/SZ93 Human Capital Development and Operations Policy HCO Working Papers The Profitability of Investment in Education: Concepts and Methods George Psacharopoulos November 1995 HCOWP 63 Papers in this series are not formal publications of the World Bank. They present preliminary and unpolished results of analysis that are circulated to encourage discussion and comment; citation and the use of such a paper should take account of its provisional character. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author(s) and should not be attributed in any manner to the World Bank, to its affiliated organizations, or to members of its Board of Executive Directors or the countries they represent. The Profitability of Investment in Education: Concepts and Methods by George Psacharopoulos Abstract This paper reviews the basic concept of the profitability of investment in education and enumerates the various techniques that have been used in the literature to estimate the rate of return to investment in education. The various estimating techniques are illustrated by using household srvey data from Venezuela and Guatemala. The paper also reviews the controversies that have appeared in the literature regarding the use of rates of return to investment in education for designing educational policy. Contents Introduction ........................1 Basic Concepts .......................1 Private Rate of Return .......................2 Social Rate of Return ......................4 The Short-cut Method .......................5 The Reverse Cost-benefit Method ......................6 The Earnings Function Method ..7....................7 Refinements and Adjustments ........................ Country Examples ...................... 10 Controversies.............................................................................................................................13 References ............................................................................................................................... 16 Introduction The early 1960s witnessed what has been descnbed in the econonics literature as the "human investment revolution in economic thought" (Bowman 1966). Expenditures on education, whether by the state or households, have been treated as investment flows that build human capital (see Schultz 1961; Becker 1964). Once education is treated as an investment, the immediate natural question is: what is the profitability of this investment in order to compare it to alternatives? Such comparison can provide priorities for the allocation of public funds to different levels of education, or can explain individual behavior regarding the demand, or lack of demand, for particular levels or types of schooling. In the three decades that followed the human investment revolution in economic thought, hundreds of estimates have been made on the profitability of investment in education in all parts of the World and for all levels and types of schooling and training (for a review, see Psacharopoulos 1994). The purpose of this paper is to take stock of the conceptual and empirical issues surrounding the profitability of investments in education and provide a how-to compendium to assist in making firther estimations. The various techniques used are illustrated by actual country data drawn from household surveys. Basic Concepts The costs and benefits of education investments can be analyzed in the same way that these are calculated for other types of projects. In education, a series of expenditures occur during school construction and while students are in school, and benefits are expected to accrue over the life-cycle of the graduates. For establishing education investment priorities at the margin, the net present value or internal rate of return of the prospective operation can be computed. The discussion below focuses on the rate of return in order to ease comparisons with other projects. (Education projects do not typically yield more than one intemal rate of retur, hence the internal rate of return criterion gives the same answer as the net present value.) 