metadata: authors: 0: author: Psacharopoulos,George
1: author: Patrinos,Harry Anthony
count: World
docna: 0: docna: Returns to investment in education : a
            decennial review of the global literature
docty: Policy Research Working Paper
subtopic: Educational Sciences,Tertiary Education,Primary Education,Economics of Education
teratopic: Education
abstracts: cdata!: Returns to investment in education based
            on human capital theory have been estimated systematically
            since the 1950s. In the 60-plus year history of such
            estimates, there have been several compilations in the
            literature. This paper reviews and highlights the latest
            trends and patterns based on a database of 1,120 estimates
            in 139 countries. The review shows that the private average
            global rate of return to one extra year of schooling is
            about 9 percent a year and very stable over decades. Private
            returns to higher education have increased over time,
            raising issues of financing and equity. Social returns to
            schooling remain high, above 10 percent at the secondary and
            higher education levels. Women continue to experience higher
            average rates of return to schooling, showing that
            girls' education remains a priority. Returns are higher
            in low-income countries. Those employed in the private
            sector of the economy enjoy higher returns than those in the
            public sector, lending support to the productive value of education.
repnme: repnme: Returns to investment in education : a
            decennial review of the global literature
display_title: Returns to investment in education :
            a decennial review of the global literature
listing_relative_url: /research/2018/04/29798773/returns-investment-education-decennial-review-global-literature
topicv3: Primary Education m1326139 480,Tertiary Education m1326349 482,Education m1327048 470,Economics of Education m1327511 1540,Returns to Education m1327513 2174
publishtoextweb_dt: 2018-04-11T00:00:00Z
historic_topic: Education
no_of_pages: 25
author_keys: 0: author_key: 91401
1: author_key: 95903
model_extractions: WPS8402 Policy Research Working Paper 8402 Returns to Investment in Education A Decennial Review of the Global Literature _George Psacharopoulos Harry Antony Patrinos_ Education Global Practice April 2018
Policy Research Working Paper 8402

# **Abstract**

Returns to investment in education based on human capital theory have been estimated systematically since the 1950s. In the 60-plus year history of such estimates, there have been several compilations in the literature. This paper reviews and highlights the latest trends and patterns based on a database of 1,120 estimates in 139 countries. The review shows that the private average global rate of return to one extra year of schooling is about 9 percent a year and very stable over decades. Private returns to higher education have increased over time, raising issues of financing and equity. Social returns to schooling remain high, above 10 percent at the secondary and higher education levels. Women continue to experience higher average rates of return to schooling, showing that girls’ education remains a priority. Returns are higher in low-income countries. Those employed in the private sector of the economy enjoy higher returns than those in the public sector, lending support to the productive value of education.

This paper is a product of the Education Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at hpatrinos@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Returns to Investment in Education: A Decennial Review of the Global Literature**

George Psacharopoulos* Harry Antony Patrinos** JEL codes: C13, J31 Keywords: Returns to schooling; Investments in education ____________________________

- Georgetown University, <u>gpsach@rcn.com.</u>

** Corresponding author, The World Bank, Washington, DC 20433, USA, hpatrinos@worldbank.org

We are grateful to all those who over the years have shared with us their work on the returns to education and to Sean Kelly for efficient research assistance.
# **Introduction**

With roots in the writings of classical economists (see, for example, Adam Smith 1776; Marshall 1890) the link between education and earnings only recently emerged. Formal modeling did not take place until much more recently (Schultz 1960, 1961; Becker 1964; Mincer 1974; Chiswick 2003). The study of earnings by schooling has led to several empirical works testing hypotheses on a great variety of social issues. These include, for example, racial and ethnic discrimination, gender discrimination, income distribution, and the determinants of the demand for education. But the dominant application that has used earnings by level of education is the estimation of the rate of return to investment in schooling.

The concept of the rate of return on investment in education is very similar to that for any other investment. It is a summary of the costs and benefits of the investment incurred at different points in time, and it is expressed in an annual (percentage) yield, like that quoted for savings accounts or government bonds. Returns on investment in education based on human capital theory have been estimated since the late 1950s. Human capital theory puts forward the concept that investments in education increase future productivity.

Estimation of the returns to education has been a popular subject in the literature (Ashenfelter and Krueger 1994; Becker 1964; Becker and Chiswick 1966; Card and Krueger 1992; Card 2001; Duflo 2001; Heckman, Lochner and Todd 2006; Oreopoulos 2006; Rosenzweig 1995; Schultz 1961). Since our last review of the literature, contributions on the subject have grown exponentially, to the point of being difficult to track (for previous compilations see Harmon, Oosterbeek and Walker 2003; Psacharopoulos 1972, 1973, 1981, 1985, 1993, 1994; Psacharopoulos and Patrinos 2002, 2004a). There have been several compilations that we have undertaken, including a few encyclopedia articles (Patrinos and Psacharopoulos 2010a, 2010b, 2002; Psacharopoulos and Patrinos 2008, 2004b). There are also a few attempts to create databases of comparable estimates of the return to schooling (Hendricks 2004; Montenegro and Patrinos 2014; Peet, Fink and Fawzi 2015).

