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Title: Nonparametric Identification and Estimation in a Generalized Roy Model
Citation Type: Working Paper
Publication Year: 2006
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Abstract: This paper studies the nonparametric identification and estimation of a generalized Roy model that includes a non-pecuniary component of utility associated with each alternative. This generalized model allows amenity or risk considerations to affect occupation choice in the classic Roy model. More generally, such a generalization would be useful in any economic setting where individuals place value on more than a single outcome of interest when choosing among discrete treatments or behaviors. The starting point for our study of identification is the well-known result for the pure Roy model in Heckman and Honore (1990): that any crosssectional dataset can be rationalized by underlying population wage distributions in which wage offers are distributed independently across sectors -- i.e., that the correlation of wage offers across sectors is unidentified. A common interpretation of this important result is that, without parametric restrictions or the availability of covariates, all of the useful content of a cross sectional dataset is absorbed in a restrictive specification that imposes independence. While this is certainly true within the pure Roy model, we demonstrate that it is in fact possible to identify, under relatively innocuous support assumptions, a common non-pecuniary component of utility associated with each sector. These results apply even in empirical settings characterized by many alternative sectors and without the need for additional covariates. We develop nonparametric estimators corresponding to two alternative assumptions under which we prove identification, describe their asymptotic properties and provide Monte Carlo evidence on their performance in small samples. We close the paper by applying our preferred estimator to study migration across US labor markets using data drawn from the 2000 US Census.
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Authors: Bayer, Patrick; Timmins, Christopher; Khan, Shakeeb
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Institution: Duke University
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Data Collections: IPUMS USA
Topics: Education, Methodology and Data Collection
Countries: United States