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Economics of Education Review 18 (1999) 183–199

Schooling indicators during Mexico’s “Lost decade”

Melissa Binder

Department of Economics, University of New Mexico, Albuquerque, NM 87131-1101, U.S.A.

Received 10 January 1997; accepted 13 February 1998

Abstract

The 1980s have been described as a “lost decade” for Latin America as a result of the sharp decline in income that followed the international debt crisis. Using the case of Mexico, this paper explores the impact of the lost decade on schooling indicators. This paper finds that falling opportunity costs in the 1980s improved schooling indicators at the same time that reductions in the level of national income worsened them. The net result of these opposing effects was relatively stagnant enrollment rates. Simulations suggest that Mexico’s secondary school enrollments would have increased considerably, had the 1980s economy grown at rates even one-half of those experienced in the 1970s. Analyses of state panel data for this period reveal that continuation rates responded more strongly to economic conditions in poorer states, and that state schooling indicators were sensitive to urbanization, sectoral structure and school spending patterns among states. [JEL I21, O54]1999 Elsevier Science Ltd. All rights reserved.

1. Introduction

The 1980s have been described as a “lost decade” for Latin America as a result of the sharp decline in income that followed the international debt crisis. Even by 1994, Latin America’s per capita GDP had yet to regain its 1980 level (IDB, 1995; 3). In Mexico, per capita income fell by 6.3 per cent in 1983 and 5.5 per cent in 1986, and grew by only 1.2 per cent annually between 1988 and 1990. Between 1983 and 1988, real manufacturing wages fell at an average annual rate of 7.3 per cent (Lustig, 1992; 40–41). Several studies have sought to evaluate the effect of the 1980s economic crisis on schooling in Latin American countries. The absolute decline in education spending is well established (Reimers, 1990; Tilak, 1989). In Mexico, real govern-ment expenditures on education fell by 40 per cent between 1981 and 1989 (Nacional Financiera, 1991). Nevertheless, the effect on schooling itself has been harder to gauge. Lustig (1992) and Kaztman and

Ger-* E-mail: [email protected]

stenfeld (1990) report with surprise an improvement in many schooling indicators during the 1980s.

In fact, human capital theory predicts two opposing responses of schooling investments to an economic downturn. The first is that opportunity costs (wages fore-gone by the student) decline, lowering the price of schooling and increasing enrollments. This price effect bodes well for future economic growth as young people acquire more human capital. But at the same time that opportunity costs fall, more students will face binding liquidity constraints as family incomes decline. If this income effect dominates, economic downturns may compromise future economic growth by reducing human capital investments.

A cursory look at enrollment rates in Mexico shows a slowdown in gains made in schooling in the 1980s, especially at the post-primary level.1 _{For secondary}

1_{Gross primary enrollment rates had already exceeded 100}

per cent by 1970. Gross enrollment rates divide matriculation by the number of school age children in the population. The rate can exceed 100 per cent if there are many older children enrolled in primary school due to grade repetition or late entry into school.

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school, enrollment rates more than doubled from 22 per cent to 46 per cent between 1970 and 1980, and then rose more slowly to 55 per cent by 1990.2_{Fig. 1 shows}

the steep rise and subsequent flattening of secondary enrollment rates. Post-secondary schooling rose even more modestly, from 14 per cent in 1980 to 15 per cent in 1990.

An analysis of time series data for the national and state levels for the 1976–77 through the 1993–94 school years suggests that changes in the secondary enrollment rate—and other schooling indicators as well—can be traced to the opposing forces of price and income effects associated with economic fluctuations. I find that at higher levels of national income, the schooling indicators improve, but during economic upturns, when employ-ment opportunities grow, children also tend to leave school. The response to economic conditions is evident even at the primary school level, although it is much larger for higher schooling levels, and is relatively stronger for vocational, as opposed to academic instruc-tion. During the 1980s, the positive effect on schooling of falling opportunity costs was countered by declining income, yielding relatively stagnant enrollment rates.

National economic indicators have similar effects on state-level schooling indicators, although the response appears to vary with the relative affluence of states. Urbanization proportion and sectoral economic structure are also important determinants of state schooling out-comes.

The paper proceeds as follows: in the next two sec-tions I outline the human capital investment model and review the results of similar studies undertaken in the United States and Great Britain. Sections 4 and 5 provide a brief overview of the Mexican schooling system, describe the data sources, and develop the empirical

Fig. 1. Secondary enrollment rates. Source: World Bank World*Data country data series, 1995.

2_{Secondary schooling includes both lower (grades 7–9) and}

upper (grades 10–12) academic and vocational secondary edu-cation.

implementation of the model. Sections 6 and 7 report the results at the national and state levels, respectively, and Section 8 concludes.

2. The human capital framework

In the human capital model, schooling is treated as an ordinary investment decision made by weighing the present value of future benefits against current costs. The benefits primarily consist of increased earnings, although consumption benefits are also possible. Costs include direct outlays for tuition, transportation, books and materials and the indirect or opportunity costs of fore-gone wages. The optimal investment occurs at the schooling level for which the marginal benefit equals the marginal cost. If the cost of schooling rises, the optimal schooling level declines, and vice versa. In the presence of imperfect capital markets that fail to supply human capital loans because of collateral problems, families must internally finance schooling expenses (Becker, 1964). Families are liquidity constrained if they cannot cover education expenses and have no access to school-ing loans: their children will receive less than the optimal amount of schooling. In addition, the liquidity con-straints will be more binding if income falls, as it did for a majority of Mexican families during the 1980s (Lustig, 1992).

Even with perfect markets for human capital loans, we would expect schooling to respond to income if students and parents also derive consumption benefits from it. For example, families may send their children to school to keep them off the streets and out of trouble, to keep them entertained, or for prestige and status (Schultz, 1963). The effect of an economic downturn on a schooling con-sumption decision is the same as the effect on a school-ing investment decisions: lower wages will reduce the opportunity cost of schooling (with a positive substi-tution effect), and at the same time reduce family income (a negative income effect).

The effects of business cycle fluctuations are not lim-ited to the cost side of the investment decision. In fact, returns to schooling tend to rise during an economic downswing (Kniesner et al., 1978). In addition, Mexico’s economic environment in the 1980s involved a dramatic policy restructuring as well as economic contraction. Over the decade Mexico embarked on a series of struc-tural reforms which liberalized trade, deregulated dom-estic markets and privatized state owned enterprises (Lustig, 1992; Aspe, 1993). These changes may have alt-ered perceived returns to schooling, thereby raising or lowering the marginal benefit of schooling. In fact, the earnings differential by schooling level appears to have widened (Cragg and Epelbaum, 1996). In a survey of 295 parents of school children, 84 per cent reported that the restructuring made higher levels of schooling more

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M. Binder / Economics of Education Review 18 (1999) 183–199 important.3

A positive relationship between an economic downturn and schooling should not then be interpreted as a pure price effect, since it also may contain a positive response to the rate of return to schooling, especially in the changing economic environment of Mexico in the 1980s.

3. Evidence from industrialized countries

Ultimately, the effect of a recession on schooling decisions is an empirical question, which has not, appar-ently, been applied to developing countries.4

Cross-sec-tional and time-series analyses for industrialized coun-tries generally find a positive income effect and negative price effects, where price is measured by both tuition (for direct costs) and the unemployment rate (for fore-gone costs).5 _{The elasticities for income are usually}

much larger than those for unemployment. This gen-eralization appears to hold for school-leaving in Great Britain (Whitfield and Wilson, 1991; Rice, 1987; Pissar-ides, 1981),6 _{as well as for high school and college}

enrollments and college entry (Betts and McFarland, 1995; Kane, 1994; Manski and Wise, 1983; Mattila, 1982; Lehr and Newton, 1978) in the United States.

In addition, the following patterns emerge: (1) high school and community college enrollments appear to be more responsive to economic variables than four-year college enrollments (Mattila, 1982; Betts and McFarland, 1995), (2) blacks tend to have higher elasticities than whites (Kane, 1994), and (3) the college enrollment rates of younger men are more elastic than those of older men (Mattila, 1982). In each of these cases, the more respon-sive group likely has a higher proportion of marginal schooling decision makers. For blacks and community college students, high elasticities may arise from low income. Low income families are more likely to face binding liquidity constraints and may have a low valu-ation of consumption component of schooling relative to the investment component.7_{Similarly, the potential high}

school population will have a lower mean income than

3_{Author’s survey conducted in Guadalajara, Arandas and}

Tijuana in 1993.

4_{Despite an extensive search, I could find no such studies in}

either the English or Spanish language literatures.

5_{Many studies control for returns to schooling, so that the}

unemployment rate can be unambiguously interpreted as a price effect.

6_{One notable exception is Micklewright et al. (1990), who}

find a positive relationship between unemployment and school-leaving.

7_{Manski and Wise (1983) find that parent schooling is more}

influential than parent income in the decision to enroll in col-lege. This may reflect a relatively larger consumption compo-nent in the decision to pursue schooling.

the college-bound population (Manski and Wise, 1983). The younger age category in (3) also contains more mar-ginal decision makers, since the decision to attend col-lege is typically made in the last year of high school. Men who begin to work rather than enroll in college face higher opportunity costs of college as they gain experi-ence in the labor market.

