Algorithmic Writing Assistance on Jobseekers' Resumes Increases Hires

• 成果类型: Article; Early Access
• 署名作者: Wiles, Emma; Munyikwa, Zanele; Horton, John
• 署名单位: Boston University; Massachusetts Institute of Technology (MIT); National Bureau of Economic Research
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2024.04528
• 发表日期: 2025
• 关键词: information systems; information systems: enabling technologies (includes AI; machine learning and data mining technologies); information systems: IT policy and management; management of IT human resources

摘要

There is a strong association between writing quality in resumes for new labor market entrants and whether they are ultimately hired. We show this relationship is, at least partially, causal: In a field experiment in an online labor market with nearly half a million jobseekers, treated jobseekers received nongenerative algorithmic writing assistance on their resumes. Treated jobseekers were hired 8% more often at 10% higher wages. Contrary to concerns that the assistance takes away a valuable signal, we find no evidence that employers were less satisfied. We find that the writing on treated jobseekers resumes had fewer errors and was easier to read. Our analysis suggests that writing is an imperfect signal of ability but better writing helps employers ascertain ability through clearer writing, suggesting digital platforms could benefit from incorporating nongenerative algorithmic writing assistance into text-based descriptions of labor services or products.