Abstract
Large Language Models (LLMs) have the potential to transform the labor market, including hiring. This paper assesses their impact on signals that job-seekers send to potential employers and how this affects labor market matching. Through two field experiments, focusing on cover letters and involving job-seekers and recruiters, we document that LLMs enhance the quality of signals, particularly benefiting lower-quality applicants. However, these improvements do not translate into increased interview invitations because the improvements are concentrated in standardized, less influential sections of the cover letters. When recruiters are explicitly informed of candidates’ use of LLMs, they place greater value on high-quality cover letters crafted without AI assistance. Our findings indicate that LLMs reduce the informativeness of signals, potentially leading to increased inefficiencies in labor market matching.
| Original language | English |
|---|---|
| Place of Publication | Tilburg |
| Publisher | CentER, Center for Economic Research |
| Pages | 1-89 |
| Volume | 2025-003 |
| Publication status | Published - 18 Mar 2025 |
Publication series
| Name | CentER Discussion Paper |
|---|---|
| Volume | 2025-003 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 4 Quality Education
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SDG 8 Decent Work and Economic Growth
Keywords
- large language models
- cover letters
- labor market
- matching
- signaling
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