State series Directory UMM :Data Elmu:jurnal:E:Economics of Education Review:Vol18.Issue2.Apr1999:

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

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. 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: logs it 5 b 1 b 1 logGDP t 1 b 2 DGDP t BEGIN 1 b 3 DGDP t END 1 b 3 TREND t 1 g 1 logGSR it 2 1 g 2 DGSR it BEGIN 1 g 3 DGSR it END 1 a i 1 m it 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 a i 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 levels, 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 US50. Results were similar using interactions with the 1990 low-income figures. 192 M. Binder Economics of Education Review 18 1999 183–199 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 20.0629 0.0591 0.6362 0.0675 20.0509 0.2216 DGDP begin 20.0485 0.0134 20.0791 0.0188 20.3662 0.0704 20.4911 0.0804 20.0940 0.2640 DGDP end 20.0581 0.0119 20.0429 0.0167 20.1179 † 0.0629 20.4941 0.0713 0.5524 0.2342 Log gross state 20.0036 0.0017 0.0040 † 0.0024 20.0097 0.0091 20.0346 0.0104 0.0282 0.0342 revenue GSR DGSR begin 0.0029 † 0.0017 20.0007 0.0024 0.0128 ‡ 0.0091 0.0266 0.0104 20.0610 † 0.0340 DGSR end 0.0015 † 0.0008 20.0005 0.0011 0.0039 0.0043 0.0110 0.0049 20.0106 0.0161 Trend 20.0016 0.0003 20.0009 0.0004 0.0004 0.0013 20.0129 0.0015 0.0010 0.0050 R 2 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. 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. 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 20.3339 0.3243 X low income 20.2769 0.1403 20.2091 0.1977 21.2148 † 0.7349 0.7951 0.8148 2.9568 2.7758 DGDP begin 20.0836 0.0316 20.0628 ‡ 0.0445 20.5151 0.1657 20.2668 †† 0.1838 20.6535 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 20.0518 0.0431 0.3999 0.1599 20.0942 0.1765 20.1472 0.6014 X low income 0.1638 0.3005 0.1169 0.4251 22.9485 † 1.5765 24.3094 1.7456 7.3976 5.9465 Log gross state 20.0046 0.0046 20.0105 †† 0.0065 20.0580 0.0243 20.0966 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 20.9995 1.0051 DGSR begin 0.0085 † 0.0052 20.0062 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 20.0698 0.2850 20.8259 0.9708 DGSR end 0.0019 0.0029 0.0049 0.0041 0.0293 † 0.0152 0.0259 †† 0.0169 20.0365 0.0575 X low income 20.0078 0.0305 20.0638 †† 0.0430 20.2890 † 0.1598 20.1587 0.1773 0.2997 0.6040 Trend 20.0016 0.0003 20.0010 0.0004 0.0002 0.0013 20.0138 0.0015 0.0017 0.0050 R 2 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. 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 low- 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 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. 194 M. Binder Economics of Education Review 18 1999 183–199 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 20.1076 0.0547 0.6190 0.0653 20.0184 0.2078 DGDP begin 20.0463 0.0134 20.0798 0.0186 20.3416 0.0695 20.4822 0.0800 20.1131 0.2602 DGDP end 20.0564 0.0119 20.0435 0.0166 0.1377 0.0621 20.4878 0.0710 0.5394 0.2311 Log gross state 20.0023 †† 0.0016 0.0037 † 0.0020 0.0040 0.0059 20.0294 0.0089 0.0181 0.0249 revenue GSR DGSR begin 0.0026 †† 0.0017 20.0006 0.0024 0.0096 0.0089 0.0256 0.0103 20.0581 † 0.0335 DGSR end 0.0011 ‡ 0.0008 20.0004 0.0011 20.0003 0.0036 0.0096 0.0046 20.0079 0.0141 Trend 20.0016 0.0002 20.0009 0.0003 0.0011 0.0013 20.0127 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 20.4590 0.1106 20.6487 0.2368 Labor force in manufacturing in 0.1419 † 0.0837 0.1459 † 0.0794 0.0677 0.1628 20.7789 0.3612 21.3345 † 0.7721 1980 R 2 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, a i , is divided into explained and random components as follows: a i 5 d 1 C i 1 h i 3 where C is a vector of state characteristics, including state economic, demographic and education financing characteristics, d 1 is a parameter, and h i is a random error term. Combining Eqs. 2 and 3 gives: logs it 5 b 1 b 1 logGDP t 1 b 2 DGDP t BEGIN 1 b 3 DGDP t END 1 b 4 TREND t 1 g 1 logGSR it 4 1 g 2 DGSR it BEGIN 1 g 3 DGSR it END 1 d 1 C i 1 h i 1 m it The random effects model assumes that the state-spe- cific error term, h i , 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 i 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 positive 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 pri- 195 M. Binder Economics of Education Review 18 1999 183–199 mary schools. The imprecision of this measure may result in its small influence on schooling indicators. 33 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. 34 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 19801990 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 2 appears to be very small for continuation rates. Per student spending appears to have larger effects in the 19801990 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