4. Overview of the Mexican schooling system8

The United States and Great Britain studies focus on post-compulsory and post-secondary schooling. In Mex-ico, relatively low levels of schooling attainment make a primary and secondary schooling focus more approate. Under the Mexican constitution, the six years of pri-mary education are compulsory, and since 1992, three years of secondary school instruction have been added to the requirement. Nevertheless, even a primary school education has yet to become universal. For example, in 1990 thirteen per cent of 15–19 year-olds had attained only four years of schooling or less (Inegi, 1992). Twenty per cent of primary schools—primarily in rural areas—offered less than a full six-year program in 1988 (Salinas de Gortari, 1989). And of those who entered first grade six years earlier, only 59 per cent graduated from primary school in 1994.

The proportion of primary school graduates who con-tinued their studies in secondary school fluctuated between 84 and 88 per cent in the 1980s, and these rates varied more widely among states. Mexico City9 _and

Sonora had continuation rates close to 100 per cent in 1993, while Guanajuato had a rate of only 75 per cent.10

Secondary school consists of separate lower and upper levels consisting of three grades each. Both levels pro-vide a vocational option. The vast majority of the lower-secondary students (86% in the 1993–94 school cycle) enroll in the academic program. The rest attend terminal junior vocational programs, where studies range from mechanics to cosmetology. More than half of all junior vocational students study in private schools. The efficiency rate of the lower-secondary academic program hovered around 60 per cent throughout the 1980s.

During the same period, between 80 and 85 per cent of the lower-secondary academic graduates enrolled in either the upper-secondary academic level or senior

8_{Unless otherwise noted, figures in this section were}

calcu-lated from published Secretary of Public Education data described in more detail below.

9_{Mexico City, the federal district, is treated as a state for the}

purposes of this study.

10_{Note that continuation rates may be inflated by students}

who enter the next schooling level several years after graduating from the previous level.

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vocational programs. Of all students at the upper-second-ary level, eighteen per cent are enrolled in vocational programs. The majority of these vocational students-about 70 per cent—study in public institutions.

5. Data sources and implementation 5.1. Schooling indicators

The schooling data are drawn primarily from annual data published by the Secretary of Public Schooling (SEP) in Mexico between 1976 and 1994 (SEP, 1983, 1984–1994).11_{The data include national and state-level}

enrollments by grade level at the start and finish of the school year for all public and private schools. These data provide the basis for calculating retention, continuation and efficiency rates, which measure the flow of students through the schooling system. The retention rate is the number enrolled at the close of the school year divided by the number who started the school year. The continu-ation rate is the number of students beginning a given school level divided by those who graduated in the pre-vious school year from the earlier school level. The efficiency rate is the number of students who graduated from a particular school level divided by those who entered the school g-1 years earlier, where g is the num-ber of grades for that school. For example, primary school consists of six grades. Students graduating in the 1993–94 cycle would have entered first grade in the 1988–89 cycle, if they successfully completed one grade a year. The efficiency rate captures leakages from the system (as students drop out of school) as well as the prevalence of grade repetition. Figs 2a, b, c and d plot the time-series for these indicators.

The SEP enrollment figures can also be combined with population censuses to determine enrollment rates, which give the fraction of all age-appropriate children that attends school. Since Mexico conducts a decennial cen-sus, population counts between censuses must be extrapolated. Primary and secondary enrollment data are drawn from the World Bank World*Data country data series, which in turn rely on UNESCO estimates. I also estimate state secondary enrollment rates for the census years of 1980 and 1990. Enrollment rates give a broad measure of the population’s participation in the school-ing system, while retention, continuation and efficiency rates measure the progress of those who have already entered the system.

11_{SEP data for the school years 1970–71 to 1975–76 report}

student enrollments for only one (undetermined) point in the school year. Thus none of the schooling indicators can be calcu-lated before the 1976–77 school year.

Fig. 2. a) Retention rates, academic sequence b) Retention rates, vocational levels c) Continuation rates d) Efficiency rates. Source: Secretary of Public Education, derived from pub-lished data.

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M. Binder / Economics of Education Review 18 (1999) 183–199

5.2. Economic indicators

The human capital framework calls for measures of income and the costs and benefits of schooling. For income, I use GDP in constant U.S. dollars from the World Bank World*Data series.12_{Since annual data on}

direct school costs are not available, the price of school-ing is determined by opportunity costs alone.13_The

stud-ies of United States and British enrollment use unem-ployment rates to proxy opportunity costs. In Mexico, however, official unemployment rates are quite low (usually under 4 per cent for the time period studied here) and not considered a reliable indicator of job mar-ket conditions, since all those who worked at least one hour in any income-generating activity in the week pre-ceding the survey are considered employed. I therefore use GDP growth rates as measures of the expansion and contraction of the economy to proxy opportunity costs.14

I will refer to the relationship between the growth rates and schooling as the price effect.

Another data issue concerns a mismatch in timing between the calendar year, for which economic variables are reported, and the school year. The school year runs from September through June and thus spans two calen-dar years. I use growth rates from both years spanned and GDP for the year in which the school year ends.15

The growth rate for the year in which the school year ends is included in the schooling series in Fig. 2a, b, c and d. Note that the schooling indicators appear to move counter-cyclically, especially in Fig. 2b and c.

Unfortunately, I could locate no data that provide wage differentials by schooling level on an annual basis for the period covered. Thus the estimated effects of the income and price measures may also include effects of changes in schooling wage differentials. As noted earlier, these differentials tend to move counter-cyclically. Thus the negative schooling impact of falling income during a recession may be underestimated and the positive schooling impact of falling opportunity costs may be

12_{Specifications which used per capita income gave similar}

results to those reported below.

13_{The bias introduced by this omission cannot be}

charac-terized a priori, since the correlation between direct and opport-unity costs is unknown.

14_{It is a stylized fact that unemployment rises in the}

downsw-ing of a business cycle and falls with a lag in upswdownsw-ings (Lilien and Hall, 1986). The mapping between economic contraction and expansion and opportunity costs is therefore not exact.

15_{I also experimented with an alternative specification for}

addressing the time mismatch, using the average of the GDP and GDP growth rates for the two calendar years spanned by the school year. This specification gave similar results to those reported here.

overestimated depending on the correlation of these vari-ables with the omitted wage-differential variable.16

Finally, a trend variable is needed to control the possi-bility that the schooling indicators trend independently from the economic variables included.17

The following reduced-form equation provides a start-ing point for the analysis:

log(s)5b01b1log(GDP)1b2%DGDPBEGIN (1)

1b3%DGDPEND1b4TREND1m

where s is a schooling indicator (retention, continuation, efficiency or enrollment rate) for a given school level,

GDP measures income, %DGDPBEGIN and %DGDPEND

measure the opportunity cost or price effect for the calen-dar years in which the school year begins and ends, and

TREND is a time-varying trend variable. The bs are coefficients andm is an error term. The semi-log form allowsb1 to be interpreted as the income elasticity of

the schooling indicator andb2andb3to be read directly

as per cent changes in the schooling indicator.18

6. Economic conditions and national schooling indicators

Table 1 reports the results of OLS and, where the Dur-bin-Watson statistic indicated the presence of serial auto-correlation, corrected Cochrane-Orcutt estimates of the model. The GDP growth rates have a negative effect on most of the schooling indicators, while national income has a positive effect. This suggests negative price and positive income effects and mirrors the results for schooling indicators in the United States and Great Bri-tain. In most cases, the responses are statistically sig-nificant at standard confidence levels and the models usually explain at least one-third of the variation of the given schooling indicator.

16_{While time series data are not available for schooling}

returns and direct costs, federal spending data for 1980–1992 provide a proxy for school availability and quality for part of the series. Real spending per student at all levels fell by 50 per cent or more during the 1980s. If per student spending is posi-tively correlated with income, the income variable may be a proxy for school availability and quality. When spending was added to the model, the income and price effects were mostly unchanged. In some cases the elasticity of income rose slightly. Estimated spending elasticities were generally tiny (never exceeding 0.08) and often negatively related to schooling indi-cators.

17_{This is in fact the case for primary-level efficiency rates}

and several measures in the state series.

18_{That is, for every percentage point increase in the growth}

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6.1. Retention rates

The negative price effect is more pronounced for economic conditions at the end of the school year for retention at lower schooling levels. At both the primary and junior vocational levels, the coefficients for GDP growth rates in the calendar year in which the school year ends are larger and more precise than the coef-ficients for growth rates in the year in which the school year begins. If the schooling response to economic con-ditions does not vary over the school year, then the end-of-school-year measure should have a greater effect, since it covers six months of the ten-month cycle.

For higher schooling levels, though, the schooling response may vary over the school year. Table 1 shows that economic conditions at the beginning of the year more strongly affect the retention rate than conditions present at the school year end for the lower-secondary and higher schooling levels. For the upper-secondary level, GDP growth in the calendar year in which the school year ends has a noisy, but decidedly positive effect on retention. These patterns may reflect the greater direct and opportunity costs incurred at higher schooling levels, and in particular the growing sunk opportunity costs as the academic year progresses. According to the 1992 Household Expenditure Survey (ENIGH), house-holds which incurred schooling services costs paid an average of N$278 quarterly on primary schools, N$338 on lower-secondary schools and N$725 on upper-sec-ondary schools (Inegi, 1993).19 _{In addition, older}

stu-dents forfeit higher wages, since their labor market pro-ductivity is higher than students at the lower-secondary level. If a student drops out before the end of the school year, the entire year must be repeated, and the fees paid and wages foregone are lost. Thus even if opportunity costs rise at the end of a school year, students may be unwilling to drop out and forfeit their sunk costs.

