The Effects of Algorithmic Labor Market Recommendations: Evidence from a Field Experiment
Open accessAbstract
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 non-recommended 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.
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Online gig platforms streamline business operations by automating job matching, scheduling, and supervision.
“Specifically, online gig platforms streamline business operations by automating job matching, scheduling, and supervision processes (e.g., Cramer andKrueger 2016, Horton 2017).”
From Skill-Biased Technical Change, Again? Online Gig Platforms and Local Employment (2025) · cites this paper as Horton 2017
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Active Labour Market Policy Evaluations: A Meta‐Analysis
Card, D., Kluve, J., & Weber, A. (2010) · The Economic Journal · doi:10.1111/j.1468-0297.2010.02387.x · Cited by 1,298 · see citing works
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Introduction—Platforms and Infrastructures in the Digital Age
Constantinides, P., Henfridsson, O., & Parker, G. G. (2018) · Information Systems Research · doi:10.1287/isre.2018.0794 · Cited by 943 · see citing works
The emergence of economic rationality of GPT
Chen, Y., Liu, T. X., Shan, Y., & Zhong, S. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2316205120 · Cited by 164 · see citing works
“, 2021); stock price forecasts (Lopez-Lira and Tang, 2023); job recruitment (Horton, 2017); product or content consumption (Adomavicius et al.”
BackgroundOK computer: Worker perceptions of algorithmic recruitment
Fumagalli, E., Rezaei, S., & Salomons, A. (2022) · Research Policy · doi:10.1016/j.respol.2021.104420 · Cited by 46 · see citing works
Designing Online Marketplaces: Trust and Reputation Mechanisms
Luca, M. (2016) · NBER Working Paper Series · doi:10.3386/w22616 · Cited by 35 · see citing works
Skill-Biased Technical Change, Again? Online Gig Platforms and Local Employment
Guo, X., Cheng, Z. (., & Pavlou, P. A. (2025) · Information Systems Research · doi:10.1287/isre.2022.0307 · Cited by 4 · see citing works
When Recruiters Meet Algorithms: Advice Taking and Bias in AI‐Assisted Recruitment
Lacroux, A., & Martin Lacroux, C. (2026) · International Journal of Selection and Assessment · doi:10.1111/ijsa.70085
- Authors
- Horton, J. J.
- Affiliations
- New York University (US)
- Corresponding author
- Horton, John J.
- ORCID
- Horton, John J.
- Venue
- Journal of Labor Economics
- Publisher
- University of Chicago Press
- Published
- 2017-04
- Volume
- 35
- Issue
- 2
- Pages
- 345-385
- ISSN
- 0734-306X (ISSN-L)
- 1537-5307
- OpenAlex
- W3122196295
- Cited by
- 203
- References
- 1
- Citations by year
- 2026: 18 · 2025: 20 · 2024: 37 · 2023: 20 · 2022: 20 · 2021: 25 · 2020: 14 · 2019: 12 · 2018: 13 · 2017: 12 · 2016: 5 · 2015: 2 · 2014: 4 · 2013: 1
- FWCI
- 42.89
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Journal of Labor Economics (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Names, Identity, and Discrimination Research
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Labor market dynamics and wage inequality
- Keywords
- digital labour markets, personnel selection, job vacancies, candidate selection, employer recruitment, field experiments
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