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Abadie's kappa and weighting estimators of the local average treatment effect

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Authors and Corporations: Słoczyński, Tymon (Author), Uysal, Selver Derya (Author), Wooldridge, Jeffrey M. (Author)
Other Authors: Uysal, Selver Derya [Author] • Wooldridge, Jeffrey M. 1960- [Author]
Type of Resource: E-Book
Language: English
Bonn, Germany IZA - Institute of Labor Economics April 2022
Series: Forschungsinstitut zur Zukunft der Arbeit: Discussion paper series ; no. 15241
Source: Verbunddaten SWB
Lizenzfreie Online-Ressourcen
Summary: In this paper we study the finite sample and asymptotic properties of various weighting estimators of the local average treatment effect (LATE), several of which are based on Abadie (2003)'s kappa theorem. Our framework presumes a binary endogenous explanatory variable ("treatment") and a binary instrumental variable, which may only be valid after conditioning on additional covariates. We argue that one of the Abadie estimators, which we show is weight normalized, is likely to dominate the others in many contexts. A notable exception is in settings with one-sided noncompliance, where certain unnormalized estimators have the advantage of being based on a denominator that is bounded away from zero. We use a simulation study and three empirical applications to illustrate our findings. In applications to causal effects of college education using the college proximity instrument (Card, 1995) and causal effects of childbearing using the sibling sex composition instrument (Angrist and Evans, 1998), the unnormalized estimates are clearly unreasonable, with "incorrect" signs, magnitudes, or both. Overall, our results suggest that (i) the relative performance of different kappa weighting estimators varies with features of the data-generating process; and that (ii) the normalized version of Tan (2006)'s estimator may be an attractive alternative in many contexts. Applied researchers with access to a binary instrumental variable should also consider covariate balancing or doubly robust estimators of the LATE.
Physical Description: 1 Online-Ressource (circa 42 Seiten); Illustrationen