Mismatch Unemployment and the Geography of Job Search
By Ioana Marinescu, Roland Rathelot. American Economic Journal: Macroeconomics, 2018
159 citations recorded by the source corpus.
Could we significantly reduce US unemployment by helping job seekers move closer to jobs? Using data from the leading employment board CareerBuilder.com, we show that, indeed, workers dislike applying to distant jobs: job seekers are 35 percent less likely to apply to a job 10 miles (mi.) away from their zip code of residence. However, because job seekers are close enough to vacancies on average, this distaste for distance is fairly inconsequential: our search and matching model predicts that relocating job seekers to minimize unemployment would decrease unemployment by only 5.3 percent. Geographic mismatch is thus a minor driver of aggregate unemployment. (JEL E24, J41, J61, J63, J64, R23)
Subfields: Economics and Econometrics, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/d3ffdf9e67c721f8fba8f3081d1194e1319d3ca9
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Citations in the corpus
- Job search during the COVID-19 crisis (2020, unknown): “Our analysis also relates more broadly to the empirical literature on job search that uses data from online job boards (Marinescu, 2017; Belot et al., 2018; Marinescu and Rathelot, 2018; Banfi and Villena-Roldan, 2019; Faberman and Kudlyak, 2019; Marinescu and Wolthoff, 2020; Kudlyak et al., 2020; B”
- Imperfect Competition, Compensating Differentials and Rent Sharing in the U.S. Labor Market (2019, unknown): “The analysis of Marinescu and Rathelot (2018) suggests distance of the firm from the workers’ home may be important.”
- Opening the Black Box of the Matching Function: The Power of Words (2020, methodology): “While CareerBuilder data is not representative by industry, in most other respects it is representative of the US labor market, as documented by Marinescu & Rathelot (2014).”