Table 1 also shows that vocational students respond more strongly to economic conditions than students enrolled in academic programs. For example, a ten per cent increase in income increases retention by about three per cent for junior vocational students, but only by 0.3 per cent for lower-secondary academic students. Each percentage point rise in the GDP growth rate reduces retention at the junior vocational level by 0.8 per cent, compared with less than one per cent in the academic program.

19_{These figures mix vocational and academic programs at}

the lower- and upper-secondary levels. Spending is not uniform across deciles. For example, top decile households spent 10 times the amount paid by the lowest decile households on pri-mary schooling (N$984 vs. N$96). The exchange rate in 1992 was about N$3 per US$1 and annual per capita income was US$1859.

Why are vocational rates so much more elastic? One possibility is that vocational programs attract marginal decision-makers, as discussed above for the case of com-munity colleges in the United States. Vocational pro-grams may be less rigorous than academic propro-grams and so involve lower costs in time and frustration to weak students. The consumption content may be lower, leav-ing students to respond more quickly to changes in the returns of their investments. Vocational students may be from low-income families with few employment contacts for jobs which require general academic training. Finally, vocational training may be more substitutable than academic training for on-the-job training so that job offers won’t compromise future productivity (and earnings).

6.2. Continuation rates

Since the marginal decision for schooling is usually an additional year of schooling, we would expect that continuation rates respond more strongly to economic conditions than retention rates. This is in fact the case. The income elasticity for continuing on to the lower-secondary from the primary schooling level is 0.51 and a two year sustained growth at five per cent will lower the continuation rate by about three per cent. The esti-mates are significant at the one per cent level and the model explains a substantial 85 per cent of the variation in the primary-to-lower-secondary continuation rate.

Estimates for continuation from lower- to upper-sec-ondary schooling are similar in magnitude to the earlier continuation rates, but there is very little precision and the explanatory power of the model is minimal. Additionally, economic conditions are a very small part of the decision-making process, explaining less than two per cent of the variation in the continuation rate over time. Mexican upper-secondary-bound students may have much in common with U.S. college-bound students, whom Manski and Wise (1983) report are affluent and more responsive to family characteristics than to external economic conditions. According to ENIGH, the top 20 per cent of households in the income distribution com-prised 46 per cent of all households with students attending upper-secondary and senior vocational schools. The bottom 20 per cent comprised only two per cent.20

Curiously, the continuation rates are responsive to economic conditions at the end of the school year, even though behavior at the beginning of the school year is

20_{The low participation of the lowest deciles in}

upper-sec-ondary schooling also reflects the life-cycle earnings hypoth-esis, which alerts us to the fact that poor householders tend to be relatively young and are thus less likely to have children old enough to be in upper-secondary and senior vocational pro-grams.

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Table 1

Time-series analysis of Mexican schooling indicators for school years 1976–77 through 1993–94(1)_{(Standard errors in parentheses)}

Log retention rates Log continuation rates Log efficiency rates Log enrollment rates Junior Lower- Senior Upper- To lower- To upper- Lower-

Upper-Primary Primary Primary Secondary(2)

vocational secondary vocational secondary secondary secondary secondary secondary

0.0342** _0.2919* _0.0274†† _0.3808** ₂_0.1648†† _0.5130** _0.4013 _0.0863 _0.2518 _1.1665†† _0.1568‡ _0.8911**

Log GDP

(0.1186) (0.1246) (0.0169) (0.1304) (0.1038) (0.0747) (0.5082) (0.2008) (0.3186) (0.7038) (0.1137) (0.1060) %DGDP 20.0422* ₂_0.0306†† ₂_0.0758* ₂_0.3285* ₂_0.2028 ₂_0.3340** ₂_0.4820‡ ₂_0.0740 ₂_0.4592† ₂_1.7894** ₂_0.1698 ₂_0.4032†

begin (0.0191) (0.2003) (0.0271) (0.1267) (0.1078) (0.0956) (0.3586) (0.1714) (0.2118) (0.4670) (0.1597) (0.2116)

20.0531** ₂_0.7691** ₂_0.0381†† ₂_0.1025 _0.1715† ₂_0.3419** _0.2637 ₂_0.2774 ₂_0.0391 _0.8186† ₂_0.1201 ₂_0.6797**

%DGDP end

(0.0174) (0.1826) (0.0247) (0.1125) (0.1008) (0.0857) (0.3878) (0.2159) (0.2447) (0.5511) (0.1533) (0.2040) Adjusted R2 _0.555 _0.562 _0.617 _0.349 _0.331 _0.851 _0.015 _0.947 _0.193 _0.745 _0.053 _0.974

d(3) _2.12 _2.23 _1.83 _1.45c _1.45c _2.17 _1.41c _1.68 _1.03c _1.06c _1.62 _1.80

0.954 0.807 0.931 0.843 0.904 0.860 0.824 0.540 0.614 0.347 1.098 0.462 Mean

(0.004) (0.031) (0.005) (0.019) (0.015) (0.026) (0.042) (0.118) (0.059) (0.158) (0.073) (0.034)

**_{Significant at the 1% level;}*_{Significant at the 5% level;}†_{Significant at the 10% level;}††_{Significant at the 15% level;}‡_{Significant at the 20% level.}

(1)_{This time period spans 18 years of published SEP data from which the following sample sizes can be derived: 18 years of retention rates, 17 years of continuation rates, 16}

years of efficiency rates at the secondary levels, and 13 years of efficiency rates at the primary level. The enrollment rates are taken from the World Bank World*Data country data series and include 15 years at the primary level (1977–1992) and 16 years at the secondary level (1975–1991). The 1981 rates are not available in either enrollment series.

(2)_{Both lower- and upper- secondary levels are included.} (3)_{Durbin-Watson statistic.}

c_{Although within the indeterminate range for auto-correlation, estimates shown are the result of Cochrane-Orcutt regressions.}

All specifications also include a constant term and trend variable. Log GDP refers to the year in which the school year ends. For example, 1977 GDP is used for the 1976–77 school year. “Begin” and “End” refer to the calendar year in effect at the beginning and end of the academic (September through June) calendar year.

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being measured.21

This result suggests that students’ enrollment decisions depend on current and expected future economic activity. Although very few families are likely to have anticipated the 1982–83 crash, sluggish growth for the rest of the 1980s was probably quite pre-dictable.

6.3. Efficiency rates22

Economic indicators are poor predictors of efficiency rates at the primary level. The high adjusted R2_results

from a positive and precise trend variable. In their study of primary education in Latin America, Wolff et al. (1994, 20–22) present anecdotal evidence that repetition rates—which bear directly on efficiency rates—depend more on arbitrary school policies than on student achievement. If school policies are not responsive to economic conditions, then efficiency rates will bear no relation to the economy. This explanation does not hold at the secondary levels, where efficiency rates are quite responsive to income and price effects: at the upper-sec-ondary level, the income elasticity is a striking 1.2, a five per cent increase in GDP growth rates reduces efficiency by nearly nine per cent, and the model explains 75 per cent of the variation in efficiency rates over time. The large elasticities and explanatory power here contrast with the weaker performance for continu-ation rates at the upper-secondary level. It appears that continuation to upper-secondary school is not well pre-dicted by economic conditions. But for the relatively elite group of students that do continue, staying on and finishing within the expected course of study does depend, to a large extent, on the economy.

6.4. Enrollment rates

Primary enrollment rates are poorly predicted by econ-omic conditions, although the income and price effects have the expected signs.23 _{Secondary enrollment rates,}

however, are very responsive to the economy, with income elasticities of close to 0.9 and a coefficient of 0.7 for the growth rate at the school-year end. Note that these enrollment rates combine both the lower and upper-secondary levels. As with the primary and junior vocational retention rates, the price effect is larger at the end of the school year. This is probably because the

21_{In other models not reported here, GDP growth rates in}

the year following the school end had tiny and insignificant effects for retention and efficiency rates.

22_{Although efficiency rates contain responses to conditions}

over several years, my analysis considers only current economic conditions. The results should be interpreted as the marginal effect of economic conditions on students who are close to graduation.

23_{See footnote 1.}

World Bank data from which the enrollment series are drawn correspond more faithfully to the calendar year than do the SEP data.

In any case, the schooling indicators by and large show positive income and negative price effects. Indi-cators that more closely reflect marginal decisions—such as the decision to continue on to the next schooling level—tend to respond more strongly to economic con-ditions. Vocational and more advanced students appear to be more responsive to price and income changes than those in academic and lower-level programs, respect-ively. Finally, while primary school enrollment rates appear to be insensitive to economic conditions, second-ary enrollment rates are among the most responsive of all the schooling indicators.

What do these estimates tell us about how Mexico’s economy has affected schooling over the past 15 years? The estimates suggest that income effects slightly domi-nate price effects. For example, a ten per cent increase in the income level will raise the continuation rate from primary to lower-secondary by five per cent, or 4.3 per-centage points. Taking the increase in five per cent growth rates over two years would reduce the continu-ation rate by slightly more than three per cent, or 2.9 percentage points. Applied to the recent experience in Mexico, the model predicts that in the 1994–95 school year (during which the economy contracted by more than six per cent), lower-secondary continuation rates would have fallen by two percentage points and the secondary enrollment rate (for both secondary levels) would have remained unchanged from the previous year. Taking as a counter factual what would have happened if the econ-omy had remained the same in 1995 as it was in 1994 (instead of declining by 6.2 per cent), then lower-second-ary continuation rates instead would have fallen by one percentage point and enrollment rates would have risen by one percentage point. The difference is not very great. However, a long period of economic decline will inten-sify the backsliding. If, for example, the 1980s economy had grown at half its average growth rate of the ten years leading up to the 1982 crash, then lower-secondary con-tinuation rates would have reached 97 per cent by 1994, eight percentage points higher than the actual figure. Sec-ondary enrollment rates would have reached 68 per cent by 1991, instead of the recorded 56 per cent. Since nega-tive income effects tend to outweigh the posinega-tive price effects of economic contraction, the cumulative effects of a stagnating and crisis prone economy are indeed dire.