2 The internal rate of return of an education project can be estimated from either the private or the social point of view. The private rate of return is used to explain the demand for education. It can also be used to assess the equity or poverty alleviation effects of public education expenditures, or the incidence of the benefits of such expenditure. The social rate of return summarizes the costs and benefits of the educational investment from the state's point of view, i.e., it includes the full resource cost of education, rather than only the portion that is paid by the recipient of education. Private Rate of Return The costs incurred by the individual are his/her foregone earnings while studying, plus any education fees or incidental expenses the individual incurs during schooling. Since education is mostly provided free by the state, in practice the only cost in a private rate of return calculation is the foregone earnings. The private benefits amount to what a more educated individual eams (after taxes), above a control group of individuals with less education. "More" and 'less" in this case usually refers to adjacent levels of education, e.g., university graduates versus secondary school graduates (see Figure 1). The private rate of return to an investment in a given level of education in such a case can be estimated by finding the rate of discount (r) that equalizes the stream of discounted benefits to the stream of costs at a given point in time. In the case of university education, for example, the formula is: 42 (W. - W.)t = + + (1) ~~~ (J+r/ +CutI r) 3 Figure 1: Stylized Age-earnings Profiles Ernf Unversiy graduates -wna wboad 0 Gg S~~~~~~~~~g ^~ ~2 65 AV 5 42 Dkuei Cost where (W.-W.) is the eamings differential between a university graduate (subscript u) and a secondary school graduate (subscript s, the control group). Cu represents the direct costs of university education (tuition and fees, books, etc.), and W. denotes the student's foregone eamings or indirect costs. A similar calculation can be made for the other levels of education. However, there is an important asymmetry between computing the returns to primary education and those to the other levels. Primary school children, mostly aged 6 to 12 years, do not forego earnings during the entire length of their studies. On the assumption that children aged 11 and 12 help in agricultural labor, two or three years of foregone eamings while in primary schooling have been used in the empirical literature. In addition, there may be no need to estimate a rate of return to justify investment in basic education - it is taken for granted that the literacy of the population is a goal that stands on its own merits for a variety of reasons other than economic considerations. However, as one climbs the educational ladder and schooling becomes more specialized, it is imperative to estimate the costs and 4 benefits of post-primary school investments, especially those in the vocational track of secondary education and higher education. Social Rate of Return The main computational difference between private and social rates of return is that, for a social rate of return calculation, the costs include the state's or society's at large spending on education. Hence, in the above example, C. would include the rental of buildings and professorial salaries. Gross earnings (i.e., before taxes and other deductions) should be used in a social rate of return calculation, and such earnings should also include income in kind where this information is available. A key assumption in a social rate of return calculation is that observed wages are a good proxy for the marginal product of labor, especially in a competitive economy using data from the private sector of the economy. Civil service pay scales are irrelevant for a social rate of return calculation, although they may be used in a private one. The "social" attribute of the estimated rate of return refers to the inclusion of the full resource cost of the investment (direct cost and foregone earnings). Ideally, the social benefits should include non-monetary or external effects of education (e.g., lower fertility or lives saved because of improved sanitation conditions followed by a more educated woman who never participates in the formal labor market). Given the scant empirical evidence on the external effects of education, social rate of return estimates are usually based on directly observable monetary costs and benefits of education (but see Summers 1992). Since the costs are higher in a social rate of return calculation relative to the one from the private point of view, social returns are typically lower than a private rate of return. The difference between the private and the social rate of return reflects the degree of public subsidization of education. The discounting of actual net age-earnings profiles is the most appropriate method of esfimating the returns to education because it takes into account the most important part of the early earning history of the individual. However, this method requires comprehensive data - one must have 5 a sufficient number of observations in a given age-educational level cell for constructing "well- behaved" age-eamnings profiles (i.e., not intersecting with each other). The Short-cut Method