Information and educational attainment

25 J. Ludwig Economics of Education Review 18 1999 17–30 Newey’s 1985 generalized method of moments GMM test does not reject the null hypothesis that these instru- ments are exogenous in the information equations. 13 The results are shown in Table 4, and suggest that for males, urban poverty residence has a negative and statistically significant effect on labor market infor- mation as measured by I-2 and I-3. When I restrict the sample to those under 16 in 1979, I produce findings that are qualitatively similar to the results shown in Tables 3 and 4.

6. Information and educational attainment

In this section, I examine the relationship between the NLSY information measures and education outcomes, measured by high school graduation and years of school completed by 1991. 14 High school graduation may be a more appropriate outcome measure than educational attainment because borrowing constraints may play an important role in preventing low-income teens from going on to post-secondary school Cameron and Heck- man, 1997. Further, the educational attainment variable may be somewhat truncated since roughly 5 percent of the sample used here is still in school in 1991, though the graduation variable should be unaffected since “there is virtually no high school graduation after age 20” Cameron and Heckman, 1997. Analysis of the NLSY suggests that more accurate information about the job market leads to more schooling. 15 Table 5 presents illustrative probit regression results for the high school completion equation, using I-2 to measure labor market information. An important concern is that the information measures serve as a proxy for student ability. One way to address this concern is by including AFQT scores in the regression equation, in addition to controlling for family, school and metropoli- tan area characteristics. Additional information as meas- ured by I-2 has a statistically significant, positive effect on the chances of graduating from high school. It is also interesting to observe that teacher–pupil ratios are posi- 13 For Eq. 1, and candidate instruments given by the N 3 J matrix Z 1 , Newey 1985’s GMM procedure examines the population moment condition for unbiased estimation, given by Z 9 1 e s 5 0. For X ss 5 [u r k X s ] and R ss 5 I 2 X ss X 9 ss X ss − 1 X 9 ss , the Newey procedure is implemented by mul- tiplying the R 2 from the regression of e s 5 R ss s on Z 1 by N, which will be distributed as a chi-squared statistic with J 2 K degrees of freedom. 14 The youngest sample member is 26 in 1991. 15 Analysis using total years of schooling completed by 1991 not shown here produces very similar results for I-2 and I-3, though somewhat weaker estimates for information effects on education using information measure I-1. tively correlated with high school graduation p , 0.10, which stands in contrast to previous research on the effects of school spending Hanushek, 1986. A high school’s dropout rate has a negative and significant relationship with graduation likelihoods, suggesting the possibility of some form of peer effect. Similar to the findings of previous research, minorities are more likely to graduate than whites once family background vari- ables are controlled Cook and Ludwig, 1997 and Bog- gess, 1998. In order to address possible differences in the edu- cational processes for African-Americans, Hispanics and non-Hispanic whites Cameron and Heckman, 1997, I re-estimated the probit with interaction terms between race and information. I find that the effects of infor- mation on high school graduation may be somewhat larger for blacks than whites, while the effects of infor- mation appear smaller for Hispanics relative to whites and blacks. I also experimented with interaction terms for gender and information, and found no consistent dif- ferences between males and females in the effects of information on education outcomes. The model described in Table 5 was re-estimated for each of the information measures, using high school graduation and then educational attainment as the depen- dent variables of interest. In Table 6, I present the coef- ficient estimates for the labor market information vari- ables from this series of regressions. Each of the labor market information measures has a positive and statisti- cally significant relationship with educational persist- ence, measured either by attainment or