Bradley Larsen

IV QUANTILE REGRESSION FOR GROUP-LEVEL TREATMENTS, WITH AN APPLICATION TO THE DISTRIBUTIONAL EFFECTS OF TRADE

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.

IV Quantile Regression for Group-Level Treatments, with an Application to the Effects of Trade on the Distribution of Wages

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). Standard quantile regression techniques are inconsistent in this setting, even if the treatment is exogenous. 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 normality. Simulations confirm the superiority of this grouped instrumental variables quantile regression estimator to standard quantile regression. An empirical application finds that low-wage earners in the U.S. from 1990-2007 were significantly more affected by increased Chinese import competition than high-wage earners. We also illustrate the usefulness of the estimation approach with additional empirical examples from urban economics, labor, regulation, and empirical auctions.

Occupational Licensing and Quality: Distributional and Heterogeneous Effects in the Teaching Profession

This paper examines a common form of entry restriction: occupational licensing. The paper studies two questions: first, how occupational licensing laws affect the distribution of quality, and second, how the effects of licensing on quality vary across regions of differing income levels. The paper uses variation in state licensing requirements for teachers and two national datasets on teacher qualifications and student outcomes from 1983-2008. Two measures of quality are used: the qualifications of candidates entering the occupation (input quality) and the quality of service provided (output quality). Results show that more restrictive licensing laws may lead some first-year teachers of high input quality to opt out of the occupation. In the sample of teachers who remain in the occupation multiple years, stricter licensing appears to increase input quality at most quantiles of the teacher quality distribution. The distribution of student test scores also increases with stricter occupational licensing, primarily in the upper half of the distribution. For most forms of licensing studied, input and output quality improvements due to stricter licensing requirements occur in high-income rather than low-income school districts, consistent with theoretical predictions of Shapiro (1986).