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New estimates of the returns to schooling in Brazil
Economics of Education Review, Vol. 10, No. 2, pp. 123-135, 1991. Printed inGreatBritain.
0272-7757/91$3.00+ 0.00 @ 1991Pergamon Press plc
New Estimates of the Returns to Schooling in Brazil* MICHAEL B. TANNEN
Professor
of Economics,
University
of the District of Columbia, Washington, DC 20008, U.S.A.
4200 Connecticut
Avenue
NW,
Abstract - Estimates of the returns to schooling in Brazil in 1980 are derived by fitting earnings functions to Census microdata. The average private rate of return has fallen by about one-third from 1970, but still remains quite respectable at about 12-13%. When schooling levels are disaggregated, the lowest private rates emerge for primary education. Those to higher education are the greatest of any level. Correction for selection bias hardly disturbs the differential among the schooling levels. Given the huge per pupil subsidy in public universities, it is not surprising that social returns are much lower than private ones for higher education. But the social rate of return is still the greatest for that level. Some types of vocational training (notably in industrial skills) also provide high returns, even when social expenditures are factored in. Geographic differences in rates of return to different schooling levels are not substantial.
questions: (1) Is education in Brazil a good investment? In particular, how large are the private and social returns? (2) How do the returns for primary, secondary and post-secondary education compare? (3) How great are regional differences in these returns? (4) Do persons with vocational education earn more than those with a general education? (5) Are there lower returns to education in government employment than in the private sector? and (6) Are there any returns to education in agriculture?
I. INTRODUCTION ALMOST overlooked
in the large body of evidence on the returns to education in less developed countries (LDCs)’ has been the recent situation in
Brazil, despite the country’s size and importance, and its spectacular successes and failures in promoting growth and development. The few studies that have been performed for that country apply to 1970 and earlier, and so much has happened in the interim that they now seem dated.2 New estimates of the returns to education for men in Brazil are developed in this paper. The analysis is based upon 1980 Census of Population microdata, supplemented by a series of interviews we conducted there with public officials, business managers, community leaders and farmers. The 1980 Census tapes provide representative and unusually detailed information for a LDC on individual employment, income, schooling and other persona1 characteristics. They are an excellent source with which to address many issues related to the labor market returns to schooling. Rate of return estimates derived from these data are used in this paper to address the following *This paper is an extension of earlier work I performed here, however, [Manuscript
are entirely my own. received 13 January 1988; revision
accepted
II. NATIONAL ESTIMATES OF THE PRIVATE RETURNS TO EDUCATION
Brazil has experienced a rapid pace of industrial development in recent decades, but human capital formation has not matched that of physical capital. The country is unusual, in that the educational attainment of the population is so low for a middleincome LDC. In 1980, the economically active population averaged only 4.19 years of schooling,3 and more than one-quarter had not completed so much as a single year.4 Public spending on social programs, meanwhile, is massive, consuming an estimated 25% of Gross under
contract
for publication 123
to The World 9 April
Bank in 1986. Views expressed
1990.1
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Economics of Education Review
Domestic Product, with roughly one-quarter devoted to education.’ But a very young and rapidly growing population has placed enormous strains on the school system. Administrative inefficiencies and the system of educational finance, dependent upon municipal and state tax revenues, have also worsened matters, with public support of schools varying enormously.6 Tuition-charging private schools fill some of the void,’ but limited or nonexistent access to any type of schooling is prevalent in some rural locations. Even where schools are convenient, however, absenteeism, repetition of grade and the dropout rate are all high.8 Traditional explanations of these problems emphasize as contributing reasons the inadequacy of facilities, shortages of educational materials and qualified teachers, high direct and opportunity costs of attending school, and a curriculum that is simply too tough.’ To what extent, though, can the low educational attainment of the population be attributed to a lack of financial incentives? The private returns to schooling are measured below using the regression approach suggested by Mincer (1974). lo The specific earnings equation used here is In Y = b0 + b,S + b2X + b3X2 + b4Majurb + b50thurb
+ b,Southeast + u,
(1)
where the dependent variable, earnings, is expressed in terms of its natural logarithm (In Y). S is the educational attainment of the individual measured by the number of years of school completed. The anticipated parabolic relationship between earnings and years of experience (X), meanwhile, is represented by the quadratic expression involving X and X2. Three geographic variables are included to capture systematic differences in earnings associated with location apart from skill. Majurb is a dummy variable representing residence in one of the major metropolitan areas in the country,” while Othurb is another dummy representing residence in any other metropolitan area. The dummy variable Southeast is added to control for the higher level of earnings in that region.12 Finally, the bs serve as the parameters to be estimated (all are expected to be positive except b3), while u is the disturbance term. The private rate of return to education is inferred from the schooling coefficient b, .I3
Equation (1) has been fitted to 1980 Census microdata for the monthly earnings in cruzeiros of employed males between the ages of 1.5and 65 in the private nonfarm sector. r4 Only persons not enrolled in school were included in the sample. The dependent variable represents the sum of both monetary earnings and the monetarized value of payments-inkind (Census data contain a self-supplied estimate of the latter15). Results are shown in column (1) of Table 1.16 The semilogarithmic earnings function, so often applied in explaining earnings differences in developed countries, also performs admirably in explaining the determinants of private nonfarm earnings in Brazil in 1980. The corrected R2 is very respectable for a cross-sectional earnings equation fitted to microdata. All coefficients have the anticipated sign, and are statistically significant at the 1% level. Our primary interest, though, is in the schooling coefficient. It implies that the private rate of return to schooling is 13.2%.” Although respectable, this is substantially lower than the rates of return estimated for Brazil in 1960 and 1970. This change does not appear to be a statistical artifact (owing to different estimation methodologies). One equation in the BehrmanBirdsall (1983) paper differs from our own only in that it applies to 15-35 year old males in 1970 and does not include geographic dummy variables. The schooling coefficient there implies an average rate of return of 20.5% for 1970. Rerunning our equation for the same age group in 1980 and dropping the geographic dummies produces an estimate of the average rate of return of 12.3%. Thus, private rates of return to schooling in Brazil have apparently dropped by more than one-third over the 1970-1980 decade. The country is hardly unique in experiencing a decline in the returns to schooling in recent years. This development seems to imply that the supply of educated labor has increased more rapidly than demand. Indications of the former are certainly there, with the number of persons enrolled in Brazilian colleges and universities more than tripling over the 1970-1980 decade, and high school enrollments growing nearly as fast. Indeed, critics have expressed the view that political pressures create a tendency to invest heavily in higher education, at a time when illiteracy limits the prospects of a sizable portion of the workforce from obtaining gainful employment.
Schooling Returns in Brazil Table 1. Regression
Explanatory variable
of monthly earnings (I)
(2)
7.58454 (533.30) 0.12430 (152.99)
Intercept Years of schooling (total) First cycle (Grades 1-4) Second cycle (Grades 5-8) High school College
0.07948 (83.91) -0.00133 (-63.38) 0.12320 (9.73) 0.02303 (1.87) 0.19161 (29.52)
Years of experience Experience squared Major urban area Other urban area Southeast dummy
private nonfarm*
7.63071 (512.55)
7.98181 (451.02)
0.12069 (50.87) 0.07810 (29.37) 0.14620 (31.57) 0.21001 (54.96) 0.07744 (82.24) -0.00131 (-62.69) 0.14219 (11.31) 0.03945 (3.23) 0.19007 (29.45)
0.10995 (46.58) 0.07960 (30.34) 0.14130 (30.91) 0.19278 (50.71) 0.04584 (35.67) -0.00055 (-18.36) 0.24543 (19.26) 0.12493 (10.16) 0.19934 (31.28) -0.99082 (-35.57) 0.4606 46,537
Mills ratio Corrected R squared Number of observations *All observations parentheses.
