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.
Document Object Identifier (DOI): 10.3386/w18265
Published: Ay?egül ?ahin & Joseph Song & Giorgio Topa & Giovanni L. Violante, 2014. "Mismatch Unemployment," American Economic Review, American Economic Association, vol. 104(11), pages 3529-64, November. citation courtesy of
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