Print Reference This Reddit This

Return on Education and Human Capital Externalities: Review of Theo...



This chapter examines the theoretical and empirical literature on return to education and human capital externalities. The purpose of the review is to highlight issues that require attention in the analysis of the impact of education on earning and externalities. In addition the review aims to identify areas widely covered in the literature and find out issues to be addressed in this thesis.

The literature that forms the basis of this thesis is varied. Given the multidimensional focus of this study, the literature review concentrates on the main broad issues .

The rest of the Chapter is organized as follows. Section 3.2 discusses the human capital literature followed by earning function literature in section 3.3. Section 3.4 presents the review of literature relating to human capital externalities followed by analasis of findings of the empirical literature in Section 3.5. The final section probides a clonclusion and shows the focus for the present study.


There is a vast body of research on the labour market benefits associated with education. The Human Capital model is the most widely used that explain the relationship between education and labour market outcomes. Human capital theory emphasizes that education provides the information and skills to enhance the productive capacities of individuals. Individuals will invest in education through schooling to acquire skills and productivity. This skills and productivity raise the value of individual to the labour market and thus lead to higher wage offers (Schultz, 1961; Ridell, 2006). Further, Okuwa (2004) demonstrates that education is an essential determinant of earnings in market economies. The higher an individual’s educational attainment, the higher the individual’s expected starting wage and the steeper the rise in earning capacity over time.

The idea that education and training constitute an investment in individuals that is analogous to investment in machinery or physical capital was first introduced by Adam Smith in 1776 in his book, The Wealth of Nations. However, the theoretical and empirical foundations of human capital are underpinned by Mincer (1958; 1962; 1974), Schultz (1961), and Becker (1962). The idea of education as a valuable investment for economic development is based on human capital theory. The essence of the theory is that investments are made in human resources in order to improve productivity, and therefore employment prospects and earnings. Individuals acquire skills through formal schooling and/or work experience, and these skills increase the individual's value to employers and therefore their future earnings. Likewise, McNabb (1997) asserts that the acquisition of human capital through education and training is an investment in the sense that the individual foregoes current income to increase his or her earning potential in the future.

According to Human Capital Theory, education is treated as an investment. It requires resources that have a cost, in terms of direct cost as well as opportunity cost through foregone earnings, and increases the productivity of the individuals taught. An individual invests time and forgone earnings in order to obtain higher future benefits. Education should continue as long as there is a positive difference between the marginal benefit and the marginal cost of education. It is assumed that individuals choose their length of schooling that equates with their marginal return from schooling to their cost of schooling. People choose different levels and types of schooling. They make different choices probably because some receive a higher benefit from a given amount of schooling or type of schooling due to learning more readily than others, or because they value future earnings more highly, or because they enjoy learning (Blundell, 2006) Human capital theory suggests that education and/or training increases the productivity of workers, hence raising workers’ future income by increasing their lifetime earnings.

Figure 3.1: Education and Alternative Income Streams


Direct cost (tuition, books)




Income stream


Income stream


Income stream










Source: Benjamin et al (2007: 258)

Figure 3.1 illustrates the individual decision to invest in education. It shows alternative income streams associated with different levels of education and different years of education. Each year of education is associated with a lifetime income stream. The earning in each year is measured in present value terms to make them comparable across different time periods. Benjamin et al (2007) explain that the shapes of the earnings streams indicate two key factors. First, for each profile, earnings increase with age, but at a decreasing rate. This concave shape reflects the fact that individuals generally continue to make human capital investments in the form of on-the-job training and work experience once they have entered the labour force. Because of the existence of diminishing returns to experience this job experience adds more to their productivity and earnings early in their careers. Second, the lifetime earnings profile of more educated individuals lies above the equivalent earnings profile of less-educated individuals. This feature is based on the assumption that education provides skills that increase the individual’s productivity and thus earning power in the labour market. As a consequence of this assumption, individuals may have the same amount of work experience, but those who have more education will earn more.

