Working Paper: NBER ID: w18265
Authors: Ayegul Sahin; Joseph Song; Giorgio Topa; Giovanni L. Violante
Abstract: We develop a framework where mismatch between vacancies and job seekers across sectors translates into higher unemployment by lowering the aggregate job-finding rate. We use this framework to measure the contribution of mismatch to the recent rise in U.S. unemployment by exploiting two sources of cross-sectional data on vacancies, JOLTS and HWOL, a new database covering the universe of online U.S. job advertisements. Mismatch across industries and occupations explains at most 1/3 of the total observed increase in the unemployment rate, whereas geographical mismatch plays no apparent role. The share of the rise in unemployment explained by occupational mismatch is increasing in the education level.
Keywords: Mismatch Unemployment; Job Seekers; Vacancies; Labor Market Dynamics
JEL Codes: E24; J24; J61; J62; J63; J64
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| geographical mismatch (J61) | no significant role in the rise of unemployment (F66) |
| mismatch across industries and occupations (J69) | increase in unemployment (J64) |
| occupational mismatch (J68) | increase in unemployment (J64) |
| sectoral mismatch (J68) | lower aggregate job-finding rate (J68) |
| lower aggregate job-finding rate (J68) | increase in unemployment (J64) |
| mismatch at the 2-digit industry level (L60) | increase in unemployment (J64) |