Male FemaleVanuatu
0.0
001.0
002.0
003.0
004.0
005
kden
sity
wag
e_1j
ob
0 10000 20000 30000x
Youth (15-29) Adults (30-64)
0.0
001.0
002.0
003.0
004
kden
sity
wag
e_1j
ob0 10000 20000 30000 40000 50000
x
Youth (15-29) Adults (30-64)Solomon Islands
0.0
002.
0004
.000
6.00
08kd
ensi
ty w
age_
1job
0 20000 40000 60000x
Youth (15-29) Adults (30-64)
0.0
002.0
004.0
006.0
008
kden
sity
wag
e_1j
ob
0 10000 20000 30000 40000x
Youth (15-29) Adult (30-64)Source: HIES and Census data, 2006
48. Looking across age groups, youth appears to have a consistently negative impact on wages, even when education and signals of maturity are taken into account (Figure 21). In the Solomon Islands, the impact of age on wages diminishes when education levels are taken into account and even further, in both Vanuatu and the Solomon Islands, when signals of maturity are taken into account.33 However, some impact of age still remains, indicating that while some employment bias against youth can be explained by characteristics such as education, some of this bias remains.
33 While signals of maturity appear to affect whether an individual is successful in finding employment, they do not significantly affect wages (see Appendix 13), indicating that these attributes in and of themselves do not increase productivity but merely indicate whether a worker is likely to be reliable.
29
Figure 21: Youth (15-24) on wages, Coefficient from Heckman wage regression
.
Vanuatu Solomon Islands
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0
UnconditionalConditional on EducationConditional on education and ma-turity signals
Source: Regression Analysis on Census and HIES data (Appendix 13).Note: Results in white denote statistically insignificant coefficients, omitted category is 50-65, the
coefficients on the dummy for age 25-49 are insignificant
49. Higher levels of experience among older adults may account for some of the remaining effect of age on wages, although this hypothesis cannot be tested with the available data. Unfortunately, the available data does not capture years of experience. While tenure may be correlated with experience, tenure only captures experience gained within the current position. The data indicate that the response of wages to tenure is small in Vanuatu and insignificant in the Solomon Islands. The limited impact confirms that tenure is not highly rewarded by employers, reflecting a sentiment expressed by the participants of focus groups. This result is counterintuitive in a context where employers assert that workers with the appropriate behavioral skills are hard to identify.34
50. In the Pacific, the concept of reservation wage is accompanied—and in cases such as Tonga, even trumped—by a reservation status. A reservation status is an unwillingness to accept employment in an occupation that is deemed less distinguished. More educated workers often hold a higher reservation wage than their less educated counterparts—that is, they are less inclined to accept employment at a wage below what they think they deserve, given the investment they have made. This higher reservation wage can manifest in two ways: either wages may be higher for educated workers if the education is deemed ‘worth it’ by employers, or unemployment may be higher if employers are not willing to offer the higher wage.
51. Looking more closely at the effect of education on wages, primary and secondary education appear to have a negative or insignificant effect. The analysis finds that primary education has a negative impact on wages in Vanuatu and an insignificant
34 This result also highlights the rationality of undertaking education and then waiting in unemployment for a better job offer, because the opportunity costs of the wait are low.
30
impact in the Solomon Islands. In both countries, secondary education has an insignificant effect on wages. These results suggest a possible inadequacy in the quality of primary school education.
52. Data from Vanuatu indicates that workers with a certificate or diploma tend to earn more than their uneducated counterparts, although basic skills may be an important prerequisite for TVET. A substantially higher variance may indicate the importance of basic skills as a prerequisite for TVET. The high variance supports anecdotal evidence showing that the productivity of graduates of TVET courses can be undermined by a lack of basic and behavioral skills (ADB) but may also be an indication of the uneven quality of TVET courses.
53. Despite their increased tendency to struggle to find employment, once employed, workers with a tertiary education have significantly higher earnings than those without an education. In Vanuatu, workers with tertiary education earn between 70-80% more than their uneducated counterparts, a phenomenon which is particularly pronounced among female wage workers (Figure 22). In the Solomon Islands, those with a tertiary education tend to earn 40% more than those without an education.
Figure 22: Impact of Education on Wages, Coefficients from a Heckman Wage Regression
Vanuatu Solomon Islands
-1.5
-1
-0.5
0
0.5
1
edprimedsecedcertedtert
Source: Regression Analysis on Census and HIES data (Appendix 14)
Error: Reference source not foundNote: Results in white denote statistically insignificant coefficients.
31
2.4 Conclusions
54. The findings presented above underscore the challenges faced by the Pacific countries in expanding productive employment opportunities for their populations. The opportunities for employment in industries outside subsistence agriculture remain quite limited. The shrinking pool of natural resources, fledgling state of the industrial sectors, and lack of capital for microenterprises mean that a significant expansion of jobs is unlikely in the near future. The relatively high unit labor costs also have the potential to exacerbate unemployment. A key question that emerges is how the PICs will handle the growing number of educated young workforce entrants who seem reluctant to engage in agriculture and who are mostly competing for a limited number of spots in the public sector.
55. The findings on paid employment and wages also help explain the unemployment trends discussed in the previous chapter. As in the case of employment and unemployment, age appears to have an impact on wage levels. However, it is difficult to discern whether age itself is influencing wages or if other factors are at work. Higher levels of education do seem to pay off eventually in the form of higher wages, although the limited number of opportunities means that graduates with higher degrees may face a longer search for employment that they believe is commensurate with their education level. This queuing for more desirable jobs with higher wages may at least partly explain the negative relationship between education and employment found in the previous chapter. Chapters 3 delves deeper into labor supply issues, examining the importance of factors such as worker skills, expectations, and motivation in the labor market.
32
CHAPTER THREE: Behind the Trends: Labor Supply
56. For the Pacific region to improve productivity and labor market outcomes, the labor force must be equipped with the appropriate skills, expectations, and motivation for employment. Particularly given the employment trends among youth described in previous chapters, it will be important for young labor market entrants to have the appropriate:
o Skills: To increase the employability and entrepreneurialism of young people, the education system must equip them with the skills demanded by the private sector.
o Expectations: To match supply and demand in both the high- and low-skilled labor markets (i.e. avoiding excess supply of high-skilled tertiary educated workers and excess demand for low-skilled workers with sound cognitive and non-cognitive skills), expectations must be aligned with opportunities.
o Motivation: To reduce unemployment or inactivity engendered by familial dependency, young people must have the motivation to plan and seek productive career paths.
57. This chapter examines the importance of these factors in the labor market, with the view that if the supply of productive labor can be strengthened, demand will follow. The chapter begins with an examination of the relevance of the skills provided by the education systems of the Pacific region. It then looks at the role played by expectations and addresses the motivation for employment, or lack thereof. Clearly, these three aspects of labor supply are highly interdependent; the skills acquired from education will play a central role in shaping the expectations of young people. Similarly, along with their skills, the motivation of young people to join the labor force and their expectations when doing so will influence the extent to which they rely on familial support in the absence of what they deem to be ‘suitable’ employment.
58. Migration is an increasingly important option for Pacific workers which will require further analysis. Many workers now look to foreign labor markets as their ultimate potential destination, particularly in Polynesia where there is a strong history of migrant labor. The following analysis does not delve deeply into the issue of migration and its requirements in terms of skills. Clearly, however, this is an important issue for Pacific policymakers as they seek to structure relevant educational systems.
3.1 Education - Providing the Necessary Basic Skills
59. The Pacific region is faced with the challenge of improving education to equip workers with the skills demanded by the market. Ensuring that education effectively addresses both individual and societal needs, providing the necessary quantity—in terms of reducing the number of drop-outs—and the necessary quality—in terms of
33
increasing skill acquisition while in school—remains a problem across the region. This section examines basic education in the Pacific in an attempt to understand the quality of labor supply and the extent to which a misaligned education system may underpin the vulnerabilities of unemployed or inactive youth. The following section then looks at the systems of further education, both tertiary and technical education.
60. Formation of critical behavioral skills is limited by the insufficient availability of early childhood education (see Table 5). Early childhood education is not easily accessible for many Pacific Island children. However, the importance of early childhood development (ECD) in preparing children for better educational performance, improving future productivity, and other social outcomes has been widely acknowledged following impact evaluations of many ECD projects undertaken internationally. That these are the very skills lacking in much of the youth population in the Pacific suggests that the limited levels of pre-primary education may affect potential productivity and social cohesion.