There is another method to arrive at approximate returns to education that is very easy to apply. Given the shape of the age-earnings profiles, one can approximate them as flat curves (see Figure 2). In such a case, the rate of return estimation is based on a simple formula: private r = - W@ (2) where W refers to the mean earnings of an individual with the subscripted educational level, and 5 is the length of the university cycle. The social rate of return in this case is simply given as: social r = W - W (3) S (W,+cg)' where C. is the annual direct cost of university education. Although the short-cut method is very easy to use, it is, by definition, inferior relative to any of the other methods described above. The weakness of the method lies in the abstraction that age- earnings profiles are concave, and that the discounting process Cn estimating the true rate of return) is very sensitive to the values of the early working ages entering the calculation. 6 Figure 2: Flat Profiles Untvmfty 6~~1 - (f Icmnduatum o 0 V 18 "23 6S Ap 5 42 *II (*m Dkind we The Reverse Cost-benefit Method This is based on the short-cut rate of return formula and amounts to asking the question: given the cost of the investment, what level of annual benefits would produce a given rate of return (10 percent, for instance) on the investment? AnnualBenefit = 0.10 (Education Cost), (4) or, in our case: (i -jW. ) = (0.10) [5 ( ± CY)] (5) Although rough, this preliminary calculation can be made easily and can precipitate further analyses on how to reduce the costs or increase the benefits to possibly justify the investment. 7 The Earnings Function Method This method is also known as the "Mincerian" method (see Mincer 1974) and involves the fitting of a function of log-wages (LnW), using years of schooling (S), years of labor market experience and its square as independent variables (see Mincer 1974). Often weeks-worked or hours-worked are added as independent variables to this function as compensatory factors. We call the above a "basic earnings function." In this semi-log specification the coefficient on years of schooling (,B) can be interpreted as the average private rate of return to one additional year of schooling, regardless of the educational level this year of schooling refers to. In fact, the b coefficient in the above semi-log basic earnings function corresponds to the rate of return as estimated by the short-cut method. This can be seen in the following discrete approximation, , 81nW Relativeearningsdifferential W.-W. 1 = W.-W = r, (6) as Education differential W. AS AS W. where W. and WO are the earnings of those with S and 0 years of schooling, respectively, and A S the difference in years of educational attainment between the two groups. The earnings function method can be used to estimate returns to education at different levels by converting the continuous years of schooling variable (S) into a series of dummy variables, say Dp, D. and D, to denote the fact that a person has completed the corresponding level of education, and that, of course, there are also people in the sample with no education in order to avoid matrix singularity. Then, after fitting an "extended earnings function" using the above dummies instead of years of schooling in the earnings function, the private rate of return to different levels of education can be derived from the following formulas: rp = PS (7) Sp 8 r, S-S ' (8) 'Ss-Sp -y =Pu5 5fl'a (9) where Sp, S,, and S. stand for the total number of years of schooling for each successive level of education (primary education completed, secondary education completed, and university education completed, respectively). Again, care has to be taken regarding the foregone eamings of primary school-aged children. In the empirical analysis that follows we have assigned only three years of foregone earnings to this group. Although convenient because it requires less data, this method is slightly inferior to the previous one as it, in fact, assumes flat age-earnings profiles for different levels of education (see Psacharopoulos and Layard 1979). Refinements and Adjustments Estimating the returns to investments in education, as for any other sector, involves an implicit projection of anticipated benefits over the "project's" lifetime. Since only the past earnings are observed, or, most commonly, only a snapshot of the relative earnings of graduates of different levels of schoolng is observed, adjustments have been used in the literature to provide a realstic projection of eamings of graduates. The most common adjustments refer to the anticipated real growth in earnings (g), mortalty (m), unemployment (u), taxes (t) and innate ability (a). Thus, starting from the observed earnings of university graduates (W.), their projected profile is adjusted as: WY = W.1 + g)(l - m)(l - uXi - t). (10) The age-earning profile of the control group (W.), say secondary school graduates, has to be adjusted in the same way, with growth, mortality, unemployment and tax rates specific to that group. In 9 addition, the resulting net benefit of higher education has been further adjusted to reflect differential ability (a ) between the two groups of graduates, Net benefit of higher education = (WY - W a)( . (11) Extensive empirical application of the adjustments described in equations (10) and (11) above, in the early literature from the 1960s on the economics of education led to the conclusion that the pluses and minuses essentially cancel out and one ends up with a net benefit almost equal to the unadjusted one. This is understood by the fact the adjustments are dealing with differences in, for example, mortality rates or unemployment rates between the two groups of graduates, and this does not amount to much in practice. In the 1970's, the adjustment to the gross earnings differential that drew the most attention was that for differential ability between the two groups of graduates, often known as the "filter" or "screening" hypothesis (see Arrow 1973). Researchers often attributed one-third of the private earnings differential to differential ability. But again, extensive research, starting from the work of Griliches (1970) to that of natural experiments using identical twins who have been separated early in ife (Ashenfelter and Krueger 1994) has shown that the ability correction is not empirically validated, hence it is dropped in contemporary practice. All the above adjustments refer to monetary measures. Yet education has often been compared to a public good, yielding benefits beyond what is captured by the individual's earnings. Differential externalities by level of education might alter the real structure of the social returns to education, hence leading to a different allocation decision. However, empirical work on the documentation of externalities is still in its infancy (but, see Weisbrod 1964). Some evidence that education as a whole might explain differential growth rates is being picked up in the new growth models (Romer 1992). Yet, in empirical applications for guiding allocation decisions at the margin between different levels of education, little has been done to change the general rule that the lower the level of education, the higher the social rate of retum. One might argue, for eaample, that university education is associated with higher externalities than primary education, in the sense that higher knowledge will lead to the classic vaccine discovery 10 case. On the other hand, applying probabilities to one graduate discovering the vaccine to several million people being illiterate and hence a burden on others, basic education might win the race in terms of externalities. Country Examples Appendix Table A-1 shows the mean earnings by level of education in Venezuela using 1989 household survey data. Figure 3 below depicts the pattern involved. Figure 3: Age-earnings Prorles by Level of Education - Venezuela, 1989 300 - University m 250- O 9 200- r 150 - 100- - 50 10 25 40 55 Age Table A-2 shows the same matrix where the social cost of the different levels of education has been entered in the early ages as negative income. Table A-I is used for the private rate of return calculation, and Table A-2 for the social rate of return 11 Note that in the case of primary education, only 3 years of foregone earnings have been assumed in either the private or the social rate of return calculation. In the social calculation, however, direct costs are incurred for 7 years (i.e., the full length of the primary education cycle). The mean earnings by level of education irrespective of age appear in Table 1. On the basis of the information provided in Tables 1, A-I and A-2, it is possible to esimate private and social retums to different levels of education (see Table 2). This can be done using any spreadsheet program where pairs of adjacent columns are used to apply Formula (1). A computer program is available on request from the author that does the estimation automatically, using as input the age-earnings profile matrix (see Psacharopoulos 1995). Table 1: Mean Earnings and Direct Cost by Level of Education, Venezuela, 1989 Mean Eamings Length of Annual Direct Cost per (Bolivares/ School Cycle School Year Educational Level year) (years) (Bolivares) No Education 39,625 n.a. n.a. Primary 69,452 7 7,668 Secondary 106,337 5 12,170 University 178,293 5 62,795 Table 2: The Returns to Education, Full Discounting Method (percent) Educational Level Private Returns Social Retuns Primary 29.4 19.5 Secondary 10.2 7.9 University 12.4 7.1 Using only the information provided in Table 1, it is possible with a hand calculator to estimate rates of return using the short-cut method. This gives the results in Table 3. 