Economic conditions and national schooling indicators

187 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 WorldData 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 upswings 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: logs 5 b 1 b 1 logGDP 1 b 2 DGDP BEGIN 1 1 b 3 DGDP END 1 b 4 TREND 1 m where s is a schooling indicator retention, continuation, efficiency or enrollment rate for a given school level, GDP measures income, DGDP BEGIN and DGDP END 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 and m is an error term. The semi-log form allows b 1 to be interpreted as the income elasticity of the schooling indicator and b 2 and b 3 to 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 rate, the school indicator will change by b 2 or b 3 per cent. 188 M. Binder Economics of Education Review 18 1999 183–199 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 N278 quarterly on primary schools, N338 on lower-secondary schools and N725 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 N984 vs. N96. The exchange rate in 1992 was about N3 per US1 and annual per capita income was US1859. 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 programs 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. 189 M. Binder Economics of Education Review 18 1999 183–199 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 20.1648 †† 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 20.0306 †† 20.0758 20.3285 20.2028 20.3340 20.4820 ‡ 20.0740 20.4592 † 21.7894 20.1698 20.4032 † 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 20.7691 20.0381 †† 20.1025 0.1715 † 20.3419 0.2637 20.2774 20.0391 0.8186 † 20.1201 20.6797 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 R 2 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 1.45 c 1.45 c 2.17 1.41 c 1.68 1.03 c 1.06 c 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 WorldData 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. 190 M. Binder Economics of Education Review 18 1999 183–199 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 rates 22 Economic indicators are poor predictors of efficiency rates at the primary level. The high adjusted R 2 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 positive price effects of economic contraction, the cumulative effects of a stagnating and crisis prone economy are indeed dire.

7. State series