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Heterogeneity in the Dynamics of Labor Earnings

Author

Listed:
  • Martin Browning

    (Department of Economics, Oxford University, Oxford OX1 3UQ, England)

  • Mette Ejrnæs

    (Department of Economics, University of Copenhagen, DK-1355 Copenhagen, Denmark)

Abstract

In this article, we survey the literature on individual earnings dynamics with a particular focus on allowing for pervasive heterogeneity across individuals. We structure the discussion around ARMA processes with nonlinear trends for each individual. We show that allowing for pervasive and codependent heterogeneity in individual parameters has a major impact on econometric modeling, estimation, and substantive conclusions. We describe an econometric method that is suitable for models with pervasive heterogeneity. We develop a long list of statistics that describe any earnings panel in great detail and provide a demanding set of features of the data for fitting. This list encompasses most moments used in the literature and provides novel statistics based on individual regressions. Finally, we present an empirical illustration using a long Danish panel. Based on this, we provide some conclusions concerning earnings dynamics but emphasize that details will vary according to the sample.

Suggested Citation

  • Martin Browning & Mette Ejrnæs, 2013. "Heterogeneity in the Dynamics of Labor Earnings," Annual Review of Economics, Annual Reviews, vol. 5(1), pages 219-245, May.
    • Handle: RePEc:anr:reveco:v:5:y:2013:p:219-245
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    File URL: http://www.annualreviews.org/doi/abs/10.1146/annurev-economics-081512-134514
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    Citations

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    Cited by:

    1. Botosaru, Irene, 2023. "Time-varying unobserved heterogeneity in earnings shocks," Journal of Econometrics, Elsevier, vol. 235(2), pages 1378-1393.
    2. Hryshko, Dmytro, 2014. "Correlated income shocks and excess smoothness of consumption," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 41-62.
    3. Jeppe Druedahl & Michael Graber & Thomas H. Jørgensen, 2021. "High Frequency Income Dynamics," CEBI working paper series 21-08, University of Copenhagen. Department of Economics. The Center for Economic Behavior and Inequality (CEBI).
    4. Aronsson, Thomas & Jenderny, Katharina & Lanot, Gauthier, 2022. "The quality of the estimators of the ETI," Journal of Public Economics, Elsevier, vol. 212(C).
    5. Zhang, Yonghui & Zhou, Qiankun, 2019. "Estimation for time-invariant effects in dynamic panel data models with application to income dynamics," Econometrics and Statistics, Elsevier, vol. 9(C), pages 62-77.
    6. Pavel K. Koval & Andrey V. Polbin, 2023. "Estimation of Heterogenous Consumption and Income Parameters," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 6, pages 76-92, December.
    7. repec:ctc:serie1:def6 is not listed on IDEAS
    8. Maurice Bun & Jasper de Winter, 2019. "Measuring trends and persistence in capital and labor misallocation," DNB Working Papers 639, Netherlands Central Bank, Research Department.
    9. Druedahl, Jeppe & Munk-Nielsen, Anders, 2018. "Identifying heterogeneous income profiles using covariances of income levels and future growth rates," Journal of Economic Dynamics and Control, Elsevier, vol. 94(C), pages 24-42.
    10. Hyungsik Roger Moon & Frank Schorfheide & Boyuan Zhang, 2023. "Bayesian Estimation of Panel Models under Potentially Sparse Heterogeneity," Papers 2310.13785, arXiv.org, revised Feb 2024.
    11. Moira Daly & Dmytro Hryshko & Iourii Manovskii, 2022. "Improving The Measurement Of Earnings Dynamics," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(1), pages 95-124, February.

    More about this item

    Keywords

    ARMA; simulated minimum distance; codependent heterogeneity; panel data;
    All these keywords.

    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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