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Is the Rate of Return to Primary Education Higher
than the Rate of Return to Higher Education?
A Study on the Buenos Aires Metropolitan Area,
1980 and 1995
[The university logo]
Paula Razquin
Monograph
International and Comparative Education
School of Education
Stanford University
July, 1999
Stanford University
School of Education
INTERNATIONAL EDUCATIONAL ADMINISTRATION AND
POLICY ANALYSIS
Is the Rate of Return to Primary Education Higher than the Rate of
Return to Higher Education?
A Study on the Buenos Aires Metropolitan Area, 1980 and 1995
Paula Razquin
July 1999
A Monograph in partial fulfillment
of the requirements for the degree of Master of Arts
Stanford University
School of Education
INTERNATIONAL EDUCATIONAL
ADMINISTRATION AND POLICY ANALYSIS
Is the Rate of Return to Primary Education Higher than the
Rate of Return to Higher Education?
A Study on the Buenos Aires Metropolitan Area,
1980 and 1995
Paula Razquin
July 1999
A Monograph in partial fulfillment
of the requirements for the degree of Master of Arts
Approvals:
ICE/IEAPA Master’s Program Director: __________________________
Colette Chabbott, Ph.D., date
Advisor: __________________________
Martin Carnoy, Ph.D., date
ABSTRACT
From the mid-1980s to the present, the World Bank has conducted several country
studies to test one standard model about the behavior of the rates of return to different levels
of education. That standard model predicts, first, that the rates of return are higher for
primary education than for higher education and, second, that the returns are stable or
decline over time. Yet, there is little evidence on the changes of the return to schooling over
time, mainly because rates of return are estimated for a single point in time.
This study uses household survey data for two years (1980 and 1995) for the
Buenos Aires Metropolitan Area to examine at what level of education the returns are
highest, and how they change over time. Results from the Mincer regression equation and
the internal rate of return formula indicate that investing in higher education yields greater
returns than investing in secondary or primary education. When examined over time, rates
of return vary depending on the level of education and sex. Findings are consistent with
Carnoy’s argument that, among countries, there is a changing patter of time-series estimates
of rates of return, which depends on the stage of economic development and educational
expansion.
i
TABLE OF CONTENTS
I. INTRODUCTION .......................................................................................................................... 1
II. RATES OF RETURN TO EDUCATION: TWO MODELS .................................................. 4
A. STUDIES ON RATES OF RETURN IN ARGENTINA ....................................................................... 8
III. RESEARCH QUESTIONS...................................................................................................... 14
IV. DATA AND METHODOLOGY ............................................................................................. 15
A. VARIABLES ............................................................................................................................. 15
B. HYPOTHESES .......................................................................................................................... 26
C. MODELS .................................................................................................................................. 26 1. The Mincer regression equation............................................................................................................ 27 2. The traditional or direct method ........................................................................................................... 28
V. FINDINGS AND DISCUSSION ............................................................................................... 30
A. VARIATIONS OF EARNINGS AND EDUCATION: FINDINGS FROM THE MINCER
REGRESSION EQUATION .................................................................................................................. 30
B. THE RATES OF RETURN CONSIDERING THE COSTS OF THE EDUCATION ................................. 36
C. COMPARING THE TWO METHODS ........................................................................................... 41
D. LIMITATIONS OF THE ANALYSIS ............................................................................................. 42
VI. CONCLUDING REMARKS ................................................................................................... 45
REFERENCES ................................................................................................................................. 46
APPENDIX 1: FINANCING OF HIGHER EDUCATION IN LATIN AMERICA.
MAP OF CURRENT POLICY OPTIONS AND REFORMS (SELECTED
STUDIES) ......................................................................................................................................... 52
APPENDIX 2: STUDIES ON RATES OF RETURN IN ARGENTINA.
DESCRIPTIVE FILES ................................................................................................................... 57
A. BUENOS AIRES, CAPITAL, CORDOBA, MENDOZA, SANTA FE ................................................ 57
B. BUENOS AIRES........................................................................................................................ 57
C. CÓRDOBA ............................................................................................................................... 58
D. MENDOZA ............................................................................................................................... 59
E. TUCUMÁN ............................................................................................................................... 60
F. SALTA ..................................................................................................................................... 60
APPENDIX 3: METHODOLOGICAL AND STATISTICAL APPENDIX ........................... 62
A. UNIT OF ANALYSIS ................................................................................................................. 62
B. VARIABLES ............................................................................................................................. 63 1. Annual Total Earnings ......................................................................................................................... 63 3. Level of Education ............................................................................................................................... 64 4. Years of Work Experience ..................................................................................................................... 65
ii
5. Marital Status ...................................................................................................................................... 65 6. Mean Annual Public Costs per Student. ................................................................................................ 65
C. LIMITATIONS OF THE ANALYSIS............................................................................................. 68 1. Autocorrelation .................................................................................................................................... 68 2. Multicollinearity ................................................................................................................................... 68
APPENDIX 4: APPENDIX TABLES. .......................................................................................... 70
APPENDIX 5: APPENDIX FIGURES ....................................................................................... 109
LIST OF TABLES
Table 1. Total Private and Social Rates of Returns to Education, by Level of Education and
Urban Area. Selected Studies .................................................................................................... 10
Table 2. Private and Social Rates of Returns to Education, by Sex, Level of education, and
Urban Area. Selected Studies. ................................................................................................... 12
Table 3. Methodology for Coding Dummy Variables for Level of Education, Mincer
Equation. .................................................................................................................................... 17
Table 4. Summary Statistics for 13 to 65 Year-Old Employed and Self-Employed
Individuals, by Year and Sex. Buenos Aires Metropolitan Area, 1980 and 1995. ................... 20
Table 5. Mean Annual Total Earnings by Sex, Level of Education, and Age Group. Buenos
Aires Metropolitan Area, October 1980 (in US$ dollars 1995). .............................................. 23
Table 6. Mean Annual Total Earnings by Sex, Level of Education, and Age Group. Buenos
Aires Metropolitan Area, May 1995 (in US$ dollars 1995). .................................................... 24
Table 7. Mean Annual Private and Social Costs in Education per Student, by Year,
Authority and Level of Education. Buenos Aires Metropolitan Area, 1980 and 1995
(in US$ dollars 1995). ............................................................................................................... 25
Table 8. OLS Estimates for the Regression of Annual Total Earnings (Logged) on
Education, Experience, Hours Worked, and Marital Status, by Sex. Buenos Aires
Metropolitan Area, 1980. ........................................................................................................... 32
Table 9. OLS Estimates for the Regression of Annual Total Earnings (Logged) on
Education, Experience, Hours Worked, and Marital Status, by Sex. Buenos Aires
Metropolitan Area, 1995. ........................................................................................................... 33
Table 10. Earnings Premiums for an Additional Level of Education Completed, by Year,
and Sex. Buenos Aires Metropolitan Area, 1980 and 1995 (In Percentages). ......................... 35
Table 11. Private and Social Rates of Return to Education: Buenos Aires Metropolitan
Area, 1980 and 1995. (In Percentages) ..................................................................................... 40
LIST OF FIGURES
Figure 1. Age-Earnings Profile for Men. Buenos Aires Metropolitan Area, 1980. .......................... 37
Figure 2. Age-Earnings Profile for Women. Buenos Aires Metropolitan Area, 1980. ..................... 38
Figure 3. Age-Earnings Profile for Men. Buenos Aires Metropolitan Area, 1995. .......................... 38
iii
Figure 4. Age-Earnings Profile for Women. Buenos Aires Metropolitan Area, 1995. ..................... 39
LIST OF APPENDIX TABLES
Appendix Table 1. Total Expenditures in Education, by Year, Authority, and Level of
Education: Argentina, 1980 (in US$ 1995 dollars). ................................................................. 70
Appendix Table 2. Coefficients for the distribution of public state funds between 19 Buenos
Aires' districts Buenos Aires, 1996. .......................................................................................... 71
Appendix Table 3. Public Expenditures in Education, by Year, Authority, and Level of
Education: Buenos Aires Metropolitan Area, 1980 and 1995 (in US$ dollars 1995). ........... 71
Appendix Table 4. Total Enrollments in Education, by Year, Authority, and Level of
Education. Argentina, 1980. ...................................................................................................... 72
Appendix Table 5. Enrollments in Public Education, by Year, Authority, and Level of
Education. Buenos Aires Metropolitan Area, 1980. ................................................................. 72
Appendix Table 6. Public Expenditures, Enrollments, and Costs per Student, by Level of
Education. State of Buenos Aires and 19 districts, 1995. ......................................................... 73
Appendix Table 7. OLS Coefficients for the Regression of Annual Total Earnings (Logged)
on Education, Experience, Hours Worked, and Marital Status by Year. Buenos Aires
Metropolitan Area. ..................................................................................................................... 74
Appendix Table 8. Mean Annual Total Earnings by Level of Education and Age, for Men.
Buenos Aires Metropolitan Area, 1980 (in 1995 US$ dollars) ............................................... 76
Appendix Table 9. Mean Annual Total Earnings by Level of Education and Age, for
Women. Buenos Aires Metropolitan Area, 1980 (in 1995 US$ dollars) ................................. 78
Appendix Table 10. Mean Annual Total Earnings by Level of Education and Age, for Men.
Buenos Aires Metropolitan Area, 1995 (in US$ dollars) ........................................................ 80
Appendix Table 11. Mean Annual Total Earnings by Level of Education and Age, for
Women. Buenos Aires Metropolitan Area, 1995 (in US$ dollars) .......................................... 82
Appendix Table 12. Costs and Benefits for an Additional Level of Education Completed for
Men. Buenos Aires Metropolitan Area, 1980 (in 1995 US$ dollars). ...................................... 84
Appendix Table 13. Costs and Benefits for an Additional Level of Education Completed for
Men. Buenos Aires Metropolitan Area, 1980 (in 1995 US$ dollars). ...................................... 86
Appendix Table 14. Costs and Benefits for an Additional Level of Education Completed for
Men. Buenos Aires Metropolitan Area, 1980 (in 1995 US$ dollars). ...................................... 88
Appendix Table 15. Costs and Benefits for an Additional Level of Education Completed for
Women. Buenos Aires Metropolitan Area, 1980 (in 1995 US$ dollars). ................................ 90
Appendix Table 16. Costs and Benefits for an Additional Level of Education Completed for
Women. Buenos Aires Metropolitan Area, 1980 (in 1995 US$ dollars). ................................ 92
Appendix Table 17. Costs and Benefits for an Additional Level of Education Completed for
Women. Buenos Aires Metropolitan Area, 1980 (in 1995 US$ dollars). ................................ 94
Appendix Table 18. Costs and Benefits for an Additional Level of Education Completed for
Men. Buenos Aires Metropolitan Area, 1995 (US$ dollars). ................................................... 96
Appendix Table 19. Costs and Benefits for an Additional Level of Education Completed for
Men. Buenos Aires Metropolitan Area, 1995 (US$ dollars). ................................................... 98
iv
Appendix Table 20. Costs and Benefits for an Additional Level of Education Completed for
Men. Buenos Aires Metropolitan Area, 1995 (in US$ dollars). ............................................. 100
Appendix Table 21. Costs and Benefits for an Additional Level of Education Completed for
Women. Buenos Aires Metropolitan Area, 1995 (in US$ dollars). ........................................ 102
Appendix Table 22. Costs and Benefits for an Additional Level of Education Completed for
Women. Buenos Aires Metropolitan Area, 1995 (in US$ dollars). ........................................ 104
Appendix Table 23. Costs and Benefits for an Additional Level of Education Completed for
Women. Buenos Aires Metropolitan Area, 1995 (in US$ dollars). ........................................ 106
LIST OF APPENDIX FIGURES
Appendix Figure 1. Box plot of Annual Total Earnings (Logged), by Sex. Buenos Aires
Metropolitan Area, 1980. ......................................................................................................... 109
Appendix Figure 2. Box plot of Annual Total Earnings (Logged), by Sex. Buenos Aires
Metropolitan Area, 1995. ......................................................................................................... 110
Appendix Figure 3. Box plot of Annual Total Earnings (Logged), by Sex. Buenos Aires
Metropolitan Area, October 1980. .......................................................................................... 111
Appendix Figure 4. Box plot of Annual Total Earnings (Logged), by Sex. Buenos Aires
Metropolitan Area, May 1995. ................................................................................................ 112
Appendix Figure 5. Scatter plot of Regression Residual versus Predicted Values, for Men.
Buenos Aires Metropolitan Area, 1980. .................................................................................. 113
Appendix Figure 6. Scatter plot of Regression Residuals versus Predicted Values, for
Women. Buenos Aires Metropolitan Area, 1980. ................................................................... 114
Appendix Figure 7. Scatter plot of Regression Residuals versus Predicted Values, for Men.
Buenos Aires Metropolitan Area, 1995. .................................................................................. 115
Appendix Figure 8. Scatter plot of Regression Residuals versus Predicted Values, for
Women. Buenos Aires Metropolitan Area, 1995. ................................................................... 116
Appendix Figure 9. Normal Probability Plot for the Regression Residuals, for Men. Buenos
Aires Metropolitan Area, 1980. ............................................................................................... 117
Appendix Figure 10. Normal Probability Plot for the Regression Residuals, for Women.
Buenos Aires Metropolitan Area, 1995. .................................................................................. 118
Appendix Figure 11. Normal Probability Plot for the Regression Residuals, for Men.
Buenos Aires Metropolitan Area, 1995. .................................................................................. 119
Appendix Figure 12. Normal Probability Plot for the Regression Residuals, for Women.
Buenos Aires Metropolitan Area, 1995. .................................................................................. 120
1
Is the Rate of Return to Primary Education Higher than the Rate of
Return to Higher Education?
A Study on the Buenos Aires Metropolitan Area, 1980 and 1995
I. INTRODUCTION
International development donors are currently claiming that higher education is in
crisis in developing countries (Winkler, 1990; World Bank, 1994; UNESCO, 1995).
Although the crisis is clearly related to reductions in public funding public efforts in higher
education have focused almost exclusively on creating and expanding private finance
strategies and do not consider, or even discuss, increasing public investment in higher
education (World Bank, 1994). The challenge for developing countries is how to preserve
or improve the quality of higher education with decreased funding.1
In Argentina, the debate about policy interventions and financing of higher
education follows a similar pattern as the one discussed by international development
donors (Gertel, 1991; Kugler, 1991; Balán, 1993a). Gertel shows, for example, that the
quality of higher education has deteriorated due to a decline in expenditures per student. He
demonstrates that a reduction in expenditures on teachers has resulted in a decrease of
average costs of higher education per student. In the context of expansion and increasing
demand for higher education, such a reduction in expenditures has even more negative
effects on the quality of higher education. The strategies Gertel suggests for reversing this
situation are, nevertheless, only related to introducing tuition fees, income taxes to
1 In the document Higher Education: the Lessons of Experience, the World Bank (1994) reports some
“successful” international experiences undertaken by developing countries in order to alleviate the effects of
the higher education crisis. Key elements of the strategies suggested by the World Bank are the reallocation of
public resources from higher education to other levels of schooling, the differentiation of higher education
institutions (universities and non-universities), the development of private institutions of higher education, the
diversification of funding resources for public higher education, the development of new funding mechanisms,
the redefinition of the role of the state in the governance of higher education, and a focus on quality,
responsiveness, and equity.
2
graduates, transferring responsibilities to private universities, and other cost-recovery
strategies.
The rather detailed description of trend and fluctuations developed in the previous
sections has revealed the magnitude of decline in per-student expenditures. This was
primarily due to decreasing public spending, and it was shown that the decline, in
turn, negatively affected quality standards in university education. Therefore, the
relevant set of questions on the financing of higher education one may ask in
Argentina is undoubtedly associated with alternative financing strategies, or more
specifically, cost-recovery strategies that could help reverse the pervasive trend
observed in the past. Since, on average, less public money is being invested each
year on per-student basis, what has been done to raise extra money? (Gertel, 1991:
74-75).
If the crisis in higher education is primarily attributed to decreased public funding,
why are policymakers not discussing the issue of increasing public funding for higher
education?2 Is there a theoretical perspective that strengthens the arguments for increasing
public expenditures for higher education? These were the initial questions that motivated
this study.
The World Bank (1994) gives different arguments to encourage developing
countries to give the highest priority to basic education instead of higher education. One is
the efficient use of resources (Blomqvist & Jimenez, 1989; Winkler, 1990; World Bank,
1995). Efficiency in education examines the ways in which educational resources should be
allocated in order to improve social benefits. The efficient criterion is the “one [that]
enables given outputs to be met at the lowest possible levels of inputs or cost (Harrold,
1992; p. 145).”
In order to evaluate efficiency, the World Bank examines the costs and benefits of
investing in different levels of education. The benefits of attending more years of
schooling—usually measured by differences in income—are compared to the costs of such
attendance in what is called the “rate of return”. According to the efficiency argument,
whenever the private rates of return to education are higher than the social returns,
policymakers should encourage families and individuals to finance their education. Public
2 Appendix 1 illustrates current policy options and reform strategies for the financing of higher education in
Latin America..
3
investment in higher education is only justified when the social benefits of the investment
exceed or at least equalize the private benefits.
From the mid-1980s to the present, the World Bank has used cross-national studies
on rates of return to prove that the returns to investments in primary education in most
countries are greater than the returns to investments in higher education. In response, many
developing countries that depend on international donors for much of their development
funding have drastically reduced the proportion of their education budgets allocated to
higher education and increased the proportion allocated to primary education.
Those studies have tested one standard model about the behavior of the rates of
return in different countries. In such a model, based on a human capital theory, differences
in income reflect differences in labor productivity with the latter being highly determined by
the skills an individual gains by attending schooling. Briefly, this standard model predicts,
first, that the returns are higher for primary education than for higher education, and second,
that the rates of return to schooling are stable or decline over time.
In this study, I examine the assumptions and methodology underlying the standard
model that supports the allocation of public investment to primary as opposed to higher
education, which was developed by Psacharopoulos in several case studies (1980, 1981,
1985, 1989, and 1993; Psacharopoulos & Woodhall, 1985). I present a different model,
based on Carnoy’s (1975) interpretation. Contrary to Psacharopoulos, Carnoy suggests that
the pattern of rates of return is not always higher for primary education and does not always
decline when examined historically. He argues that the returns to education are determined
by the dynamics of the labor market and the educational expansion of a given country. In
this study, I use sample data for two years—1980 and 1995—for the Buenos Aires
Metropolitan Area to examine at what level of education the rates of return are highest, and
how they change over time.
4
II. RATES OF RETURN TO EDUCATION: TWO MODELS
Scholars have developed two different models to explain how and why the returns
differ among levels of education, and how and why they change or do not change over time.
Examples of one of the models can be found in the several country studies that
Psacharopoulos (1980, 1981, 1985, 1989, and 1993) conducted. This model is based on
human capital theory. According to this theory, the skills that education enhances in the
individual make that individual more productive on the job. Since differences in wages
represent differences in productivity, and since productivity is determined in part by the
education of the individual, then differences in wages can ultimately be attributed to
differences in education.
Do rates of return differ among levels of education? Why? According to the human
capital model, rates of return differ depending on the level of education. The returns to the
investment in primary education—that is, the differences in earnings for attending additional
schooling, when costs are considered—are higher than the returns to the investment in
higher education. That is not to say that higher education does not make an individual more
productive and have higher earnings. On the contrary, it does. But as the educational level
of an individual increases, the increase in the rate of productivity of that individual due to
education tends to diminish. When the level of education of an individual is low, for
example when he or she has only primary education, small increments in education add
substantially to the labor productivity of that individual. When an individual has a higher
level of schooling, the increases in productivity that the individual gains by attending
additional schooling tends to decline, even though they are still positive.
Do rates of return change over time? Why? According to Psacharopoulos and
Woodhall (1985), rates of return tend to be relatively stable over time, if not slightly
declining or falling slowly. The main reason is that, in a market economy, the supply of
educated individuals and demand for educated labor tend to equilibrium. The authors shows
that in some developing countries, for example in Colombia, the expansion of the
educational system has kept pace with the increasing demand for educated labor, partly due
5
to technological changes and the rates of return have been constant. Nevertheless, there is
little evidence with respect to the changes of the return to schooling over time, mainly
because in most country studies the rates of return are estimated for a single point in time.
According to the human capital model, diminishing returns to schooling and supply
and demand adjustments over time apply to every country with a market economy.
Therefore, the behavior of the rates of return to education is expected to follow the same
pattern in all countries (Cippolone, 1994). Rates of return studies can function as a decision
criterion for indicating efficient resource allocation in education, whatever the country, and
regardless of the structure and dynamics of the educational systems or the particularities of
the labor markets. In an article in the Economics of Education Review, Psacharopoulos
(1989) presents the results of a series of country studies. As “a stock-taking exercise,” he
provides evidence of the expected pattern of rates of return in developing countries.3
Bearing in mind this limitation, the overall trend in the returns to education is a mild
over time decline. Out of 85 pairs of end-year estimates in the two Appendix tables,
the returns to education have declined in 55 cases between the earlier and later date
(Psacharopoulos, 1989: 226-227).
