Full Citation
Title: IV Quantile Regression for Group-Level Treatments, with an Application to the Distributional Effects of Trade
Citation Type: Working Paper
Publication Year: 2015
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Abstract: We present a methodology for estimating the distributional effects of an endogenous treatment that varries at the group level when there are group-level unobservable, 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-by-group 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. An empirical application that low-wage earners in the US from 1990-2007 were significantly more affected by increased Chinese import competition than high-wage earners. Our approach applies to a broad range of settings in labor, industrial organization, trade, public finance, and other applied fields.
Url: http://web.stanford.edu/~bjlarsen/Grouped_IV_Quantile_Regression_%282015%29.pdf
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Authors: Chetverikov, Denis; Larsen, Bradley; Palmer, Christopher
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Publication Number: 21033
Institution: NBER
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Data Collections: IPUMS USA
Topics: Labor Force and Occupational Structure, Methodology and Data Collection
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