7. State series

In this section I consider the marked variation among states to study first, whether the aggregate patterns exist at the state level and second, what determines the uneven performance in schooling indicators among states.

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191

M. Binder / Economics of Education Review 18 (1999) 183–199 Tables 6 and 7 in Appendix A present summary statistics

for schooling indicators and state-level characteristics, respectively. States vary considerably in all schooling indicators, but the differences are particularly acute for enrollment rates. For example, the upper-secondary enrollment rate varies from 15 per cent (in the state of Guanajuato) to 56 per cent (in Mexico City).24 _Other

state characteristics are equally diverse. For example, Mexico City had a per capita income nearly five times that of the poorest state of Oaxaca in 1988. In 1990, only eight per cent of Nuevo Leon’s population was rural, compared to 61 per cent in Oaxaca; and per student spending in Mexico City was more than three times the spending in Guanajuato.

In investigating income and price effects at the state level, I add measures of state economic performance to the analysis to distinguish between national and local economic conditions. State-level GDP data are not avail-able annually. I therefore use annual Gross State Rev-enues (GSR) as a proxy for state income (Inegi, 1986).25

GSR measures the total income accruing to a state government in a calendar year, including local taxes and the receipt of federal funds. In a state cross-section, the correlation between GSR and state-level GDP in each of the years for which both are available (1980, 1985 and 1988) was greater than 0.94. I begin with a fixed effects model which measures the response of state-level schooling indicators to national and state-level price and income effects:

log(sit)5b01b1log(GDPt)

1b2%D(GDPt)BEGIN1b3%D(GDPt)END

1b3TRENDt1g1log(GSRit) (2)

1g2%D(GSRit)BEGIN1g3%D(GSRit)END1ai

1mit

where the beta terms duplicate Eq. (1), GSR proxies the state-level income effect, the %DGSR’s proxy the

state-level price effects at the beginning and end of the school year, respectively, and the g’s are coefficients. The

schooling indicator, s, is now subscripted by state i and

24_{Enrollment rates were calculated by dividing total lower}

and upper-academic secondary matriculation by the number of 13—15 year-olds and 16–18 year-olds, respectively for each state in 1990. Because some matriculated students may be older than expected based on normal progress through the schooling system, the rates are likely to be inflated. See footnote 1.

25_{GSR figures were converted to constant 1980 pesos by}

using metropolitan price index data also available in the

Anua-rio Estadı´stico (op. cit.). States were assigned the index

pro-vided by the sample city within the state with an index. Seven states had no city included in the sample: their indices were drawn from an average of their bordering states.

time t, and ai represents a time invariant state fixed

effect.

Table 2 shows estimates of Eq. (2) for retention and continuation rates for the academic sequence.26_{The state}

schooling indicators display similar responses to the aggregate data with respect to income and price effects

measured by GDP and %DGDP. The magnitudes are

similar to those reported in Table 1, except for dramatic changes in the continuation rates to upper-secondary school. Compared to the national totals, the state analy-ses show a sign reversal for income, a drastic decline in the negative price effect at the start of the school year, and an increase in the positive price effect at the school-year end.27 _{This instability in estimates reinforces the}

interpretation of the imprecise estimates in Table 1 that continuation to the upper-secondary school is not con-sistently determined by current economic conditions.

The state time-series economic indicators are very small in magnitude and generally imprecisely measured: the largest income elasticity is 0.03.28_{Moreover, in three}

of the five models reported in Table 2, the income and price effects are exactly reversed. That is, for primary and upper-secondary retention and lower-secondary con-tinuation rates, higher state income has a negative effect and higher state economic growth has a positive effect, holding national economic conditions constant. Suppose in a given year both national and state-level income lev-els rise. In states with higher income levlev-els, the overall income effect will be less. Similarly, the negative price effect will be attenuated by a positive response at the state level. Given the small magnitudes of the state-level effects, the overall income and price effects will remain with the expected signs, but richer states appear to exhi-bit less elastic schooling indicators with respect to econ-omic conditions. We should expect that better-off states would be less responsive to current changes in economic conditions, since fewer families are likely to face binding liquidity constraints (Becker and Tomes, 1986). In any case, these results alert us to the possibility that the response of schooling indicators to economic conditions may vary with the affluence of the state.

To explore this possibility, I interact the economic indicators with the proportion of low-income workers in each state in 1980 (Pick et al., 1989).29_{Table 3 provides}

the results of fixed effects models that include these

26_{Efficiency rates are omitted from this analysis because of}

strong evidence of serial auto-correlation (see Table 1).

27_{Decomposition analyses of these changes showed that they}

are due to a combination of the use of the state series and the inclusion of the GSR variables.

28_{These results also persisted in random effect specifications,}

not reported here, that controlled for population size.

29_{This designation includes workers who earned less than}

1081 pesos in 1980, or about US$50. Results were similar using interactions with the 1990 low-income figures.

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Table 2

Fixed effects analysis using state panel for school years 1976–77 to1990–91(1)_{(Standard errors in parentheses)}

Log retention rate Log continuation rate

Primary school Lower-secondary Upper-secondary To lower-secondary To upper-secondary Log GDP 0.0391**_(0.0112) _0.0448**_(0.0158) ₂_{0.0629 (0.0591)} _0.6362**_(0.0675) ₂_{0.0509 (0.2216)}

%DGDP begin 20.0485**_(0.0134) ₂_0.0791**_(0.0188) ₂_0.3662**_(0.0704) ₂_0.4911**_(0.0804) ₂_{0.0940 (0.2640)}

%DGDP end 20.0581**_(0.0119) ₂_0.0429**_(0.0167) ₂_0.1179†_(0.0629) ₂_0.4941**_(0.0713) _0.5524*_(0.2342)

Log gross state

20.0036*_(0.0017) _0.0040†_(0.0024) ₂_{0.0097 (0.0091)} ₂_0.0346*_(0.0104) _{0.0282 (0.0342)}

revenue (GSR)

%DGSR begin 0.0029†_(0.0017) ₂_{0.0007 (0.0024)} _0.0128‡_(0.0091) _0.0266**_(0.0104) ₂_0.0610†_(0.0340)

%DGSR end 0.0015†_(0.0008) ₂_{0.0005 (0.0011)} _{0.0039 (0.0043)} _0.0110*_(0.0049) ₂_{0.0106 (0.0161)}

Trend 20.0016**_(0.0003) ₂_0.0009**_(0.0004) _{0.0004 (0.0013)} ₂_0.0129**_(0.0015) _{0.0010 (0.0050)}

R2_within _0.170 _0.156 _0.103 _0.329 _0.020

between 0.000 0.056 0.198 0.005 0.051

overall 0.016 0.095 0.003 0.018 0.037

**_{Significant at the 1% level;}*_{Significant at the 5% level;}†_{Significant at the 10% level;}††_{Significant at the 15% level;}‡_Significant

at the 20% level.

(1)_{The series is limited by the availability of GSR only though 1991.}

Table 3

Fixed effects analysis using interactions of the proportion of low-income workers with economic indicators for school years 1976– 77 to 1990–91 (Standard errors in parentheses)

Log retention rate Log continuation rate

Primary school Lower-secondary Upper-secondary To lower-secondary To upper-secondary Log GDP 0.0607**_(0.0164) _0.0620**_(0.0231) _{0.0410 (0.0858)} _0.5961**_(0.0952) ₂_{0.3339 (0.3243)}

X low income 20.2769*_(0.1403) ₂_{0.2091 (0.1977)} ₂_1.2148†_(0.7349) _{0.7951 (0.8148)} _{2.9568 (2.7758)}

%DGDP begin 20.0836**_(0.0316) ₂_0.0628‡_(0.0445) ₂_0.5151**_(0.1657) ₂_0.2668††_(0.1838) ₂_{0.6535 (0.6261)}

X low income 0.3814 (0.3036) 20.0875 (0.4629) 1.7249 (1.5897) 22.3131‡_(1.7635) _{5.3366 (6.0076)}

%DGDP end 20.0724*_(0.0304) ₂_{0.0518 (0.0431)} _0.3999**_(0.1599) ₂_{0.0942 (0.1765)} ₂_{0.1472 (0.6014)}

X low income 0.1638 (0.3005) 0.1169 (0.4251) 22.9485†_(1.5765) ₂_4.3094*_(1.7456) _{7.3976 (5.9465)}

Log gross state

20.0046 (0.0046) 20.0105††_(0.0065) ₂_0.0580*_(0.0243) ₂_0.0966**_(0.0270) _0.1193‡_(0.0918)

revenue (GSR)

X low income 0.0251 (0.0508) 0.1698*_(0.0714) _0.5746*_(0.2659) _0.5853*_(0.2950) ₂_{0.9995 (1.0051)}

%DGSR begin 0.0085†_(0.0052) ₂_{0.0062 (0.0073)} _0.0444†_(0.0270) _{0.0304 (0.0300)} _{0.0278 (0.1021)}

X low income 20.0564 (0.0491) 0.0460 (0.0691) 20.3451‡_(0.2569) ₂_{0.0698 (0.2850)} ₂_{0.8259 (0.9708)}

%DGSR end 0.0019 (0.0029) 0.0049 (0.0041) 0.0293†_(0.0152) _0.0259††_(0.0169) ₂_{0.0365 (0.0575)}

X low income 20.0078 (0.0305) 20.0638††_(0.0430) ₂_0.2890†_(0.1598) ₂_{0.1587 (0.1773)} _{0.2997 (0.6040)}

Trend 20.0016**_(0.0003) ₂_0.0010**_(0.0004) _{0.0002 (0.0013)} ₂_0.0138**_(0.0015) _{0.0017 (0.0050)}

R2_Within _0.189 _0.089 _0.128 _0.385 _0.034

Between 0.455 0.066 0.000 0.172 0.085

Overall 0.342 0.066 0.003 0.123 0.063

**_{Significant at the 1% level;}*_{Significant at the 5% level;}†_{Significant at the 10% level;}††_{Significant at the 15% level;}‡_Significant

at the 20% level.