The popularity of estimating returns to education stems from the resulting efficiency, equity and financing implications. The rank order of returns to a level or type of education, and a comparison with the returns of alternative investments can assist education policy makers to make informed investment decisions.

Previous compilations have shown that private returns to primary education decline over time, but slightly (Psacharopoulos 1981). Previous work also shows that returns are highest for primary education, the general curricula, the education of women, and countries with the lowest per capita income (Psacharopoulos 1985). Also, primary education continues to exhibit the highest social profitability in all world regions. Social and private returns at all levels generally decline by the level of a country's per capita income. Overall, the returns to female education are higher than those to male education. The returns to the academic secondary school track are higher than the vocational track – since the unit cost of vocational education is much higher; and the returns for those who work in the private (competitive) sector of the economy are higher than in the public (noncompetitive) sector (Psacharopoulos 1994).

The classic pattern of falling returns to education by level of economic development and level of 2
education is maintained. The private returns to higher education are increasing, highest returns are recorded for low-income and middle-income countries, average returns to schooling are highest in Latin America and the Caribbean (Psacharopoulos and Patrinos 2004a).

In this update we follow the tradition (see, for example, Psacharopoulos and Patrinos 2004) and present the latest estimates and patterns. We review estimates from 139 countries over several years resulting in a panel of 1,120 country-year cases.

# **Methods**

Returns to education in the literature have been estimated using two main methods – the fulldiscounting method and the Mincerian earnings function. For an explanation of these methods see Psacharopoulos (1995) and Psacharopoulos and Mattson (1998). Over the years, researchers have given preference to the Mincerian method because of its convenience (Mincer, 1974).

_The concept of rate of return to education_ The rate of return to schooling equates the value of lifetime earnings of the individual to the net present value of costs of education. For an investment to be economically justified, the rate of return should be positive, and should be higher than the alternative rate of return. For the individual, weighing costs and benefits means investing if the rate of return exceeds the private discount rate (the cost of borrowing and an allowance for risk).

The costs incurred by the individual are the foregone earnings while studying, plus any schooling fees or incidental expenses incurred. The private benefits amount to how much extra an educated individual earns (after taxes) compared with an individual with less education. _More_ and _less_ in this case refer to adjacent levels of education – e.g., university graduates compared to secondary school leavers.

The social rate of return includes the society’s spending on education – for example, money spent on renting buildings and professorial salaries. The social attribute of the estimated rate of return refers to the inclusion of the full resource cost of the investment – the direct costs by government and the foregone earnings of students as they invest in their education. Ideally, the social benefits should include non-monetary benefits of education, such as the number of lives saved because of improved sanitation conditions followed by a woman because she has received more education. Given the scant empirical evidence on the social benefits of education, the social rate of return estimates are usually based on directly observable monetary costs and benefits of education. Since the costs are higher in a social rate of return calculation relative to the one from the private point of view, social returns are typically lower than a private rate of return. The difference between the private and the social rates of return reflects the degree of public subsidization of education – since practically the only difference is the addition of social costs.

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# _Estimation issues_

The Mincerian earnings function has been the subject of controversy in the literature (Psacharopoulos and Layard, 1979; Heckman et al. 2006). One issue with the Mincerian method of estimating returns to education is missing variables, e.g., ability bias. Griliches (1977) analyzed the issue many years ago. He found that the bias is small or negative. Adding more variables to the equation will not solve the problem and might add other biases (Patrinos 2016).

The earnings premium associated with the level of education suggests that productivity increases as people acquire additional qualifications. An alternative view is that earnings increase with education due to credential effects. This refers to the idea that higher levels of schooling are associated with higher earnings, not because they directly raise productivity, but because they certify that the worker is likely to be productive. In this sense, education merely sorts workers according to their unobserved attributes; it does not necessarily augment their intrinsic productivity. For public policy reasons it is important to distinguish between the human capital (productivity) and screening hypotheses about returns to education. In very basic terms, these two hypotheses mean, respectively: schooling imparts skills that enhance productivity; hence, increases in earnings are due to the increased productivity brought about by investments in schooling (human capital); while the screening hypothesis maintains that employers select workers with higher qualifications to reduce their risk of hiring someone with a lower capacity to learn; in this case, higher earnings may not be due to productivity alone (screening). With these concepts in mind, if the only purpose of schooling is to sort prospective employees, then questions arise as to the appropriateness of public investment in the expansion or improvement of schooling.

