The paper examines the role of education in earnings determination by using all three rounds of the Urban Worker Survey of Ghana for 2004-2006 conducted by CSAE. The analysis is begun by investigating and comparing heterogeneity in earnings determinants amongst self-employed (informal sector), private sector workers and public sector workers with particular focus on education. An examination of the role education, individual and household characteristics play in facilitating entry into employment sectors is further undertaken in a multinomial logit model of occupational attainment in addition to analyzing the pattern of returns to education along the earnings distribution. After addressing biases associated with estimating earnings equations, it is observed that education plays an important role in promoting access to lucrative formal sector jobs, especially employment in the public sector but has no direct impact on earnings within the sector.
The importance of education in modern labour market outcomes cannot be over-emphasized. The theoretical underpinnings have been provided by Schultz [
The evidence on returns to education in Sub-Saharan Africa is inconclusive although generally biased towards convexity. The indication of convexity in recent studies by Söderbom and Teal [
Labour markets in most developing countries are segmented into broadly defined formal and informal sectors [
In Ghana, self-employment is the fastest growing segment of the labour force across rural and urban areas constituting about 65% of total employment [
This paper aims to fill gaps in the earnings determination literature by investigating and comparing heterogeneity in earnings determinants across occupations (public and private formal sectors and the informal (self- employment) sector) with particular attention on schooling. First is an examination of the role of education in the labour market in terms of earnings by occupation as a baseline model. This is followed by an examination of how education, individual and household characteristics facilitate entry into employment sectors in a multinomial logit model of occupational attainment and subsequently address selectivity bias in the earnings equation following Heckman [
Analysis of the demand for education has been driven by the concept of the human capital pioneered by Schultz [
where wi is the monthly earnings of individual i, Xi is a vector of worker characteristics and ui is the residual. According to Card [
Two main sources of bias in OLS estimates of effects of education on earnings are endogeneity (omitted variable) bias and sample selectivity bias. The former relates to the concern in earnings literature that education may be positively correlated with unobserved ability which will lead to an upward bias of estimates of the returns to education.1 Therefore, to be sure our findings are not due to the failure to allow for such endogeneity, instrumental variables are used to control for endogenous education.
Endogeneity is caused by a correlation between explanatory variables and the disturbance term. This means
This results in biased and inconsistent estimates; which is corrected by an identification of variable(s) that are not correlated with the residual but correlated with the endogenous variable. Subsequently, parent’s education (mother and father’s education) are used as instruments for education in an instrumental variable estimation.
In Ghana as in most developing countries (particularly in Africa), having a job in the formal wage sector is typical of outcomes in the labour market (selection into different sectors of employment are correlated with the potential determinants of earnings). In addition, observations with no earnings information are excluded, if exclusion of such observations is not random (lower earners are less likely to work), such sample selection bias may bias OLS estimates. Estimation of equations over an endogenously selected population requires the implementation of selection correction methods following Heckman [
I therefore model occupational outcomes by focusing on the way in which education and other individual and household characteristics influence people’s decisions to participate in the formal public or private sectors and self-employment relative to unemployment for consistent estimates of the earning function. Following Heckman [
where
It is possible that earnings determinants, particularly education may be different for individuals at different points in the earnings distribution. Standard OLS techniques concentrate on estimating the mean of the dependent variable subject to values of the independent variables where variables are included as uncentred regressors. As an alternative to OLS, quantile regression is based on the entire sample available and allows us to estimate the return to education within different quantiles of the earnings distribution [
The urban household worker surveys for Ghana were conducted by the Centre for the Study of African Economies (CSAE)3 in collaboration with the Ghana Statistical Office (GSO) based on a stratified random sample of urban households from the 2000 census.Urban areas covered include Accra (and neighboring Tema), Kumasi, Takoradi and Cape Coast. The surveys have been conducted for 2004, 2005 and 2006 and collect information on incomes, education and labour market experience (tenure) household characteristics and numerous other modules for labour market participants in urban areas. The study uses all three rounds of the UHWS in pooled sample estimations. Although it has been a while since the data was collected, the UHWS is the only known labour market survey in Ghana which tried to track individuals overtime, although the high attrition rate made it impossible to conduct any meaningful analysis using the data as panel. For this reason, it is deemed necessary to research into the labour market using this data set.
