Instrumental variable analysis with censored data in the presence of many weak instruments: Application to the effect of being sentenced to prison on time to employment

Publication Abstract

Ertefaie, Ashkan, Anh Nguyen, David J. Harding, Jeffrey Morenoff, and Wei Yang. 2018. "Instrumental variable analysis with censored data in the presence of many weak instruments: Application to the effect of being sentenced to prison on time to employment." The Annals of Applied Statistics, 12(4): 2647-2673.

This article discusses an instrumental variable approach for analyzing censored data that includes many instruments that are weakly associated with the endogenous variable. We study the effect of imprisonment on time to employment using an administrative data on all individuals sentenced for felony in Michigan in the years 2003-2006. Despite the large body of research on the effect of prison on employment, this is still a controversial topic, especially since some of the studies could have been affected by unmeasured confounding. We take advantage of a natural experiment based on the random assignment of judges to felony cases and construct a vector of instruments based on judges' ID that can avoid the confounding bias. However, some of the constructed instruments are weakly associated with the sentence type, that is, the endogenous variable, which can potentially lead to misleading results. Using a dimension reduction technique, we propose a novel semi-parametric estimation procedure in a survival context that is robust to the presence of many weak instruments. Specifically, we construct a test statistic based on the structural failure time model and provide inference by inverting the testing procedure. Under some assumptions, the optimal choice of the test statistic has also been derived. Analyses show a significant negative impact of imprisonment on time to employment which is consistent with some of the previous results. Our simulation studies highlight the importance of accounting for weak instruments in the analyses in terms of both bias and inflated type-I error rates.

DOI:10.1214/18-AOAS1174 (Full Text)

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