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The Effects of Algorithmic Labor Market Recommendations: Evidence from a Field Experiment

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  • John J. Horton

Abstract

Algorithmically recommending workers to employers for the purpose of recruiting can substantially increase hiring: in an experiment conducted in an online labor market, employers with technical job vacancies that received recruiting recommendations had a 20% higher fill rate compared to the control. There is no evidence that the treatment crowded out hiring of nonrecommended candidates. The experimentally induced recruits were highly positively selected and were statistically indistinguishable from the kinds of workers employers recruit “on their own.” Recommendations were most effective for job openings that were likely to receive a smaller applicant pool.

Suggested Citation

  • John J. Horton, 2017. "The Effects of Algorithmic Labor Market Recommendations: Evidence from a Field Experiment," Journal of Labor Economics, University of Chicago Press, vol. 35(2), pages 345-385.
  • Handle: RePEc:ucp:jlabec:doi:10.1086/689213
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    Cited by:

    1. John Horton & William R. Kerr & Christopher Stanton, 2017. "Digital Labor Markets and Global Talent Flows," NBER Chapters,in: High-Skilled Migration to the United States and its Economic Consequences, pages 71-108 National Bureau of Economic Research, Inc.
    2. Eliason, Marcus & Hensvik, Lena & Kramarz, Francis & Nordström Skans, Oskar, 2017. "The causal impact of social Connections on firms' outcomes," Working Paper Series 2017:11, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    3. Cristiano Codagnone & Fabienne Abadie & Federico Biagi, 2016. "The Future of Work in the ‘Sharing Economy’. Market Efficiency and Equitable Opportunities or Unfair Precarisation?," JRC Working Papers JRC101280, Joint Research Centre (Seville site).
    4. Chen Liang & Yili Hong & Bin Gu, 2017. "Home Bias in Global Employment," Working Papers 17-06, NET Institute.
    5. Girum Abebe & Stefano Caria & Marcel Fafchamps & Paolo Falco & Simon Franklin & Simon Quinn & Forhad Shilpi, 2017. "Matching Firms and Workers in a Field Experiment in Ethiopia," SERC Discussion Papers 0225, Spatial Economics Research Centre, LSE.
    6. Michael Luca, 2016. "Designing Online Marketplaces: Trust and Reputation Mechanisms," NBER Chapters,in: Innovation Policy and the Economy, Volume 17, pages 77-93 National Bureau of Economic Research, Inc.
    7. Michael Luca, 2016. "Designing Online Marketplaces: Trust and Reputation Mechanisms," NBER Working Papers 22616, National Bureau of Economic Research, Inc.

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