Some scholars disagree with Psacharopoulos and argue for an alternative
interpretation of the rates of return (Carnoy, 1975; Ryoo, 1988; Ryoo, Nam, & Carnoy,
1993; and Carnoy, 1994). According to these scholars, other factors should be considered
when analyzing differences in income. Education is one, but other characteristics of the
labor market should be considered as well, for example, characteristics of the productive
sector, types and number of jobs in a given sector, technology employed, unemployment
rates, and institutional factors such as government regulations.
Carnoy (1994) explains that the returns to education are not necessarily always
higher for lower levels of schooling, i.e. primary, when compared to higher levels of
schooling, i.e. secondary or higher education. In addition, they are not always stable when
examined historically. On the contrary, he argues that time-series estimates of rates of return
among different countries might reveal changing patterns, depending on the stage of
3 For Latin America, rates of return studies are: Kugler and Psacharopoulos (1989) for Argentina, Riveros
(1990) for Chile, Gomez-Castellano and Psacharopoulos (1990) for Ecuador, Psacharopoulos and Alam
(1991) for Venezuela, and Psacharopoulos and Velez (1994) for Uruguay.
6
economic development and the development of educational systems of a given country. His
thesis is that the rates of return “would depend largely on the demand for educated labor
(which depended, in turn, on changes in technology and the demand for final goods) and on
changes in the relative number of educated persons (Carnoy, 1975: 313).”
From Carnoy’s (1994) viewpoint, if rates of return studies are to function as a
criterion to determine in which level of education to invest, then it is important to examine
the changes in rates of return historically. One of the limitations of studies developed for a
single point in time is that they might not be correct for estimating future earnings nor future
returns, because they estimate earnings for decisions about schooling made in the past and
they might show only short-term fluctuations.
Over time studies for four different countries—United States (Carnoy, 1994),
Colombia and Hong Kong, (both cited in Carnoy, 1994), and South Korea (Ryoo, 1988)—
tend to support this second approach. Carnoy points out that, in those countries, the returns
to education fell with educational expansion, first for lower levels of schooling. This
phenomenon is reflected in higher rates of return for higher education relative to primary
education.
Rates of return to education are estimated by comparing the costs of attending
additional levels or years of schooling with the benefits that having more education yields—
this is called the traditional or direct method. When the costs considered are only those
incurred by the individual and the benefits are also the additional earnings received by the
individual, private rates of return are estimated. When public costs to education are added to
the private costs and when social benefits are also considered, social rates of returns are
estimated.
Most rates of return studies measure private costs as the costs of tuition (for those
attending a private education), fees, other costs such as books and school material, and also
the earnings foregone by the individual when he or she is attending schooling. By
considering income foregone, rates of return studies assume that if a person was not
attending school, he or she would be employed or self-employed and, therefore, receiving
an income. Private benefits are the additional earnings an individual receives, after
7
discounting taxes, for having more years of schooling or having completed an additional
level of education.
Social costs are calculated as the costs incurred by the individuals and those
incurred by the public sector, the Federal, State or Local governments, depending on the
area or country studied. Social benefits are also the additional earnings received by an
individual4 and the social benefits, and taxes paid by the individuals. Although it is argued
that education brings other benefits to societies5, most studies use the aggregated
individual’s earnings as a measure of social benefit.
The traditional or direct method for calculating the rates of return to different levels
of education (comparing the costs with the benefits) uses what is called an internal rate of
return formula. The return to the investment in education is the discount rate at which the
additional costs and benefits for attending a higher level of schooling equals zero6:
0 = Y - C
(1-r)i=1
i i
i
n
The internal rate of return formula illustrates the discounted difference in earnings
that can be expected over a person’s lifetime, based upon different levels or years of
schooling.
When data on costs are not available or difficult to obtain, a second way of
calculating the returns to education can be used—the Mincer regression equation or Mincer
earnings function. To be precise, rather than calculating the rate of return, this second
method estimates how having more years or levels of schooling affects earnings. In this
case, rates of return are the coefficients for years of schooling or levels of education in a
regression equation, where variations in earnings (the dependent variable) are explained by
4 Other private benefits of being more educated are difficult to measure, for example, psychic benefits or
higher self-esteem, to name some. 5 One example of other social benefits, or externalities, could be having lower crime rates.
6 Yi= the difference in average income in period i between those with one level of schooling and those with
the next highest level. Ci= the cost of schooling in period i, where private cost equals income forgone (taken at
75% of annual income of those with the lower level of schooling), and social costs equals income forgone
plus average institutional costs. r = the marginal internal rate of return to schooling. n = the number of periods
from the beginning of the level of school being analyzed to the end of the working life (Carnoy, 1975: 34).
8
education, among other independent variables. The standard Mincer regression equation
with levels of education can be represented as,7
ln Yi = a + b1j Si +b2 EXi + b3 EX2i + ui …
The Mincer regression equation assumes that the only costs of schooling are the
earnings foregone. Using this function is more reliable when other private costs such as
tuition or school material are small when compared to social costs, for example, in countries
where education is primarily a public good (Ryoo, 1988).
A. Studies on rates of return in Argentina
Several studies estimate rates of return for different areas and levels of education in
Argentina. Petrei and Delfino (1988) were the first to analyze the relationship between
education and earnings for different cities and years. Using data from different household
surveys—Encuesta Permanente de Hogares (EPH)—they estimate and compare social rates
of return for Capital Federal, Buenos Aires, Córdoba, Mendoza, and Santa Fe for 1974,
1980, and 1985. They conclude that the returns to the investment in education in Argentina
are higher for primary education than for secondary and higher education, and that social
rates of returns decline over time (see rates of return for selected studies in Table 1 and
descriptive files for each study in Appendix 2).
Kugler and Psacharopoulos (1989) and FIEL (1994) estimated the rates of return to
education for the Buenos Aires Metropolitan Area for a single point in time. Although using
data on earnings from the EPH also for 1985, results from Kugler and Psacharopoulos are
slightly different than those that Petrei and Delfino obtained. Social rates of return for
primary education are higher than the ones obtained by Petrei and Delfino (16.7% for men
and 13.9% for women, see Table 1), but the returns for secondary and higher education are
lower than the ones obtained by the other authors. When private rates of return and sex are
analyzed, it is observed that the returns for higher education are higher for men but lower
for women. According to Kugler and Psacharopoulos, social investment in primary
7 where: Y= earnings, S= a vector of dummy variables for levels of education, and EX= years of work
experience.
9
education is “fully justified,” but social investment in the other levels of education should be
targeted more carefully.
10
Table 1. Total Private and Social Rates of Returns to Education, by Level of Education and
Urban Area. Selected Studies
Total
Urban area and Study Primary
complete
Secondary
complete
Higher education
complete
Private Social Private Social Private Social
Buenos Aires Metropolitan Area
Petrei and Delfino, 1974 (1)
… 17.8 … 9.3 … 8.7
Petrei and Delfino, 1980 (1)
… 11.3 … 12.4 … 8.8
Petrei and Delfino, 1985 (1)
… 13.9 … 10.3 … 7.9
Kugler and Psacharopoulos,
1985 (2)
30.0 16.7 9.0 6.4 11.0 7.1
FIEL, 1993
EPH, 25 to 54 years old … … … … … …
FIEL's survey. All ages … … 10.9 … 13.4 …
Capital Federal
Petrei and Delfino, 1974 (1)
… 20.4 … 9.2 … 8.3
Petrei and Delfino, 1980 (1)
… 15.4 … 10.8 … 9.7
Petrei and Delfino, 1985 (1)
… 16.0 … 9.7 … 6.8
Cordoba
Petrei and Delfino, 1974 (1)
… 14.4 … 12.0 … 11.0
Petrei and Delfino, 1980 (1)
… 23.0 … 9.2 … 9.0
Petrei and Delfino, 1985 (1)
… 16.2 … 5.8 … 5.5
Giordano and Montoya, 1883
(3)
… … … … … …
Mendoza
Ferrá and Claramount, 1980 (4)
14.0 9.8 12.8 9.6 … …
Petrei and Delfino, 1985 (1)
… 11.0 … 6.0 … 6.4
Santa Fe
Petrei and Delfino, 1980 (1)
… 16.7 … 4.8 … 10.0
Petrei and Delfino, 1985 (1)
… 15.2 … 9.1 … …
Tucumán
FIEL and FBET, 1995 (5)
EPH, 25-54 years old … … … … … …
FIEL's survey, all ages … … 11.0 … 11.3 …
Salta
del Rey and Mena de Méndez,
1985
… … 10.5 … … …
Sources: Petrei and Delfino (1988), Kugler and Psacharopoulos (1989), FIEL (1994), Giordano and
Montoya (1989), Ferrá and Claramount (1985), FIEL, FBET and Fundación Banco de Crédito Argentino (1996),
del Rey and Mena de Méndez (1986). (1)
The rates of return for higher education are only for university. (2)
The rates
of return are for individuals 14 to 65 years old. (3)
Rates of return assume neither repetition nor dropouts. (4)
The
rates of return are for individuals 6 to 59 years old. The rates of return are for the number of years of schooling for
a given level, 7 years for primary and 5 years for secondary. The rates of return are for a scenario where the annual
rate of increase of wages is 0, and the amount of earnings discounted for retirement are a 100% valuable for
estimating the private rates of return. (5)
Mean rates of return (not marginal). Rates of return are for levels of
education, not years of schooling.
11
A third study for the Buenos Aires Metropolitan Area—FIEL (1994)—presents
policy recommendations based on the rates of return they estimate for 1993. FIEL’s study
also examines other aspects of the relationship of education and earnings, but with respect
to the returns to different levels of education, the results show that private rates of return are
higher for higher education than for secondary education (Table 1). After considering
alternative hypotheses, the authors conclude that higher returns for higher levels of
schooling are evidence of a higher demand for highly skilled workers required to
complement physical capital or to make that physical capital more efficient. The study
suggests, also, that skilled human capital is not exchangeable for human capital with lower
levels of education.
Rates of return to education were also estimated for other urban areas, such as
Capital Federal (Petrei & Delfino, 1988), Córdoba (Petrei & Delfino, 1988; and Giordano
& Montoya, 1989), Mendoza (Ferrá & Claramount, 1985; and Petrei & Delfino, 1988),
Santa Fe (Petrei & Delfino, 1988), Tucumán (FIEL et al., 1996), and Salta (del Rey &
Mena de Méndez, 1986). Rates of return for these other cities are also showed in Table 1. It
is shown that there is no clear pattern in the returns to different levels of education. In
Capital Federal, Córdoba, and Mendoza, for example, social total rates of returns for both
men and women decline with the level of education, but in Santa Fe (1980) and Tucumán
the returns increase for higher levels of education when compared to lower levels. Rates of
return over time do not show a unique pattern either.
Rates of returns studies are not only difficult to compare because of their different
results, reference city and year, but also because they are methodologically different. Some
of them aggregate men and women when earnings are analyzed. In others, age groups differ,
or several adjustments are made on earnings and costs data. Except for Petrei and Delfino’s
(1988) study, all of the rates of return studies are estimated for a single year and city, thus
making it impossible to arrive to any conclusions with respect to over time variations. The
results found in this monograph examine variations at two time points—1980 and 1995—
and provide information to understand the relationship between education and earnings in
the Buenos Aires Metropolitan Area.
12
Table 2. Private and Social Rates of Returns to Education, by Sex, Level of education, and
Urban Area. Selected Studies.
Men
Urban area and Study Primary
complete
Secondary
complete
Higher education
complete
Private Social Private Social Private Social
Buenos Aires Metropolitan Area
Petrei and Delfino, 1974 (1)
… … … … … …
Petrei and Delfino, 1980 (1)
… … … … … …
Petrei and Delfino, 1985 (1)
… … … … … …
Kugler and Psacharopoulos, 1985 (2)
… … 9.0 … 13.0 …
FIEL, 1993
EPH, 25 to 54 years old … … 11.2 … 13.8 …
FIEL's survey. All ages … … … … … …
Capital Federal
Petrei and Delfino, 1974 (1)
… … … … … …
Petrei and Delfino, 1980 (1)
… … … … … …
Petrei and Delfino, 1985 (1)
… … … … … …
Cordoba
Petrei and Delfino, 1974 (1)
… … … … … …
Petrei and Delfino, 1980 (1)
… … … … … …
Petrei and Delfino, 1985 (1)
… … … … … …
Giordano and Montoya, 1883 (3)
18.3 10.7 14.3 12.9 8.5 7.2
Mendoza
Ferrá and Claramount, 1980 (4)
… … … … … …
Petrei and Delfino, 1985 (1)
… … … … … …
Santa Fe
Petrei and Delfino, 1980 (1)
… … … … … …
Petrei and Delfino, 1985 (1)
… … … … … …
Tucumán
FIEL and FBET, 1995 (5)
EPH, 25-54 years old … … 12.0 … 12.7 …
FIEL's survey, all ages … … … … … …
Salta
del Rey and Mena de Méndez, 1985 … … … … … …
13
Table 2 (cont.). Private and Social Rates of Returns to Education, by Sex, Level of
Education, and Urban Area. Selected Studies.
Women
Urban area and Study Primary
complete
Secondary
complete
Higher education
complete
Private Social Private Social Private Social
Buenos Aires Metropolitan Area
Petrei and Delfino, 1974 (1)
… … … … … …
Petrei and Delfino, 1980 (1)
… … … … … …
Petrei and Delfino, 1985 (1)
… … … … … …
Kugler and Psacharopoulos, 1985 (2) … … 12.0 … 8.0 …
FIEL, 1993
EPH, 25 to 54 years old … … … … … …
FIEL's survey. All ages … … … … … …
Capital Federal
Petrei and Delfino, 1974 (1)
… … … … … …
Petrei and Delfino, 1980 (1)
… … … … … …
Petrei and Delfino, 1985 (1)
… … … … … …
Cordoba
Petrei and Delfino, 1974 (1)
… … … … … …
Petrei and Delfino, 1980 (1)
… … … … … …
Petrei and Delfino, 1985 (1)
… … … … … …
Giordano and Montoya, 1883 (3)
8.6 3.3 12.5 7.7 18.6 9.8
Mendoza
Ferrá and Claramount, 1980 (4) … … … … … …
Petrei and Delfino, 1985 (1)
… … … … … …
Santa Fe
Petrei and Delfino, 1980 (1)
… … … … … …
Petrei and Delfino, 1985 (1)
… … … … … …
Tucumán
FIEL and FBET, 1995 (5)
EPH, 25-54 years old … … … … … …
FIEL's survey, all ages … … … … … …
Salta
del Rey and Mena de Méndez, 1985 … … … … … …
Sources: Petrei and Delfino (1988), Kugler and Psacharopoulos (1989), FIEL (1994), Giordano and
Montoya (1989), Ferrá and Claramount (1985), FIEL, FBET and Fundación Banco de Crédito Argentino
(1996), del Rey and Mena de Méndez (1986). (1)
The rates of return for higher education are only for
university. (2)
The rates of return are for individuals 14 to 65 years old. (3)
Rates of return assume neither
repetition nor dropouts. (4)
The rates of return are for individuals 6 to 59 years old. The rates of return are for
the number of years of schooling for a given level, 7 years for primary and 5 years for secondary. The rates of
return are for a scenario where the annual rate of increase of wages is 0, and the amount of earnings
discounted for retirement are a 100% valuable for estimating the private rates of return. (5)
Mean rates of return
(not marginal). Rates of return are for levels of education, not years of schooling.
14
III. RESEARCH QUESTIONS
Following Carnoy’s (1975) model on the determinants of the returns to education, I
use data for the Buenos Aires Metropolitan Area for 1980 and 1995 to address the
following questions:
1. How do the rates of return to various levels of education behave in a city where
educational attainment has expanded to levels similar to that of developed countries but
employment opportunities have decreased to a level that corresponds to developing
countries? More specifically, what is the rate of return to different levels of education? Is
the rate of return to primary education really higher than the rate of return to higher
education?
2. Are rates of return stable when examined historically, or do they tend to increase or
decrease for certain levels of education?
3. What are the returns to different levels of education when two different methods of
estimation are used, namely the Mincer regression equation and the traditional or direct
method?
The purpose of this study is, first, to analyze how having different levels of
education affect the level of earnings of men and women, and, second, to examine if the
rates of return differ between 1980 and 1995. I argue that the pattern of rates of return in the
Buenos Aires Metropolitan Area does not follow Psacharopoulos’s (1980) model of higher
returns to primary education and declining returns when examined historically. On the
contrary, the Argentinean economy has worsened during the last fifteen years and
unemployment has dramatically increased. Since 1985, higher education has expanded to
reach a level that qualifies it as mass education. I argue that the returns to primary education
are higher than those to higher education in cases where employment opportunities are not
constrained. That is not the case for the Buenos Aires Metropolitan Area, where having a
higher educational degree not only helps but might also determine the probability of having
a paid or self-employment.
15
Following these considerations, I hypothesize in general terms that: (a) the rate of
return to higher education is higher than the rate of return to primary education, and (b) the
rates of return to higher education tend to increase when examined historically.
IV. DATA AND METHODOLOGY
The data comes from the National Institute of Statistics and Census (INDEC)8 for
the Buenos Aires Metropolitan Area, for October 1980 and May 1995. The INDEC uses a
cluster sampling procedure to select individuals to be interviewed. The sample used in this
study represents a sub-sample of the INDEC’s sample. I examine how education affects the
earnings of individuals aged 13 to 65 years old. Since schooling affects that part of the
income received either by wages or self-employment or a combination of both, I examine
only individuals who receive an income from wages or self-employment. In 1980, 3413
individuals are in the sample—2283 men and 1130 women. In 1995, there are 3333
individuals—2071 men and 1262 women (See Appendix 3 for more detailed information
on the unit of analysis of this study).
A. Variables
I compare the rates of return to different levels of education as calculated by two
different methods—the Mincer regression equation and the direct method. In this section, I
introduce the variables of this study according to the two methods used. For the Mincer
equation, the variables are:
Annual Total Earnings. This is the conceptual dependent variable and is measured
in U.S. dollars9. The annual total earning of a person is the weekly or monthly earnings
times the number of weeks or months worked during the year. The number of weeks a
person works during the year depends on whether he or she is attending school. For those
declaring they are not enrolled in school, I compute earnings for 12 months a year (no
8 Instituto Nacional de Estadísticas y Censos (INDEC), Encuesta Permanente de Hogares (EPH).
9 Since 1992, by law one Argentinean peso equals one U.S. dollar. I automatically transformed the unit pesos
to dollars, even though there might be very slightly differences.
16
matter what was the last level of education attended was). I compute 2 months of work
(during the two months of summer vacation) for those persons going to school, except for
those attending higher education. For individuals attending higher education, I calculate 2
months of earnings during the summer and 1 month during the winter vacation (3 months).
Because the distribution of annual total earnings is not symmetric, I use the logarithm of
annual total earnings as the dependent variable (See Appendix 3, Variables, Annual Total
Earnings, for more details).
Level of Education. This is the main explanatory variable in the Mincer regression
equation and is measured as the last level of education attained. It includes primary
incomplete, primary complete, secondary complete, secondary technical complete, and
higher education complete. Higher education includes both university and other post-
secondary type of education. I coded level of education as a set of four dummy variables,
primary incomplete being the reference category (see Table 3). A more detailed explanation
on how this variable is measured in both years can be found in Appendix 3, Variables,
Level of Education.
17
Table 3. Methodology for Coding Dummy Variables for Level of Education, Mincer
Equation.
Dummy variables
Level of education Primary Secondary Secondary Higher
education
(original variable) Complete complete technical complete complete
Primary incomplete 0 0 0 0
Primary complete 1 0 0 0
Secondary incomplete 1 0 0 0
Secondary complete 1 1 0 0
Secondary technical incomplete 1 0 0 0
Secondary technical complete 1 0 1 0
Higher education incomplete 1 1 0 0
Higher education complete 1 1 0 1
Years of Work Experience. This is one of the control variables in the Mincer
equation. I compute work experience as age minus years of schooling minus 4 (I assume
that the individual starts kindergarten at the age of five). (See Appendix 3, Variables, Years
of Work Experience).
Years of Work Experience Squared. I include this second control variable in order
to correct for the non-linear relationship between experience and earnings.
Hours Worked per Week. This is the third control variable and it accounts for the
variations in earnings according to the number of hours an individual works.
Marital Status. This is the fourth control variable and is coded as a dummy, 1
meaning not married and 0 otherwise (See Appendix 3, Variables, Marital Status).
When the rates of return are estimated using the direct method, the variables are:
18
Age. It is measured in years.
Mean Annual Total Earnings. It is computed as the mean of the variable Annual
Total Earnings.
Level of Education. It is measured as in the Mincer regression equation. For the
direct method, level of education does not include secondary technical education (See
Appendix 3, Variables, Level of Education).