Human capital theory argues intuitively that education endows an individual with productivity enhancing human capital, and that this increased productivity results in raising their market value in the labour market. The theory stresses how education increases the productivity and efficiency of workers by increasing the level of cognitive stock of economically productive human capability, which is a product of innate abilities and investment in human beings. The provision of formal education is seen as a productive investment in human capital, which the proponents of the theory have considered as equally worthwhile as that of physical capital (Olaniyan and Okemakinde, 2008)

Blundell (2006) highlights three core elements of human capital theory. First, human capital theory is a theory of investment decisions: individuals incur costs at the present time in return for benefits in the future. This investment dimension is particularly important because the benefits of human capital acquisition typically accrue over a long period, in the form of a higher earnings stream over many years. Second, human capital investments are generally a risky investment because of the existence of uncertainty about the extent to which the investments will pay off. Third, the opportunity cost -- the income foregone by not working -- is the main component of the costs of obtaining human capital.

Another significant aspect of the human capital theory is that the investment in knowledge and skills not only benefits the individual, but could also increase an employer’s or country’s human capital resource pool and potential productivity. Human capital externality suggests that increasing the human capital of one person will have some effects, not only in terms of the earnings and returns to education for that individual, but also on earnings and returns to education for other individuals (Kimenyi et al).

In summary, Human Capital Theory explains four major arguments. First, investments are made in human resources in order to improve individual productivity and therefore their earnings. Second, it is an investment because costs are acquired, both in term of direct costs (fees) and indirect costs (opportunity cost). Third, the optimal choice is dependent on the balance between benefits and costs. Fourth, the investment in education will benefit both the individual and the society.


Estimation of the causal link between schooling and earnings has been puzzling labour economists for several decades. One of the major questions regarding the relationship between education and earning is: how much is the return to education? Many methodologies have been proposed to answer this question, but one that has become a cornerstone in this empirical research is human capital earning function proposed by Mincer which reveals how wages relate to schooling and work experience. In his study, Schooling, Experiences and Earning, Mincer (1974) argues that the investments in human capital studied take two complementary forms: formal schooling measured by years of school completed, and work experience measured by potential years in the labour force subsequent to the completion of schooling. Formal schooling can be treated as an investment in future earnings. After entering the labour force, individuals continue to devote time and money to furthering job skills and acquiring job-related information.

Chiswick (2003) states that the human capital earning function introduced by Mincer has several distinct characteristics that makes it particularly attractive. First, the functional form is an equation based on the optimizing behaviour of individuals and represents the outcome of a labour market process. Second, it converts the monetary cost of the investment in human capital into years of schooling and years of labour market experience. In other words, it converts the ‘immeasurable’ into the ‘measurable’. Third, the function is adaptable to inclusion of other variables that affect earnings. Fourth, it allows comparisons across time and demographic groups, since the coefficients of the regression equation have economic interpretations. Fifth, although earnings are positively skewed and the inequality of earnings rises with the level of schooling, by using the natural logarithm of earnings as the dependent variable, the residuals are closer to being normally distributed and homoskedastic. Sixth, the functional form generates a commonly used measure of relative inequality, the variance of the natural logarithm of earnings, thereby facilitating the study of earnings and income inequality across time and space.

The theoretical foundations of Mincer’s specification can arise from accounting identity framework (Mincer, 1974). Mincer (1974) proposed a simple model relating years of completed schooling to lifetime earnings. The model assumes that the income that an individual will obtain is fixed and related directly to the amount of schooling they obtain. The private cost of schooling is the cost of delaying the income stream another year. He argues that potential earnings for the present day depend on investments in human capital made at former times.

