Political Diversity in U.S. Police Agencies Journal Version
American Journal of Political Science (2025)
Bocar Ba, Haosen Ge, Jacob Kaplan, Dean Knox, Mayya Komisarchik, Gregory Lanzalotto, Rei Mariman, Jonathan Mummolo, Roman Rivera, and Michelle Torres
Partisans are increasingly divided on policing policy, which may affect officer behavior. Wemergerosters from 99 of the 100 largest local U.S. agencies—over one third of local law enforcement nationwide—with voter files to study police partisanship. Police skew more Republican than their jurisdictions, with notable exceptions. Using fine-grained data in Chicago and Houston, we compare behavior by Democratic and Republican officers facing commoncircumstances. Overall, wefindfewpartisandifferences after correcting for multiple comparisons. But consistent with prior work, we find Black officers make fewer stops and arrests in Chicago, and they use force less in both cities. Comparing same-race Democratic and Republican officers, we find only that White Democrats make more violent-crime arrests than White Republicans in Chicago. Our results suggest that despite Republicans’ preference for more punitive law enforcement policy and their overrepresentation in policing, partisan divisions do not translate into detectable differences in on-the-ground enforcement.

Action vs. Attention Signals for Human-AI Collaboration: Evidence from Chess SSRN
Stefanos Poulidis; Haosen Ge; Hamsa Bastani; Osbert Bastani
Machine learning is increasingly employed to support human decision-makers by offering algorithmic advice in high-stakes domains such as healthcare, law, and finance. While most prior work has studied action signals, which recommend specific actions to decision-makers, many practical implementations actually rely on attention signals, which flag key decisions but do not prescribe a course of action. While superficially similar, attention signals provide very different kind of information to the decision-maker—e.g., in hospitals, attention signals may trigger upon encountering high-risk patients, while action signals may suggest specific treatments for those patients. We study the impact of action and attention signals on human decision-making via an extensive behavioral experiment in the context of chess, a challenging and well-studied decision-making problem. We find that both signal types can effectively improve decision-making, with attention signals achieving at least 40% of the benefits of action signals. More interestingly, action and attention signals improve performance through very different mechanisms. Action signals improve decision-making only in the specific states where they are provided. However, they can also guide decision-makers into "uncharted waters," where they are unsure how to make effective decisions, thereby degrading performance. In contrast, attention signals, while requiring human effort to be effective, improve decision-making quality not only in states where they are given, but also have positive spillovers to subsequent states. Our findings have significant implications for the deployment of algorithmic signals to improve decision-making in practice.

Measuring Regulatory Barriers Using Annual Reports of Firms Journal Version
Asian Review of Political Economy (2024)
Haosen Ge
 ​Existing studies show that regulation is a major barrier to global economic integration. Nonetheless, identifying and measuring regulatory barriers remains a challenging task for scholars. I propose a novel approach to quantify regulatory barriers at the country-year level. Utilizing information from annual reports of publicly listed companies in the U.S., I identify regulatory barriers business practitioners encounter. The barrier information is first extracted from the text documents by a cutting-edge neural language model trained on a hand-coded training set. Then, I feed the extracted barrier information into a dynamic item response theory model to estimate the numerical barrier level of 40 countries between 2006 and 2015 while controlling for various channels of confounding. I argue that the results returned by this approach should be less likely to be contaminated by major confounders such as international politics. Thus, they are well-suited for future political science research.