Reconsidering the Benefits of Minority Cohesion: The Difference Between Relative and Absolute Cohesion in the U.S. House of Representatives

Cohesion has long been a topic of interest for those studying minorities in the U.S. Congress. Generally, it is thought that cohesion gives minorities an advantage in legislative bargaining, which ultimately helps them influence the legislative process. However, this only assumes absolute not relative cohesion. When minorities work more with each other they are less able to work with those outside their group. This makes legislative influence more difficult for minorities. By examining sponsorship-cosponsorship networks from the 97th-103rd Congresses, I show that cohesion actually hurts the ability of minorities to influence the legislative process. This affect is felt differently for minority “leaders” and “followers,” suggesting that cohesion benefits some more than others

Seeing racial avoidance on New York City streets

Here, using publicly available traffic camera feeds in combination with a real-world field experiment, we examine how pedestrians of different races behave in the presence of racial out-group members. Across two different New York City neighbourhoods and 3,552 pedestrians, we generate an unobtrusive, large-scale measure of inter-group racial avoidance by measuring the distance individuals maintain between themselves and other racial groups. We find that, on average, pedestrians in our sample (93% of whom were phenotypically non-Black) give a wider berth to Black confederates, as compared with white non-Hispanic confederates

Stand up and be counted: Using traffic cameras to assess voting behavior in real time

Despite their ubiquity, few have used traffic camera networks for social science research. Using 1,312,977 images collected from 768 London-based cameras leading up to the 2015 UK general election, this study not only demonstrates how traffic camera data can be used to effectively measure same-day turnout, but we also provide ways such data can be used to assess political behavior more broadly. Such automated enumeration is especially important in countries where official results are only returned for the current election, making it difficult for those interested in assessing turnout at lower levels of aggregation, even when those elections are next on the calendar. Although we are not the first to suggest the value of images-as-data, this study hopes to underline the importance of video-as-data, while simultaneously offering an important foundation for future research

Emotional Arousal Predicts Voting on the U.S. Supreme Court

Do judges telegraph their preferences during oral arguments? Using the U.S. Supreme Court as our example, we demonstrate that Justices implicitly reveal their leanings during oral arguments, even before arguments and deliberations have concluded. Specifically, we extract the emotional content of over 3,000 hours of audio recordings spanning 30 years of oral arguments before the Court. We then use the level of emotional arousal, as measured by vocal pitch, in each of the Justices’ voices during these arguments to accurately predict many of their eventual votes on these cases. Our approach yields predictions that are statistically and practically significant and robust to including a range of controls; in turn, this suggests that subconscious vocal inflections carry information that legal, political, and textual information do not

Replication Data for: Pitch Perfect: Vocal Pitch and the Emotional Intensity of Congressional Speech on Women

All files needed to replicate figures and tables for "Pitch Perfect: Vocal Pitch and the Emotional Intensity of Congressional Speech on Women" by Bryce J. Dietrich, Matthew Hayes, and Diana Z. O'Brien. Please refer to README files for descriptions of each file and variable

Gender, language, and representation in the United States Senate

We explore how gendered language in Senate floor debates evolves between the 101st and 109th sessions (N=229,526 speeches). We hypothesize that female Senators speak like women in the general population, that their speeches focus on traditionally designated women\u27s issues, and that they use female linguistic strategies found in the general population when discussing low politics or women\u27s issues. We also expect women to speak like legislators, adopting more male linguistic approaches for high politics issues or in election year speeches and for female senators to use more male linguistics as time served in the Senate increases. Using a suite of computational linguistics approaches such as topic modeling (Latent Dirichlet Allocation), syntax and semantic analysis (Coh-Metrix), and sentiment analysis (LIWC), our analyses highlight the distinct roles of women speaking for women (e.g. promoting issues like education or healthcare), women speaking like women (e.g. using personal pronouns), and women speaking as Senators