Abstract

We apply recent advances in machine learning to measure Congressmember personality traits using floor speeches from 1996 to 2014. We also demonstrate the superiority of text-based measurement over survey-based measurement by showing that personality traits are correlated with survey response rates for members of Congress. Finally, we provide one empirical application showcasing the importance of personality on congressional behavior.