Making Talk Cheap: Generative AI and Labor Market Signaling

TL;DR

This paper examines how large language models like ChatGPT reduce the cost of written communication, disrupting traditional labor market signaling mechanisms and leading to less meritocratic hiring outcomes.

Contribution

It introduces a novel LLM-based measure of application customization and develops a structural model to quantify the impact of reduced signaling on labor market outcomes.

Findings

Employers valued customized applications before LLMs but not after.

Disruption of signaling reduces meritocracy, with top workers hired less often.

Bottom workers are hired more often due to diminished signaling effectiveness.

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 have a high willingness to pay for workers with more customized applications in the period before LLMs are 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.