0.4351 46,537
have been weighted
0.4459 46,537
by the appropriate
They maintain there is a welfare loss due to misallocation, believing that output could be raised by rechanneling resources toward primary education. A cursory view of the facts gives credence to this view. Illiteracy rates, though falling, remain high. ‘s Financial support for higher education, meanwhile, consumes 23% of all public spending on education, although only 5% of the total student population is at that level. I9 Brazilian policymakers have generally resisted this suggestion for reallocation, responding that the country’s continued rapid industrial development requires a growing supply of skilled manpower. A more sophisticated investigation of the resource allocation question, however, would examine private and social rates of return.20 Consider the private financial incentives first (social returns are discussed in a later section). To investigate the issue, Eqn (1) is modified by replacing the single schooling variable with separate ones that represent years of school completed at each of the levels that characterize the Brazilian
(3)
sampling weights. I statistics are in
educational system. There, primary school is broken up into two cycles, grades one to four (the first cycle), and grades five through eight (the second cycle). After primary school, the system is typical, consisting of high school, college and graduate programs.‘l Results from fitting this expanded earnings equation to the data appear in column (2) of Table 1. Private returns of 12.8 and 8.1% are implied for the first and second cycles of primary education, respectively. 22Returns to high school are greater, at 15.7% per year. Most striking, though, are the even larger returns to higher education. A year of college or university training has an impressive private return of 23.4%.23 These results, however, are potentially subject to a type of selection bias. Specifically, the equation has been estimated from data on working males, resulting in a censored sample of the entire male population.24 A method of correcting for this problem is Heckman’s (1976) technique. This involves a two-step procedure in which in the first
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stage, the probability that an individual will be gainfully employed (and out of school) is determined according to a probit regression equation in which a series of personal and locational characteristics serve as the regressors. From these results, a selection variable (the inverse Mills ratio term) is created,25 and then inserted into the RHS of the earnings equation. That equation is then re-estimated for the initial sample (i.e. those with earnings), yielding consistent estimates of the coefficients free of censoring bias. Results using this procedure appear in column (3) of Table 1.26 There are some changes in the estimated returns to education, though none are of a drastic nature. The rate of return to the first cycle of primary school is somewhat lower than before, at 11.6%, but the rate of return to the second cycle, at 8.3%, is little affected. A downward adjustment is evident in the other schooling coefficients, with the returns to high school reduced slightly, to 15.2%, while those to a year of college are reduced more, to 21.5%. Nonetheless, the returns to college remain the highest of any schooling level. III. REGIONAL
DIFFERENCES
The results in Table 1 raise the question of why the pattern of returns to education in Brazil is dissimilar from that in other LDCs. A possible explanation is differences in the extent of economic development. Brazil may be atypical because of the economic dominance of the highly industrialized Southeastern region of the country. The presence of such a large modern sector requiring many skilled workers could result in a strong demand for educated labor (relative to supply), even though the situation in other parts of the country might be similar to that in other LDCs. Regional differences such as this might have been obscured in estimating Eqn (1). While that equation did allow for differences in the level of regional earnings in terms of a simple “lump-sum” (i.e. dummy variable) value, the estimated rates of return were not permitted to vary by region. As a result, an average of the returns in different regions appeared. In this section, further estimates are obtained by separating the data for each region. To focus more clearly on the extremes in regional differences, only the Southeast and the Northeast*’ are considered below. The Southeast was selected because it is the most prosperous region in the
country. The Northeast, on the other hand, offers the most striking contrast within Brazil. The region is large in both area and population (30% of the nation resides there). It is, however, so pervasively poor that it contains the greatest concentration of poverty in all of Latin America. Indeed, there is a substantial difference in educational attainment between these regions, with many individuals in the Northeast reporting never having attended schoo1.28 Estimates of the private educational returns in each of these regions are shown in Table 2. Columns (1) and (2) apply to the situation in the Southeast, while results for the Northeast appear in columns (3) and (4). Columns (2) and (4) include the correction for censoring (inverse Mills ratio term). Consider first the findings for the Southeast. In column (2), the rate of return for a year of schooling in the first cycle of primary school is 11 .O%, while that for the second cycle is smaller, at 8.0%. The returns to high school and college, however, are considerably larger, at 15.2 and 20.7%, respectively. The magnitude and pattern of rates of return in the Northeast differ somewhat, but pronounced differences simply do not appear. There, returns to the first and second cycles of elementary school are quite similar to one another (at 9.6 and 9.9%). As in the Southeast, however, rates of return to more advanced schooling are greater. For high school the estimate is 16..5%, while that to college is a very impressive 24.3%. Interestingly, despite the region’s relative lack of economic development, the returns to college in the Northeast exceed those in the Southeast by more than 3.5 percentage points.*” We can conclude that there is little similarity between the pattern of returns for different schooling levels in either region and that observed in most other LDCs. The suggestion that regional aggregation is responsible for this difference draws no support from the results just presented. There is simply no indication here of exceptionally high returns associated with primary education. Nor is there any indication of a serious overinvestment in higher education, at least as far as private returns are concerned. IV. SOCIAL
RETURNS
Private rates of return are important in understanding individual incentives to attending school at different levels, but they are not a sufficient guide to resource allocation when public monies are in-
127
Schooling Returns in Brazil Table 2. Regression
of monthly
earnings
by region* Northeast
Southeast Explanatory
variable
(1)
Intercept
7.74147 (321.64) 0.11600 (35.24) 0.07577 (22.99) 0.14583 (25.36) 0.20713 (45.00) 0.07963 (65.68) -0.00134 (-49.96) 0.23432 (11.22) 0.09510 (4.50)
First cycle (Grades l-4) Second cycle (Grades 5-8) High school College Years
of experience
Experience
squared
Major
urban
area
Other
urban
area
Mills ratio Corrected R squared Number of observations *All observations
0.4525 26,817
have been weighted
by the appropriate
volved. Education in Brazil is financed to a considerable extent by federal, state and local governments. These subsidies vary enormously, from very small per pupil expenditures for primary education in the Northeast, to very large amounts at public universities throughout the country. Social rates of return, which incorporate these public expenditures, are considered in this section. Their estimation involves two additional problems. The first is data availability. A recent World Bank report on Public Spending in Brazil (1989) concludes that comparable data on school expenditures throughout the country do not exist at the present time. That study, however, does provide some tentative information regarding per pupil expenditures at the primary, secondary and public university levels,30 and this is used below in computing social rates of return. The second problem is methodological. Mincer’s approach needs to be modified to incorporate subsidies so as to calculate social rates of return. Eaton (1985) shows this can be accomplished using the following procedure. First, the earnings gain from an additional year of schooling is calculated from the earnings regression results as Y* = 12[exp
8.11435 (302.46) 0.10462 (32.07) 0.07721 (23.80) 0.14109 (24.93) 0.18844 (41.21) 0.04660 (28.52) -0.00055 (-14.48) 0.34502 (16.52) 0.18952 (9.02) - 1.02763 (-29.59) 0.4698 26,817 sampling
(4)
(3)
(2)
weights.
7.65219 (253.96) 0.10235 (19.68) 0.09031 (11.23) 0.15670 (11.61) 0.23009 (20.31) 0.06988 (29.83) -0.00114 (-22.22) 0.07055 (2.74) -0.01220 (-0.51)
7.95668 (202.78) 0.09415 (18.11) 0.09168 (11.50) 0.15251 (11.40) 0.21757 (19.30) 0.04243 (13.00) -0.00048 (-6.43) 0.16070 (6.04) 0.06369 (2.61) -0.86482 (-11.96) 0.4147 7895
0.4040 7895 t statistics
are in parentheses.
(a + bI)-exp (a)], where Y* is the annual earnings gain, a is the intercept term, bI is the schooling coefficient and exp is the exponential function.31 Next, the present value of the lifetime gain in earnings (G*) is computing by integrating Y* over the working life, yielding
G* = Y*(-l/r)[exp
(-4lr)-exp
(-r)],
(2)
where r is the rate of return on a year of schooling at that level, and the individual is assumed to have a working life of 41 years.32 In equilibrium, this gain equals its cost, so that the following condition applies G* = C* = P*(-llr)[exp
(-r)-11.
(3)
In Eqn (3) P* represents the opportunity cost of another year of schooling to the individual. Finally, the social rate of return, r’, is implicitly defined by the equation Y*(-llr’)[exp
(-4lr’)-exp(-r’)] (-llr’)[exp (-r’)-11,
= (P*+PS) (4)
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where PS denotes the per pupil public subsidy. The equation is then solved for r’ as the root of the polynomial expression. Applying this procedure to our data generates the following results. As expected, the incorporation of public subsidies yields estimates of social rates of return below those of private rates at every level of education. The return to the first cycle of primary education is reduced by 2 percentage points. That to the second cycle and to high school are each reduced by 1.2 percentage points. Not surprisingly, given the huge public expenditure per pupil reported for university education; the greatest difference (a decline of 9 percentage points) appears there. Universities, though, can provide external benefits that are not captured by this calculation. The expenditure data, moreover, apply to public universities, which enroll about half of all higher education students. Subsidies to private colleges and universities are presumably much less. How sensitive might the social rates of return be to these factors? The consequence of one guestimate, in which the public expenditure figure used earlier is reduced by 50%) is that the social rate of return rises to 15.6%, the highest of any level of education. Regardless of which cost estimate is used for higher education, though, it is clear that the evidence simply does not support the position that level of education is being oversubsidized. There is no indication here of an imbalance between private incentives and public expenditures on higher education.33 V. VOCATIONAL EDUCATION AND TRAINING The finding of large social rates of return to higher education would probably surprise many Brazilian entrepreneurs. A frequent complaint we heard from owners and managers of large industrial firms was the “overemphasis” on higher education by the students, the schooling system and the State. These managers maintained that what was needed instead were more persons with vocational training who could perform technician and related supervisory jobs admirably. There often was, they maintained, a shortage of qualified individuals who can fill the available blue-collar and supervisory vacancies. To help improve the situation, the managers cited a growing reliance upon industrial vocational training schools established as a cooperative venture between the government and the private sector
(SENAI%). But while these schools were generally given good marks for providing decent training in basic industrial and supervisory skills, they were also criticized as unable to provide the more sophisticated or specialized training needed by the firms. The managers suggested that individuals and the economy would be well served if vocational education programs were better funded. Interestingly, these remarks run counter to the criticism leveled against vocational education in LDCs by some policy analysts. Newburg and Martin (1972), for example, maintain that much of the “educational crisis” in developing countries is the result of undue emphasis on vocational education. This type of education is relatively expensive to provide, and yet it is, the authors state, not particularly useful. Many vocational graduates go on to obtain jobs which require only the general academic skills they have learned; their vocational education is wasted. Vocational schools nonetheless manage to survive and even thrive in LDCs, according to the authors because their degrees serve as a useful (but too expensive) screening device. Brazil, though, by virtue of its size and industrial development, has a much larger blue-collar job market than most LDCs, and it can absorb a much greater number of persons with vocational training in specialized skills. The returns to vocational education there may be atypical. But what does the quantitative evidence reveal? The Census questionnaire asked individuals whether they had received vocational training in the highest level of school they had attended. Specific kinds of vocational specialties in primary school and high school were listed for the respondents to check. We divided the responses for the primary school level into vocational skills which were intended to serve the commercial sector, those which were more trade or industry oriented, and those which fit neither category. A dummy variable was assigned to each and inserted into the earnings equation. Such a division was not practical at the secondary school level, due to the nature of the Census coding categories. As a result, only a single dummy for vocational training in high school was included. Results obtained using this modified earnings equation are shown in Table 3. The column (2) specification includes the Mills term, although its addition has only a slight effect on the other coefficients [compare with column (I)]. The important finding is that two of the four coefficients on
129
Schooling Returns in Brazil Table 3. Vocational education and monthly earnings* Explanatory variable
(1)
Intercept
7.62983 (512.38) 0.12039 (50.58) 0.08273 (19.47) 0.13362 (22.24) 0.20485 (21.95) 0.15576 (4.01) 0.05169 (2.18) -0.02152 (-1.32) 0.04743 (1.12) 0.07756 (82.20) -0.00131 (-62.73) 0.14239 (11.32) 0.03895 (3.19) 0.19011 (29.45)
First cycle (Grades 1-4) Second cycle (Grades 5-8) High school College Industrial education -
Primary school
Commercial education -
Primary school
Other vocational education Vocational education -
Primary school
Secondary school
Years of experience Experience squared Major urban area Other urban area Southeast Mills ratio Corrected R squared Number of observations
*All observations parentheses.