Summary of the sample are provided in
The unemployed in Ghana possess mean education that are close to those in formal sector (private sector), an indication that in Ghana the unemployed seem to queue for suitable job opportunities in the formal sector. Average years of tenure on the job are highest for self-employment followed by public sector and the least is the private sector. The overall low tenure on the job suggests a high labour turnover in Ghana particularly in the private sector. Average monthly earnings by employment sector and gender presented in
Employment Status | All (%) | Female (%) | Male (%) | Education (years) | Tenure (years) |
---|---|---|---|---|---|
Public | 7.2 | 4.7 | 9.3 | 10.6 | 6.7 |
Private | 46.9 | 30.8 | 60.6 | 9.1 | 2.6 |
Self-employment | 25.7 | 42.1 | 11.7 | 6.6 | 8.8 |
Unemployment | 20.1 | 22.3 | 18.4 | 8.6 | - |
Total | 2,107 | 970 | 1,125 | 8.16 | 5.70 |
Source: Calculations from Ghana UHWS 2004, 2005 and 2006 [
Employment Sector | All ($) | Female ($) | Male ($) | |||
---|---|---|---|---|---|---|
Mean | Std. Dev | Mean | Std. dev | Mean | Std. dev | |
Public | 111.18 | 100.18 | 120.85 | 122.86 | 107.16 | 89.56 |
Private | 87.63 | 124.44 | 61.42 | 63.25 | 97.79 | 139.96 |
Self-employment | 74.19 | 180.20 | 55.87 | 55.49 | 131.38 | 346.60 |
Total | 84.60 | 145.10 | 60.71 | 64.83 | 104.43 | 184.84 |
Source: Calculations from Ghana UHWS 2004, 2005 and 2006 [
Baseline OLS results of the earnings equation are presented in
The average premium to an additional year of education in Ghana is highest in the private sector 8.7%, followed by the public sector (8.4%) and self-employment (2.3%). The low returns to education in self-employ- ment in Ghana are worrying since self-employment is the fastest growing occupation in the country. The implication here is that education may not be an effective means by which incomes can be increased and poverty levels reduced among the working population that is growing the fastest.
Results in
Self | Private | Public | Pooled (1) | Pooled (2) | |
---|---|---|---|---|---|
Education (years) | 0.023* | 0.087*** | 0.084*** | 0.079*** | 0.062*** |
(0.013) | (0.011) | (0.022) | (0.008) | (0.008) | |
Log of hours | −0.006 | 0.142 | 0.248 | −0.009 | 0.064 |
(0.199) | (0.089) | (0.205) | (0.085) | (0.083) | |
Tenure | 0.106*** | 0.042* | 0.003 | 0.060*** | 0.070*** |
(0.033) | (0.025) | (0.017) | (0.020) | (0.021) | |
Tenure2 | −0.004*** | −0.000 | 0.000 | −0.002* | −0.002** |
(0.001) | (0.001) | (0.000) | (0.001) | (0.001) | |
Sex | 0.361 | 0.201*** | 0.058 | 0.319*** | 0.198** |
(0.249) | (0.078) | (0.110) | (0.073) | (0.081) | |
Firm size | 0.608*** | 0.679*** | |||
(0.072) | (0.070) | ||||
Private | −0.108 | ||||
(0.076) | |||||
Self | −0.023 | ||||
(0.127) | |||||
Accra | −0.250 | 0.021 | 0.139 | 0.041 | −0.001 |
(0.231) | (0.069) | (0.125) | (0.062) | (0.062) | |
Constant | 2.988*** | 1.906*** | 1.924* | 3.007*** | 2.566*** |
(1.043) | (0.481) | (1.087) | (0.446) | (0.438) | |
R-squared | 0.095 | 0.278 | 0.294 | 0.159 | 0.216 |
Observations | 387 | 736 | 130 | 1253 | 1253 |
Notes: Dependent variable is the log of monthly earnings. Robust standard errors in parenthesis; ***p < 0.01, **p < 0.05, *p < 0.1. OLS estimation method.
found to be associated with working in the private sector or in self-employment relative to the public sector, an indication of non-existence of compensating earnings differential in the Ghanaian labour market. In addition, men in private sector enjoy earnings premium of 22% more than women but no such evidence is found for public and self-employment.
OLS estimates of the Mincerian equation potentially suffer from sample selection bias and endogeneity bias. First, we address endogeneity of education by estimating the earnings function in instrumental variables estimation. Mother and father’s (parents) education are used as instruments for education due to the high positive correlation between individual’s education and that of their parents. Weak erogeneity test, test of over-identifying restrictions and Hausman test of endogeneity prove that indeed education is endogenous in the sample. Results are not presented for brevity. It is observed that correcting for endogeneity bias changes returns to education estimates as IV estimates are appreciably higher. The average premium to an additional year of schooling in Ghana increases to 10.9% and within occupations returns to education in private sector increases to 11.7% but no evidence is found in self-employment and public sector similar to Rankin, Sandefur and Teal [
Notes: Dependent variable is the logarithm of monthly earnings. Robust standard errors in parenthesis; ***p < 0.01, **p < 0.05, *p < 0.1.
unemployment as the based for normalisation, results are presented in Table A2, average partial effects from the multinomial logit estimates are presented in Table 5.
Results highlight the strict preference for formal sector employment by the educated in Ghana, as all levels of education are found to reduce the likelihood of being in self-employment and increase the probability of formal sector employment particularly in the public sector. In addition, individuals with secondary education prefer to wait in unemployment for formal sector jobs rather than enter into self-employment as secondary education is found to increase the likelihood of unemployment. Being a man increases probability of formal sector employment and reduces probability of self-employment.
