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

• 成果类型: Article
• 署名作者: Horton, John J.
• 署名单位: New York University
• 刊物名称: JOURNAL OF LABOR ECONOMICS
• ISSN/ISSBN: 0734-306X
• DOI: 10.1086/689213
• 发表日期: 2017
• 页码: 345-385
• 关键词: internet job search; VACANCIES

摘要

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.