Greater school choice leads to lower demand for private tutoring according to various international studies, but this has not been explicitly tested for the U.S. context. To estimate the causal effect of charter school appearances on neighboring private tutoring prevalence, we employ a comparative event study model combined with a longitudinal matching strategy to accommodate differing treatment years. In contrast to findings from other countries, we estimate that charter schools increase, rather than decrease, tutoring prevalence in the United States. We further find that the effect varies considerably based on the characteristics of the treated neighborhood: areas with the highest income, educational attainment, and proportion Asian show the greatest treatment impacts, while the areas with the least show null effects. Moreover, methodologically this investigation offers a pipeline for flexibly estimating causal effects with observational, longitudinal, geographically located data.
This study introduces the signal weighted teacher value-added model (SW VAM), a value-added model that weights student-level observations based on each student’s capacity to signal their assigned teacher’s quality. Specifically, the model leverages the repeated appearance of a given student to estimate student reliability and sensitivity parameters, whereas traditional VAMs represent a special case where all students exhibit identical parameters. Simulation study results indicate that SW VAMs outperform traditional VAMs at recovering true teacher quality when the assumption of student parameter invariance is met but have mixed performance under alternative assumptions of the true data generating process depending on data availability and the choice of priors. Evidence using an empirical data set suggests that SW VAM and traditional VAM results may disagree meaningfully in practice. These findings suggest that SW VAMs have promising potential to recover true teacher value-added in practical applications and, as a version of value-added models that attends to student differences, can be used to test the validity of traditional VAM assumptions in empirical contexts.