12 Table 3: Short-cut Estimates of the Returns to Education (percent) Educational Level Private Retums Social Returns Primary 25.0 16.9 Secondary 10.6 11.5 University 13.5 12.0 When individual data are available, one can fit the so-called Mincerian functions to estimate the private returns to an investment in education. When the basic Mincerian eamings function is fitted to Guatemalan data, it gives an overall private rate of return to investment in education of the order of 15 percent. (The detailed earnings functions results are reported in Psacharopoulos and Ng, 1992, Annex 3). Such a rate of return, of course, refers to the marginal year of schooling that spans all educational levels. When the same function is fitted to different sub-groups of the population, we get the typical result of the females exhibiting a higher rate of return on their education investment relative to nales. (See Table 4). The public-private sector split gives, again, the typical result hat the private sector rewards more investment in human capital, whereas the public sector pay scales yield flat age-earnings profiles and a lower rate of return. Table 4. Returns to Education in Guatemala: Basic Earnings Function Method (percent) Entire sample Rate of Return Entire Sample 14.9 Males 14.2 Females 16.3 Private Sector 14.1 Public Sector 8.7 13 When an extended earnings function is fitted to the same data set, where the educational variable enters as a string of dummy variables rather than as a continuous variable, one gets the set of rates of return to investment in the different levels of education reported in Table 5. Table 5: Returns to Education in Guatemala: Extended Earnings Function Method (percent) Education Level Rate of Retum Primary 31.0 Secondary 15.0 Higher 14.7 The rather high rate of return to investment in primary education is due to the fact that one- third of the workforce is illiterate, hence there is a big payoff at the margin when someone completes primary education. Controversies Perhaps the most debated hypothesis in the economics of education is the one referring to the so-called "screening hypothesis," namely that earnings differences might be due to the superior ability of the more educated, rather than to their extra education. Among the several tests reported in the literature, the one by Ashenfelter and Krueger (1994) using pairs of twins as units of observation deserves mention because of the quasi-experimental "design" of the sample: twins who were separated early in life and received different amounts of education were observed. The authors found no bias in the estimated returns to schooling. On the contrary, they found that measurement errors in self- reported schooling differences resulted in a substantial underestimation from conventionally-estimated returns to investment in education. (For similar results, although not based on experimental data, see Katz and Ziderman (1980) using Israeli data; Cohn, Kiker and de Oliveira (1987) using United States data; Boissiere, Knight and Sabot (1985) using Tanzanian and Kenyan data, Chou and Lau (1987) 14 using Thai data; Bound, Griliches and Hall (1986) using United States data; Glewwe (1991) on Ghana, and Psacharopoulos and Velez (1992) using Colombian data). The crux of the matter is that the undisputable and universal positive correlation between education and earnings can be interpreted in many different ways. As Ashenfelter (1991) put it, the causation issue on whether education really affects earnings can only be answered with experimental data generated by exposing at random different people to various amounts of education. Given the fact that moral and pragmatic considerations prevent the generation of such pure data, researchers will have to make do with indirect inferences or natural experiments. Three recent papers report the results of using natural experiments in order to asses the effect of selectivity bias on the returns to education. One example of such a natural experiment was carried out with identical twins who received different amounts of education (as to control for differences in genetic ability). In fact, Angrist and Krueger (1992) found that a rate of return to the extra years of schooling was 10 percent higher than conventional rate of return estimates. Angrist and Krueger (1991) found a very similar rate of return to investment