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Title: Deliberation in Collective Decision-Making: The Case of the FOMC
Authors: Lopez Moctezuma, Gabriel
Advisors: Iaryczower, Matias
Contributors: Politics Department
Keywords: Committee
Monetary Policy
Probabilistic Topic Model
Structural Estimation
Subjects: Political science
Issue Date: 2016
Publisher: Princeton, NJ : Princeton University
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.
Alternate format: The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog:
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Politics

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