Borui Sun

• ''The Determinants and Consequences of Political Selection in the Developing World: Evidence from India'' (with Apurva Bamezai, Siddharth George, and M.R. Sharan) 

Draft available here.

• ''Team Expansion and the Costs of Homogeneity: Evidence from Indian Local Governance'' 

Status: Empirical work ongoing.
Abstract. Organizations routinely expand teams to meet growing demands, yet adding members often creates coordination challenges. This paper studies how social diversity shapes the organizational costs of team expansion. I exploit arbitrary voter-population thresholds that determine the number of elected representatives in West Bengal’s Panchayat Samiti councils to estimate the causal effect of expanding governing teams. I find that the average effect of team expansion masks substantial heterogeneity by team composition. Adding an additional representative reduces local infrastructure provision when a Gram Panchayat’s representatives are from the same caste, but weakly improves provision when representatives are from different castes. These results suggest that diversity can mitigate, and potentially reverse, the organizational costs of larger teams. Consistent with this interpretation, the negative effects of expansion in homogeneous teams are accompanied by greater inequality in infrastructure allocation, with benefits becoming increasingly concentrated among already-favored groups. To identify the mechanisms underlying these patterns, I develop a complementary lab-in-the-field experiment with elected representatives that separately tests the roles of collusion, information aggregation, and credit attribution. Together, the project provides new evidence on how team composition shapes organizational performance and the design of representative institutions.  

• "Reducing Inequality Through Efficient Policing" (with Erich Battistin, and Richard Fletcher Hahn)

Status: Empirical work ongoing.
Abstract. This project studies the causal impact of routine police deployment decisions on crime and public safety inequality. The central question is how within-city shifts in patrol intensity affect crime rates at a fine geographic scale. We exploit a natural experiment created by the redrawing of Police Service Area (PSA) boundaries in Washington, DC, which induced quasi-random variation in police presence at the census block level. Unlike prior work focusing on citywide staffing levels or short-lived localized interventions such as hotspot policing, we examine routine structural changes in patrol allocation in a jurisdiction notably under-studied in the crime literature. Our research design draws on three sources of variation: which blocks were reassigned to new PSAs; temporal changes in block-level crime vulnerability; and algorithmic redistricting rules governing size balance and geographic contiguity. We construct the ex-ante reassignment probability for each census block from these exogenous rules, and compare blocks with similar reassignment likelihoods (some reassigned, others not) to estimate the causal effect of PSA change on subsequent crime outcomes. We develop a spatial equilibrium model of criminal behavior, estimated using two sources of granular longitudinal data: public incident-level police reports and measures of patrol intensity derived from anonymized GPS data from officer smartphones. Together, these allow us to simulate welfare-relevant counterfactuals under alternative deployment strategies. The findings are intended to inform place-based public policy and the equitable deployment of limited policing resources.

• ''Algorithmic Bias and Gender Pay Gaps in Digital Labor Markets'' (with Gaurav Chiplunkar, Erin Kelley, and Greg Lane)

Status: Empirical work ongoing.
Abstract. The rise of gig economy platforms has sparked debate on whether algorithmic hiring practices mitigate or exacerbate gender pay disparities. Using data from a leading Indian job portal, we document that algorithmic bias reinforces existing gender wage gaps: employers offer lower salaries to women, and the platform's algorithm systematically recommends lower-paying jobs to them. To address this, we conduct a randomized experiment modifying the platform's wage suggestion algorithm. Employers nudged by the revised algorithm offered higher salaries to female workers without increasing hiring requirements, and treated vacancies attracted higher-quality applicants. Our findings demonstrate how small algorithmic adjustments can mitigate pay disparities in digital labor markets.

• ''Washington, D.C.'s Crime Decline and Its Lessons for American Policing'' (with Erich Battistin, Richard Fletcher Hahn, and Samantha Pérez-Dávila). Niskanen Center Report, June 2026.  Media Coverage: Washington Post, New York Times, NPR, The Atlantic, USA Today, The Hill, The Independent, Fox 5 DC, among others.