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Identification of the covariance structure of earnings using the GMM estimator

Author

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  • Aedín Doris
  • Donal O’Neill
  • Olive Sweetman

Abstract

In recent years there has been a rapid growth in the number of studies that have used the GMM estimator to decompose the earnings covariance structure into its permanent and transitory parts. Using a heterogeneous growth model of earnings, we consider the performance of the estimator in this context. We use Monte Carlo simulations to examine the sensitivity of parameter identification to key features such as panel length, sample size, the degree of persistence of earnings shocks and the specification of the earnings model. We show that long panels allow the identification of the model, even when persistence in transitory shocks is high. Short panels, on the other hand, are insufficient to identify individual parameters of the model even with moderate levels of persistence. Copyright Springer Science+Business Media, LLC. 2013

Suggested Citation

  • Aedín Doris & Donal O’Neill & Olive Sweetman, 2013. "Identification of the covariance structure of earnings using the GMM estimator," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 11(3), pages 343-372, September.
    • Handle: RePEc:kap:jecinq:v:11:y:2013:i:3:p:343-372
    DOI: 10.1007/s10888-012-9216-5
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    Cited by:

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    2. P. Jenkins, Stephen & Jäntti, Markus, 2013. "Income mobility," ISER Working Paper Series 2013-23, Institute for Social and Economic Research.
    3. Otto Kässi, 2014. "Earnings dynamics of men and women in Finland: permanent inequality versus earnings instability," Empirical Economics, Springer, vol. 46(2), pages 451-477, March.
    4. Stephen Jenkins & Peter Lambert, 2011. "Robert Moffitt and Peter Gottschalk’s 1995 paper ‘Trends in the covariance structure of earnings in the US: 1969–1987’," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 9(3), pages 433-437, September.
    5. Maurice J.G. Bun & Jasper Winter, 2022. "Capital and labor misallocation in the Netherlands," Journal of Productivity Analysis, Springer, vol. 57(1), pages 93-113, February.
    6. Cappellari, Lorenzo & Jenkins, Stephen P., 2014. "Earnings and labour market volatility in Britain, with a transatlantic comparison," Labour Economics, Elsevier, vol. 30(C), pages 201-211.
    7. Gustafsson, Johan & Holmberg, Johan, 2022. "Permanent and transitory earnings dynamics and lifetime income inequality in Sweden," Umeå Economic Studies 1005, Umeå University, Department of Economics.
    8. P. Jenkins, Stephen & Cappellari, Lorenzo, 2013. "Earnings and labour market volatility in Britain," ISER Working Paper Series 2013-10, Institute for Social and Economic Research.
    9. Cappellari, Lorenzo & Jenkins, Stephen P., 2014. "Earnings and labour market volatility in Britain, with a transatlantic comparison," Labour Economics, Elsevier, vol. 30(C), pages 201-211.
    10. Gustafsson, Johan & Holmberg, Johan, 2023. "Permanent and transitory earnings dynamics and lifetime income inequality in Sweden," Labour Economics, Elsevier, vol. 85(C).
    11. Gustafsson, Johan & Holmberg, Johan, 2019. "Earning dynamics in Sweden: The recent evolution of permanent inequality and earnings volatility," Umeå Economic Studies 963, Umeå University, Department of Economics.
    12. Maurice Bun & Jasper de Winter, 2019. "Measuring trends and persistence in capital and labor misallocation," DNB Working Papers 639, Netherlands Central Bank, Research Department.

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    More about this item

    Keywords

    Identification; GMM; Covariance structure of earnings; J31; D31;
    All these keywords.

    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution

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