Layard and Psacharopoulos (1974) found no support for the screening hypothesis. Psacharopoulos (1979) made a distinction between the weak and the strong versions of the screening hypothesis. It is true that, at the hiring point, employers do not have enough information on the prospective productivity of a job applicant, so they might offer a premium to those who have a higher level of schooling relative to the rest. This is the weak version of screening. But after years on the job, such premium should diminish if the worker did not live up to the expectations. Such finding contradicts empirical data showing the earnings gap between more and less educated workers increases over time.

Card (2001) provides a rigorous test of the screening hypothesis by taking advantage of natural experiments such as changes in the school leaving age or college openings. By and large, while some evidence of weak screening is revealed, education is generally associated with higher earnings due to productivity rather than to screening. A recent analysis that uses rigorous evaluation techniques to compare the earnings of workers who barely passed versus those who barely failed high school exit exams finds little evidence of diploma screening effects (Clark and Martorell, 2014).

More recent analyses, which exploit data that permit the disaggregation of earnings by years of completed schooling, have questioned the linear nature of the earnings function approach. Furthermore, due to ongoing and rapid technological progress, cross-sectional data based on observations from many years in the past may produce biased estimates of the returns to schooling. Some have even questioned whether it is still possible to interpret the coefficient on schooling as 4
a rate of return (Heckman, Lochner and Todd 2006). For instance, some researchers argue that the literature following Mincer’s estimated returns to schooling using cross-sectional data assumes that younger workers base their earnings expectations on the current experiences of older workers. However, if prices (the cost of schooling, for example, tuition associated with university) change over time and workers can at least partially anticipate these changes, then estimates of the returns to different schooling levels based on cross-sectional data may not represent the ex-ante rates of return governing human capital investment decisions. Relying on past cohorts to assess current investment decisions requires several strong assumptions, such as stability of the economic environment and perfect certainty of future earnings streams. This is difficult in the current context, given that emerging evidence suggests that wage patterns have changed substantially over time, making it difficult to use cross sections to approximate lifecycle earnings. One solution could be to follow actual cohorts over their entire educational and employment lifecycles to measure their earnings patterns to estimate the returns to education.

The debate is ongoing about the external validity and causality of rate of return estimates. More conclusive evidence will emerge once data on the lifetime earnings profiles of beneficiaries of voucher programs can be obtained, as many of these programs use lotteries to assign places, thus giving researchers access to randomized data. In 1981, Chile introduced nationwide school choice by providing vouchers to any student wishing to attend a “voucher school” (essentially, a private school participating in the program, whereby the funding from the voucher would be used to pay the fees and tuition). Beneficiaries of the vouchers obtained more schooling and subsequently earned more than non-voucher students. Also, it is estimated that formal sector earnings are higher for lottery winners in Colombia’s large-scale government program which used a lottery to distribute scholarships for private secondary school to socially disadvantaged students. Institutional factors have also been used to more precisely estimate the returns to schooling, including the effect of birth date. Those who are forced to remain in school because of their birth date and the school attendance law receive the same rate of return to education as those who voluntarily continue schooling.

The Mincerian method gives private returns, whereas the full discounting method can give private and social returns. In interpreting the evidence presented below, the reader should bear in mind that Mincerian and full-discount returns are not directly comparable because they differ in scale. This is because, by construction, the Mincerian method tacitly assumes foregone earnings even for children aged 6 to 12 years, thus underestimating the true size of the returns (Psacharopoulos and Mattson 1998; Psacharopoulos 1995).

In our exposition below we start with private returns based on the Mincerian method, then move on to the full-discounting method that includes social returns.

# **Results**

_Mincerian private returns_ The coefficient on years of schooling of the basic Mincerian function gives an overall picture of the returns to education. Based on 705 estimates, over the years 1950 to 2014, the private rate of return to an additional year of schooling is 8.8 percent (see Annex 1; given the size of the database, 5
only summary statistics are reported here, while the rest can be found online in Annex 2<sup>1</sup> ). This is lower by about one percentage point relative to our (Psacharopoulos and Patrinos 2004a) compilation. It should be noted that the decline over time of the returns to education is very gradual and statistically insignificant (see Figure 1). This could be interpreted as a “race between education and technology” as the price of education fails to decline proportionately in the face of rapid supply increases (Tinbergen 1975). Thus, demonstrating that the demand for skill is outpacing the growth in supply of skills.

**Figure 1: Rate of Return to Schooling over Time**

<!-- Start of picture text --> (%)<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1950 1960 1970 1980 1990 2000 2010<br><!-- End of picture text --> Note: Regressing the overall Mincerian rate of return on the year of the estimate, gives: _Return_ = 49.611 – 0.020 _Year;_ R<sup>2</sup> =0.003 (t = 1.4) Regressing the Mincerian overall return on the country’s years of schooling ( _S_ ) in the survey year, yields a very small but statistically significant decline of the returns of about 0.2 percentage points for every additional year of schooling (see Figure 2).