high school graduation. Moreover, the magnitude of the effects are not trivial. For example, for an African-American male living within an urban poverty area, a one-standard-devi- ation increase in I-1 increases the likelihood of gradu- ation by 2.5 percentage points evaluated at the mean of I-1 and the other explanatory variables, equal to around one-fifth of the dropout rate among such teens. Black males in urban poverty ZIP codes with educational expectations or aspirations that are within 2 standard deviations of the average education of their occupational aspiration I-2 or I-3 5 1 are 4 to 5 percentage points more likely to graduate than similar males with less congruence between educational and occupational aspir- ations I-2 or I-3 5 0. When I allow for varying effects of information by race, I find uniformly significant effects of information on educational outcomes for blacks. For whites, the evi- dence for information effects on educational attainment are almost as strong: in 5 of the 6 educational outcome equations, information is statistically significant at the 5 percent threshold, while in the last case the information coefficient is significant at 10 percent. Labor market information appears to have a less important role in determining the educational outcomes of Hispanic youth. Re-estimating these equations using only those respon- 26 J. Ludwig Economics of Education Review 18 1999 17–30 Table 4 Labor market information measure used as dependent variable: Explanatory variable: I-1 I-2 I-3 Male 20.09 0.06 0.60 0.05 0.61 0.04 Age 0.04 0.01 0.02 0.01 0.02 0.01 African-American 20.40 0.07 0.14 0.071 0.13 0.051 Hispanic 20.14 0.08 0.28 0.08 0.24 0.07 Family income 3 10,000 0.10 0.03 0.000.03 0.02 0.02 Mother’s education 0.07 0.01 0.03 0.01 0.03 0.01 Father not in HH 0.01 0.07 20.04 0.07 20.06 0.07 At 14, any HH Member: Subscribe to mags 0.29 0.05 0.10 0.051 0.10 0.051 Subscribe to paper 0.06 0.06 0.12 0.051 0.06 0.05 Have library card 0.19 0.05 0.09 0.05 0.05 0.05 Mom work when R 14 20.03 0.06 0.18 0.06 0.15 0.06 Dad work when R 14 20.03 0.07 20.03 0.07 20.08 0.07 white clr mom’s indstry 0.70 0.41 0.96 0.411 1.17 0.40 white clr dad’s indstry 1.14 0.37 20.23 0.36 20.04 0.35 Resid in urban pov ZIP 0.42 0.29 0.55 0.271 0.53 0.27 maleurban pov resid 20.42 0.30 20.93 0.29ˆ 20.94 0.28ˆ City, not urban pov 0.02 0.06 20.03 0.05 20.02 0.05 rural out of MSA 0.10 0.06 0.01 0.06 0.04 0.06 Adj. R-sqlog likelihood 0.12 22281 22391 N 3,371 3,372 3,372 NOTE: Standard errors given in parentheses, 5 statistically significant at 1 level. 1 5 significant at 5 level. Other explanatory variables include: constant term, guidance counselor-pupil ratio, and holdings in R’s school library.ˆ 5 Likelihood ratio test or F- test indicates sum of coefficients for urban poverty residence and urban poverty residence-male interaction variables significantly different from zero at 5 level. Variable definitions: I-1: Total number of correct answers to the NLSY Occupational Duty Questions. I-2: 5 1 if [R’s educational aspiration] 2 [CPS-estimated average education of workers under 35 years of age in R’s aspired occupational group] , 2sd of R’s occupational group education. I-3: 5 1 if [R’s expected educational attainment] 2 [CPS-estimated average education of workers under 35 years of age in R’s aspired occupational group] , 2sd of R’s occupational group. dents in the NLSY who are 15 or younger in 1979 pro- duces qualitatively similar results. Fewer of the labor market information coefficient estimates are statistically significant, which is not surprising given that the sample size decreases by around 45 percent when this age restriction is imposed. I also experiment with two-stage estimation methods to explore the possible endogeneity of the information variable in the education-outcome equations. Different combinations of the school- and MSA-level instru- ments 16 produce qualitatively similar results: large posi- 16 Candidate instruments include variables that may reflect a family’s neighborhood choice set within a metropolitan area or influence labor market information: percent of the MSA or county population that is African-American or has a high school degree, percent of households within the MSA or county that are headed by a female, and the ratio of guidance counselors to students in the respondent’s school. Specification tests based on Newey 1985 suggest that these