61. Although primary school enrolment is relatively high in the Pacific, further progress is needed. Below the age of 15, approximately 80 percent of youth in Vanuatu remain in full-time education. This varies little by gender and only incrementally by rural-urban location (Figure 24). Nevertheless, significant room for improvement remains in some countries, and those countries with rapid population growth will be challenged to provide sufficient facilities and resources for expanding student populations.
34
Table 5: Regional Enrolment levels in pre-primary education
Gross Enrolment Ratio in Pre-Primary Education, 2007
Fiji 16Samoa 48Solomon Islands 35Tonga 23Vanuatu 7Thailand 95China 42Philippines 48New Zealand 93Japan 86Australia 100
Source: UNESCO Education for All, Global Monitoring Report 2010
Figure 23: Proportion of Young People in Full Time Education
Figure 24 Proportion of 15-19 year olds enrolled in Full Time Education
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 300%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Kiribati Solomon Islands Vanuatu Samoa
Kiribati Vanuatu Solomon Islands0
10
20
30
40
50
60
70
80
Urban rural
Source: HIES
62. School dropout rates, particularly in the transition from primary to secondary school, are high in many Pacific countries (Figure 23). The transition rate from primary to secondary education was under 65% in both Tonga and Vanuatu. Thus, the proportion of young people enrolled in full-time education fell dramatically between the ages of 15-19, to approximately 35-40% in Vanuatu. Notably, it is at this age that unemployment and inactivity starts to become increasingly prominent, before peaking between the ages of 20-24. The decline in school attendance is particularly notable in rural areas (Figure 24). The overall transition rate from primary to secondary education is reported as relatively high in Solomon Islands—88.1% in 2009 with school drop-out increasing but still only 4% at Pre-Secondary level35. By the age of 15, many young people in the Pacific have left full-time education, with the rates in Melanesia lower at all points (Figure 23).
63. Many of the early dropouts are involuntary, as many youth are pushed out due to rationing of places (Box 3). In Vanuatu, over 40% of dropouts are caused by progressive selection examinations to enter higher levels of school education (Box 3). The consequences of such selective education systems are profound. In the Caribbean, not only did such a system of rationing have implications for the skill acquisitions of young people, but it also sent a strong message that those who ‘failed the test’ were second-class citizens, leading to anger, frustration, and possibly the accompanying negative behaviors. Solomon Islands is seeking to address the challenge of a system designed to push out students as it increases transition to junior secondary. The advent of Community High Schools has been a significant initiative to expand access to the full nine years of basic education, including junior secondary level.
35 Performance Assessment Framework 2007-2009.
35
Box 3: Push-out from the Formal Education Sector in VanuatuEducation in Vanuatu has, until recently, been neither free nor compulsory. In cases where financial constraints necessitated a choice, families have usually chosen to educate the males. School fees have therefore been a major contributing factor to the lower representation of girls in education. However, recent progress has been made toward gender parity, and while females are less likely to have attended school than their male counterparts, current gross enrolment rates show that this finding should diminish.
A serious concern is the push-out rate for both boys and girls in school years 7, 11, and 13 as a result of national exams. Approximately 6,000 children take the national exams that are given in sixth grade, vying for 1,000 places in secondary school. In Year 10, children then take more national exams to compete for roughly 300 seats in Year 11. In Year 12, more exams are taken to get into Year 13, which is the final pre-university year and for which only 100 or so seats are available. The push-out rate for girls is particularly high in years 11,12, and 13, and the development of an educated elite is the inevitable outcome.
Source: Strachan, J: Gender and the formal education sector in Vanuatu (2004) Development Studies Network
64. However, more than poor completion rates, it is poor quality that inhibits skill formation among Pacific youth. Basic education should provide graduates with sound behavioral, literacy, and numeracy skills upon which they can build in secondary school or take into the workforce. While evidence in this area remains weak across the region, the existing evidence suggests that basic education in many PICs fails to adequately impart critical cognitive, non-cognitive, and behavioral skills, and many youth leave school without the basic skills necessary to be productive additions to the labor force.
65. Indeed, many students leave school because they feel that the education system does not provide enough benefits relative to the costs of schooling . While some youth leave school involuntarily due to the rationing of places described above, many leave because they feel that the education system, geared toward preparing students for employment in the public sector (where employment prospects are extremely limited), is anachronistic. They do not believe that the education system represents a financial investment sound enough to justify the costs of schooling (Figure 25). Countries in which Fee-Free Basic Education policies do not exist or are not fully implemented or compensated experience a cost burden on families of school attendance.36,37 In Solomon Islands, a survey of Honiara and Malaita found that 50% of respondents cited geographic distance as a barrier to attending primary school, 47% cited parents who wanted them to stay home to help, 39% cited school fees as too high, and 27% as other
36 Lovegrove, ASPBAE, Papua New Guinea 2007.37 Barriers to Education Study, December 2011.
36
costs being too high38. Even when fee-free basic education is introduced, costs remain and pose a barrier to participation.39
Figure 25: Reason for leaving school, Vanuatu
Male Female Male FemaleUrban Rural
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
DisabilityFamily problemsExpelled from schoolDidn't pass examsDistance to travelSchool fees problem
Note: Excludes those who have finished school (and ‘other’).Source: HIES, Vanuatu.
66. A telling indicator of quality of education is low literacy rates. In the Solomon Islands, the functional literacy of those gaining a primary education is as low as 28.1%, and even of those who have completed secondary school, less than half are literate. 40 In addition, preliminary findings from a recent World Bank Early Grade Reading Assessment in Tonga imply that only 43% of grade 3 students could read and understand a text provided in the survey.41 A sound understanding of the extent of this problem will be critical in any attempt toward addressing youth unemployment, and baseline studies of core skills acquisition are needed across the region in order to assess the efficiency of the education system. Part of the challenge is proper diagnosis and official recognition of the scale of the problem, as official literacy figures typically massively overstate functional literacy as measured by more reliable tools (Box 4).
38 Solomon Islands Educational Experience Survey 2007.39 Barriers to Education Study, 2011.40 Solomon Islands Educational Experience Survey 2007.41 Tonga Early Grade Reading Assessment (EGRA) Baseline Survey. World Bank. 2010.
37
Box 4: Functional literacy vs. reported literacyUsing census-derived data as a basis for calculating literacy rates is widely acknowledged to be flawed. With answers given by the heads of households on behalf of other members of the family, it represents household opinion and is a very inaccurate proxy for true functional literacy.
Figure 26: Reported LiteracyFor example, while the Solomon Islands Household Income and Expenditure Survey estimates literacy rates of over 80% (Figure 27), the results of recent survey work to test functional literacy in the Solomon Islands (ASPBAE 2007) indicate that just 17% of respondents were functionally literate, 42.7% semi-literate, and 40.2% non literate. A similar report commissioned by AusAid in Vanuatu found a 27% literacy rate42, contrasting starkly with the rate of over 90% derived from household survey data (Figure 27).
Source: GMR and HIESs
3.2 Tertiary and TVET - Managing Expectations
67. Although the PICs have focused increasingly on expanding TVET, an unrealistic reservation status among those who complete their education may be contributing to youth unemployment and inactivity. Regional policies have focused on creating and/or expanding TVET institutions to help increase productivity and fill skill shortages. While only 10% of 30-34 year olds in Tonga have TVET qualifications, this figure rises to 12% among 25-29 year olds (Figure 27). However, it appears that the strong emphasis on formal qualifications engenders an unrealistic reservation status among those who complete their education. As discussed earlier, many educated youth feel reluctant to accept low-skilled employment for which they feel overqualified and instead prefer to wait in unemployment, supported by their family networks, for what they consider to be a suitable public sector job. Indeed, informal youth consultations suggested that it is a widely accepted ‘career path’ to wait for employment for 6-12 months after leaving school. This is particularly true for those who have completed higher-level education (Figure 28 and Figure 29).
