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Nonparametric Identification and Estimation in a Generalized Roy Model

dc.contributor.author Bayer, Patrick J
dc.contributor.author Khan, Shakeeb
dc.contributor.author Timmins, Christopher
dc.date.accessioned 2010-03-09T15:42:53Z
dc.date.available 2010-03-09T15:42:53Z
dc.date.issued 2008
dc.identifier.uri https://hdl.handle.net/10161/2054
dc.description.abstract This paper considers nonparametric identification and estimation of a generalized Roy model that includes a non-pecuniary component of utility associated with each choice alternative. Previous work has found 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 of Roy sorting behavior that imposes independence on wage draws. While this is true, we demonstrate that it is also possible to identify (under relatively innocuous assumptions and without the use of covariates) a common nonpecuniary component of utility associated with each choice alternative. 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. 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.
dc.format.extent 484123 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.publisher SSRN
dc.subject Migration
dc.subject Roy Model
dc.subject non-pecuniary returns
dc.subject returns to college education
dc.title Nonparametric Identification and Estimation in a Generalized Roy Model
dc.type Journal article


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