RESEARCH
RESEARCH
Abstract: We assess whether men and women are treated differently when presenting their research in economics seminars. We collected data on every interaction between presenters and audience members across thousands of seminars, job market talks and conference presentations, leveraging both human judgment and audio processing algorithms to measure the number, tone and type of interruptions. Within a seminar series, women are interrupted more than men, and this finding holds when controlling for characteristics of the presenter and their paper topic. This differential treatment appears to reflect in part greater engagement with female speakers - resulting in larger attendance and seminar engagement, and in part greater hostility toward female speakers - resulting in more negative, mid-sentence, and declarative interruptions.
Abstract: This paper shows that the wording of Federal Reserve communication affects expectations and other economic variables over and above the effects of setting the federal funds rate. Adapting neural network methods for text analysis from the computer science literature, I analyze how the wording in statements of the Federal Open Market Committee (FOMC) impacts fed funds futures (FFF) prices when these statements are announced. Using text analysis on FOMC statements and internal meeting materials, I create a new monetary policy “text shock” series for 2005-2014 that isolates the variation of FFF prices induced by the FOMC’s forward guidance, not their current assessment of the economy. I find that the wording of FOMC statements accounts for four times more variation in FFF prices than direct announcements of changes in the target federal funds rate. I also find that the impact of forward guidance on real interest rates is twice as large when using text shocks over other measures, like changes in FFF prices. Furthermore, the text shock produces responses in output and inflation that are qualitatively consistent with workhorse macroeconomic models, whereas changes in FFF prices do not.
Abstract: The Federal Open Market Committee (FOMC) claims that their post-meeting statements shift market expectations of future monetary policy. In this paper, I provide evidence supporting this claim. I apply a methodology from computational text analysis to produce a pairwise-statement similarity measure that compares wording between two FOMC statements. This similarity measure documents that FOMC statements have become more similar over time. With an event-study approach, I find that a decrease in the similarity of sequential FOMC statements is correlated with an increase the variation of federal funds rate expectations, calculated from high-frequency fed funds futures prices. This relationship persists even after controlling for changes in the target federal funds rate and Federal Reserve Chair. Standard monetary regressions omit any measure of policy statement texts and are thus biased. Adding the sequential statement similarity measure to a regression of federal funds rate expectations on the target rate accounts for 1.5 times the variation in market expectations. This paper suggests that more detailed text analysis on FOMC statements will improve modeling of monetary policy expectations.
Abstract: Does the Federal Reserve follow a communication rule? We propose a simple framework to estimate communication rules, which we conceptualize as a systematic mapping between the Fed’s expectations of macroeconomic variables and the words they use to talk about the economy. Using text analysis and regularized regressions, we find strong evidence for systematic communication rules that vary over time, with changes in the rule often being associated with changes in the economic environment. We also find that shifts in communication rules increase disagreement among professional forecasters and correlate with monetary policy surprise measures. Our method is general and can be applied to investigate systematic communication in a wide variety of settings.
Abstract: This paper measures seminar dynamics using a replicable, scalable, machine-learning approach and finds a gender-tone gap in economics presentations. We train a deep convolutional neural network to impute labels for gender and tone-of-voice. We apply this to recorded presentations from the 2022 NBER Summer Institute to measure tone at a high frequency, which allows us to provide novel results on how economists interact with each other in talks. We find that female economists are less likely to be spoken to in a positive tone and more likely to be addressed with a serious and stern tone. Female economists are also more likely to speak in a positive tone. Overall, we show that gender differences in economics presentations exist across fields and presentation formats.
"Reputation for Confidence," with Laura Gáti
"Joint Taxation and Labor Supply," with Ross Batzer
"Historical Measures of Market Concentration," with Carola Frydman, Mark (Zhenzhi) He, Jane Olmstead-Rumsey, and Dimitris Papanikolaou
"ECB Communication and its Impact on Financial Markets" by Istrefi, Odendahl, & Sestieri [Discussion Slides]
Central Bank Communications: Theory and Practice Conference 2024 (FRB Cleveland, OH)
"Estimating the Effects of Political Pressure on the Fed: A Narrative Approach with New Data," by Drechsel [Discussion Slides]
NBER Monetary Economics Spring Workshop 2024 (Chicago, IL)
"Policymakers' Uncertainty," by Cieslak, Hansen, McMahon & Xiao [Discussion Slides]
Western Finance Association 2023 (San Francisco, CA)