Publications
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. Using a structural model of bidding and hiring, we find that the selection effects we observe would also occur in equilibrium.
In the Media:
- New York Times – If a Law Bars Asking Your Past Salary, Does It Help or Hurt
- New York Times – How a Common Interview Question Fuels the Gender Pay Gap
Platform marketplaces can use several means to steer buyers to select sellers, including recommendations and guarantees. Guarantees—which create a direct financial stake for the platform in seller performance—might be particularly effective, as they align buyer and platform interests. 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. This preference for guaranteed sellers was not the result of their lower financial risk, but rather because buyers viewed the platform’s decision to guarantee as informative about relative seller quality. Indeed, a follow-up experiment showed that simply recommending the sellers that platform would have guaranteed was equally effective at steering buyers towards those selected sellers.
We examine how firms engage with the big data capabilities of two-sided matching platforms. While such platforms can use artificial intelligence (AI) and big data techniques to help firms find better transaction partners, the effectiveness of these technologies depends on the concentration of transactions undertaken on the platform, giving rise to a potential small numbers bargaining problem, as users become increasingly dependent on the platform to find transaction partners. We argue that firms deal with this appropriation challenge through strategies of partial reliance: they make use of 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. We test this argument in the context of an online labor platform, using a regression discontinuity design to causally identify the effect of the platform’s recommendations on the hiring choices of firms at every stage of the recruiting process. Consistent with our theory, we find that firms rely heavily on the platform’s recommendations when screening job applicants to view, but ignore these recommendations when deciding whom to hire from among those interviewed, with the reliance on the platform’s recommendations being weaker for more specialized jobs and for more experienced employers. The study contributes to our understanding of how firms use big data technologies, highlighting the challenges these technologies pose for organizational governance and scope choice, and providing a bridge between research on AI and big data and work on two-sided markets.
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 discuss applications of our framework across settings, extensions to other types of entrepreneurship, and the viability of opportunity as an orienting construct for entrepreneurship research.
Submitted Papers
“Equity Implications of Filters.” (with Sibo Lu), Under review at Management Science