12 research outputs found
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The central role of noise in evaluating interventions that use test scores to rank schools
Several countries have implemented programs that use test scores to rank schools, and to reward or penalize them based on their students' average performance. Recently, Kane and Staiger (2002) have warned that imprecision in the measurement of school-level test scores could impede these efforts. There is little evidence, however, on how seriously noise hinders the evaluation of the impact of these interventions. We examine these issues in the context of Chile's P-900 program-a country-wide intervention in which resources were allocated based on cutoffs in schools' mean test scores. We show that transitory noise in average scores and mean reversion lead conventional estimation approaches to greatly overstate the impacts of such programs. We then show how a regression discontinuity design that utilizes the discrete nature of the selection rule can be used to control for reversion biases. While the RD analysis provides convincing evidence that the P-900 program had significant effects on test score gains, these effects are much smaller than is widely believed
Class-Size Caps, Sorting, and the Regression-Discontinuity Design
This paper examines how schools’ choices of class size and households’ choices of schools affect regression-discontinuity-based estimates of the effect of class size on student outcomes. We build a model in which schools are subject to a class-size cap and an integer constraint on the number of classrooms, and higher-income households sort into higher-quality schools. The key prediction, borne out in data from Chile’s liberalized education market, is that schools at the class-size cap adjust prices (or enrollments) to avoid adding an additional classroom, which generates discontinuities in the relationship between enrollment and household characteristics, violating the assumptions underlying regression-discontinuity research designs