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Can Labor Markets Function in the Age of AI? The Evaluation Bottleneck in Hiring — arXiv:2609.30058

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.30058
License (as archived): CC0-1.0
Archived: 2026-10-08T04:27:59.384Z
SHA-256 of the archived text: 03e43ba160a22d64a70caf85b25d1787d08f64841e669b8c055cf3887f4b3eb5
Stable URL of this page: https://metraipolis.com/documento/dc_f6c00a031893

How to cite

Can Labor Markets Function in the Age of AI? The Evaluation Bottleneck in Hiring — arXiv:2609.30058 — arXiv (en), https://arxiv.org/abs/2609.30058 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f6c00a031893 (Metraipolis archive dc_f6c00a031893, sha256 03e43ba160a22d64a70caf85b25d1787d08f64841e669b8c055cf3887f4b3eb5)

BibTeX

@misc{dcf6c00a031893,
  title = {Can Labor Markets Function in the Age of AI? The Evaluation Bottleneck in Hiring — arXiv:2609.30058},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.30058},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f6c00a031893; sha256 03e43ba160a22d64a70caf85b25d1787d08f64841e669b8c055cf3887f4b3eb5},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.30058. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-25 in cs.AI).
Categorie arXiv: Computer Science and Game Theory (cs.GT) ; Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Theoretical Economics (econ.TH).
Pagina del lavoro: https://arxiv.org/abs/2609.30058 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T03:50:58.224Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-25?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).