INFERENCE ON CAUSAL EFFECTS IN A GENERALIZED REGRESSION KINK DESIGN

• 成果类型: Article
• 署名作者: Card, David; Lee, David S.; Pei, Zhuan; Weber, Andrea
• 署名单位: University of California System; University of California Berkeley; National Bureau of Economic Research; IZA Institute Labor Economics; Princeton University; Cornell University; University of Mannheim
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA11224
• 发表日期: 2015
• 页码: 2453-2483
• 关键词: unemployment-insurance; DISCONTINUITY DESIGNS; MODEL ESTIMATION; identification; EQUATIONS

摘要

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.