IV Quantile Regression for Group-level Treatments, with an Application to the Distributional Effects of Trade / Denis Chetverikov, Bradley Larsen, Christopher Palmer.

Format:

Book

Author/Creator:

Chetverikov, Denis.

Contributor:

National Bureau of Economic Research.

Larsen, Bradley.

Palmer, Christopher J.

Series:

Working Paper Series (National Bureau of Economic Research) no. w21033.

NBER working paper series no. w21033

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2015.

Summary:

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. An empirical application indicates 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.

Notes:

Print version record

March 2015.