Nonparametric identification and estimation in a generalized Roy model
By: Patrick Bayer, Shakeeb Khan, Christopher Timmins.
Contributors: Khan, Shakeeb, 1969-; Timmins, Christopher.; National Bureau of Economic Research.
Series: NBER working paper series -- working paper 13949; Working paper series (National Bureau of Economic Research : Online) -- working paper no. 13949.
Publisher: National Bureau of Economic Research
Published: 2008
Place of publication: Cambridge, MA
Language: eng
Format: Electronic resource
Description: "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 non-pecuniary 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"--National Bureau of Economic Research web site.
Notes: Title from PDF file as viewed on 7/2/2008.

Includes bibliographical references.

Also available in print.

System requirements: Adobe Acrobat Reader.

Mode of access: World Wide Web.