PreprintCited by 6Labor market dynamics and wage inequalityEmployer Branding and e-HRMDigital Economy and Work Transformation

Generative AI and Labor Market Matching Efficiency

Open access
Wiles, Emma, Horton, John J.·2025·SSRN Electronic Journal

Integrity signals

Registry

Crossref · 10.2139/ssrn.5187344

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Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Algorithmic assistance on job application essays can increase hiring rates significantly.

    “For the latter, algorithmic assistance on job application essays can increase hiring rates significantly (Wiles et al. 2025).”

    From The Coasean Singularity? Demand, Supply, and Market Design with AI Agents (2025) · cites this paper as Wiles et al. 2025

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Authors
Wiles, E., & Horton, J. J.
Affiliations
Boston University (US)
Corresponding author
Wiles, Emma
Venue
SSRN Electronic Journal
Publisher
Elsevier BV
Published
2025
ISSN
1556-5068 (ISSN-L)
OpenAlex
W4409472832
Cited by
6
References
0
Citations by year
2026: 5 · 2025: 1
Type
posted content
Language
en
Open access
Locations
SSRN Electronic Journal (acceptedVersion)
Topics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Labor market dynamics and wage inequality
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Employer Branding and e-HRM
  • Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
Keywords
generative AI, job matching, job advertisements, market efficiency, negative externalities, welfare effects

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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