have been weighted
0.4307 46,537 by the appropriate
vocational education variables have a positive sign and are statistically significant in column (1) [one of which is marginally insignificant at the 5% level in column (2)]. The coefficient for commercial edu-
cation at the primary school level implies a private return of 4.7% for this kind of training (relative to what others receive with similar years of general academic training). The private returns to industrial education in primary school are much greater, at 16.4%. There is no evidence of any special economic value to the other two broad types of training, but because these categories are heterogeneous, the possibility remains that some types of training within them could lead to higher returns. Private returns, of course, do not tell the whole story. Public expenditures on vocational schools in Brazil are quite large, but the amount of the subsidy
(2) 7.98074 (450.96) 0.10961 (46.29) 0.08502 (20.28) 0.12849 (21.67) 0.18776 (20.37) 0.15221 (3.98) 0.04552 (1.94) -0.02550 (-1.58) 0.04424 (1.06) 0.04599 (35.75) -0.00055 (-18.45) 0.24549 (19.27) 0.12440 (10.12) 0.19934 (31.27) -0.99058 (-35.57) 0.4462 46,537
sampling weights. t statistics are in
varies with the type of program. A recent World Bank report estimates those to SENAI and SENAC training in 1985 to be $200 and $51 per pupil, respectively. 35 Using the expenditure data to calculate the social rate of return to industrial education at the primary school level (Eaton’s method) yields an estimated return of 14.4%) over and above the return to general academic training at that level. For commercial training in primary school the social rate of return is 4.4%. VI. SECTOR OF EMPLOYMENT (A) Public Sector Employment Administrative wage setting policies in LDCs tend to compress the wage and salary scale in the public sector. This can result in lower salaries of many
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government employees, and also reduce the returns to schooling, relative to those in the private enterprise. Does Brazil follow this general tendency? Employment by all three branches of government (federal, state and municipality) is considerable. As is typical, different pay scales exist, with federal salaries usually the highest (although some states match the federal pay scale), while salaries for state employees generally exceed those for municipal workers (the latter two vary by state and by municipality). To examine first whether the salary level is lower in public (relative to private) employment, three dummy variables representing the level of pay scales were created: one corresponding to employment in the federal government, another to employment in a state government, and the third for municipal
employment. The earnings equation was then run including these variables on a sample which consisted of males employed in the private nonfarm sector or in any of the government services (but persons engaged in military service were excluded). Results are shown in column (1) of Table 4. The coefficient on each government employment variable measures how earnings for persons employed in that service compare to earnings in the private sector, controlling for the effects of differential schooling, experience and location. Each of these coefficients has the expected sign, and is statistically significant. Their magnitude also follows the anticipated pattern. Pay in civilian government jobs is 7.5% less than that in the private nonfarm sector, while salaries of state employees run about 11% less than comparable skills would command in
Table 4. Monthly earnings in government Explanatory variable Intercept First cycle (Grades l-4) Second cycle (Grades 5-8) High school College Years of experience Experience squared Major urban area Other urban area Southeast Federal government State government Municipal government Mills ratio Corrected R squared Number of observations *All observations parentheses.
Government and private (1) 7.98757 (467.63) 0.10986 (48.16) 0.08187 (32.74) 0.13547 (31.53) 0.19572 (56.05) 0.04445 (36.20) -0.00051 (-18.15) 0.25365 (20.57) 0.12580 (10.58) 0.19874 (32.65) -0.08252 (-4.89) -0.12012 (-3.16) -0.22054 (-14.39) -0.99085 (-37.01) 0.4700 50,270
have been weighted by the appropriate
and agriculture*
Government only (2) 7.99599 (69.10) 0.09611 (5.27) 0.09231 (7.65) 0.06623 (3.75) 0.18750 (14.92) 0.02940 (4.30) -0.00017 (-1.08) 0.32800 (3.91) 0.19256 (2.36) 0.28681 (8.89)
-0.92577 (-5.66) 0.4937 1586
Agriculture (3) 7.75781 (397.19) 0.14000 (45.90) 0.12025 (12.03) 0.23526 (9.97) 0.16852 (6.05) 0.02833 (16.77) -0.00026 (-7.03) 0.25577 (7.38) 0.23269 (17.48) 0.13637 (12.86)
-0.85362 (-17.41) 0.2295 25,288
sampling weights. t statistics are in
Schooling
Returns in Brazil
private industry. Municipal employees fare the worst, with their salaries lagging 19.5% behind those of the private sector. While government pay overall is lower than that in the private sector, this does not necessarily imply that the returns to schooling are lower. These are examined in column (2). To avoid potential heterogeneity problems in pay among employees of different branches of government, these results apply to federal employees. Comparing these results to those for the private sector (see Table l), the only substantial difference in returns to education occurs for persons with a high school education. Government employees in this group receive a rate of return to their incremental schooling of less than 7%) compared with the 15% received by comparably educated persons in the private sector. This salary compression is not evident for employees with a college education, who receive a rate of return comparable to that in the private sector. Only 6% of federal employees in the sample, moreover, fall in the high school category, so overall, there is not much difference in the rates of return to education received in the federal and private sectors. (B) Agriculture and Nonagricultural Differences The earnings of agricultural workers in Brazil are quite low, compared to those in other industries. Agricultural workers also have less schooling, but prior analyses of agriculture in Brazil have produced mixed results regarding the effects of education on earnings. Patrick and Graber (1977), for example, found that functional literacy was associated with higher farm income in three out of four small regions in Brazil. Phillips (1987), however, pointed out that of nine studies of the returns to farmer education in Brazil, only two featured a positive and significant effect of education on output. To provide further evidence on this issue, the earnings equation was re-estimated using a national sample of agricultural workers. Results are shown in column (3) of Table 4. According to these, the private rate of return to all levels of education
131
except college are actually higher than they are in the private nonfarm sector (see Table 1). The greatest difference is for high school, where agricultural workers with such education receive more than twice the rate of return similarly schooled persons in the private nonfarm sector realize.a6 VII. SUMMARY
AND CONCLUDING
REMARKS
The average private rate of return to schooling in Brazil in 1980 was respectable, but substantially less than what was observed for that country in 1970. When the returns to different schooling levels were examined, those to higher education were the greatest of any level. Little wonder, then, that enrollment growth has been most rapid at that level. The relatively low returns to primary education, meanwhile, help explain why the majority of Brazilian students do not complete that level. As expected, social rates of return were lower than private rates at all levels, with the greatest difference occurring for higher education. Still, the social returns to higher education were at least comparable to those for secondary education and exceeded those for primary education. Interestingly, although a great regional economic disparity exists between the most prosperous Southeast and the least prosperous Northeast, rates of return to different education levels in the private nonfarm sector are generally comparable between the regions. Vocational training in industrial skills, meanwhile, was observed to yield sizable private and social returns over an academic curriculum at the primary school level. There was also some evidence of positive but smaller private and sociai returns to commercial training at the primary school level. The only notable difference in the returns to education between private and federal government employees occurred for persons who attended high school. They fared substantially less well in the federal sector. And despite low wages in agricultural pursuits in general, rates of return to education there were more than comparable to those in other sectors.
NOTES 1. See Psacharopoulos (1985) for a survey of the literature. 2. Estimates for Brazil by Birdsall and Behrman (1983) use 1970 Census data. Psacharopoulos (1973) cited earlier estimates in 1970 PhD dissertations by C. de M. Castro, S.D. Hewlett, M.O. Lerner, and
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Economics
of Education
Review
A.J. Rogers, III. A World Bank staff paper by the same author reached me after this manuscript was substantially completed. It uses 1980 Census data, and covers some of the same ground, but does not consider either social returns or selection bias. 3. Brazil: Economic Memorandum (World Bank, 1984) Table 10.1, p. 305. 4. Calculations based upon the sample of employed males (all industries) used in this paper. Percentages do not vary much by age groupings (10 year intervals). 5. Brazil L Public Spending on So&l ho&a& (World Bank, 1989, p. iii). 6. As one indication. the World Education Encvcloaedia (Facts on File Pub.. v. 161) notes “Primarv teachers’ salaries paid by the State of Sao Pa& were‘ 367% higher than ‘salaries paid by some municipalities in the same state”. 7. Private and religious schools are found at all levels of education, with 52% of all secondary schools in 1980 being private. 97% of all secondary schools, however, were in urban areas (ibid., pp. 153-55). 8. The country has made gains in achieving high enrollment rates, which were 80% of the school-age population in 1970 and 90% in 1980 (Economic Memorandum, ibid., Table 4.2, p. 128), but less so with respect to attrition and repetition. In the 196Os, 15% of first graders went on to complete grade four. Two decades later, only 17% completed primary school (World Encyclopedia, ibid., pp. 15354). 9. Among those 7-24 years old in 1982 who never attended school, 24% listed either no school available or no space in school as the reason why. (Zndicudores Sociais, Vol. 2, IBGE, 1984, p. 196.) Another 22.7% said they had to work. Also, the National Pedagogical Studies Institute (INEP) reported that curricular content in primary school was substantially higher in Brazil than in the U.S., France or Switzerland (World Encyclopedia, ibid., p. 153). 10. Mincer (1974). Compared to the older method of equalizing lifetime earnings streams, this approach uses individual, rather than grouped observations, and individual differences in experience and location can thus be directly controlled for. It produces unbiased estimates, however, only if the disturbance term is uncorrelated with the schooling variable. This assumption has been challenged on several grounds, including self-selection (censoring) bias, which we consider later. 11. These are Belem, Fortaleza, Recife, Salvador, Belo Horizonte, Rio de Janeiro, Sao Paula, Curitiba, Porto Allegre and Brasilia. 12. The “Southeast” Census region contains the states of Rio de Janeiro, Sao Paulo, Minas Gerais and Espirito Santo. Other regression results which included only the first two states in the dummy were very similar. 13. Given reasonable assumptions [see Mincer (1974)], the private rate of return is equal to the antilog of the schooling coefficient minus one. These assumptions are not challenged here, but future work could examine their validity. For example, one assumption is that the direct costs of schooling are offset by the part-time earnings of students. Plank (1986) noted such direct costs can be large in Brazil. 