> 1 <u>http://datatopics.worldbank.org/education/files/GlobalAchievement/ReturnsEdAnnex2.xlsx.</u>

6
<!-- Start of picture text --> Figure 2: Return to Schooling by Years of Schooling<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0<br>Years of schooling<br>Rate of return (%)<br><!-- End of picture text --> Note: Regressing the overall Mincerian rate of return on the year of the estimate, gives: _Return_ = 10.749 - 0.246 _S_ , R<sup>2</sup> = 0.027 (t = 4.4) Nevertheless, there has been an increase in the returns to schooling since 2000. Parsing the sample of estimates into pre- and post-2000 estimates, the returns have increased, albeit at a slower rate relative to the increase in the years of schooling (see Table 1). The schooling revolution of the 20<sup>th</sup> century continues, with an average increase in schooling years of more than 10 percent in the first decade and a half of the 21<sup>st</sup> century, as compared with the latter half of the 20<sup>th</sup> century. The returns to schooling increased in the 21<sup>st</sup> century by 4 percent relative to the latter half of the 20<sup>th</sup> century.

**<u>Table 1: Years of schooling and returns over time</u>**

|Period|Mean years of<br>schooling|Overall Mincerian<br>rate of return (%)|Number of studies|
|---|---|---|---|
|Pre 2000|7.8|8.7|511|
|Post 2000|8.6|9.1|194|

Restricting the sample to the most recent estimate for each country, we get an average rate of return of 9.5 percent, just slightly lower than the 9.7 percent we obtained in our Psacharopoulos and Patrinos (2004a) update. Again, this could refer to Tinbergen’s (1975) race between education and technology. The higher returns since 2000 suggest that the price of education is increasing even though supply is going up. This points to the interpretation that education is no longer winning the race.

Focusing on a single country is illustrative. For Colombia, we have a record of 35 return estimates over time (Psacharopoulos 1985, 1994; Patrinos 1995; Kaboski 2003; Sanchez Torres and Nunez Mendez 2003; Psacharopoulos, Arriagada and Velez 1992; Psacharopoulos and Velez 1992; Tenjo et al. 2015) since 1965. Taken together, we note a convergence to a 10 percent overall rate of return to a year of schooling. At the same time, average schooling had gone up considerably, from 7
less than 5 years in the 1960s to more than 10 years by 2013 (see Figure 3).

**Figure 3: Returns and years of schooling over time: Colombia**

<!-- Start of picture text --> 16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1974 1979 1984 1989 1994 1999 2004 2009 2014<br>Years of schooling Rate of return (%)<br><!-- End of picture text --> Focusing on Argentina, for which we have Mincerian returns (Kaboski 2003; Psacharopoulos 1994; Kugler and Psacharopoulos 1989; Lopez Boo 2010; Fiszbein, Giovagnoli and Patrinos 2007) over a 25-year period, we notice a remarkable stability of such returns even during the country’s economic crisis (Figure 4). Note that the wide variation of the returns over time is due to different samples and methodologies.

**Figure 4: Returns to Schooling and Economic Growth: Argentina**

<!-- Start of picture text --> 15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>1980 1985 1990 1995 2000 2005<br>Returns Growth<br><!-- End of picture text --> Note: Source of change in GDP per capita is World Bank World Development Indicators This finding can be interpreted using Tinbergen’s (1975) race between education and technology, in the sense that over time the education supply curve has been shifting more to the right relative 8
to the demand curve. This is most evident in the returns to higher education (see Figure 5). While enrollment in higher education has gone up three-fold since 1970, the returns have not changed overall. The increased share of the labor force with higher education should have reduced the rate of return on the investment. Yet, the rates of return over time do not fluctuate much because as the supply of educated labor increases, so does the demand for higher skills, hence not depressing the returns to education. As discussed by Goldin and Katz (2010), the race reflects, on the one hand, the skill-biasedness of technological progress with its consequences for income inequality and, on the other hand, to the pivotal role of education in mediating this relation. While higher education increased substantially, the premium on high skills continued to increase. This suggests that educational advancements were insufficient to countervail demand due to technological progress. The rising inequality implies that technology is winning this race.

**Figure 5: The Race between Higher Education and Technology**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1970 1980 1990 2000 2010<br>Returns Enrollment<br><!-- End of picture text --> _Income level and regional differences_ Private returns to schooling are higher in low-income countries by about one percentage point relative to high-income countries (see Table 2). This is the case even though the mean years of schooling differs by a factor of almost two between country groups. In fact, years of schooling will peak, to about 10 years by 2050.

