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Title: Nonparametric Identification and Estimation in a Generalized Roy Model
Citation Type: Miscellaneous
Publication Year: 2007
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Abstract: This paper offers a novel approach to the nonparametric identification and estimation of a generalized Roy model that includes a non-pecuniary component of utility associated with each choice alternative. The original Roy model (1951) was used as a tool to describe occupational sorting based on only wages, but it has broader applications to many polychotomous choice problems if the non-pecuniary component of utility is included. We begin with a well-known result for the pure Roy model described in Heckman and Honore (1990)-without parametric restrictions or the availability of covariates, all of the useful content of a cross-sectional dataset is absorbed in a restrictive specification of sorting behavior that imposes independence on wage draws. While this is certainly true within the context of the pure Roy model, we demonstrate that it is possible to identify, under relatively innocuous assumptions and without the use of covariates, a common non-pecuniary component of utility associated with each sector. We develop nonparametric estimators corresponding to two alternative assumptions under which we prove identification, derive asymptotic properties, and illustrate small sample properties with a series of Monte Carlo experiments. We demonstrate the usefulness of one of these estimators with an empirical application based on Dahl (2001). Micro data from the 2000 Census are used to calculate the returns to a college education. If high-school and college graduates face different costs of migration, this would be reflected in different degrees of Roy-sorting-induced bias in their observed wage distributions. Correcting for this bias, the observed returns to a college degree are cut in half. We would like to thank Richard Blundell, James Heckman, and seminar participants at SUNY Albany for their helpful comments. All remaining errors and omissions are our own. Correspondence should be directed to all three authors at the
Url: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.596.7899&rep=rep1&type=pdf
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Authors: Bayer, Patrick; Khan, Shakeeb; Timmins, Christopher
Publisher: Duke University
Data Collections: IPUMS USA
Topics: Education, Methodology and Data Collection
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