market design for academic hiring
A Statistical Market-Design Framework for Academic Job Markets
A centralized questionnaire converts scarce candidate signals into structured information, improving interview selection, matching efficiency, and welfare across the market.
Market pulse
Departments cannot identify genuine interest and candidates cannot credibly signal fit. Interviews are allocated with too little information.
2023–24 hiring cycle
Market frictions
Congestion, information asymmetry, and weak coordination create lost opportunities for both candidates and departments.
Offers cluster; some positions remain unfilled.
Difficult to identify sincere interest.
Departments may miss out on ideal candidates.
Interview slots go to candidates unlikely to accept.
Proposed mechanism
A preference-signaling layer for academic hiring: candidates complete a single questionnaire and departments use the resulting signals to inform interview decisions.
Signaling stage
A standardized questionnaire captures candidate preferences, and each candidate selects which departments receive their responses.
Interview stage
Questionnaire signals are combined with traditional materials to estimate acceptance probabilities and inform interview selection.
Matching stage
Departments extend offers as usual, but more informed decisions move the market closer to stable outcomes.
Results
Theoretical guarantees
Simulation design
Simulations merge 2026 U.S. News rankings with College Scorecard data across 103 departments in four prestige tiers.
300 candidates/year, 10-year horizon, 200 replications. Participation rates ρ ∈ {0%, 5%, 20%, 50%, 90%, 100%} tested.
Click any figure to expand
Questionnaire
The questionnaire covers dimensions central to academic job decisions.
Potential questionnaire categories
Paper and Code
@article{academicmarket2026,
title={A Statistical Market-Design Framework
for Academic Job Markets},
author={Kaazempur-Mofrad, Ali and
Dai, Xiaowu and He, Xuming},
year={2026}
}