IV Quantile Regression for Group-Level Treatments, With an Application to the Distributional Effects of Trade

• 成果类型: Article
• 署名作者: Chetverikov, Denis; Larsen, Bradley; Palmer, Christopher
• 署名单位: University of California System; University of California Los Angeles; Stanford University; National Bureau of Economic Research; University of California System; University of California Berkeley
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA12121
• 发表日期: 2016
• 页码: 809-833
• 关键词: panel-data; nonseparable models; UNITED-STATES; identification; inference; TECHNOLOGY; variables; MARKET; wages

摘要

We present a methodology for estimating the distributional effects of an endogenous treatment that varies at the group level when there are group-level unobservables, a quantile extension of Hausman and Taylor, 1981. Because of the presence of group-level unobservables, standard quantile regression techniques are inconsistent in our setting even if the treatment is independent of unobservables. In contrast, our estimation technique is consistent as well as computationally simple, consisting of group-by-group quantile regression followed by two-stage least squares. Using the Bahadur representation of quantile estimators, we derive weak conditions on the growth of the number of observations per group that are sufficient for consistency and asymptotic zero-mean normality of our estimator. As in Hausman and Taylor, 1981, micro-level covariates can be used as internal instruments for the endogenous group-level treatment if they satisfy relevance and exogeneity conditions. Our approach applies to a broad range of settings including labor, public finance, industrial organization, urban economics, and development; we illustrate its usefulness with several such examples. Finally, an empirical application of our estimator finds that low-wage earners in the United States from 1990 to 2007 were significantly more affected by increased Chinese import competition than high-wage earners.