42 Early and Tamtam 2007.
38
Figure 28: Time Spent by Unemployed in Vanuatu Searching for a Job by Age
Figure 29: Searching for work for the first time after leaving school, Samoa (%) by age
<15 15-19 20-24 25-35 35+0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
1 week1 month3 months6 months1 year
34
32
30
28
26
24
22
20
18
16
14
0 0.005 0.01 0.015 0.02 0.025 0.03 0.035
Source: HIES Vanuatu* Excludes long term unemployment (>1yr)
Source: Samoa census 2006*Excludes those currently enrolled in education
68. A lack of basic skills among TVET entrants limits the ability of TVET institutions to match the expectations of graduates with the increased productivity demanded by employers. Limited literacy, numeracy, and behavioral skills represent a significant constraint to the ability of TVET institutions to provide graduates with the necessary technical and vocational skills. As a result, many private sector employers are choosing not to hire TVET graduates and are either increasingly employing overseas workers or hiring employees with good basic skills and training them on the job. The result of the disconnect between the supply and demand for skills is the co-existence of skill
39
Figure 27: Increasing Focus on Vocational Training - Proportion of age group whose highest qualification is Vocational/Tertiary, Tonga.
<15 15-19 20-24 25-35 35+0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
1 week1 month3 months6 months1 year
Source: HIES
shortages and unemployment. In Fiji, approximately half of the 15,000 school leavers will go on to become the ‘educated unemployed’43, while at the same time Fiji is experiencing a skill shortage necessitating the expensive recruitment of skilled workers from overseas.
69. While an emphasis on formal TVET may make sense for some Pacific countries, others may find informal on-the-job training for local markets to be more appropriate. In PICs such as Tonga, Fiji, Palau, Samoa, and the Cook Islands that have greater access to international labor markets, formal TVET institutions that can provide clear signals of ability through internationally recognized qualifications may be attractive. For land-rich, low-income countries with larger populations such as PNG, the Solomon Islands, and Vanuatu, informal on-the-job training for local markets is likely to remain the top priority. This type of training may include government-sponsored internships or regulations such as those recently adopted in Tonga, whereby the age until which education is compulsory (defined broadly to include apprenticeships) was raised until 18. International experience suggests that uneven quality, high costs, and inflexibility to labor market demands can often undermine the efficacy of formal TVET. Furthermore, given the size of some Pacific economies and the limited opportunities for migration to those countries where TVET skills are needed, the creation of large formal TVET institutions runs the risk of creating excess supply of any particular skill in which a course is run.
70. Formal, sponsored learning-by-doing approaches that put less emphasis on the qualifications that engender high expectations but that instead emphasize behavioral skills could be highly effective. Such approaches represent a vital complement both to formal TVET institutions and to a well-functioning and inclusive formal education system that furnishes students with the necessary prerequisites in the form of literacy, numeracy, and behavioral skills. Box 5 provides an example of a successful on-the-job training initiative for new graduates in Kiribati.
43 UNICEF 2005.
40
Box 5: Increased dialogue with the private sector – successful initiativesSuccessful on-the-job training programs allow employers to benefit from a structured trial of workers while at the same time providing information for training institutions to improve their curriculum and better target those skills demanded by the private sector. Ensuring that TVET graduates have the appropriate skills for their business benefits both TVET institutions and employers.
The Kiribati Major Employers Organization (KIMEO), in cooperation with the Kiribati Institute of Technology (KIT) and the Ministry of Labour and Human Resource Development (MLHRD), provides a good example. Eleven of KIMEO’s members provided 23 six-week placements for graduates of KIT’s office administration/accounting, mechanical, electrical and carpentry programs. Of these 23 graduates, eight young people were offered permanent jobs. MLHRD has submitted a paper to the Cabinet on centralizing the allocation of funding for temporary job placements in government agencies.
3.3 Social Norms - Providing the Motivation to Work
71. Motivation to work is critical to labor supply decisions, but many inactive individuals in the PICs have no interest in finding work thanks to support from strong family networks (Figures 29 and 30). The finding that in Tonga, 67% of inactive men between the ages of 15 and 25 have no interest in finding work, compared to only 15% who believe no work is available, refutes the simplistic belief that unemployment is driven by limited labor demand. The luxury of this lethargy is likely due in large part to the mutually supportive nature of family networks, a very salient feature of Pacific cultures, which enable young people to rely on their families for food, support, and shelter. In Kiribati, after taking age, gender, and education into account, those individuals living in households receiving large remittances were 8% more likely to be inactive than those without such supportive flows of liquidity (see Appendix 9).
41
Figure 30: Proportion at each age still living with parents
Figure 31: Proportion of Household Heads by age
15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 400
10
20
30
40
50
60
70
80
90
100
Vanuatu Kiribati
15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 650%
1%
2%
3%
4%
5%
6%
Solomon Islands Samoa
Source: HIES
72. The high degree of resource sharing among extended family which is prevalent in Pacific Island culture can create problems for the development of enterprise. The expectation that wealth will be shared among networks can lead to substantial demands by relations as well as others within the community on the income and assets of a potential entrepreneur. Combined with a lack of financial literacy to enable adequate separation of business from personal accounts, this culture of sharing renders many new businesses unviable. Success in business has often meant finding ways to quarantine the business from such demands44 (Box 6).
44 Chamber of Commerce, Tonga, Duncan and Nakagawa 2007.
42
Box 6: Addressing Cultural Factors in the Labor Market – The Young Farmers Course in FijiIn order to address the factors that affect productive labor supply (skills, expectations, motivation), the Young Farmers Course, run by the Marist Training Centre in Tutu, Fiji, has developed several mechanisms to ensure that the young people in its training program gainnot only the necessary skills in farming but also the ability to stave off the demands of their family and community. Trainees must:
- Provide written evidence of village land being available for their future farm development and
- Demonstrate their willingness to use such land by planting 500 yaqona before entering the course.
In addition, new applicants are:- Visited in their villages to discuss with them and their families the course and
the need for land, and - All income received from sales will be deposited in individual savings accounts
and used, with the approval of the Principal, for the development of the home project.
In addition, by publishing the (high) earnings of the entrepreneurs who graduated from the scheme, the Marist Training Centre is helping to overcome the inability of young people to see entrepreneurialism as a ‘reputable’ occupation.
Source: USP (1986), SPC (2010)
3.4 Conclusions
73. Clearly, labor supply issues are contributing to the problems of unemployment and inactivity in the Pacific region. Although it is easy to blame such problems on limited labor demand, private sector demand is dependent on the quality of labor supply. The analysis in this chapter shows that limited early childhood development, poor quality primary education, and high expectations engendered by TVET and tertiary qualifications are contributing to labor supply problems in terms of limited behavioral and cognitive skills coupled with high expectations and low motivation among young labor force entrants. The following chapter looks at the other side of the equation, labor demand, to see how it may be affecting labor market outcomes.
43
CHAPTER FOUR: The Missing Link: Labor Demand
74. In addition to the quantity and quality of labor supply, labor demand is a critical determinant of labor market outcomes. While Chapter 1 examined the labor market equilibrium in five Pacific countries and Chapter 3 explored the facets of the labor supply, this chapter addresses the missing link: labor demand. The chapter begins by examining the domestic demand for labor then looks at the demands for Pacific labor from international markets.
75. Due to data constraints, the analysis of domestic demand must rely on the revealed preferences of the market from factors that are correlated with unemployment, employment, and productivity (wages). In the absence of an explicit indication of the skills required by the private sector—for example, from a formal employer survey—the best indication of demands for labor must be deduced from the revealed preferences of the market in the form of the correlates of unemployment, employment, and productivity (wages). The following section therefore returns to the results of chapters and 2 to examine the role of cognitive skills (proxied by formal educational attainment), non-cognitive behavioral skills (proxied by maturity, which can be signaled by age or by increased household responsibilities), and the role of technical skills (proxied by certificates and diplomas attained in TVET). Although experience may account for some of the remainder as discussed in previous sections, the magnitude of skills gained through experience is hard to quantify with existing data. Anecdotal evidence gathered during informal consultations with the private sector in Tonga and the Solomon Islands and an informal employer survey conducted by the Tongan Chamber of Commerce are used to support the analysis.
4.1 Domestic Demand
76. Education at lower levels does not appear to be valued highly by employers. The finding that primary school education increases the probability of entering unemployment (compared to no education) and has a negative impact on wages among individuals who are employed indicates that the quality of primary education does not adequately furnish its students with the skills valued by employers. This lack of quality in primary education is corroborated by the lack of functional literacy (Box 6).
77. While tertiary education appears to raise productivity and has value to employers, there is also evidence of an oversupply of graduates with a higher education. The higher wage levels found among graduates with tertiary education suggest that such education is valued by employers. At the same time, the ‘queuing’ for
44
appropriate employment indicates an oversupply of graduates (see Appendices 17 & 18).
