Labor Reallocation, Employment, and Earnings: Vector Autoregression Evidence

Henry Hyatt and Tucker McElroy (tucker.s.mcelroy@census.gov)

Working Papers from U.S. Census Bureau, Center for Economic Studies

Abstract: 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.

Pages: 79 pages
Date: 2017-01
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https://www2.census.gov/ces/wp/2017/CES-WP-17-11R.pdf Revised version, 2017 (application/pdf)
https://www2.census.gov/ces/wp/2017/CES-WP-17-11.pdf First version, 2017 (application/pdf)

Related works:
Journal Article: Labor Reallocation, Employment, and Earnings: Vector Autoregression Evidence (2019)
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