Deliberation and Policy Outcomes: Evidence from the Textual Analysis of FOMC Transcripts *

Alessandro Riboni, Francisco Ruge-Murcia and Linh Tran ()
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Linh Tran: McGill University = Université McGill [Montréal, Canada]

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Abstract: Natural language processing is used to extract information from FOMC transcripts and construct quantitative text-based measures of voiced policy stance, emotions, and collaboration. These measures are inputs in an econometric model of deliberation where members interact with one another across rounds of a meeting and over time across meetings. Evidence shows that members learn from one another during within-meeting deliberation and exert influence across meetings. Although emotional tone has limited effects on policy stances and decisions, it has strong predictive power for dissent behavior

Keywords: JEL classification: D7 E5 Collective decision-making monetary committee natural language processing dissent transparency; JEL classification: D7; E5 Collective decision-making; monetary committee; natural language processing; dissent; transparency (search for similar items in EconPapers)
Date: 2025-11-20
Note: View the original document on HAL open archive server: https://cnrs.hal.science/hal-05375535v1
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