Fixed effects model

Abstract: About the Author Series Editor's Introduction 1. Introduction 2. Linear Fixed Effects Models: Basics 3. Fixed Effects Logistic Models 4. Fixed Effects Models for Count Data 5. Fixed Effects Models for Events History Data 6. Structural Equation Models With Fixed Effects Appendix 1 Appendix 2 References Author Index Subject Index

Abstract: This paper empirically tests and rejects classical competitive theories of wage determination by examining differences in wages for equally skilled workers across industries. Human capital earnings functions are estimated using cross-sectional and longitudinal data from the CPS and QES. The major finding is that the dispersion in wages across industries as measured by the standard deviation in industry wage differentials is substantial. Furthermore, F tests of the joint significance of industry dummy variables are decisively rejected. These differences are very difficult to link to unobserved differences in ability or to compensating differentials for working conditions. Fixed effects models are estimated using two longitudinal data sets to control for constant, unmeasured worker characteristics that might bias cross-sectional estimates. Because measurement error is a serious problem in looking at workers who report changing industries, we use estimates of industry classification error rates to adjust the longitudinal results. In the fixed effects analysis, the industry wage differentials are sizable and are very similar to the cross-sectional estimates. In addition, the fixed effects estimates are robust under a variety of assumptions about classification errors and are similar using both data sets. These findings cast doubt on explanations of industry wage differentials based on unmeasured ability. Additional analysis finds that the industry wage structure is highly correlated for workers in small and large firms, in different regions of the U.S., and with varying job tenures. Finally, evidence is presented demonstrating that turnover has a negative relationship with industry wage differentials. These findings suggest that workers in high wage industries receive noncompetitive rents.

Abstract: We propose a general, nonlinear mixed effects model for repeated measures data and define estimators for its parameters. The proposed estimators are a natural combination of least squares estimators for nonlinear fixed effects models and maximum likelihood (or restricted maximum likelihood) estimators for linear mixed effects models. We implement Newton-Raphson estimation using previously developed computational methods for nonlinear fixed effects models and for linear mixed effects models. Two examples are presented and the connections between this work and recent work on generalized linear mixed effects models are discussed.