market design for academic hiring

A Statistical Market-Design Framework for Academic Job Markets

Preference signaling for the academic job market.

A centralized questionnaire converts scarce candidate signals into structured information, improving interview selection, matching efficiency, and welfare across the market.

Ali Kaazempur-Mofrad1, Xiaowu Dai1, Xuming He2

1University of California, Los Angeles  •  2Washington University in St. Louis

Market pulse

Noisy, congested, hard to coordinate.

Departments cannot identify genuine interest and candidates cannot credibly signal fit. Interviews are allocated with too little information.

  • Offers concentrate on a few candidates
  • Interviews wasted on low-probability matches
  • Ideal pairs fail to connect

2023–24 hiring cycle

641 Statistics PhDs awarded
82 Accepted permanent academic positions
185 Statistics PhDs with no position secured
130 Assistant professor openings posted
42 Searches left unfilled

Market frictions

Why the system fails

Congestion, information asymmetry, and weak coordination create lost opportunities for both candidates and departments.

01

Congestion

Offers cluster; some positions remain unfilled.

02

Noisy interest

Difficult to identify sincere interest.

03

Coordination failure

Departments may miss out on ideal candidates.

04

Resource waste

Interview slots go to candidates unlikely to accept.

Proposed mechanism

Three-stage design

A preference-signaling layer for academic hiring: candidates complete a single questionnaire and departments use the resulting signals to inform interview decisions.

1

Signaling stage

Candidates complete one questionnaire

A standardized questionnaire captures candidate preferences, and each candidate selects which departments receive their responses.

2

Interview stage

Departments rank with calibrated confidence

Questionnaire signals are combined with traditional materials to estimate acceptance probabilities and inform interview selection.

3

Matching stage

Standard offers, sharper information

Departments extend offers as usual, but more informed decisions move the market closer to stable outcomes.

Overview of the signaling, interview, and matching stages in the proposed market-design framework.
Process map

Results

Theoretical guarantee and simulation summary

Theoretical result

Truthful participation is a dominant strategy

  • Universal disclosure dominates for candidates
  • Misreporting incentives vanish under market competition
  • Aggregate welfare is nondecreasing in participation
  • Better information pushes toward stable outcomes

Average per year over 10 simulated years

Hiring outcomes under baseline and questionnaire cases

Per simulated year
63 positions 300 candidates
Market size held fixed across both scenarios.
Without questionnaire 24 matches
39 searches unfilled 276 candidates unmatched
With questionnaire 38 matches
25 searches unfilled 262 candidates unmatched
View Detailed Results

Questionnaire

A market-wide comparable signal

The questionnaire covers dimensions central to academic job decisions.

Potential questionnaire categories

Location
Compensation and resources
Teaching
Research environment
Example questionnaire Open in new tab
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Paper and Code

Paper Code repository
BibTeX 
@article{kaazempurmofrad2026marketdesign,
  title   = {A Statistical Market-Design Framework for Academic Job Markets},
  author  = {Kaazempur-Mofrad, Ali and Dai, Xiaowu and He, Xuming},
  journal = {arXiv e-prints},
  year    = {2026},
  doi     = {10.48550/arXiv.2604.14230}
}

Platform

Interactive demo for truthful reporting

Use the platform to enter a set of true preferences, then adjust the reported version and see how the estimated interview probability changes.

Try the demo

Test truthful and alternative reports side by side

  • Complete a sample questionnaire covering preferences like location, teaching, and research environment.
  • See an estimated interview probability based on departments matching your true preferences.
  • Change reported preferences and observe how deviations from truthfulness affect the displayed probability.
Open Platform Demo

Inside the demo

A simple three-step walkthrough

1
Report true preferences

Fill out the questionnaire using the preferences you would actually want a department to satisfy.

2
View the baseline probability

See the estimated interview probability associated with truthful reporting.

3
Test deviations in real time

Modify the reported profile and compare the new result against the truthful benchmark.