Let Et be potential at time t. Investments in training are expressed as a fraction of potential earnings invested, Ct = kt Et, where kt is the fraction invested at time t, and let λt be the return to training in investments made at time t, then Et+1 = Et +Ctλt = Et (1 + ktλt) (1)

Repeated substitution yields (2)

Formal schooling is defined as years spent in full-time investments (kt=1). Assume that the rate of return on formal schooling is constant (λt = λs) and that formal schooling takes place at the beginning of life. Also, assume that the rate of return to post-school investment, λt is constant over time and equals λ0. Then, we can write (in logs)


Which yields the approximate relationship (for small λs and λ0) using the fact that ln(1+ λ) λ, if λ < 0.2,


To establish a relationship between potential earnings and years of labour market experience, Mincer (1974) further assumes a linearly declining rate of post-school investment:

Where x = t-s ≥ 0 is the amount of work experience as of age t and Z is the length of working life. Then the relationship between potential earnings, schooling and experience is given by:


Observed earnings equal potential earnings less investment cost, yield the following relationship for observed earnings:


When Es and K are uncorrelated, the variance of log earnings will be minimized at 1/λ0.


We obtain the standard Mincer equation:


Using Mincer’s model, many modern empirical studies estimate the association between years of schooling and labour market outcomes, such as wages and earnings, using data on individuals. Formally, the basic model has


where Ei denotes earnings or wages of individual i, Si denotes years of schooling, EXi denotes years in labour market or experience, and εi denotes the error term, which embodies the effect of all of the determinants of wages or earnings besides schooling and experience.

The equation states that the natural logarithm of earnings or wage depends linearly on years of schooling controlling for experience and experience squared. The use of natural logarithms allows the interpretation of βs as the percentage effect of an additional year of schooling. This coefficient represents a private internal rate of return to schooling. This earnings function shows that earnings levels are related to human capital investments. This implies that the more human capital investments an individual makes the higher his or her earnings. Further, the coefficient on the schooling variable reflects the rate of return to schooling. Earnings should be related to the quality of schooling. Those attending higher quality schools should earn more.

According to Polachek (2007), the Mincer earnings function yields at least three important empirical implications. Firstly, the function suggests that earnings levels are related to human capital investments. This indicates the more individual makes human capital investments the higher the earnings that will be obtained. Then, the coefficient on the schooling variable reflects the rate of return to schooling. Secondly, earnings functions are concave. This means that earnings rise faster for the young, than earnings increases taper off mid-career. Thirdly, the model has implications regarding the distribution of earnings. The impact of schooling is most apparent in what Mincer calls the “overtaking” subset of earnings distributions. The overtaking year of work experience is that time at which the earnings of continuing investors in human capital are equal to the earnings of those with equal schooling who did not continue to invest. In other words, the overtaking point is the moment in one’s lifecycle when observed earnings just equal one’s potential earnings at graduation, were there no post-school investment.

Figure 3.2: Earning Profiles









Years of work experience





Source: Mincer (1974: 17)

Figure 3.2. ilustrates the shape of net earnings Yj and gross earning Ej during the post-school investment period OP. The concave curve Y0YjYp plotted over the lifecycle reflects observed earnings, which are gross earnings Ej minus human capital investment Cj. Overtaking year of experience is showed by Q. At the overtaking year of experience Q, net earnings Yj be the same as gross earnings upon graduation, so that Yj = E0 = Ys.

3.4 Human Capital Externalities

It is widely accepted that education is beneficial, both to the individual and the society. An individual’s educational attainment affects not only the individual’s productivity but also that of others, this is called externalities. Education externalities have become the focus of economic and policy debate for the last couple of decades. Different theoretical explanations have been developed. Halfdanarson et al (2008) claim, that in general, three different types of externalities have been identified in the literature. Market externalities, which can be further grouped into two main categories: technological externalities (or non-pecuniary) and pecuniary externalities - are frequently put next to non-market externalities. Technological and pecuniary externalities were already mentioned by Marshall in his famous book. However, they were not further developed until recently. Romer (1986) and Lucas (1988) elaborated technological externalities. They explained that processes of learning by doing and exchange of ideas are more likely to occur in an area with higher average levels of education.