Earnings Differential. It represents the benefits an individual receives for
completing an additional level of education. Differences in earnings are calculated as the
mean earnings of a person of a given age and level of education minus the mean earnings of
a person with the same age but a previous level of education. For example, mean earnings
that a woman with secondary complete and 27 years old receives are subtracted from the
mean earnings that a woman with higher education complete and the same age receives.
Mean Annual Earnings Foregone per Student. This variable is part of the formula
for computing private costs per student. Earnings forgone for a given age and level of
education are the same as the mean annual total earnings for the same age and level of
education.
Mean Annual Total Private Costs per Student. Because there are no data on private
costs per student for 1980, I use earnings forgone per student as the measure of private
costs per student. For calculations in 1995, mean private costs for a given age and level of
education are added to the earnings forgone to obtain total private costs per student.
Mean Annual Public Cost per Student. For the 1980 dataset, public expenditures on
different levels of education are divided by the number of students enrolled in the same level
to obtain costs per student (See Appendix 3, Variables, Mean Annual Social Costs per
Student). No transformation was needed for the data for 1995.
Mean Annual Total Social Cost per Student. For 1980, the total social costs are the
income foregone added to the public costs per student. For 1995, social costs per student
are calculated as the average of the public and private costs per student.
Tables 4, 5 and 6 provide summary statistics for the different variables considered
in this study. Table 4 indicates that, although there are more women in the 1980 INDEC’s
19
sample (5,722 men and 6,178 women), women who earn income from wages or self-
employment represent less than one-half the number of men sub-sampled for this study,
31.9% and 14.9% for men and women, respectively (Table 4, number of cases 2,283 and
1,130). The participation of women in paid or self-employed jobs improves in 1995, where
the number of women, 22.5% of the INDEC’s sample, is near two-thirds the number of
men, 41.48% of the INDEC’s sample. More men and women have entered the paid or self-
employed labor force over these 15 years, but the sex gap has been reduced.
20
Table 4. Summary Statistics for 13 to 65 Year-Old Employed and Self-Employed
Individuals, by Year and Sex. Buenos Aires Metropolitan Area, 1980 and 1995.
1980 1995
Mean or % Mean or %
Variables Men Women Men Women
Annual total earnings 13,623 8,510 9,967 6,949
(In US$ 1995 dollars)
Level of education
Primary incomplete 20.1 16.5 8.3 9.3
Primary complete 38.9 31.9 34.0 24.5
Secondary incomplete 12.1 13.9 14.6 14.4
Secondary complete 6.7 17.9 9.6 18.4
Secondary technical incomplete 7.8 0.4 8.5 0.7
Secondary technical complete 3.9 1.0 5.3 1.7
Higher education incomplete 6.0 9.6 10.6 13.2
Higher education complete 4.5 8.9 9.2 17.9
Years of work experience 26.1 23.3 23.3 21.7
Hours worked per week 47.6 38.2 48.8 37.4
Marital status
Single 26.0 40.8 27.8 37.1
Married 71.6 45.0 69.2 49.5
Separated or divorced 1.6 9.1 2.4 9.0
Widow 0.8 5.0 0.6 4.4
Age 37.2 34.7 36.9 36.5
Number of cases 2283 1130 2071 1262
Source: Encuesta Permanente de Hogares (1980 and 1995).
On average, men earn more than women, both in 1980 and 1995. But similar to the
increase of women’s participation in the paid and self-employed labor force, the gap of
mean earnings between men and women narrowed. In 1980, women earn on average US$
5,112 less than men, whereas in 1995 they earn US$ 3,017 less (Table 4). However, mean
21
earnings for men and women decreased between 1980 and 1995. Although it is not the
purpose of this study to examine women’s labor strategies, it is nevertheless interesting to
remark that one possible way in which women are improving their condition in the labor
market relative to men is by increasing their participation rather than by increasing their
mean earnings.
Data on education show that more than one third of the population sub-sampled
completed primary education in the 80’s (Table 4). The percentages decrease during the
period of analysis, mainly because a higher percentage of individuals completes secondary
and higher education. In both years, the percentage of women with secondary education
complete is twice as high as the percentage of men; more women also complete higher
education. However, it is important to remark that this level does not discriminate between
universities from other kinds of post-secondary education where women are highly
represented (i.e., teacher education). The percentages for secondary technical education
show that this type of education remains mainly masculine.
With respect to years of work experience, it can be observed in Table 4 that men
have, on average, more years of work experience than women, although this difference
declines from 1980 to 1995. Men also work more hours a week than do women (9 hours
more in 1980 and 11 hours more in 1995). Men who receive income from wages or self-
employment are, mostly, married, about 70% of them. Although that is also the case for
women, the percentages are not as high as for men (between 45 and 50%). These
percentages make sense if we consider that most women decide not to participate in the
labor force when they are married. The mean age is about 35-37 years for both sexes and
years.
Mean annual total earnings by age group are presented in Tables 5.a and 5.b. In
1980, individuals of all age groups earn more the higher the level of education attained,
except for those aged 19 to 23 whose earnings decrease slightly with the completion of
primary and secondary education compared to those with primary incomplete. A similar
situation occurs in 1995. When mean earnings for a single level are analyzed, the general
conclusion is that earnings tend to diminish with age. Age-income profiles presented in the
22
Findings section provide a better representation of how earnings behave according to the
level of education and the age group.
Table 7 shows mean annual costs per student, information that is used for the
internal rate of return’s calculations. Costs used are average public, private10
, and total
costs. As the data available indicate, in 1980, mean public expenditures per student are
higher the higher the level of education—1,814 for primary, 3,285 for secondary, and 5,131
for higher education. This is not the case for 1995 where average public costs are lower for
higher education compared to secondary education; average public costs per student also
decline from 1980 to 1995 for each level of education. These differences and the decline
could be due to the transformations done to the data. Average private costs per student for
1995 also indicate that costs are higher the higher the level of education.
10
Table 7 does not take into consideration income foregone.
23
Table 5. Mean Annual Total Earnings by Sex, Level of Education, and Age Group. Buenos Aires Metropolitan Area, October 1980 (in
US$ dollars 1995).
Men (n=2,283) Women (n=1,130)
Level of Education Level of Education
Age Higher Higher
Primary Secondary Education Primary Secondary Education
Group Incomplete Complete Complete Complete Incomplete Complete Complete Complete
13 to 18 4,339 5,635 7,471 … 4,302 4,540 6,552 …
(19) (135) (4) (5) (54) (8)
19 to 23 8,715 8,218 8,577 11,207 5,825 5,540 6,930 8,440
(19) (156) (68) (2) (8) (78) (99) (12)
24 to 27 9,449 11,144 11,703 18,639 5,417 7,360 8,227 12,425
(19) (134) (64) (6) (10) (61) (45) (23)
28 to 34 9,676 12,287 19,175 28,971 5,194 6,922 10,737 21,226
(68) (237) (98) (23) (30) (87) (61) (21)
35 to 44 9,683 14,636 24,191 40,178 5,933 7,839 11,201 18,901
(121) (299) (58) (34) (55) (116) (59) (23)
45 to 54 10,481 13,675 25,330 44,813 6,284 9,429 13,129 25,473
(128) (239) (53) (21) (53) (84) (37) (17)
55 to 65 10,828 13,587 22,651 41,420 5,768 7,469 10,872 8,694
(85) (143) (33) (17) (25) (42) (12) (5)
Number (459) (1,343) (378) (103) (186) (522) (321) (101)
of cases
Source: Encuesta Permanente de Hogares (1980). Note: Number of cases in each cell is in parenthesis.
24
Table 6. Mean Annual Total Earnings by Sex, Level of Education, and Age Group. Buenos Aires Metropolitan Area, May 1995 (in US$
dollars 1995).
Men (n=2,071) Women (n=1,262)
Level of Education Level of Education
Age Higher Higher
Primary Secondary Education Primary Secondary Education
Group Incomplete Complete Complete Complete Incomplete Complete Complete Complete
13 to 18 2,840 3,398 3,306 … 3,865 2,353 2,621 …
(10) (67) (5) (4) (36) (5)
19 to 23 5,642 5,960 4,343 12,718 3,869 4,799 3,634 7,090
(7) (159) (93) (6) (1) (62) (101) (13)
24 to 27 5,938 6,467 6,542 14,987 3,641 5,518 4,909 9,241
(8) (108) (102) (13) (2) (50) (72) (29)
28 to 34 7,273 8,292 10,904 21,051 4,356 5,022 7,700 11,139
(17) (216) (92) (50) (11) (79) (61) (60)
35 to 44 6,520 9,704 14,971 25,574 3,955 5,549 8,211 14,603
(36) (295) (121) (60) (35) (116) (90) (73)
45 to 54 6,160 8,873 15,134 28,332 4,715 6,475 11,354 12,927
(57) (222) (73) (47) (43) (111) (62) (43)
55 to 65 5,929 8,789 12,184 27,326 4,280 5,282 7,018 13,860
(36) (114) (43) (14) (21) (46) (28) (8)
Number (171) (1,181) (529) (190) (117) (500) (419) (226)
of cases
Source: Encuesta Permanente de Hogares (1995). Note: Number of cases in each cell is in parenthesis.
25
Table 7. Mean Annual Private and Social Costs in Education per Student, by Year, Authority and Level of Education. Buenos Aires
Metropolitan Area, 1980 and 1995 (in US$ dollars 1995).
1980 1995
Level of Authority Authority
Average State Average Private
education Ministry State Municipality Public Ministry 19
Districts
City Bs.
As.
Public 19
Districts
City Bs.
As.
Average
Primary 2,433 1,791 1,680 1,814 770 532 1,008 770 843 931 887
Secondary 3,929 2,039 1,144 3,285 1,405 1,170 1,640 1,405 1,334 2,317 1,826
Higher
education
5,255 3,444 … 5,131 718 97 1,340 718 2,484 2,782 2,633
Sources: For 1980, Appendix Tables 3 and 5. For 1995, public costs for the 19 districts, Appendix Table 6. Public costs for the city of Buenos Aires, and
private costs, information provided by Programa de Estudios de Costos, Ministry of Education, 1998.
26
B. Hypotheses
The first hypothesis stated in this study is that increases in earnings are related to
education. This hypothesis is congruent with the two models for explaining the behavior of
the rates of return to education.
Hypothesis 1: As the level of education increases, earnings tend to increase, when
experience, hours worked, and marital status are controlled.
The second hypothesis represents the idea that the rates of return to education do not
tend to diminish; on the contrary, the additional earnings from completing higher education
versus completing secondary school are higher than the additional earnings from completing
primary education compared to not completing it.
Hypothesis 2: Additional earnings due to adding higher education to secondary
education are higher than the additional earnings due to adding primary education to
primary incomplete, ceteris paribus.
The third hypothesis represents the idea that, in the Buenos Aires Metropolitan
Area, the returns to education tend to increase when comparing returns for 1980 to those for
1995, at least for higher education. As explained above, unemployment rates increased from
1980 to 1995, resulting in more opportunities for individuals with higher levels of schooling
than those with less schooling.
Hypothesis 3: The difference in earnings due to additional levels of education tends
to increase for higher education, when differences between 1995 and 1980 are
compared.
Because I use two different samples for 1980 and 1995, I am not able to statistically
test the variations of earnings over time.
C. Models
I use two models for calculating the rates of return to different levels of education.
On one hand, I use the Mincer regression equation to estimate how education affects
earnings. On the other hand, I also calculate the returns using the traditional method or
internal rate of return formula. This second model accounts for costs of education. For the
27
second model, several transformations are made to the data to obtain costs per student. In
that respect, the resulting data is not as accurate as it would be if no or fewer
transformations had been done. For example, for the costs in the 1980, public expenditures
in education aggregate expenditures for primary and secondary. For that reason, I had to use
different percentages from other sources to obtain expenditures for each level. Similarly, the
data on expenditures also aggregate expenditures for different states, so I use other
percentages to estimate expenditures for the city and for the state of Buenos Aires. Finally,
the area considered in this study is the Buenos Aires Metropolitan Area—the city of Buenos
Aires and 19 districts belonging to the state of Buenos Aires. Because expenditures for the
state of Buenos Aires include expenditures for all the districts and not the 19 belonging to
the Metropolitan Area, I had to use other percentages to estimate expenditures for the 19
districts. Even though the two models are estimated, results obtained by the Mincer
equation method are of superior quality.
1. The Mincer regression equation
I use a linear regression model and the ordinary least squares method to estimate the
earnings function, that is, the variation of earnings as explained by a set of independent
variables. In the literature on rates of returns to education, this kind of earnings function
receives the name “Mincer earnings function,” as first developed by that scholar (Mincer,
1974). I first estimate the regression coefficients without considering sex interactions
(Model 1 in Appendix Table 7). I then include sex interactions with each independent
variable (Model 2 in Appendix Table 7). Adding sex interactions significantly improves the
estimations for both years (see F-test model 1 versus model 2, Appendix Table 7). Because
estimating earnings including sex interactions is the same as computing separate
estimations for both sexes, I then use separate samples for men and women to estimate the
earnings function.
In this study, the earnings function is represented by the following equation (1):
logY a b PC b SC b STC b HEC b EX b EX2 b HRS b NOTMi 1 i 2 i 3 i 4 i 5 i 6 i 7 i 8 i
where: Y= annual total earnings,
28
PC= primary complete,
SC= secondary complete,
STC= secondary technical complete,
HEC= higher education complete,
EX= years of work experience,
EX2= years of work experience squared,
HRS= hours worked per week,
NOTM= not married.
In this earnings function, the coefficient for each level of education represents the
additional earnings the completion of that level yields, when compared to the completion of
the previous level. The intercept represents the earnings of an individual whose primary
schooling is incomplete and who is married (the reference categories for the dummies for
level of education and marital status), other things being equal.
2. The traditional or direct method
The methodology for estimating rates of return to education includes two steps.
First, I calculate earnings differentials for an additional level of education; for instance, I
subtract mean earnings for an individual aged 19 to 23 with secondary complete from mean
earnings for an individual within the same age group but with higher education complete.
This difference is called the “undiscounted” value of education. It assumes that the
difference in earnings for different levels of schooling does not account for differences in the
costs of the same level. For the purpose of this method, it is assumed that an individual’s
expected lifetime earnings for a given year are the same as the earnings of an older
individual in the same year and with the same level of education. Lifetime earnings are
represented in what is called an age-income profile.
Second, I estimate the rates of return by using a formula that represents the discount
rate at which the net present value of the additional costs and benefits for attending a higher
level of schooling equals zero. The discount rate formula requires solving for “r” in the
following formula (2):
29
0 = Y - C
(1-r)i=1
i i
i
n
where:
Yi= the difference in average income in period i between those with one level of
schooling and those with the next highest level
Ci= the cost of schooling in period i, where private cost equals income forgone
(taken at 75% of annual income of those with the lower level of schooling),
and social costs equals income forgone plus average institutional costs
r = the marginal internal rate of return to schooling, and
n = the number of periods from the beginning of the level of school being
analyzed to the end of the working life (Carnoy, 1975: 34).
30
V. FINDINGS AND DISCUSSION
In this section, I present and compare the rates of return as estimated by the two
different methods. I analyze variations in earnings by level of education using the results for
the Mincer regression equation and the internal rates of return when costs are accounted for.
A. Variations of earnings and education: findings from the Mincer regression
equation
By looking at the results from the Mincer regression equation, it is possible to ask:
How can we explain the variations in earnings in the Buenos Aires Metropolitan Area? As I
discussed in the previous section, including sex and sex interactions in the regression
function significantly improves the estimation of earnings. Last level of education
completed, years of work experience, hours worked, and not being married explain about
39% of the variation in earnings (logged) in 1980 (see Table 8, Adjusted R2). If we include
sex in the equation, we can predict 31% of variation. Although the adjusted R2
difference is
negative, the improvement of the model is statistically significant at p<.001 (see Appendix
Table 7, F-test for the difference between model 1 without sex versus model 2 with sex). In
1995, adding sex and sex interactions to education, experience, hours worked, and marital
status also significantly improve the model.
How much variation is explained by education? First of all, I examine the intercepts.
In 1980, a married man with primary education incomplete, no work experience, and no
hours worked who wanted to enter the labor market would start earning US$ 2,786 (inverse
log of 3.445, Table 8). However, the analysis of the intercepts is misleading, because when
the person starts accumulating hours of work, his log earnings increase by about .004. A
woman in the same situation in 1980 would start earning less than a man, or only US$
1,239 (about 1,547 U.S. dollars less than a man). In 1995, the starting annual earnings
would be US$ 1,265 for a man and US$ 789 for a woman (Table 9). It can be observed that
mean earnings for a married individual with primary incomplete who wants to start working
decreased between 1980 and 1995.
31
In 1980, completing primary education makes a positive difference in the level of
earnings. A married man with no work experience and primary complete has a starting
earning (logged) of 11% more than the same person without primary complete. For the case
of a woman, having primary complete represents an increase of 8% in log earnings.
Completing secondary education seems to have more of an effect in increasing starting
earnings, considering that it represents an addition of about 9% to 12% to that increase
already earned. Men with secondary technical complete earn more than those with a regular
secondary education—29% more than the log earnings for primary education complete.
Having higher education in the 1980s is one of the best predictors of increases in earnings,
given that it implies an increase of 33% for men and 36% for women to the earnings
(logged) received when having secondary education complete (see Table 8).
32
Table 8. OLS Estimates for the Regression of Annual Total Earnings (Logged) on
Education, Experience, Hours Worked, and Marital Status, by Sex. Buenos Aires
Metropolitan Area, 1980.
1980
Model 1 Model 2
Independent variables Total Men Women
Intercept 3.344 *** 3.445 *** 3.093 ***
(0.03) (0.04) ( 0.05)
Level of education (1) (2) 33.264 36.4273
Primary complete 0.102 *** 0.107 *** 0.084 ***
( 0.01) ( 0.01) (0.02)
Secondary complete 0.115 *** 0.122 *** 0.089 **
(0.02) (0.02) (0.03)
Secondary technical complete 0.287 *** 0.285 *** N/A
(0.04) (0.04)
Higher education complete 0.343 *** 0.333 *** 0.364 ***
(0.03) (0.04) (0.04)
Control variable
Years of work experience 0.022 *** 0.021 *** 0.024 ***
(0.00) (0.00) (0.00)
Years of work experience square 0.000 *** 0.000 *** 0.000 ***
(0.00) (0.00) (0.00)
Hours worked per week 0.006 *** 0.004 *** 0.007 ***
(0.00) (0.00) (0.00)
Marital status (Not married=1) -0.044 *** -0.094 *** 0.016
(0.01) (0.02) (0.02)
Gender (Female=1) -0.172 *** … …
(0.01)
R-square 0.392 0.311 0.360
Adjusted R-square 0.389 0.308 0.354
F-test model 1 vs. model 2 (3) 10.269 ***
Degrees of freedom 9 7 8 7
Number of cases 3414 3414 2283 1130
Source: Encuesta Permanente de Hogares (1980). Note: Standard errors of coefficients are in
parentheses. N/A: parameter not estimated because there are only 11 individuals out of 1130. (1)
Reference
category: primary incomplete. (2)
Dummy variables. See Table 3 for methodology for coding dummy
variables. (3)
F-test, df1=7 and df2=3347. * p<.05 ** p<.01 *** p<.001 (one-tailed tests).
33
Table 9. OLS Estimates for the Regression of Annual Total Earnings (Logged) on
Education, Experience, Hours Worked, and Marital Status, by Sex. Buenos Aires
Metropolitan Area, 1995.
1995
Model 1 Model 2
Independent variables Total Men Women
Intercept 3.023 *** 3.102 *** 2.897 ***
(0.03) (0.04) (0.04)
Level of education (1) (2) 38.9795 33.7844
Primary complete 0.098 *** 0.116 *** 0.057 **
(0.02) (0.03) (0.03)
Secondary complete 0.114 *** 0.097 *** 0.129 ***
(0.01) (0.02) (0.02)
Secondary technical complete 0.218 *** 0.228 *** 0.123 **
(0.03) (0.03) (0.06)
Higher education complete 0.358 *** 0.390 *** 0.338 ***
(0.02) (0.03) (0.02)
Control variable
Years of work experience 0.027 *** 0.026 *** 0.027 ***
(0.00) (0.00) (0.00)
Years of work experience square 0.000 *** 0.000 *** 0.000 ***
(0.00) (0.00) (0.00)
Hours worked per week 0.007 *** 0.006 *** 0.008 ***
(0.00) (0.00) (0.00)
Marital status (Not married=1) -0.059 *** -0.120 *** -0.003
(0.01) (0.02) (0.02)
Gender (Female=1) -0.086 *** … …
(0.01)
R-square 0.419 0.405 0.405
Adjusted R-square 0.417 0.402 0.401
F-test model 1 vs. model 2 (3) 6.309 ***
Degrees of freedom 9 8 8 8
Number of cases 3333 3333 2071 1262
Source: Encuesta Permanente de Hogares (1995). Note: Standard errors of coefficients in
parentheses. N/A: parameter not estimated because there are only 11 individuals out of 1130. (1)
Reference
category: primary incomplete. (2)
Dummy variables. See Table 3 for methodology for coding dummy
variables. (3)
F-test, df1=7 and df2=3347. * p<.05 ** p<.01 *** p<.001 (one-tailed tests).
