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Laura Caron

Publications and source records attributed to Laura Caron.

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The Short- and Long-Term Impacts of Expanding Public Education for Disabled Students

Between 1949 and 1980, every U.S. state mandated public schools to provide educational services for disabled students. This is one of the largest education reforms in U.S. history, but little is known about its impacts. Given scarce data in this period, I compile survey and administrative datasets and set up a difference-in-difference design using variation in the mandates' timing. I show that the mandates increased both services for disabled students and preschool enrollments. In adulthood, disabled individuals below school age at a mandate's implementation became about 20% less likely to have no education, attained up to 0.23 more years of education, and were more likely to have worked. Although this policy could have taken away resources from non-disabled students, in fact, education and employment also increased for non-disabled individuals. These effects align with evidence that the mandates increased spending per student by up to 15%. Families were also impacted: the mandates increased employment among mothers of disabled children and the probability that disabled individuals became household heads. Over the long term, the mandates paid for themselves by generating government revenues in excess of their cost. These results provide new evidence on the large, broad impacts of expanding access to education for disabled students.

econ.GN

Triple Difference Designs with Heterogeneous Treatment Effects

Triple difference designs have become increasingly popular in empirical economics. The advantage of a triple difference design is that, within a treatment group, it allows another subgroup of the population -- potentially less impacted by the treatment -- to serve as a comparison for the subgroup of interest. While literature on difference-in-differences has discussed heterogeneity in treatment effects between treated and control groups or over time, relatively little attention has been given to triple difference designs and the implications of heterogeneity in treatment effects in this setting. In this paper, I show that the parameter identified under common triple difference assumptions does not allow for causal interpretation of differences between subgroups when subgroups may differ in their underlying (unobserved) treatment effects. I propose a new parameter of interest, the controlled difference in average treatment effects on the treated, which allows for causal comparisons between subgroups. I then propose identification assumptions and doubly-robust estimators for this parameter. I use a simulation study to highlight the desirable finite-sample properties of these estimators, as well as to show the difference between the two parameters. An empirical application shows the importance of considering treatment effect heterogeneity in practical applications.

econ.EM