Figure 3.3: Types of Human Capital Externalities

Human Capital Externalities

Non-Market Externalities

Market Externalities

Pecuniary Externalities

Technological Externalities

Source: Halfdanarson et al (2008)

Various theoretical explanations of market externalities have been constructed.Technological externalities (non-pecuniary externality) was re-discovered by the work of Romer (1986) and Lucas (1988). They explained theoretically that the process of sharing or exchanging of knowledge, and learning by doing, in turn nurture technological progress, and this tends to occur in an area with a higher average levels of education. While pecuniary externality was re-discovered by the works of Krugman (1991a). This type of education externality is not created through technological channels, but through improving the firm-worker searching process. This process occurs if the average level of education of workers is high, so firms will invest more in physical capital because of the high cost of firms and workers finding each other in a labour market (Acemoglu, 1996).

Another type of human capital externality is non-market externality. It is important to recognize the distinction between non-market effects and nonmarket externalities of human capital. In order to understand the differences between them McMahon (2007) developed a diagram of the total net benefits of education (figure 3.4.).

McMahon (2007) argues that there are two outcomes from the direct effects of education. The first category is market outcomes, such as earnings and economic growth. The second category is non-market outcomes (private non-market benefits and non-market social benefits), which yield additional non-market benefits to human welfare. Indirect effects occur as education works through some other intervening variables to affect either market or non-market outcomes. Further, he states that the indirect effects are externalities because the education of one person benefits others in the family, the community, and/or in future generations. The benefits of these indirect effects are not enjoyed by the individual as the direct result of his or her education investments, but are freely available to all.

Figure 3.4: Total Net Benefits of Education

Source: McMahon (2007)

Figure 3.4 suggests that the effects of education can be grouped into two main effects: direct and indirect. Further these two types of effects of education are divided into three categories for each type. Under the direct effects of education there are market benefits, including earning and economic growth in panel A-1; private non-market benefits including better personal health and greater longevity in panel A-2; and non-market social benefits in panel A-3. The indirect effects of education consist of indirect effects as a part of earnings and economic growth as seen in panel B-1, indirect effects as a part of non market private benefits such as lower fertility rate, and reduced infant mortality as seen in panel B-2. In panel B-3 is indirect effects as a part of public goods, such as education’s net effects on reduction of poverty, lower crime rates, improvement in civic senses of individuals.

Most Economists agree that there are social benefits from education, but they disagree on the size of these externalities. In addition, most conventional estimates of the Social Rate of Returns do not take into account the non-monetary externalities, as these are difficult to measure. This is why the literature on the non-market externalities of education is very rare.

Empirical Evidences of Return to Education and Human Capital Externalities

A large body of literature investigates the returns to education and human capital externalities. Estimated returns to education are, in general, larger in developing countries than in developed countries. Kimenyi et al (2006) find found that the private returns to education in Kenya generally increase with the level of education. In the rural areas, returns to university education are lower than returns to secondary and college education. O’Donoghue (1999) applies micro simulation methods to compare returns to education in four European countries, Germany, Ireland, Italy and the United Kingdom. This author uses two measures of the return to education i.e. the internal rate of return and the marginal benefit. His findings are consistent with those of other international studies. The internal rates of return tend to diminish with education level and these rates of return for women usually exceed to those of men. Turning to the marginal benefit of education, O’Donoghue finds that private returns are higher than social and fiscal returns to education. This would suggest that individuals gain more from education than society in general, and more than the public finances do. Also, it seems that those with higher incomes have higher private marginal benefits than those with lower incomes.

Based on a Mincerian earnings function method, a number of studies on education and earnings in Africa find that the private rate of return to an additional year of schooling is quite high. For countries in Africa, it is commonly asserted that the private returns to investment in education are highest at primary level, and thus primary education should be the number one investment priority (Psacharopoulos, 1985; 1994). However, a number of later studies on education in Africa have found that the private rates of return not only are relatively lower than suggested in the conventional pattern, but also increase with the level of education (Kifle, 2007).

