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Regression Kink Design: Theory and Practice

aUC Berkeley, CA, USA
bNBER, Cambridge, MA, USA
cIZA, Bonn, Germany
dPrinceton University, Princeton, NJ, USA
eCornell University, Ithaca, NY, USA
fCentral European University, Budapest, Hungary

Regression Discontinuity Designs

ISBN: 978-1-78714-390-6, eISBN: 978-1-78714-389-0

ISSN: 0731-9053

Publication date: 13 May 2017

Abstract

A regression kink design (RKD or RK design) can be used to identify casual effects in settings where the regressor of interest is a kinked function of an assignment variable. In this chapter, we apply an RKD approach to study the effect of unemployment benefits on the duration of joblessness in Austria, and discuss implementation issues that may arise in similar settings, including the use of bandwidth selection algorithms and bias-correction procedures. Although recent developments in nonparametric estimation (Calonico, Cattaneo, & Farrell, 2014; Imbens & Kalyanaraman, 2012) are sometimes interpreted by practitioners as pointing to a default estimation procedure, we show that in any given application different procedures may perform better or worse. In particular, Monte Carlo simulations based on data-generating processes that closely resemble the data from our application show that some asymptotically dominant procedures may actually perform worse than “sub-optimal” alternatives in a given empirical application.

Acknowledgements

Acknowledgments

We thank Sebastian Calonico, Matias Cattaneo, Pauline Leung, Tim Moore, two anonymous referees, and seminar participants at CUFE, Econometric Society China Meeting, Hanyang, Tsinghua and Sichuan University for helpful comments. Suejin Lee provided outstanding research assistance.

Citation

Card, D., Lee, D.S., Pei, Z. and Weber, A. (2017), "Regression Kink Design: Theory and Practice", Regression Discontinuity Designs (Advances in Econometrics, Vol. 38), Emerald Publishing Limited, Bingley, pp. 341-382. https://doi.org/10.1108/S0731-905320170000038016

Publisher

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Emerald Publishing Limited

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