78. It appears that while technical skills may be valued by employers, they are not substantially rewarded in terms of wages. While individuals who have obtained a certificate or diploma are less likely to be unemployed, there is mixed evidence that those with a certificate or diploma earn more than those who have no education. Workers in Vanuatu with a certificate or diploma tend to earn more than their uneducated counterparts, but in Solomon Islands, the effect is insignificant (see Appendix 13). Lower unemployment among these individuals suggests that technical skills are valued by employers, or it could indicate that those who have undergone TVET are more able or motivated to search for employment. The finding that these individuals are not substantially rewarded in terms of wages suggests that their technical skills do not fully translate into increased productivity, which could perhaps indicate a lack of basic and behavioral skills.
79. Marriage and status as household head—in addition to age—may be used by employers as signals of maturity and of enhanced behavioral skills. Marriage and being a household head decrease the probability of unemployment and increase the probability of being in paid work. Furthermore, the finding that neither marriage nor status as a household head affects employment in the subsistence sector suggests that the result is driven by labor demand rather than increased supply among these groups. The fact that wages in paid employment do not increase in response either marriage or household head status suggests that they are purely signals, not an indication of productivity.45
80. Employers indicate that recruitment of local staff is hampered by difficulties finding employees with both the necessary technical and behavioral skills. Employers in Tonga were found to have difficulty in finding managers that were ‘skilled and trustworthy’ (TCCI – see Figure 30). They reported that ‘When potential employees have the right academic records, they don’t have the business skills or practical knowledge to go with it.’ A lack of behavioral skills such as motivation, punctuality, and honesty were frequently cited as impediments to recruitment by the Tongan private sector. Employers explained that they were forced to recruit staff from overseas (mostly Fiji and the Philippines) due to a low response rate among locals to job advertisements. Employers reported that ‘there is a general lack of interest in working; loyalty is a problem, as well as that many people don’t think a job is essential or don’t have a business mindset’ and ‘Potential employees have the wrong attitude; business owners have to teach them punctuality, attendance, honesty, etc46’.
45 This assumes that employers are screening effectively through this or other means, such that those they employ have enhanced behavioral skills. Otherwise, these behavioral skills would translate into increased productivity.46 TCCI, 2010.
45
Figure 32: Most sought after characteristics in Tongan staff
Most important characteristics in staff
28%
16%
6%12%
12%
4%
5%
2%2%
3%3%2%1% 4%
Honesty
Punctuality/attendance
Ability to work in a team/communication/negotiation
Customer skills
Hard Work/commitment/desire to learn
Independence/initiative/problem solving skills
Foreign language skills (eg. English)
Time management/Organizational skills
Management/Leadership skills
Computer skills
Degree achieved/educational institution attended
Literacy and numeracy skills
Theoretical knowledge of the job
Practical knowledge of the job
Source: TCCI, 2010
81. Focus groups repeatedly highlighted the importance of networks in obtaining employment, and many youth felt disheartened by the futility of qualifications in the face of family connections. This sentiment seems to be supported by findings that, particularly in the Solomon Islands, having a household head in government increases one’s chances of being employed in government, and having a household head working in the private sector increases one’s chances of being employed in the private sector.47 Indeed in the Solomon Islands, having the head of household working for the government increases employment prospects there by more than a secondary education does. The importance of family connections likely stems in part from the culture of sharing among family networks, but it is also likely to be a further indication that employers are seeking some unobservable characteristic, such as behavioral skills, for which networks can act as a signal.
47 Furthermore, the converse is also true, that is having a household head in government decreases the probability of employment in the private sector, and vice versa.
46
4.2 International Demand
82. Emigration has long played a role in Samoa and Tonga, in large part because of the relatively easy entry their citizens have to New Zealand.48 Australia, New Zealand, and the United States have relatively large concentrations of Samoans and Tongans. Best estimates indicate that around 3,000 people on average migrate from Samoa each year and around 1,500 from Tonga. For Fiji, RMI, and FSM, it is estimated that the rate of emigration is around 0.5 per cent of their population (see Figure 32). 49
Figure 33: Gross International Migration Rate
Source: Gibson and Nero (2007)Note: This map does not show intra-regional migration
83. While the permanent migration of skilled labor may result in short-run capacity constraints, the benefits of temporary migration are wide-ranging. Short-term migrants acquire new skills, gain employment experience, and earn an income of which a share is often remitted to family members and community organizations back home (Figure 34).50 Temporary migrants are also likely to save some of their income to
48 The same applies to Cook Islands, Niue, and Tokelau, while 75 citizens from both Kiribati and Tuvalu are granted access to New Zealand each year under the Pacific Access Scheme.49 As former territories of the United States, nationals of RMI and FSM have easy access to the United States under the terms of the Compact of Free Association. In the case of Fiji, around 5000-6000 people, mostly Indo-Fijians, have been migrating since the coups of 1987 according to official statistics, although actual figures might be considerably higher.50 Over half of Tonga’s GDP is estimated to come from remittances, while the share is around 20% in Samoa. The Reserve Bank of Fiji estimates that foreign exchange earnings from remittances are at
47
reinvest for when they move back to their home communities.51 Although the migration of many skilled Indo-Fijians following the coups had an adverse impact on the level and availability of skills in Fiji, this ‘brain drain’ was assuaged to some extent as many Indo-Fijian families educated their children to maximize their chances of gaining permanent residency overseas.52 More recently, indigenous Fijians have moved overseas on a short-term basis to work in occupations such as security services, care giving, nursing and teaching. This mutually beneficial migration is a response to the high demand for these services overseas53 and the lack of employment opportunities and lower wages offered in Fiji. This rise in temporary overseas employment has led to a substantial increase in remittances in Fiji.
Figure 34: Remittances 2007 (% GDP)
World Bank (2009)
84. Emigration and remittances do not play an important role in Papua New Guinea, Solomon Islands, and Vanuatu. These countries have a small proportion of people with professional training, and they also lack the overseas access for less-skilled workers that is enjoyed by countries such as Samoa, Tonga, and the U.S. Compact countries. Despite the lack of preferential labor market access, remittances do play a more important role in Kiribati, which has had a long-term program of training seamen and fishermen to work on foreign ships. In addition, AusAID has recently begun to train I-Kiribati nurses for overseas employment.
least F$300 million a year and could be as much as F$500 million, making it the second largest source of foreign exchange earnings next to tourism.51 The World Bank estimates that free movement of labor globally would increase gross wages by US$356 billion, or 0.6% of world income –the equivalent of three times that of all aid flows during 2003 World Bank (2006). For detailed modeling, refer to Walmsley and Winters (2005).52 Clemens 2009.53 Much of this migration is to New Zealand and Australia. However, some of this migration is within the region, and Fijians are now working in countries short of vocational skills such as Cook Islands and RMI.
48
85. Despite the variable migration situation of PICs currently, demographic change in developed countries is likely to ensure that demand for migrant workers will continue to rise, including for less skilled workers. In particular, demand for labor in the horticulture, construction, health, domestic services and hospitality sectors is expected to increase. Overseas demand is also expanding beyond the more skilled sectors to include temporary employment of lesser-skilled and unskilled workers. Box7describes a scheme for the seasonal employment of fruit pickers that is being tested by New Zealand.
Box 7: Regional mobility schemes for low-skilled workersThrough their circular labor mobility programs, both New Zealand and Australia are beginning to provide labor market access to low- and un-skilled Pacific Island labor. New Zealand’s ‘Recognised Seasonal Employer’ (RSE) scheme and Australia’s ‘Pacific Seasonal Worker Pilot Scheme’ (PSWPS) aim to address domestic labor market shortages in horticulture. Under both schemes, employers must pay the minimum wage, guarantee 30 hours per week, and provide acceptable working conditions and accommodation. Formal employment may be supplemented through additional training in the PSWPS and the RSE. Pacific seasonal workers may access skills training for financial literacy, basic literacy, and numeracy. Within the PSWPS, additional training opportunities will become available in Pacific seasonal workers’ home countries.
Assessments of New Zealand’s RSE program have shown the scheme to have positive impacts on pro-poor development outcomes (Gibson et al 2008). While the development impacts from the PSWPS have not yet been assessed, their impact in terms of workers received is likely to have been smaller than the RSE.
