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Labor Reallocation, Employment, and Earnings: Vector Autoregression Evidence


  • Henry R. Hyatt
  • Tucker S. McElroy


Analysis of the labor market has given increasing attention to the reallocation of jobs across employers and workers across jobs. However, whether and how job reallocation and labor market “churn” affects the health of the labor market remains an open question. In this paper, we present time series evidence for the U.S. 1993-2013 and consider the relationship between labor reallocation, employment, and earnings using a vector autoregression (VAR) framework. We find that an increase in labor market churn by 1 percentage point predicts that, in the next quarter, employment will increase by 100 to 560 thousand jobs, lowering the unemployment rate by 0.05 to 0.25 percentage points. Job destruction does not predict future changes in employment but a 1 percentage point increase in job destruction leads to an increase in future unemployment 0.14 to 0.42 percentage points. We find mixed results on the relationship between labor reallocation rates and earnings: we nd that, especially for earnings derived from administrative records data, a 1 percentage point increase to either job destruction or churn leads to increased earnings of less than 2 percent. Results vary substantially depending on the earnings measure we use, and so the evidence inconsistent on whether productivity-enhancing aspects of churn and job destruction provide earnings gains for workers in aggregate. Our findings on churn leading to increased employment and a lower unemployment rate are consistent with models of replacement hiring and vacancy chains.

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  • Henry R. Hyatt & Tucker S. McElroy, 2017. "Labor Reallocation, Employment, and Earnings: Vector Autoregression Evidence," Working Papers 17-11r, Center for Economic Studies, U.S. Census Bureau.
  • Handle: RePEc:cen:wpaper:17-11r

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    References listed on IDEAS

    1. Pierre Cahuc & Fabien Postel-Vinay & Jean-Marc Robin, 2006. "Wage Bargaining with On-the-Job Search: Theory and Evidence," Econometrica, Econometric Society, vol. 74(2), pages 323-364, March.
    2. Champagne, Julien & Kurmann, André & Stewart, Jay, 2017. "Reconciling the divergence in aggregate U.S. wage series," Labour Economics, Elsevier, vol. 49(C), pages 27-41.
    3. Tucker McElroy, 2008. "Exact formulas for the Hodrick-Prescott filter," Econometrics Journal, Royal Economic Society, vol. 11(1), pages 209-217, March.
    4. Stephanie Aaronson & Tomaz Cajner & Bruce Fallick & Felix Galbis-Reig & Christopher Smith & William Wascher, 2014. "Labor Force Participation: Recent Developments and Future Prospects," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 45(2 (Fall)), pages 197-275.
    5. Timothy Dunne & Lucia Foster & John Haltiwanger & Kenneth R. Troske, 2004. "Wage and Productivity Dispersion in United States Manufacturing: The Role of Computer Investment," Journal of Labor Economics, University of Chicago Press, vol. 22(2), pages 397-430, April.
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