14. Data used in this study were obtained by taking a one in 10 sample from the national sample Census tapes. Because cross-sectional earnings differences are examined, the serious inflation the Brazilian economy has experienced is not a source of confusion here. The analysis is restricted to males because of their more consistent labor force participation. 15. In the private nonfarm sector such payments are not uncommon, but are usually small. For example, in visits to factories, we noted that employees often received lunches or discounts on the goods produced. The payment-in-kind information in the Census data was, however, sparsely reported. 16. The earnings of the self-employed are not distinguished from the earnings of employees here. Chiswick (1976) argues that the earnings determinants for the self-employed are different than those for employees. But Henderson (1983) found similar determinants for the two groups. Lee (1987), meanwhile, observed no selection bias present, but a greater percentage of the variation in earnings of employees that the self-employed can be explained by a human capital function. 17. Because the estimate does not distinguish the effect of school quantity from that of “quality” the schooling coefficient should be interpreted as measuring the returns to another year of schooling at the existing average quality. It seems obvious that quality matters, particularly when differences are great. Researchers, however, have had a difficult time documenting its importance [see Hanushek (1986)]. In Behrman and Birdsall (1983), quality is represented by the educational attainment of teachers in a geographic location. Inserting this measure into the earnings function via a quadratic interaction with years of schooling, the authors find large returns to school quality. Hanushek points out, however, this assumes teacher’s education is a viable measure of quality. A large number of studies have tried to directly measure the effect of teacher education on student performance, but its coefficient is statistically significant in only 11 out of 106 cases, with five significant cases having the wrong sign. The Behrman-Birdsall estimate is also based upon an
Schooling Returns in Brazil interaction term between years of schooling and quality squared, even though its coefficient is highly insignificant. 18. These were 40.6% in 1970, 31.3% in 1980 (Economic Memorandum, ibid., p. 127). 19. Public Spending, ibid., p. 39. 20. Psacharopoulos (1985) reports that social rates of return in LDCs usually decline with additional schooling. Private rates, however, are sometimes greater for university than secondary education, although both are below those of primary school. In seven studies he cited for Latin America, this was the case for Mexico (1963), Columbia (1974), Costa Rica (1974) and Venezuela (1981). The computation of social rates conveniently ignores several issues. Externalities are overlooked, but university research, for example, provides access to a knowledge base that might not otherwise exist in a community. On the other hand, Mincer’s interpretation of Eqn (1) assumes that education raises earnings because it raises productivity. In the competing “screening view”, education merely certifies that an individual is more productive, even if education is not the reason why. If the latter is more correct, the calculation of the private returns is unaffected (because more schooling still leads to higher earnings), but the social returns need to be reduced. 21. Prior to 1971, grades five to eight were considered to be the first phase of secondary education. With the passage of Law 5692 in 1971, primary education was redefined as grades one to eight. High school is usually for 3 years, although students sometimes attend a fourth year for college preparation. 22. These estimates are derived from the Mincer-type equation which assumes that each year of schooling involves a year of earnings foregone. (This assumption applies whether separate schooling variables for each level or a single schooling variable is used.) But students in primary school in Brazil have required almost 3 years to complete a single grade. Children not in school, however, often work at very low wages in small businesses or factories or providing menial services. I plan to address these issues in a subsequent paper. 23. The higher education variable includes years of graduate school training. There were few cases of the latter. 24. The problem is that the (unobserved) wage offers of those not working are probably lower than those for persons in the sample [see Reimers (1983) for more explanation]. 25. Results from the probit equation we ran explaining inclusion in our sample are shown in Appendix Table 1. The Mills term is defined asfl.)lF(.). where f is the normal density and F is its cumulative function. The expression in parentheses (.) denotes the sum of the products of the probit regression coefficients and the regressors. 26. When the Mills term is inserted into the column (1) specification, the estimate of the average rate of return to schooling becomes 12.5%. 27. The Northeast consists of the states of Maranhao, Piaui, Ceara, Rio Grande do Norte, Paraiba, Pernambuco, Alagoas, Sergipe and Bahia. Because much of the financial resources for public primary and secondary schools come from municipal and state governments, schools in the Northeast are less well funded than those in the Southeast, often considerably so. In the rural Northeast, primary school teachers themselves often have no more than 4 years of education, and books, papers and physical facilities are in short supply. A reasonable hypothesis, therefore, is that pronounced differences in school quality between the regions would lead to differences in rates of return. 28. This disparity is less when the nonfarm private sector is considered, but remains considerable. About 75% of agricultural workers in the Northeast report no formal schooling compared to 30% of nonfarm workers. For the region as a whole 54.3% have no school years completed, compared to 16.5% in the Southeast. 29. The Northeast Development Agency (SUDENE) has helped manufacturing firms from the Southeast and abroad set up facilities in the Northeast. These have no doubt increased job opportunities for educated labor in the region. There is no consideration of the migration motive here, e.g. more educated persons in the Northeast may move south to enhance job prospects. It should also be recognized that the rate of return is based upon the gain in earnings. Equal (or unequal) rates of return do not necessarily imply equal (or unequal) salaries. 30. These estimates (for 1983) are $149 for primary school, $144 for secondary school and $2586 for public university education. We deflated them to 1980 dollars and then converted to cruzeiros using the August 1980 parallel exchange rate. 31. Since the Census data apply to monthly earnings, it is necessary to multiply by 12 to get annual earnings. 32. Eaton assumed a working life of 41 years, but the calculation is robust with respect to alternate assumptions. 33. The distributional issue is not considered here. There is evidence that persons from above average incomes benefit most from subsidized higher education (Public Spending, ibid.). 34. Servicio National de Aprendizagem Industrial. There are also cooperative arrangements for
134
Economics of Education Review commercial training (SENAC), training in rural skills (SENAR) and other vocational programs, such as the elite federally funded schools. SENAI courses are taught at the primary and secondary school levels. These are usually brief; in 1980 students attended an average of only 114 h (So&is Indicadores, ibid., p. 203). 35. Brazil - Issues in Secondary Education (1989) p. 19. These estimates apply to non-secondary school training. Estimates of the cost of secondary education programs are much higher. 36. But pay in agricultural pursuits is still 25% lower than that in other occupations after standardizing for differential schooling, experience and location. Note that if agricultural workers with more schooling also have access to better capital goods (including land), the schooling coefficients will likely contain an upward bias.
REFERENCES BEHRMAN, J.R. (1987) Schooling in developing countries: which countries are the over- and underachievers and what is the schooling impact? Econ. Educ. Rev. 6, 111-127. BEHRMAN, J.R. and Birdsall, N. (1983) The quality of schooling: quantity alone is misleading. Am. Econ. Rev. 73, 928-946. BIRDSALL, N. and BEHRMAN, J.R. (1984) Does geographical aggregation cause overestimates of the returns to schooling? Oxford Bull. Econ. Statist. 46, 55-72. CHISWICK, CU. (1976) On estimating earnings functions for LDCs. J. Develop. Econ. 3, 67-78. DOUGHERTY, C. and PSACHAROPOULOS, G. (1977) Measuring the cost of misallocation of investment in education. J. Hum. Resour. 12, 446-459. EATON, P. (1985) The quality of schooling: comment. Am. Econ. Rev. 75, 1195-1201. FUNDACAO INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATICA (IBGE) (1985) Indicadores Sociais Tabelas Selectionadas, Vol. 2 - 1984. Rio de Janeiro. HANUSHEK, E.A. (1986) The economics of schooling. J. Econ. Lit. 24, 1141-1177. HECKMAN, J. (1976) The common structure of statistical models of truncation, sample selection, and limited dependent variables and a simple estimator for such models. Ann. Econ. Social Measurement 5, 475-492. HENDERSON, J.W. (1983) Earnings functions for the self employed. J. Develop. Econ. 13, 97-102. LEE YING SOON (1987) Self employment versus wage employment. Estimation of earnings functions in LDCs. Econ. Educ. Rev. 7, 81-89. MINCER, J. (1974) Schooling, Experience and Earnings. New York: National Bureau of Economic Research. NEWBURG, B.C. and MARTIN, K.L. (1972) The educational crisis in the lesser developed countries. J. Develop. Areas 6, 155-162. PATRICK, G.F. and GRABER, K.L. (1977) Income generation among small farmer households in Brazil. J. Develop. Areas 11. PHILLIPS, J.M. (1987) A comment on farmer education and farm efficiency: a survey. Econ. Develop. Cult. Change 35. PLANK, D.N. (1986) State action and the distribution of school enrollments in Brazil, 1970. Econ. Educ. Rev. 5, 403-414. PSACHAROPOULOS, G. (assisted by HINCHLIFFE, K.) (1973) Returns to Education: An International Comparison. San Francisco: Jossey-Bass Press. PSACHAROPOULOS, G. (1985) Returns to education: a further international update and implications. J. Hum. Resour. XX, 5‘83-664. REIMERS, C.W. (1983) Labor market discrimination against hispanic and black men. Rev. Econ. Statist. 65, 570-579. WORLD BANK (1984) Brazil: Economic Memorandum. Washington, DC. WORLD BANK (1988) Brazil - Public Spending on Social Programs; Issues and Options, Vol. I. Washington, DC, 27 May 1988. WORLD BANK (1989) Brazil - Issues in Secondary Education, Vol. I. Washington, DC, 28 November 1989.
135
Schooling Returns in Brazil APPENDIX Table 1. Probit Explanatory
regression
variable
Intercept First cvcle , (Grades l-4) Second cycle (Grades 5-8) High school College Major
urban
area
Other
urban
area
Relation to household Son Relation to household Grandfather Relation to household Grandchild Age 25-34
head: head: head:
Age 35-44 Age 45-54 Age 55+ Married South Out of school Black Mixed Recently
arrived
migrant
Log likelihood ratio (multiplied by -2) Number of observations
of employment
status* (1) 0.64153 (21.83) 0.04254 (10.23) 0.00450 (0.77) 0.02011 (1.99) 0.07556 (7.02) -0.51308 (-30.58) -0.42861 (-28.32) -0.58817 (-26.99) -0.40129 (-4.95) -0.36785 (-13.90) 0.31845 (16.48) -0.03261 (-1.39) -0.56721 (-24.20) - 1.24345 (-52.48) 0.53044 (27.44) 0.00215 (0.18) 0.95311 (60.45) -0.02939 (-1.27) -0.01489 (-1.20) 0.01089 (0.39) 19743.2 100,265
*All observations have been weighted by the appropriate sampling weights. t statistics are in parentheses.
