Code for: Information Frictions and Employee Sorting Between Startups

Principal Investigator(s):  View help for Principal Investigator(s) Kevin Bryan, University of Toronto; Mitchell Hoffman, UC Santa Barbara; Amir Sariri, Purdue University

Version:  View help for Version V1

Name File Type Size Last Modified
  Replication_Package 11/03/2025 08:51:AM

Project Citation: 

Bryan, Kevin, Hoffman, Mitchell, and Sariri, Amir. Code for: Information Frictions and Employee Sorting Between Startups. Nashville, TN: American Economic Association [publisher], 2026. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2026-03-03. https://doi.org/10.3886/E232242V1

Project Description

Summary:  View help for Summary
Would workers apply to better firms if they were more informed about firm quality? Collaborating with 26 science-based startups, we create a custom job board and invite business school alumni to apply. The job board randomizes across applicants to show coarse expert ratings of all startups' science and/or business model quality. Making ratings visible strongly reallocates applications toward higher-rated firms. This reallocation holds restricting to high-quality workers. Treatments operate in part by shifting worker beliefs about firms' right-tail outcomes. Despite these benefits, workers make post-treatment bets indicating highly overoptimistic beliefs about startup success, suggesting a problem of broader informational deficits. This is the code accompanying the article.


Scope of Project

Subject Terms:  View help for Subject Terms Hiring; job applications; startups; overconfidence
JEL Classification:  View help for JEL Classification
      M50 Personnel Economics: General
      M51 Personnel Economics: Firm Employment Decisions; Promotions


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Published Versions

V1 [2026-03-03]

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