All specifications also include a constant term. Log GDP refers to the year in which the school year ends. For example, 1977 GDP is used for the 1976–77 school year. “Begin” and “End” refer to the calendar year in effect at the beginning and end of the academic year. “Low-Income” refers to proportion of workers in each state with very low 1980 earnings. See note 29. The X terms are interactions with the previous variable.

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193

M. Binder / Economics of Education Review 18 (1999) 183–199 interactions. The interactions with log GDP are

uni-formly negative for retention rates and significant for the primary and upper-secondary levels. This means that states with more low-income workers respond less elasti-cally to changes in income than states with fewer income workers. Note that, for the range of possible low-income proportion values (see Table 7), the national income effect is always positive, but it is nonetheless quite small: in Mexico City, with only 3.2% of workers earning very low wages, the estimated income elasticity for primary school retention is 0.052; in Yucatan, with a low-income proportion of 21.1%, the elasticity is only 0.002. Still, the expected Becker-Tomes effect is work-ing opposite to what had been expected. One possible explanation for this puzzle is that in richer states, school-ing is more accessible. This would mean that poorer chil-dren are more likely to be involved in schooling, but since they are more likely to drop out,30_{retention rates}

will be worse.

Even if this is the case, the effect is very small. For retention at the primary level, no other interactions are statistically significant. For lower-secondary retention, there is a significant and positive interaction for the log of GSR-the income for state income. This means that poorer states respond more strongly to changes in state income. Although this appears to contradict the idea that poorer states have fewer economically marginal students, the effect is again extremely small: for the state average of 9.35% low income workers, the effect of gross state revenue is close to zero. For Yucatan the effect is posi-tive, but still tiny, with an elasticity of 0.025. Overall, both the primary and lower-secondary retention rates do not vary greatly among states of different income levels. For upper-secondary retention the interactions appear to be much more important. Poorer states still have lower national GDP elasticities (the interaction coefficient is negative), but all four of the other significant interaction terms suggest greater elasticity for states with more low-income workers: the state low-income interaction coefficient is positive and all significant price interaction coef-ficients are negative. Thus in poorer states, the upper-secondary retention rates are more responsive to econ-omic conditions. It is certainly true, as mentioned earlier, that the direct and opportunity costs are much more pro-nounced for this schooling level than for earlier levels. As such, even the middle-class students that comprise a much greater share of upper-secondary schooling are more likely to be affected by economic fluctuations at this schooling level, and more so in states with lower wages.

30_{Positive effects of income or wealth on various schooling}

indicators in developing countries are reported in Glewwe and Jacoby (1994), Jamison and Lockheed (1987) and Birdsall (1985).

The continuation rates to lower-secondary also appear to be more responsive to economic conditions in poorer states. The interaction terms for the national price effects are large and negative and the interaction for the state income effect is large and positive. In contrast, the con-tinuation rate to upper-secondary does not appear at all sensitive to economic conditions when interaction terms are present. This finding mirrors the unstable estimates for upper-secondary continuation rates in earlier dis-cussions of Tables 1 and 2. In any case, the interaction analysis has shown that upper-secondary retention and lower-secondary continuation rates are more responsive to economic conditions in poorer states. In these states, deteriorating economic conditions will have harsher consequences for schooling outcomes.

States vary not only in their sensitivity to economic conditions but also in their economic structure and edu-cational spending patterns. These differences may also influence schooling outcomes. For example, the supply of accessible schooling may be greater in states with more heavily concentrated populations, states that spend more per student may provide higher quality education, and more industrialized states may provide more income security but also higher opportunity costs.31_The

follow-ing analyses incorporate state characteristics in an effort to identify the variation among states in schooling out-comes. Unfortunately, annual time series data are not available for urbanization, sectoral structure and school spending measures.32 _{I therefore use two alternative}

approaches for exploring the role of these state character-istics. In the first approach, I use only one value of these measures from an early or middle point in the series. In the second approach, I limit the analysis to 1980 and 1990, years for which the structural measures are avail-able. Although the second approach severely restricts the number of observations, it has the advantage of exploring the effects of changes in urbanization, sectoral structure and school spending on schooling outcomes. In addition, I am able to expand the studied schooling outcomes to include state-level enrollment rates, since the decennial censuses provide state population counts by age for these years.

For both approaches I estimate random effects models which facilitate the analysis of between-state variation, while still controlling for correlated error terms for

same-31_{Another potentially important variable is state-to-state}

migration, which may influence schooling indicators on the demand side. Net in-migration, though, was so closely (and positively) correlated with state income, sectoral structure and urbanization that identifying separate effects was impossible. The variables that were included may therefore proxy migration inflows: their estimated effects should be interpreted cautiously.

32_{Urbanization measures are available decennially, as is the}

labor force distribution by sector. School spending by state is available annually after 1985.

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Table 4

Fixed state characteristics and log schooling indicators using random effects models for school years 1976–77 to 1990–91 (Standard errors in parentheses)

Log retention rates Log continuation rates

Primary school Lower-secondary Upper-secondary To lower-secondary To Upper-Secondary Log GDP 0.0350**_(0.0110) _0.0459**_(0.0151) ₂_0.1076*_(0.0547) _0.6190**_(0.0653) ₂_{0.0184 (0.2078)}

%DGDP begin 20.0463**_(0.0134) ₂_0.0798**_(0.0186) ₂_0.3416**_(0.0695) ₂_0.4822**_(0.0800) ₂_{0.1131 (0.2602)}

%DGDP end 20.0564**_(0.0119) ₂_0.0435**_(0.0166) _0.1377*_(0.0621) ₂_0.4878**_(0.0710) _0.5394*_(0.2311)

Log gross state

20.0023††_(0.0016) _0.0037†_(0.0020) _{0.0040 (0.0059)} ₂_0.0294**_(0.0089) _{0.0181 (0.0249)}

revenue (GSR)

%DGSR begin 0.0026††_(0.0017) ₂_{0.0006 (0.0024)} _{0.0096 (0.0089)} _0.0256**_(0.0103) ₂_0.0581†_(0.0335)

%DGSR end 0.0011‡_(0.0008) ₂_{0.0004 (0.0011)} ₂_{0.0003 (0.0036)} _0.0096*_(0.0046) ₂_{0.0079 (0.0141)}

Trend 20.0016**_(0.0002) ₂_0.0009**_(0.0003) _{0.0011 (0.0013)} ₂_0.0127**_(0.0015) _{0.0005 (0.0048)}

Log per capita

20.0050 (0.0109) 20.0021 (0.0105) 0.0304‡_(0.0224) _0.1024*_(0.0478) _{0.0818 (0.1049)}

income in 1980 Log per student

20.0100 (0.0210) 20.0001 (0.0203) 20.0759†_(0.0429) _0.1233‡_(0.0919) _{0.1298 (0.2010)}

spending in 1985

% Rural in 1980 0.0514*_(0.0256) _{0.0293 (0.0243)} _{0.0287 (0.0499)} ₂_0.4590**_(0.1106) ₂_0.6487**_(0.2368)

% Labor force in

manufacturing in 0.1419†_(0.0837) _0.1459†_(0.0794) _{0.0677 (0.1628)} ₂_0.7789*_(0.3612) ₂_1.3345†_(0.7721)

1980

R2_Within _0.169 _0.156 _0.097 _0.328 _0.020

Between 0.228 0.188 0.235 0.657 0.444

Overall 0.216 0.176 0.148 0.592 0.264

**_{Significant at the 1% level;}*_{Significant at the 5% level;}†_{Significant at the 10% level;}††_{Significant at the 15% level;}‡_Significant

at the 20% level.

All models include a constant term. Log GDP refers to the year in which the school year ends. For example, 1977 GDP is used for the 1976-277 school year. “Begin” and “End” refer to the calendar year in effect at the beginning and end of the academic year.

state observations. In these models, the time-invariant state error term in Eq. (2),ai, is divided into explained

and random components as follows:

ai5d1Ci1hi (3)

where C is a vector of state characteristics, including state economic, demographic and education financing characteristics,d1is a parameter, andhiis a random error

term. Combining Eqs. (2) and (3) gives: log(sit)5b01b1log(GDPt)

1b2%D(GDPt)BEGIN1b3%D(GDPt)END

1b4TRENDt1g1log(GSRit) (4)

1g2%D(GSRit)BEGIN1g3%D(GSRit)END1d1Ci

1hi1mit

The random effects model assumes that the state-spe-cific error term, hi , is uncorrelated with the other

explanatory variables. A Hausman specification test sup-ports this assumption.

