Agree to Disagree: Measuring Hidden Dissent in FOMC Meetings

TL;DR

This paper introduces a deep learning approach to quantify hidden dissent in FOMC meetings, revealing its prevalence, drivers, and market impact, thus providing new insights into monetary policy decision-making.

Contribution

It develops a novel method to measure unobserved dissent in FOMC meetings using transcripts and deep learning, linking it to economic conditions and market responses.

Findings

Hidden dissent is widespread and influenced by macroeconomic factors.

It correlates with divergent economic projections and policy sub-optimality.

Financial markets respond to the inferred hidden dissent.

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.