Marriage reduces the probability of unemployment and increases the probability of self-employment whereas access to non-labour income increases the likelihood of self-employment. This is a reflection of the phenomenon
Note: These results are based on multinomial logit estimates reported in Appendix A2. Levels of significance are; *p < 0.1; **p < 0.05; ***p < 0.01.
particularly among low educated women in Ghana who otherwise would have remained unemployed, are set up in trading by their husbands after marriage. Having children increases unemployment probability by about 5% and reduces the probability of private sector employment by about 7%. In a similar manner, being a household head reduces the tendency of unemployment by about 4% due mainly to the economic burden on such individuals in Ghana. Also in Ghana, the probability of unemployment is largely reduced (about 42%) by residing in Accra relative to other urban areas. Finally, young people are more prone to unemployment than the old as age is found to reduce unemployment.
Results with selectivity correction based on the multinomial logit model are reported in Table 6. Weighted least squares are applied in the second stage regression to account for heteroskedasticity present in the model due to selectivity and standard errors are bootstrap to account for the two stage nature of the procedure. Generally, results with sample selection correction are fairly similar to OLS estimates with a convex earning education relation found in the private sector. The selection correction term is however positive and significant in the public sector but insignificant in private and self-employment. The implication here is that, earnings of a worker
Notes: Dependent variable is the log of monthly earnings. Bootstrap standard errors in parenthesis ***p < 0.01, **p < 0.05, *p < 0.1. Regressions include two year dummies to control for any time specific effects but not included for brevity. Result obtained via Stata Selmlog command.
with average characteristics are higher than that of any worker who would be drawn randomly into the public sector. Conversely, earnings of a worker with average characteristics in either private or self-employment do not significantly differ from those of a worker who would otherwise be randomly drawn into the sector.
Quantile regression estimates to determine earnings heterogeneity are presented in Table 7. Hours of work, tenure, sex and residence in Accra increase earnings along the earnings distribution though not always significantly so at all quantiles of the conditional earnings distribution. Firm size premium is highest at the bottom quantile but the premium declines along the conditional earnings distribution. All educational levels compared to no education, are associated with earnings premiums across quantiles in the Ghanaian labour market (though not always significantly) with larger premiums to high levels of education evident of a convex relationship between earnings and education. Results from F-tests statistics confirm the differential earnings premiums across quantiles. What emerges from these results is that, in the Ghanaian labour market, high ability individuals (in the top quartile of the earnings distribution) have more premiums with all levels of education (primary, secondary and tertiary) compared to low ability ones. The implication here therefore is that, contrary to expectations, education may not be inequality reducing in urban Ghana.
Notes: Dependent variable is the logarithm of monthly earnings. Bootstrap standard errors in parenthesis ***p < 0.01, **p < 0.05, *p < 0.1. Regressions include year dummies to control for time specific effects but not included for brevity.
In addressing the objectives of the study, ordinary least squares and quantile regression techniques are applied in addition to addressing the two main endogeneity issues in the earnings equations which were omitted variable bias and sample selection bias by the use of instrumental variables and sample selection correction technique by Heckman [14] and Lee [15] .
Findings indicate that education plays an important role in promoting access to lucrative formal sector jobs especially in the public sector but has no direct impact on earnings within the sector. Results show a strong convex education-earnings relationship, consistent with Quinn and Teal [22] and Rankin et al. [11] , especially in the private sector as individuals with higher levels of education have higher earnings premiums than those with lower levels of education. Sample selection appears a very important aspect of the Ghanaian labour market consistent with labour market segmentation as hypothesised by Harris and Todaro [23] based on the positive self-selection within the public sector. Estimated earnings determinants along the earnings distribution point to a convex education earnings relationship particularly at the top quartile of the earnings distribution as found in the OLS, this further indicates education is not inequality-reducing among urban workers in Ghana, as high ability individuals have higher premiums with all levels of education than low ability ones.
Although educational attainment is a primary factor in determining labour market participation and earnings, it negatively determines participation and weakly explains earnings in the low-earning and the fastest growing informal sector amidst stagnant employment growth in the formal sector. As a result, even though the informal sector is characterised as low earnings sector, many job seekers find jobs in the informal sector. Educational investment therefore, needs to be augmented with improvements in employment opportunities in quality alternative occupations similar to those found in the public and private sectors.
It is, however, important to note that predictions of the findings are limited due to the cross sectional nature of the data used for which reason results are not necessarily causative. The high attrition rate in the data is the main limitation which made it impossible to conduct a meanings panel analysis. In addition, although quantile regression is informative in terms of the differential effects of education on earnings, it is not able to indicate the rationale for the differences, further research is therefore needed to decompose the differential impact education has on earnings.
I am grateful to the Office of Research, Innovation and Development (ORID) in University of Ghana for the financial support I received in the form of Faculty Development Grant towards my PhD.