Our earlier estimates (reported in Table 3) appear to be somewhat sensitive to the exclusion of public sector employees. The average returns to education obtained for the full sample is 5.7%. For males, the estimate is 5.2% as against 6.3% for the full sample. Estimate for the female sample is however much lower: 9.6% compared to the earlier estimate of 13%. Similarly, there is a fall in the size of the coefficient for the urban sample from 8.1 to 7.2%. For rural worker, the new estimate is 4.1% as against 5.8%. The sample selection term, lambda, however remains insignificant throughout. ii) Additional control for observed determinants of wages: To minimise omitted variable related bias in OLS estimates, past studies have often employed superior control for observed correlate of wages using data on, for instance, school quality (e.g. Behrman and Birdsall, 1983). The HIES 2000 collected information on types of school attended by an individual. Thus we produce further estimates of earnings functions with additional control for school types which arguably proxy for school quality in Bangladesh. Results are reported in Appendix Table 2. Individuals who studied in public schools earn more in the labour market than those who attended private, aided or religious (Islamic) schools. This finding is consistent with Asadullah (2005) who finds that individuals appearing in secondary school certificate (SSC) examinations from public schools have higher achievements (measured in terms of pass rate in first division). However, our primary intention here is to assess whether the estimate of coefficient on schooling variable is sensitive to the exclusion of control for type of school attended by sample individuals. Comparison of OLS and Heckman estimates of the coefficient on schooling variable with and without school type dummies reveals that the estimate of returns to education is not affected by control for “school quality” in our data.
Ali, Liaqat, and NaveedAkhtar (2014) investigated An Analysis of The Gender Earning Differentials in Pakistan used time series data for the time period 2010-11. The study used province, literacy, education, occupation, industry, status of job, age, martial status as key words and used OLS method for estimation. It is estimated that female invest more on education but earning is low rather than males invest low on education but earning is high.Kavuma, SusanNamirembe, Oliver Morrissery and Richard Upward (2014) investigated Private Returns to Education for the Wage-employees and Self-employed in Uganda used panel data for the time period 2005-06 and 2009-10. The study used homogenous and heterogeneous technique for analyses and used variables as future earning, cost of education, schooling, and present value. The study estimated that returns from education in Uganda are very high.
Low returns to education do not necessarily imply that education has no value in China. It may be because the value of education has not been properly reflected as private economic returns in labour markets. Fleisher and Wang (2004) find that the wages of educated workers are well below their marginal product in China, and the social returns to education will exceed the estimated private returns. Hence, the most widely accepted explanation of lower Chinese educationreturns may be the explanation of labour markets transition. Before 1978, wages of all workers were determined and controlled through a rigid system in China, designed to reduce labour costs during the rapid industrialization. Low wages were made possible by state-subsidized food prices and state provision of non-wage benefits to workers and their families. Throughout the economic reforms in China into the early 1990s, the wage differentials by levels of skill and schooling still remained narrow. After the “socialist market economy” was authorized in the early 1990s, the rigid wage system was gradually replaced by the flexible wage system. 1 Thus, the wage reform in China freed up the compressed wage differentials and thereafter had similar implications for the economic returns to education.
Our samples, which consist of individuals in employment aged between 16 and 65, drawn from the BHPS, the GSOEP and the PSID comprise 3,486, 5,548 and 1,123 heads of households respectively. We exclude the self-employed, agricultural workers and individuals with more than one job. For each country we explore how investments held in financial assets affect estimated returns to human capital by comparing the returns to education in a standard mincerian wage equation with the returns to education allowing for interactions between human capital investment and risky financial investments. To be specific, for each country we initially estimate the following semi log mincerian wage equation:
Economic activity and wages by sex presented in table 3 show that women are mainly concentrated in the public sector and service private sector. Women’s participation in the public sector is over twice that of men. The public sector accommodates about 34% of women and 17% of men. Participation in the private sector is larger for men than women. About 64% of men are in the private sector compared to about 51% of women. Division by sectors of the economy shows that women dominate services with a participation of about 66% compared to about 46% for men. In services, women have the largest participation in education and health. Men on the other hand are mainly concentrated in industry. About 34% of men are in industry compared to 20% of women. The vast majority of women in industry are in manufacturing, whereas men are in construction.Wages are consistently lower for women within and across sectors. Agriculture is the lowest paying sector for both sectors and especially for women. Average public sector wages are higher for men and women compared to the private sector. Wage differentials between men and women are larger in the private sector. The lower difference in the public sector may in part be as a result of Albanian legislature regarding wages in this sector. Women’s wages are higher in services than industry, whereas for men it is the opposite. Wage differentials for women between services and industry are larger than those for men. Within services women’s highest wages are in transport and communications followed by education and health. The former two are as a result of the continuous increases of wages in these two sectors. Higher public sector wages part of which due to the continuous increases of wages in health and education are expected to have an impact on returns to education by sector.
