I am a Ph.D. student at the Princeton University Department of Economics interested in industrial organization, market design, labor markets, online platforms, and matching theory. I am on the academic job market, 2025-2026.

Email: jesseas@princeton.edu

Research:

Making Talk Cheap: LLMs and Signaling on Digital Labor Platforms (with Anaïs Galdin).

[Work in progress]

Abstract:

In many markets, writing is used to signal quality: for example, workers send cover letters to employers, and prospective college students send application essays to admissions offices, and so on. However, the advent of generative AI and large language models (LLMs) has dramatically lowered the cost of producing written content, thus threatening the usefulness of writing as a signal. In this paper, we study how markets that rely on costly written communication are affected by the flattening of writing costs due to the widespread adoption of LLMs, and which alternative market designs may mitigate efficiency losses in the face of this technological change. To do so, we use data from Freelancer.com, a major digital labor platform, to isolate and quantify how the introduction of LLMs affects matching efficiency in online labor markets. We develop a novel measure of text customization that uses a LLM to quantify how well a cover letter responds to a given job description. We show that, before LLMs, this measure is significantly predictive of labor demand, and that job posters have a high willingness to pay for it, but not so after LLMs are introduced. Motivated by this finding, we develop a model that combines three typically distinct modeling approaches: (1) a Spence signaling model in which workers invest costly effort to produce noisy signals that positively correlate with their ability in equilibrium, (2) a demand model in which employers value worker applications following the literature on discrete choice demand, and (3) a scoring auction in which workers submit applications competing on multiple dimensions to win a contract. We estimate the model with a novel simulation-based estimator that exploits the information structure of our model’s equilibrium to identify workers’ joint distributions of ability and reservation wages, as well as how employers trade off worker ability and price. Having estimated the model, we then simulate a counterfactual equilibrium in which we imagine that LLMs have completely eroded workers’ ability to signal their ability, while holding fixed pre-LLM supply and demand. We thus are able to quantity the impact that LLMs have on market efficiency through their effects on signaling alone. Finally, we draw conclusions on how alternative labor contract designs can regain some of the efficiency lost due to LLMs reducing written communication to cheap talk.

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.