Nonparametric Identification and Estimation in a Generalized Roy Model
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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.
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Gilhuly Family Professor in Economics
Bayer's research focuses on wide range of subjects including racial inequality and segregation, social interactions, housing markets, education, and crime. He has received numerous grants from the National Science Foundation, Social Science and Humanities Council of Canada, and the US Department of Education. His most recent work has been published in the Quarterly Journal of Economics, the Journal of Political Economy, the Journal of Environmental Economics, and American Economics Association
Professor of Economics
Professor Khan is on leave at Boston College for the 2016-17 academic year.Professor Khan specializes in the fields of mathematical economics, statistics, and applied econometrics. His studies have explored a variety of subjects from covariate dependent censoring and non-stationary panel data, to causal effects of education on wage inequality and the variables affecting infant mortality rates in Brazil. He was awarded funding by National Science Foundation grants for his projects ent
Professor of Economics
Christopher D. Timmins is a Professor in the Department of Economics at Duke University, with a secondary appointment in Duke’s Nicholas School of the Environment. He holds a BSFS degree from Georgetown University and a PhD in Economics from Stanford University. Professor Timmins was an Assistant Professor in the Yale Department of Economics before joining the faculty at Duke in 2004. His professional activities include teaching, research, and editorial responsibilities. Professor Timmi
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