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Inference on Causal Effects in a Generalized Regression Kink Design

Author(s): Card, David; Lee, David S; Pei, Zhuan; Weber, Andrea

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Abstract: © 2015 The Econometric Society. We consider nonparametric identification and estimation in a nonseparable model where a continuous regressor of interest is a known, deterministic, but kinked function of an observed assignment variable. We characterize a broad class of models in which a sharp "Regression Kink Design" (RKD or RK Design) identifies a readily interpretable treatment-on-the-treated parameter (Florens, Heckman, Meghir, and Vytlacil (2008)). We also introduce a "fuzzy regression kink design" generalization that allows for omitted variables in the assignment rule, noncompliance, and certain types of measurement errors in the observed values of the assignment variable and the policy variable. Our identifying assumptions give rise to testable restrictions on the distributions of the assignment variable and predetermined covariates around the kink point, similar to the restrictions delivered by Lee (2008) for the regression discontinuity design. Using a kink in the unemployment benefit formula, we apply a fuzzy RKD to empirically estimate the effect of benefit rates on unemployment durations in Austria.
Publication Date: 1-Nov-2015
Citation: Card, D, Lee, DS, Pei, Z, Weber, A. (2015). Inference on Causal Effects in a Generalized Regression Kink Design. Econometrica, 83 (6), 2453 - 2483. doi:10.3982/ECTA11224
DOI: doi:10.3982/ECTA11224
ISSN: 0012-9682
EISSN: 1468-0262
Pages: 2453 - 2483
Type of Material: Journal Article
Journal/Proceeding Title: Econometrica
Version: Final published version. Article is made available in OAR by the publisher's permission or policy.

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