Publication database

We initiate the study of naivete-based discrimination, the practice of conditioning offers on external information about consumers’ naivete. Knowing that a consumer is naive increases a monopolistic or competitive firm's willingness to generate inefficiency to exploit the consumer's mistakes, so naivete-based discrimination is not Pareto-improving, can be Pareto-damaging, and often lowers total welfare when classical preference-based discrimination does not. Moreover, the effect on total welfare depends on a hitherto unemphasized market feature: the extent to which the exploitation of naive consumers distorts trade with different types of consumers. If the distortion is homogenous across naive and sophisticated consumers, then under an arguably weak and empirically testable condition, naivete-based discrimination lowers total welfare. In contrast, if the distortion arises only for trades with sophisticated consumers, then perfect naivete-based discrimination maximizes social welfare, although imperfect discrimination often lowers welfare. And if the distortion arises only for trades with naive consumers, then naivete-based discrimination has no effect on welfare. We identify applications for each of these cases. In our primary example, a credit market with present-biased borrowers, firms lend more than socially optimal to increase the amount of interest naive borrowers unexpectedly pay, creating a homogenous distortion. The condition for naivete-based discrimination to lower welfare is then weaker than prudence.

This is an open access article.

We analyze conditions facilitating profitable deception in a simple model of a competitive retail market. Firms selling homogenous products set anticipated prices that consumers understand and additional prices that naive consumers ignore unless revealed to them by a firm, where we assume that there is a binding floor on the anticipated prices. Our main results establish that “bad" products (those with lower social surplus than an alternative) tend to be more reliably profitable than “good" products. Specifically, (1) in a market with a single socially valuable product and sufficiently many firms, a deceptive equilibrium - in which firms hide additional prices - does not exist and firms make zero profits. But perversely, (2) if the product is socially wasteful, then a profitable deceptive equilibrium always exists. Furthermore, (3) in a market with multiple products, since a superior product both diverts sophisticated consumers and renders an inferior product socially wasteful in comparison, it guarantees that firms can profitably sell the inferior product by deceiving consumers. We apply our framework to the mutual-fund and credit-card markets, arguing that it explains a number of empirical findings regarding these industries.

This is an open access article.

Peer evaluations place organizational members in a dual role: they evaluate their peers and are being evaluated by their peers. We theorize that when evaluating their peers, they anticipate how their evaluations will be perceived and adjust their evaluations strategically to be evaluated more positively themselves when their peers assess them. Building on this overarching claim of role duality resulting in strategic peer evaluations, we focus on a dilemma that evaluating members face: they want to leverage their evaluations of peers to portray themselves as engaged and having high standards, but at the same time, they must be careful not to offend anyone as doing so may cause retaliation. We suggest that organizational members about to be evaluated resolve this dilemma by participating in more peer evaluations but carefully targeting in which evaluations they participate. We test our theory by analyzing peer evaluations on Wikipedia, supplemented by in-depth semi-structured interviews. Our study informs research on peer evaluation and organizational design by revealing how being an evaluator and evaluated can make evaluations more strategic.

© 2023, INFORMS

Early-stage technology startups rely critically on talented scientists and engineers to commercialize new technologies. And yet, they compete with large technology firms to hire the best workers. Theories of ability sorting predict that high ability workers will choose jobs in established firms that offer greater complementary assets and higher pay, leaving low ability workers to take lower-paying and riskier jobs in startups. We propose an alternative view in which heterogeneity in both worker ability and preferences enable startups to hire talented workers who have a taste for a startup environment, even at lower pay. Using a longitudinal survey that follows 2,394 science and engineering PhDs from graduate school into industrial employment, we overcome common empirical challenges by observing ability and stated preferences prior to first-time employment. We find that both ability and career preferences strongly predict startup employment, with high ability workers who prefer startup employment being the most likely to work in a startup. We show that this is due in part to the dual selection effects of worker preferences resulting in a large pool of startup job applicants, and startups “cherry picking” the most talented workers to make job offers to. Additional analyses confirm that startup employees earn approximately 17% lower pay. This gap is greatest for high ability workers and persists over workers’ early careers, suggesting that they accept a negative compensating differential in exchange for the non-pecuniary benefits of startup employment. This is further supported by data on job attributes and stated reasons for job choice.

© 2022, INFORMS

With the help of more than 700 reviewers, we assess the reproducibility of nearly 500 articles published in the journal Management Science before and after the introduction of a new Data and Code Disclosure policy in 2019. When considering only articles for which data accessibility and hardware and software requirements were not an obstacle for reviewers, the results of more than 95% of articles under the new disclosure policy could be fully or largely computationally reproduced. However, for 29% of articles, at least part of the data set was not accessible to the reviewer. Considering all articles in our sample reduces the share of reproduced articles to 68%. These figures represent a significant increase compared with the period before the introduction of the disclosure policy, where only 12% of articles voluntarily provided replication materials, of which 55% could be (largely) reproduced. Substantial heterogeneity in reproducibility rates across different fields is mainly driven by differences in data set accessibility. Other reasons for unsuccessful reproduction attempts include missing code, unresolvable code errors, weak or missing documentation, and software and hardware requirements and code complexity. Our findings highlight the importance of journal code and data disclosure policies and suggest potential avenues for enhancing their effectiveness.

© 2024, INFORMS

While external search allows organizations to source diverse ideas from people outside the organization, it often generates a narrow set of non-diverse ideas. We theorize that this result stems from an interplay between organizations’ selection of ideas and the external generation of ideas: an organization selects ideas shared by external contributors, and the external contributors, who strive to see their ideas selected, use the prior selection as a signal to infer what kind of ideas the organization is looking for. Contributors whose ideas are not aligned with the organization’s selection tend to stop submitting ideas (i.e., self-selection) or adjust the ideas they submit so that they correspond (i.e., self-adjustment), resulting in a less diverse pool of ideas. Our central hypothesis is that the more consistent organizations are in their selection, the stronger the co-evolutionary lock-in: organizations with greater selection consistency receive future ideas with lower content variety. We find support for these predictions by combining large-scale network analysis and natural language processing across a large number of organizations that use crowdsourcing. Our findings suggest a reconceptualization of external search: organizations are not simply passive receivers of ideas but send signals that shape the pool of ideas that externals share.

With permission of the Academy of Management