Deliberation in Collective Decision-Making: The Case of the FOMC

Abstract

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 deliberation in

policy-relevant decision-making bodies. To fill this gap, this

dissertation provides an empirical analysis of the process of deliberation within the

Federal Open Market Committee (FOMC), which is the body in charge of

implementing monetary policy in the United States.

I study the deliberation

records from FOMC meetings to quantify the extent to which allowing

participants to communicate with one another results in decisions that

pool the information of individual members. In the first chapter, I empirically

assess whether the information FOMC members provide in their economic forecasts is

truthfully reported. I provide evidence that their predictions are

systematically biased and fail to incorporate publicly available

information. I exploit the variation among FOMC members' appointment

process and career experiences to show that

the biased nature of these forecasts is consistent with members' heterogeneity

in preferences. In the second chapter, I explore the systematic biases

in the verbal content of FOMC members' deliberation process. I estimate a probabilistic topic model that allows me to extract the time

FOMC members spend deliberating different aspects of monetary

policy-making and its relationship with members' characteristics and

forecast biases. In the third

chapter, I show

that there is a substantial amount of information transmitted through the

sequential deliberation of policy recommendations. 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. 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.