Agree to Disagree: Measuring Hidden Dissents in FOMC Meetings

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 both economic outlooks and policy preferences. Furthermore, we show that the financial markets respond to the hidden dissent implied in FOMC minutes.

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

JEL Classification: E52, E58, C55