**<u>Table 2: Private Returns to Schooling by Income Group</u>**

|Country income level|Overall rate of return (%)|Mean years of schooling|
|---|---|---|
|Low|9.3|5.0|
|Middle|9.2|7.0|
|High|8.2|9.2|
|World average|8.8|8.0|
|Notes: Country per capita<br>2015 US$: low=$1045 or|income levels based on World<br>less; middle=$1046-$12,735|Bank (2016) classifications in<br>; high=$12,736 or more|

9
Disaggregating further by world region, private returns to schooling are highest in Latin America and Sub-Saharan Africa and lowest in the Middle East and North Africa (see Table 3). The Middle East and North Africa is an outlier, given the relatively low average level of schooling. Kingsbury (2018) provides several hypotheses for the low returns citing such factors as corruption, natural resources and poor academic performance. While South Asia exhibits relatively low returns, within the region India has seen increasing returns since the economic liberalization program of the 1990s.

**<u>Table 3: Private Returns to schooling by region</u>**

|<br>Region|<br>Overall rate<br>of return (%)|<br>Mean years<br>of schooling|
|---|---|---|
|Latin America and Caribbean|11.0|7.3|
|Sub-Saharan Africa|10.5|5.2|
|East Asia and Pacific|8.7|6.9|
|South Asia|8.1|4.9|
|Advanced Economies|8.0|9.5|
|Europe and Central Asia|7.3|9.1|
|Middle East and North Africa|5.7|7.5|
|World average|8.8|8.0|

# _Gender_

As in previous reviews, the private returns to female education exceed that of males by about two percentage points (see Figure 6). The gap has increased. The female advantage was just over one percentage point in previous updates (Psacharopoulos and Patrinos 2004a; Psacharopoulos 1994). This does not imply that earnings are higher for females, but only that education is a good investment for women and girls, and a development priority.

**Figure 6: Private Mincerian Returns to Education by Gender**

<!-- Start of picture text --> 12<br>10<br>8<br>6<br>4<br>2<br>0<br>Males Females<br><!-- End of picture text --> 10
# _Private sector of employment_

The returns for those working in the private sector of the economy are higher than for those working in the public sector. The finding lends credibility that, where productivity matters, education is recognized (Psacharopoulos 1983; Harmon, Oosterbeek and Walker 2003). There is a clear earnings advantage for workers in the private sector (see Figure 7).

**Figure 7: Returns to Schooling by Sector of Employment**

<!-- Start of picture text --> 12<br>10<br>8<br>6<br>4<br>2<br>0<br>Public sector Private sector<br><!-- End of picture text --> _Full discounting method and social returns_ Based on 166 estimates using the full discounting method, the returns to schooling fall by level of economic development (see Table 4). The private returns to primary education are still high, but there are fewer countries where this calculation can be made given the drive for universal primary education. The private returns to higher education are substantial and have remained high since the last update. They have increased since the last update in both low- and high-income countries, falling only in middle-income countries. The returns to secondary education have declined, but not in high-income countries.

**<u>Table 4: Returns by income and educational level (%)</u>**

|Per capita income level|Primary|Private<br>Secondary|Higher|Primary|Social<br>Secondary|Higher|
|---|---|---|---|---|---|---|
|Low|25.4|18.7|26.8|22.1|18.1|13.2|
|Middle|24.5|17.7|20.2|17.1|12.8|11.4|
|High|28.4|13.2|12.8|15.8|10.3|9.7|
|Average|25.4|15.1|15.8|17.5|11.8|10.5|

Note: The “high” private return to primary education in high-income countries is due to an outlier 1959 estimate of 65% for Puerto Rico, a country classified as high-income under our current-per-capita income classification system.

11
The social returns follow the well-known pattern of falling by level of development and level of education. Social returns to education are universally lower than private returns because of the public subsidization of education. Social returns are lower than private because researchers can account for full social costs, but they do not include social benefits. The costs of higher levels of schooling are much higher, hence the lower returns (see Figure 8).

**Figure 8: The Pattern of Private and Social Returns**

<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>Primary Secondary Tertiary<br>Private Social<br><!-- End of picture text --> As a rule, returns are higher in lower-income countries relative to higher-income countries (see Figures 9 and 10). This can be attributed to the relative scarcity of human capital in the two types of countries. For example, the mean years of schooling of the labor force in Sub-Saharan Africa is 5.1, compared with 10.2 in advanced economies.

**Figure 9: The Structure of Private Returns**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Primary Secondary Tertiary<br>Low income Middle income High income<br><!-- End of picture text --> 12
**Figure 10: The Structure of Social Returns**

<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>Primary Secondary Tertiary<br>Low income Middle income High income<br><!-- End of picture text --> _Private and social returns by region_ In our previous analysis (Psacharopoulos and Patrinos 2004a) we presented estimates from 83 countries and showed that the returns to education were highest in Sub-Saharan Africa, the region with the lowest levels of schooling. With near universal primary education in most regions, it is becoming difficult to estimate returns to primary schooling using recent surveys and the full discounting method. Our update – using estimates since 2000 – is dominated by such returns from high-income countries, which exclude returns to primary education (since the comparison group, that is, workers with no education, is absent). Overall, private returns to secondary and higher education in high-income countries are high, at 13 and 12 percent (see Table 5).