Source: Pacific Economic Survey
4.3 Conclusions
86. Whether to meet domestic or overseas demand, it is clear that improving the worker skills is a key priority for improving labor market outcomes in the PICs. The analysis of domestic demand underscores the need to improve access to and quality of education—starting even from the pre-primary level—to ensure that workers have the skills being sought by employers. For those Pacific countries with access to overseas employment opportunities, it will also be important to equip workers with the necessary skills to meet overseas labor demand. Such temporary migration may provide not only a safety valve for population growth and but also more attractive opportunities for the growing number of unemployed youth.
49
CHAPTER 5: Conclusions
87. The Pacific Island countries face numerous challenges in ensuring productive employment opportunities for their growing populations. Currently, the potential for growth in domestic employment appears limited due to the continued exhaustion of natural resources and lack of private sector opportunities. While emigration is an option for workers in countries that benefit from overseas agreements, those in other countries do not have such opportunities and therefore must rely on domestic employment. Further work is needed to identify measures that could help expand productive employment opportunities in PICs, e.g., policies to encourage investment in microenterprises. Such efforts could not only create more employment opportunities but would also help foster longer-term economic growth.
88. On the labor supply side, the plentiful human resources and current youth bulge in the Pacific region could prove to be a valuable resource, particularly if the quality of the workforce can be improved. A key finding in this report is that while demand for workers does exist among domestic employers, workers lack the necessary skills. Labor market outcomes are unlikely to improve unless countries boost the quality of education at all levels to ensure that workers can meet the needs of employers. The findings of this report underscore how improving both cognitive and behavioral skills will be critical in this regard. Additional information and analysis are needed to understand the specific needs of the private sector, identify skills gaps, and determine how the education system can best address such gaps.
89. Beyond equipping workers with the necessary skills, it will also be important to ensure that workers have expectations for employment that are commensurate with their skills as well as the motivation to engage in productive work. As described in Chapter 3, labor supply decisions are complex and can be influenced by a number of factors, including reservation status and the availability of family support. Influencing expectations and motivation, particularly in country settings where inactivity among potential labor force entrants is not uncommon, will undoubtedly take time. If workers can be equipped with the skills sought by employers and if the availability of sought-after employment opportunities can be expanded, unemployment and inactivity would likely decrease as potential labor force entrants would have greater motivation to seek work.
90. It is hoped that the findings of this report may serve as a starting point for future analysis and policy development. As noted, the purpose of this report is to provide an overview of labor market outcomes and their determinants. The report does not attempt to make policy prescriptions, but it identifies numerous areas in which further information and analysis is needed to inform future policies. At a minimum, it will be important to improve the frequency and quality of data collection to develop an
50
accurate understanding of the current labor market situation. On the labor supply side, testing on cognitive and non-cognitive abilities at different levels of education would be highly informative. On the demand side, formal employer surveys would be useful for understanding the challenges faced by employers in recruiting workers and identifying the skills that employers are seeking. Such information could provide invaluable inputs to future policymaking and help ensure that efforts and resources are focused on the areas that are most critical to improving labor market outcomes.
51
Appendices
Appendix 1: Summary of Quantitative Data Sources
Kiribati HIES 2006 - Questionable data quality
Samoa Census 2006 - Limited variable availability
Solomon Islands
HIES 2006 - No labor force component.- Questionable data quality
Tonga Census 2006 - Raw data unavailable, only cross-tabulations possible. - Unemployment likely to be substantially under measured
Vanuatu HIES 2006
52
Appendix 2: List of consultations undertaken in support of quantitative findings
NEW ZEALANDNZAID Vicki Platter Economic Advisor (Trade & Development)NZAID Trish Nally Education AdvisorNZAID Christine Briasco Health AdvisorMinistry of Labour Stephen Dunstan MigrationMinistry of Pacific Island Affairs Colin Tukuittonga, Chief ExecutiveMinistry of Pacific Island Affairs Debbie McLeod Director of Policy and MonitoringMinistry of Pacific Island Affairs Linda AumuaSOLOMON ISLANDSCountry Office Edith Bowles Country ManagerNZAID NicciSimmonds Second SecretaryMDPAC (development planning and aid coordination)
Daniel Rove Director, Social Services Division
MDPAC Lyn Legua Supervising PSMCIE Robinson Gegue Labour Division, work permitsMCIE Joelle Industry department, working on financing
micro enterprisesMCIE Eric Private Sector Scholarships ProgramAusAID KamalAzmi Counsellor, DevelopmentAUSAID Aileen Croghan First Secretary, Rural LivelihoodsMEHRD MylynKuve Permanent SecretaryMEHRD Donald Malasa Under SecretaryMEHRD Audrey Rusa Co-ordinating Secretariat of MEHRDMEHRD Johnson Fangalasu’u Advisor literacy programHoniara City Council - Youth Group
Mary Jinoisi Youth Coordinator, HCC
COESI Samuel Maeniuta Co-ordinatorLASI – Literacy Association of Solomon Islands
Priscilla Maeniuta Co-ordinator
Employer – SZETU Enterprises Peter Tan Employer, Chair of Association of Manufacturers
NEW CALEDONIASPC Jimmie Rodgers Director GeneralSPC Richard Mann Deputy Director GeneralSPC Bill Parr Director, Social Resources DivisionSPC Gerald Haberkorn Manager, Statistics and DemographySPC Arthur Jorari DemographerSPC Mike Batty Director, Fisheries DivisionSPC Rose Maebiru Youth AdvisorSPC Aleki S Director, Land Resources DivisionSPC Thierry Jubeau Manager, Public Health DivisionSPC DennieIniakwala HIV/STISPC George Malefo’asi Adolescent HealthSPC LiaMaka Co-ordinator CETC (community education
training centre)SPC Emily Hasleman Co-ordinator PATVET SPC TarusilaBradburgh Co-ordinator of Pacific Youth Council
53
AUSTRALIAAusAID Jane Lake Assistant Director General, PacificAusAID Jerry Strudwick Education AdvisorAusAID Debbie Bowman Human Development DirectorAusAID Steve Passingham Education AdvisorAusAID Bernie Whiler Social Protection AdvisorPrime Minister & Cabinet Damian White Senior AdvisorParliament Bob McMullan Parliamentary Secretary for International
Development AssistanceTONGAWB Focal Office Saia Faletau Development Banks RepresentativesTNYC Vanessa Lolohea Executive Director of Tonga National Youth
CongressAusAID Debbie Reschke First, SecretaryNZAID MalakaiKaufusi Development Program CoordinatorTCCI Tricia Emberson Member of Chamber of CommerceTCCI Cecile Quesada Program Officer, implementing employer
surveyMOTEYS TanielaFusimalohi CEO of Ministry of Training, Employment and
YouthMEWAC ViliamiFukofuka CEO of Ministry of EducationFWCES Rev SavinataMoala Free Wesleyan Church Christian Education
SystemTDoS Ata’ataFinau Government Statistician for TongaMLCI Hon LisiateAkolo Minister of Labour Commerce and IndustriesPET & YES Mrs Temaleti Director Pasifika Employment and TrainingP&P TufuiFaletau, National Planning and Policy DivisionTNYC Tongan Youth
GroupOrganised by TNYC
TDB BalwynFa’otousia Aid and Project Management, Ministry of Finance
VANUATUNational training Council David Lambukly Director, responsible for accreditationAusAID David Burns,
Belynda McNaughton
OXFAM Kelly Dent
54
Appendix 3: Unemployment in Vanuatu
Two measures of unemployment in Vanuatu
1. activity ‘unemployed’: of these 399 are in subsistence 25 list themselves as working for pay a further 25 as help family business and 61 other work no pay.
