A Simple Method to Estimate Large Fixed Effects Models Applied to Wage Determinants and Matching

CERGE-EI Working Paper Series No. 532

62 Pages Posted: 28 Mar 2015

See all articles by Nikolas Mittag

Nikolas Mittag

University of Chicago - Harris School of Public Policy

Multiple version iconThere are 2 versions of this paper

Date Written: March 1, 2015

Abstract

Models with high dimensional sets of fixed effects are frequently used to examine, among others, linked employer-employee data, student outcomes and migration. Estimating these models is computationally difficult, so simplifying assumptions that cause bias are often invoked to make computation feasible and specification tests are rarely conducted. I present a simple method to estimate large two-way fixed effects (TWFE) and worker-firm match effect models without additional assumptions. It computes the exact OLS solution including estimates of the fixed effects and makes testing feasible even with multi-way clustered errors. An application using German linked employer-employee data illustrates the advantages: The data reject the assumptions of simpler estimators and omitting match effects biases estimates including the returns to experience and the gender wage gap. Specification test detect both problems. Firm fixed effects, not match effects, are the main channel through which job transitions drive wage dynamics, which underlines the importance of firm heterogeneity for labor market dynamics.

Keywords: multi-way fixed effects, linked employer-employee data, matching, wage dynamics

JEL Classification: J31, J63, C23, C63

Suggested Citation

Mittag, Nikolas, A Simple Method to Estimate Large Fixed Effects Models Applied to Wage Determinants and Matching (March 1, 2015). CERGE-EI Working Paper Series No. 532, Available at SSRN: https://ssrn.com/abstract=2585952 or http://dx.doi.org/10.2139/ssrn.2585952

Nikolas Mittag (Contact Author)

University of Chicago - Harris School of Public Policy ( email )

1155 East 60th Street
Chicago, IL 60637
United States

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