Explaining Demographic Heterogeneity in Cyclical Unemployment

Eliza Forsythe, Jhih Chian Wu

Research output: Contribution to journalArticlepeer-review

Abstract

We investigate the sources of heterogeneity in the levels and cyclical sensitivity of unemployment rates across demographic groups. We develop a new methodology to decompose cyclical and level differences in unemployment rates between groups into flows between three states (employment, unemployment, and out-of-the-labor-force). We find that increases in unemployment rates during recessions for young, non-white, and less-educated groups of workers are primarily driven by reductions in the job-finding rates, which can explain more than 60% of cyclical fluctuations in the unemployment rate across demographic groups, compared with under 20% driven by separations. However, separations are the most important factor in explaining the persistent gap in unemployment rates between each disadvantaged group and their respective counterpart group, with important differences between groups. For less-educated workers, separation rates explain most of the unemployment gap, with 75% of the separation rate attributable to industry and occupation. Less-educated workers also spend less time searching. For younger workers, we find separation rates explain all of the unemployment gap, while industry and occupation explain only 60% of their elevated separation rates. For non-white workers, hiring explains almost half of the unemployment gap. Non-white workers search more intensely for work than other groups, but spend less time interviewing per search time, suggesting that labor market discrimination contributes to non-white workers’ persistently high unemployment rates.

Original languageEnglish (US)
Article number101955
JournalLabour Economics
Volume69
DOIs
StatePublished - Apr 2021

Keywords

  • Employment exit rate
  • Gross worker flows
  • Job finding rate
  • Separation rate
  • Unemployment Gap
  • Unemployment rate

ASJC Scopus subject areas

  • Economics and Econometrics
  • Organizational Behavior and Human Resource Management

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