**-** **<u>Table 5: Private and Social Returns to Schooling by Region, post 2000 (%)</u>**

||<br>Priv<br>Secondary|<br>ate<br>Higher|<br>Soc<br>Secondary|ial<br>Higher|
|---|---|---|---|---|
|Average|13.2|12.4|10.2|10.6|
|Developing countries|--|--|10.2|16.4|
|Number of studies|50|54|59|61|

Note: For private returns, sample consists of high income countries plus Turkey; for social returns, two are from Africa, two are from Latin America, one is from Europe, one is from East Asia, and the rest are from high income countries.

In terms of social returns, these are higher than any plausible social discount rate though lower than private, across all income groups. The social returns to higher education are particularly high, but these are driven by returns in Africa, where the social returns to higher education are 35 percent in Malawi (Chirwa and Matita 2009) and 22 percent in South Africa (Salisbury 2015) – this of course implies that private returns to higher education are even higher.

13
# **Discussion**

The rate of return patterns found in previous updates are upheld and reinforced. Regarding efficiency in the use of resources, spending on human capital is a good investment. For example, in the United States the long-term 1966 to 2015 average return on stocks and bonds is 2.4 percent (Damodaran 2016) versus a 10.5 percent overall private return to investment in education in our database for education.

Recent research indicates that the social rates of return reported above are under-estimates of the true returns because of the omission of externalities (Munich and Psacharopoulos 2017). Taking just one externality into account, the social rate of return to investment in education could be 50% higher than the one traditionally estimated. Monetizing the value of just one externality of education - reduced mortality - Pradhan et al. (2018) found that the social rate of return to investment in one extra year of schooling in low-income countries is 16%, relative to 11% based solely on earnings differentials.

In allocating an education budget among different levels of schooling, priority should be given to the lower levels of education in countries that have not yet achieved universal primary.

In countries with disparities between male and female enrollment, priority should be given to the education of girls.

The size of the private returns to education and difference between private and social rates calls for selective cost recovery in higher education.

The overall 10 percent private return to one year of schooling has marginally declined since first estimated over half a century ago (see, for example, Becker 1964).

When making education decisions, it is very tempting to use estimates as those presented above for countries that lack such information. We emphasize that there is no substitute for a countryspecific study.

In such case, we recommend using the full discounting method as it is more appropriate relative to the Mincerian method.

Future updates will attempt to include estimates of the returns to school quality. Although there are plenty of macro studies in this respect, micro studies on the returns to investing in specific school quality inputs are emerging.

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# **Text references**

(References to the database used in this compilation are found on-line in Annex 3.<sup>2</sup> )

- Ashenfelter, O. and A. Krueger. 1994. “Estimates of the economic return to schooling from a new sample of twins.” _American Economic Review_ 84(5): 1157-1173.

- Becker, G.S. 1964. _Human Capital: A Theoretical and Empirical Analysis with Special Reference to Education_ . New York: National Bureau of Economic Research.

- Becker, G.S. and B.R. Chiswick. 1966. “Education and the distribution of earnings.” _American Economic Review_ 56(2): 358−369.

- Card, D. and A.B. Krueger. 1992. “Does School Quality Matter? Returns to Education and the Characteristics of Public Schools in the United States.” _Journal of Political Economy 100_ (1): -40.

- Card, D. 2001. “Estimating the return to schooling: Progress on some persistent econometric problems.” _Econometrica_ 69(5): 1127−1160.

- Chirwa, E. W., and M.M. Matita. 2009. “The Rate of Return of Education in Malawi.” University of Malawi Working Paper 2009/01.

- Chiswick, B.R. 2003. “Jacob Mincer, Experience and the Distribution of Earnings.” _Review of Economics of the Household_ 1: 343-361.

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**<u>Annex 1: Returns by educational level and type, most recent survey year (%)</u>**

