CDS Faculty Fellow Bryant J Moy receives Susan Clarke Young Scholars Award from the American Political Science Association
CDS Faculty Fellow Bryant J Moy received the Susan Clarke Young Scholars Award from the Urban and Local Politics Section of the American Political Science Association (APSA). The honor recognizes scholars who have completed their PhD within the last three years and have a promising future in the study of urban politics. In addition, Moy recently presented a poster “Racial Threat and Criminal Activity Nuisance Ordinances” at the Society for Political Methodology where he previously earned a Graduate Student Best Poster Award for his paper on “Responsiveness in a Fragmented Local Politics.”
Moy is an American politics scholar interested in local politics, race, and data science. In addition to joining CDS this July, Moy will be starting a new role as a Visiting Assistant Professor of Politics at NYU this fall and will go on to join the Department of Politics in 2024 as Assistant Professor. Moy graduated in 2022 with a PhD in Political Science from Washington University in St. Louis and has earned bachelor’s and master’s degrees in political science from Arkansas State University. We caught up with him to discuss his recent research, the intersection of machine learning and the study of politics, and how his work will impact the future of data science.
Congratulations on your recent presentation at the Society for Political Methodology! What initially sparked your interest in this particular topic?
I presented a project on the emergence of discriminatory housing ordinances. Specifically, I looked at a housing ordinance called criminal activity nuisance ordinances. These policies require landlords to evict residents if they interact with the criminal justice system. Local governments have disproportionately used these policies on people of color. I want to understand where these policies emerge and whether we have social science theories that would predict their emergence.
For multi-racial democracies to survive, there must be social and legal equality. This is true for national governments and local governments. We are learning more about how local governments in the United States create and perpetuate injustice and inequality, particularly through their housing policies.
Can you tell us one thing that you learned in your research that surprised you?
I was surprised by how well the data fit the theoretical expectations. The theory suggests that discriminatory policies should not emerge in places with a low level of minorities. But as the minority population approaches 50%, we should see a significant increase in discriminatory policy adoption. Conversely, after 50%, we should see these policies cease to emerge, suggesting that the minority population is politically powerful enough to protect their interest. This non-linear relationship shows up in the data as predicted.
You recently received the Susan Clarke Young Scholars Award from the Urban and Local Politics Section of the American Political Science Association. Through your research how have you seen the field of data science benefiting research in politics and other social fields?
I see data science becoming an essential part of the study of politics, especially local and urban politics. I think data science benefits research in politics because we can start to build and test more complex theories. We no longer need to assume that our parameters have linear and constant effects. Thus, with machine learning, we can start to build and test more complex social scientific theories and extract new insights about the world.
I hope winning the Susan Clarke Young Scholar Award will amplify my upcoming research into local politics and show others that data science, machine learning, and politics can be intertwined to benefit the discipline.
What impact do you hope your work will have on the future of data science?
I largely see data science as adding value to the enterprise of scientific discovery and inference. I want data scientists to continue tackling more social science problems, like understanding the cause and effect of discriminatory policies. I hope this research sparks interest from data science practitioners to use their skills for social good.
For more information on Moy, read our introduction on the CDS blog.
By Meryl Phair