34
Control variables do not explain as much of a difference in earnings as education
does. It is interesting to note that not being married is not the best scenario for a man who
wants to start working, given that it implies a negative effect on log earnings (-9%).
However, a single woman earns more than a married one, other things being equal (2%
more, Table 8).
Completing primary education in 1995 is as important for men as completing
primary education in 1980 (12% more earnings than primary incomplete, Table 9).
However, that is not the case for women, as they receive more earnings for completing
primary education but less than they used to receive in the 1980s (2% less in 1995 than in
1980). Completion of secondary education implies, for both sexes, more earnings than in
the 1980s. For men, having a technical secondary education still yields more earnings than
having a regular secondary education; but for women, a regular secondary education yields
more earnings than a technical one, when compared to the earnings received with primary
complete.
For men, higher education is more important than it used to be in the 1980s. It
represents an addition of 39% when compared to secondary education complete (Table 9).
For women, having higher education in 1995 yields more earnings than not having it, but
the additional log earnings are slightly lower than they were in 1980 (36% and 34% for
1980 and 1995 respectively). Further research should consider whether earnings for non-
university and university education decrease or decrease for 1995 and how that affects the
variations in earnings for women.
What can we conclude about the earnings premiums for having additional levels of
education? Table 8 summarizes the findings from the Mincer regression equation. The rates
of return to different levels of education are the earnings premium for an additional level
completed (the percentages of the coefficients estimated in Tables 6.a and 6.b). We can see
that the higher average premiums for both men and women (total) is due to higher education
complete, for both 1980 and 1995.
35
Table 10. Earnings Premiums for an Additional Level of Education Completed, by Year,
and Sex. Buenos Aires Metropolitan Area, 1980 and 1995 (In Percentages).
1980 1995
Level of education Total Men Women Total Men Women
Primary complete 10.2 10.7 8.4 9.8 11.6 5.7
(vs. primary incomplete)
Secondary complete 11.5 12.2 8.9 11.4 9.7 12.9
(vs. primary complete)
Secondary technical complete 28.7 28.5 21.8 22.8 12.3
(vs. primary complete)
Higher education complete 34.3 33.3 36.4 35.8 39.0 33.8
(vs. secondary complete)
Source: Based on earnings functions in Tables 6.a and 6.b.
By looking at Table 8 we can accept the hypothesis that additional earnings due to
adding higher education to secondary education are higher than the additional earnings due
to adding primary education to primary incomplete, other things being controlled
(Hypothesis 2). If wages are a proxy for productivity, we cannot say that the returns to
education tend to diminish when adding higher education to an individual’s portfolio. If that
were the case, we would expect the premium to higher education to be less than 11% for
men and less than 8% for women, in 1980. On the contrary, this level of education increases
earnings in 33% and 37% for men and women, respectively, when compared to secondary
complete. The same analysis can be made for 1995 for both sexes.
Table 8 also shows that hypothesis 3 is supported. When examined historically, the
returns to schooling are not stable, an argument sustained by Psacharopoulos (1980).
Rather, the rates of return vary depending on sex and the level of education. Total rates of
return for primary education complete decrease over time, but the returns for men increase
whereas the ones for women decrease. The inverse occurs with the returns for secondary
education, when compared to the ones for primary education complete. While the average
36
or total rate of return is stable over time, the returns for men decrease and the ones for
women increase.
Earnings premiums for higher education are higher than the premiums for any other
level of schooling, and they tend to increase over time, at least in average (Table 10). But
when analyzed by sex, the over time patterns vary, increasing for men and with a slight
decrease for women. Again, when higher education aggregates university and non-
university education, the rates of return should be analyzed carefully, given that we can
assume that the effect of education on earnings is different for those with a university degree
when compared to those with a non-university degree.
B. The rates of return considering the costs of the education
Before presenting the rates of return, it is important to observe the age-earnings
profiles for both years and sexes (Figures 1.a to 1.d). These profiles show some of the
common structural characteristics addressed by Cippolone (1994) for all age-earnings
profiles. On one hand, the absolute level of earnings at any time is higher for people with
higher levels of schooling. The profiles for men and women for 1980 and 1995 indicate that
mean annual total earnings for those having higher education complete is higher at any age
group, except when the individuals are attending university (age 13 to 18) and for women
aged 55 to 65 in 1980 (Figure 2). For this group, mean earnings are below the earnings of
women with secondary complete.
In 1980, mean earnings for those with secondary complete are also higher than
mean earnings for those with primary complete. But in 1995, mean earnings for those with
secondary complete are higher only after the age of 24 for men and 28 for women (Figures
1.c and 1.d). Similarly, individuals with primary complete earn, on average, more than those
with primary incomplete at any time; the only exception being women aged 13 to 18 in
1995. For this group, primary complete pays more after the age of 19 (Figure 4).
37
Level of education
Primary incomplete
Primary complete
Secundario completo
Higher education
complete
N=2,283
Age group
55 to 6545 to 5435 to 4428 to 3424 to 2719 to 2313 to 18
Mea
n A
nn
ual
To
tal
Ear
nin
gs (
19
95
do
lars
) 50000
40000
30000
20000
10000
0
Figure 1. Age-Earnings Profile for Men. Buenos Aires Metropolitan Area, 1980.
On the other hand, the shape of the age-earnings profiles is also concave, as
suggested by Cippolone (1994). Earnings increase with age at a decreasing rate, although in
1980 and 1995 women’s higher-education earnings peak, flatten and then increase
substantially. A similar situation occurs with the earnings’ curve for women with secondary
complete in 1995. Finally, earnings increase slightly faster for those with more education
before they peak; that is to say, the slope is positively correlated with the level of schooling.
However, earnings do not peak at a later age for individuals with more education.
38
Level of education
Primary incomplete
Primary complete
Secondary complete
Higher education
complete
N=1,130
Age group
55 to 6545 to 5435 to 4428 to 3424 to 2719 to 2313 to 18Mea
n A
nn
ual
To
tal
Ear
nin
gs (
11
99
5 U
.S.
do
llars
)
30000
25000
20000
15000
10000
5000
0
Figure 2. Age-Earnings Profile for Women. Buenos Aires Metropolitan Area, 1980.
Level of education
Primary incomplete
Primary complete
Secondary complete
Higher education
complete
N=2,071
Age group
55 to 6545 to 5435 to 4428 to 3424 to 2719 to 2313 to 18Mea
n A
nn
ual
To
tal
Ear
nin
gs (
19
95
U.S
. d
olla
rs)
30000
25000
20000
15000
10000
5000
0
Figure 3. Age-Earnings Profile for Men. Buenos Aires Metropolitan Area, 1995.
39
Level of education
Primary incomplete
Primary complete
Secondary complete
Higher education
complete
N=1,262
Age interval
55 to 6545 to 5435 to 4428 to 3424 to 2719 to 2313 to 18Mea
n A
nn
ual
To
tal
Ear
nin
gs (
19
95
U.S
. d
olla
rs)
15000
10000
5000
0
Figure 4. Age-Earnings Profile for Women. Buenos Aires Metropolitan Area, 1995.
Table 11 shows the rates of return to different levels of education as estimated by
the direct method (see also Appendix Tables 8, 9, and 10). Private rates of return were
estimated using income foregone and private costs (when available) of education; whereas
the social rates of return were estimated adding the public costs to the private rates of
return. It is important to recall that because several transformations have been done to the
data on costs, the rates of return might be distorted.
In 1980, adding higher education to secondary education yields greater returns than
adding secondary education to primary education, privately and socially—for men, private
rates of return are 14.8% for higher education and 10% for secondary education, and for
women, 13.4% and 8.3% for higher and secondary education, respectively. This could be
explained, in part, because, although costs are higher, earnings are also higher for those
individuals who finished higher education, particularly in a context where higher education
(university) has not yet expanded and there was less supply of highly educated individuals.
However, in 1995, the returns to higher education (versus secondary education) are also
40
higher than the returns to secondary education (versus primary education), being 18.8%.
For this year, higher returns to higher education can be explained by the fact that costs per
student for higher education declined whereas earnings are still higher than earnings for
individuals with secondary education.11
Table 11. Private and Social Rates of Return to Education: Buenos Aires Metropolitan
Area, 1980 and 1995. (In Percentages)
1980 1995
Men Women Men Women
Level of education Private Social Private Social Private Social Private Social
Secondary complete 10.0 7.7 8.3 5.4 7.2 6.0 5.6 4.6
(vs. primary
complete)
Higher education
complete
14.8 11.1 13.4 8.9 18.8 15.5 10.7 9.8
(vs. secondary
complete)
Higher education
complete
12.1 9.3 10.9 7.1 11.4 10.2 9.2 7.7
(vs. primary
complete)
Source: Based on Appendix Tables 9.a to 9.f and 10.a to 10.f.
Table 11 also indicates that private rates of return are higher than social rates of
return, for both years and, of course, both sexes. This is the expected result, given that
social costs include both private and public costs of schooling. Nevertheless, the gap
between private and social returns is narrowing, at least as calculated with the data
available. For example, in 1980, the difference between private and social rates of return for
men is about 4 points for higher education (versus secondary education) whereas in 1995
that difference is about 3 points.
11
Distortion in the costs data refer mainly to public costs for 1995, so if rates of return were estimated with
more precise data the only part of the return that would change are the social rates of return, not the private
ones.
41
Sex differences are also observed in the rates of return to different levels of
education as estimated by the direct method. In both years, private and social returns are
higher for men. Since average costs of schooling are assumed to be the same for both men
and women, differences in rates of return are explained because of differences in mean
annual earnings among sexes.
The behavior of the rates of return over time varies, depending on the level of
education and sex. For men and women, the returns to secondary education decline between
1980 and 1995, and faster for private rates of return than for social ones (Table 11). On the
contrary, private rates of return for higher education (versus secondary education) for men
increase over time (14.8% to 18.8%), whereas for women they decrease for the same level
(from 13.4% to 10.7%). Social rates of return for women with higher education increase
when compared to women with secondary education.
C. Comparing the two methods
What can we conclude about the behavior of the rates of return by level of education
and the variation over time? As expected, rates of return differ when estimated by the two
different methods—the Mincer regression equation and the direct method. However, it
seems that there is a pattern observed using both estimations.
Going back to the research questions and hypotheses that guide this study, I
conclude the following. On one hand, it can be concluded for the Buenos Aires
Metropolitan Area that as the level of education increases, earnings tend to increase
(Hypothesis 1). This can be observed by the results from the Mincer regression equation
(Table 8) and the age-earnings profiles (Figures 1). Both methods also show that rates of
return to higher education are higher than any other level of education (Hypothesis 2). The
regression coefficients demonstrate that investing in higher education yields greater returns
than investing in secondary or primary complete (Table 8). Similarly, the internal rates of
return indicate that investing in higher education generates greater returns than investing in
secondary (Table 10).
42
Finally, when examined over time, rates of return vary depending on level of
education and sex (Hypothesis 3). Results from the Mincer regression equation show that
the returns to primary education increase for men and decrease for women12
. For secondary
education (versus primary complete), the results from the Mincer equation indicate that the
returns decrease for men and increase for women (Table 8), whereas results from the direct
method show that the rates of return decrease for both sexes (Table 11). Rates of return to
higher education increase for men and decrease for women, as both regression coefficients
and internal rates of return indicate—except for the social rate of return estimated by the
direct method, which increase slightly for women.
D. Limitations of the analysis
Some considerations should be made when interpreting the findings of this study.
First, overestimation of the annual earnings and rates of return is possible. Data on income
in INDEC’s sample for 1995 does not discriminate between different sources of income—
wages and income from self-employment. I combined the data on income with the sources
of income to obtain the earnings that correspond to wages and self-employment. Because I
also included in the sample individuals who receive income from rents or interests when
these sources are combined with wages or self-employment (i.e., wages and rents, or self-
employment and rents and interests), the earnings due to wages and self-employment might
be overestimated, as well as the rates of return. This is not the case for the sample of 1980.
I also made the assumption of full-time studies when computing annual earnings.
However, this is not the reality of students’ lives in Buenos Aires, particularly for higher
education, where many students work and study at the same time. This computation
overestimates earnings foregone, hence probably underestimates the rates of return to
higher education.
A second consideration is that, in order to guarantee comparability among years, I
could not discriminate between post-secondary education and university education. These
12
Because of the sub-sample’s age range, primary rates of return using the direct method were not estimated.
Costs for primary education are made while individuals are 6 to 12 years old, and the individuals sampled for
this study are 13 to 65 years old.
43
two types of education are condensed in the category “higher education”. However
interesting it might have been to examine university education, for example, such an
analysis would have been possible only for 1980.
Third, the findings presented in this study can only be generalized to the population
of the Buenos Aires Metropolitan Area. Studies for other urban areas in Argentina, such as
Córdoba, Santa Fe, or Mendoza, might yield similar patterns. However, one should not
expect a similar pattern when studying rural areas or other cities that do not present
characteristics similar to Buenos Aires.
It could be argued that one of the assumptions of the linear regression (Mincer
equation) model has not been met. The relationship between levels of education completed
and earnings might best be described as a step function rather than a linear one, particularly
if completing a level of education has a credentialing effect on earnings. Education
measured as years of schooling could have best met the assumption of a linear relationship,
but for 1995 this consideration was not possible.
The model for the Mincer regression equation has met other assumptions for a linear
regression. By plotting the residuals against the predicted values, I checked that the
assumptions of homogeneity of variance (homoscedasticity) and equality of variance are met
for the estimations for the two sexes and years. Appendix Figures 5 to 8 show that the
variance of errors does not change with predicted values for the dependent variables—for
the Mincer regression equation, the mean annual total earnings (logged). Errors are
normally distributed, as showed by the normal p-plots for both sexes and years (Appendix
Figures 9 and 10). I use the Durbin-Watson statistic to test the hypothesis of no positive
correlation. The hypothesis could not be rejected for any of the four regression models;
therefore, autocorrelation is not significant (See Appendix 3, Limitations of the Analysis,
Autocorrelation, for d values). I tested for multicollinearity by analyzing the tolerance
values for the independent variables (see Appendix 3, Limitations of the Analysis,
Multicollinearity, for more details).
Rates of return as estimated by the direct method should be analyzed carefully for
two reason. One, since costs for 1995 come from information provided by the city and state
of Buenos Aires, public costs corresponding to the Ministry of Education that apply to
44
universities have not been considered. Second, several transformation have been done to the
data, except for costs per student for 1995 corresponding to the city of Buenos Aires as well
as private costs per student for the same year.
45
VI. CONCLUDING REMARKS
The title of this paper asks whether the rate of return to primary education is always
higher than the returns to higher education. By estimating the coefficients of the regression
of log earnings on levels of education and other control variables, and by using a cost-
benefit analysis, I observed that the returns to primary education in at least one country,
Argentina, are not higher than the returns to higher education.
If we consider that some international donors recommend redirecting public funding
to primary education based on rates of return studies, then the evidence provided in this
study is highly controversial. The case of the Buenos Aires Metropolitan Area shows that,
for example in 1980, the returns to higher education are higher than those for primary.
I did not include in this paper’s title the question of whether the returns change or
are stable when examined historically. Changes over time are stable and are explained, in
the model developed by Psacharopoulos (1980, 1981, 1985, and 1993) as an adjustment
between demand for and supply of educated labor. Even though variations in different
points in time are important from the standpoint of his model, the truth is that very few
studies have engaged in examining the pattern of the returns to education historically. This
study intended to fill that gap by using data for the Buenos Aires Metropolitan Area for two
years, 1980 and 1995.
46
REFERENCES
Albrecht, D., & Ziderman, A. (1992). Funding Mechanisms for Higher Education.
Financing for Stability, Efficiency, and Responsiveness. (Vol. 153). Washington, DC: The
World Bank.
Balán, J. (1993a). Introduction. Higher Education, 25(Special Issue on Higher
Education in Latin America), 1-120.
Balán, J. (1993b). Políticas de financiamiento y gobierno de las Universidades
Nacionales bajo un régimen democrático: Argentina 1983-1992. In H. Courard (Ed.),
Políticas Comparadas de Educación Superior en América Latina. Santiago de Chile:
FLACSO.
Blomqvist, A., & Jimenez, E. (1989). The Public Role in Private Post-Secondary
Education. A review of Issues and Options. (Vol. PPR Working Papers Nr. 240).
Washington, DC: The World Bank.
Bour, E. (1993). La descentralización de la educación superior: elementos de un
programa de reforma (Documento de Trabajo 38). Buenos Aires: Fundación de
Investigaciones Económicas Latinoamericanas--FIEL.
Brunner, J. J. (1993a). Chile's higher education: between market and state. Higher
Education, 25, 35-59.
Brunner, J. J. (1993b). Estudio Comparado sobre Financiamiento de la Educación
Superior en Seis Países de América Latina. Estado Actual, Tendencias e Innovaciones
(Serie Educación y Cultura Documento de Trabajo 32). Santiago de Chile: FLACSO.
Brunner, J. J. (1994). Educación superior en América Latina: coordinación,
financiamiento y evaluación. In C. Marquis (Ed.), Evaluación Universitaria en el
MERCOSUR . Buenos Aires: Ministerio de Cultura y Educación.
Brunner, J. J., & Briones, G. (1992). Higher Education in Chile: Effects of the 1980
Reform. In L. Wolff & D. Albrecht (Eds.), Higher Education Reform in Chile, Brazil, and
Venezuela. Towards a Redefinition of the Role of the State (Vol. 34, pp. II.1-44).
Washington DC: The World Bank.
Carnoy, M. (1975). The Return to Schooling in the United States, 1939-1969.
Journal of Human Resources, X (3), 312-331.
47
Carnoy, M. (1994). Rates of Return to Education. International Encyclopedia of
Education, 4913-4918.
Cippolone. (1994). Education and Earnings. International Encyclopedia of
Education.
Congreso de la Nación. (1995). Ley de Educación Superior 24.510. In Boletín
Oficial de la República Argentina (Ed.) . Buenos Aires: Congreso de la Nación.
Contaduria General de la Provincia de Buenos Aires. (1996). El Gasto Público
Provincial y su Incidencia en los Municipios. Buenos Aires: Provincia de Buenos Aires.
Cox, C. (1993). Políticas de Educación Superior en Chile, 1970-1990: Generación
y Resultados. In H. Courard (Ed.), Políticas Comparadas de Educación Superior en
América Latina . Santiago de Chile: Facultad Latinoamericana de Ciencias Sociales
(FLACSO).
del Rey, E. C., & Mena de Mendez, N. C. (1986). Rendimiento de la inversion en
educacion secundaria en Salta. In Universidad Nacional de Salta (Ed.), Anales de la
Asociacion Argentina de Economia Politica. XXI Reunion Anual (Vol. 2, pp. 509-529).
Salta: Universidad Nacional de Salta. Facultad de Ciencias Economicas, Juridicas y
Sociales.
Delfino, J., & Gertel, H. (1995). Modelo para la Asignación del Presupuesto
Estatal entre las Universidades Nacionales. Buenos Aires: Ministerio de Cultura y
Educación.
Diéguez, H. L., Llach, J. J., & Petrecolla, A. (1990a). El Gasto Público Social.
(Vol. I y II). Buenos Aires: Ministerio de Economía, Obras y Servicios Públicos
(MEOySP), Secretaría de Economía.
Diéguez, H. L., Llach, J. J., & Petrecolla, A. (1990b). El Gasto Público Social.
Sector Educación. (Vol. III-A Apéndices). Buenos Aires: Ministerior de Economía, Obras
y Servicios Públicos (MEOySP), Secretaría de Economía.
Ferrá, C., & Claramount, A. M. (1985). Rentabilidad de la Educación Primaria y
Secundaria en Mendoza. Mendoza: Universidad Nacional de Cuyo, Facultad de Ciencias
Económicas.
FIEL. (1994). Educación y Mercado de Trabajo en la Argentina. In ADEBA (Ed.),
Desafíos y Opciones para Crecer. Actas y Documentos Técnicos (pp. 329-417). Buenos
Aires: ADEBA.
FIEL, FBET, & Fundación Banco de Crédito Argentino. (1996). Educación y
Mercado de Trabajo en la Provincia de Tucumán. Buenos Aires: FIEL.
48
Gertel, H. R. (1991). Issues and perspectives for higher education in Argentina in
the 1990s. Higher Education, 21(Special Issue on Higher Education in Developing
Countries), 63-81.
Giordano, O., & Montoya, S. (1989). Rentabilidad de la educación en Córdoba.
Estudios, Año XII. No. 50(Abril/Junio), 57-67.
Gomez-Castellanos, L., & Psacharopoulos, G. (1990). Earnings and Education in
Ecuador: Evidence from the 1987 Household Survey. Economics of Education Review, 9,
219-227.
