Multidimensional Screening with Element-wise Monotone Independence

SSRN Electronic Journal · 2026-01-01

Does AI Cheapen Talk? Theory and Evidence from Global Entrepreneurship and Hiring

Management Science · 2026-04-27

Screening human capital based on signals such as job applications or entrepreneurial pitches is crucial for organizations. Signals are often informative insofar as they require differential knowledge and effort to produce. Generative AI (GAI) complicates screening by lowering the cost of producing impressive signals. We model the informational effects of GAI, showing that applicants’ access to GAI can increase—and also decrease—an evaluator’s screening mistakes. This result depends on how GAI affects experts’ signals compared with nonexperts’. Using experiments in hiring and start-up investing, we estimate that senders’ access to GAI (ChatGPT) lowers screening accuracy by 4%–9% for employers and start-up investors. Consistent with our model, senders’ access to GAI also improves screening accuracy in some settings, in our case, among senders from non–English-speaking countries. These results show that GAI can profoundly shape screening accuracy. This paper was accepted by Anindya Ghose, information systems. Funding: We are grateful for the Columbia Business School Digital Future Initiative Grant for helping fund this project. B. Cowgill thanks the Kauffman Foundation Emerging Scholars Program, the Columbia Center for Political Economy, the NET Institute, and the Stellar Development Foundation. P. Hernandez-Lagos thanks the Yeshiva University Sy Syms Dean’s Research Fund. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.07027 .

The Tradeoffs of Transparency: Measuring Inequality When Subjects Are Told They Are in a Study

AEA Randomized Controlled Trials · 2025-06-26

The Tradeoffs of Transparency: Measuring Inequality When Subjects Are Told They Are in a Study

AEA Randomized Controlled Trials · 2025-06-26

The Tradeoffs of Transparency: Measuring Inequality When Subjects Are Told They Are in a Study

AEA Randomized Controlled Trials · 2025-06-26

Salary History and Employer Demand: Evidence from a Two-Sided Audit

American Economic Journal Applied Economics · 2025-06-26 · 1 citations

We study how salary disclosures affect employer demand using a field experiment featuring hundreds of recruiters evaluating over 2,000 job applications. We randomize the presence of salary questions and the candidates' disclosures for male and female applicants. Our findings suggest that extra dollars disclosed yield higher salary offers, willingness to pay, and perceptions of outside options by recruiters (all similarly for men and women). Recruiters make negative inferences about the quality and bargaining positions of nondisclosing candidates, though they penalize silent women less. (JEL C93, D82, J22, J23, J31)

The Tradeoffs of Transparency: Measuring Inequality When Subjects Are Told They Are in a Study

AEA Randomized Controlled Trials · 2025-06-26

The Tradeoffs of Transparency: Measuring Inequality When Subjects Are Told They Are in a Study

AEA Randomized Controlled Trials · 2025-06-26

The Tradeoffs of Transparency: Measuring Inequality When Subjects Are Told They Are in a Study

AEA Randomized Controlled Trials · 2025-06-26

Clause and Effect: Theory and Field Experimental Evidence on Noncompete Clauses

SSRN Electronic Journal · 2024-01-01 · 1 citations