A process of deliberation, in which policymakers exchange information prior to formal voting procedures, precedes almost every collective decision. Yet, beyond scarce evidence coming from field and laboratory experiments, few studies have analyzed the role played by sequential deliberation in policy-relevant decision-making bodies. To fill this gap, I estimate an empirical model of policy-making that incorporates social learning via deliberation. In the model, committee members speak in sequence, allowing them to weight their own information and biases against recommendations made by others. The empirical application uses historical transcripts from the Federal Open Market Committee (FOMC), which is the body in charge of implementing monetary policy in the United States. I find the process of deliberation significantly changes members’ policy recommendations compared to the case where members follow their private information. Incorporating sequential learning explains the pattern of individual recommendations and collective choices extremely well and improves the fit over behavioral models that ignore deliberation.

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