Publications

I theorize that candidate filters, a core technology in digital labor markets considered central to cost-effective matching, redistributes costs from the search-and-screening stage to the job-offer stage of hiring. Filters cause employers to focus on applicants who display common preferred characteristics. At the search-and-screening stage, filters reduce the number of applicants interviewed and alter the composition of the interviewed pool by removing both lower- and higher-quality candidates. At the job-offer stage, filters lead employers to make job offers to candidates with easily observable signals of quality and higher wage demands. Additionally, I theorize that if many firms target the same signals of quality, they may all pursue similar candidates. This increases bargaining power for those applicants, leading to more rejected job offers and higher wage demands. A randomized field experiment in a large online labor market confirms that filters redistribute hiring costs. The experiment shows that employers with access to filters interview 3.4% fewer applicants. Using a machine learning algorithm to proxy for unobservable-to-the-employer applicant quality, I show that filters cause employers to interview 28.9% fewer lower-quality applicants and 36.9% fewer high-quality applicants per job. Among employers who actively used filters, I find suggestive evidence of worse job-offer outcomes: offers are 8.9% more likely to be rejected, and contracted wages are 2.5% higher. I discuss the implications of this cost redistribution for organizations, labor markets, and strategy research.

Extending the opportunity-discovery perspective on Kirznerian entrepreneurship, we propose a general framework in which new businesses emerge from three distinct mechanisms situated at the individual-opportunity nexus: discovery, discernment, and exploitation. We propose observing opportunities in market exchanges and characterizing their profitability potential based on a component that is common to all observers and one that is specific to individual observers who vary in access to market information. Analysis of an online platform for freelance labor demonstrates our contributions in theory, measurement, and inference. In this context, discovery and exploitation mechanisms shape individuals’ entrepreneurial transitions from freelancer to founder.

We report the results of a field experiment in which treated employers could not observe the compensation history of their job applicants. Treated employers responded by evaluating more applicants, and evaluating those applicants more intensively. They also responded by changing what kind of workers they evaluated: treated employers evaluated workers with 7% lower past average wages and hired workers with 16% lower past average wages. Conditional upon bargaining, workers hired by treated employers struck better wage bargains for themselves.

Platform marketplaces can use several means to steer buyers to select sellers, including recommendations and guarantees. We report the results of an experiment in which a platform marketplace guaranteed selected sellers for a group of would-be buyers. Offering a guarantee did not increase sales overall, suggesting financial risk was not determinative for the marginal buyer. However, the intervention did cause buyers to preferentially contract with guaranteed sellers — not because of lower financial risk, but because buyers viewed the platform’s decision to guarantee as informative about relative seller quality.

We examine how firms engage with the big data capabilities of two-sided matching platforms. We argue that firms deal with the appropriation challenge these platforms pose through strategies of partial reliance: they use the platform’s AI-driven recommendations to identify an initial set of generally acceptable partners, then rely on their own internal capabilities to select the best firm-specific match. Using a regression discontinuity design on an online labor platform, we find that firms rely heavily on the platform’s recommendations when screening applicants but ignore them when deciding whom to hire.

We study how Artificial Intelligence (AI) impacts organizational decision-making through the lens of the classic tradeoff between errors of omission and errors of commission in a hierarchy. We propose a theoretical framework that unpacks AI’s influence into two opposing effects. We argue that while AI successfully mitigates errors of omission during the search phase at the bottom of the hierarchy, it simultaneously increases the potential for errors of commission during the evaluation phase. We empirically examine these effects by studying a sequential, two-stage decision process within the radiology department of a major research hospital. Leveraging advanced textual analysis on a dataset of approximately 213,000 junior-drafted and senior-corrected CT (Computed Tomography) scan reports, we can observe and measure both error types. We find that AI significantly broadens the search space, increasing the number of distinct diagnostic topics identified by junior radiologists. These topics are incorporated into the final diagnostic reports of senior radiologists, suggesting that AI is enabling the location of diagnostically pertinent information that might have otherwise been missed, mitigating errors of omission. We also observe that AI decreases the semantic similarity between the analysis and recommendations contained in junior and senior reports; this rise in senior correction activity implies an increase in the need to mitigate commission errors. Thus, AI redistributes the costs of decision-making from the initial search stage to the final evaluation stage, increasing the burden of gatekeepers.

Generative AI (GenAI) can produce high-quality images at near-zero cost, generating debate over whether it will displace human artists. The dominant task-based view predicts displacement wherever GenAI can perform an artist’s tasks at acceptable quality. We argue that capability is necessary but not sufficient because it ignores whether buyers can capture the value of GenAI output. Capturing that value requires exclusive rights that copyright law grants to human-created work but withholds from AI-generated work, so GenAI displaces artists only where buyers need not own the work. Because the same artists serve both commercial and non-commercial buyers, we further predict that displaced artists carry the shock into the commercial segment, depressing wages even where demand persists. We test these predictions on Artmug.kr, a Korean art-commission platform that bans AI-generated work and requires buyers to declare commercial or non-commercial intent. After DALL·E 2’s April 2022 release, non-commercial illustration demand fell 40% relative to commercial commissions. Although commercial demand showed no discontinuous decline, the wage premium of commercial over non-commercial illustration fell from roughly 29% before the release to 8% by the end of the sample period. Post-hoc investigations trace the wage convergence not to intensified competition but to artists’ degraded outside options and selection among remaining non-commercial buyers. Task-based measures thus mispredict GenAI’s toll on commercial artists: demand for their work persists, but the shock reaches them through reduced wages.