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

Author

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  • Nikolas Mittag
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.

Suggested Citation

  • Nikolas Mittag, 2015. "A Simple Method to Estimate Large Fixed Effects Models Applied to Wage Determinants and Matching," CERGE-EI Working Papers wp532, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    • Handle: RePEc:cer:papers:wp532
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    References listed on IDEAS

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    Cited by:

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    2. Köllö, János & Boza, István & Balázsi, László, 2021. "Wage gains from foreign ownership: evidence from linked employer-employee data," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 55, pages 1-3.
    3. Aquilante, Tommaso & Livio, Luca & Potoms, Tom, 2020. "On-the-job training and intra-family dynamics," Bank of England working papers 873, Bank of England.
    4. Fehn, Rebecca & Frings, Hanna, 2018. "Decomposing the Returns to Regional Mobility," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181609, Verein für Socialpolitik / German Economic Association.
    5. Dmytro Vikhrov, 2017. "Immigration policy index," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 25(1), pages 3-46, January.
    6. Kory Kantenga, 2016. "Sorting and Wage Inequality," 2016 Meeting Papers 660, Society for Economic Dynamics.
    7. Frings, Hanna & Kamb, Rebecca, 2021. "What explains the urban wage premium? Sorting, non-portable or portable agglomeration effects?," Ruhr Economic Papers 916, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.

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    More about this item

    Keywords

    multi-way fixed effects; linked employer-employee data; matching; wage dynamics;
    All these keywords.

    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J63 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Turnover; Vacancies; Layoffs
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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