Files in this item



application/pdf3160943.pdf (8MB)Restricted to U of Illinois
(no description provided)PDF


Title:Three Essays Exploring Heterogeneity Using Quantile Regression
Author(s):Reck, Clayton Gregory
Doctoral Committee Chair(s):Kevin F. Hallock
Department / Program:Economics
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Economics, Labor
Abstract:This study examines heterogeneity using quantile regression in several different areas in labor economics. Quantile regression calculates different estimates of the relationship between two variables at different points on the conditional distribution and, therefore, allows heterogeneity in these relationships to be explored. The first part of this study explores the relationship between CEO pay and firm performance relationships. I find that significant heterogeneity exists in pay for performance sensitivities with high conditional wage CEOs tied more closely to firm performance than low conditional wage CEOs. The second and third parts of this study explore heterogeneity in pay differences between blacks and whites and between males and females respectively. Using quantile regression, I show that pay differences between both blacks and whites, and between males and females exhibit significant heterogeneity. Since sample selection is likely to be a problem in estimation of wage differences due to large differences in labor force participation rates between the groups studied, I develop a technique to control for this type of selectivity bias in the quantile regression setting. After controlling for sample selection bias, I find that throughout the 1980s both males and females, and blacks and whites show significant heterogeneity in pay differences, with these pay differences becoming more homogeneous over time.
Issue Date:2004
Description:161 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2004.
Other Identifier(s):(MiAaPQ)AAI3160943
Date Available in IDEALS:2015-09-25
Date Deposited:2004

This item appears in the following Collection(s)

Item Statistics