in education to the one conventionally estimated. Another debated issue in the literature has been the role of socioeconomic background. Card and Krueger (1992) find that, holding school quality constant, there is no evidence that parental income or education affects state-level returns to education. But Neuman (1991), using Israeli data, found that the returns to schooling are higher to those coming from more favorable socioeconomic backgrounds. When the sample is split by gender, typically the returns to female education are higher than those for males. It should be remembered that such calculations are based on the observed wages of women who are working in the labor market. Several other women have chosen to work at home, tacitly placing a higher value on their household-activities time than on market wages. In addition, the truncation of women's earnings' samples leads to classic econometric biases documented by Heckman (1979). In recent work, correction for selectivity bias does not appear to change significantly the returns on investment in women's education (see Psacharopoulos and Tzannatos 1992). However, the fact remains that rates of return for women do not take into account household production. Regarding the "earnings-reflect-productivity" assumption, the returns in the private/competitive sector of the economy are higher than for those who work in the public/non-competitive sector. Dabos and Psacharopoulos (1991) analyzed the earnings of Brazilian males in 1980 and found sizeable returns to education across labor market "segments," especially among rwal workers and the self-emnployed. 15 This finding was upheld even after correcting for dependent variable selectivity bias regarding who enters a particular economic sector. Perhaps the best and most cited finding in this area refers to agricultural production. Jamison and Lau (1982) found that, other things being equal, four years of education for farmers translates to a nearly 10 percent increase in physical agricultural output. On the issue of whether or not earnings really reflect productivity, Chou and Lau (1987) repeated the Jamison and Lau (1982) production function methodology for Thailand and upheld the results. They found that one additional year of schooling adds about 10 percent to farm output. In East Asia, for example, one additional year of education contributed over three percent to real GDP. (See also Azhar (1991) reporting similar results for Pakistan.) References Angrist, J.D. and AB. Krueger. 1991. "Does Compulsory School Attendance Affect Schooling and Earnings?" The Quarterly Journal of Economics 106, no. 4(November). 1992. "Estimating the Payoff to Schooling Using the Vietnam-era Draft Lottery." Working Paper No. 4067. National Bureau of Economic Research, New York. Arrow. 1973. "Higher Education as a Filter." Journal of Public Economics 2:193-216. Ashenfelter, Orley. 1991. "How Convincing is the Evidence Linking Education and Income?" Working Paper No. 292. Industrial Relations Section, Princeton University, Princeton, New Jersey. Ashenfelter, Orley and A Krueger. 1994. "Estimates of the Economic Return of Schooling from a New Sample of Twins." American Economic Review (December). Azhar, R 1991. "Education and Technical Efficiency during the Green-revolution in Pakistan." Economic Development and Cultural Change 39, no. 3: 651-665. Becker, Gary S. 1964. Human Capital. New York: National Bureau of Economic Research. Boissiere, M., J.B. Knight and RH. Sabot. 1985. "Earnings, Schooling, Ability and Cognitive Skills." American Economic Review 75:1016-30. Bound, J., Z. Griliches and B.H. Hall. 1986. "Wages, Schooling and IQ of Brothers and Sisters: Do the Family Factors Differ? Internaional Economic Review 27, no. 1 (February): 77-105. Bowman, M.J. 1966. "The Human Investment Revolution in Economic Thought." Sociology of Education (Spring): 117-37. Card, D. and AB. Krueger. 1992. "Does School Quality Matter? Returns to Education and the Characteristics of Public Schools in the United States." Journal of Polifical Economy 100, no. 1:1-39. 17 Chou, E.C. and L.J. Lau. 1987. "Farmer Ability and Fann Productivity: A Study of Farm Households in the Chiangmai Valley, Thailand, 1972-1978." Discussion Paper 62. Education and Training Department, The World Bank, Washington, D.C. Cohn, E., B.F. Kiker and M.M. de Oliveira. 1987. "Further Evidence on the Screening Hypothesis." Economics Letter 25:289-94. Dabos, M and G. Psacharopoulos. 