Harrold, R. (1992). Resource Allocation. In B. R. Clark & G. R. Neave (Eds.), The
Encyclopedia of Higher Education (pp. 1465-1476). Oxford and New York: Pergamon
Press.
Kugler, B. (1991). Argentina. Reallocating Resources for the Improvement of
Education. Washington DC: The World Bank.
Kugler, B., & Psacharopoulos, G. (1989). Earnings and Education in Argentina: an
Analysis of the 1985 Buenos Aires Household Survey. Economics of Education Review, 8,
353-365.
Mincer, J. (1974). Schooling, Experience, and Earnings. New York: National
Bureau of Economic Research.
Petrei, A. H., & Delfino, J. A. (1988). La educacion y la estructura de ingresos en
el mercado laboral (Proyecto MEJ/PNUD. Proyecto MEJ/ME/Banco Mundial/PNUD
87/012 87/009). Buenos Aires: Ministerio de Educacion y Justicia.
Psacharopoulos, G. (1980). Higher education in developing countries. A cost-
benefit analysis. Washington DC: The World Bank.
Psacharopoulos, G. (1981). Returns to Education: An Updated International
Comparison. Comparative Education, 17 (3), 321-341.
Psacharopoulos, G. (1985). Returns to Education: A Further International Update
and Implications. Journal of Human Resources, XX, Nro. 4, 583-604.
Psacharopoulos, G. (1989). Time Trends of the Returns to Education: Cross-
National Evidence. Economics of Education Review, 8, n. 3, 225-231.
Psacharopoulos, G. (1993). Returns to Investment. A Global Update. (Vol. 1067).
Washington, DC: The World Bank.
49
Psacharopoulos, G., & Alam, A. (1991). Earnings and Education in Venezuela: An
Update from the 1987 Household Survey. Economics of Education Review, 10, 29-36.
Psacharopoulos, G., & Ng, Y. C. (1992). Earnings and Education in Latin
America. Assessing Priorities for Schooling Investments. (Vol. 1056). Washington, DC:
The World Bank.
Psacharopoulos, G., & Velez, E. (1994). Education and the Labor Market in
Uruguay. Economics of Education Review, 13, 19-27.
Psacharopoulos, G., & Woodhal, M. (1985). Cost-benefit analysis of educational
investment. In G. Psacharopoulos & M. Woodhal (Eds.), Education for Development. An
Analysis of Investment Choices (pp. 29-71). Washington, DC: Oxford University Press.
Riveros, L. A. (1990). The Economic Return to Schooling in Chile. An Analysis of
its Long-term Fluctuations. Economics of Education Review, 9, 111-121.
Ryoo, J.-K. (1988). Changes in Rates of Return to Education Over Time: The Case
Study of Korea. Unpublished Doctor of Philosophy, Stanford University, Stanford.
Ryoo, J.-K., Nam, Y.-S., & Carnoy, M. (1993). Changing Rates of Return to
Education over Time: A Korean Case Study. Economics of Education Review, 12, 71-80.
Schwartzman, S. (1993). Policies for higher education in Latin America: the
context. Higher Education, 25, 9-20.
Secretaria de Programacion y Evaluacion Educativa. (1996). Censo Nacional de
Docentes y Establecimientos Educativos. Matricula Escolar. (Vol. 1). Buenos Aires:
Ministerio de Cultura y Educacion.
UNESCO. (1995). Policy Paper for Change and Development in Higher
Education. France: UNESCO.
V. de Flood, C., Harriague, M. M., Gasparini, L., & Vélez, B. (1994). El Gasto
Público Social y su Impacto Redistributivo. Buenos Aires: Ministerio de Economía.
Winkler, D. R. (1990). Higher Education in Latin America. Issues of Efficiency
and Equity. (Vol. 77). Washington , DC: The World Bank.
Wolff, L., Albrecht, D., & Saliba, A. (1992). Higher Education in Brazil: Issues and
Efforts at Reform. In L. Wolff & D. Albrecht (Eds.), Higher Education Reform in Chile,
Brazil, and Venezuela. Towards a Redefinition of the Role of the State (Vol. 34, pp. III-1,
III-38). Washington, DC: The World Bank.
50
Wolff, L., & Brunner, J. J. (1992). Higher Education in Venezuela: Issues and
Options for Reform. In L. Wolff & D. Albrecht (Eds.), Higher Education Reform in Chile,
Brazil, and Venezuela. Towards a Redefinition of the Role of the State (Vol. 34, pp. IV-1,
IV-19). Washington, DC: The World Bank.
World Bank. (1994). Higher Education: the Lessons of Experience. Washington
DC: The World Bank.
World Bank. (1995). Priorities and Strategies for Education. A World Bank
Review. Washington, D. C.: The World Bank.
51
Appendix 1
Financing Higher Education in Latin America: Current Policy
Options and Reforms
52
APPENDIX 1: Financing of Higher Education in Latin America. Map of Current Policy Options and
Reforms (selected studies)
Developing Countries Latin America Argentina
Albrecht
and
Ziderman
(1992)
World
Bank
(1994)
UNES-
CO
(1995)
Winkler/
World
Bank
(1990)
Balán
(1993b)
Brunner
(1993b,
and
1994)
Schwartz
man
(1993)
Kugler/
World
Bank
(1991)
Balán
(1993a)
Investment in higher education
Increase public expenditures
for higher education
X
Reallocate resources from
higher education to other levels
X
X
X
Sources
Deregulation: educational market
(private institutions and diversification
of institutions in HE system)
X
X
X
X
X
X
Diversification: cost-sharing with
students, alumni, external sources,
income generating activities
X
X
X
X
X
X
X
X
X
Resource allocation
Resources to students/Subside demand
(loans and grants)
X
X
X
X
X
Incentives/Efficiency
(input, output, quality based
evaluations)
X
X
X
X
X
X
X
53
Accountability
(intra-institutional efficiency and self-
assessment criteria)
X
X
X
X
X
X
X
X
Research
(productivity, competition for funds)
X
X
X
Sources: Albrecht, D., & Ziderman, A. (1992), World Bank (1994), UNESCO (1995), Winkler, D. R. (1990) , Balán, J. (1993b), Brunner, J. J. (1993b),
Brunner, J. J. (1994), Schwartzman, S. (1993), Kugler, B. (1991) , Balán, J. (1993a).
54
Appendix 1 (cont.). Financing of Higher Education in Latin America. Map of Current Policy Options and Reforms
(selected studies) Argentina Chile Brazil Venez.
Bour/
FIEL
(1993)
FIEL
(IEL,
1994)
Congreso
Nación
(1995)
Delfino
and
Gertel
(1995)
Brunner
and
Briones
(1992)
Brunner
(1993a)
Cox
(1993)
Wolff,
et. al.
(1992)
Wolff
and
Brunner
(1992)
Investment in HE
Increase public expenditures
for higher education
Reallocate resources from
HE to other levels of schooling
X
X
Sources
Deregulation: educational market
(private institutions and diversification
of institutions in HE system)
X
X
X
X
X
X
X
Diversification: cost-sharing with
students, alumni, external sources,
income generating activities
X
X
X
X
X
Resource allocation
Resources to students/Subside demand
(loans and grants)
X
X
X
X
X
X
X
Incentives/Efficiency
(input, output, quality based
evaluations)
X
X
X
X
X
X
X
X
55
Accountability
(intra-institutional efficiency and self
assessment criteria)
X
X
X
Research
(productivity, competition for funds)
X
X
X
X
Sources: Bour, E. (1993) , FIEL (1994) , Congreso de la Nación, (1995), Delfino, J., & Gertel, H. (1995) , Brunner, J. J., & Briones, G. (1992) , Brunner,
J. J. (1993a) , Cox, C. (1993) , Wolff, L., Albrecht, D., & Saliba, A. (1992) , Wolff, L., & Brunner, J. J. (1992).
56
Appendix 2
Studies on Rates of Return in Argentina: Descriptive Files
57
APPENDIX 2: Studies on Rates of Return in Argentina. Descriptive Files
A. Buenos Aires, Capital, Cordoba, Mendoza, Santa Fe
Petrei, A. H., & Delfino, J. A. (1988). La educacion y la estructura de ingresos en el
mercado laboral (Proyecto MEJ/PNUD. Proyecto MEJ/ME/Banco Mundial/PNUD 87/012
87/009). Buenos Aires: Ministerio de Educacion y Justicia.
Area: Buenos Aires Metropolitan Area, Capital, Córdoba Metropolitan Area, Mendoza,
and Santa Fe, 1974, 1980 and 1985
Theoretical model: Does not specify (human capital)
Sample: Encuesta Permanente de Hogares. Individuals in the labor forced (employed or
unemployed) were sampled. Sample aggregates men and women.
Type of rates of return: Rates of return by level of education. Social rates of return.
Levels of education: Primary, secondary and higher education
Method for estimating rates of return: Internal rate of return
Private costs: family expenditures on education. Source: Survey on Household’s
Expenditures. Earnings foregone. Costs were adjusted by the repetition rates.
Public costs: Federal and State Governments, and National University of Buenos
Aires, Centro, Córdoba, Cuyo and Litoral. Costs per student. Costs were adjusted by
repetition rates. Adjusted capital expenditures were included
Private benefits: Earnings. Earnings are adjusted by the probability of survival,
occupational level, and changes in productivity. Earnings are from wages, self-
employment and other utilities and benefits.
B. Buenos Aires
Kugler, B., & Psacharopoulos, G. (1989). Earnings and Education in Argentina: an
Analysis of the 1985 Buenos Aires Household Survey. Economics of Education Review, 8,
353-365.
Area: Buenos Aires Metropolitan Area, 1985
Theoretical model: Human capital
Sample: Encuesta Permanente de Hogares, April 1985. Men and women. Age: 14 to 65.
N=4,501. Individuals with positive earnings from labor or self-employment.
Type of rates of return: Average rates of return. Rates of return by level of education.
Private and social rates of return. Returns for dependent employment.
Levels of education: Primary, secondary and higher education .
Method for estimating rates of return: Mincer regression equation. Estimates for years of
schooling and for levels of education. Internal rate of return
58
FIEL. (1994). Educación y Mercado de Trabajo en la Argentina. In ADEBA (Ed.),
Desafíos y Opciones para Crecer. Actas y Documentos Técnicos (pp. 329-417). Buenos
Aires: ADEBA.
Area: Buenos Aires Metropolitan Area, 1993
Theoretical model: Does not specify (human capital)
Sample: Two samples:
(1) sample of firms not randomly selected, 1995. Does not specify the size of the sample.
Aggregates men and women
(2) Encuesta Permanente de Hogares, October 1993. Men 25-54 years old. N=not
specified. Individuals included in the sample: not specified. Types of earnings
considered: not specified.
Type of rates of return: Aggregated rate of return. Private. Rates of return by level of
education. Mean and marginal rates of return.
Levels of education: Secondary and higher education
Method for estimating rates of return: Mincer regression equation. Estimates for years of
schooling and for levels of education.
C. Córdoba
Giordano, O., & Montoya, S. (1989). Rentabilidad de la educación en Córdoba. Estudios,
Año XII. No. 50(Abril/Junio), 57-67.
Area: Cordoba Metropolitan Area, 1983
Theoretical model: Human capital
Sample: Encuesta Permanente de Hogares, October 1983. Men and women. Does not
specify age.
Type of rates of return: Private rates of return. Rates of return by level of education.
Levels of education: Secondary and higher education
Method for estimating rates of return: Mincer regression equation and Internal rate of
return
Private costs: Direct costs (transportation, school materials, clothes). Does not
specify how they estimated direct costs. Income foregone.
Public costs: State expenditures on education (does not specify if they include
Federal government). Capital expenditures were adjusted to account for the annual
cost of use of public good. Costs are adjusted by repetition and dropout rates.
Private benefits: Direct benefits, earnings differentials. Earnings are adjusted by
hours worked, the probability of being employed, and rates of survival.
59
D. Mendoza
Ferrá, C., & Claramount, A. M. (1985). Rentabilidad de la Educación Primaria y
Secundaria en Mendoza. Mendoza: Universidad Nacional de Cuyo, Facultad de Ciencias
Económicas.
Area: Mendoza Metropolitan Area (Gran Mendoza), 1980
Theoretical model: Does not specify
Sample: Encuesta Permanente de Hogares, October 1980. The sample aggregates men and
women. Age: 6 to 59. Estimations for 2 samples: (a) N=1,416; the sample includes
individuals who receive an income from wages or self-employment (individuals with other
sources of income are not included); (b) N=1,488; the sample includes individuals who
receive an income from wages or self-employment (individuals with other sources if income
such as utilities and benefits are included).
Type of rates of return: Mean rates of return (not marginal), private and social
Level of education: Primary and secondary
Method for estimating rates of return: Internal Rate of Return (traditional or direct method)
Private costs: Direct costs (transportation, school materials, clothes). Direct costs
were estimated by information provided by the mothers. Earnings foregone.
Public costs: Inputs provided by the school. Federal and State Governments, and
National University of Cuyo (expenditures on secondary schools belonging to the
university). Costs are adjusted by the probability of dropping-out a grade or course.
Private benefits: Earnings. Taxes are not discounted from the original data (it is
assumed that earnings declared in the EPH are net earnings). Health insurance and
retirement are discounted from the original data. Individuals are assumed to work 12
months, “Christmas gift” is included. Earnings are adjusted by the probability of
being alive for a given age and level of education.
60
E. Tucumán
FIEL, FBET, & Fundación Banco de Crédito Argentino. (1996). Educación y Mercado de
Trabajo en la Provincia de Tucumán. Buenos Aires: FIEL.
Area: Tucumán Metropolitan Area, 1995
Theoretical model: Human capital
Sample: Two samples:
(1) sample of 41 firms not randomly selected by the researchers, April-August 1995.
Sample of: 17 owners and managers, human resources’ managers, and 93 employees.
Aggregates men and women.
(2) Encuesta Permanente de Hogares, May 1995. Men 25-54 years old. N=not specified.
Individuals included in the sample: not specified. Types of earnings considered: not
specified.
Type of rates of return: Aggregated rate of return. Private. Rates of return by level of
education. Mean and marginal rates of return.
Levels of education: Secondary and higher education.
Method for estimating rates of return: Mincer regression equation. Estimates for years of
schooling and for levels of education.
F. Salta
del Rey, E. C., & Mena de Mendez, N. C. (1986). Rendimiento de la inversion en
educacion secundaria en Salta. In Universidad Nacional de Salta (Ed.), Anales de la
Asociacion Argentina de Economia Politica. XXI Reunion Anual (Vol. 2, pp. 509-529).
Salta: Universidad Nacional de Salta. Facultad de Ciencias Economicas, Juridicas y
Sociales.
Area: Salta Metropolitan Area, 1984
Theoretical model: Human capital
Sample: Encuesta Permanente de Hogares, April 1984. 13 to 45 years old. Aggregates men
and women
Type of rates of return: Private rates of return.
Levels of education: Secondary
Method for estimating rates of return: Internal rate of return.
Private costs: Direct costs (tuition, transportation, school materials, clothes).
Source: survey elaborated for the study. Income foregone. Costs are adjusted by
repetition and dropout rates.
Benefits: earnings with discounts.
61
Appendix 3
Methodological and Statistical Appendix
62
APPENDIX 3: Methodological and Statistical Appendix
A. Unit of analysis
The steps to determine the unit of analysis were the following:
1. From the INDEC’s sample, I selected those individuals aged 13 to 65. In 1980, men
in this age group represent 39.9% and women are only 18.3% of the INDEC’s
sample. In 1995, men from 13 to 65 years old are 37.7% and women are 20.8% of
the same sample.
2. For the 1980 dataset, individuals sub-sampled were those that declare having
income from wages or self-employment greater than zero. Men receiving wages
represent 10.5% and women 4.2% of the population sampled by INDEC. Men
having income from self-employment are 31.9% and women 14.9%.
3. For the 1995 dataset, individuals sub-sampled were those whose earnings per hour
are greater than zero. I excluded individuals who receive income from rents, or
interests when these are the only sources of income, but I included individuals with
income from rents and interests when they are combined with income from wage or
self-employment. Men whose earnings are greater than zero represent 41.48% and
women 22.55% from the INDEC’s sample.
4. For both years, individuals who did not know or did not answer regarding their level
of education attained or did not know or did not answer whether they completed a
given level were excluded from the sub-sample used in this study.
5. In 1980, 3 cases were excluded from the sub-sample. These cases were identified as
the lower extreme values in the distribution of the log annual income (dependent
variable for the Mincer regression equation, consult Variables’ section in this
Appendix). Cases excluded were 2 men, cases # 288 and 371, who received annual
income of $ 373.6, and 1 woman, case # 6153, who had an annual income of $
208.2 (Appendix Figure 1). However, being a extreme value was not the only
criteria used to exclude a case from the INDEC’s sample. In order to decide which
case to exclude, I ran an exploratory regression of annual total earnings (logged) on
education, experience, hours worked, and marital status for each sex. Cases
excluded were those identified as outliers and unusual influential cases, as measured
by Cooks’s distance. For cases # 288 and # 371, the Cook’s distance was .016,
greater than the size adjusted cutoff for men, .002 (n=2287). For case # 6153, the
Cook’s distance was .028, greater than the .004 cutoff for women (n=1131).
6. In 1995, 3 cases were also excluded from the sub-sample. One lower extreme case
in the distribution of log annual income was a man having an annual income of $
70.4 (case # 4023), and two upper extreme values, 2 women having income of $
81,994 and $ 115,926 annually (cases # 11414 and # 5729, respectively, Appendix
Figure 2). The same criteria for being an outlier in the exploratory regression and
being an unusual influential case were used. For case # 4023, the Cook’s distance
was .049, greater than the size-adjusted cutoff for men .002 (n=2072). For case
#11414, the Cook’s distance was .006 and for # 5729 it was .009, both greater than
the size adjusted cut off for women .003 (n=1264).
63
7. Other extreme values in the distribution of log annual income neither identified as
outliers in the exploratory regression nor as unusual influential cases as measured by
Cook’s distance are not excluded from the sub-sample.
8. The sub-sample used in this study is the same for the Mincer regression equation
and the direct method.
B. Variables
1. Annual Total Earnings
Several transformations are done to the original data (as reported by INDEC, EPH)
in order to obtain the values for annual total earnings. For 1980, the original variables used
are called “income from wages” and “income from self-employment,” both measured in
thousands of “pesos ley” per month. The data are transformed as follows:
1. I sum up both incomes (wages + self-employment) to obtain an aggregated monthly
income.
2. I multiply the individuals’ monthly income by the number of months worked during
the year.
3. I make several assumptions with respect to the number of weeks worked, varying
according to whether the individual attains schooling and the level of education
attained. First, if a person is not attending school when interviewed by the INDEC—
either never attended or dropped out from education—I assume the person either
works 12 month a year or works 11 months and has 1 month of paid vacation.
Second, if a person is going to primary or secondary education by the time of the
interview, I assume he or she works only during the 2 summer months. Third, if a
person is attaining higher education (university or non-university), I assume the
number of months worked is 3 (2 in the summer and 1 month during the winter).
For both the second and third assumptions, I consider individuals have no paid
vacations. I do not either consider any earnings from “Christmas’ gifts”. I assume
full-time students in every educational level, even though these assumptions might
not be accurate, particularly for higher education students. In consequence, annual
total earnings are sub-estimated.
4. Earnings are deflated using the Consumer Price Index to obtain values as in pesos
1995.
In the 1995 dataset, the original variable used is called “income per hour” and is
measured in “pesos”. I also use a variable called “total number of hours worked during the
week.” Data are transformed as follows:
1. I multiply the income per hour by the total hours worked by the individual during
the week, obtaining income per week. Because the number of hours worked during
the week includes hours worked in other occupations (for those persons who have
more than one job), I assume the income per hour in the other occupations is the
same as the income received in the main occupation.
64
2. Data for “total number of hours worked during the week” is originally presented by
intervals (for example, from 1 to 19 hours per week). I use the midpoint values to
obtain data in hours (for the same example, hours worked equals 10). For the
interval 62 and more hours per week, I assume that the maximum number of hours
possible to work during the week is 98; therefore, the midpoint is 80.
3. I multiply income per week by the number of weeks during the year to obtain annual
income. I follow the same assumptions as in 1980 with respect to the number of
weeks worked during the year.
For both years, because the distribution of annual total earnings is positively
skewed, I use the logarithm of annual total earnings as the dependent variable for the
Mincer regression equation (Appendix Figures 3 and 4).
3. Level of Education
The following transformations were done to the original data provided by the
INDEC in order to obtain the categories for level of education. In 1980, the original
variables used are called “level” and “finish studies?” The transformations are the
following:
1. I aggregate the variable “level” to obtain categories similar to the data for 1995. I
recode Primary as Primary Education, Secondary—National, Commercial,
“Normal” (teacher training), and other secondary—as Secondary Education,
Secondary Technical as Secondary Technical, Post-Secondary (non-university) and
University as Higher Education.
