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.
Abstract. The competence and representativeness of a political class are widely seen as defining features of democratic quality. Yet, whether the two can be achieved jointly in developing democracies is an open question. We address this question by linking administrative records on 97 million rural citizens in Bihar, India, to nearly one million candidates contesting village council elections, providing the first large-scale evidence on political entry, selection, and governance. We show that while candidates are drawn from a socioeconomically elite subset of society, they are strongly positively selected on competence. Within this pool, however, a sharp trade-off emerges: winners are more competent yet less representative than the candidates they defeat, while candidates for higher-tier offices are more competent yet less representative than those contesting lower tiers. Using close elections, we show that this trade-off has consequences for governance: more competent leaders have better knowledge of governance rules, boast wider bureaucratic networks and implement skill-intensive programs more effectively, but align government spending less closely with marginalized groups. Finally, using a regression discontinuity design, we show that affirmative action attenuates the trade-off: political reservations in favour of marginalized Scheduled Castes (SCs) improve both competence and representativeness. On the other hand, gender-based reservations democratize the candidate pool by drawing women from less elite households. Our findings identify a fundamental tension in political selection in developing democracies, while showing that institutional design can mitigate its costs.

• ''Team Expansion and the Costs of Homogeneity: Evidence from Indian Local Governance'' (Job Market Paper)
  Status: Manuscript in preparation.
  Abstract. 
• "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, NPR, The Atlantic, USA Today, The Hill, The Independent, Fox 5 DC, among others.