|E||Oll|||Full dis<br>|counting||||
|---|---|---|---|---|---|---|---|---|---|
|conomy|Y|vera<br>Mii||Private|||Social||S<sup>3</sup>|
||ear|nceran|Prim|Sec|Higher|Prim|Sec|Higher|ource|
|Albania|2012|8.0|||||||Miluka (2015)|
|Algeria|2001|2.2|||||||Arbak (2012)|
|Argentina|2003|11.0|||||||Lopez Boo (2010)|
|Australia|2010|8.3|||||||Mariotti and<br>Meinecke (2011)|
|Austria|2011|||10.4|9.9||8.8|7.1|OECD (2015)|
|Azerbaijan|1995|3.7|||||||Peet et al (2015)|
|Bangladesh|2006|10.0|||||||Taposh and Lin<br>(2009)|
|Belarus|2002|6.9|||||||Yemtsov et al<br>(2006)|
|Belgium|2009||||10.7|||14.5|<br>OECD (2013)|
|Belize|2005|10.0|||||||Hausman and<br>Klinger (2007)|
|Bolivia|2002|10.3|||||||Patrinos et al 2009|
|Bosnia &<br>Herzegovina|2002|8.1|||||||Peet et al (2015)|
|<br>Brazil|2001|15.7|||||||Patrinos et al 2009|
|Bulgaria|2003|6.7|||||||Flabbi et al (2008)|
|Cambodia|2007|6.7|||||||Lall and<br>Sakellariou (2010)|
|Cameroon|1995|6.0|||||||Banerjee and<br>Duflo (2005)|
|Canada|2011|||11.2|12.9||6.9|7.4|<br>OECD (2015)|
|Chile|2011||||14.8||8|15.5|OECD (2015)|
|China|2009|10.3|||||||Ding et al (2013)|
|Colombia|2014|11.3|||||||Tenjo et al (2015)|
|Costa Rica|1992|8.5|||||||Banerjee and<br>Duflo (2005)|
|Cyprus|1994|5.2|||||||Banerjee and<br>Duflo (2005)|
|Czech<br>Republic|2011|||14.2|19.9||22.6|15.3|<br>OECD (2015)|
|<br>Denmark|2011|||14.4|7.9||8.7|3.9|OECD (2015)|
|Djibouti|1996|11.6|||||||Anos-Casero and<br>Seshan (2006)|
|Ecuador|2002|6.5|||||||<br>Peet et al (2015)|
|Egypt, Arab<br>Rep.|2011|3.4|||||||Rizk (2016)|
|<br>El Salvador|1992|7.6|||||||Funkhouser (1996)|
|Eritrea|2002|10.9|||||||Kifle (2007)|
|Estonia|2011|||21.9|17.1||6.6|5.6|OECD (2015)|

3 References are listed on-line in Annex 3 (http://datatopics.worldbank.org/education/files/GlobalAchievement/ReturnsEdAnnex3.docx).

20
|Ethiopia|2011|12.5|||||||Peet et al (2015)|
|---|---|---|---|---|---|---|---|---|---|
|Finland|2011|||6.1|8.4||20.6|6.1|OECD (2015)|
|France|2009|||9.4|9.5||6.6|6.9|OECD (2013)|
|Gambia, The|2003|6.8|||||||Foltz and Gajigo<br>(2012)|
|Georgia|2006|6.9|||||||Botchorishvili<br>(2007)|
|Germany|2011||||||||OECD (2015)|
|Ghana|2007|4.2|||||||Peet et al (2015)|
|Great Britain|2011|||11|12.2||45.6|30.3|OECD (2015)|
|Greece|2009|||5.9|8.6||5.4|11.6|OECD (2013)|
|Guatemala|2000|||||11.2|19|13.1|Laguna and Porta<br>(2004)|
|Honduras|1991|9.3|||||||<br>Funkhouser (1996)|
|Hong Kong<br>SAR, China|1996|||||6.3|9.5|10|Yeung (1999)|
|<br>Hungary|2011|||14.2|20.8||26.5|20.5|OECD (2015)|
|Iceland|2003|||7.2|5||7.9|5.6|OECD (2006)|
|India|2008|10.8|||||||World Bank<br>(2011)|
|Indonesia|2007|10.7|||||||<br>Sohn (2013)|
|Iraq|2006|0.7|||||||Peet et al (2015)|
|Ireland|2009|||20.9|17||6.2|15.4|OECD (2013)|
|Israel|2011|||11.1|12.1||5.1|9.5|OECD (2015)|
|Italy|2010|||8.1|9.5||6.6|8.6|OECD (2015)|
|Jamaica|1989|28.8|20.4|15.7||17.7|7.9||Psacharopoulos<br>(1994)|
|Japan|2007||||7.6|||7.3|<br>OECD (2013)|
|Jordan|2001|6.7|||||||Arbak (2012)|
|Kazakhstan|2001|8.0|||||||Arabsheibani and<br>Mussurov (2006)|
|Kenya|1995||||||||Wambugu (2002)|
|Korea, Rep.|2011|||3.7|5.9||-0.7|0|OECD (2015)|
|Kuwait|2010|4.8|||||||Alqattan (2012)|
|Latvia|2012|7.7|||||||Vilerts et al (2015)|
|Liberia|1983||99|30.5|17|41|17|8|Psacharopoulos<br>(1994)|
|Luxembourg|2010|||10.2|||10.1||OECD (2015)|
|Madagascar|1993|15.1|||||||Cogneau (2003)|
|Malaysia|2002|10.0|||||||Ismail (2007)|
|Malawi|2012|8.8|||||||Gondwe (2015)|
|Maldives|2004|8.1|||||||World Bank<br>(2011)|
|Malta|2001|25.7|||||||<br>Arbak (2012)|
|Mexico|2002|13.2|||||||Metzger and|
||||||||||Patrinos (2005)|
|Moldova|2003|8.0|||||||<br>Flabbi et al (2008)|

