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We study identification and estimation in the Regression Discontinuity Design (RDD) with a multivalued treatment variable. We also allow for the inclusion of covariates. We show that without additional information, treatment effects are not identified. We give necessary and sufficient conditions that lead to identification of LATEs as well as of weighted averages of the conditional LATEs. We show that if the first stage discontinuities of the multiple treatments conditional on covariates are linearly independent, then it is possible to identify multivariate weighted averages of the treatment effects with convenient identifiable weights. If, moreover, treatment effects do not vary with some covariates or a flexible parametric structure can be assumed, it is possible to identify (in fact, over-identify) all the treatment effects. The over-identification can be used to test these assumptions. We propose a simple estimator, which can be programmed in packaged software as a Two-Stage Least Squares regression, and packaged standard errors and tests can also be used. Finally, we implement our approach to identify the effects of different types of insurance coverage on health care utilization, as in Card, Dobkin and Maestas (2008).

We develop a novel strategy to identify the relative importance of school and neighborhood factors in determining school segregation. Using detailed student enrollment and residential location data, our research design compares differences in student composition between adjacent Census blocks served by different schools to analogous differences between those schools. Our findings indicate that neighborhood factors explain around 62% of racial segregation and 44% of economic segregation across all schools, playing an even more pronounced role in urban areas, where school segregation has been especially acute. These findings suggest that the involvement of urban planners is essential when attempting to confront inequality of opportunity through education. [Support was provided from the Bonbright Center at the University of Georgia.]

This paper reports the results of an experiment designed to test for the presence of debt aversion. The population who participated in the experiment were recent financial aid candidates and the experiment focused on student loans. The goal is to shed new light on different aspects of the perceptions with respect to debt. These perceptions can prevent agents from choosing an optimal portfolio or from undertaking attractive investment opportunities, such as in education. The study design disentangles two types of debt aversion: one that is studied in the previous literature, which encompasses both framing and labeling effects, and another that controls for framing effects and identifies only what we denote labeling debt aversion. The results suggest that participants in the experiment exhibit debt aversion, and most of the debt aversion is due to labeling effects. Labeling a contract as a "loan"' decreases its probability of being chosen over a financially equivalent contract by more than 8 percent. The analysis also provides evidence that students are willing to pay a premium of about 4 percent of the financed value to avoid a contract labeled as debt. Survey questions are appended. (Contains 9 tables and 10 footnotes.)

We propose a simple test of the main identification assumption in models where the treatment variable takes multiple values and has bunching. The test consists of adding an indicator of the bunching point to the estimation model and testing whether the coefficient of this indicator is zero. Although similar in spirit to the test in Caetano (2015), the dummy test has important practical advantages: it is more powerful at detecting endogeneity, and it also detects violations of the functional form assumption. The test does not require exclusion restrictions and can be implemented in many approaches popular in empirical research, including linear, two-way fixed effects, and discrete choice models. We apply the test to the estimation of the effect of a mother's working hours on her child's skills in a panel data context (James-Burdumy 2005). [ABSTRACT FROM AUTHOR]

We show that in models with endogeneity, bunching at the lower or upper boundary of the distribution of the treatment variable may be used to build a correction for endogeneity. We derive the asymptotic distribution of the parameters of the corrected model, provide an estimator of the standard errors, and prove the consistency of the bootstrap. An empirical application reveals that time spent watching television, corrected for endogeneity, has roughly no net effect on cognitive skills and a significant negative net effect on non-cognitive skills in children. [ABSTRACT FROM AUTHOR]

We study the effects of enrichment activities such as reading, homework, and extracurricular lessons on children's cognitive and non-cognitive skills. We take into consideration that children forgo alternative activities, such as play and socializing, in order to spend time on enrichment. Our study controls for selection on unobservables using a novel approach which leverages the fact that many children spend zero hours per week on enrichment activities. At zero enrichment, confounders vary but enrichment does not, which gives us direct information about the effect of confounders on skills. Using time diary data available in the Panel Study of Income Dynamics (PSID), we find that the net effect of enrichment is zero for cognitive skills and negative for non-cognitive skills, which suggests that enrichment may be crowding out more productive activities on the margin. The negative effects on non-cognitive skills are concentrated in higher-income students in high school, consistent with elevated academic competition related to college admissions. JEL Codes: I21, I2, J01, C24. Keywords: cognitive skills, non-cognitive skills, bunching, enrichment, homework, college, time use, skill development. [ABSTRACT FROM AUTHOR]