0272-7757/91$3.00+ 0.00 @ 1991Pergamon Press plc
New Estimates of the Returns to Schooling in Brazil* MICHAEL B. TANNEN
Professor
of Economics,
University
of the District of Columbia, Washington, DC 20008, U.S.A.
4200 Connecticut
Avenue
NW,
Abstract - Estimates of the returns to schooling in Brazil in 1980 are derived by fitting earnings functions to Census microdata. The average private rate of return has fallen by about one-third from 1970, but still remains quite respectable at about 12-13%. When schooling levels are disaggregated, the lowest private rates emerge for primary education. Those to higher education are the greatest of any level. Correction for selection bias hardly disturbs the differential among the schooling levels. Given the huge per pupil subsidy in public universities, it is not surprising that social returns are much lower than private ones for higher education. But the social rate of return is still the greatest for that level. Some types of vocational training (notably in industrial skills) also provide high returns, even when social expenditures are factored in. Geographic differences in rates of return to different schooling levels are not substantial.
questions: (1) Is education in Brazil a good investment? In particular, how large are the private and social returns? (2) How do the returns for primary, secondary and post-secondary education compare? (3) How great are regional differences in these returns? (4) Do persons with vocational education earn more than those with a general education? (5) Are there lower returns to education in government employment than in the private sector? and (6) Are there any returns to education in agriculture?
I. INTRODUCTION ALMOST overlooked
in the large body of evidence on the returns to education in less developed countries (LDCs)’ has been the recent situation in
Brazil, despite the country’s size and importance, and its spectacular successes and failures in promoting growth and development. The few studies that have been performed for that country apply to 1970 and earlier, and so much has happened in the interim that they now seem dated.2 New estimates of the returns to education for men in Brazil are developed in this paper. The analysis is based upon 1980 Census of Population microdata, supplemented by a series of interviews we conducted there with public officials, business managers, community leaders and farmers. The 1980 Census tapes provide representative and unusually detailed information for a LDC on individual employment, income, schooling and other persona1 characteristics. They are an excellent source with which to address many issues related to the labor market returns to schooling. Rate of return estimates derived from these data are used in this paper to address the following *This paper is an extension of earlier work I performed here, however, [Manuscript
are entirely my own. received 13 January 1988; revision
accepted
II. NATIONAL ESTIMATES OF THE PRIVATE RETURNS TO EDUCATION
Brazil has experienced a rapid pace of industrial development in recent decades, but human capital formation has not matched that of physical capital. The country is unusual, in that the educational attainment of the population is so low for a middleincome LDC. In 1980, the economically active population averaged only 4.19 years of schooling,3 and more than one-quarter had not completed so much as a single year.4 Public spending on social programs, meanwhile, is massive, consuming an estimated 25% of Gross under
contract
for publication 123
to The World 9 April
Bank in 1986. Views expressed
1990.1
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Economics of Education Review
Domestic Product, with roughly one-quarter devoted to education.’ But a very young and rapidly growing population has placed enormous strains on the school system. Administrative inefficiencies and the system of educational finance, dependent upon municipal and state tax revenues, have also worsened matters, with public support of schools varying enormously.6 Tuition-charging private schools fill some of the void,’ but limited or nonexistent access to any type of schooling is prevalent in some rural locations. Even where schools are convenient, however, absenteeism, repetition of grade and the dropout rate are all high.8 Traditional explanations of these problems emphasize as contributing reasons the inadequacy of facilities, shortages of educational materials and qualified teachers, high direct and opportunity costs of attending school, and a curriculum that is simply too tough.’ To what extent, though, can the low educational attainment of the population be attributed to a lack of financial incentives? The private returns to schooling are measured below using the regression approach suggested by Mincer (1974). lo The specific earnings equation used here is In Y = b0 + b,S + b2X + b3X2 + b4Majurb + b50thurb
+ b,Southeast + u,
(1)
where the dependent variable, earnings, is expressed in terms of its natural logarithm (In Y). S is the educational attainment of the individual measured by the number of years of school completed. The anticipated parabolic relationship between earnings and years of experience (X), meanwhile, is represented by the quadratic expression involving X and X2. Three geographic variables are included to capture systematic differences in earnings associated with location apart from skill. Majurb is a dummy variable representing residence in one of the major metropolitan areas in the country,” while Othurb is another dummy representing residence in any other metropolitan area. The dummy variable Southeast is added to control for the higher level of earnings in that region.12 Finally, the bs serve as the parameters to be estimated (all are expected to be positive except b3), while u is the disturbance term. The private rate of return to education is inferred from the schooling coefficient b, .I3
Equation (1) has been fitted to 1980 Census microdata for the monthly earnings in cruzeiros of employed males between the ages of 1.5and 65 in the private nonfarm sector. r4 Only persons not enrolled in school were included in the sample. The dependent variable represents the sum of both monetary earnings and the monetarized value of payments-inkind (Census data contain a self-supplied estimate of the latter15). Results are shown in column (1) of Table 1.16 The semilogarithmic earnings function, so often applied in explaining earnings differences in developed countries, also performs admirably in explaining the determinants of private nonfarm earnings in Brazil in 1980. The corrected R2 is very respectable for a cross-sectional earnings equation fitted to microdata. All coefficients have the anticipated sign, and are statistically significant at the 1% level. Our primary interest, though, is in the schooling coefficient. It implies that the private rate of return to schooling is 13.2%.” Although respectable, this is substantially lower than the rates of return estimated for Brazil in 1960 and 1970. This change does not appear to be a statistical artifact (owing to different estimation methodologies). One equation in the BehrmanBirdsall (1983) paper differs from our own only in that it applies to 15-35 year old males in 1970 and does not include geographic dummy variables. The schooling coefficient there implies an average rate of return of 20.5% for 1970. Rerunning our equation for the same age group in 1980 and dropping the geographic dummies produces an estimate of the average rate of return of 12.3%. Thus, private rates of return to schooling in Brazil have apparently dropped by more than one-third over the 1970-1980 decade. The country is hardly unique in experiencing a decline in the returns to schooling in recent years. This development seems to imply that the supply of educated labor has increased more rapidly than demand. Indications of the former are certainly there, with the number of persons enrolled in Brazilian colleges and universities more than tripling over the 1970-1980 decade, and high school enrollments growing nearly as fast. Indeed, critics have expressed the view that political pressures create a tendency to invest heavily in higher education, at a time when illiteracy limits the prospects of a sizable portion of the workforce from obtaining gainful employment.
Schooling Returns in Brazil Table 1. Regression
Explanatory variable
of monthly earnings (I)
(2)
7.58454 (533.30) 0.12430 (152.99)
Intercept Years of schooling (total) First cycle (Grades 1-4) Second cycle (Grades 5-8) High school College
0.07948 (83.91) -0.00133 (-63.38) 0.12320 (9.73) 0.02303 (1.87) 0.19161 (29.52)
Years of experience Experience squared Major urban area Other urban area Southeast dummy
private nonfarm*
7.63071 (512.55)
7.98181 (451.02)
0.12069 (50.87) 0.07810 (29.37) 0.14620 (31.57) 0.21001 (54.96) 0.07744 (82.24) -0.00131 (-62.69) 0.14219 (11.31) 0.03945 (3.23) 0.19007 (29.45)
0.10995 (46.58) 0.07960 (30.34) 0.14130 (30.91) 0.19278 (50.71) 0.04584 (35.67) -0.00055 (-18.36) 0.24543 (19.26) 0.12493 (10.16) 0.19934 (31.28) -0.99082 (-35.57) 0.4606 46,537
Mills ratio Corrected R squared Number of observations *All observations parentheses.