Table 4 shows estimates of the random effects model in Eq. (4) using all available years and the following fixed measures for the Ci vector: the log of state per

capita income in 1980 (this is a direct measure that does not use the GSR proxy), the log of per student spending in 1985, the proportion of the population living in rural communities in 1980 and the proportion of the labor force employed in manufacturing in 1980. Since the national and state-level price and income coefficients are similar to those reported in Table 2, they are not repeated in Table 4.

Per capita income has a small positive effect on upper-secondary retention and a more pronounced (but still modest) positive effect on continuation to the lower-sec-ondary level. Per student spending in 1985 (the earliest year for which this measure is available) has small nega-tive effects on high school retention and posinega-tive (but imprecise) effects on continuation rates into both second-ary levels of schooling. Note, though, that the spending measure is very broad: it divides the total of all federal, state and private expenditures for education by the num-ber of students enrolled in academic programs at the pri-mary and secondary levels. The true per student figure is much lower, since vocational, pre-school and post-sec-ondary students are excluded. In addition, the measure is an average over all schooling levels: it does not dis-tinguish between a state that spends lavishly on univer-sities and one that spends proportionately more on

(1)

Table 4

Fixed state characteristics and log schooling indicators using random effects models for school years 1976–77 to 1990–91 (Standard errors in parentheses)

Log retention rates Log continuation rates

Primary school Lower-secondary Upper-secondary To lower-secondary To Upper-Secondary Log GDP 0.0350**_(0.0110) _0.0459**_(0.0151) ₂_0.1076*_(0.0547) _0.6190**_(0.0653) ₂_{0.0184 (0.2078)} %DGDP begin 20.0463**_(0.0134) ₂_0.0798**_(0.0186) ₂_0.3416**_(0.0695) ₂_0.4822**_(0.0800) ₂_{0.1131 (0.2602)} %DGDP end 20.0564**_(0.0119) ₂_0.0435**_(0.0166) _0.1377*_(0.0621) ₂_0.4878**_(0.0710) _0.5394*_(0.2311) Log gross state

20.0023††_(0.0016) _0.0037†_(0.0020) _{0.0040 (0.0059)} ₂_0.0294**_(0.0089) _{0.0181 (0.0249)} revenue (GSR)

%DGSR begin 0.0026††_(0.0017) ₂_{0.0006 (0.0024)} _{0.0096 (0.0089)} _0.0256**_(0.0103) ₂_0.0581†_(0.0335) %DGSR end 0.0011‡_(0.0008) ₂_{0.0004 (0.0011)} ₂_{0.0003 (0.0036)} _0.0096*_(0.0046) ₂_{0.0079 (0.0141)} Trend 20.0016**_(0.0002) ₂_0.0009**_(0.0003) _{0.0011 (0.0013)} ₂_0.0127**_(0.0015) _{0.0005 (0.0048)} Log per capita

20.0050 (0.0109) 20.0021 (0.0105) 0.0304‡_(0.0224) _0.1024*_(0.0478) _{0.0818 (0.1049)} income in 1980

Log per student

20.0100 (0.0210) 20.0001 (0.0203) 20.0759†_(0.0429) _0.1233‡_(0.0919) _{0.1298 (0.2010)} spending in 1985

% Rural in 1980 0.0514*_(0.0256) _{0.0293 (0.0243)} _{0.0287 (0.0499)} ₂_0.4590**_(0.1106) ₂_0.6487**_(0.2368) % Labor force in

manufacturing in 0.1419†_(0.0837) _0.1459†_(0.0794) _{0.0677 (0.1628)} ₂_0.7789*_(0.3612) ₂_1.3345†_(0.7721) 1980

R2_Within _0.169 _0.156 _0.097 _0.328 _0.020

Between 0.228 0.188 0.235 0.657 0.444

Overall 0.216 0.176 0.148 0.592 0.264

**_{Significant at the 1% level;}*_{Significant at the 5% level;}†_{Significant at the 10% level;}††_{Significant at the 15% level;}‡_Significant at the 20% level.

state observations. In these models, the time-invariant

state error term in Eq. (2),

a

, is divided into explained

and random components as follows:

a

5 d

Ci

1 h

(3)

where C is a vector of state characteristics, including

state economic, demographic and education financing

characteristics,

d

is a parameter, and

h

is a random error

term. Combining Eqs. (2) and (3) gives:

log(s

)

5 b

1 b

log(GDP

)

1 b

%

D

(GDP

)

BEGIN

1 b

%

D

(GDP

)

END

1 b

TRENDt

1 g

log(GSR

)

(4)

1 g

%

D

(GSR

)

BEGIN

1 g

%

D

(GSR

)

END

1 d

Ci

1 h

1 m

The random effects model assumes that the

state-spe-cific error term,

h

, is uncorrelated with the other

explanatory variables. A Hausman specification test

sup-ports this assumption.

Table 4 shows estimates of the random effects model

in Eq. (4) using all available years and the following

fixed measures for the C

vector: the log of state per

capita income in 1980 (this is a direct measure that does

not use the GSR proxy), the log of per student spending

in 1985, the proportion of the population living in rural

communities in 1980 and the proportion of the labor

force employed in manufacturing in 1980. Since the

national and state-level price and income coefficients are

similar to those reported in Table 2, they are not repeated

in Table 4.

Per capita income has a small positive effect on

upper-secondary retention and a more pronounced (but still

modest) positive effect on continuation to the

lower-sec-ondary level. Per student spending in 1985 (the earliest

year for which this measure is available) has small

nega-tive effects on high school retention and posinega-tive (but

imprecise) effects on continuation rates into both

second-ary levels of schooling. Note, though, that the spending

measure is very broad: it divides the total of all federal,

state and private expenditures for education by the

num-ber of students enrolled in academic programs at the

pri-mary and secondary levels. The true per student figure

is much lower, since vocational, pre-school and

post-sec-ondary students are excluded. In addition, the measure

is an average over all schooling levels: it does not

dis-tinguish between a state that spends lavishly on

univer-sities and one that spends proportionately more on

(2)

pri-mary schools. The imprecision of this measure may

result in its small influence on schooling indicators.

The coefficient on the portion of the population that

is rural is positive for the retention rates and significantly

negative for the continuation rates, suggesting that rural

states do a better job of retaining students, but are worse

at inducing students to continue from one level to the

next. Schools may be relatively less accessible in states

where larger proportions of the population are rural, so

that the relatively better-off attend school and with

econ-omic conditions constant, are less likely to leave school.

In the 1988–89 school cycle, for example, 20 per cent

of primary schools nationally offered less than a six-year

program, compared with 44 per cent in the rural state of

Chiapas (Salinas de Gortari, 1989).

At the same time, continuation rates are likely to be

lower in rural states because secondary schools tend to

be concentrated in larger towns. A state with a rural

pro-portion at half the national median of 41 per cent would

have a lower-secondary continuation rate nine

percent-age points higher than a state with the median proportion

and the upper-secondary continuation rate would be 13

percentage points higher.

The per cent of the labor force employed in

manufac-turing has a modest positive effect on retention rates and

a large negative effect on continuation rates. Inasmuch

as manufacturing jobs offer higher wages and more

stab-ility than jobs in other sectors, the results suggest that

manufacturing jobs enhance the ability of students to

complete the academic year of schooling, once they have

begun it. However, higher opportunity costs appear to

reduce continuation rates considerably: every percentage

point increase in the manufacturing share of labor

reduces the lower-secondary continuation rate by 0.8 per

cent and the upper-secondary continuation rate by 1.3

per cent.

Differences in per capita income, urbanization, school

spending and manufacturing prominence are quite

suc-cessful in explaining between-state variation, especially

for lower- and upper-secondary continuation rates, where

the models account for 66 and 44 per cent of the

observed variation, respectively.

Table 5 shows parameter estimates of the second

approach, which uses data for 1980 and 1990. The trend

variable picks up changes in national economic

con-ditions (and any other changes that are uniform among

states).

_{The patterns for retention rates and continuation}

33_{1985 was also a recession year, so that the cross-section} may not accurately reflect school quality among states. This problem is at least partly corrected in the 1980/1990 analysis that follows, in which 1990 school spending is also included.

34_{Because only two years are available for each state, only} one time-varying variable that is identical for all states can be identified by the model.

rates controlling for changes in state characteristics are

similar to the point-in-time state characteristics used in

Table 4. For example, larger rural population and

manu-facturing labor proportions lead to better retention rates

and worse continuation rates, although the within-state

effect (measured by the R

_{) appears to be very small for}

continuation rates. Per student spending appears to have

larger effects in the 1980/1990 model, with positive

elas-ticities of around 0.2 for the continuation rates. The

response of enrollment rates to state characteristics is

similar in direction to the response of continuation rates,

although the magnitude of the response is attenuated: the

log of per student school spending has a positive effect

on enrollment, while the proportion of the population

that is rural and the proportion of the labor force

employed in manufacturing have negative effects. For

enrollments, the effect of within-state changes of these

characteristics appears to be quite large: the model

accounts for more than two-thirds of the within-state

variation.

