Philippe Jehiel

We develop a dynamic model where successive, decentralized policy makers must decide whether to implement affirmative action policies aimed at improving the performance of future generations of a targeted group. Employers do not perfectly observe if a worker benefited from affirmative action, but take that possibility into account, resulting in the devaluation of the worker’s credentials and an associated feeling of injustice. We establish that, in equilibrium, affirmative action is implemented perpetually by benevolent policy makers, despite the feeling of injustice that eventually dominates the anticipated benefits. This contrasts with the first best, which requires affirmative action to be temporary.

We study the existence of efficient auctions in private value settings in which some bidders form their expectations about the distribution of their competitor’s bids based on the accessible data from past similar auctions consisting of bids and expost values. We consider steady states in such environments with a mix of rational and data-driven bidders, and we allow for correlation across bidders in the signal distributions about the ex post values. After reviewing the working of the approach in second-price and first-price auctions, we establish our main result that there is no efficient auction in such environments.

Lack of diversity in higher education is partly driven by long-run belief distortions about admission chances at elite colleges. We depart from the rational expectation framework and propose a simple model of expectation formation in which students estimate their admission chances by sampling a pool of given size τ of peers who previously applied to elite colleges. Assuming students consider peers with abil-ity as close as possible to their own, two types of inefficiencies arise in steady state: high-achieving disadvantaged students self-select out of elite colleges, and average students from advantaged families apply to elite colleges even though their true admission chances are null. We then explore the working of the model when students from several possibly dissimilar neighborhoods compete for the same positions, thereby highlighting externalities related to the comparative neighborhood com-positions. Several policy instruments such as quotas or the mixing of neighborhoods are considered.

This study explores selection neglect in an experimental investment game where individuals can learn from others’ outcomes. Experiment 1 examines aggregate-level equilibrium behavior. We find strong evidence of selection neglect and corroborate several comparative static predictions of Jehiel’s (2018) model, showing that the severity of the bias is aggravated by the sophistication of other individuals and moderated when information is more correlated across individuals. Experiment 2 focuses on individual decision-making, isolating the influence of beliefs from possible confounding factors. This allows us to classify individuals according to their degree of naivety and explore the limits of, and potential remedies for, selection neglect.