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Agree to disagree: Measuring hidden dissent in FOMC meetings

Author

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  • Tsang, Kwok Ping
  • Yang, Zichao

Abstract

Using FOMC transcripts and customized deep learning models, we quantify “hidden dissent”, or disagreement in the FOMC that is unobserved in formal votes. We find hidden dissent to be prevalent and systematically driven by macroeconomic conditions like inflation and unemployment. It strongly correlates with divergent member projections (SEP) and measures of policy sub-optimality, reflecting heterogeneity among members in policy preferences. Furthermore, we show that the financial markets respond to the hidden dissent implied in FOMC minutes.

Suggested Citation

  • Tsang, Kwok Ping & Yang, Zichao, 2025. "Agree to disagree: Measuring hidden dissent in FOMC meetings," Journal of Economic Dynamics and Control, Elsevier, vol. 180(C).
    • Handle: RePEc:eee:dyncon:v:180:y:2025:i:c:s0165188925001630
    DOI: 10.1016/j.jedc.2025.105197
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    JEL classification:

    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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