Is the Rate of Return to Primary Education Higher …cd663nx5858...Rate of Return to Higher Education? A Study on the Buenos Aires Metropolitan Area, 1980 and 1995 Paula Razquin July

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



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


Buenos Aires Metropolitan Area, 1995 (in US$ dollars) ........................................................ 80


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






Women. Buenos Aires Metropolitan Area, 1980 (in 1995 US$ dollars). ................................ 90






Men. Buenos Aires Metropolitan Area, 1995 (US$ dollars). ................................................... 96


Men. Buenos Aires Metropolitan Area, 1995 (US$ dollars). ................................................... 98

iv


Men. Buenos Aires Metropolitan Area, 1995 (in US$ dollars). ............................................. 100


Women. Buenos Aires Metropolitan Area, 1995 (in US$ dollars). ........................................ 102





LIST OF APPENDIX FIGURES

Appendix Figure 1. Box plot of Annual Total Earnings (Logged), by Sex. Buenos Aires

Metropolitan Area, 1980. ......................................................................................................... 109


Metropolitan Area, 1995. ......................................................................................................... 110


Metropolitan Area, October 1980. .......................................................................................... 111


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.



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.


Appendix Figure 11. Normal Probability Plot for the Regression Residuals, for Men.




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


… 11.3 … 12.4 … 8.8


… 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


… 20.4 … 9.2 … 8.3


… 15.4 … 10.8 … 9.7


… 16.0 … 9.7 … 6.8

Cordoba


… 14.4 … 12.0 … 11.0


… 23.0 … 9.2 … 9.0


… 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 … …


… 11.0 … 6.0 … 6.4

Santa Fe


… 16.7 … 4.8 … 10.0


… 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


complete

Secondary

complete

Higher education

complete




… … … … … …


… … … … … …


… … … … … …

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


… … … … … …


… … … … … …


… … … … … …

Cordoba


… … … … … …


… … … … … …


… … … … … …

Giordano and Montoya, 1883 (3)

18.3 10.7 14.3 12.9 8.5 7.2

Mendoza

Ferrá and Claramount, 1980 (4)

… … … … … …


… … … … … …

Santa Fe


… … … … … …


… … … … … …

Tucumán


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


complete

Secondary

complete

Higher education

complete




… … … … … …


… … … … … …


… … … … … …

Kugler and Psacharopoulos, 1985 (2) … … 12.0 … 8.0 …

FIEL, 1993

EPH, 25 to 54 years old … … … … … …

FIEL's survey. All ages … … … … … …

Capital Federal


… … … … … …


… … … … … …


… … … … … …

Cordoba


… … … … … …


… … … … … …


… … … … … …

Giordano and Montoya, 1883 (3)

8.6 3.3 12.5 7.7 18.6 9.8

Mendoza

Ferrá and Claramount, 1980 (4) … … … … … …


… … … … … …

Santa Fe


… … … … … …


… … … … … …

Tucumán


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



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




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


Reference



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


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


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

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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


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


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


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.


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


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


1995. Men. Durbin-Watson d = 1.952, sample size 2,071 and 8 independent


1995. Women. Durbin-Watson d = 1.904, sample size 1,262 and 8 independent


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


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


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


Reference



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


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


Women. Buenos Aires Metropolitan Area, 1980 (in 1995 US$ dollars) (1)

Higher



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


Mean annual total earnings for the

age group in bold.

80


Men. Buenos Aires Metropolitan Area, 1995 (in US$ dollars) (1)

Higher



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


Mean annual total earnings for the age group in

bold.

82


Women. Buenos Aires Metropolitan Area, 1995 (in US$ dollars) (1)

Higher



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



Higher education complete (vs. secondary complete)

Social



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



88



Higher education complete (vs. primary complete)

Social



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



90


for Women. Buenos Aires Metropolitan Area, 1980 (in 1995 US$ dollars).


Social



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



92




Social



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)



94




Social



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)



96


for Men. Buenos Aires Metropolitan Area, 1995 (US$ dollars).


Social



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



98


for Men. Buenos Aires Metropolitan Area, 1995 (US$ dollars).


Social



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



100


for Men. Buenos Aires Metropolitan Area, 1995 (in US$ dollars).


Social



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



102


for Women. Buenos Aires Metropolitan Area, 1995 (in US$ dollars).


Social



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



104




Social



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)



106




Social



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



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



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



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


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


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


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


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


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



118


1.00.75.50.250.00

Exp

ecte

d C

um

. P

rob

.

1.00

.75

.50

.25

0.00



119


1.00.75.50.250.00

Exp

ecte

d C

um

. P

rob

.

1.00

.75

.50

.25

0.00



120


1.00.75.50.250.00

Exp

ecte

d C

um

. P

rob

.

1.00

.75

.50

.25

0.00



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