2. ILO definition: not work but looking for work (only 187, of which 10 also list themselves as ‘help family business’, ‘subsistence’ or ‘other work no pay’, furthermore 1 lists as working full time, 5 work part time, 1 has own business, 7 sell product 41 own hh consumption (only 95 unemployed) 25 domestic duties 12 other
Only 8 individuals are in the intersection of both of these measures. 23 of those who purport to work for pay list their main activity as household duties, similarly 3 of those who work for pay and full time education (included in labour force)
Status
ActivityWork for pay
Help family business Subsistence
Other Total
Work Full time 1,491 24 37 13 1,565Work Part time 197 14 38 14 263Own business 55 91 40 10 196Sell product 11 8 428 18 465Own HH consumption 59 78 3,495 157 3,789Unemployed 25 25 399 61 510Domestic duties 20 19 721 132 892Full-time Education 3 0 9 6 18Others 18 6 133 45 202
55
Appendix 4: Students (13-15) who reported using substances on a weekly basis, %
Boys Girls Boys GirlsVanuatu Tonga
0%
10%
20%
30%
40%
50%
60%
70%
80%
Ever tried Smoking
Weekly cigarette Smoking
Been drunk two or more times
Kava use Weekly
Methylated spirits use weekly
Marijuana use weekly
Glue sniffing weekly
Source: Phongsavan et al (2005)
56
Appendix 5: Gender Parity and Access to Education
Gender parity with respect to access to education has a good record in most of the Pacific Island Countries and 8 of the 10 PIC’s for which data is available have a higher Gross Enrollment in Early Childhood Education for females than for males (UNESCO 2007).54 And, with some notable exceptions (PNG), primary enrollment is close to parity with nearly all countries for which there is data (Fiji, Marshall Islands, Nauru, Solomon Islands, Vanuatu all having a GPI of .99 or higher55). In addition to the high gender parity rates in the region, women tend to repeat grades at a lower rate.
Figure 35: Proportion of 15-19 year olds Currently in Education, by gender
Repeaters (%)
Solomon Islands Samoa Vanuatu Kiribati Tonga0
10
20
30
40
50
60
70
80
90
100
Fiji Solomon Islands Tonga Vanuatu0
2
4
6
8
10
12
14
16
18
Male Female
Source: HIES Source: UNESCO (2007)
However, these positive beginnings do not maintain throughout and beyond the education system (see Box 3. At tertiary level, gender biases are more evident in access of female students, award of scholarships and fields of study. Whilst the degree of gender equality varies considerably across the region, there is a general pattern of equal early enrolment, followed by higher male employment (see Figure 36).
Figure 36: Low representation of women in the labor forceTonga Vanuatu
15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65+0
0.05
0.1
0.15
0.2
0.25
Male Female
10-14 15-19 20-24 25-29 30-39 40-49 50-59 60-64 65+0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
Solomon Islands Samoa
54 The exceptions are Vanuatu and the Cook Islands. 55 The exception is the Cook Islands for which the GPI is .96.
57
15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 640%
5%
10%
15%
20%
25%
30%
35%
15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 640.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
Source: HIES (female employees as % of female labor force compared with male employees as % of male labor force)
Box 8: Selectivity BiasSample selectivity arises because earnings functions are estimated on separate subsamples of workers, and because these subsamples may be systematically different in certain ways, that is they may not represent random draws from the population as a whole.56If more highly motivated or more ambitious people systematically select into particular occupations, for example, into public sector work, then people in the public sector subsample would, on average, be more motivated and ambitious than the wider population.
This bias can be assuaged to some degree using the Heckman correction method (see Heckman 1979 for further details). To make this correction the probability of selection into each occupation type is first estimated by fitting a model of occupational attainment. Thus, whilst modeling occupational outcomes is a useful exercise in itself, it also enables an estimation of the expected log of wage conditional on the individuals selection into occupation x. The coefficient on the selectivity term, based upon this model,57 is then a measure of the bias due to nonrandom sample selection.
The selectivity term for those that work full time in Vanuatu is insignificant, indicating the absence of a significant selectivity bias –characteristics that effect selection into full time work, but also systematically effect wages. However, selectivity is a problem for the self-employed youth and for women who work in government in Vanuatu. In Solomon Islands selectivity bias is not an issue.Appendix 6: Impact of age on unemployment, dProbits
Vanuatu Unemployment
Sol Island Unemployment
Samoa Unemployment Kiribati Unemployment
age1534 0.0549*** 0.0108 0.0777*** -0.0520*** 0.0173*** 0.00629*** 0.111*** 0.0329***(0.00965) (0.0107) (0.0148) (0.0171) (0.00169) (0.00110) (0.0235) (0.00963
)age3549 0.0183 0.0156 -0.0672*** -0.0990*** 0.00477*** 0.00194* 0.0512**
*0.0134*
56 This condition violates one of the fundamental Gaussian assumptions on which OLS analysis relies.57 Inverse Mills Ratio formula.
58
(0.0119) (0.0117) (0.0162) (0.0168) (0.00169) (0.00113) (0.009) (0.0071)
Controls NO YES NO YES NO YES NO YESObs 10061 10061 11193 11193 87651 87651 3654 3654
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
The base or reference categories are as follows: age: persons aged 50-64 years old; education: persons with no education. Controls include: age, gender, location, marital status position in household, number of dependents, remittances, number of employed members of household.
Source: HIES and Census.
59
Appendix 7: Unemployment, Education vs Maturity
VARIABLES Vanuatu Unemployment Sol Is Unemployment(1) (2) (3) (1) (2) (3)
age1534 0.0533*** 0.0514*** 0.0103 0.0695*** 0.0578*** -0.0357**(0.00959) (0.00969) (0.0107) (0.0149) (0.0153) (0.0167)
age3549 0.0176 0.0164 0.0124 -0.0722*** -0.0739*** -0.0865***(0.0119) (0.0119) (0.0115) (0.0163) (0.0164) (0.0165)
female 0.0198*** 0.0194*** -0.00243 0.133*** 0.129*** 0.0183(0.00626) (0.00627) (0.00830) (0.00935) (0.00949) (0.0121)
urban 0.0468*** 0.0477*** 0.0481*** -0.0126 0.00962 0.00194(0.00649) (0.00649) (0.00634) (0.0140) (0.0145) (0.0147)
edprim 0.0123* 0.0149** 0.0346** 0.0505***(0.00744) (0.00738) (0.0135) (0.0136)
edsec 0.00791 0.00807 0.0334** 0.0472***(0.00881) (0.00872) (0.0150) (0.0152)
edcert -0.0332** -0.0230 -0.188*** -0.145***(0.0159) (0.0169) (0.0218) (0.0238)
edtert 0.0550 0.0662 -0.151*** -0.0854(0.0419) (0.0436) (0.0467) (0.0520)
married -0.0577*** -0.0273**(0.00966) (0.0126)
head -0.0496*** -0.245***(0.00911) (0.0134)
Observations 10061 10061 10061 11193 11193 11193Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
VARS Samoa Unemployment Kiribati Unemployment58
(1) (2) (3) (1) (2) (3)
age1534 0.0169*** 0.0155*** 0.00929*** 0.0968*** 0.0770*** 0.0605***(0.00167) (0.00159) (0.00134) (0.0152) (0.0147) (0.0151)
age3549 0.0047*** 0.00411*** 0.00263* 0.0401** 0.0299 0.0282(0.0017) (0.00155) (0.00135) (0.0197) (0.0182) (0.0179)
female -0.00142*** -0.00138*** -0.000421 -0.00575 -0.00514 -0.0112(0.000411) (0.000395) (0.000386) (0.00720) (0.00682) (0.00762)
urban 0.00297*** 0.00317*** 0.00281*** 0.00270 0.000852 0.000948(0.000595) (0.000597) (0.000551) (0.00823) (0.00813) (0.00806)
edprim 0.00295 0.00193
0.0578*** 0.0607***(0.00269) (0.00225)
(0.0213) (0.0215)edsec 0.00424*** 0.00397***
0.0593*** 0.0588***(0.000586) (0.000537)
(0.0184) (0.0184)
58 Tertiary education perfectly predicts employment and hence cannot be used in a probit regression.
60
edcert 0.00909*** 0.00953***
0.0437 0.0506(0.00266) (0.00268)
(0.0419) (0.0438)edtert 0.00106 0.00175
(0.00232) (0.00244)
English Literacy
0.0307***
(0.00785)
0.0309***
(0.00776)
married -0.00602*** -0.0121(0.000678) (0.00883)
head -0.00158**
-0.0263***(0.000659)
(0.00904)
Obs 87651 87651 87651 3,898 3,879 3,879Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
The base or reference categories are as follows: age: persons aged 50-64 years old; education: persons with no education. Employed members variable is the number of members in the household that are employed. Number of dependants variable corresponds to the number of individuals between the age groups 0 to 14 and 65 years and over
Source: HIES
61
Appendix 8: Impact of literacy on unemployment
Kiribati Unemployment Samoa UnemploymentFemale Male
Local Literacy -0.0102 0.00108 -0.00207**(0.0308) (0.00104) (0.00102)
litE 0.0309*** 0.00445** 0.0117***(0.00776) (0.00185) (0.00319)
Controls YES YES YES
Observations 1815 39212 46054Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
The base or reference is persons who have no literacy skills. Controls include: age, gender, location, marital status position in household, number of dependents, remittances, number of employed members of household. Education is omitted from the controls to avoid multicollinearity.