The paper statistically evaluates the trends in private returns to education in Pakistan for the period 1990-91 to 2012-13. The data of 16 nationally representative Labor Force Surveys during this period are utilized to estimate the standard Mincerian Earning Functions with some modifications. Trends are also disaggregated for gender, region, province, sectors and educational attainments. In addition, the study also employs the pseudo-panel approach for the first time in Pakistan for estimating overall returns to education to control unobserved individual heterogeneity which is common to estimate returns from data on individuals. The estimate using the traditional approach with individual LFS cross-section data suggests 5.5 percent yearly returns for wage earners after controlling for the heterogeneity in the regional and provincial labor markets in Pakistan. Nonetheless, the study found considerably larger returns to education from the pseudo-panels with year fixed effects. The estimates of earning equation with birth specific cohort data reveal about 9.2 percent returns for overall Pakistani labor market.
whether it contributes through increasing the efficiency of individuals who acquire higher levels of education or through externalities that also increase the wages of those who have lower human capital is a subject that has been investigated only very recently. The paper finds a strong correlation between the aggregate level of education and wages regardless how local human capital is measured, or the methodology used to estimate the magnitude of spillovers. The private returns to education in Turkey are found to be around 5%, lower than typical estimates in most developed countries (Card, 1999; Mid- dendorf, 2008). Considering the rather scarce human capital in Turkey, one would expect higher returns. Yet similar estimates are reported for China and Russia, though these countries have higher levels of education. This could be due to either the quality of education in Turkey being lower, or that human capital unless accompanied with the appropriate physical capital and technol- ogy is not as productive as it should be.
Card (1993) notes that educational attainment is not randomly distributed across the population but decided upon by individuals. As a result, return to schooling may be over- or under-estimated. Card (2001) developed a model in which individuals maximize lifetime utility which depends on consumption, schooling, and work; subject to an intertemporal budget constraint that equates consumption to earnings based on prior schooling plus earnings given current schooling less tuition cost. The first order conditions identify the marginal benefit and marginal cost of schooling, which, when equated, define optimal schooling. Di↵erences in school- ing can therefore arise both from di↵erences in marginal benefits and di↵erences in marginal costs of schooling. From the marginal benefit, Card derived a model for log earnings with an individual-specific intercept and schooling coefficient. He shows how individual heterogeneity in both the intercept and slope results in an inconsistent and biased estimate of returns to education. If the distributions of the individual-specific initial wages (with no schooling) and marginal returns to school- ing are highly skewed/asymetric, schooling will be correlated with the error term in an ordinary least squares estimation and returns to schooling will be overestimated. People with greater returns to schooling have a motivation to obtain more educa- tion, which results in an upward bias in the returns to schooling. Even when there is no heterogeneity in the slope, OLS estimates still su↵er omitted variable bias due to the correlation between ability and marginal cost of schooling. If marginal costs are less for people who can earn more for any given schooling, returns to education would be overestimated.
According to the literature, the rates of returns are highest to primary education followed by secondary and then university levels (See Psacharopoulos, 1994). The declining rate of return, by level of education, is also observed across different levels of per capita income. The largest improvements in productivity occur during the early years of primary education. However, our estimates suggest that the lowest returns to education are to the first few years of schooling and the highest are to university. Thus our finding of an increasing rate of returns seems unconventional. Cohen and House (1994) find similar pattern in Khartoum, the Sudan. Their estimate of the rate of returns to university education is around 12 percent. In Malaysia, Mazumdar (1994) finds increasing return to education at levels higher than lower secondary, while Gindling et al. (1995) find that private rates of return to education in Taiwan are highest for the higher levels (university levels) and lowest for the lower educational levels.
