Seminars 2019-2020

We study the problem of assigning workers to short-term jobs in online marketplaces. In settings where workers’ most relevant skills and attributes are readily observed (e.g., Uber), the marketplace platform should clearly prioritize matches of the workers with the best attributes. However, in many important and growing settings, workers are distinguished in quality by skills and attributes that are difficult to measure at scale.  Information about these attributes is perceived by marketplace participants through effort and cost (e.g., interviewing) – in particular, mounting evidence suggests that reputational systems do not bridge the gap.  We expect marketplace to increasingly encounter this challenge as the online gig economy expands from its current niche, at 0.5% of the overall US labor force.

We use data covering millions of job postings and transactions on a major online platform for sourcing freelance labor. We structurally estimate employers’ demand preferences, including the extent to which they hire based on uncertain information about workers’ quality-relevant competencies, in a setting featuring an asymptotically large number of choices (freelancers) sorted into essentially unique consideration sets (rather than each being one of large N instances).  We recommend how and when the platform should prioritize matching for compatible skills, matching for repeat relationships, and matching that encourages exploration.

Using archival field data and experiments, I provide evidence of novel factors that influence diversity-related hiring decisions in organizations. First, I explore the implications of impression management as a driver of diversity. If organizations have impression management concerns around diversity, they may strive to match the levels of diversity found among peer organizations, thereby conforming to the descriptive social norm for diversity. I examine this prediction in the context of gender diversity on U.S. corporate boards and find that significantly more S&P 1500 boards include exactly two women (the descriptive social norm) than would be expected by chance. Experimental data corroborate these findings and provide additional evidence that social norms, visibility, and impression management concerns all affect organizational preferences for diversity. Second, I explore how a common feature of personnel selection decisions–the fact that they are made in isolation–can affect the diversity of hired candidates. In a series of experiments, I show individuals select less diversity when making decisions in isolation, as opposed to making collections of choices, because diversity is less salient when selection decisions are made in isolation. Together, these projects illuminate novel factors that determine when and why organizations demand diversity. Understanding these factors can provide guidance about potential interventions to increase diversity in organizations.

How do people decide whether to incur costs to increase their likelihood of success? In investigating this question, we offer a theory called prospective outcome bias. According to this theory, people tend to make decisions that they expect to feel good about after the outcome has been realized. Because people expect to feel best about decisions that are followed by successes – even when the decisions did not cause those successes – they will pay more to increase their chances of success when success is already likely (e.g., people will pay more to increase their probability of success from 80% to 90% than from 10% to 20%). We find evidence for prospective outcome bias in nine experiments. In Study 1, we establish that people evaluate costly decisions that precede successes more favorably than costly decisions that precede failures, even when the decisions did not cause the outcome. Study 2 establishes, in an incentive-compatible laboratory setting, that people are more motivated to increase higher chances of success. Studies 3-5 generalize the effect to other contexts and decisions, and Studies 6-8 indicate that prospective outcome bias causes it (rather than regret aversion, waste aversion, goals-as-reference-points, probability weighting, or loss aversion). Finally, in Study 9, we find evidence for another prediction of prospective outcome bias: people prefer small increases in the probability of large rewards (e.g., a 1% improvement in their chances of winning $100) to large increases in the probability of small rewards (e.g., a 10% improvement in their chances of winning $10).