0.4351 46,537
have been weighted
0.4459 46,537
by the appropriate
They maintain there is a welfare loss due to misallocation, believing that output could be raised by rechanneling resources toward primary education. A cursory view of the facts gives credence to this view. Illiteracy rates, though falling, remain high. ‘s Financial support for higher education, meanwhile, consumes 23% of all public spending on education, although only 5% of the total student population is at that level. I9 Brazilian policymakers have generally resisted this suggestion for reallocation, responding that the country’s continued rapid industrial development requires a growing supply of skilled manpower. A more sophisticated investigation of the resource allocation question, however, would examine private and social rates of return.20 Consider the private financial incentives first (social returns are discussed in a later section). To investigate the issue, Eqn (1) is modified by replacing the single schooling variable with separate ones that represent years of school completed at each of the levels that characterize the Brazilian
(3)
sampling weights. I statistics are in
educational system. There, primary school is broken up into two cycles, grades one to four (the first cycle), and grades five through eight (the second cycle). After primary school, the system is typical, consisting of high school, college and graduate programs.‘l Results from fitting this expanded earnings equation to the data appear in column (2) of Table 1. Private returns of 12.8 and 8.1% are implied for the first and second cycles of primary education, respectively. 22Returns to high school are greater, at 15.7% per year. Most striking, though, are the even larger returns to higher education. A year of college or university training has an impressive private return of 23.4%.23 These results, however, are potentially subject to a type of selection bias. Specifically, the equation has been estimated from data on working males, resulting in a censored sample of the entire male population.24 A method of correcting for this problem is Heckman’s (1976) technique. This involves a two-step procedure in which in the first
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Economics of Education Review
stage, the probability that an individual will be gainfully employed (and out of school) is determined according to a probit regression equation in which a series of personal and locational characteristics serve as the regressors. From these results, a selection variable (the inverse Mills ratio term) is created,25 and then inserted into the RHS of the earnings equation. That equation is then re-estimated for the initial sample (i.e. those with earnings), yielding consistent estimates of the coefficients free of censoring bias. Results using this procedure appear in column (3) of Table 1.26 There are some changes in the estimated returns to education, though none are of a drastic nature. The rate of return to the first cycle of primary school is somewhat lower than before, at 11.6%, but the rate of return to the second cycle, at 8.3%, is little affected. A downward adjustment is evident in the other schooling coefficients, with the returns to high school reduced slightly, to 15.2%, while those to a year of college are reduced more, to 21.5%. Nonetheless, the returns to college remain the highest of any schooling level. III. REGIONAL
DIFFERENCES
The results in Table 1 raise the question of why the pattern of returns to education in Brazil is dissimilar from that in other LDCs. A possible explanation is differences in the extent of economic development. Brazil may be atypical because of the economic dominance of the highly industrialized Southeastern region of the country. The presence of such a large modern sector requiring many skilled workers could result in a strong demand for educated labor (relative to supply), even though the situation in other parts of the country might be similar to that in other LDCs. Regional differences such as this might have been obscured in estimating Eqn (1). While that equation did allow for differences in the level of regional earnings in terms of a simple “lump-sum” (i.e. dummy variable) value, the estimated rates of return were not permitted to vary by region. As a result, an average of the returns in different regions appeared. In this section, further estimates are obtained by separating the data for each region. To focus more clearly on the extremes in regional differences, only the Southeast and the Northeast*’ are considered below. The Southeast was selected because it is the most prosperous region in the
country. The Northeast, on the other hand, offers the most striking contrast within Brazil. The region is large in both area and population (30% of the nation resides there). It is, however, so pervasively poor that it contains the greatest concentration of poverty in all of Latin America. Indeed, there is a substantial difference in educational attainment between these regions, with many individuals in the Northeast reporting never having attended schoo1.28 Estimates of the private educational returns in each of these regions are shown in Table 2. Columns (1) and (2) apply to the situation in the Southeast, while results for the Northeast appear in columns (3) and (4). Columns (2) and (4) include the correction for censoring (inverse Mills ratio term). Consider first the findings for the Southeast. In column (2), the rate of return for a year of schooling in the first cycle of primary school is 11 .O%, while that for the second cycle is smaller, at 8.0%. The returns to high school and college, however, are considerably larger, at 15.2 and 20.7%, respectively. The magnitude and pattern of rates of return in the Northeast differ somewhat, but pronounced differences simply do not appear. There, returns to the first and second cycles of elementary school are quite similar to one another (at 9.6 and 9.9%). As in the Southeast, however, rates of return to more advanced schooling are greater. For high school the estimate is 16..5%, while that to college is a very impressive 24.3%. Interestingly, despite the region’s relative lack of economic development, the returns to college in the Northeast exceed those in the Southeast by more than 3.5 percentage points.*” We can conclude that there is little similarity between the pattern of returns for different schooling levels in either region and that observed in most other LDCs. The suggestion that regional aggregation is responsible for this difference draws no support from the results just presented. There is simply no indication here of exceptionally high returns associated with primary education. Nor is there any indication of a serious overinvestment in higher education, at least as far as private returns are concerned. IV. SOCIAL
RETURNS
Private rates of return are important in understanding individual incentives to attending school at different levels, but they are not a sufficient guide to resource allocation when public monies are in-
127
Schooling Returns in Brazil Table 2. Regression
of monthly
earnings
by region* Northeast
Southeast Explanatory
variable
(1)
Intercept
7.74147 (321.64) 0.11600 (35.24) 0.07577 (22.99) 0.14583 (25.36) 0.20713 (45.00) 0.07963 (65.68) -0.00134 (-49.96) 0.23432 (11.22) 0.09510 (4.50)
First cycle (Grades l-4) Second cycle (Grades 5-8) High school College Years
of experience
Experience
squared
Major
urban
area
Other
urban
area
Mills ratio Corrected R squared Number of observations *All observations
0.4525 26,817
have been weighted
by the appropriate
volved. Education in Brazil is financed to a considerable extent by federal, state and local governments. These subsidies vary enormously, from very small per pupil expenditures for primary education in the Northeast, to very large amounts at public universities throughout the country. Social rates of return, which incorporate these public expenditures, are considered in this section. Their estimation involves two additional problems. The first is data availability. A recent World Bank report on Public Spending in Brazil (1989) concludes that comparable data on school expenditures throughout the country do not exist at the present time. That study, however, does provide some tentative information regarding per pupil expenditures at the primary, secondary and public university levels,30 and this is used below in computing social rates of return. The second problem is methodological. Mincer’s approach needs to be modified to incorporate subsidies so as to calculate social rates of return. Eaton (1985) shows this can be accomplished using the following procedure. First, the earnings gain from an additional year of schooling is calculated from the earnings regression results as Y* = 12[exp
8.11435 (302.46) 0.10462 (32.07) 0.07721 (23.80) 0.14109 (24.93) 0.18844 (41.21) 0.04660 (28.52) -0.00055 (-14.48) 0.34502 (16.52) 0.18952 (9.02) - 1.02763 (-29.59) 0.4698 26,817 sampling
(4)
(3)
(2)
weights.
7.65219 (253.96) 0.10235 (19.68) 0.09031 (11.23) 0.15670 (11.61) 0.23009 (20.31) 0.06988 (29.83) -0.00114 (-22.22) 0.07055 (2.74) -0.01220 (-0.51)
7.95668 (202.78) 0.09415 (18.11) 0.09168 (11.50) 0.15251 (11.40) 0.21757 (19.30) 0.04243 (13.00) -0.00048 (-6.43) 0.16070 (6.04) 0.06369 (2.61) -0.86482 (-11.96) 0.4147 7895
0.4040 7895 t statistics
are in parentheses.
(a + bI)-exp (a)], where Y* is the annual earnings gain, a is the intercept term, bI is the schooling coefficient and exp is the exponential function.31 Next, the present value of the lifetime gain in earnings (G*) is computing by integrating Y* over the working life, yielding
G* = Y*(-l/r)[exp
(-4lr)-exp
(-r)],
(2)
where r is the rate of return on a year of schooling at that level, and the individual is assumed to have a working life of 41 years.32 In equilibrium, this gain equals its cost, so that the following condition applies G* = C* = P*(-llr)[exp
(-r)-11.
(3)
In Eqn (3) P* represents the opportunity cost of another year of schooling to the individual. Finally, the social rate of return, r’, is implicitly defined by the equation Y*(-llr’)[exp
(-4lr’)-exp(-r’)] (-llr’)[exp (-r’)-11,
= (P*+PS) (4)
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Economics of Education Review
where PS denotes the per pupil public subsidy. The equation is then solved for r’ as the root of the polynomial expression. Applying this procedure to our data generates the following results. As expected, the incorporation of public subsidies yields estimates of social rates of return below those of private rates at every level of education. The return to the first cycle of primary education is reduced by 2 percentage points. That to the second cycle and to high school are each reduced by 1.2 percentage points. Not surprisingly, given the huge public expenditure per pupil reported for university education; the greatest difference (a decline of 9 percentage points) appears there. Universities, though, can provide external benefits that are not captured by this calculation. The expenditure data, moreover, apply to public universities, which enroll about half of all higher education students. Subsidies to private colleges and universities are presumably much less. How sensitive might the social rates of return be to these factors? The consequence of one guestimate, in which the public expenditure figure used earlier is reduced by 50%) is that the social rate of return rises to 15.6%, the highest of any level of education. Regardless of which cost estimate is used for higher education, though, it is clear that the evidence simply does not support the position that level of education is being oversubsidized. There is no indication here of an imbalance between private incentives and public expenditures on higher education.33 V. VOCATIONAL EDUCATION AND TRAINING The finding of large social rates of return to higher education would probably surprise many Brazilian entrepreneurs. A frequent complaint we heard from owners and managers of large industrial firms was the “overemphasis” on higher education by the students, the schooling system and the State. These managers maintained that what was needed instead were more persons with vocational training who could perform technician and related supervisory jobs admirably. There often was, they maintained, a shortage of qualified individuals who can fill the available blue-collar and supervisory vacancies. To help improve the situation, the managers cited a growing reliance upon industrial vocational training schools established as a cooperative venture between the government and the private sector
(SENAI%). But while these schools were generally given good marks for providing decent training in basic industrial and supervisory skills, they were also criticized as unable to provide the more sophisticated or specialized training needed by the firms. The managers suggested that individuals and the economy would be well served if vocational education programs were better funded. Interestingly, these remarks run counter to the criticism leveled against vocational education in LDCs by some policy analysts. Newburg and Martin (1972), for example, maintain that much of the “educational crisis” in developing countries is the result of undue emphasis on vocational education. This type of education is relatively expensive to provide, and yet it is, the authors state, not particularly useful. Many vocational graduates go on to obtain jobs which require only the general academic skills they have learned; their vocational education is wasted. Vocational schools nonetheless manage to survive and even thrive in LDCs, according to the authors because their degrees serve as a useful (but too expensive) screening device. Brazil, though, by virtue of its size and industrial development, has a much larger blue-collar job market than most LDCs, and it can absorb a much greater number of persons with vocational training in specialized skills. The returns to vocational education there may be atypical. But what does the quantitative evidence reveal? The Census questionnaire asked individuals whether they had received vocational training in the highest level of school they had attended. Specific kinds of vocational specialties in primary school and high school were listed for the respondents to check. We divided the responses for the primary school level into vocational skills which were intended to serve the commercial sector, those which were more trade or industry oriented, and those which fit neither category. A dummy variable was assigned to each and inserted into the earnings equation. Such a division was not practical at the secondary school level, due to the nature of the Census coding categories. As a result, only a single dummy for vocational training in high school was included. Results obtained using this modified earnings equation are shown in Table 3. The column (2) specification includes the Mills term, although its addition has only a slight effect on the other coefficients [compare with column (I)]. The important finding is that two of the four coefficients on
129
Schooling Returns in Brazil Table 3. Vocational education and monthly earnings* Explanatory variable
(1)
Intercept
7.62983 (512.38) 0.12039 (50.58) 0.08273 (19.47) 0.13362 (22.24) 0.20485 (21.95) 0.15576 (4.01) 0.05169 (2.18) -0.02152 (-1.32) 0.04743 (1.12) 0.07756 (82.20) -0.00131 (-62.73) 0.14239 (11.32) 0.03895 (3.19) 0.19011 (29.45)
First cycle (Grades 1-4) Second cycle (Grades 5-8) High school College Industrial education -
Primary school
Commercial education -
Primary school
Other vocational education Vocational education -
Primary school
Secondary school
Years of experience Experience squared Major urban area Other urban area Southeast Mills ratio Corrected R squared Number of observations
*All observations parentheses.
