Quantile Treatment Effects in the Regression Discontinuity Design: Process Results and Gini Coefficient

By Markus Frölich, Blaise Melly. Social Science Research Network, 2010

165 citations recorded by the source corpus.

This paper shows nonparametric identification of quantile treatment effects (QTE) in the regression discontinuity design. The distributional impacts of social programs such as welfare, education, training programs and unemployment insurance are of large interest to economists. QTE are an intuitive tool to characterize the effects of these interventions on the outcome distribution. We propose uniformly consistent estimators for both potential outcome distributions (treated and non-treated) for the population of interest as well as other function-valued effects of the policy including in particular the QTE process. The estimators are straightforward to implement and attain the optimal rate of convergence for one-dimensional nonparametric regression. We apply the proposed estimators to estimate the effects of summer school on the distribution of school grades, complementing the results of Jacob and Lefgren (2004).

Subfields: Economics and Econometrics, Sociology and Political Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/7bfda516c09172ad903db86e58d6915014eeff52

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Citations in the corpus

Testing for Manipulation in the Regression Discontinuity Design When the Running Variable Is Discrete (2016, background): “The following standard continuity assumption is sufficient to identify the conditional average treatment effect (Hahn et al., 2001; Frandsen et al., 2012): Assumption 1 Regarded as functions of r, E [Y (0) |R∗ = r] and E [Y (1) |R∗ = r] are continuous at zero.”
Wanna Get Away? Regression Discontinuity Estimation of Exam School Effects Away From the Cutoff (2015, methodology): “As noted recently by Frandsen, Frölich, and Melly (2012), the weighting approach used by Abadie, Angrist, and Imbens (2002) and Abadie (2003) breaks down in a conventional RD framework because the distribution of treatment status is degenerate conditional on the running variable.”
Robust Nonparametric Confidence Intervals for Regression‐Discontinuity Designs (2014, methodology): “…bandwidth selection procedures for local-linear estimators, Otsu and Xu (2011) study empirical likelihood methods applied to local-linear estimators , Frandsen, Frölich, and Melly (2012) consider quantile treatment e¤ects, Card, Lee, Pei, and Weber (2012), Dong (2012) and Dong and Lewel (2012)…”
Regression Discontinuity Applications with Rounding Errors in the Running Variable (2015, background): “Instead of estimating the mean treatment effect at the cutoff, Frandsen et al. (2012) estimate quantile RD treatment effects for standard fuzzy design RD models.”
A GENERAL DOUBLE ROBUSTNESS RESULT FOR ESTIMATING AVERAGE TREATMENT EFFECTS (2014, methodology): “(23) This approach to identifying quantile treatment effects was used by Cattaneo (2010), Frandsen, Frölich, and Melly (2012), Chernozhukov et al. (2013), Frölich and Melly (2013), Donald and Hsu (2014), and Sant’Anna (2016), among others.”