Using elaborate model and earnings function methodologies, Hossain (1976) estimates both social and private rates of return to three levels of education in China. The social rates of return were highest for primary education at 14.4%, followed by 12.9% for secondary education and 11.3% for higher education. The private rates of return were also highest for primary education (18.0%), and this was followed by higher education and secondary, respectively. Cohen and House (1994) examine the relevance of the human capital approach to explain the variance in workers’ productivity and earnings in the labour market of urban Khartoum, Sudan. One of their important findings is that returns to primary education are lower than the average for other developing countries, while returns to college education are higher. The results oppose the popular view observed by Psacharopolous (1994). Based on this empirical evidence, Cohen and House conclude that the patterns of returns to education at different levels remain inconclusive. Table 3.1 summarizes selected papers on private returns to education.

Table3.2. Private Return to education


Data Set


Econometric Technique(s)


Bedi and Gaston (1999)

May 1990 survey of Honduran households



The IV estimates are significantly higher than OLS estimates. The higher rate of return estimates are driven by the greater schooling attainment and the higher marginal returns for individuals from more privileged family backgrounds.

Brunello, and Miniaci (1999)

1975-1995 survey on income and wealth of Italian households



They find evidence that the return increases with higher levels of educational attainment.

Duflo (2001)

1995 intercensal survey of Indonesia (SUPAS



Combining differences across regions in the number of schools constructed with differences across cohorts induced by the timing of the program suggests that each primary school constructed per 1,000 children led to an average increase of 0.12 to 0.19 years of education, as well as a 1.5 to 2.7 percent increase in wages. This implies estimates of economic returns to education ranging from 6.8 to 10.6 percent.

Harmon and Walker (1995)

1978 to 1986 Family Expenditure Survey (FES).



Years of education have a positive effect on return to education

Hevia (2008)

European Household Panel 2000 data for Spain


O LS, IV, and double selection

There are incentives in Spain for investing in education not only because it means an increase in wages but also because it raises the probability of obtaining any wage at all.

Hyder (2007)

Labour Force Survey (LFS) of Pakistan for 2001-02.


Multinomial Logit Model (MNL)

The estimates for the labour market in Pakistan shows that males are more advantaged in terms of earnings as compared to female counterparts.

Kifle (2007)

363 employees (salary and/or wage earners) working in public and private sectors of the Eritrean economy 2001-2002.



the rates of returns to education increase with the increase in levels of education.

Leigh and Ryan (2008)

Labour Dynamics in Australia (HILDA)



The naive OLS returns to an additional year of schooling (controlling for age and gender) was around 13%. The implied ability bias is 9% when instrumenting with changes in school-leaving laws, 10–28% estimating a fixed effects model with identical twins, and 39% instrumenting with month of birth.

Liu et al (2000)

1990 Taiwan “Human Resource Utilization Survey”.



The estimated wage function is convex; returns to schooling increase with the level of education. The effect of father’s schooling is larger than the effect of mother’s schooling in the wage function; however, the effect of wife’s schooling is even larger.

O’Donoghue (1999)

The German Socio-Economic Panel, the Irish Survey on Income Distribution, Poverty, and the Usage of State Service ,Research Institute and the Survey of Italian Households, UK Family Expenditure Survey (1987)

Germany, Ireland, Italy and the United Kingdom


Private returns are higher than social and fiscal returns to education. This would suggest that individuals gain more from education than society in general and more than the public finances do.

Psacharopoulos (1994)

Cross countries data

78 countries

Full or elaborate method, and earning function method (OLS)

Primary education continues to be the number one investment priority in developing countries; educating females is marginally more profitable than educating males

Rummery et al (1999)

1985 wave of the Australian Longitudinal Survey



An additional year of schooling is associated with an increase in wages of approximately 8%.

Toh and Wong (1999)



Cost- benefits approach

The rates of returns to education in general increase with the level of education.

Trostel et al (2002)

International Social Survey Programme data, 1985–1995.

28 countries


A conventional OLS estimates suggest a worldwide average rate of return to schooling of just under 5% for men, and a little under 6% for women. There is no evidence for a worldwide rising rate of return to education from 1985 through 1995. Indeed, the worldwide rate of return declines slightly over this period. In general, instrumental-variable estimates (using spouse’s and parents’ schooling as determinants of schooling) are over 20% higher than ordinary least-squares estimates.