62
Appendix 9: Correlates of unemployment, disaggregated by sex
Vanuatu Unemployment
Sol Is Unemployment Samoa Unemployment
Female Male Female Male Female Maleage1534 0.0117 0.00465 -0.0314 -0.069*** 0.00933*** 0.00551***
(0.0160) (0.0142) (0.024) (0.0235) (0.00179) (0.00143)age3549 0.00397 0.0288* -0.082*** -0.1*** 0.00507** 0.00143
(0.0171) (0.0159) (0.0247) (0.0220) (0.00201) (0.00166)urban 0.0468*** 0.0520*** 0.124*** 0.0941*** 4.81e-05 0.00445***
(0.00966) (0.00780) (0.0213) (0.0228) (6.79e-05) (0.000861)married -0.073*** -0.0139 -0.0422** -0.00651 -0.000159* -0.00157**
(0.0135) (0.0150) (0.0169) (0.0196) (9.05e-05) (0.000760)edprim 0.00939 -0.107*** 0.0414** -0.320*** 0.000393 -0.0043***
(0.0107) (0.0185) (0.0181) (0.0181) (0.000492) (0.000768)edsec 0.00796 0.0158 0.0764*** 0.0847*** 0.000278* 0.00372
(0.0132) (0.00979) (0.0212) (0.0211) (0.000165) (0.00377)edcert -0.069*** 0.0152 -0.160*** 0.0887*** 0.00165* 0.00413***
(0.0218) (0.0119) (0.0386) (0.0225) (0.000997) (0.000776)edtert 0.119 0.0331 -0.251** -0.0130 0.000421 0.0139***
(0.0849) (0.0271) (0.117) (0.0360) (0.000601) (0.00468)head -0.0424** -0.008*** -0.170*** -0.016*** 0.00021***
(0.0176) (0.00251) (0.0370) (0.00376) (7.65e-05)Controls YES YES YES YES YES YESObs 5017 5044 5742 5448 82295 52666
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1The base or reference categories are as follows: age: persons aged 50-64 years old; education: persons with no education. .Controls include: number of dependents, remittances, number of employed members of household.
63
Appendix 10: Correlates of Inactivity
Vanuatu Inactivity Kiribati Inactivity
(excl domestic duties) (incl domestic duties) (excl domestic duties) (incl domestic duties)
age1534 0.00722** -0.0110 -0.0392* -0.103***
(0.00345) (0.0113) (0.0215) (0.0305)
age3549 0.00704 -0.0232** -0.0387** -0.0853***
(0.00510) (0.0111) (0.0194) (0.0288)
female -0.00203 0.0773*** 0.00976 0.0688***
(0.00230) (0.00984) (0.0158) (0.0218)
urban -0.00246 0.107*** -0.0826*** -0.0485**
(0.00221) (0.00644) (0.0145) (0.0227)
married -0.00740** -0.0129 -0.0385** 5.43e-05
(0.00321) (0.00978) (0.0195) (0.0251)
head -0.000543 -0.0448*** -0.0426** -0.0978***
(0.00282) (0.0107) (0.0174) (0.0253)
edprim 0.00262 -0.0515*** 0.00145 -0.0143
(0.00215) (0.00729) (0.0199) (0.0293)
edsec -0.00466** -0.0311*** -0.0776*** -0.158***
(0.00198) (0.00873) (0.0214) (0.0309)
edcert -0.00688*** -0.0926*** -0.124*** -0.265***
(0.00263) (0.0135) (0.0212) (0.0446)
edtert -0.120*** -0.401***
(0.0152) (0.0607)
rem 0.0242 0.0874*
64
(0.0334) (0.0491)
hhempl 0.00174*** -0.0482*** -0.0102* -0.0541***
(0.000651) (0.00348) (0.00567) (0.00792)
litL -0.0518 -0.171**
(0.0573) (0.0719)
Observations 9978 10061 3190 3206
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
65
Appendix 11: Impact of Maturity Signals on Government Employment
Vanuatu Solomon Islands Kiribati
age1534 -0.135*** -0.0872*** -0.142*** -0.0955*** -0.0279 0.0434(0.0259) (0.0281) (0.0264) (0.0283) (0.0490) (0.0528)
age5064 0.0415 0.0391 -0.0645 -0.0665 -0.160* -0.175**(0.0411) (0.0407) (0.0414) (0.0411) (0.0854) (0.0867)
edprim -0.095*** -0.0920*** -0.181*** -0.177*** 0.0697 0.0677(0.0346) (0.0347) (0.0427) (0.0431) (0.118) (0.115)
edsec 0.114*** 0.123*** 0.0679 0.0734* 0.203* 0.208*(0.0305) (0.0307) (0.0440) (0.0443) (0.117) (0.113)
edcert 0.235*** 0.232*** 0.281*** 0.272*** 0.156 0.152(0.0426) (0.0428) (0.0461) (0.0468) (0.114) (0.111)
edtert 0.299*** 0.285*** 0.138** 0.135* -0.187 -0.197(0.0784) (0.0792) (0.0694) (0.0705) (0.193) (0.189)
married 0.146*** 0.171*** 0.139*(0.0305) (0.0353) (0.0773)
head 0.00793 0.0415 0.152**(0.0380) (0.0353) (0.0596)
female 0.00761 0.0168 0.0353 0.0679* 0.0232 0.110*(0.0255) (0.0366) (0.0273) (0.0352) (0.0491) (0.0577)
urban 0.0653** 0.0659** -0.129*** -0.125*** 0.114** 0.132***(0.0293) (0.0293) (0.0258) (0.0260) (0.0455) (0.0456)
Obs 1489 1489 1683 1683 469 469Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
The base or reference categories are as follows: age: persons aged 35-49 years old; education: persons with no education. Employed members variable is the number of members in the household that are employed. Number of dependants variable corresponds to the number of individuals between the age groups 0 to 14 and 65 years and over. In the government regressions, the omitted category is non-governmental paid work
66
Appendix 12: Impact of Maturity Signals on Subsistence Employment
(1) (2) (3) (4) (7) (8)subs subs subs subs subs subs
Vanuatu Solomon Islands Samoaage1534
0.0501*** 0.0356** 0.0546*** 0.0277 -0.0306*** -0.0565***
(0.0128) (0.0140) (0.0161) (0.0177) (0.00522) (0.00593)age5064
0.0620*** 0.0651*** 0.0434* 0.0489** 0.120*** 0.137***
(0.0184) (0.0184) (0.0239) (0.0242) (0.00763) (0.00782)edprim 0.0975*** 0.0992*** -0.00240 0.00341 0.192*** 0.193***
(0.0128) (0.0128) (0.0208) (0.0209) (0.0162) (0.0162)edsec -0.157*** -0.153*** -0.217*** -0.212*** -0.0125 -0.0124
(0.0149) (0.0150) (0.0210) (0.0212) (0.00841) (0.00842)edcert -0.407*** -0.403*** -0.459*** -0.451*** -0.471*** -0.470***
(0.0198) (0.0203) (0.0133) (0.0141) (0.00384) (0.00386)edtert -0.326*** -0.321*** -0.451*** -0.448*** -0.426*** -0.425***
(0.0469) (0.0472) (0.0140) (0.0149) (0.00426) (0.00437)married 0.0454*** 0.0481** -0.0206***
(0.0163) (0.0214) (0.00596)head -0.119*** -0.153*** -0.0567***
(0.0184) (0.0223) (0.00668)female 0.0298*** -0.0476*** 0.202*** 0.106*** -0.0627*** -0.0765***
(0.0112) (0.0167) (0.0145) (0.0208) (0.00511) (0.00556)urban -0.239*** -0.240*** -0.429*** -0.429*** -0.422*** -0.422***
(0.0119) (0.0120) (0.0145) (0.0143) (0.00397) (0.00398)
Obs 8548 8548 5497 5497 52724 52724Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
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Appendix 13: Impact of Maturity Signals on Wage Employment
Vanuatu Solomon Islands Kiribati Samoawrkft wrkft wrkft wrkft wrkft wrkft paid paid
age1534 -0.0510*** -0.0248*** -0.0780*** -0.00972 -0.0984*** -0.0350*** -0.081*** -0.0123***(0.00778) (0.00841) (0.00737) (0.00725) (0.0111) (0.0117) (0.0034) (0.00387)
