ESSAYS IN LABOR ECONOMICS

Abstract

My dissertation is composed of two essays in Labor Economics. The first chapter examines how employers learn about workers' unobserved productivity when learning is asymmetric between incumbent and outside firms. I develop an asymmetric employer learning model in which endogenous job mobility is both a direct result of intensified adverse selection and a signal used by outside employers to update their expectations about workers' productive ability. I derive, from the model, empirical implications regarding the relationship between wage rates, ability, schooling and overall measures of job separations that contrasts the public learning models and the two-period mover-stayer models. Testing the model with data from the National Longitudinal Survey of Youth 1979 (NLSY-79), I find strong evidence supporting the three-period asymmetric employer learning model.

The second chapter concerns economics of fertility and investigates to what extent the observed correlation between adolescent fertility and poor maternal educational attainment is causal. Semi-parametric kernel matching estimator is applied to estimate the effects of teenage childbearing on schooling outcomes. The matching method estimates the conditional moments without imposing any functional form restrictions and attends directly to the common support condition. Using data from the NLSY-79, kernel matching estimates suggest that half of the cross-sectional educational gaps remains after controlling for individual and family covariates. The difference between matching estimates and regression-based estimates implies that part of the conditional difference in parametric models is due to the functional assumption. The robustness check following Altonji, Elder, and Taber (2005) reveals that a substantial amount of correlation is required within a parametric framework to make the negative effect of teen motherhood on educational attainment go away. Further evidence obtained by simulation-based nonparametric sensitivity analysis suggests that the matching estimates are quite robust with regard to a wide range of specifications of the simulated unobservables.