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Employer Credit Checks: Poverty Traps versus Matching Efficiency

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  • Dean Corbae
  • Andrew Glover

Abstract

We develop a framework to understand pre-employment credit screening through adverse selection in labor and credit markets. Workers differ in an unobservable characteristic that induces a positive correlation between labor productivity and repayment rates in credit markets. Firms therefore prefer to hire workers with good credit because it correlates with high productivity. A poverty trap may arise, in which an unemployed worker with poor credit has a low job finding rate, but cannot improve her credit without a job. In our calibrated economy, this manifests as a large and persistent wage loss from default, equivalent to 2.3% per month over ten years. Banning employer credit checks eliminates the poverty trap, but pools job seekers and reduces matching efficiency: average unemployment duration rises by 13% for the most productive workers after employers are banned from using credit histories to screen potential hires.

Suggested Citation

  • Dean Corbae & Andrew Glover, 2018. "Employer Credit Checks: Poverty Traps versus Matching Efficiency," NBER Working Papers 25005, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:25005
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    References listed on IDEAS

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    1. Menzio, Guido & Shi, Shouyong, 2010. "Block recursive equilibria for stochastic models of search on the job," Journal of Economic Theory, Elsevier, vol. 145(4), pages 1453-1494, July.
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    3. Kyle F Herkenhoff, 2019. "The Impact of Consumer Credit Access on Unemployment," Review of Economic Studies, Oxford University Press, vol. 86(6), pages 2605-2642.
    4. Kyle Herkenhoff, 2016. "The Impact of Consumer Credit Access on Employment, Earnings and Entrepreneurship," 2016 Meeting Papers 781, Society for Economic Dynamics.
    5. Kyle Herkenhoff & Gordon Phillips & Ethan Cohen-Cole, 2016. "How Credit Constraints Impact Job Finding Rates, Sorting & Aggregate Output," Working Papers 16-25, Center for Economic Studies, U.S. Census Bureau.
    6. Marieke Bos & Emily Breza & Andres Liberman, 2018. "The Labor Market Effects of Credit Market Information," Review of Financial Studies, Society for Financial Studies, vol. 31(6), pages 2005-2037.
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    9. Satyajit Chatterjee & Dean Corbae & Makoto Nakajima & José-Víctor Ríos-Rull, 2007. "A Quantitative Theory of Unsecured Consumer Credit with Risk of Default," Econometrica, Econometric Society, vol. 75(6), pages 1525-1589, November.
    10. Igor Livshits & James C. Mac Gee & Michèle Tertilt, 2016. "The Democratization of Credit and the Rise in Consumer Bankruptcies," Review of Economic Studies, Oxford University Press, vol. 83(4), pages 1673-1710.
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    Cited by:

    1. Gajendran Raveendranathan & Georgios Stefanidis, 2020. "The Unprecedented Fall in U.S. Revolving Credit," Department of Economics Working Papers 2020-05, McMaster University.
    2. Satyajit c & Dean Corbae & Kyle Dempsey & Jose-Victor Rios-Rull, 2020. "A Quantitative Theory of the Credit Score," PIER Working Paper Archive 20-030, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    3. Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," Working Papers 2019-056, Human Capital and Economic Opportunity Working Group.

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    More about this item

    JEL classification:

    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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