劳动力市场能否在人工智能时代运转？招聘中的评估瓶颈
Can Labor Markets Function in the Age of AI? The Evaluation Bottleneck in Hiring
AI总结：
本研究分析AI使申请材料信息量降低，导致企业更依赖经验，损害缺乏经验的高匹配申请者，并提出多阶段招聘作为内生应对，恢复评估机会以维持市场功能。
AI中文摘要：
人工智能辅助的求职工具通过简化寻找和申请工作的流程而日益流行。但通过让申请者更容易生成和定制申请材料，这些工具也可能降低材料对申请者匹配度的信息量。我们在一个招聘市场中研究这一权衡，其中申请者在经验和潜在匹配质量上存在差异，企业使用有噪声的申请材料来决定筛选谁。我们探讨人工智能如何影响下游的筛选和招聘，以及哪些申请者受到最不利的影响。随着申请材料信息量的减少，贝叶斯企业理性地更依赖于诸如先前经验等粗略的可观测特征。在由经验和岗位兼容性定义的四种申请者类型中，缺乏经验但兼容的申请者面临的风险最大：他们缺乏可观测的经验，并失去了能将其与其他缺乏经验的候选者区分开来的个性化信息。当筛选成本高昂时，这些变化还可能产生低效的筛选失败，即企业不筛选任何申请者或仅筛选有经验的申请者。随后我们表明，多阶段招聘可以作为企业的内生反应出现：一种相对廉价的中期评估使企业能在昂贵的全面筛选之前获取新的匹配证据。这可以恢复在单阶段招聘下消失的筛选机会，并为缺乏经验但兼容的申请者提供筛选途径。我们的结果表明，人工智能如何将招聘中的核心摩擦从提交申请转变为获得可信的评估，从而为没有先前经验的高匹配度工人设置进入壁垒。多阶段招聘可以内生地出现以应对这一情况，恢复否则会消失的评估机会，并帮助维持市场运转。
英文摘要：
AI-assisted job-search tools have become increasingly popular by making it easier to find and apply to jobs. But by making it easier for applicants to generate and tailor application materials, they can also reduce how informative those materials are about applicant fit. We study this tradeoff in a hiring market where applicants differ in experience and latent match quality and firms use noisy application materials to decide whom to screen. We ask how AI affects downstream screening and hiring, and which applicants are most adversely affected. As application materials become less informative, a Bayesian firm rationally relies more heavily on coarse observables such as prior experience. Among the four applicant types defined by experience and compatibility for the job, inexperienced-compatible applicants are the most exposed: they lack observable experience and lose the individualized information that could distinguish them from other inexperienced candidates. When screening is costly, these changes can also generate inefficient screening failures in which firms screen no applicants or screen only experienced applicants. We then show that multistage hiring can arise as an endogenous firm response: a relatively inexpensive intermediate assessment allows firms to acquire new evidence of fit before costly full screening. This can restore screening opportunities that disappear under one-stage hiring and give inexperienced-compatible applicants a path to screening. Our results show how AI can shift the central friction in hiring from submitting applications to obtaining credible evaluation, creating entry barriers for high-fit workers without prior experience. Multistage hiring can endogenously arise in response, restoring evaluation opportunities that would otherwise disappear and helping preserve market functioning.
