Agree to Disagree: Measuring Hidden Dissents in FOMC Meetings

59 Pages Posted: 20 Aug 2023 Last revised: 2 Oct 2023

See all articles by Kwok Ping Tsang

Kwok Ping Tsang

Virginia Tech

Zichao Yang

Wenlan School of Business, Zhongnan University of Economics and Law

Date Written: October 1, 2023

Abstract

Based on a record of dissents on FOMC votes and transcripts of the meetings from 1976 to 2017, we develop a deep learning model based on self-attention modules to create a measure of disagreement for each member in each meeting. While dissents are rare, we find that members often have reservations with the policy decision. The level of disagreement is mostly driven by current or predicted macroeconomic data at both the individual and meeting levels, while personal characteristics of the members matter only at the individual level. We also use our model to evaluate speeches made by members between meetings, and we find a weak correlation between the level of disagreement revealed in them and that of the following meeting. Finally, we find that the level of disagreement increases whenever monetary policy action is more aggressive.

Keywords: Natural language processing, disagreement, monetary policy, FOMC

JEL Classification: E52, E58, C55

Suggested Citation

Tsang, Kwok Ping and Yang, Zichao, Agree to Disagree: Measuring Hidden Dissents in FOMC Meetings (October 1, 2023). Available at SSRN: https://ssrn.com/abstract=4546049 or http://dx.doi.org/10.2139/ssrn.4546049

Kwok Ping Tsang (Contact Author)

Virginia Tech ( email )

250 Drillfield Drive
Blacksburg, VA 24061
United States

HOME PAGE: http://https://sites.google.com/site/byrontkp/

Zichao Yang

Wenlan School of Business, Zhongnan University of Economics and Law ( email )

No.143, Wuluo Road
Wuhan, Hubei 430073
China

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