8. Conclusions

Schooling indicators in Mexico clearly respond to

economic conditions as predicted by the human capital

model. During a recession, falling opportunity costs

improve schooling indicators at the same time that

reduced income worsens them. As found in studies of

schooling in industrialized countries, the negative

income effect of falling income generally exceeds the

positive effect of lower opportunity costs. The responses

are evident even at the primary level of schooling, but

they are more pronounced at higher schooling levels.

Vocational students in Mexico respond more strongly to

economic incentives, just as community college students

in the United States exhibit larger income and price

elas-ticities than do students in four-year institutions. This

pattern can be explained by the greater concentration of

marginal schooling decision makers in vocational

pro-grams due to lower family income or greater

substitut-ability of school and on-the-job training. In addition,

enrollment rates—which measure the broad participation

of children in the education system—tend to respond

more strongly to economic conditions than do the

reten-tion, efficiency and continuation rates of those who have

already entered the system.

Although the magnitudes of schooling indicator

responses to economic conditions are often small, a

pro-tracted period of economic decline and stagnation will

hobble human capital formation. The estimates presented

here show that secondary enrollment rates would have

been eleven percentage points higher in 1991 (68 per

cent instead of 56 per cent), had the 1980s experienced

growth rates of just one-half of those in the preceding

decade.

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Table 5

Random effects models for changing state characteristics and schooling indicators for the 1979–80 and 1989–90 school years (Standard errors in parentheses)

Log retention rates Log continuation rates Enrollment rates

Lower- Upper- Lower- Upper- Lower-

Upper-Primary

secondary secondary secondary secondary secondary secondary Log state per

20.0085 0.0039 0.0479† _0.0437 ₂_0.0812†† _0.0474 ₂_0.0129

capita

(0.0088) (0.0112) (0.0264) (0.0448) (0.1084) (0.0557) (0.0327)

income(1)

20.0005 0.0017 20.0124 20.0058 0.0202 20.0409 20.0058

%DGSR begin

(0.0052) (0.0075) (0.0182) (0.0304) (0.0844) (0.0372) (0.0211)

Log per

0.0043 20.0094 20.0172 0.1619* _0.2502†† _0.0901 _0.0722††

student

(0.0118) (0.0161) (0.0384) (0.0646) (0.1685) (0.0796) (0.0459)

spending(2)

0.0607** _0.0061 _0.1458* ₂_0.4526** ₂_0.7908** ₂_0.4620** ₂_0.3810** % Rural

(0.0230) (0.0268) (0.0622) (0.1060) (0.2519) (0.1328) (0.0791)

% Labor force

0.1099* _0.0625 _0.2633† ₂_0.5467* ₂_0.9409†† _0.0355 ₂_0.3497* in

(0.0488) (0.0603) (0.1411) (0.2397) (0.5837) (0.2989) (0.1761)

manufacturing

20.0010* _0.0000 _0.0010 _0.0025 _0.0002 _0.0107** _0.0073**

Trend

(0.0005) (0.0006) (0.0015) (0.0025) (0.0068) (0.0031) (0.0018)

R2_Within _0.271 _0.000 _0.170 _0.000 _0.096 _0.679 _0.735

Between 0.227 0.121 0.108 0.759 0.434 0.618 0.570

Overall 0.232 0.078 0.121 0.672 0.320 0.634 0.618

**_{Significant at the 1% level;}*_{Significant at the 5% level;}†_{Significant at the 10% level;}††_{Significant at the 15% level;}‡_Significant at the 20% level.

All models also include a contstant term. “Begin” refers to the calendar year in effect at the begining of the school year. (1)_{1980 and 1988 values.}

(2)_{1985 and 1990 values.}

Analyses of a state panel data revealed similar patterns

in terms of state-level schooling indicators and national

economic conditions. State-level economic conditions

appeared to be less important in determining state

schooling indicators. Nevertheless, a state’s affluence

appears to determine the responsiveness of schooling

indicators to conditions in both the national and state

economy. In particular, retention rates at the

upper-sec-ondary level and continuation rates to lower-secupper-sec-ondary

schooling appeared to be more sensitive to economic

indicators in states with higher proportions of

low-income workers. Finally, state characteristics appear to

play an important role in determining differences in

schooling outcomes among states. In analyses that used

characteristics both at one point in time and between

1980 and 1990, less urbanized states showed better

reten-tion rates but worse enrollment and continuareten-tion rates,

states that spent more per student between 1980 and

1990 and had smaller shares of the labor force in

manu-facturing had better continuation and enrollment rates at

the secondary level.

Acknowledgements

This paper benefitted greatly from discussions with

Alok Bohara, Guillermina Engelbrecht, Phil Ganderton,

Cecilia Garcia, Steve Hoenack, Andrew Morrison, Omar

Rivera, Mike McKee and Christine Sauer. Remaining

errors are my own. I wish also to acknowledge the Latin

America Institute at UNM for providing funds for travel

to Mexico, Carolyn Mountain for locating state-level

economic data, Ken Baker for creating the

state-school-ing series, and Lou Ann Lora-Platt for editorial

assist-ance.

Appendix A

Tables 6 and 7

For population figures, rural population and

manufac-turing labor proportions: Pick, James B. and Edgar W.

Butler. (1994) The Mexico Handbook: Economic and

Demographic Maps and Statistics. Boulder: Westview

(4)

Table 6

1990 Schooling indicators by state

State Retention rates Continuation rates Efficiency rates Enrollment rates

Upper- To lower- To upper- Upper-

Upper-Prim Lower-sec Prim Lower-sec Lower-sec

sec sec sec sec sec

Aguascalientes 0.94 0.92 0.85 0.79 0.72 0.68 0.62 0.41 0.67 0.27

Baja California 0.91 0.89 0.84 0.99 1.05 0.69 0.50 0.38 0.79 0.32

Baja California

0.91 0.92 0.81 0.97 1.03 0.65 0.66 0.46 0.81 0.44

Sur

Campeche 0.97 0.93 0.85 0.84 1.11 0.42 0.54 0.36 0.62 0.30

Coahuila 0.95 0.93 0.87 0.88 0.78 0.66 0.61 0.18 0.78 0.28

Colima 0.89 0.90 0.83 0.92 0.82 0.54 0.56 0.36 0.74 0.31

Chiapas 0.97 0.90 0.87 0.79 0.65 0.30 0.60 0.40 0.45 0.16

Chihauhua 0.95 0.91 0.86 0.77 0.87 0.54 0.50 0.34 0.64 0.24

DF (Mexico

0.94 0.93 0.96 1.12 1.12 0.75 0.52 0.31 1.07 0.56

City)

Durango 0.93 0.90 0.86 0.77 0.71 0.53 0.54 0.38 0.95 0.24

Guanajuato 0.97 0.92 0.85 0.68 0.58 0.57 0.54 0.28 0.54 0.15

Guerrero 0.96 0.92 0.91 0.78 0.84 0.39 0.63 0.45 0.63 0.30

Hidalgo 0.96 0.94 0.93 0.81 0.59 0.60 0.65 0.49 0.73 0.25

Jalisco 0.95 0.90 0.97 0.77 0.86 0.55 0.55 0.55 0.66 0.37

Mexico 0.96 0.93 0.89 0.87 0.54 0.67 0.59 0.35 0.75 0.20

Michoacan 0.93 0.90 0.90 0.74 0.72 0.42 0.54 0.17 0.55 0.17

Morelos 0.95 0.93 0.88 0.94 0.68 0.73 0.62 0.41 0.83 0.34

Nayarit 0.97 0.92 0.90 0.87 0.48 0.56 0.67 0.41 0.74 0.24

Nuevo Leon 0.96 0.95 0.94 0.94 0.65 0.69 0.77 0.07 0.85 0.26

Oaxaca 0.94 0.92 0.84 0.73 0.66 0.42 0.61 0.34 0.52 0.20

Puebla 0.97 0.95 0.93 0.77 0.65 0.52 0.64 0.49 0.63 0.28

Queretero 0.95 0.94 0.94 0.78 0.67 0.63 0.60 0.34 0.66 0.22

Quintana Roo 0.93 0.91 0.85 0.95 0.73 0.59 0.54 0.36 0.64 0.18

San Luis Potosı´ 0.94 0.92 0.86 0.81 0.65 0.55 0.53 0.10 0.69 0.21

Sinaloa 0.95 0.94 0.87 0.89 1.10 0.54 0.57 0.39 0.73 0.41

Sonora 0.92 0.91 0.84 0.98 0.82 0.60 0.58 0.37 0.85 0.36

Tabasco 0.96 0.94 0.84 0.83 0.87 0.49 0.65 0.35 0.70 0.30

Tamaulipas 0.94 0.94 0.93 0.88 0.74 0.64 0.63 0.30 0.74 0.28

Tlaxcala 0.98 0.96 0.86 0.89 0.81 0.71 0.66 0.37 0.84 0.36

Veracruz 0.98 0.93 0.93 0.85 0.85 0.42 0.59 0.40 0.64 0.28

Yucatan 0.96 0.93 0.82 0.90 0.80 0.42 0.61 0.29 0.63 0.24

Zacatecas 0.93 0.89 0.86 0.71 0.64 0.52 0.55 0.21 0.54 0.17

Mean 0.95 0.92 0.88 0.85 0.77 0.56 0.59 0.35 0.71 0.28

Standard

0.02 0.02 0.04 0.10 0.17 0.11 0.06 0.11 0.13 0.09

deviation

Minimum 0.89 0.89 0.81 0.68 0.48 0.30 0.50 0.70 0.45 0.15

Maximum 0.98 0.96 0.97 1.12 1.12 0.75 0.77 0.55 1.07 0.56

For per capita state income in 1988: Inegi. (1996)

Sis-tema de Cuentos Nacionales de Me´xico: Producto

Interno Bruto por Entidad Federativa 1993 and Pick and

Butler, op cit.