1991. "An Analysis of the Sources of Earnings Variation among Brazilian Males." Economics ofEdAcation Rewew 10, no. 4. Glewwe, P. 1991. "Schooling, Skills, and the Returns to Government Investment in Education." Working Paper No. 76. LSMS, The World Bank, Washington, D.C. Griliches, Z. 1970. "Notes on the Role of Education in Production Functions and Growth Accounting." In W.L. Hansen, ed., E4ucation; Income and Human Capital Studies in Income and Wealth, Vol 35. New York: National Bureau of Economic Research. Heckman, J.J. 1979. "'Sample Selection as a Specification Error." Economefrica 47, no. 1: 153-161. Jamison, D.T. and L. Lau. 1982. Fawmer Education and Farm Efficiency. Baltimore: Johns Hopkins University Press. Katz, E. and A. Ziderman. 1980. "On Education, Screening and Human Capital." Economics Letters 6:81-8. Mincer, J. 1974. Schooling, Experience and Earnings. New York: National Bureau of Economic Research. Neuman, S. 1991. "Parental Background, Educational Attainments and Returns to Schooling and to Marriage: The Case of Israel." AppliedEconomics 23:1325-34. 18 Psacharopoulos, G. 1994. "Returns to Investment in Education: A Global Update." World Development Septeber. 1995. '"RR Manual: A Computer Program to Estimate Rates of Return to Investments in Education " Human Resources Development and Operations Policy, The World Bank Washington, D.C. Psacharopoulos, G. and R Layard. 1979. "Human Capital and Earnings: British Evidence and a Critique." Review of Economic Sfudies 46: 485-503. Psacharopoulos, G. and Z. Tzannatos. 1992. Women's Employment and Pay in Latin America: Overview andMediodology. Washington, D.C.: The World Bank. Psacharopoulos, G. and Yng Chu Ng. 1992. '"anings and Education in Latin America: Assessing Priorities for Schooling Investments." Working Paper 1056, Latin America and the Canbbean Region, Technical Department. Washington D.C.: The World Bank. Psacharopoulos, G. and E. Velez. 1992. "Schooling, Ability and Earnings in Colombia, 1988." Economic Development and Cultural Change 40, no. 3 (April): 629-43. Romer, P.M. 1992. "Two Strategies for Economic Development: Using Ideas and Producing Ideas." 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Department of Economics, Industial Relations Section, Princeton University, Princeton, N.J. 19 Table A-1: Age-earnings Profiles by Level of Education, Venezuela 1989 (Bolivareslyear) Age No Education PFimary Secondary University 10 3610 0 0 0 11 7220 0 0 0 12 10830 0 0 0 13 14440 10240 0 0 14 18050 20480 0 0 15 21660 30720 0 0 16 23520 35601 0 0 17 23469 37877 0 0 18 27122 42370 57489 0 19 30228 46116 59291 0 20 31794 48942 58575 0 21 34960 50975 60508 0 22 38031 54131 62766 0 23 41467 54544 64781 80336 24 40758 56105 66761 100328 25 41920 58237 68972 105261 26 43941 61061 69760 119689 27 42988 63808 70917 116799 28 41760 64704 72517 124479 29 42920 66335 75243 123156 30 46935 67814 80441 131632 31 50520 68955 79834 134410 32 48685 67255 87892 137396 33 43667 68643 90215 142869 34 42855 69555 94084 150093 35 41122 74418 96817 151344 36 42313 72928 96047 158180 37 38829 72389 108305 165724 38 42536 70499 102851 180251 39 44501 73360 101546 184283 40 45550 77284 97804 194548 41 42199 78429 103540 187500 42 41501 80738 111413 181436 43 45967 79879 116198 174210 44 47254 85688 113672 190298 45 49470 82868 120159 195430 46 44248 85805 137510 185111 47 44601 83548 152400 178025 48 46964 88722 159748 189884 49 49708 86274 159546 204518 50 47631 85285 155425 227195 51 48484 82225 154261 230408 52 49709 86156 149292 227355 53 55390 93201 142913 231263 54 52662 95409 133447 216012 55 50674 91418 134487 230675 56 49356 86116 145527 241332 57 48695 86057 149536 270229 58 49541 92595 142173 261698 59 46420 99668 133933 227602 60 42159 99803 133929 224172 20 Table A-2: Age-earnings Profiles and Direct Costs by Level of Education, Venezuela 1989 (Bolivareslyear) Input to Social Rate of Return Calculation Age No Education Primaxy Secondary University 6 0 -7668 0 0 7 0 -7668 0 0 8 0 -7668 0 0 9 0 -7668 0 0 10 3610 -7668 0 0 11 7220 -7668 0 0 12 10830 -7668 0 0 13 14440 10240 -12170 0 14 18050 20480 -12170 0 15 21660 30720 -12170 0 16 23520 35601 -12170 0 17 23469 37877 -12170 0 18 27122 42370 57489 -62796 19 30228 46116 59291 -62796 20 31794 48942 58575 -62796 21 34960 50975 60508 -62796 22 38031 54131 62766 -62796 23 41467 54544 64781 80336 24 40758 56105 66761 100328 25 41920 58237 68972 105261 26 43941 61061 69760 119689 27 42988 63808 70917 116799 28 41760 64704 72517 124479 29 42920 66335 75243 123156 30 46935 67814 80441 131632 31 50520 68955 79834 134410 32 48685 67255 87892 137396 33 43667 68643 90215 142869 34 42855 69555 94084 150093 35 41122 74418 96817 151344 36 42313 72928 96047 158180 37 38829 72389 108305 165724 38 42536 70499 102851 180251 39 44501 73360 101546 184283 40 45550 77284 97804 194548 41 42199 78429 103540 187500 42 41501 80738 111413 181436 43 45967 79879 116198 174210 