We consider private contributions to threshold public good projects that succeed when the number of contributors exceeds a threshold. For standard distributions of contribution costs, valuable threshold public good projects are more likely to succeed when they require more contributors. Raising the success threshold reduces free-riding incentives, and this strategic effect dominates the direct effect. Remarkably, common sense holds and easier projects are more likely to succeed for cost distributions with right tails fatter than Cauchy. Our results suggest government grants reduce the likelihood that valuable, threshold public good projects succeed, unless the tail of contribution costs is sufficiently fat.
We generalize the school choice problem by defining a notion of allowable priority violations. In this setting, a weak axiom of stability (partial stability) allows only certain priority violations. We introduce a class of algorithms called the student exchange under partial fairness (SEPF). Each member of this class gives a partially stable matching that is not Pareto dominated by another partially stable matching (i.e., constrained efficient in the class of partially stable matchings). Moreover, any constrained efficient matching that Pareto improves upon a partially stable matching can be obtained via an algorithm within the SEPF class. We characterize the unique algorithm in the SEPF class that satisfies a desirable incentive property. The extension of the model to an environment with weak priorities enables us to provide a characterization result that proves the counterpart of the main result in Erdil and Ergin (2008).
We oﬀer a model in which heterogeneous agents make individual decisions with negative external effects such as the extent of social distancing during pandemics. Because of the externality, the agents have different individual and political preferences over the policy response. Personally, they might prefer a low-level response, yet would vote for a higher one because it deters the others – even if simultaneously decreasing their personal benefits. The effect is even more pronounced in information acquisition: agents would want one level of slant in the information they base their actions on and a different level of slant in public announcements. The model accounts for numerous empirical regularities of the public response to COVID-19.
How do market opportunities influence the formation and development of long-term relationships? To answer this question, I build a model of employment relationships where the worker has private information about match quality, the firm learns about match quality over time, and the firm makes a match-specific investment. Improved market opportunities for workers promote productive relationships because they let the worker signal her firm-specific productivity by forgoing market opportunities. Signaling allows the firm to bypass the learning stage and encourages investment (signaling effect). Improved market opportunities for firms, however, discourage long-term relationships and undermine investment incentives (layoffs effect). I embed the relationship game in a search market equilibrium where market opportunities for both parties depend on search frictions and market thickness. With intermediate values of market thickness, relationship productivity and worker welfare are u-shaped in search frictions: when search frictions decrease from high to intermediate levels, the layoffs effect dominates; when search frictions are sufficiently low, the signaling effect dominates.
We present a model of media capture, a politician having control over the editorial policies of media. At the heart of the model is the trade-off faced by a politician who wants to persuade the citizens: she wants to capture the media and produce news in her favor, but capture leads the citizens to not follow the media as they find them uninformative. The model is a Bayesian persuasion model (à la Kamenica and Gentzkow (2011)) with an audience of heterogeneous priors. We identify conditions on the distribution of priors that guarantee full information revelation and no information revelation by the captured media. The model also has several testable predictions: (i) the information content of the news provided by the captured media decreases as the politician becomes more popular, (ii) in societies with more extremists than moderates, the media are more likely to produce "negative" news than "positive" ones, and (iii) in societies where the media are less accessible to citizens, they are more informative.
Work in Progress
Priority-Based Matching with a Social Objective: Contract Design for Access and Equity, with Umut Dur, Parag A. Pathak and Tayfun Sönmez.
Exit, Voice and Institutions, with Daron Acemoglu.
Are Dynamic Matching Platforms Natural Monopolies?, with Omer Karaduman.