Uncovering the effect of school racial composition is difficult because racial mixing is not accidental but instead an outcome of government and family choices. Using rich panel data on the achievement of Texas students, we disentangle racial composition effects from other aspects of school quality and from differences in abilities and family background. The estimates strongly indicate that a higher percentage of black schoolmates reduces achievement for blacks, while it implies a much smaller and generally insignificant effect on whites. These results suggest that existing levels of segregation in Texas explain a small but meaningful portion of the racial achievement gap.

The extensive investigation of the contribution of teachers to student achievement produces two generally accepted results. First, there is sub stantial variation in teacher quality as measured by the value added to achievement or future aca demic attainment or earnings. Second, variables often used to determine entry into the profession and salaries, including post-graduate schooling, experience, and licensing examination scores, appear to explain little of the variation in teacher quality so measured, with the exception of early experience. Together these findings underscore explicitly that observed teacher characteristics do not represent teacher quality.

This paper disentangles the impact of schools and teachers in influencing achievement with special attention given to the potential problems of omitted or mismeasured variables and of student and school selection. Unique matched panel data from the UTD Texas Schools Project permit the identification of teacher quality based on student performance along with the impact of specific, measured components of teachers and schools. Semiparametric lower bound estimates of the variance in teacher quality based entirely on within-school heterogeneity indicate that teachers have powerful effects on reading and mathematics achievement, though little of the variation in teacher quality is explained by observable characteristics such as education or experience. The results suggest that the effects of a costly ten student reduction in class size are smaller than the benefit of moving one standard deviation up the teacher quality distribution, highlighting the importance of teacher effectiveness in the determination of school quality.

Most discussion of special education has centered on the costs of providing mandated programs for children with disabilities and not on their effectiveness. As in many other policy areas, inferring program effectiveness is difficult because students not in special education do not provide a good comparison group. By following students who move in and out of targeted programs, however, we are able to identify program effectiveness from changes over time in individual performance. We find that the average special education program significantly boosts mathematics achievement of special-education students, particularly those classified as learning-disabled or emotionally disturbed, while not detracting from regular-education students. These results are estimated quite precisely from models of students and school-by-grade-by-year fixed effects in achievement gains, and they are robust to a series of specification tests.

This paper attempts to reconcile the contradictory findings in the debate over school resources and school effectiveness by highlighting the role of aggregation in the presence of omitted variables bias. While data aggregation for well-specified linear models yields unbiased parameter estimates, aggregation alters the magnitude of any omitted variables bias. In general, the theoretical impact of aggregation is ambiguous. In a very relevant special case where omitted variables relate to state differences in school policy, however, aggregation implies clear upward bias of estimated school resource effects. Analysis of High School and Beyond data provides strong evidence that aggregation inflates the coefficients on school resources. Moreover, the pattern of results is not consistent with an errors-in-variables explanation, the alternative explanation for the larger estimated impact with aggregate estimates. Since studies using aggregate data are much more likely to find positive school resource effects on achievement, these results provide further support to the view that additional expenditures alone are unlikely to improve student outcomes.

We examine the effect of air pollution on school absences using administrative data for elementary and middle school children in 39 of the largest school districts in Texas merged with air quality information maintained by the Environmental Protection Agency. We address potentially confounding factors with a difference-in-difference-in-differences strategy that controls for persistent characteristics of schools, years, and attendance periods. Of the pollutants considered, we find that high carbon monoxide (CO) levels, even when below federal air quality standards, significantly increase absences. Our results suggest that the substantial decline in CO levels over the past two decades has yielded economically significant health benefits.