Trostel (2005)

1985-1995 the International Social Survey Programme (ISSP)

33 countries


The marginal rate of return is increasing significantly at low levels of education, and decreasing significantly at high levels of education.

Tsakloglou and Cholezas (2001)

Household Budget Surveys HBSs (1974, 1988 and 1994).



Returns to education are increasing as the level of education rises.

Uusitalo (1999)

1970, 1975, 1980, 1985 and 1990, censuses.



Cognitive abilities, as measured by the army test scores in 1970, are found to have a significant and fairly large effect both on the choice of the length of schooling and on subsequent earnings. Instrumental variable estimates that utilize family background variables as instruments produce estimates of the return to schooling that are approximately 60% higher than the least squares estimates.

Even though there are many good reasons to argue in favour of schooling externalities, cross-country evidence on human capital externalities has proved to be surprisingly mixed. Some studies suggest that the ideal field for empirical research on human capital externalities should be local labour markets, such as in cities. Following this suggestion, Rauch (1993), Acemoglu and Angrist (2000, 2001), Moretti (2004), and Ciccone and Peri (2002) estimate Mincerian wage-equations, augmented with an average human capital term meant to capture the productivity externalities generated by schooling.

The Mincerian approach to human-capital externalities was introduced by Rauch (1993) to estimate average-schooling externalities in a cross section of the U.S. cities in 1980. He uses the US individual data from the Public Use Microdata Sample of the 1980 Census of Population, collected for the Standard Metropolitan Statistical Areas (SMSA) as the basis. He finds that the SMSA average education and the SMSA average experience are both significant in raising individual wages. An additional average year of schooling means an increase of 5.1% in the wage, and an additional year of experience means a 0.46% increase.

Acemoglu and Angrist (1999, 2000, 2001) extend the approach to a panel of the US states and account for state-fixed effects as well as for the endogeneity of average and individual schooling. In this study they use samples of white men aged 40-49 from the 1960-80 US Censuses. They employ the variation in educational attainment associated with compulsory schooling laws and child labour laws in the US to examine whether there is evidence of external returns to higher average schooling at the state level. Their findings suggest that there is no evidence of significant schooling externalities between 1960 and 1980.

Moretti (1999) analyses regional externalities using panel data for American cities. His results show that an increase in the supply of college graduates raises less skilled workers’ wages as well as college graduates’ wages. Moretti (2004) finds evidence that a greater share of college-graduates in cities leads to an increase in their wages in 1980 and 1990.

Dalmazzo and Blasio (2005) find a positive and robust effect of local human capital on rents in Italy. In other words, the concentration of human capital at the local level generates positive externalities. Kimenyi et al (2006) find that human capital externalities have a positive effect on earnings in Kenya. A general increase in the level of education benefits all workers in terms of higher earnings. Riddell (2004) finds that there is evidence of human capital externalities in terms of economic growth, knowledge spillovers, non-market external benefits and taxation.

Rudd (2000) investigates whether the average level of human capital in a region affects the earnings of an individual residing in that region in a manner external to the individual’s own human capital. He uses data from 1978-1991 March Current Population Survey (CPS). His results show that there is little evidence of an external effect of human capital, which suggest that human capital spillovers of the form postulated by the new growth literature are unlikely to matter much in practice. Yamarik (2008) finds that the US schooling generates little to no positive externalities. Ciccone and Peri (2006) estimate the human capital externalities in the US cities between 1970 and 1990. They find no evidence for significant average-schooling externalities to US cities and states between 1970-1990. All these studies provide mixed evidence in patterns of return to investment in education and existence of human capital externalities. A summary of human capital externalities studies is provided in Table 3.2.

Table 3.2: Human Capital Externalities


Data Set


Econometric Technique(s)


Acemoglu and Angrist (2000)

1960-80 US Censuses.



Estimates using CSLs as instruments for average schooling in 1960-80 data generate statistically insignificant social return estimates ranging from -1 to less than 2 percent. The results in precisely estimated private returns to education of about seven percent, and small social returns, typically less than one percent, that are not significantly different from zero.

