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QUANTILE TREATMENT EFFECTS IN REGRESSION KINK DESIGNS

Published online by Cambridge University Press:  17 March 2020

Heng Chen,
Harold D. Chiang and
Yuya Sasaki
Heng Chen
Affiliation:
Bank of Canada
Harold D. Chiang
Affiliation:
Vanderbilt University
Yuya Sasaki*
Affiliation:
Vanderbilt University
*
Address correspondence to Yuya Sasaki, Department of Economics, Vanderbilt University, VU Station B #351819, 2301 Vanderbilt Place, Nashville, TN 37235-1819, USA; e-mail: yuya.sasaki@vanderbilt.edu.
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Abstract

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The literature on regression kink designs develops identification results for average effects of continuous treatments (Nielsen et al., 2010, American Economic Journal: Economic Policy 2, 185–215; Card et al., 2015, Econometrica 83, 2453–2483), average effects of binary treatments (Dong, 2018, Jump or Kink? Identifying Education Effects by Regression Discontinuity Design without the Discontinuity), and quantile-wise effects of continuous treatments (Chiang and Sasaki, 2019, Journal of Econometrics 210, 405–433), but there has been no identification result for quantile-wise effects of binary treatments to date. In this article, we fill this void in the literature by providing an identification of quantile treatment effects in regression kink designs with binary treatment variables. For completeness, we also develop large sample theories for statistical inference, present a practical guideline on estimation and inference, conduct simulation studies, and provide an empirical illustration.

Information

Type
MISCELLANEA
Information
Econometric Theory , Volume 36 , Issue 6 , December 2020, pp. 1167 - 1191
Copyright
© Cambridge University Press 2020

Footnotes

First arXiv date: March 15, 2017. We thank Peter Phillips (Editor), Arthur Lewbel (Co-Editor), three anonymous referees, Yingying Dong, Robert Moffitt, and participants at New York Camp Econometrics XIII for very useful comments and suggestions. All the remaining errors are ours.

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