I am a Ph.D. student at the Princeton University Department of Economics. I am on the 2025-2026 academic job market.

I study industrial organization in the context of labor markets, with a particular focus on the economics of AI, information, and matching.

My CV can be found here.

Research Fields

• Industrial Organization
• Labor Economics

Email: jesseas@princeton.edu

Job Market Paper:

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

Abstract:

Large language models (LLMs) like ChatGPT have significantly lowered the cost of producing written content. This paper studies how LLMs, through lowering writing costs, disrupt markets that 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 the extent to which an application is tailored to a given job posting. Taking the measure to the data, we find that employers had a high willingness to pay for workers with more customized applications in the period before LLMs were introduced, but not after. To isolate and quantify the effect of LLMs’ disruption of signaling on equilibrium outcomes, we develop and estimate a structural model of labor market signaling, in which workers invest costly effort to produce noisy signals that predict their ability in equilibrium. We use the estimated model to simulate a counterfactual equilibrium in which LLMs render written applications useless in signaling workers’ ability. Without costly signaling, employers are less able to identify high-ability workers, causing the market to become significantly less meritocratic: compared to the pre-LLM equilibrium, workers in the top quintile of the ability distribution are hired 19% less often, workers in the bottom quintile are hired 14% more often.

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).