have been weighted
0.4307 46,537 by the appropriate
vocational education variables have a positive sign and are statistically significant in column (1) [one of which is marginally insignificant at the 5% level in column (2)]. The coefficient for commercial edu-
cation at the primary school level implies a private return of 4.7% for this kind of training (relative to what others receive with similar years of general academic training). The private returns to industrial education in primary school are much greater, at 16.4%. There is no evidence of any special economic value to the other two broad types of training, but because these categories are heterogeneous, the possibility remains that some types of training within them could lead to higher returns. Private returns, of course, do not tell the whole story. Public expenditures on vocational schools in Brazil are quite large, but the amount of the subsidy
(2) 7.98074 (450.96) 0.10961 (46.29) 0.08502 (20.28) 0.12849 (21.67) 0.18776 (20.37) 0.15221 (3.98) 0.04552 (1.94) -0.02550 (-1.58) 0.04424 (1.06) 0.04599 (35.75) -0.00055 (-18.45) 0.24549 (19.27) 0.12440 (10.12) 0.19934 (31.27) -0.99058 (-35.57) 0.4462 46,537
sampling weights. t statistics are in
varies with the type of program. A recent World Bank report estimates those to SENAI and SENAC training in 1985 to be $200 and $51 per pupil, respectively. 35 Using the expenditure data to calculate the social rate of return to industrial education at the primary school level (Eaton’s method) yields an estimated return of 14.4%) over and above the return to general academic training at that level. For commercial training in primary school the social rate of return is 4.4%. VI. SECTOR OF EMPLOYMENT (A) Public Sector Employment Administrative wage setting policies in LDCs tend to compress the wage and salary scale in the public sector. This can result in lower salaries of many
130
Economics of Education Review
government employees, and also reduce the returns to schooling, relative to those in the private enterprise. Does Brazil follow this general tendency? Employment by all three branches of government (federal, state and municipality) is considerable. As is typical, different pay scales exist, with federal salaries usually the highest (although some states match the federal pay scale), while salaries for state employees generally exceed those for municipal workers (the latter two vary by state and by municipality). To examine first whether the salary level is lower in public (relative to private) employment, three dummy variables representing the level of pay scales were created: one corresponding to employment in the federal government, another to employment in a state government, and the third for municipal
employment. The earnings equation was then run including these variables on a sample which consisted of males employed in the private nonfarm sector or in any of the government services (but persons engaged in military service were excluded). Results are shown in column (1) of Table 4. The coefficient on each government employment variable measures how earnings for persons employed in that service compare to earnings in the private sector, controlling for the effects of differential schooling, experience and location. Each of these coefficients has the expected sign, and is statistically significant. Their magnitude also follows the anticipated pattern. Pay in civilian government jobs is 7.5% less than that in the private nonfarm sector, while salaries of state employees run about 11% less than comparable skills would command in
Table 4. Monthly earnings in government Explanatory variable Intercept First cycle (Grades l-4) Second cycle (Grades 5-8) High school College Years of experience Experience squared Major urban area Other urban area Southeast Federal government State government Municipal government Mills ratio Corrected R squared Number of observations *All observations parentheses.
Government and private (1) 7.98757 (467.63) 0.10986 (48.16) 0.08187 (32.74) 0.13547 (31.53) 0.19572 (56.05) 0.04445 (36.20) -0.00051 (-18.15) 0.25365 (20.57) 0.12580 (10.58) 0.19874 (32.65) -0.08252 (-4.89) -0.12012 (-3.16) -0.22054 (-14.39) -0.99085 (-37.01) 0.4700 50,270
have been weighted by the appropriate
and agriculture*
Government only (2) 7.99599 (69.10) 0.09611 (5.27) 0.09231 (7.65) 0.06623 (3.75) 0.18750 (14.92) 0.02940 (4.30) -0.00017 (-1.08) 0.32800 (3.91) 0.19256 (2.36) 0.28681 (8.89)
-0.92577 (-5.66) 0.4937 1586
Agriculture (3) 7.75781 (397.19) 0.14000 (45.90) 0.12025 (12.03) 0.23526 (9.97) 0.16852 (6.05) 0.02833 (16.77) -0.00026 (-7.03) 0.25577 (7.38) 0.23269 (17.48) 0.13637 (12.86)
-0.85362 (-17.41) 0.2295 25,288
sampling weights. t statistics are in
Schooling
Returns in Brazil
private industry. Municipal employees fare the worst, with their salaries lagging 19.5% behind those of the private sector. While government pay overall is lower than that in the private sector, this does not necessarily imply that the returns to schooling are lower. These are examined in column (2). To avoid potential heterogeneity problems in pay among employees of different branches of government, these results apply to federal employees. Comparing these results to those for the private sector (see Table l), the only substantial difference in returns to education occurs for persons with a high school education. Government employees in this group receive a rate of return to their incremental schooling of less than 7%) compared with the 15% received by comparably educated persons in the private sector. This salary compression is not evident for employees with a college education, who receive a rate of return comparable to that in the private sector. Only 6% of federal employees in the sample, moreover, fall in the high school category, so overall, there is not much difference in the rates of return to education received in the federal and private sectors. (B) Agriculture and Nonagricultural Differences The earnings of agricultural workers in Brazil are quite low, compared to those in other industries. Agricultural workers also have less schooling, but prior analyses of agriculture in Brazil have produced mixed results regarding the effects of education on earnings. Patrick and Graber (1977), for example, found that functional literacy was associated with higher farm income in three out of four small regions in Brazil. Phillips (1987), however, pointed out that of nine studies of the returns to farmer education in Brazil, only two featured a positive and significant effect of education on output. To provide further evidence on this issue, the earnings equation was re-estimated using a national sample of agricultural workers. Results are shown in column (3) of Table 4. According to these, the private rate of return to all levels of education
131
except college are actually higher than they are in the private nonfarm sector (see Table 1). The greatest difference is for high school, where agricultural workers with such education receive more than twice the rate of return similarly schooled persons in the private nonfarm sector realize.a6 VII. SUMMARY
AND CONCLUDING
REMARKS
The average private rate of return to schooling in Brazil in 1980 was respectable, but substantially less than what was observed for that country in 1970. When the returns to different schooling levels were examined, those to higher education were the greatest of any level. Little wonder, then, that enrollment growth has been most rapid at that level. The relatively low returns to primary education, meanwhile, help explain why the majority of Brazilian students do not complete that level. As expected, social rates of return were lower than private rates at all levels, with the greatest difference occurring for higher education. Still, the social returns to higher education were at least comparable to those for secondary education and exceeded those for primary education. Interestingly, although a great regional economic disparity exists between the most prosperous Southeast and the least prosperous Northeast, rates of return to different education levels in the private nonfarm sector are generally comparable between the regions. Vocational training in industrial skills, meanwhile, was observed to yield sizable private and social returns over an academic curriculum at the primary school level. There was also some evidence of positive but smaller private and sociai returns to commercial training at the primary school level. The only notable difference in the returns to education between private and federal government employees occurred for persons who attended high school. They fared substantially less well in the federal sector. And despite low wages in agricultural pursuits in general, rates of return to education there were more than comparable to those in other sectors.
NOTES 1. See Psacharopoulos (1985) for a survey of the literature. 2. Estimates for Brazil by Birdsall and Behrman (1983) use 1970 Census data. Psacharopoulos (1973) cited earlier estimates in 1970 PhD dissertations by C. de M. Castro, S.D. Hewlett, M.O. Lerner, and
132
Economics
of Education
Review
A.J. Rogers, III. A World Bank staff paper by the same author reached me after this manuscript was substantially completed. It uses 1980 Census data, and covers some of the same ground, but does not consider either social returns or selection bias. 3. Brazil: Economic Memorandum (World Bank, 1984) Table 10.1, p. 305. 4. Calculations based upon the sample of employed males (all industries) used in this paper. Percentages do not vary much by age groupings (10 year intervals). 5. Brazil L Public Spending on So&l ho&a& (World Bank, 1989, p. iii). 6. As one indication. the World Education Encvcloaedia (Facts on File Pub.. v. 161) notes “Primarv teachers’ salaries paid by the State of Sao Pa& were‘ 367% higher than ‘salaries paid by some municipalities in the same state”. 7. Private and religious schools are found at all levels of education, with 52% of all secondary schools in 1980 being private. 97% of all secondary schools, however, were in urban areas (ibid., pp. 153-55). 8. The country has made gains in achieving high enrollment rates, which were 80% of the school-age population in 1970 and 90% in 1980 (Economic Memorandum, ibid., Table 4.2, p. 128), but less so with respect to attrition and repetition. In the 196Os, 15% of first graders went on to complete grade four. Two decades later, only 17% completed primary school (World Encyclopedia, ibid., pp. 15354). 9. Among those 7-24 years old in 1982 who never attended school, 24% listed either no school available or no space in school as the reason why. (Zndicudores Sociais, Vol. 2, IBGE, 1984, p. 196.) Another 22.7% said they had to work. Also, the National Pedagogical Studies Institute (INEP) reported that curricular content in primary school was substantially higher in Brazil than in the U.S., France or Switzerland (World Encyclopedia, ibid., p. 153). 10. Mincer (1974). Compared to the older method of equalizing lifetime earnings streams, this approach uses individual, rather than grouped observations, and individual differences in experience and location can thus be directly controlled for. It produces unbiased estimates, however, only if the disturbance term is uncorrelated with the schooling variable. This assumption has been challenged on several grounds, including self-selection (censoring) bias, which we consider later. 11. These are Belem, Fortaleza, Recife, Salvador, Belo Horizonte, Rio de Janeiro, Sao Paula, Curitiba, Porto Allegre and Brasilia. 12. The “Southeast” Census region contains the states of Rio de Janeiro, Sao Paulo, Minas Gerais and Espirito Santo. Other regression results which included only the first two states in the dummy were very similar. 13. Given reasonable assumptions [see Mincer (1974)], the private rate of return is equal to the antilog of the schooling coefficient minus one. These assumptions are not challenged here, but future work could examine their validity. For example, one assumption is that the direct costs of schooling are offset by the part-time earnings of students. Plank (1986) noted such direct costs can be large in Brazil. 14. Data used in this study were obtained by taking a one in 10 sample from the national sample Census tapes. Because cross-sectional earnings differences are examined, the serious inflation the Brazilian economy has experienced is not a source of confusion here. The analysis is restricted to males because of their more consistent labor force participation. 15. In the private nonfarm sector such payments are not uncommon, but are usually small. For example, in visits to factories, we noted that employees often received lunches or discounts on the goods produced. The payment-in-kind information in the Census data was, however, sparsely reported. 16. The earnings of the self-employed are not distinguished from the earnings of employees here. Chiswick (1976) argues that the earnings determinants for the self-employed are different than those for employees. But Henderson (1983) found similar determinants for the two groups. Lee (1987), meanwhile, observed no selection bias present, but a greater percentage of the variation in earnings of employees that the self-employed can be explained by a human capital function. 17. Because the estimate does not distinguish the effect of school quantity from that of “quality” the schooling coefficient should be interpreted as measuring the returns to another year of schooling at the existing average quality. It seems obvious that quality matters, particularly when differences are great. Researchers, however, have had a difficult time documenting its importance [see Hanushek (1986)]. In Behrman and Birdsall (1983), quality is represented by the educational attainment of teachers in a geographic location. Inserting this measure into the earnings function via a quadratic interaction with years of schooling, the authors find large returns to school quality. Hanushek points out, however, this assumes teacher’s education is a viable measure of quality. A large number of studies have tried to directly measure the effect of teacher education on student performance, but its coefficient is statistically significant in only 11 out of 106 cases, with five significant cases having the wrong sign. The Behrman-Birdsall estimate is also based upon an
Schooling Returns in Brazil interaction term between years of schooling and quality squared, even though its coefficient is highly insignificant. 18. These were 40.6% in 1970, 31.3% in 1980 (Economic Memorandum, ibid., p. 127). 19. Public Spending, ibid., p. 39. 20. Psacharopoulos (1985) reports that social rates of return in LDCs usually decline with additional schooling. Private rates, however, are sometimes greater for university than secondary education, although both are below those of primary school. In seven studies he cited for Latin America, this was the case for Mexico (1963), Columbia (1974), Costa Rica (1974) and Venezuela (1981). The computation of social rates conveniently ignores several issues. Externalities are overlooked, but university research, for example, provides access to a knowledge base that might not otherwise exist in a community. On the other hand, Mincer’s interpretation of Eqn (1) assumes that education raises earnings because it raises productivity. In the competing “screening view”, education merely certifies that an individual is more productive, even if education is not the reason why. If the latter is more correct, the calculation of the private returns is unaffected (because more schooling still leads to higher earnings), but the social returns need to be reduced. 21. Prior to 1971, grades five to eight were considered to be the first phase of secondary education. With the passage of Law 5692 in 1971, primary education was redefined as grades one to eight. High school is usually for 3 years, although students sometimes attend a fourth year for college preparation. 22. These estimates are derived from the Mincer-type equation which assumes that each year of schooling involves a year of earnings foregone. (This assumption applies whether separate schooling variables for each level or a single schooling variable is used.) But students in primary school in Brazil have required almost 3 years to complete a single grade. Children not in school, however, often work at very low wages in small businesses or factories or providing menial services. I plan to address these issues in a subsequent paper. 23. The higher education variable includes years of graduate school training. There were few cases of the latter. 24. The problem is that the (unobserved) wage offers of those not working are probably lower than those for persons in the sample [see Reimers (1983) for more explanation]. 25. Results from the probit equation we ran explaining inclusion in our sample are shown in Appendix Table 1. The Mills term is defined asfl.)lF(.). where f is the normal density and F is its cumulative function. The expression in parentheses (.) denotes the sum of the products of the probit regression coefficients and the regressors. 26. When the Mills term is inserted into the column (1) specification, the estimate of the average rate of return to schooling becomes 12.5%. 27. The Northeast consists of the states of Maranhao, Piaui, Ceara, Rio Grande do Norte, Paraiba, Pernambuco, Alagoas, Sergipe and Bahia. Because much of the financial resources for public primary and secondary schools come from municipal and state governments, schools in the Northeast are less well funded than those in the Southeast, often considerably so. In the rural Northeast, primary school teachers themselves often have no more than 4 years of education, and books, papers and physical facilities are in short supply. A reasonable hypothesis, therefore, is that pronounced differences in school quality between the regions would lead to differences in rates of return. 28. This disparity is less when the nonfarm private sector is considered, but remains considerable. About 75% of agricultural workers in the Northeast report no formal schooling compared to 30% of nonfarm workers. For the region as a whole 54.3% have no school years completed, compared to 16.5% in the Southeast. 29. The Northeast Development Agency (SUDENE) has helped manufacturing firms from the Southeast and abroad set up facilities in the Northeast. These have no doubt increased job opportunities for educated labor in the region. There is no consideration of the migration motive here, e.g. more educated persons in the Northeast may move south to enhance job prospects. It should also be recognized that the rate of return is based upon the gain in earnings. Equal (or unequal) rates of return do not necessarily imply equal (or unequal) salaries. 30. These estimates (for 1983) are $149 for primary school, $144 for secondary school and $2586 for public university education. We deflated them to 1980 dollars and then converted to cruzeiros using the August 1980 parallel exchange rate. 31. Since the Census data apply to monthly earnings, it is necessary to multiply by 12 to get annual earnings. 32. Eaton assumed a working life of 41 years, but the calculation is robust with respect to alternate assumptions. 33. The distributional issue is not considered here. There is evidence that persons from above average incomes benefit most from subsidized higher education (Public Spending, ibid.). 34. Servicio National de Aprendizagem Industrial. There are also cooperative arrangements for
134
Economics of Education Review commercial training (SENAC), training in rural skills (SENAR) and other vocational programs, such as the elite federally funded schools. SENAI courses are taught at the primary and secondary school levels. These are usually brief; in 1980 students attended an average of only 114 h (So&is Indicadores, ibid., p. 203). 35. Brazil - Issues in Secondary Education (1989) p. 19. These estimates apply to non-secondary school training. Estimates of the cost of secondary education programs are much higher. 36. But pay in agricultural pursuits is still 25% lower than that in other occupations after standardizing for differential schooling, experience and location. Note that if agricultural workers with more schooling also have access to better capital goods (including land), the schooling coefficients will likely contain an upward bias.
REFERENCES BEHRMAN, J.R. (1987) Schooling in developing countries: which countries are the over- and underachievers and what is the schooling impact? Econ. Educ. Rev. 6, 111-127. BEHRMAN, J.R. and Birdsall, N. (1983) The quality of schooling: quantity alone is misleading. Am. Econ. Rev. 73, 928-946. BIRDSALL, N. and BEHRMAN, J.R. (1984) Does geographical aggregation cause overestimates of the returns to schooling? Oxford Bull. Econ. Statist. 46, 55-72. CHISWICK, CU. (1976) On estimating earnings functions for LDCs. J. Develop. Econ. 3, 67-78. DOUGHERTY, C. and PSACHAROPOULOS, G. (1977) Measuring the cost of misallocation of investment in education. J. Hum. Resour. 12, 446-459. EATON, P. (1985) The quality of schooling: comment. Am. Econ. Rev. 75, 1195-1201. FUNDACAO INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATICA (IBGE) (1985) Indicadores Sociais Tabelas Selectionadas, Vol. 2 - 1984. Rio de Janeiro. HANUSHEK, E.A. (1986) The economics of schooling. J. Econ. Lit. 24, 1141-1177. HECKMAN, J. (1976) The common structure of statistical models of truncation, sample selection, and limited dependent variables and a simple estimator for such models. Ann. Econ. Social Measurement 5, 475-492. HENDERSON, J.W. (1983) Earnings functions for the self employed. J. Develop. Econ. 13, 97-102. LEE YING SOON (1987) Self employment versus wage employment. Estimation of earnings functions in LDCs. Econ. Educ. Rev. 7, 81-89. MINCER, J. (1974) Schooling, Experience and Earnings. New York: National Bureau of Economic Research. NEWBURG, B.C. and MARTIN, K.L. (1972) The educational crisis in the lesser developed countries. J. Develop. Areas 6, 155-162. PATRICK, G.F. and GRABER, K.L. (1977) Income generation among small farmer households in Brazil. J. Develop. Areas 11. PHILLIPS, J.M. (1987) A comment on farmer education and farm efficiency: a survey. Econ. Develop. Cult. Change 35. PLANK, D.N. (1986) State action and the distribution of school enrollments in Brazil, 1970. Econ. Educ. Rev. 5, 403-414. PSACHAROPOULOS, G. (assisted by HINCHLIFFE, K.) (1973) Returns to Education: An International Comparison. San Francisco: Jossey-Bass Press. PSACHAROPOULOS, G. (1985) Returns to education: a further international update and implications. J. Hum. Resour. XX, 5‘83-664. REIMERS, C.W. (1983) Labor market discrimination against hispanic and black men. Rev. Econ. Statist. 65, 570-579. WORLD BANK (1984) Brazil: Economic Memorandum. Washington, DC. WORLD BANK (1988) Brazil - Public Spending on Social Programs; Issues and Options, Vol. I. Washington, DC, 27 May 1988. WORLD BANK (1989) Brazil - Issues in Secondary Education, Vol. I. Washington, DC, 28 November 1989.
135
Schooling Returns in Brazil APPENDIX Table 1. Probit Explanatory
regression
variable
Intercept First cvcle , (Grades l-4) Second cycle (Grades 5-8) High school College Major
urban
area
Other
urban
area
Relation to household Son Relation to household Grandfather Relation to household Grandchild Age 25-34
head: head: head:
Age 35-44 Age 45-54 Age 55+ Married South Out of school Black Mixed Recently
arrived
migrant
Log likelihood ratio (multiplied by -2) Number of observations
of employment
status* (1) 0.64153 (21.83) 0.04254 (10.23) 0.00450 (0.77) 0.02011 (1.99) 0.07556 (7.02) -0.51308 (-30.58) -0.42861 (-28.32) -0.58817 (-26.99) -0.40129 (-4.95) -0.36785 (-13.90) 0.31845 (16.48) -0.03261 (-1.39) -0.56721 (-24.20) - 1.24345 (-52.48) 0.53044 (27.44) 0.00215 (0.18) 0.95311 (60.45) -0.02939 (-1.27) -0.01489 (-1.20) 0.01089 (0.39) 19743.2 100,265
*All observations have been weighted by the appropriate sampling weights. t statistics are in parentheses.