21
|Mongolia|2002|8.5|||||||Patrinos et al 2009|
|---|---|---|---|---|---|---|---|---|---|
|Morocco|2001|2.8|||||||Arbak (2012)|
|Namibia|2003|28.0|||||||Godana and<br>Ashipala (2006)|
|Nepal|2008|7.9|||||||World Bank<br>(2011)|
|Netherlands|2011|||5.5|9.1||10.8||<br>OECD (2015)|
|New Zealand|2011|||8.3|7.6||7.7|5.7|OECD (2015)|
|Nicaragua|2001|||||5.2|9.5|11.4|Belli and Ayadi<br>(1998)|
|Niger|2011|11.1|||||||<br>Peet et al (2015)|
|Nigeria|2011|5.7|||||||Peet et al (2015)|
|Northern<br>Ireland|1995|17.4|||||||Trostel et al (2002)|
|Norway|2011||||8.7||7.1|4.5|OECD (2015)|
|Pakistan|2009|6.2|||||||World Bank<br>(2011)|
|Palestine|2011|5.1|||||||Rizk (2016)|
|Panama|2009|10.9|||||||Freire et al (2015)|
|Papua New<br>Guinea|1987|11.1|||||||Gibson and Fatai<br>(2006)|
|Paraguay|1990|11.6|23.7|14.6|13.7|20.3|12.7|10.8|<br>Psacharopoulos<br>(1994)|
|Peru|2001|10.7|||||||Trivelli (2006)|
|Philippines|2000|12.6|||||||Kaboski (2003)|
|Poland|2010|||10.2|26.6||6.9|13.9|OECD (2015)|
|Portugal|2011|||13.6|19.6||5.7|11.3|OECD (2015)|
|Puerto Rico|1989|15.1|||||||Griffin and Cox<br>Edwards (1993)|
|Romania|2009|11.3|||||||Ion Zgreaban 2013|
|Russian<br>Federation|2002|9.2|||||||Gorodnichenko<br>and Klara (2005)|
|Senegal|1985||33.7|21.3||23|8.9||Psacharopoulos<br>(1994)|
|Serbia|2013|10.0|||||||<br>Popov (2013)|
|Sierra Leone|1971|||||20|22|9.5|Psacharopoulos<br>(1994)|
|Singapore|2000|13.2|||||||Low et al 2004|
|Slovak<br>Republic|2011|||29.5|17.7||18.9|12.6|OECD (2015)|
|<br>Slovenia|2011|||10.3|16.8||10.1|12.7|OECD (2015)|
|Somalia|1983||59.9|13|33.2|20.6|10.4|19.9|Psacharopoulos<br>(1994)|
|Spain|2011|||12.9|9.8||9.6|6.5|<br>OECD (2015)|
|Sri Lanka|2008|8.9|||||||World Bank<br>(2011)|
|Suriname|1992|9.9|||||10.8||<br>Horowitz and<br>Schenzler (1999)|
|Sweden|2011|||18.3|7.8||21|1.7|<br>OECD (2015)|

22
|Switzerland|2011||||||7.6|2.8|OECD (2015)|
|---|---|---|---|---|---|---|---|---|---|
|Thailand|2010|12.6||||3.4|5.7|11.3|Tangtipongkul<br>(2015)|
|Timor-Leste|2007|3.9|||||||Santos (2014)|
|Tunisia|2011|7.0|||||||Rizk (2016)|
|Turkey|2008|11.8|||||||Tansel and Daoud<br>(2014)|
|Uganda|2008|11.9|||||||Peet et al (2015)|
|Ukraine|2002|4.5|||||||Gorodnichenko<br>and Klara (2005)|
|United States|2011|||20.5|14||11.9|11.9|OECD (2015)|
|Uruguay|1990|9.1|||||||Kaboski (2003)|
|Venezuela,<br>RB|2008|6.4|||||||Gonzales and<br>Oyelere (2009)|
|Vietnam|2014|5.7|||||||<br>Doan et al (2016)|
|Yemen, Rep.|1985||10|41|56|2|26|24|Psacharopoulos<br>(1994)|
|Zambia|1995|10.7|||19.2|||5.7|Banerjee and<br>Duflo (2005)|
|Zimbabwe|1994|5.6|16.6|48.5|5.1|11.2|47.6|-4.6|Banerjee and<br>Duflo (2005)|

23