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Collective Learning and Distributive Uncertainty

Ginzburg, Boris (2022): Collective Learning and Distributive Uncertainty.

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Abstract

I study a committee that is considering a costly project whose distributive consequences are unknown. The committee is divided into two factions. Support of both factions is required for the project to be approved. By delaying approval, the committee can gradually learn which faction benefits from the project. I show that a project that gives a lower payoff to everyone is more likely to be approved than a more socially efficient project. Furthermore, the equilibrium amount of learning is excessive, and a deadline on adopting the project is socially optimal in a wide range of settings.

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