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

Alessandro Riboni, Francisco Ruge-Murcia and Linh Tran

No 20840, CEPR Discussion Papers from Centre for Economic Policy Research

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.

JEL-codes: D7 E5 (search for similar items in EconPapers)
Date: 2025-11
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