For proportion of low-income workers in 1980: Pick,

James B., Edgar W. Butler and Elizabeth L. Lanzer.

(1989) Atlas of Mexico. Boulder: Westview Press.

For retention, continuation and efficiency rates: SEP.

Various years. Estadı´stica ba´sica del sistema educativo

nacional: fin de cursos.

For enrollment rates: Inegi. 1984 and 1992. Censo

general de poblacion y vivienda 1980 and 1990 and SEP

op cit.

For education spending: SEP. (1995) Compendio

estadı´stico del gasto educativo 1994 and SEP op cit.

Conversions to US $s were based on exchange rate

figures published in the Inter-American Development

Bank’s, Economic and Social Progress in Latin

(5)

Table 7

State characteristics, various years

1990 Per 1988 Per 1990 Proportion of Proportion of

Population 1990 student

capita state Proportion of low-income workers in

State growth 1970– Population spending (in

income (In population that workers in manufacturing

90(1) _(1000s) _current

current US $s) is rural 1980 1990

US$s)(2)

Aguascalientes 1.13 720 1724 0.23 0.08 0.24 428

Baja California 0.91 1661 2599 0.09 0.04 0.23 621

Baja California

1.48 318 2492 0.22 0.04 0.09 655

Sur

Campeche 1.13 535 1809 0.30 0.10 0.09 522

Coahuila 0.77 1972 2571 0.14 0.07 0.25 509

Colima 0.78 429 2116 0.17 0.06 0.10 482

Chiapas 1.05 3210 1028 0.60 0.11 0.06 337

Chihauhua 0.51 2442 2259 0.23 0.06 0.26 462

DF (Mexico

0.20 8236 4665 0.00 0.03 0.21 809

City)(3)

Durango 0.44 1349 1657 0.43 0.09 0.16 418

Guanajuato 0.75 3983 1408 0.37 0.10 0.24 261

Guerrero 0.64 2621 1220 0.48 0.12 0.09 316

Hidalgo 0.58 1888 1530 0.55 0.18 0.15 336

Jalisco 0.61 5303 2170 0.18 0.08 0.23 363

Mexico 1.56 9816 1972 0.16 0.06 0.28 299

Michoacan 0.53 3548 1200 0.38 0.11 0.15 328

Morelos 0.94 1198 1809 0.14 0.09 0.16 379

Nayarit 0.52 525 2356 0.38 0.09 0.10 402

Nuevo Leon 0.83 3099 3469 0.08 0.05 0.29 519

Oaxaca 0.50 3020 963 0.61 0.13 0.98 298

Puebla 0.65 4126 1277 0.36 0.15 0.17 288

Queretero 1.17 1051 2119 0.40 0.09 0.25 337

Quintana Roo 4.60 493 2482 0.26 0.09 0.06 504

San Luis

0.56 2003 1567 0.45 0.13 0.17 336

PotosR

Sinaloa 0.74 2204 1729 0.36 0.07 0.10 422

Sonora 0.66 1824 2562 0.21 0.05 0.16 475

Tabasco 0.95 1502 2099 0.50 0.09 0.08 367

Tamaulipas 0.54 2250 2069 0.19 0.08 0.18 560

Tlaxcala 0.81 761 1274 0.24 0.13 0.25 379

Veracruz 0.63 6228 1549 0.44 0.13 0.11 358

Yucatan 0.80 1363 1452 0.21 0.21 0.15 355

Zacatecas 0.34 1276 1366 0.54 0.10 0.08 325

Mean 0.88 2530 1955 0.31 0.09 0.16 420

Standard

0.74 2224 746 0.16 0.04 0.07 121

deviation

Minimum 0.20 318 963 0 0.03 0.06 261

Maximum 4.60 9816 4665 0.61 0.21 0.29 809

(1)_{Factor of increase.}

(2)_{Total spending divided by primary and lower- and upper-secondary (academic) students. For comparison, average per student} spending for primary and secondary schooling in the United States was $5399 in 1990. (U.S. Department of Education, The Condition

of Education 1992, Washington, DC: 1992, p. 130.).

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References

Aspe, P. (1993) Economic Transformation the Mexican Way. MIT Press, Cambridge, MA.

Becker, G. (1964) Human Capital. University of Chicago Press, Chicago, IL.

Becker, G., & Tomes, N. (1986). Human capital and the rise and fall of families. Journal of Labor Economics, 4(3,pt.2), S1–S39.

Betts, J., & McFarland, L. (1995). Safe port in a storm: The impact of labor market conditions on community college enrollments. Journal of Human Resources, 30(4), 741–766. Birdsall, N. (1985). Public inputs and child schooling in Brazil.

Journal of Development Economics, 18(1), 67–86.

Cragg, M., & Epelbaum, M. (1996). Why has wage dispersion grown in Mexico? Is it the incidence of reforms or the grow-ing demand for skills? Journal of Development Economics,

51(1), 99–118.

Glewwe, P., & Jacoby, H. (1994). Student achievement and schooling choice in low-income countries: Evidence from Ghana. Journal of Human Resources, 23(3), 843–864. Inegi. (1986) Anuario Estadı´stico de Los Estados Unidos

Mex-icanos 1984-1985. Inegi, Aguascalientes, Mexico.

Inegi. (1992) XI Censo General de Poblacion y Vivienda, 1990:

Resumen General. Inegi, Aguascalientes, Mexico.

Inegi. (1993) ENIGH-92: Encuesta Nacional de Ingresos y

Gastos de Los Hogares. Inegi, Aguascalientes, Mexico.

Inter-American Development Bank (IDB). (1995) Economic

and Social Progress in Latin America, 1995 Report.

Inter-American Development Bank, Washington, DC.

Jamison, D.T., & Lockheed, M.E. (1987). Participation in schooling: Determinants and learning outcomes in Nepal.

Economic Development and Cultural Change, 35(2), 279–

306.

Kane, T.J. (1994). College entry by blacks since 1970: The role of college costs, family background, and the returns to edu-cation. Journal of Political Economy, 102(5), 878–911. Kaztman, R., & Gerstenfeld, P. (1990). The complexity of

eval-uating social development. CEPAL Review, 40(August), 31–44.

Kniesner, T., Padilla, A., & Polachek, S. (1978). The rate of return to schooling and the business cycle. Journal of

Human Resources, 13(2), 264–277.

Lehr, D., & Newton, J. (1978). Time series and cross-sectional investigations of the demand for higher education.

Econ-omic Inquiry, 16(3), 411–422.

Lilien, D. and Hall, R. (1986) Cyclical fluctuations in the labor market. Handbook of Labor Economics, Volume II, pp. 1001–1035.

Lustig, N. (1992) Mexico: The Remaking of an Economy. Brookings, Washington, DC.

Manski, C. and Wise, D. (1983). College Choice in America. Harvard University Press, Cambridge, MA.

Mattila, J.P. (1982). Determinants of male school enrollments: A time-series analysis. Review of Economics and Statistics,

64(2), 242–251.

Micklewright, J., Pearson, M., & Smith, S. (1990). Unemploy-ment and early school leaving. Economic Journal, 100(Conference), 163–169.

Nacional Financiera. (1991) La economı´a mexicana en cifras. Nacional Financiera, Mexico, DF.

Pick, J.B., Butler, E.W. and Lanzer, E.L. (1989) Atlas of

Mex-ico. Westview Press, Boulder, CO.

Pissarides, C. (1981). Staying-on at school in England and Wales. Economica, 48(November), 345–363.

Reimers, F. (1990). Deuda externa, ajuste estructural y educa-cio´n en Ame´rica Latina. Tiempos de crisis y reformas.

Revi-sta Latinoamericana de Estudios Educativos (Mexico), 20(1), 49–83.

Salinas de Gortari, C. (1989) Primer Informe de Gobierno

1989: Anexo. Estados Unidos Mexicanos, Mexico, DF.

Secretarı´a de Educacio´n Pu´blica (SEP). (1983) Estadı´stica

ba´s-ica del sistema educativo nacional: serie histo´rba´s-ica de fin de cursos 1976–1977/1981–1982. SEP, Mexico, DF.

Secretarı´a de Educacio´n Pu´blica (SEP). (1984–1994)

Estadı´-stica ba´sica del sistema educativo nacional: fin de cursos.

SEP, Mexico, DF.

Rice, P.G. (1987). The demand for post-compulsory education in the UK and the effects of educational maintenance allow-ances. Economica, 54(November), 465–475.

Schultz, T.W. (1963) The Economic Value of Education. Columbia University Press, New York, NY.

Tilak, J. (1989). The recession and public investment in edu-cation in Latin America. Journal of Interamerican Studies

and World Affairs, 31(Spring), 125–146.

Whitfield, K., & Wilson, R.A. (1991). Staying on in full-time education: The educational participation rate of 16-Year-Olds. Economica, 58(231), 391–404.

Wolff, L., Schiefelbein, E. and Valenzuela, J. (1994) Improving the quality of primary education in Latin America and the Caribbean. World Bank Discussion Paper Series #257. World Bank, Washington, DC.