age5064 -0.0464*** -0.0489*** -0.0415*** -0.0434*** -0.0753*** -0.0756*** -0.111*** -0.131***(0.00893) (0.00866) (0.00826) (0.00771) (0.0101) (0.00894) (0.0039) (0.00387)
edprim -0.0409*** -0.0426*** -0.0103 -0.0196** 0.0408* 0.0367 -0.064*** -0.0618***(0.00784) (0.00772) (0.00936) (0.00906) (0.0231) (0.0224) (0.0094) (0.00949)
edsec 0.168*** 0.162*** 0.0975*** 0.0857*** 0.158*** 0.155*** 0.071*** 0.0697***(0.0121) (0.0120) (0.0121) (0.0117) (0.0238) (0.0235) (0.0047) (0.00474)
edcert 0.530*** 0.514*** 0.518*** 0.460*** 0.531*** 0.515*** 0.682*** 0.678***(0.0286) (0.0293) (0.0274) (0.0288) (0.0627) (0.0652) (0.0047) (0.00496)
edtert 0.425*** 0.412*** 0.478*** 0.393*** 0.627*** 0.564*** 0.690*** 0.685***(0.0548) (0.0540) (0.0571) (0.0584) (0.116) (0.132) (0.0057) (0.00622)
married -0.0204** 0.0282*** 0.0492*** 0.0990***(0.0101) (0.00775) (0.0114) (0.00359)
head 0.123*** 0.207*** 0.141*** 0.0763***(0.0129) (0.0136) (0.0191) (0.00509)
female -0.0857*** -0.0145 -0.0903*** 0.000861 -0.0789*** -0.0252** -0.140*** -0.134***(0.00671) (0.00969) (0.00640) (0.00813) (0.0103) (0.0117) (0.0029) (0.00326)
urban 0.0382*** 0.0381*** 0.222*** 0.235*** 0.0961*** 0.101*** 0.178*** 0.184***(0.00722) (0.00712) (0.0138) (0.0139) (0.0143) (0.0143) (0.0039) (0.00393)
Obs 10061 10061 11193 11193 3552 3552 100999 100999Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1The base or reference categories are as follows: age: persons aged 35-49 years old; education: persons with no education. Employed members variable is the number of members in the household that are employed. Number of dependants variable corresponds to the number of individuals between the age groups 0 to 14 and 65 years and over. In the paid work regressions, the omitted category is population of working age, not in education who are not in wage labor.
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Appendix 14: Determinants of wages among the employed, impact on ln(wages)
A. Age and Educational Dummies
Vanuatu Solomon Islands
Age15-24 - 0.258* - 0.463*** - 0.278** -0.618*** -0.592*** -0.540***
(0.098) (0.076) (0.098) (0.117) (0.123) (0.083)
Age24-49 0.0485 0.083 0.0198 -0.0693 -0.0759 -0.0886
(0.06997) (0.067) (0.0705) (0.0833) (0.0831) (0.0836)
edprim -0.267*** -0.257*** -0.209** -0.0922
(0.053) (0.0576) (0.0942) (0.1078)
edsec 0.006 -0.0107 -0.222*** -0.922
(0.055) (0.061) (0.100) (0.108)
edcert 0.268*** 0.275*** -0.013 0.225
(0.075) (0.159) (0.1203) (0.137)
edtert 0.727*** 0.793*** 0.182 0.445**
(0.150) (0.051) (0.2047) (0.2204)
married -0.026 0.836
(0.068) (0.0875)
head 0.110 0.294**
(0.069) (0.0978)
tenure 0.014*** -0.294
(0.003) (0.0014)
female -0.054 -0.063 -0.004 -0.054 -0.089 -0.013
(0.0558) (0.042) (0.068) (0.0558) (0.0558) (0.0706)
urban 0.521*** 0.010 0.038 0.521*** 0.557*** 0.703***
(0.0587) (0.049) (0.051) (0.0587) (0.0639) (0.0760)
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Heck YES YES YES YES YES YES
Obs 1409 1409 1409 1615 1615 1615
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
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A. Age and Educational Ordinal Variables, Youth Only (15-34)
Wage work Government work Self employmentVARIABLES Vanuatu Solomons Vanuatu Solomons Vanuatu Solomons
age 0.0863 0.0767*** 0.206** 0.0751*** 0.128 0.0746***(0.0596) (0.0214) (0.103) (0.0213) (0.278) (0.0214)
agesq -0.000926 -0.000917*** -0.00297 -0.000898*** -0.00211 -0.000902***(0.00113) (0.000271) (0.00194) (0.000270) (0.00520) (0.000272)
education -0.191*** -0.305*** -0.430*** -0.243*** -0.298 -0.213**(0.0636) (0.0926) (0.110) (0.0886) (0.176) (0.0890)
edsq 0.0938*** 0.0955*** 0.135*** 0.0746*** 0.123** 0.0976***(0.0198) (0.0240) (0.0394) (0.0267) (0.0523) (0.0240)
tenure 0.0107*** -0.00125 0.0244* -0.00131 0.0358** -0.00126(0.00388) (0.00145) (0.0133) (0.00145) (0.0174) (0.00145)
married -0.0538 0.00386 -0.369*** 0.0172 -0.110 0.0597(0.0627) (0.0919) (0.112) (0.0930) (0.274) (0.0879)
head 0.0352 0.0389 0.164 0.0915 0.806*** 0.221***(0.0708) (0.103) (0.123) (0.0934) (0.254) (0.0742)
female 0.0299 0.0142 0.260** -0.00134 0.252 0.0383(0.0598) (0.0701) (0.109) (0.0710) (0.242) (0.0709)
urban -0.00217 0.615*** -0.00754 0.715*** 0.473 0.822***(0.0610) (0.0765) (0.123) (0.0555) (0.427) (0.0605)
Selectivity -0.804** -1.312*** -1.221 -1.685** 0.893 -0.825*(0.394) (0.507) (1.560) (0.760) (2.957) (0.436)
Constant 6.461*** 5.671*** 5.642*** 6.011*** 4.102 5.137***(0.846) (0.549) (1.836) (0.725) (4.335) (0.468)
Obs 648 1777 185 1777 45 1777R-squared 0.207 0.148 0.275 0.147 0.386 0.147
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
B. Age and Educational Ordinal Variables, All Ages
(1) (2) (3) (4) (5) (6)Vanuatu Solomons Vanuatu Solomons Vanuatu Solomons
age 0.0595*** 0.0782*** 0.0674*** 0.0798*** 0.0591 0.0952***(0.0128) (0.0191) (0.0259) (0.0190) (0.0449) (0.0187)
agesq -0.000670*** -0.000934*** -0.000807** -0.000950*** -0.000843 -0.00112***(0.000168) (0.000246) (0.000322) (0.000245) (0.000565) (0.000245)
education -0.235*** -0.312*** -0.433*** -0.248*** -0.171 -0.210**(0.0507) (0.0912) (0.0892) (0.0888) (0.184) (0.0892)
edsq 0.110*** 0.0953*** 0.144*** 0.0687*** 0.0945 0.0996***(0.0157) (0.0240) (0.0257) (0.0252) (0.0572) (0.0241)
tenure 0.0129*** -0.00123 0.00770 -0.00127 0.0306*** -0.00118(0.00292) (0.00145) (0.00584) (0.00145) (0.0100) (0.00146)
female -0.0664 6.09e-05 -0.0385 -0.0430 -0.102 -0.0928*(0.0438) (0.0612) (0.0774) (0.0577) (0.152) (0.0560)
urban 0.0495 0.600*** -0.106 0.699*** 0.276 0.817***(0.0501) (0.0633) (0.0974) (0.0537) (0.275) (0.0603)
selectivity -0.590** -1.436*** -1.085 -2.123*** -2.318 -0.877**(0.259) (0.327) (0.774) (0.539) (1.678) (0.421)
Constant 6.822*** 5.764*** 7.718*** 6.344*** 7.905*** 4.948***(0.312) (0.488) (0.850) (0.626) (1.694) (0.460)
Obs 1268 1777 426 1777 108 1777R-squared 0.180 0.148 0.172 0.147 0.149 0.142
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
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