Comparing Figure 6 and Table 5 allows us to disentangle the trend in the relative labour market value of diplomas in terms of wages from the trend in the private return to these diplomas, which is also influenced by developments in the relative length of studies 7 . Not surprisingly, the higher the educational level, the higher the wage premium. Particularly for women, there seems to be a high bonus in pursuing university studies. However, once controlled for the different length of studies, the hierarchy changes. The master degree yields by far the highest return, both for men and women. This is attributable to its short length of studies, compared, for instance, to tertiary level studies. However, a decreasing trend is observable in the returns to the master degree. For men, this is entirely due to the fact that the study duration gap between master craftsmen and the reference group has increased somewhat, whereas the wage premium do not decrease. For women, however, this is both the outcome of a declining labour market valuation of female masters and of a change in the duration gap. A striking feature is that the wage premium for employees with a high school diploma (with or without an additional vocational degree) has decreased sharply. This is particularly true for women, for whom the returns to high school dropped from 12.5% in 1984 to 7.4% in 1997. For men, this decrease only started in 1993, but is also quite strong (from 8- 9% to less than 6%). This decrease is only the result of a declining labour market valuation of this degree, and not of changes in the duration gap. As a result, a high school degree yields the lowest return at the end of the period, for both men and women. Having this degree in addition to an apprenticeship does not seem to bring any further return. The returns to education are higher for women than for men in all educational categories except for holders of a higher technical degree. The trend in the return to higher technical college is constant over time for men, and slightly declining for women. This is mainly due to the fact that more and more women complete an apprenticeship prior to higher technical college studies, which increases the duration of studies. At the very end of the period, the returns to higher technical college are similar for both genders.
a measure of expected returns, present returns to education represent some signal of returns to education in the future. Second, rates of return to education not only in a child’s state of residence but also in other states could affect his participation in child labor and schooling. Even though inter-state migration is relatively low in India (due to language barriers), education provides individuals with greater mobility in labor markets. Yet, returns to education in one’s own state may be the only signal individuals have of employment opportunities for educated workers. An extension to our analysis could include the rates of returns to education not only in one’s own state but also in neighboring states as explanatory variables.
There may be other factors that affect the policy and economic interpretation of the statistical estimates: there may be “over”education where, because of labour market rigidities of some form, relative wages for different types of workers does not clear the markets for those types. For example, if the wage for highly educated workers is too high to clear the market, then this type of worker may take a job that requires only a lower level of skill and commands a lower wage. This overeducation would manifest itself as a lower estimate of the average return to education and ought to result, in the long run, in a decline in education levels. That is, if there is some factor that prevents relative wages to adjust then quantities will adjust instead. A related issue is the extent to which there is heterogeneity in the returns to education: returns may differ across individuals because they differ in the efficiency with which they can exploit education to raise their productivity. There may be individual-specific skills, for example social or analytical skills, which are complementary to formal education so that individuals with a large endowment of such skills reap a higher return to their investment in education than those with a low endowment. Thus, for example, some college graduates may not be well endowed with these complementary skills and may appear to be overeducated: in fact, they are simply less productive than other graduates in graduate jobs.
This report is concerned with the effect of education on earnings and, in particular, with the financial return to education. The methodology used is to estimate, using large survey datasets that contain information on education, earnings and other characteristics, the relationship between (log) wages and education. The work largely uses straightforward regression methods to estimate coefficients that pick up the effect of either a year of education or the possession of a specific qualification (for example, a degree) on (log) wages 1 . The empirical specifications are based on the theory of human capital, whereby individuals make decisions on acquiring human capital, such as education, up to the point where the returns to education are driven down to the real return on other assets. With additional assumptions, such a framework implies that (log) wages are linearly related to education (or qualifications) and a quadratic function of work experience. Furthermore, the coefficient on education in such a model (i.e. the effect of a year of education on wages) can be interpreted as the financial rate of return to education providing the only costs of education are the opportunity costs of the forgone earnings. That is, the coefficient that we estimate is a measure both of the effect of a year of education on individual wages and the financial return to an individual investing in his/her human capital. Indeed, our analysis suggests that the returns estimated here are not very sensitive to whether we include the (modest) real costs that are associated with education – for example, including the recently introduced fees for higher education into a financial rate of return calculation makes little difference to the impression given by the coefficients that we estimate 2 .