44 47254 85688 113672 190298 45 49470 82868 120159 195430 46 44248 85805 137510 185111 47 44601 83548 152400 178025 48 46964 88722 159748 189884 49 49708 86274 159546 204518 50 47631 85285 155425 227195 51 48484 82225 154261 230408 52 49709 86156 149292 227355 53 55390 93201 142913 231263 54 52662 95409 133447 216012 55 50674 91418 134487 230675 56 49356 86116 145527 241332 57 48695 86057 149536 270229 58 49541 92595 142173 261698 59 46420 99668 133933 227602 60 42159 99803 133929 224172 Human Capital Development and Operations Policy Working Paper Series Contact tor Title Author Date paper HROWP30 Language and Education in S.M. Cummings May 1994 M. Espinosa Latin America: An Overview Stella Tamayo 37599 HROWP31 Does Participation Cost the Jesko Hentschel June 1994 D. Jenkins World Bank More? Emerging 37890 Evidence HROWP32 Research as an Input into Harold Alderman June 1994 P. Cook Nutrition Policy Formation 33902 HROWP33 The Role of the Public and Deepak Lal June 1994 M. Espinosa Private Sectors in Health 37599 Financing HROWP34 Social Funds: Guidelines for Soniya Carvalho July 1994 K Labrie Design and Implementation 31001 HROWP35 Pharmaceutical Policies: Graham Dukes July 1994 0. Shoffner Rationale and Design Denis Broun 37023 HROWP36 Poverty, Human Development Harsha Aturupane August 1994 P. Cook and Growth: An Emerging Paul Glewwe 30864 Consensus? Paul Isenman HROWP37 Getting the Most out of Helen Saxenian September 1994 0. Shoffner Pharmaceutical Expenditures 37023 HROWP38 Procurement of Denis Broun September 1994 0. Shoffner Pharmaceuticals in World 37023 Bank Projects HROWP39 Notes on Education and Harry Anthony Patrinos September 1994 I. Conachy Economic Growth: Theory and 33669 Evidence HROWP40 Integrated Early Child Mary Eming Young October 1994 0. Shoffner Development: Challenges and 37023 Opportunities HROWP41 Labor Market Insurance and Deepak Lal October 1994 M. Espinosa Social Safety Nets 37599 HROWP42 Institutional Development in Alberta de Capitani October 1994 S. Howard Third World Countries: The Douglass C. North 30877 Role of the World Bank HROWP43 Public and Private Secondary Marlaine E. Lockheed November 1994 M. Verbeeck Schools in Developing Emmanuel Jimenez 34821 Countries HROWP44 Integrated Approaches to T. Paul Schultz November 1994 M. Espinosa Human Resource Development 37599 HROWP45 The Costs of Discrimination in Harry Anthony Patrinos November 1994 I. Conachy Latin America 33669 HROWP46 Physician Behavioral Nguyen X. Nguyen December 1994 M. Espinosa Response to Price Control 37599 HROWP47 Evaluation of Integrated T. Paul Schultz January 1995 M. Espinosa Human Resource Programs 37599 Human Capital Development and Operations Policy Working Paper Series Contact for Title Author Date paper HROWP48 Cost-Effectiveness and Health Philip Musgrove January 1995 0. Shoffner Sector Reform 37023 HROWP49 Egypt: Recent Changes in Susan H. Cochrane February 1995 0. Shoffner Population Growth Ernest E. Massiah 37023 HROWP50 Literacy and Primary Kowsar P. Chowdhury February 1995 M. Espinosa Education 37599 HROWP51 Incentives and Provider Howard Barnum March 1995 0. Shoffner Payment Methods Joseph Kutzin 37023 Helen Saxenian HROWP52 Human Capital and Poverty Gary S. Becker March 1995 M. Espinosa Alleviation 37599 HROWP53 Technology, Development, and Carl Dahlman April 1995 M. Espinosa the Role of the World Bank 37599 HROWP54 International Migration: Sharon Stanton Russell May 1995 0. Shoffner Implications for the World 37023 Bank HROWP55 Swimming Against the Tide: Nancy Birdsall May 1995 A. Colbert Strategies for Improving Equity Robert Hecht 34479 in Health HROWP56 Child Labor: Issues, Causes Faraaz Siddiqi June 1995 I Conachy and Interventions Harry Anthony Patrinos 33669 HCOWP57 A Successful Approach to Roberto Gonzales July 1995 K. Schrader Partcipation: The World Bank's Cofino 82736 Relationship with South Africa HCOWP58 Protecting the Poor During K. Subbarao July 1995 K. Labrie Adjustment and Transitions Jeanine Braithwaite 31001 Jyotsna Jalan HCOWP59 Mismatch of Need, Demand Philip Musgrove August 1995 Y. Attkins and Supply of Services: 35558 Picturing Different Ways Health Systems can go Wrong HCOWP60 An Incomplete Educational Armando Montenegro August 1995 M. Bennet Reform: The Case of 80086 Colombia HCOWP61 Education with and with out the Edwin G. West September, 1995 M. Espinosa State. 37599 HCOWP62 Interactive Technology and Michael Crawford October 1995 P. Warrick Electronic Networks in Higher Thomas Eisemon 34181 Education and Research: Lauritz Holm-Nielsen Issues & Innovations HCOWP63 The Profitability of Investment George Psacharopoulos November 1995 M. Espinosa in Education: Concepts and 37599 Methods