2. I combined the aggregated level of education (1) with the variable “finish
studies?”—yes or no—to obtain the following categories: Primary Incomplete and
Complete, Secondary Incomplete and Complete, Secondary Technical Incomplete
and Complete, and Higher Education Incomplete and Complete.
For the 1995 dataset, the original variable used is called “level” and is categorized
as (2).
For both years, those individuals having a given level incomplete, I recode them as
having completed the previous level. In other words, a person with secondary incomplete is
coded as having primary complete; and a person that responds having higher education
incomplete is coded as having completed secondary. Therefore, I assume that those
attending higher education or those that drop out from this level went to a regular secondary
education (not technical). Because I do not have data on costs for secondary technical
education (only aggregated as secondary education), for the Internal Rate of Return method
I code those with Secondary Technical Complete as Secondary (regular) Complete.
Table 3 shows how level of education is coded as a set of four dummy variables for
the Mincer equation. I coded the dummies in a way in which completion of higher levels of
education implies having completed the lower levels. For example, an individual with
higher education complete is coded as having higher education, secondary and primary
complete. Individuals who have not completed a certain level of education are coded as
having completed the previous level.
65
4. Years of Work Experience
For the 1980 dataset, the original variable “last year approved” varies from 1 to 8
grade or year in a given level of education. Because theoretically this variable should range
from 1 to 7—the maximum number of years possible to approve in a cycle are 7 (primary
education)—for those having 8 years approved I count as having 7. To obtain years of
schooling, for those having primary (variable “level”) and 1 year approved, I compute it as
having 2 years of schooling (1 for kindergarten and 1 for primary). This means that I add
one year of schooling to those declared by the individuals.
In the 1995 sample, I use the variable “level of education.” For those having a given
level incomplete, I count as having the previous level complete; therefore, I count the
number of years corresponding to that level. For example, if a person has secondary
incomplete, I consider he or she has 7 years of primary. For those attending higher
education, I assume they have completed a regular secondary education, not technical (5
years of schooling). However if a person declares having secondary technical complete, I do
count 6 years of schooling.
For both years, even though it was not compulsory, I assume all the individuals
attained kindergarten. To obtain years of schooling, I also add one year of schooling to those
declared by the individuals; therefore, I over-estimate the years of schooling a person has.
To obtain the values for years of work experience, I compute (age) – (years of schooling) -
4.
5. Marital Status
For both years, the original variable Marital Status is coded as Single, Married,
Separated or Divorced, and Widow. I code marital status as a dummy, 1 standing for not
married individuals (single, divorced, and widow) and 0 for married.
6. Mean Annual Public Costs per Student.
1980. Expenditures. For the 1980 rates of return, several transformation are made
to the source data to obtain public expenditures in education for the Buenos Aires
Metropolitan Area.
1. Source data on public expenditures for Ministry, State (Diéguez, Llach, &
Petrecolla, 1990a) and Municipality’s (V. de Flood, Harriague, Gasparini, & Vélez,
1994) authorities in all the Argentinean states were deflated to obtain data converted
to 1995 U.S. dollars. Because data on expenditures for Municipalities aggregates
primary and secondary education, I use the percentages of teachers in primary and in
secondary education to calculate the Municipality’s expenditures for each level.
Specifically, I calculate the percentage of teachers in Municipality’s primary
education over the number of teachers in Municipal primary and secondary
education and use that percentage (97.33%) to obtain Municipality’s expenditures
66
for primary education. The same criterion is used to compute Municipal
expenditures for secondary education (2.67%). In 1980, higher education was not
financed by municipalities. Deflated expenditures are presented in Appendix Table
1. Percentages come from Diéguez et al. (Diéguez et al., 1990a) , Table 2.2, p.22).
Note: because the data corresponds to the entire country, more transformations are
made to obtain expenditures for the Buenos Aires Metropolitan Area (see step 2).
2. Because data are aggregated at the country level, I use the percentages of teachers in
both the city and state of Buenos Aires over the total number of teachers in the
country to obtain desegregated expenditures. For example, I use the percentage of
teachers in primary education in the city of Buenos Aires (public and private) over
the total number of teachers in primary education in the country (public and private)
to obtain public expenditures on primary education for the city of Buenos Aires. The
same procedure is used to get expenditures for other levels of education and for the
state of Buenos Aires. For primary education, the percentages are 9.25% and
29.85% for the city and state of Buenos Aires, respectively. To calculate
expenditures for secondary education, I use percentages of 13.30% for the city and
33.52% for the state of Buenos Aires. For higher education, I use only percentages
of teachers in university level; the percentages being 27.37% and 23.34% for the
city and state of Buenos Aires, respectively. I use source data from Diéguez et al
(Diéguez, Llach, & Petrecolla, 1990b), Tables A.22, p. 25; A.23, p.26; and A.27,
p.27). Note: the Buenos Aires Metropolitan Area includes the city of Buenos Aires
and only 19 districts from the state of Buenos Aires; therefore, more transformations
are made to obtain accurate data on expenditures (see step 3).
3. In order to obtain estimated expenditures for the 19 districts from the state of
Buenos Aires belonging to the Buenos Aires Metropolitan Area, I use a coefficient
of distribution for each of the 19 districts. Such a coefficient indicates the
percentage each district receives when public state funds are distributed. The
coefficient for the 19 districts is 40.52%13
. I then use this percentage to calculate the
part of expenditures that belong to the 19 districts. Appendix Table 3 shows the
coefficient for each district.
4. Because Municipality’s expenditures for primary and secondary education are also
aggregated at the country level, I use the percentages corresponding to the state of
Buenos Aires for each level—step (2)—and then the ones corresponding to the 19
districts—step (3). I do not compute Municipality’s expenditures for the city of
Buenos Aires.
5. Expenditures for the city of Buenos Aires from step (2) and the ones for the 19
districts from steps (3) and (4) are added to obtain expenditures for the Buenos
Aires Metropolitan Area, 1980 (Appendix Table 3).
1980. Enrollments. Similar transformations are made to obtain data for enrollments
for 1980:
13
I use the same coefficient for 1980 and 1995, the source being Contaduría General de la Provincia de
Buenos Aires (1996). I assume the coefficient did not change between these two years.
67
1. Source data on enrollments (Diéguez et al., 1990b) are aggregated at the country
level (Appendix Table 4). Percentages of schools in both the city and state of
Buenos Aires are used to calculate percentages of enrollments for the two areas at
each level of education and authority—Ministry, State, and Municipalities. For
primary education, percentages used are 4.44% and 26.43% for the city and state of
Buenos Aires, respectively. For secondary education, I use 11.60% for the city of
Buenos Aires and 31.84% for the state. Percentages for higher education are
18.93% and 18.20% for both the city and state of Buenos Aires, respectively
(Diéguez et al., 1990c, Tables A.22, p.25; A.23, p. 26; and A.24, p. 27).
2. In order to obtain estimated enrollments for the 19 districts from the state of Buenos
Aires, I use the same coefficient of distribution for each of the 19 districts—
Expenditures, step (3), Appendix Table 2. I use this percentage to calculate the part
of the state enrollments corresponding to the 19 districts.
3. Enrollments for the city and state of Buenos Aires are added to obtain enrollments
for the Buenos Aires Metropolitan Area, 1980, for different authorities—Ministry,
State and Municipalities—and levels of education. Data on enrollments are
presented in Appendix Table 5.
1980. Public Costs per Student. Social expenditures in education (Appendix Table
3) are divided by the enrollments (Appendix Table 6) to obtain the mean social costs per
student by authority, and level of education for the Buenos Aires Metropolitan Area. Table
2 shows the costs per students used for the internal rate of return method.
1995. Mean Public Costs per Student. The following notes are important:
1. Costs per student for the Ministry authority are calculated as the average of the costs
per student for the 19 districts and for the city of Buenos Aires.
2. Costs per student for the 19 districts of the state of Buenos Aires are calculated by
dividing the expenditures for the 19 districts by enrollments for the 19 districts
(Appendix Table 6). Source data on expenditures for the state of Buenos Aires were
provided by the Program for the Study of Educational Costs, Ministry of Education
and the percentages for the 19 districts are calculated using the same procedures and
coefficient explained previously. Data on enrollments come from Secretaría de
Programación y Evaluación Educativa (1996). I also calculated the percentages for
the 19 districts (Appendix Table 6).
3. Source data on costs per student for the city of Buenos Aires come from the
Program for the Study of Educational Costs, Ministry of Education. No
transformations are necessary (Table 7).
4. I calculate total costs per student as the average of the Ministry, 19 districts, and the
city of Buenos Aires’ costs per student (Table 7).
5. Mean private costs per student for the state of Buenos Aires were provided by the
Program for the Study of Educational Costs, Ministry of Education. Because I
cannot compute the mean costs for the 19 districts as a percentage of the state’s
cost, I assume the average private costs per student for the 19 districts is the same as
the average for the state (Table 7). The Program also provided private costs for the
city of Buenos Aires for the Study of Educational Costs.
68
6. Total private costs are calculated as the average of 19 districts and the city of
Buenos Aires’ private costs (Table 7).
7. Mean total social costs are the average of the private and public costs per student
(Table 7).
C. Limitations of the Analysis
1. Autocorrelation
1980. Men. Durbin-Watson d = 1.903, sample size 2,283 and 8 independent
variables. Upper critical value for d is 1.65 at p = .01.
1980. Women. Durbin-Watson d = 1.865, sample size 1,130 and 7 independent
variables. Upper critical value for d is 1.65 at p = .01.
1995. Men. Durbin-Watson d = 1.952, sample size 2,071 and 8 independent
variables. Upper critical value for d is 1.65 at p = .01.
1995. Women. Durbin-Watson d = 1.904, sample size 1,262 and 8 independent
variables. Upper critical value for d is 1.65 at p = .01.
2. Multicollinearity
1980. Men. The lowest tolerance values are found for the variables Years of Work
Experience (tolerance = .0414) and Years of Work Experience Square (tolerance = .0478).
This means that only about 4% of the variation in experience and experience square is
independent of the other variables. Because Years of Work Experience Square is
introduced to correct for the non-linear relationship between experience and earnings, these
two variables are collinear, and therefore, the tolerance values are low. Separate effects for
Years of Work Experience and Years of Work Experience Square cannot be generalized
beyond the sample for this study. However, t-tests are still valid. Tolerance values for the
other variables are above .62.
1980. Women. Lowest tolerance values are .0523 for Years of Work Experience
and .0587 for Years of Work Experience Square. Tolerance values for other variables are
above .47.
1995. Men. Tolerance value for Years of Work Experience = .0475 and for Years of
Work Experience Square = .0523. Tolerance values for other variables are above .63.
1995. Women. Tolerance for Years of Work Experience = .0611 and for Years of
Work Experience Square = .0617. Tolerance values for other variables are above .64.
69
Appendix 4
Appendix Tables
70
APPENDIX 4: Appendix Tables.
Appendix Table 1. Total Expenditures in Education, by Year, Authority, and Level of
Education: Argentina, 1980 (in US$ 1995 dollars).
1980
Level of education Ministry State Municipality (1)
Total
Primary 239,292,308 3,949,876,923 230,013,411 4,419,182,642
Secondary 2,159,846,154 581,138,462 6,315,280 2,747,299,896
Higher education 1,354,953,846 65,261,538 … 1,420,215,385
Sources: For 1980 Ministry and State's expenditures, Diéguez et al. (1990, Tables 4.13, p. 129; and
4.14, p. 131). For 1980 Municipalities' expenditures, V. de Flood et al. (1994, Table GP5, p. 62). For 1995
Ministry's expenditures, V. de Flood, et al. (1994, Table GN1, p. 44). For 1995 State of Buenos Aires'
expenditures, information provided by Programa Estudio de Costos del Sistema Educativo, Ministry of
Education. (1)
I use data from Diéguez et al. (1990) to calculate percentages for primary and secondary
education, Table 2.2 (p. 22).
71
Appendix Table 2. Coefficients for the distribution of public state funds between 19
Buenos Aires' districts Buenos Aires, 1996.
Almirante Brown 2.38507
Avellaneda 1.79918
Berazategui 1.4506
Esteban Echeverría 1.42132
Florencio Varela 2.33206
General San Martín 2.21995
General Sarmiento
La Matanza 6.44869
Lanús 2.03238
Lomas de Zamora 2.63273
Merlo 2.93822
Moreno 2.28221
Morón 1.75595
Quilmes 2.63637
San Fernando 1.00832
San Isidro 2.04932
Tigre 1.33649
Tres de Febrero 1.33027
Vicente López 2.46133
Total for 19 districts 40.52046
Source: Contaduría General de la Provincia de Buenos Aires (1996), p. 93.
Appendix Table 3. Public Expenditures in Education, by Year, Authority, and Level of
Education: Buenos Aires Metropolitan Area, 1980 and 1995 (in US$ dollars 1995).
1980
Level of education Ministry State Municipality Total
Primary 51,076,386 843,092,035 27,819,584 921,988,005
Secondary 580,640,952 156,230,012 857,679 737,728,643
Higher education 499,058,703 24,037,231 … 523,095,934
Source: Based on Appendix Table 1.
72
Appendix Table 4. Total Enrollments in Education, by Year, Authority, and Level of
Education. Argentina, 1980.
Authority
Level of education Ministry State Municipality Total
Primary 138,520 3,106,723 154,574 3,399,817
Secondary 600,564 312,694 5,816 919,074
Higher education 360,991 26,529 … 387,520
Source: Diéguez, et al. (1990b, Table 3.1, p. 56).
Appendix Table 5. Enrollments in Public Education, by Year, Authority, and Level of
Education. Buenos Aires Metropolitan Area, 1980.
1980
Level of education Ministry State Municipality Total
Primary 20,993 470,831 16,556 508,380
Secondary 147,175 76,629 750 224,555
Higher education 94,971 6,979 … 101,950
Source: Based on Appendix Table 4.
73
Appendix Table 6. Public Expenditures, Enrollments, and Costs per Student, by Level of
Education. State of Buenos Aires and 19 districts, 1995.
Expenditures Enrollments Costs per
Level of State of 19 Districts State of 19 Districts Student
Education Buenos Aires 40.52% Buenos Aires 40.52% 19 Districts
Primary 672,263,225 272,404,420 1,264,084 512,213 532
Secondary 604,927,848 245,119,789 517,017 209,498 1,170
Higher education 91,383,560 37,029,075 944,868 382,865 97
Source: For expenditures, data provided by Programa Estudio de Costos del Sistema Educativo,
Ministry of Education. For enrollments, Secretaría de Programación y Evaluación Educativa (1996, Table
A.2.1., p. 29). Note: Enrollments on higher education include only non-university education.
74
Appendix Table 7. OLS Coefficients for the Regression of Annual Total Earnings
(Logged) on Education, Experience, Hours Worked, and Marital Status by Year. Buenos
Aires Metropolitan Area.
1980 1995
Independent variables Model 1 Model 2 Model 1 Model 2
Intercept 3.344 *** 3.445 *** 3.023 *** 3.102 ***
( 0.03) ( 0.04) ( 0.03) ( 0.04)
Level of education (1) (2)
Primary complete 0.102 *** 0.107 *** 0.098 *** 0.116 ***
( 0.01) ( 0.01) ( 0.02) ( 0.03)
Secondary complete 0.115 *** 0.122 *** 0.114 *** 0.097 ***
( 0.02) ( 0.02) ( 0.01) ( 0.02)
Secondary technical complete 0.287 *** 0.285 *** 0.218 *** 0.228 ***
( 0.04) ( 0.04) ( 0.03) ( 0.03)
Higher education complete 0.343 *** 0.333 *** 0.358 *** 0.390 ***
( 0.03) ( 0.04) ( 0.02) ( 0.02)
Control variables ***
Years of work experience 0.022 *** 0.021 *** 0.027 *** 0.026 ***
( 0.00) ( 0.00) ( 0.00) ( 0.00)
Years of work experience square 0.000 *** 0.000 *** 0.000 *** 0.000 ***
( 0.00) ( 0.00) ( 0.00) ( 0.00)
Hours worked per week 0.006 *** 0.004 *** 0.007 *** 0.006 ***
( 0.00) ( 0.00) ( 0.00) ( 0.00)
Marital status (not -0.044 *** -0.094 *** -0.059 *** -0.120 ***
married=1) ( 0.01) ( 0.02) ( 0.01) ( 0.02)
Sex (female=1) -0.172 *** -0.352 *** -0.086 *** -0.205 ***
( 0.01) ( 0.06) ( 0.01) ( 0.06)
Female*primary … -0.022 … -0.059
( 0.03) ( 0.04)
Female*secondary … -0.034 … 0.032
( 0.04) ( 0.03)
Female*secondary … N/A … -0.105
technical ( 0.07)
Female*higher education … 0.032 … -0.052
( 0.06) ( 0.03)
Female*experience … 0.003 … 0.002
( 0.00) ( 0.00)
Female*experience … 0.000 … 0.000
squared ( 0.00) ( 0.00)
Female*hours worked … 0.003 *** … 0.002 ***
( 0.00) ( 0.00)
75
Female*not married … 0.111 *** … 0.118 ***
( 0.03) ( 0.02)
R-square 0.392 0.404 0.419 0.428
Adjusted R-square 0.389 0.400 0.417 0.425
F-test model 1/model 2 (3)
10.269 *** 6.309 ***
Degrees of freedom 9 7 16 9 8 17
Number of cases 3414 3414 3333 3333 3333
Source: Encuesta Permanente de Hogares (1980 and 1995). Note: Standard errors of coefficients in
parentheses. N/A: parameter not estimated because there are only 11 individuals out of 3462. (1)
Reference
category: primary incomplete. (2)
Dummy variables. See Table 3 for methodology for coding dummy
variables. (3)
For 1980, df1=7 and df2=3397. For 1995, df1=8 and df2=3315. * p<.05 ** p<.01 *** p<.001
(one-tailed tests).
76
Appendix Table 8. Mean Annual Total Earnings by Level of Education and Age, for Men.
Buenos Aires Metropolitan Area, 1980 (in 1995 US$ dollars) (1)
Higher
Age Primary Secondary Education
Incomplete Complete Complete Complete
13 2,646 5,160 0 0
14 3,211 5,318 0 0
15 3,775 5,476 0 0
16 4,339 5,635 0 0
17 5,214 5,793 0 0
18 6,089 6,309 7,471 0
19 6,964 7,185 7,840 0
20 7,840 7,701 8,209 0
21 8,715 8,218 8,577 0
22 8,861 8,803 9,202 0
23 9,008 9,388 9,828 11,207
24 9,155 9,973 10,453 13,684
25 9,302 10,559 11,078 16,161
26 9,449 11,144 11,703 18,639
27 9,494 11,372 13,197 20,705
28 9,540 11,601 14,692 22,771
29 9,585 11,829 16,186 24,838
30 9,631 12,058 17,680 26,904
31 9,676 12,287 19,175 28,971
32 9,677 12,548 19,732 30,216
33 9,678 12,809 20,289 31,461
34 9,679 13,070 20,847 32,706
35 9,679 13,331 21,404 33,952
36 9,680 13,592 21,961 35,197
37 9,681 13,853 22,519 36,442
38 9,682 14,114 23,076 37,687
39 9,682 14,375 23,634 38,932
40 9,683 14,636 24,191 40,178
41 9,763 14,540 24,305 40,641
42 9,843 14,444 24,419 41,105
43 9,922 14,347 24,533 41,568
44 10,002 14,251 24,647 42,032
45 10,082 14,155 24,761 42,495
46 10,162 14,059 24,874 42,959
47 10,241 13,963 24,988 43,422
48 10,321 13,867 25,102 43,886
49 10,401 13,771 25,216 44,349
77
50 10,481 13,675 25,330 44,813
51 10,515 13,666 25,062 44,474
52 10,550 13,657 24,794 44,134
53 10,585 13,649 24,526 43,795
54 10,619 13,640 24,258 43,456
55 10,654 13,631 23,990 43,117
56 10,689 13,622 23,722 42,777
57 10,723 13,614 23,455 42,438
58 10,758 13,605 23,187 42,099
59 10,793 13,596 22,919 41,760
60 10,828 13,587 22,651 41,420
61 10,665 13,439 22,067 40,469
62 10,502 13,290 21,483 39,519
63 10,339 13,142 20,899 38,568
64 10,177 12,993 20,315 37,617
65 10,014 12,844 19,731 36,666
Source: Encuesta Permanente de Hogares (1980). (1)
Mean annual total earnings for the age group in
bold.
