We study a competitive labor market with imperfect information. In our basic model, the labor market consists of heterogeneous workers and ex ante identical firms who have only imperfect private information about workers' productivities. Firms compete by posting wages in order to cherry-pick...

Using nationally representative data in China, we find substantial positive partial correlations of both parents' education with one's wage. In addition, returns to father's education are higher in more monopsonistic and less meritocratic labor markets, including non-coastal regions, the...

The paper reexamines the employer size-wage puzzle using NLSY79 data. The empirical results show that even for those who never receive any training from their employers, size-wage premium still exists and is quantitatively important. Wage increases associated with receiving on-the-job training...

The March Current Population Survey (CPS) is the primary data source for estimation of levels and trends in labor earnings and income inequality in the USA. Time-inconsistency problems related to top coding in theses data have led many researchers to use the ratio of the 90th and 10th...

Displaced workers, especially long tenured workers, face large human capital losses. Private firms frequently offer insurance against this threat in the form of severance pay – scheduled benefits linked in expectation to the worker’s human capital loss. We explore this linkage, first...

We investigate the link between agricultural productivity and net migration in the United States using a county-level panel for the most recent period of 1970-2009. In rural counties of the Corn Belt, we find a statistically significant relationship between changes in net outmigration and...

This paper develops a two-period labor market model with imperfect information and on-the-job training, and uses data from National Longitudinal Survey of Youth 1979 Cohorts (NLSY79) to test its predictions. We find that training does not explain the positive relationship between employer size...