Ciccone and Peri (2006)

Public-use microdata samples (PUMS) of the U.S. Census (U.S. Bureau of Census, 1970, 1990).



Empirical results yield no evidence of statistically significant average-schooling externalities at the city level or the state level

Dalmazzo and Blasio (2007)

1993, 1995, 1998 and 2000 Survey of Household Income and Wealth (SHIW)



Human capital generates relevant externalities on firms’ productivity levels.

Isacsson (2005)

1993 and 1998 Statistics for Sweden.


OLS, Fixed Effects

The cross-sectional models suggest, in general, that externalities are positive and significantly different from zero. However, after accounting for individual fixed effects and dummy variables for the county in which the individual works, the results indicate no statistically significant external effects of education on earnings in Sweden.

Kimenyi et al (2006)

Welfare Monitoring Survey (WMS) of 1994



The private returns to education generally increase with the level of education. In general, the results of this analysis show that public policies that expand schooling opportunities for underprivileged social groups benefit the whole society via the externality effects of education. The benefits are in terms of improved productivity and earnings.

Maani (1996)

The 1991 New Zealand Census of Population and Dwellings

New Zealand


The estimates of the social rates of return to education were further positive and significant, indicating that investments in education result in positive economic returns.

Moretti (2004)

1979-1994 Metropolitan Statistical Area (MSA), National Longitudinal Survey of Youth



A percentage point increase in the supply of college graduates raises high school drop-outs’ wages by 1.9%, high school graduates’ wages by 1.6%, and college graduates wages by 0.4%. The effect is larger for less educated groups, as predicted by a conventional demand and supply model. But even for college graduates, an increase in the supply of college graduates increases wages, as predicted by a model that includes conventional demand and supply factors as well as spillovers

Muravyev (2008)

1994 Russia Longitudinal Monitoring Survey



1 percentage point increase in the share of city residents with a university degree results in an increase of wages of city residents by about 1 percent.

Rauch (1993)

US individual data from Public Use Microdata Sample of the 1980 Census of Population


OLS, least-squares dummy-variable (LSDV)

The SMSA average education and the SMSA average experience are both significant in raising individual wages.

Rudd (2000)

1978-1991 of the March Current Population Survey (CPS).


OLS, least-squares dummy-variable (LSDV)

Much of the observed correlation between state educational attainment and individual earnings is due to the fact that the average level of education in a state is a good proxy for other, truly productive factors.

Yamarik (2008)



U.S. schooling generates little to no positive externalities.


This chapter provides an outline of the literature relating to returns to education and human capital externalities. It highlights the key issues that are significant in the development of this thesis. The first part of this chapter explores the human capital theory. It concludes that this theory is the basis for analysing return to education and human capital externalities of education.

This chapter also provides a brief overview of the human capital earning function and the theoretical foundation of Mincer’s equation. Then it discusses the three types of human capital externalities. The chapter argues that investment in education and training not only provides pivate benefits but also social benefit. Furthermore, it is emphazised that the education externalities consist of market and non-market externalities.

After discussing human capital externalities, this chapter summarizes some empirical evidence of returns to education and human capital externalities. There is an enormous literature devoted to estimating rates of returns to education and human capital externalities, but there are few empirical studies from Indonesia. Research is needed in order to assess the effectiveness of the current education in terms return earning in Indonesia. This study attempts to addresses this issue and fill this research vacuum.

Most of the previous studies emphasize to private returns to education and market externalities of education. Little attention has been given to analysing the non-monetary externalities of education because these are difficult to measure. In contrast to the previous studies on returns to education and human capital externalities, this study seeks to examine private returns, market externalities, and non-market externalities of education in Indonesia. The subsequent chapters will address these issues.

Need help with your literature review?

Our qualified researchers are here to help. Click on the button below to find out more:

Literature Review Service

Related Content

In addition to the example literature review above we also have a range of free study materials to help you with your own dissertation: