19738 A View from LATHR No. 26 A COST BENEFIT ANALYSIS OF EDUCATIONAL INVESTMENT IN VENEZUELA, 1989 by Ariel Fiszbein and George Psacharopoulos Human Resources Division Technical Department Latin America and the Caribbean Region The World Bank November 1991 *A View from LATHR" is a series of occasional flyers produced by the Human Resources Division of Latin America and the Caribbean Technical Department of the World Bank for the purpose of stimulating discussion among staff on key issues facing the sector. The views expressed here are those of the authors and should not be attributed to the World Bank. Abstract In this paper we use data from the latest Venezuelan Household Survey . (1989, second semester) to assess macro priorities among educational investment in that country. An analysis of earnings by educational level reveals persisting, although declining over time, premia to the more educated . relative to the less educated. E.g., primary education graduates earn nearly twice as much as those with no education. Among secondary education graduates, those who followed a vocational/technical curriculum earn 22 percent above those with general/academic education. University graduates earn 87 percent above secondary school graduates. When the above earnings premia are combined with the costs of educational provision we get the social returns to the different levels of the education system. The cost-benefit analysis indicates that primary education is on top of the benefit-cost hierarchy: it yields the highest returns per unit of its low social cost. Higher education exhibits the lowest returns among the three levels of education, mainly due to the high cost of university provision. Secondary education lies in-between the above on the cost-benefit calculus. Among the two streams of the secondary level, technical/vocational education has less than one percentage point social rate of return advantage over general/academic education -- a result that runs counter to what has been found in the literature in other countries. The overall rate of return to education has been dropping over time, a result consistent with human capital theory. 勺 I. Introduction Venezuela is a country with well developed educational indicators for its level of per capita income. Primary education is almost universal: in 1988 the gross enrollment ratio was 106 percent while the net enrollment ratio was 94 percent.' However, repetition and school drop out are still significant problems. According to Schiefelbein and Heikkinen (1991) the repetition rate among first graders is approximately 28 percent while the repetition rate for all grades is 23 percent. The same authors indicate that 63 percent of the students complete sixth grade, and only 46 percent complete the nine grades of basic education. In 1988, secondary coverage was 54 percent2 , a fraction of it (20.7 percent) being technical-vocational. A major issue in current educational policy discussions within the country is the future of this level of education, especially on whether the vocational/technical dimension should be intensified or not. Higher education covers 27 percent of the population aged 18-24. A major issue here is the fact that the tertiary level of education absorbs more than 40 percent of the public resources for education 3 , an outlier by world standards. This indicates the importance of establishing the cost- effectiveness of Venezuela's heavy investment in higher education. In this paper we use data from the 1989 (Second Semester) "Encuesta de Hogares por Muestreo", a household survey conducted by the Venezuelan Office of Statistics (OCEI) in order to provide a snapshot of educational investment priorities in Venezuela. Ii. Previous Work Venezuela is a country for which cost-benefit analyses in education have been conducted since 1959. Shoup (19S9) used essentially tax records to estimate the returns to education, yielding an impressive 82 percent for primary education. We are grateful to the members of the World Bank's education sector mission for their help in gathering the cost data in this paper, and to Hongyu Yang and Jin Qian for the graphics and rate of return estimations. We benefitted from comments by Barbara Bruns and Cecilia Valdivieso on an earlier version of the paper. We are solely responsible for any remaining mistakes. Information on enrollment rates was obtained from World Bank (1991a). 2 The 54% coverage rate for secondary education corresponds to grades 7-11. In strict sense, secondary school starts at grade 10. Gross enrollment rate in the "upper secondary" level is much lower (23%, and the net enrollment rate is 17%). 3 World Bank (1991b). 2 Psacharopoulos and Steier (1988), using the "Encuesta de Hogares", found rates of return to education for males of 13.7 percent for 1975 and 11.4 percent for 1984. More recently, Psacharopoulos and Alam (1991) estimated returns to education for 1987. They found the overall rate of return to education to be 10.7 percent and that for males 9.7 per cent, while the rate for those employed in the private sector of the economy was 10.3 percent. These results seemed to indicate that returns to education were diminishing through time. III. Sample and Data Description Out of the 315,650 individual observations of the Encuesta we selected those aged 15 to 65 with positive income (Y). We have worked with the variables shown in Table 1. Years of schooling (S) has been constructed by combining information of the individual's highest level of education attended and the last grade completed in that level (Oultimo grado aprovado"). Experience has been constructed in the traditional Mincerian way, i.e. Age - S - 6. Education has been coded as in Table 2 that presents mean earnings by level of education as well as by type of employment. When compared with those presented in Psacharopoulos and Alam (1991), the numbers in Table 1 indicate that the Venezuelan labor force has become more educated (average schooling increased by almost one year) a trend already noticed by those authors. At the same time the labor force became younger (the mean age fell by less than one year) and correspondingly less experienced (average experience fell by one and one half years). In terms of the spread of earnings by level of education it is interesting to note that, on average, university graduates earn 2.3 times as much as graduates from primary school, an almost identical ratio to the 2.4 value Psacharopoulos and Alam found for 1987. Age-earnings profiles by level of education are well-behaved, as shown in Figure 1. The numbers are 3-year moving averages of mean earnings within single cells, age, and by level of education. The negative numbers in the beginning of the profile (shown in Annex 2) are the annual social costs while a person is still in school in order to complete the respective educational level. IV. The Determinants of Earnings In this section we fit Mincerian earnings functions with education in continuous and dummy forms. The functions are fitted to the sample as a whole and within the private and public sub-sectors of the economy, and by sex. The results shown in Table 3 indicate an overall private rate of return to schooling of 9.6 percent, and a rate of 9.4 percent for males. Thus, the declining trend in rates of return to education observed in section II appears 3 to be confirmed. Given the fact the mean educational level has increased from 4.6 years in 1975, to 6.6 in 1984, 6.8 years in 1987, and 7.7 years in 1989, our estimates confirm what appears to be strong evidence of diminishing returns to schooling. Returns were estimated independently for males and females, and for private and public sector employees. Returns to education are found to be higher for females than for males, a result already found in the previous studies. Returns to education in the public sector are found to be 1.7 percentage points lower than in the private sector. This result is consistent with the findings of previous studies, although the difference is larger than the ones estimated by Psacharopoulos and Steier and Psacharopoulos and Alam. Also consistent with previous analyses is the finding that returns to experience are significantly lower in the public than in the private sector. Finally, the Mincerian earnings function was fitted for males employees working in the private sector, obtaining a slightly lower rate of return to education (9.2 percent). These results seem to indicate that our estimates of the rate of return to education are robust and, as a result, can be taken to be an adequate indicator of the expected returns to new investments in education. Finally, in addition to estimating the traditional earnings equations, we also estimated earnings as a function of the level of education rather than of the years of schooling. We considered eight levels/types of education ranging from no education to complete university education.4 We have distinguished between two types of secondary education: general and technical. General secondary education encompasses the humanities and science fields, while technical secondary education includes all other fields.S Complete primary education was used as the reference group. Table 4 shows the earnings differentials by level of education, in index form, obtained from this estimation. It is confirmed that individuals with complete secondary-technical education earn more than those with general secondary education, although the differential is smaller than what the mean earnings shown in Table 2 suggested. Between levels, earnings differentials estimated in this fashion tend to be larger than the those obtained by comparing average incomes. Also, while average incomes seemed to suggest that 4 In the current system, the six-year primary education has been replaced by a nine-year basic education ("Educacion Basica"), followed by a cycle of "Educacion Media Diversificada y Profesional". The household survey, however, maintains the old distinction between primary and secondary levels. * As a result, we are not able to estimate the returns to the last three years of basic education independently of those to secondary education ("Educacion Media"). Our results should be interpreted as indicating the returns to the first six years of basic education, and the returns to completing "Educacion Media". 5 See Annex 1 for a detailed description of the way in which the unit costs were estimated for each of these two types of secondary education. 4 workers with incomplete university education earned less than workers with complete technical education, this apparent anomaly disappears when we control for number of hours worked and experience. Table 1 Means and standard deviations of variables in the sample Variable Mean Standard deviation Income (Y) (Bls/month) 6,706 5,745 Hours worked 42.8 9.5 Years of schooling 7.7 4.1 Age 34.8 11.8 Experience 21.0 12.9 Education dummies - no education .06 .24 - primary incomplete .15 .36 - primary complete .27 .44 - secondary incomplete .27 .44 - secondary . general .12 .32 . technical .02 .13 - university incomplete .04 .18 - university complete*/ .07 .25 Literate .94 .24 Urban .85 .36 Male .68 .47 Private sector employee .47 .50 Public sector employee .24 .42 Self-employed .23 .42 Employer .07 .25 Source: "Encuesta de Hogares por Muestreo," 1989 (2nd Semester), N=65009. Note : */ Unless specified otherwise, the term university refers to higher education, both in universities and other tertiary institutions ("Institutos" and "Colegios"). 5 Age-earning Profiles by Education Level Venezuela, 1989 Meaithly lesome (Vefezvels et.) 25000- 20000- University 15000- 10000 Secondary Primary 5000- noompiete prnary No Education 9 15 20 25 30 35 40 45 50 55 60 65 Age 6 Table 2 Mean earnings by selected worker characteristics Characteristic Bola/Month Educational Level No education 3,778 Incomplete primary 4,870 Primary complete 5,983 Secondary incomplete 6,472 Secondary complete 7,880 - General 7,554 - Technical 10,001 University incomplete 8,792 University complete 13,759 Type of Employment Private sector employee 5,801 Public sector employee 7,766 Self-employed 5,535 Employer 13,259 7 Table 3 The Determinants of earnings (dependent variable = InY) Entire sample Private Private Public sector Variable Mincerian Dummy form MaLes Females sector sector Mates Constant 4.541 5.166 4.740 5.019 4.618 5.202 4.576 S .096 .094 .113 .095 .078 .092 (152.86) (133.95) (92.31) (104.64) (78.25) (93.89) EX .040 .040 0.041 0.042 .046 .024 .046 (62.85) (61.49) (57.14) (35.26) (51.02) (22.01) (47.16) EX2 -.0004 -.0005 *.0005 -.0006 *.0006 -.0003 -.0006 (40.14) (39.82) (37.58) (23.22) (32.93) (12.19) (31.60) Log hours .738 .733 .712 .509 .695 .675 .741 (93.92) (92.49) (61.07) (45.25) (47.52) (38.05) (43.01) No education -.570 (52.48) Primary incompL. -.287 (38.58) Primary complete- .000 (.000) Secondary incompt. .207 (32.41) Secondary general .441 (53.14) Secondary techn. .637 (35.56) University incompl. .648 (48.45) University complete 1.069 (106.43) R2 .363 .356 .370 .386 .334 .356 .380 N 60,903 60,903 41,940 18,963 29,469 13,564 20,796 Numbers in parenthesis are absolute t-ratios. Dummy variable base. 8 V. The Returns to Education These are estimated for each completed level of education (relative to the previous one) based on the actual age-earnings profiles by level of education. The private rate of return is the discount rate that brings the net present value of the profile to zero. In the case of the private returns to education the only cost to the individual is forgone earnings for the duration of the respective schooling cycle. In the case of primary education it was assumed that children at this level incur only three years of foregone earnings. The social returns were estimated using the actual age-earnings profiles, and estimates of unit annual costs for each level/type of education shown in Table 6. Details about those costs can be found in Annex 1. The matrices used for the calculation of the social rates of return were included in Annex 2. Returns were estimated both for the entire sample and for male employees working in the private sector. The results are shown in Table 5. Private returns are found to be significantly larger for primary education than for the other two levels, a result consistent with previous findings for Venezuela as well as with the international evidence. A rate of return of the order of 27 percent is somewhat lower than the average rate for primary education corresponding to countries in Latin America (Psacharopoulos (1985)). A social return to primary education of 18.2 percent is also below the average for Latin America, but high enough to indicate the high priority which should be given to investments in this sector. Both the private and the social rates of return are higher when the estimation is restricted to males working in the private sector, probably a better indicator of returns in the competitive sector of the Venezuelan economy. Private rates of return to technical secondary education are found to be 4 percentage points higher than those to general secondary education. However, this difference is reduced to only 1.4 percentage points in the case of male employees in the private sector, a result that suggests that part of the above noted difference may be the result of non-competitive wage policies in the public sector. Given the higher unit costs of technical education, the difference in social rates of return to both types of secondary schooling appears to be significantly lower, although still existent, than the difference in private rates.6 Our best estimate of the difference between the social returns to the two types of education is 0.7 percentage points, based on males in the competitive sector of the labor market (last column of Table 5). 6 It must be noted that, the household survey includes individuals which studied under a variety of systems. In the 1960s there were independent technical schools, which were eliminated in 1969. Since then, and until 1977, the technical specialties were taught in the diversified schools. Since then, some technical fields are taught both in diversified and professional schools. Thus, our results should not be interpreted as evidence of higher external efficiency of one of the two current branches of the secondary level. 9 Table 4 Earnings Premiums by Level of Education (index) Estimated Differentials Mean Earnings No Education 56.6 63.1 Primary incomplete 75.1 81.4 Primary complete 100.0 100.0 Secondary incomplete 123.0 108.2 Secondary general 155.4 126.3 Secondary technical 189.1 167.2 University incomplete 191.2 147.0 University complete 291.2 230.0 Table 5 The Returns to Education by Level (percent) Private Returns Social Returns Entire sample Private sector Entire sample Private sector Education Level both sexes males both sexes males Primary 27.4 29.8 18.2 19.5 (rel. to no education) Secondary (rel. to primary) - general 11.9 11.7 8.9 8.9 - technical 15.9 13.1 10.8 9.6 University (rel. to general sec.) 12.0 11.9 7.0 7.3 (rel. to techn. sec.) 9.2 11.0 5.4 5.9 Note: Based on elaborate method, 3-year moving average. (See Annex for entire profile). Primary education graduates foregone earnings start at age 9. 10 Table 6 Unit annual costs (in 1989 second semester Bolivares) Educational Level Cost (Blsfyearl Primary 7,668 Secondary general 12,170 Secondary technical 18,198 Higher Education 62,795 Source: See Annex 1 International evidence on comparative returns to general and technical secondary education is not abundant. Psacharopoulos (1987) reviewed evidence from seven countries. Only two of those countries (Colombia and Taiwan) had returns to technical education higher than those to general education. Nevertheless, our results seem to confirm those of Psacharopoulos and Steier (1988) for Venezuela, which indicated that, in 1975 and 1984, both private and social returns were higher for technical secondary education. Finally, the social returns to higher education are significantly lower than those to the previous two levels -- a near universal finding in other countries. VI. Concluding Remarks What do the above results mean in terms of educational investment priorities in Venezuela? First, our results must be qualified by the limited and aggregate nature of the data in hand. For example, more detailed analysis (e.g., regarding the quality of education at the different streams) can address the reasons why technical/vocational education has a slight edge over general/academic education. But one thing is more or less clear from the preceding macro-analysis: Primary education is investment priority number one, and higher education the least. The small rate of return advantage of secondary technical/vocational education might be tempting to expand it. Such policy, however, might be self-defeating if it were the most expensive technical/vocational streams (e.g. industry and agriculture) that would be expanded at the margin. The most appropriate strategy appears to be one of balanced growth, with special attention to maintaining the equilibrium in quality and cost effectiveness between specialties. REFERENCES Psacharopoulos, G. (1985), "Returns to education: A further international update and implications", Journal of Human Resources, XX, 4. Peacharopoulos, G. (1987), "To vocationalize or not to vocationalize? This is the curriculum question", International Review of Education, 33, 187- 211. Psacharopoulos, G. and Steier, F. (1988), "Education and the labor market in Venezuela, 1975-1984", Economics of Education Review, 7, 321-332. Psacharopoulos, G. and Alam, A. (1991), "Earnings and education in Venezuela: An update from the 1987 Household Survey", Economics of Education Review, 10, 29-36. Schiefelbein, E. and Heikkinen, S. (1991), Venezuela. Access, repetition and efficiency in primary education, OREALC, (mimeo), October. Shoup, C. (1959), The Fiscal System of Venezuela, Baltimore: Johns Hopkins University Press. World Bank (1991a), World Development Report 1991, Oxford: Oxford University Press. World Bank (1991b), Venezuela Poverty Study: From Generalized Subsidies to Targeted Programs. s 3 12 Annex 1: Unit Costs in Education The following are estimates of unit costs for the different levels of the Venezuelan education system. The year of reference, unless explicitly indicated, is 1990. Given the nature of the data available, there is a negative relationship between the level of disagregation and the level of expected accuracy. This is so because a number of assumptions had to be made in order to estimate costs by field of specialization. All assumptions are clearly stated in the text. I. Unit Costs for Pre-School, Basic, and Secondary Education in Venezuela. Three types of expenditures were taken into account: (i) expenditures incured by the Ministry of Education (MOE) which cannot be assigned to any particular program (indirect costs A), (ii) investment expenditures incured by FEDE and MINDUR (indirect costs B), and (iii) expenditures incured by the MOE which can be assigned to individual programs (direct costs). "Indirect costs A" have been prorated among all programs (pre-school to higher education) according to enrollment. "Indirect costs B" have been prorated among all programs excluding higher education, which receives a separate budget for construction, according to enrollment. Expenditures correspond to 1990 and are current Bolivares effectively sprent. These are unpublished number prepared by the finance department of the MOE. 1. Indirect Costs A. The following are programs which, indirectly, support all education levels. Programs, as well as individual sub-programs, unrelated to formal education were excluded.7 Program 01 (Central Services): 8,061,735,033 Program 02-01 (Support Services): 357,505,817 Program 02-02 (Support Services): 28,145,095 Program 03 (Planning): 290,739,041 Program 04 (Social Programs)': 380,735,027 Program 96 (Federal Programs): 109,488,983 Program 97 (Collective Contracts): 3,277,525,873 TOTAL 12,505,874,869 7 Details on these excluded items are available from the authors on request. I Excluding transfers. 13 2. Indirect Costs B': FEDE: MOE 402,809,700 Own Income 10,000,000 Other Incomes 412,391,162 Balance 1989 603,861,737 MINDUR 798,602,959 MINDUR 22,341,724 TOTAL 2,250,007,282 3. Direct Costs: Pre-School 1,917,662,197 Basic 11,563,795,334 Special 607,916,261 Secondary (Media Diversificada and Media Profesional) 2,578,177,407 4. Enrollment: Pre-School 379,309 Basic 2,241,276 Secondary 209,573 Special 132,250 Adults 372,963 Higher Non-University 54,547 5. Unit Indirect Costs: Indirect costs A were prorated among all levels according to enrollment. Indirect costs B were prorated among all levels, excluding higher education, according to enrollment. The unit indirect cost resulting is Bs 4,364. 6. Unit Costs: Unit costs are the sum of unit indirect costs and direct costs calculated as the ratio of total direct costs and enrollment by level. 9 Expenditure for FEDE and MINDUR from Memoria v Cuenta, Ministerio de Educacion, Caracas 1990. 14 Unit Direct Cost Unit Cost Pre-School 5,056 9,419 Basic 5,159 9,523 Secondary 12,302 16,666 Special 4,597 8,960 II. Unit Cost by Type of Secondary Education The available information on expenditures does not distinguish between types of secondary education. Thus, there is no direct way of estimating independent costs for "Educacion Media Diversificada" and "Educacion Media Profesional", or for fields of study (industrial, agropecuaria, etc.).10 However as personnel represents 97% of all expenditures, using information on teachers should be a reasonably proxy. Unfortunately, the information on teachers by field is also incomplete. Through a number of assumptions, it is possible to come up with a crude estimate of unit costs by field of study. (1) From the Memoria v Cuenta 1990 we obtained the teacher/student ratios for "Educacion Media Diversificada" (0.0978) and "Educaci6n Media Profesional" (0.1658). It should be noted that this is a very crude measure to the extent that it does not take into account the share of full and part-time teachers in each branch. (2) We assumed that the ratio for "Diversificada" applies to "science" and "humanities" which we labeled "General"; and that the ratio for "Profesional" applies to all other fields which we labeled "Technical". (3) Using those ratios and the enrollment by field (provided by the Planning Department of MOE) we estimated unit direct costs, and unit costs for Secondary General and Technical education. (4) Next, we used information published by the "Direccion de Educacion Media Profesional" on the distribution of teachers by field within technical education. We weighted the shares by average number of hours (teaching and in administrative duties) and came up with the following distribution of teachers by field: Agriculture 16.93% Industrial 40.79% Commercial 39.80% Social 2.42% 10 It should be noticed that costs are also incurred directly by students. In the case of secondary education, they have been estimated to be: textbooks (US$56.21), transportation ($46.55), food ($62.07) and uniform ($8.62). 15 (5) We used these shares in order to estimate unit direct costs, and unit costs for each of the four technical fields. Direct Cost Enrollment Unit Direct Unit Cost Cost General 1,785,982,368 166,132 10,750 15,114 Technical 729,195,040 43,441 18,236 22,600 Agriculture 134,118,620 5,794 23,148 27,512 Industrial 323,136,357 13,859 23,316 27,680 Commercial 315,293,626 23,028 13,692 18,055 Social 19,171,120 503 38,114 42,477 III. Unit Costs for Higher Education We have distinguished between Universities on the one hand, and "Institutos y Colegios Universitarios" on the other. Expenditure Enrollment Unit Cost Higher Education 30,151,927,560 386,638 77,985 University 27,850,347,936 332,091 83,864 Non-University 2,301,579,624 54,547 42,194 Source: OPSU Finally, the all the estimated unit costs are expressed in dollars and in terms of constant Bolivares corresponding to the second half of 1989. This last transformation is required in order to make them comparable with the incomes obtained from the "Encuesta de Hogares por Muestreo" corresponding to that period. 16 Unit Costs Unit Costs Unit Costs Current Bs. U$ 198911 Prices Pre-School 9419.4 196.24 7584.6 Basic 9523.2 198.40 7668.2 Special 8960.45 186.68 7215.1 Secondary 16665.78 347.20 13419.5 General 15114.11 314.88 12170.1 Technical 22599.84 470.83 18197.7 Agriculture 27511.57 573.16 22152.7 Industry 27679.72 576.66 22288.1 Commercial 18055.48 376.16 14538.5 Social 42477.29 884.94 34203.4 Higher 77984.9 1624.69 62794.6 University 83863.6 1747.16 67528.3 Non-University 42194.43 879.05 33975.6 솥 17 Annex 2 Entire Sample: net Age-Earnings Profiles Age NoEd Prim SeGn SeTc Univ 6 0 -7668 0 0 0 7 0 -7668 0 0 0 8 0 -7668 0 0 0 9 3094 -7668 0 0 0 10 6189 -7668 0 0 0 11 9283 -7668 0 0 0 12 12377 7514 -12170 -18198 0 13 15471 15028 -12170 -18198 0 14 18566 22542 -12170 -18198 0 15 21660 30056 -12170 -18198 0 16 23520 35601 -12170 -18198 0 17 23469 37655 56988 37745 -62795 is 27122 42370 57489 66785 -62795 19 30228 46116 59291 59361 -62795 20 31794 48942 S8575 69531 -62795 21 34960 50975 60508 74030 -62795 22 38031 54131 62766 69153 77199 23 41467 54S44 64781 68313 80336 24 40758 56105 66761 74950 100328 25 41920 58237 68972 78539 105261 26 43941 61061 69760 94065 119689 27 42988 63808 70917 91927 116799 28 41760 64704 72517 100888 124479 29 42920 66335 75243 91663 123156 30 46935 67814 80441 95845 131632 31 50520 689S5 79834 95547 134410 32 48685 672S5 87892 123125 137396 33 43667 68643 90215 123086 142869 34 42855 69555 94084 123297 150093 35 41122 74418 96817 101201 151344 36 42313 72928 96047 105012 158180 37 38829 72389 108305 109497 165724 38 42536 70499 102851 119306 180251 39 44501 73360 101546 122946 184283 40 45550 77284 97804 135416 194548 41 42199 78429 103540 142207 187500 42 41501 80738 111413 191078 181436 43 45967 79879 116198 213432 174210 44 47254 85688 113672 220423 190298 45 49470 82868 120159 173925 195430 46 44248 85805 137510 138645 185111 47 44601 83548 152400 155454 178025 48 46964 88722 159748 158400 189884 49 49708 86274 159546 159067 204518 Continued 18 Age NoEd Prim SeGn SeTc Univ 50 47631 85285 155425 108584 227195 51 48484 82225 154261 105450 230408 52 49709 86156 149292 98650 227355 53 55390 93201 142913 119560 231263 54 52662 95409 133447 .130520 216012 55 50674 91418 134487 140520 230675 56 49356 86116 145527 144000 241332 57 48695 86057 149536 80440 270229 58 49541 92595 142173 67240 261698 59 46420 99668 133933 131240 227602 60 42159 99803 133929 136200 224172 61 38773 132836 147492 96000 253952 62 36798 117585 162492 96000 280148 63 40065 116176 203192 96000 242543 64 42974 70345 178156 96000 197843 65 58172 95829 195556 37333 163743 Sub-sample: Males in Private Sector Age NoEd Prim SeGn SeTc Univ 6 0 -7668 0 0 0 7 0 -7668 0 0 0 8 0 -7668 0 0 0 9 3043 -7668 0 0 0 10 6085 -7668 0 0 0 11 9128 -7668 0 0 0 12 12170 7908 -12170 -18198 0 13 15213 15817 -12170 -18198 0 14 18255 23726 -12170 -18198 0 15 21298 31634 -12170 -18198 0 16 24458 39652 -12170 -18198 0 17 24129 37545 58909 20400 -62795 18 28476 43399 58559 40800 -62795 19 31718 47415 62852 74707 -62795 20 33856 53033 60646 59622 -62795 21 36622 55180 62096 74358 -62795 22 39765 56635 65129 72023 70973 23 41339 57687 61213 73623 82658 24 41603 58015 67099 76932 93757 25 44323 60611 68248 76685 92558 26 48743 61744 76056 80283 116264 27 47374 66109 74698 83211 117375 28 45207 68757 76846 103917 128173 . 29 41887 70894 73419 105415 130185 - Continued - 19 Age NoEd Prim SeGn SeTc Univ 30 45385 70533 82083 96842 145271 31 52705 70088 83346 90996 140933 32 52338 69659 95197 90280 144422 33 47268 71634 102188 118072 159532 34 39709 74960 109285 112460 162483 35 39706 79489 126127 128193 155389 36 39767 78703 121158 113539 167481 37 38986 75878 128879 109031 206696 38 49865 71429 107110 125621 203010 39 50515 74433 106135 116848 221065 40 50172 77266 101327 129823 211145 41 40138 79922 117980 127933 207005 42 40794 84261 118456 200751 177959 43 39537 85497 139823 310557 183444 44 45642 91111 125779 314857 244306 45 49553 88237 140231 226473 264812 46 54066 91883 118430 118633 262338 47 48100 92760 118618 163262 237945 48 42808 97670 113551 222692 212745 49 44557 94996 135661 156462 193400 50 45010 92194 149493 75000 216914 51 57860 91169 151873 75000 242914 52 53168 83086 137873 75000 226714 53 59487 79479 127847 57933 222172 54 48720 73644 115147 113933 219950 55 52736 76361 118677 170900 260379 56 48571 76246 143236 84000 245206 57 52083 79188 160756 48660 245143 58 47828 89318 153892 72990 154000 59 46899 97388 111733 168660 150000 60 42082 94826 161333 180000 217200 61 42649 86959 154533 180000 187200 62 40127 73694 199800 180000 191200 63 44530 67748 69200 180000 177600 64 45692 65037 73000 180000 200400 65 48325 64525 101800 48000 117600 Note: Negative numbers at the beginning of the profile are social direct costs of the respective educational level. ► ► , л - Continuedftom back Page No. 16 "91tat do woe think about Health Care Finance in Latin America and the Caribbean?' by Philip Musgrove, September 1991 No. 17 "Population Gromh, Eaernalities and Poverty" by Nancy Birdsall and Charles Griffin, September 1991 No. 18 "Wage Trends in Latin America * by Alejandra Cox Edwards, September 1991 No. 19 'Investment in Science Research and Training: 7he Case of Brazil and Implications for Other Countries" by Laurence Wolff, with contributions from George Psacharopoulos, Aron Kuppermann, Charles Blitzer, GeoffTey Shepherd, Carlos Primo Braga and Alcyone Saliba, September 1991 No. 20 'Prenatal and Perinatal Health Care: A Diagnostic Instrument' by Francisco Mardones, September 1991 No. 21 "Maternal Anthropometry in Prenatal Care: A New Maternal Weight G;ain GWt' by Pedro Rosso, September 1991 No. 22 'Poveny and Inequality in Latin America and the Caribbean During the 70s and 80s: An Overview of the Evidence" by Dominique van de Walle, September 1991 No. 23 'Social Indicators in Latin America and the Caribbean: A Compilation of Statistics from 1970 to the Present* by George Psacharopoulos and Bill Wood, October 1991 No. 24 "ICETEX - A Student Loan Success Story in Colombia " by Samuel Carlson, October 1991 No. 25 'Educational Development and Costing in Mexico, 1977-1990.- A Cross-State 771me- Series Analysis' by Juan Prawda and George Psacharopoulos, November 1991 No. 26 *A Cost-Benefit Analysis of Educational Investment in Venezuela, 1989 a by Ariel Fiszbein and George Psacharopoulos, November 1991 Views from LATHR No. 0 "The Magnitude of Poverty in Latin America in the 1980s" September, 1990 No. I "An Ounce of Prevention is Worth How Much Cure? Thinking about the Allocation of Health Care Spending" by Philip Musgrove, September 1990. No. 2 "Decentralization and Educational Bureaucracies" by Juan Prawda, November, 1990 No. 3 "What Should Social Funds Finance?: Portfolio Mix, Targeting, and Efficiency Criteria" by Margaret E. Grosh, December 1990 No. 4 "Financial Balance in Chile: The ISAPRES (anstituciones de Salud Previsional) Health Care System and the Public Sector" by Philip Musgrove, January, 1991 No. 5 "Population, Health and Nutrition Issues in the Latin American and Caribbean Region and the Agenda for the 90's" by Oscar Echeverri, January, 1991 No. 6 "Population and Family Planning in the 1990's. Reconciling Macro and Micro Issues" by Bruce D. Carlson, February, 1991 No. 7 "The Feasibility of Student Loans in Latin America: A Simulation" by Samuel Carlson and Guozhong Xie, March, 1991 No. 8 "Transforming the Vicious Orcle - The Costs and Savings of School Inefficiency in Mexico" by Samuel Carlson, April 1991 No. 9 "Colombia's "Escuela Nueva *': An Education Innovation" by Eduardo Velez, May 1991 No. 10 "Health Technology Development and Assessment: Do LAC Countries Have a Choice?" by Oscar Echeverri, June 1991 No. 11 "The Recurrent Cost Factor in the PHR Sector" by Jacob van Lutsenburg Maas, July 1991 No. 12 "The Burden of Death at Diferent Ages: Assumptions, Parameters and Values" by Phillip Musgrove, August 1991 No. 13 "Government Expenditure on Social Sectors in Latin America and the Caribbean: Statistical Trends" by Hongyu Yang, August 1991 No. 14 "From Manpower Planning to Labor Market Analysis" by George Psacharopoulos, September 1991 No. 15 "An Update on Cholera in the Americas" by Francisco Mardones, August 1991 - Continued on inside Page