I am a Ph.D. student at the Princeton University Department of Economics. I study empirical industrial organization, the economic impacts of AI, labor markets, online platforms, and matching theory. I am on the academic job market, 2025-2026.

Email: jesseas@princeton.edu

Job Market Paper:

Making Talk Cheap: Generative AI and Labor Market Signaling (with Anaïs Galdin).

[Draft coming soon!]

Abstract:

This paper studies how large language models (LLMs) like ChatGPT lower the cost of producing written communication and disrupt markets that have traditionally relied on writing as a costly signal of quality (e.g., job applications, college essays). Using data from Freelancer.com, a major digital labor platform, we explore the effects of LLMs’ disruption of labor market signaling on equilibrium market outcomes. We develop a novel LLM-based measure to quantify how tailored an application is to a given job posting. Taken to the data, the measure significantly predicts labor demand in the period before LLMs are introduced, but not after. Motivated by this finding, we develop a structural model of labor market signaling, in which workers invest costly effort to produce noisy signals that predict their ability in equilibrium. We estimate the model on pre-LLM data using a novel simulation-based estimator, and then simulate a counterfactual equilibrium in which LLMs reduce writing costs to zero, dismantling workers’ capacity to signal their ability. Our counterfactual analysis suggests that LLMs cause employers to divert hiring away from higher ability workers towards lower ability workers, thereby lowering wages and leading to a reduction in worker surplus and virtually no effect on employer surplus.

Working Papers:

Job Matching without Price Discrimination (with Wilbur Townsend).

[Previously circulated as “Stable Matching in Monopsonistic Labor Markets”]

Revise and Resubmit, Games and Economic Behavior

Abstract:

In many labor markets, firms do not price discriminate among their workers. In this paper, we study how a labor market with uniform salaries matches workers to jobs. To do so, we construct a job matching model in which each firm views workers as interchangeable and must pay all its workers the same salary. While an efficient stable outcome always exists, inefficient outcomes can be stable as well. Workers’ preferred stable outcome is efficient. In contrast, firms prefer inefficient stable outcomes in which they pay lower salaries. Though a strategyproof mechanism that implements an efficient stable outcome can elicit how workers value employment, it cannot elicit firms’ production technologies.

Work in Progress:

Congestion and Effortful Information Provision in Two-Sided Markets (with Anaïs Galdin).

Attentional Market Power on Digital Labor Platforms (with Anaïs Galdin and Yiying Tan).