78
Appendix Table 9. Mean Annual Total Earnings by Level of Education and Age, for
Women. Buenos Aires Metropolitan Area, 1980 (in 1995 US$ dollars) (1)
Higher
Age Primary Secondary Education
Incomplete Complete Complete Complete
13 2,458 4,254 0 0
14 3,073 4,349 0 0
15 3,687 4,445 0 0
16 4,302 4,540 0 0
17 4,607 4,635 0 0
18 4,911 4,835 6,552 0
19 5,216 5,140 6,678 0
20 5,520 5,340 6,804 0
21 5,825 5,540 6,930 0
22 5,743 5,904 7,189 0
23 5,662 6,268 7,449 8,440
24 5,580 6,632 7,708 9,768
25 5,498 6,996 7,967 11,096
26 5,417 7,360 8,227 12,425
27 5,372 7,272 8,729 14,185
28 5,328 7,185 9,231 15,945
29 5,283 7,097 9,733 17,705
30 5,239 7,009 10,235 19,466
31 5,194 6,922 10,737 21,226
32 5,276 7,024 10,788 20,967
33 5,358 7,126 10,840 20,709
34 5,440 7,228 10,892 20,451
35 5,522 7,330 10,943 20,192
36 5,604 7,431 10,995 19,934
37 5,687 7,533 11,046 19,676
38 5,769 7,635 11,098 19,418
39 5,851 7,737 11,149 19,159
40 5,933 7,839 11,201 18,901
41 5,968 7,998 11,394 19,558
42 6,003 8,157 11,586 20,215
43 6,038 8,316 11,779 20,873
44 6,073 8,475 11,972 21,530
45 6,108 8,634 12,165 22,187
46 6,143 8,793 12,358 22,844
47 6,178 8,952 12,551 23,501
48 6,214 9,111 12,744 24,159
79
49 6,249 9,270 12,937 24,816
50 6,284 9,429 13,129 25,473
51 6,232 9,233 12,904 23,795
52 6,181 9,037 12,678 22,117
53 6,129 8,841 12,452 20,439
54 6,078 8,645 12,226 18,762
55 6,026 8,449 12,001 17,084
56 5,975 8,253 11,775 15,406
57 5,923 8,057 11,549 13,728
58 5,871 7,861 11,323 12,050
59 5,820 7,665 11,098 10,372
60 5,768 7,469 10,872 8,694
61 5,665 7,469 10,872 7,017
62 5,562 7,367 11,155 5,339
63 5,459 7,266 11,437 3,661
64 5,356 7,164 11,720 1,983
65 5,253 7,063 12,003 305
Source: Encuesta Permanente de Hogares (1980). (1)
Mean annual total earnings for the
age group in bold.
80
Appendix Table 10. Mean Annual Total Earnings by Level of Education and Age, for
Men. Buenos Aires Metropolitan Area, 1995 (in US$ dollars) (1)
Higher
Age Primary Secondary Education
Incomplete Complete Complete Complete
13 1,819 1,860 0 0
14 2,159 2,373 0 0
15 2,500 2,885 0 0
16 2,840 3,398 0 0
17 3,401 3,910 0 0
18 3,961 4,423 3,306 0
19 4,521 4,935 3,652 0
20 5,081 5,448 3,997 0
21 5,642 5,960 4,343 0
22 5,701 6,061 4,783 0
23 5,760 6,163 5,223 12,718
24 5,819 6,264 5,662 13,474
25 5,879 6,366 6,102 14,231
26 5,938 6,467 6,542 14,987
27 6,205 6,832 7,414 16,200
28 6,472 7,197 8,287 17,413
29 6,739 7,562 9,159 18,626
30 7,006 7,927 10,032 19,839
31 7,273 8,292 10,904 21,051
32 7,190 8,449 11,356 21,554
33 7,106 8,606 11,808 22,057
34 7,022 8,763 12,260 22,559
35 6,939 8,919 12,711 23,062
36 6,855 9,076 13,163 23,564
37 6,771 9,233 13,615 24,067
38 6,687 9,390 14,067 24,569
39 6,604 9,547 14,519 25,072
40 6,520 9,704 14,971 25,574
41 6,484 9,621 14,987 25,850
42 6,448 9,538 15,003 26,126
43 6,412 9,455 15,020 26,402
44 6,376 9,372 15,036 26,677
45 6,340 9,289 15,052 26,953
46 6,304 9,206 15,069 27,229
47 6,268 9,122 15,085 27,505
48 6,232 9,039 15,101 27,780
81
49 6,196 8,956 15,118 28,056
50 6,160 8,873 15,134 28,332
51 6,137 8,865 14,839 28,231
52 6,114 8,856 14,544 28,131
53 6,091 8,848 14,249 28,030
54 6,068 8,839 13,954 27,929
55 6,045 8,831 13,659 27,829
56 6,021 8,822 13,364 27,728
57 5,998 8,814 13,069 27,628
58 5,975 8,806 12,774 27,527
59 5,952 8,797 12,479 27,426
60 5,929 8,789 12,184 27,326
61 5,762 8,561 11,896 26,104
62 5,595 8,333 11,615 24,883
63 5,429 8,106 11,341 23,661
64 5,262 7,878 11,073 22,439
65 5,095 7,650 10,811 21,218
Source: Encuesta Permanente de Hogares (1995). (1)
Mean annual total earnings for the age group in
bold.
82
Appendix Table 11. Mean Annual Total Earnings by Level of Education and Age, for
Women. Buenos Aires Metropolitan Area, 1995 (in US$ dollars) (1)
Higher
Age Primary Secondary Education
Incomplete Complete Complete Complete
13 3,863 886 0 0
14 3,864 1,375 0 0
15 3,865 1,864 0 0
16 3,865 2,353 0 0
17 3,866 2,842 0 0
18 3,867 3,331 2,870 0
19 3,867 3,820 3,124 0
20 3,868 4,310 3,379 0
21 3,869 4,799 3,634 0
22 3,823 4,943 3,889 0
23 3,778 5,086 4,144 7,090
24 3,732 5,230 4,399 7,807
25 3,687 5,374 4,654 8,524
26 3,641 5,518 4,909 9,241
27 3,784 5,419 5,467 9,621
28 3,927 5,320 6,025 10,000
29 4,070 5,221 6,584 10,380
30 4,213 5,121 7,142 10,759
31 4,356 5,022 7,700 11,139
32 4,311 5,081 7,757 11,523
33 4,267 5,139 7,813 11,908
34 4,222 5,198 7,870 12,293
35 4,178 5,256 7,927 12,678
36 4,133 5,315 7,984 13,063
37 4,089 5,374 8,041 13,448
38 4,044 5,432 8,097 13,833
39 4,000 5,491 8,154 14,218
40 3,955 5,549 8,211 14,603
41 4,031 5,642 8,525 14,435
42 4,107 5,734 8,839 14,268
43 4,183 5,827 9,154 14,100
44 4,259 5,920 9,468 13,932
45 4,335 6,012 9,782 13,765
46 4,411 6,105 10,097 13,597
47 4,487 6,197 10,411 13,429
48 4,563 6,290 10,725 13,262
83
49 4,639 6,383 11,040 13,094
50 4,715 6,475 11,354 12,927
51 4,672 6,356 10,920 13,020
52 4,628 6,237 10,487 13,113
53 4,585 6,117 10,053 13,207
54 4,541 5,998 9,620 13,300
55 4,498 5,879 9,186 13,393
56 4,454 5,759 8,753 13,487
57 4,411 5,640 8,319 13,580
58 4,367 5,521 7,885 13,673
59 4,324 5,401 7,452 13,767
60 4,280 5,282 7,018 13,860
61 4,258 5,153 8,088 13,288
62 4,236 5,024 9,157 12,717
63 4,214 4,895 10,226 12,145
64 4,193 4,765 11,295 11,573
65 4,171 4,636 12,365 11,001
Source: Encuesta Permanente de Hogares (1995). (1) Mean annual total earnings for the age group
in bold.
84
Appendix Table 12. Costs and Benefits for an Additional Level of Education Completed
for Men. Buenos Aires Metropolitan Area, 1980 (in 1995 US$ dollars).
Secondary complete (vs. primary complete)
Social
Age Earnings Earnings Private Private Public total
differential foregone costs benefits costs benefits
13 (5,160) (5,160) 0 (5,160) (3,285) (8,445)
14 (5,318) (5,318) 0 (5,318) (3,285) (8,603)
15 (5,476) (5,476) 0 (5,476) (3,285) (8,762)
16 (5,635) (5,635) 0 (5,635) (3,285) (8,920)
17 (5,793) (5,793) 0 (5,793) (3,285) (9,078)
18 1,162 1,162 1,162
19 655 655 655
20 507 507 507
21 359 359 359
22 399 399 399
23 439 439 439
24 479 479 479
25 519 519 519
26 559 559 559
27 1,825 1,825 1,825
28 3,091 3,091 3,091
29 4,357 4,357 4,357
30 5,622 5,622 5,622
31 6,888 6,888 6,888
32 7,184 7,184 7,184
33 7,481 7,481 7,481
34 7,777 7,777 7,777
35 8,074 8,074 8,074
36 8,370 8,370 8,370
37 8,666 8,666 8,666
38 8,963 8,963 8,963
39 9,259 9,259 9,259
40 9,555 9,555 9,555
41 9,765 9,765 9,765
42 9,975 9,975 9,975
43 10,185 10,185 10,185
44 10,395 10,395 10,395
45 10,605 10,605 10,605
46 10,815 10,815 10,815
47 11,025 11,025 11,025
48 11,235 11,235 11,235
85
49 11,445 11,445 11,445
50 11,655 11,655 11,655
51 11,396 11,396 11,396
52 11,137 11,137 11,137
53 10,878 10,878 10,878
54 10,618 10,618 10,618
55 10,359 10,359 10,359
56 10,100 10,100 10,100
57 9,841 9,841 9,841
58 9,582 9,582 9,582
59 9,323 9,323 9,323
60 9,064 9,064 9,064
61 8,628 8,628 8,628
62 8,193 8,193 8,193
63 7,758 7,758 7,758
64 7,322 7,322 7,322
65 6,887 6,887 6,887
Internal Rate of Return 10.0% 7.7%
Source: For earnings, Encuesta Permanente de Hogares (1980). For costs, Table 7.
86
Appendix Table 13. Costs and Benefits for an Additional Level of Education Completed
for Men. Buenos Aires Metropolitan Area, 1980 (in 1995 US$ dollars).
Higher education complete (vs. secondary complete)
Social
Age Earnings Earnings Private Private Public total
differential foregone costs benefits costs benefits
13 0 0 0 0 0 0
14 0 0 0 0 0 0
15 0 0 0 0 0 0
16 0 0 0 0 0 0
17 0 0 0 0 0 0
18 (7,471) (7,471) 0 (7,471) (5,131) (12,602)
19 (7,840) (7,840) 0 (7,840) (5,131) (12,971)
20 (8,209) (8,209) 0 (8,209) (5,131) (13,340)
21 (8,577) (8,577) 0 (8,577) (5,131) (13,708)
22 (9,202) (9,202) 0 (9,202) (5,131) (14,333)
23 1,379 1,379 1,379
24 3,231 3,231 3,231
25 5,083 5,083 5,083
26 6,936 6,936 6,936
27 7,508 7,508 7,508
28 8,080 8,080 8,080
29 8,652 8,652 8,652
30 9,224 9,224 9,224
31 9,796 9,796 9,796
32 10,484 10,484 10,484
33 11,172 11,172 11,172
34 11,860 11,860 11,860
35 12,547 12,547 12,547
36 13,235 13,235 13,235
37 13,923 13,923 13,923
38 14,611 14,611 14,611
39 15,299 15,299 15,299
40 15,987 15,987 15,987
41 16,336 16,336 16,336
42 16,686 16,686 16,686
43 17,035 17,035 17,035
44 17,385 17,385 17,385
45 17,735 17,735 17,735
46 18,084 18,084 18,084
47 18,434 18,434 18,434
48 18,784 18,784 18,784
87
49 19,133 19,133 19,133
50 19,483 19,483 19,483
51 19,412 19,412 19,412
52 19,340 19,340 19,340
53 19,269 19,269 19,269
54 19,198 19,198 19,198
55 19,126 19,126 19,126
56 19,055 19,055 19,055
57 18,984 18,984 18,984
58 18,912 18,912 18,912
59 18,841 18,841 18,841
60 18,769 18,769 18,769
61 18,403 18,403 18,403
62 18,036 18,036 18,036
63 17,669 17,669 17,669
64 17,302 17,302 17,302
65 16,935 16,935 16,935
Internal Rate of Return 14.8% 11.1%
Source: For earnings, Encuesta Permanente de Hogares (1980). For costs, Table 7.
88
Appendix Table 14. Costs and Benefits for an Additional Level of Education Completed
for Men. Buenos Aires Metropolitan Area, 1980 (in 1995 US$ dollars).
Higher education complete (vs. primary complete)
Social
Age Earnings Earnings Private Private Public total
differential foregone costs benefits costs benefits
13 (5,160) (5,160) 0 (5,160) (3,285) (8,445)
14 (5,318) (5,318) 0 (5,318) (3,285) (8,603)
15 (5,476) (5,476) 0 (5,476) (3,285) (8,761)
16 (5,635) (5,635) 0 (5,635) (3,285) (8,920)
17 (5,793) (5,793) 0 (5,793) (3,285) (9,078)
18 (6,309) (6,309) 0 (6,309) (5,131) (11,440)
19 (7,185) (7,185) 0 (7,185) (5,131) (12,316)
20 (7,701) (7,701) 0 (7,701) (5,131) (12,832)
21 (8,218) (8,218) 0 (8,218) (5,131) (13,349)
22 (8,803) (8,803) 0 (8,803) (5,131) (13,934)
23 1,818 1,818 1,818
24 3,711 3,711 3,711
25 5,603 5,603 5,603
26 7,495 7,495 7,495
27 9,333 9,333 9,333
28 11,171 11,171 11,171
29 13,008 13,008 13,008
30 14,846 14,846 14,846
31 16,684 16,684 16,684
32 17,668 17,668 17,668
33 18,652 18,652 18,652
34 19,637 19,637 19,637
35 20,621 20,621 20,621
36 21,605 21,605 21,605
37 22,589 22,589 22,589
38 23,574 23,574 23,574
39 24,558 24,558 24,558
40 25,542 25,542 25,542
41 26,102 26,102 26,102
42 26,661 26,661 26,661
43 27,221 27,221 27,221
44 27,780 27,780 27,780
45 28,340 28,340 28,340
46 28,900 28,900 28,900
47 29,459 29,459 29,459
48 30,019 30,019 30,019
89
49 30,578 30,578 30,578
50 31,138 31,138 31,138
51 30,807 30,807 30,807
52 30,477 30,477 30,477
53 30,146 30,146 30,146
54 29,816 29,816 29,816
55 29,486 29,486 29,486
56 29,155 29,155 29,155
57 28,825 28,825 28,825
58 28,494 28,494 28,494
59 28,164 28,164 28,164
60 27,833 27,833 27,833
61 27,031 27,031 27,031
62 26,229 26,229 26,229
63 25,426 25,426 25,426
64 24,624 24,624 24,624
65 23,822 23,822 23,822
Internal Rate of Return 12.1% 9.3%
Source: For earnings, Encuesta Permanente de Hogares (1980). For costs, Table 7.
90
Appendix Table 15. Costs and Benefits for an Additional Level of Education Completed
for Women. Buenos Aires Metropolitan Area, 1980 (in 1995 US$ dollars).
Secondary complete (vs. primary complete)
Social
Age Earnings Earnings Private Private Public total
differential foregone costs benefits costs benefits
13 (4,254) (4,254) 0 (4,254) (3,285) (7,539)
14 (4,349) (4,349) 0 (4,349) (3,285) (7,634)
15 (4,445) (4,445) 0 (4,445) (3,285) (7,730)
16 (4,540) (4,540) 0 (4,540) (3,285) (7,825)
17 (4,635) (4,635) 0 (4,635) (3,285) (7,921)
18 1,716 1,716 1,716
19 1,538 1,538 1,538
20 1,464 1,464 1,464
21 1,390 1,390 1,390
22 1,285 1,285 1,285
23 1,181 1,181 1,181
24 1,076 1,076 1,076
25 972 972 972
26 867 867 867
27 1,457 1,457 1,457
28 2,046 2,046 2,046
29 2,636 2,636 2,636
30 3,226 3,226 3,226
31 3,815 3,815 3,815
32 3,765 3,765 3,765
33 3,714 3,714 3,714
34 3,664 3,664 3,664
35 3,614 3,614 3,614
36 3,563 3,563 3,563
37 3,513 3,513 3,513
38 3,462 3,462 3,462
39 3,412 3,412 3,412
40 3,362 3,362 3,362
41 3,395 3,395 3,395
42 3,429 3,429 3,429
43 3,463 3,463 3,463
44 3,497 3,497 3,497
45 3,531 3,531 3,531
46 3,565 3,565 3,565
47 3,599 3,599 3,599
48 3,633 3,633 3,633
91
49 3,667 3,667 3,667
50 3,701 3,701 3,701
51 3,671 3,671 3,671
52 3,641 3,641 3,641
53 3,611 3,611 3,611
54 3,582 3,582 3,582
55 3,552 3,552 3,552
56 3,522 3,522 3,522
57 3,492 3,492 3,492
58 3,463 3,463 3,463
59 3,433 3,433 3,433
60 3,403 3,403 3,403
61 3,403 3,403 3,403
62 3,787 3,787 3,787
63 4,172 4,172 4,172
64 4,556 4,556 4,556
65 4,940 4,940 4,940
Internal Rate of Return 8.3% 5.4%
Source: For earnings, Encuesta Permanente de Hogares (1980). For costs, Table 7.
92
Appendix Table 16. Costs and Benefits for an Additional Level of Education Completed
for Women. Buenos Aires Metropolitan Area, 1980 (in 1995 US$ dollars).
Higher education complete (vs. secondary complete)
Social
Age Earnings Earnings Private Private Public total
differential foregone costs benefits costs benefits
13 0 0 0 0 0 0
14 0 0 0 0 0 0
15 0 0 0 0 0 0
16 0 0 0 0 0 0
17 0 0 0 0 0 0
18 (6,552) (6,552) 0 (6,552) (5,131) (11,683)
19 (6,678) (6,678) 0 (6,678) (5,131) (11,809)
20 (6,804) (6,804) 0 (6,804) (5,131) (11,935)
21 (6,930) (6,930) 0 (6,930) (5,131) (12,061)
22 (7,189) (7,189) 0 (7,189) (5,131) (12,320)
23 991 991 991
24 2,060 2,060 2,060
25 3,129 3,129 3,129
26 4,198 4,198 4,198
27 5,456 5,456 5,456
28 6,714 6,714 6,714
29 7,972 7,972 7,972
30 9,231 9,231 9,231
31 10,489 10,489 10,489
32 10,179 10,179 10,179
33 9,869 9,869 9,869
34 9,559 9,559 9,559
35 9,249 9,249 9,249
36 8,940 8,940 8,940
37 8,630 8,630 8,630
38 8,320 8,320 8,320
39 8,010 8,010 8,010
40 7,700 7,700 7,700
41 8,165 8,165 8,165
42 8,629 8,629 8,629
43 9,093 9,093 9,093
44 9,558 9,558 9,558
45 10,022 10,022 10,022
46 10,486 10,486 10,486
47 10,951 10,951 10,951
48 11,415 11,415 11,415
93
49 11,879 11,879 11,879
50 12,344 12,344 12,344
51 10,891 10,891 10,891
52 9,439 9,439 9,439
53 7,987 7,987 7,987
54 6,535 6,535 6,535
55 5,083 5,083 5,083
56 3,631 3,631 3,631
57 2,179 2,179 2,179
58 727 727 727
59 (725) (725) (725)
60 (2,177) (2,177) (2,177)
61 (3,855) (3,855) (3,855)
62 (5,816) (5,816) (5,816)
63 (7,777) (7,777) (7,777)
64 (9,737) (9,737) (9,737)
65 (11,698) (11,698) (11,698)
Internal Rate of Return 13.4% 8.9%
Source: For earnings, Encuesta Permanente de Hogares (1980). For costs, Table 7.
94
Appendix Table 17. Costs and Benefits for an Additional Level of Education Completed
for Women. Buenos Aires Metropolitan Area, 1980 (in 1995 US$ dollars).