Monetary uncertainty in return to education has received a much smaller empirical attention. Since the return to education is not constant among individuals and materi- alizes possibly only several years after a choice of education has been made, educational investment has an inherent uncertainty to it. As when estimating mean returns to edu- cation, endogenous selection also complicates the estimation of uncertainty of returns to education measured by variances. For example, a direct comparison of income variances between university and high school educated people might give an incorrect picture of the effect of education on the income variance, because we cannot observe counterfac- tual income streams of the same people with different education levels. Consequently, the observed variance of income may not be a good measure of uncertainty, because it is comprised of two distinct components: unobserved heterogeneity and uncertainty. The intuition for this dichotomy follows from private information: wage uncertainty, or risk, is the part of the wage variance, which is not foreseeable by the decision-maker.
Given the mostly ad hoc nature of the macro-economic specifications, there is no strong a priori reason to assume a linear relationship between human capital and productivity levels or growth. In fact one might expect diminishing returns to a factor (as in the conventional log-log Cobb- Douglas production function). One of the few studies that has examined this issue is Krueger and Lindahl (1998). They find evidence for non-linearities, in particular they find that a quadratic form for schooling fits the data better (a squared term is significant). The inverted-U pattern sug- gests that there are diminishing returns to education, with the peak effect at about 7.5 years. 15 The presence of non-linearities is also consistent with other forms of mis-specification (generally simple aggregation of a non-linear micro relationship renders the coefficients on the nonlinear macro equation uninterpretable).
This study measures the private rates of return to education in Algeria using both basic and extended Mincerian earnings functions. To do so, a random sample of employees from Saida province in Algeria has been used. The findings of the study show that an additional year of schooling increases earnings by 9,5%. Returns from secondary education are the highest while returns from middle education are the lowest. It is also interesting to note that female workers tend to have higher returns than male workers. Furthermore, returns to education are higher in rural and public sector compared to urban and private sector respectively.
As Abelson (2003, pp.313-315) points out, the externalities of education are easier to list than they are to measure. Generally speaking, attempts to quantify social returns to education have found them to be modest at best. For example, Acemoglu and Angrist (2000) estimate that external returns to education are around 1 percent and not significantly different from zero, while a literature review by Psacharopoulos and Patrinos (2004) find mixed evidence, suggesting that social returns might be lower or higher than private returns. Recent work by the OECD (2006, p.130) compares a measure of private returns (the increase in after-tax earnings less costs of undertaking education) with a proxy for social returns (the sum of private returns, plus increased tax revenue, less the cost of providing education). For most of the 11 developed countries in the study, the OECD finds that the social returns to education are lower than the private returns (though since the study did not cover Australia, it should be regarded as suggestive rather than definitive).
The survey has information on household characteristics: household residence (rural or urban), household size, membership of a social group, and religion; individual characteristics: age, education (number of standard years completed), gender, marital status and relation to the household head. The survey also has information on occupation, industry, number of hours work in a usual day and wages and salaries of individuals, and the principal source of income for the household. The components of household income include farm income, income from interests (or dividend or capital gains), property, pension, income from other sources etc. A household belongs to one of the following social groups: Scheduled Caste (SCs), Scheduled Tribe (STs), Other Backward Classes (OBCs) and Others. 10 The dataset provides additional information: whether an individual failed or repeated a class, whether he/she can converse in English and his/her division in secondary board examination.
The misreporting problem is well known to be prevalent in voter turnout data. There is evidence that survey respondents, especially those with higher level of education, are more likely to overstate their civic participation. One important source of misreporting that could be correlated with schooling is the potential for embarrassment. A respondent might be embarrassed to admit that he/she did not vote in the last general election to the interviewer, leading to untruthful responses. However, the fact that the voting question in TILDA is contained in the SCQ could help to avoid this problem. The fact that the voter turnout statistics (See Figure 1) derived using the TILDA sample are very similar to the official data is also reassuring.