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Labor Reallocation and Unemployment Fluctuations: A Tale of Two Tails

Author

Listed:
  • Dimitrios Bakas

    (Nottingham Business School, Nottingham Trent University, UK; Rimini Centre for Economic Analysis)

  • Theodore Panagiotidis

    (Department of Economics, University of Macedonia, Greece)

  • Gianluigi Pelloni

    (Rimini Centre for Economic Analysis; The Johns Hopkins University, SAIS-Bologna, Italy; Research Centre, Orizzonti Holding, Italy)

Abstract

This paper examines the sectoral shifts hypothesis for the US regional labor market using a quantile panel framework. We use a monthly panel dataset that spans over 1990-2016 for the 48 US states and employ a dynamic quantile panel data regression approach to investigate the asymmetric nature of the relationship between sectoral labor reallocation and unemployment fluctuations. The empirical evidence suggests that the impact of the employment dispersion index is relatively small and insignificant for lower levels of unemployment but becomes positive and highly significant for higher rates. Our findings bear out the asymmetry of reallocation disturbances for the US labor market.

Suggested Citation

  • Dimitrios Bakas & Theodore Panagiotidis & Gianluigi Pelloni, 2023. "Labor Reallocation and Unemployment Fluctuations: A Tale of Two Tails," Working Paper series 23-07, Rimini Centre for Economic Analysis.
  • Handle: RePEc:rim:rimwps:23-07
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    References listed on IDEAS

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    1. Newey, Whitney K & Powell, James L & Walker, James R, 1990. "Semiparametric Estimation of Selection Models: Some Empirical Results," American Economic Review, American Economic Association, vol. 80(2), pages 324-328, May.
    2. Zheng, John Xu, 1998. "A Consistent Nonparametric Test Of Parametric Regression Models Under Conditional Quantile Restrictions," Econometric Theory, Cambridge University Press, vol. 14(1), pages 123-138, February.
    3. Contoyannis, Paul & Forster, Martin, 1999. "The distribution of health and income: a theoretical framework," Journal of Health Economics, Elsevier, vol. 18(5), pages 603-620, October.
    4. Rosenberg, Mark W. & Wilson, Kathleen, 2000. "Gender, poverty and location: how much difference do they make in the geography of health inequalities?," Social Science & Medicine, Elsevier, vol. 51(2), pages 275-287, July.
    5. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    6. Khan, Shakeeb & Powell, James L., 2001. "Two-step estimation of semiparametric censored regression models," Journal of Econometrics, Elsevier, vol. 103(1-2), pages 73-110, July.
    7. Anonymous, 1958. "World Health Organization," International Organization, Cambridge University Press, vol. 12(3), pages 391-394, July.
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    Cited by:

    1. Ignacio Moral-Arce & Stefan Sperlich & Ana Fernández-Saínz & Maria Roca, 2012. "Trends in the Gender Pay Gap in Spain: A Semiparametric Analysis," Journal of Labor Research, Springer, vol. 33(2), pages 173-195, June.

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    More about this item

    Keywords

    Unemployment; Labor Reallocation; Sectoral Shifts; Dynamic Panel Data; Quantile Regression;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

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