Higher education complete (vs. primary complete)
Social
Age Earnings Earnings Private Private Public total
differential foregone costs benefits costs benefits
13 (4,254) (4,254) 0 (4,254) (3,285) (7,539)
14 (4,349) (4,349) 0 (4,349) (3,285) (7,634)
15 (4,445) (4,445) 0 (4,445) (3,285) (7,730)
16 (4,540) (4,540) 0 (4,540) (3,285) (7,825)
17 (4,635) (4,635) 0 (4,635) (3,285) (7,920)
18 (4,835) (4,835) 0 (4,835) (5,131) (9,966)
19 (5,140) (5,140) 0 (5,140) (5,131) (10,271)
20 (5,340) (5,340) 0 (5,340) (5,131) (10,471)
21 (5,540) (5,540) 0 (5,540) (5,131) (10,671)
22 (5,904) (5,904) 0 (5,904) (5,131) (11,035)
23 2,172 2,172 2,172
24 3,136 3,136 3,136
25 4,101 4,101 4,101
26 5,065 5,065 5,065
27 6,913 6,913 6,913
28 8,760 8,760 8,760
29 10,608 10,608 10,608
30 12,456 12,456 12,456
31 14,304 14,304 14,304
32 13,944 13,944 13,944
33 13,583 13,583 13,583
34 13,223 13,223 13,223
35 12,863 12,863 12,863
36 12,503 12,503 12,503
37 12,143 12,143 12,143
38 11,782 11,782 11,782
39 11,422 11,422 11,422
40 11,062 11,062 11,062
41 11,560 11,560 11,560
42 12,058 12,058 12,058
43 12,556 12,556 12,556
44 13,055 13,055 13,055
45 13,553 13,553 13,553
46 14,051 14,051 14,051
47 14,549 14,549 14,549
48 15,048 15,048 15,048
95
49 15,546 15,546 15,546
50 16,044 16,044 16,044
51 14,562 14,562 14,562
52 13,080 13,080 13,080
53 11,599 11,599 11,599
54 10,117 10,117 10,117
55 8,635 8,635 8,635
56 7,153 7,153 7,153
57 5,671 5,671 5,671
58 4,189 4,189 4,189
59 2,707 2,707 2,707
60 1,226 1,226 1,226
61 (452) (452) (452)
62 (2,029) (2,029) (2,029)
63 (3,605) (3,605) (3,605)
64 (5,181) (5,181) (5,181)
65 (6,757) (6,757) (6,757)
Internal Rate of Return 10.9% 7.1%
Source: For earnings, Encuesta Permanente de Hogares (1980). For costs, Table 7.
96
Appendix Table 18. Costs and Benefits for an Additional Level of Education Completed
for Men. Buenos Aires Metropolitan Area, 1995 (US$ dollars).
Secondary complete (vs. primary complete)
Social
Age Earnings Earnings Private Private Public total
differential foregone costs benefits costs benefits
13 (1,860) (1,860) (1,826) (3,686) (1,405) (5,091)
14 (2,373) (2,373) (1,826) (2,373) (1,405) (3,778)
15 (2,885) (2,885) (1,826) (2,885) (1,405) (4,290)
16 (3,398) (3,398) (1,826) (3,398) (1,405) (4,803)
17 (3,910) (3,910) (1,826) (3,910) (1,405) (5,315)
18 (1,117) (1,117) (1,117)
19 (1,283) (1,283) (1,283)
20 (1,450) (1,450) (1,450)
21 (1,617) (1,617) (1,617)
22 (1,279) (1,279) (1,279)
23 (940) (940) (940)
24 (602) (602) (602)
25 (264) (264) (264)
26 75 75 75
27 582 582 582
28 1,090 1,090 1,090
29 1,597 1,597 1,597
30 2,105 2,105 2,105
31 2,612 2,612 2,612
32 2,907 2,907 2,907
33 3,202 3,202 3,202
34 3,497 3,497 3,497
35 3,792 3,792 3,792
36 4,087 4,087 4,087
37 4,382 4,382 4,382
38 4,677 4,677 4,677
39 4,972 4,972 4,972
40 5,267 5,267 5,267
41 5,366 5,366 5,366
42 5,466 5,466 5,466
43 5,565 5,565 5,565
44 5,664 5,664 5,664
45 5,764 5,764 5,764
46 5,863 5,863 5,863
47 5,963 5,963 5,963
48 6,062 6,062 6,062
97
49 6,161 6,161 6,161
50 6,261 6,261 6,261
51 5,974 5,974 5,974
52 5,688 5,688 5,688
53 5,401 5,401 5,401
54 5,115 5,115 5,115
55 4,828 4,828 4,828
56 4,542 4,542 4,542
57 4,255 4,255 4,255
58 3,969 3,969 3,969
59 3,682 3,682 3,682
60 3,396 3,396 3,396
61 3,335 3,335 3,335
62 3,282 3,282 3,282
63 3,235 3,235 3,235
64 3,195 3,195 3,195
65 3,161 3,161 3,161
Internal Rate of Return 7.2% 6.0%
Source: For earnings, Encuesta Permanente de Hogares (1995). For costs, Table 7.
98
Appendix Table 19. Costs and Benefits for an Additional Level of Education Completed
for Men. Buenos Aires Metropolitan Area, 1995 (US$ dollars).
Higher education complete (vs. secondary complete)
Social
Age Earnings Earnings Private Private Public total
differential foregone costs benefits costs benefits
13 0 0 0 0 0 0
14 0 0 0 0 0 0
15 0 0 0 0 0 0
16 0 0 0 0 0 0
17 0 0 0 0 0 0
18 (3,306) (3,306) (2,633) (5,939) (718) (6,657)
19 (3,652) (3,652) (2,633) (6,285) (718) (7,003)
20 (3,997) (3,997) (2,633) (6,630) (718) (7,348)
21 (4,343) (4,343) (2,633) (6,976) (718) (7,694)
22 (4,783) (4,783) (2,633) (7,416) (718) (8,134)
23 7,495 7,495 7,495
24 7,812 7,812 7,812
25 8,129 8,129 8,129
26 8,445 8,445 8,445
27 8,786 8,786 8,786
28 9,126 9,126 9,126
29 9,467 9,467 9,467
30 9,807 9,807 9,807
31 10,147 10,147 10,147
32 10,198 10,198 10,198
33 10,249 10,249 10,249
34 10,299 10,299 10,299
35 10,350 10,350 10,350
36 10,401 10,401 10,401
37 10,451 10,451 10,451
38 10,502 10,502 10,502
39 10,553 10,553 10,553
40 10,603 10,603 10,603
41 10,863 10,863 10,863
42 11,122 11,122 11,122
43 11,382 11,382 11,382
44 11,641 11,641 11,641
45 11,901 11,901 11,901
46 12,160 12,160 12,160
47 12,420 12,420 12,420
48 12,679 12,679 12,679
99
49 12,938 12,938 12,938
50 13,198 13,198 13,198
51 13,392 13,392 13,392
52 13,587 13,587 13,587
53 13,781 13,781 13,781
54 13,975 13,975 13,975
55 14,170 14,170 14,170
56 14,364 14,364 14,364
57 14,558 14,558 14,558
58 14,753 14,753 14,753
59 14,947 14,947 14,947
60 15,142 15,142 15,142
61 14,208 14,208 14,208
62 13,268 13,268 13,268
63 12,320 12,320 12,320
64 11,367 11,367 11,367
65 10,407 10,407 10,407
Internal Rate of Return 18.8% 17.5%
Source: For earnings, Encuesta Permanente de Hogares (1980). For costs, Table 7.
100
Appendix Table 20. Costs and Benefits for an Additional Level of Education Completed
for Men. Buenos Aires Metropolitan Area, 1995 (in US$ dollars).
Higher education complete (vs. primary complete)
Social
Age Earnings Earnings Private Private Public total
differential foregone costs benefits costs benefits
13 (1,860) (1,860) (1,826) (3,686) (1,405) (5,091)
14 (2,373) (2,373) (1,826) (4,199) (1,405) (5,604)
15 (2,885) (2,885) (1,826) (4,711) (1,405) (6,116)
16 (3,398) (3,398) (1,826) (5,224) (1,405) (6,629)
17 (3,910) (3,910) (1,826) (5,736) (1,405) (7,141)
18 (4,423) (4,423) (2,633) (7,056) (718) (7,774)
19 (4,935) (4,935) (2,633) (7,568) (718) (8,286)
20 (5,448) (5,448) (2,633) (8,081) (718) (8,799)
21 (5,960) (5,960) (2,633) (8,593) (718) (9,311)
22 (6,061) (6,061) (2,633) (8,694) (718) (9,412)
23 6,555 6,555 6,555
24 7,210 7,210 7,210
25 7,865 7,865 7,865
26 8,520 8,520 8,520
27 9,368 9,368 9,368
28 10,216 10,216 10,216
29 11,064 11,064 11,064
30 11,912 11,912 11,912
31 12,760 12,760 12,760
32 13,105 13,105 13,105
33 13,451 13,451 13,451
34 13,796 13,796 13,796
35 14,142 14,142 14,142
36 14,488 14,488 14,488
37 14,833 14,833 14,833
38 15,179 15,179 15,179
39 15,525 15,525 15,525
40 15,870 15,870 15,870
41 16,229 16,229 16,229
42 16,588 16,588 16,588
43 16,947 16,947 16,947
44 17,306 17,306 17,306
45 17,664 17,664 17,664
46 18,023 18,023 18,023
47 18,382 18,382 18,382
48 18,741 18,741 18,741
101
49 19,100 19,100 19,100
50 19,459 19,459 19,459
51 19,366 19,366 19,366
52 19,274 19,274 19,274
53 19,182 19,182 19,182
54 19,090 19,090 19,090
55 18,998 18,998 18,998
56 18,906 18,906 18,906
57 18,814 18,814 18,814
58 18,722 18,722 18,722
59 18,629 18,629 18,629
60 18,537 18,537 18,537
61 17,543 17,543 17,543
62 16,549 16,549 16,549
63 15,556 15,556 15,556
64 14,562 14,562 14,562
65 13,568 13,568 13,568
Internal Rate of Return 11.4% 10.2%
Source: For earnings, Encuesta Permanente de Hogares (1995). For costs, Table 7.
102
Appendix Table 21. Costs and Benefits for an Additional Level of Education Completed
for Women. Buenos Aires Metropolitan Area, 1995 (in US$ dollars).
Secondary complete (vs. primary complete)
Social
Age Earnings Earnings Private Private Public total
differential foregone costs benefits costs benefits
13 (886) (886) (1,826) (2,712) (1,405) (4,117)
14 (1,375) (1,375) (1,826) (3,201) (1,405) (4,606)
15 (1,864) (1,864) (1,826) (3,690) (1,405) (5,095)
16 (2,353) (2,353) (1,826) (4,179) (1,405) (5,584)
17 (2,842) (2,842) (1,826) (4,668) (1,405) (6,073)
18 (462) (462) (462)
19 (696) (696) (696)
20 (930) (930) (930)
21 (1,164) (1,164) (1,164)
22 (1,053) (1,053) (1,053)
23 (942) (942) (942)
24 (831) (831) (831)
25 (720) (720) (720)
26 (609) (609) (609)
27 48 48 48
28 706 706 706
29 1,363 1,363 1,363
30 2,020 2,020 2,020
31 2,678 2,678 2,678
32 2,676 2,676 2,676
33 2,674 2,674 2,674
34 2,672 2,672 2,672
35 2,670 2,670 2,670
36 2,669 2,669 2,669
37 2,667 2,667 2,667
38 2,665 2,665 2,665
39 2,663 2,663 2,663
40 2,662 2,662 2,662
41 2,883 2,883 2,883
42 3,105 3,105 3,105
43 3,327 3,327 3,327
44 3,548 3,548 3,548
45 3,770 3,770 3,770
46 3,992 3,992 3,992
47 4,214 4,214 4,214
48 4,435 4,435 4,435
103
49 4,657 4,657 4,657
50 4,879 4,879 4,879
51 4,565 4,565 4,565
52 4,250 4,250 4,250
53 3,936 3,936 3,936
54 3,622 3,622 3,622
55 3,308 3,308 3,308
56 2,993 2,993 2,993
57 2,679 2,679 2,679
58 2,365 2,365 2,365
59 2,051 2,051 2,051
60 1,736 1,736 1,736
61 2,935 2,935 2,935
62 4,133 4,133 4,133
63 5,331 5,331 5,331
64 6,530 6,530 6,530
65 7,728 7,728 7,728
Internal Rate of Return 5.6% 4.6%
Source: For earnings, Encuesta Permanente de Hogares (1995). For costs, Table 7.
104
Appendix Table 22. Costs and Benefits for an Additional Level of Education Completed
for Women. Buenos Aires Metropolitan Area, 1995 (in US$ dollars).
Higher education complete (vs. secondary complete)
Social
Age Earnings Earnings Private Private Public total
differential foregone costs benefits costs benefits
13 0 0 0 0 0 0
14 0 0 0 0 0 0
15 0 0 0 0 0 0
16 0 0 0 0 0 0
17 0 0 0 0 0 0
18 (2,870) (2,870) (2,636) (5,506) (718) (6,224)
19 (3,124) (3,124) (2,636) (5,760) (718) (6,478)
20 (3,379) (3,379) (2,636) (6,015) (718) (6,733)
21 (3,634) (3,634) (2,636) (6,270) (718) (6,988)
22 (3,889) (3,889) (2,636) (6,525) (718) (7,243)
23 2,946 2,946 2,946
24 3,408 3,408 3,408
25 3,870 3,870 3,870
26 4,332 4,332 4,332
27 4,153 4,153 4,153
28 3,975 3,975 3,975
29 3,796 3,796 3,796
30 3,617 3,617 3,617
31 3,439 3,439 3,439
32 3,767 3,767 3,767
33 4,095 4,095 4,095
34 4,423 4,423 4,423
35 4,751 4,751 4,751
36 5,079 5,079 5,079
37 5,408 5,408 5,408
38 5,736 5,736 5,736
39 6,064 6,064 6,064
40 6,392 6,392 6,392
41 5,910 5,910 5,910
42 5,428 5,428 5,428
43 4,946 4,946 4,946
44 4,464 4,464 4,464
45 3,982 3,982 3,982
46 3,500 3,500 3,500
47 3,018 3,018 3,018
48 2,536 2,536 2,536
105
49 2,054 2,054 2,054
50 1,572 1,572 1,572
51 2,099 2,099 2,099
52 2,626 2,626 2,626
53 3,153 3,153 3,153
54 3,680 3,680 3,680
55 4,207 4,207 4,207
56 4,734 4,734 4,734
57 5,261 5,261 5,261
58 5,788 5,788 5,788
59 6,315 6,315 6,315
60 6,842 6,842 6,842
61 5,201 5,201 5,201
62 3,560 3,560 3,560
63 1,919 1,919 1,919
64 278 278 278
65 (1,363) (1,363) (1,363)
Internal Rate of Return 10.7% 9.8%
Source: For earnings, Encuesta Permanente de Hogares (1995). For costs, Table 7.
106
Appendix Table 23. Costs and Benefits for an Additional Level of Education Completed
for Women. Buenos Aires Metropolitan Area, 1995 (in US$ dollars).
Higher education complete (vs. primary complete)
Social
Age Earnings Earnings Private Private Public total
differential foregone costs benefits costs benefits
13 (886) (886) (1,826) (2,712) (1,405) (4,117)
14 (1,375) (1,375) (1,826) (3,201) (1,405) (4,606)
15 (1,864) (1,864) (1,826) (3,690) (1,405) (5,095)
16 (2,353) (2,353) (1,826) (4,179) (1,405) (5,584)
17 (2,842) (2,842) (1,826) (4,668) (1,405) (6,073)
18 (3,331) (3,331) (2,633) (3,331) (718) (4,049)
19 (3,820) (3,820) (2,633) (3,820) (718) (4,538)
20 (4,310) (4,310) (2,633) (4,310) (718) (5,028)
21 (4,799) (4,799) (2,633) (4,799) (718) (5,517)
22 (4,943) (4,943) (2,633) (4,943) (718) (5,661)
23 2,004 2,004 2,004
24 2,577 2,577 2,577
25 3,150 3,150 3,150
26 3,723 3,723 3,723
27 4,202 4,202 4,202
28 4,680 4,680 4,680
29 5,159 5,159 5,159
30 5,638 5,638 5,638
31 6,116 6,116 6,116
32 6,443 6,443 6,443
33 6,769 6,769 6,769
34 7,095 7,095 7,095
35 7,422 7,422 7,422
36 7,748 7,748 7,748
37 8,074 8,074 8,074
38 8,401 8,401 8,401
39 8,727 8,727 8,727
40 9,054 9,054 9,054
41 8,793 8,793 8,793
42 8,533 8,533 8,533
43 8,273 8,273 8,273
44 8,013 8,013 8,013
45 7,752 7,752 7,752
46 7,492 7,492 7,492
47 7,232 7,232 7,232
48 6,972 6,972 6,972
107
49 6,712 6,712 6,712
50 6,451 6,451 6,451
51 6,664 6,664 6,664
52 6,877 6,877 6,877
53 7,089 7,089 7,089
54 7,302 7,302 7,302
55 7,515 7,515 7,515
56 7,727 7,727 7,727
57 7,940 7,940 7,940
58 8,153 8,153 8,153
59 8,365 8,365 8,365
60 8,578 8,578 8,578
61 8,135 8,135 8,135
62 7,693 7,693 7,693
63 7,250 7,250 7,250
64 6,808 6,808 6,808
65 6,365 6,365 6,365
Internal Rate of Return 9.2% 7.7%
Source: For earnings, Encuesta Permanente de Hogares (1995). For costs, Table 7.
108
Appendix 5
Appendix Figures
109
Appendix 5: Appendix Figures
594860655748
6153
62056778
1090809626136230371288
132894
11312285N =
Gender
WomenMen
An
nu
al T
ota
l E
arnin
gs (
Logg
ed)
5.0
4.0
3.0
2.0
Appendix Figure 1. Box plot of Annual Total Earnings (Logged), by Sex. Buenos Aires
Metropolitan Area, 1980.
110
6801843986447280847792278478761011107105937029770495891071811345100956175907964347918652963546281971210556817810796719097048526588790438257
66918542554378815505690011272105091094511230
114145729
35130701155543429849312425508721783871982248549453127472594271551275119235044640251491923533624953964366480526391092210224214043269941380110315491849350873232323314403909342147279092280511016934884293821183039314415447402709155932641157383517476116114107476294234639683939314671926783060246625922450380767834263156261479240611890
8195384
4023
224536323394008282110645823487677479930341608588110444821487350712821616179010623028179817443774270753371910242053019052338467927192341
12642072N =
Gender
WomenMen
An
nu
al T
ota
l E
arnin
gs (
Logg
ed)
6
5
4
3
2
1
Appendix Figure 2. Box plot of Annual Total Earnings (Logged), by Sex. Buenos Aires
Metropolitan Area, 1995.
111
594857486065
62056778
10828841090809626230136
132894
11302283N =
Extreme values removed
Gender
WomenMen
An
nual
Tota
l E
arnin
gs (
Logg
ed)
5.5
5.0
4.5
4.0
3.5
3.0
2.5
2.0
Appendix Figure 3. Box plot of Annual Total Earnings (Logged), by Sex. Buenos Aires
Metropolitan Area, October 1980.
112
67834263156261479240611890
8195384
12622071N =
Extreme values removed
Gender
WomenMen
An
nual
Tota
l E
arnin
gs (
Logg
ed)
5.5
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
Appendix Figure 4. Box plot of Annual Total Earnings (Logged), by Sex. Buenos Aires
Metropolitan Area, May 1995.
113
Predicted Value
4.84.64.44.24.03.83.63.4
Res
idu
al
1.0
.5
0.0
-.5
-1.0
-1.5
Appendix Figure 5. Scatter plot of Regression Residual versus Predicted Values, for Men.
Buenos Aires Metropolitan Area, 1980.
114
Predicted Value
4.84.64.44.24.03.83.63.43.2
Res
idu
al
1.5
1.0
.5
0.0
-.5
-1.0
Appendix Figure 6. Scatter plot of Regression Residuals versus Predicted Values, for
Women. Buenos Aires Metropolitan Area, 1980.
115
Predicted Value
4.84.64.44.24.03.83.63.43.23.0
Res
idu
al
1.0
.5
0.0
-.5
-1.0
-1.5
Appendix Figure 7. Scatter plot of Regression Residuals versus Predicted Values, for
Men. Buenos Aires Metropolitan Area, 1995.
116
Predicted Value
4.64.44.24.03.83.63.43.23.0
Res
idu
al
1.0
.5
0.0
-.5
-1.0
-1.5
Appendix Figure 8. Scatter plot of Regression Residuals versus Predicted Values, for
Women. Buenos Aires Metropolitan Area, 1995.
117
Residuals Cumulative Probability
1.00.75.50.250.00
Exp
ecte
d C
um
. P
rob
.
1.00
.75
.50
.25
0.00
Appendix Figure 9. Normal Probability Plot for the Regression Residuals, for Men.
Buenos Aires Metropolitan Area, 1980.
118
Residuals Cumulative Probability
1.00.75.50.250.00
Exp
ecte
d C
um
. P
rob
.
1.00
.75
.50
.25
0.00
Appendix Figure 10. Normal Probability Plot for the Regression Residuals, for Women.
Buenos Aires Metropolitan Area, 1995.
119
Residuals Cumulative Probability
1.00.75.50.250.00
Exp
ecte
d C
um
. P
rob
.
1.00
.75
.50
.25
0.00
Appendix Figure 11. Normal Probability Plot for the Regression Residuals, for Men.
Buenos Aires Metropolitan Area, 1995.
120
Residuals Cumulative Probability
1.00.75.50.250.00
Exp
ecte
d C
um
. P
rob
.
1.00
.75
.50
.25
0.00
Appendix Figure 12. Normal Probability Plot for the Regression Residuals, for Women.
Buenos Aires Metropolitan Area, 1995.