instruments are valid, while tests based on Hausman 1978 cannot reject the null hypothesis that the information measures are exogeneous in the education- outcome equations. tive point estimates for the effects of information on high school graduation, with large standard errors; the esti- mates for the effects of information on educational attainment vary in magnitude and even sign depending on the choice of instruments and model specification. In light of the Hausman test results and possible problems with two-stage procedures in finite samples with “weak” instruments Guilkey et al., 1992 and Bound et al., 1993, a persuasive case might be made for placing greater emphasis on the naive estimates presented above. The remaining issue is whether the coefficient esti- mates on these labor market information variables reflect the effects of information on education, or instead serve as proxies for the effects of student ability or poverty despite the inclusion of AFQT scores and family back- ground variables in our regression models. In order to address this issue, I rank the sample on the basis of AFQT scores and re-estimate the educational-outcome regressions for the bottom, middle and top third of respondents on the basis of these scores. If the infor- mation measures simply proxy for ability, the relation- ship between information and educational outcomes should be smaller within the AFQT groupings relative 27 J. Ludwig Economics of Education Review 18 1999 17–30 Table 5 Explanatory variable: 1 2 Labor mkt Info I-2 0.16 0.06 0.17 0.081 I-2 3 black 0.13 0.13 ˆ I-2 3 hispanic 20.26 0.15 AFQT 0.03 0.002 0.03 0.002 Male 20.22 0.06 20.22 0.06 Age 0.12 0.03 0.12 0.03 Age-squared 20.004 0.0021 20.004 0.0021 African-American 0.67 0.07 0.59 0.11 Hispanic 0.17 0.081 0.33 0.12 Family income 3 10,000 0.11 0.04 0.11 0.04 Mother’s education 0.02 0.011 0.02 0.011 Father not in HH 20.23 0.08 20.23 0.08 At 14, any HH Member: Subscribe to mags 0.14 0.061 0.13 0.061 Subscribe to paper 0.02 0.06 0.01 0.06 Have library card 0.06 0.06 0.06 0.06 Mom work when R 14 0.01 0.06 0.02 0.06 Dad work when R 14 0.05 0.07 0.04 0.07 Teachersstudents 4.65 2.45 4.55 2.44 School dropout rate 20.004 0.001 20.004 0.001 Resid in urban pov ZIP 20.12 0.09 20.012 0.10 City, not urban pov 0.07 0.07 0.07 0.07 Rural out of MSA 0.10 0.08 0.10 0.08 log likelihood 21345 21342 N 3,372 3,372 NOTE: Standard errors given in parentheses, 5 statistically significant at 1 level. 1 5 significant at 5 level. Other explanatory variables include: constant term, and MSA unemployment and poverty rates. ˆ 5 Likelihood ratio test indicates sum of coefficients for labor market information plus interaction term between either black or Hispanic variable is statistically significant at 5 level. Variable definitions: I-2: 5 1 if [R’s educational aspiration] 2 [CPS-estimated average education of workers under 35 years of age in R’s aspired occupational group] , 2sd of R’s occupational group education. Table 6 Coefficient estimates for labor market information effects on high school graduation Information measure: High school graduation High school graduation Highest grade completed Highest grade completed I-1 0.06 0.02 0.05 0.03 0.06 0.02 0.07 0.031 I-1 3 Black 0.04 0.05 ˆ 0.02 0.05 ˆ I-1 3 Hispanic 20.03 0.05 20.08 0.06 I-2 0.16 0.06 0.17 0.081 0.26 0.06 0.31 0.08 I-2 3 Black 0.13 0.13 ˆ 20.09 0.14 ˆ I-2 3 Hispanic 20.26 0.15 20.17 0.17 ˆ I-3 0.17 0.06 0.16 0.081 0.35 0.06 0.41 0.06 I-3 3 Black 0.19 0.13 ˆ 20.09 0.13 ˆ I-3 3 Hispanic 20.20 0.15 20.16 0.16 ˆ NOTES: Coefficients derived from estimating OLS and probit models specified as in Table 4. 5 significant at 1 percent, 1 5 significant at 5 percent, ˆ 5 likelihood ratio test for significance of information plus information interacted with race exceeds 5 percent significance threshold. to the full sample regressions. I find that the magnitudes of the information effects are generally as large within the AFQT groups as they are in the full sample analy- sis. 17 When I stratify the sample on the basis of residence within an urban poverty area, I again find that the infor- mation coefficients are typically as large in the stratified samples as in the full sample regression; in fact, the esti- mated effect of information measures I-2 and I-3 with the urban poverty sample are larger than with the full sample. 28 J. Ludwig Economics of Education Review 18 1999 17–30

7. Discussion