Gender and Tone in Recorded Economics Presentations: Audio Analysis with Machine Learning

44 Pages Posted: 3 Jan 2023 Last revised: 21 Mar 2023

See all articles by Amy Handlan

Amy Handlan

Brown University

Haoyu Sheng

Brown University - Department of Economics

Date Written: January 1, 2023

Abstract

This paper studies gender and tone in economics presentations using a replicable,
scalable, machine-learning approach. We train a deep convolutional neural network to
impute labels for gender, age, and tone-of-voice. We apply this to recorded presentations
from the 2022 NBER Summer Institute to measure tone at a high-frequency
level. We find that female economists are more likely to speak in a positive tone and
less likely to be spoken to in a positive tone, even by other women. We find that male
economists are significantly more likely to sound angry or stern compared to female
economists.

Keywords: machine learning, audio analysis, gender, seminar dynamics, tone

JEL Classification: A1, C8, C45

Suggested Citation

Handlan, Amy and Sheng, Haoyu, Gender and Tone in Recorded Economics Presentations: Audio Analysis with Machine Learning (January 1, 2023). Available at SSRN: https://ssrn.com/abstract=4316513 or http://dx.doi.org/10.2139/ssrn.4316513

Amy Handlan (Contact Author)

Brown University ( email )

64 Waterman Street
Providence, RI 02912
United States

Haoyu Sheng

Brown University - Department of Economics ( email )

64 Waterman Street
Providence, RI 02912
United States

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