The Making of U.S. Monetary Policy: A Linguistic Analysis of FOMC Transcripts

This dissertation uses 152 verbatim FOMC transcripts (2000–2018) to quantify how leadership and gender shape deliberation and to infer individual policy preferences from what participants say behind closed doors. I build speaker-level measures of participation (speech share and frequency) and opinion content, then apply modern NLP with three reasoning-capable LLMs (GPT-4o, Claude 3, Gemini 2.5) to classify belief-marked sentences (e.g., “I think,” “I believe,” “I’m concerned”) as hawkish or dovish, aggregating to a meeting-speaker Hawkish Index. Deliberation decentralizes across chairs, and a gender gap that widened under Bernanke narrows markedly with Janet Yellen’s appointment; a difference-in-differences design attributes roughly 0.6–0.8 percentage-point gains in women’s speaking share to her leadership. Validating the textual measure against individualized behavior, higher hawkishness robustly predicts tighter discount-rate recommendations (a 10-point rise maps to ≈0.7–2.8 bps higher proposals with meeting fixed effects) and a greater probability of dissent (≈1–2 percentage points for a 10-point rise), with consistent directional signals across models. Together, the results show that who speaks, how much, and—critically—how they speak leave measurable fingerprints on the making of U.S. monetary policy, and that carefully identified subjective language provides a scalable, interpretable proxy for policymakers’ underlying stance.

Author dc:creator

• Chen Xie (114508)

• Gender gap and policy stance within FOMC

Statement dc:rights

• In Copyright

DOI dc:identifier

https://doi.org/10.25417/uic.31451317.v1

OAI identifier oai:identifier

oai:figshare.com:article/31451317