Nikhil Rao

Working Papers

Measuring Discrimination using Natural Experiments [Draft]

with James Reeves

Disparities between social groups (e.g., gender, race) exist at crucial decision points in many contexts, from hiring to policing. These disparities are difficult to interpret when unobservable factors vary by group. To overcome this challenge, we use a binary instrumental variable (IV) strategy that measures discrimination after adjusting for group differences in unobserved potential outcomes. Assumptions on selection behavior recover the distribution of potential outcomes for each group, which we directly condition on to bound or point identify discrimination. This approach broadens the set of opportunities to measure such discrimination since existing tools rely on randomly assigned decision-makers. We illustrate our methodology through studying racial discrimination in misdemeanor prosecution, the most common form of contact with the criminal court system in the United States. We use a difference-in-difference IV strategy generated by a budget cut that reduced prosecution rates in King County, Washington, but did not affect adjacent counties. Before the cut, we find no evidence of discrimination in prosecution conditional on unobserved potential recidivism. After the cut, minority defendants became less likely to be prosecuted than white defendants with similar unobserved potential recidivism. We find suggestive evidence that cases involving minority defendants were more likely to be lower quality, potentially due to disparities created in previous stages of the criminal legal system (e.g., arrests), and that prosecutors responded to the budget cut by focusing on high quality, less resource-intensive cases.

In Progress

Using Network Data to Measure Social Returns and Improve Targeting of Crime-Reduction Interventions 

with Ashley Craig and Sara Heller

Funded by NSF, Russell Sage Foundation, J-PAL, and University of Michigan

[Slides: Spillovers via multiple networks and RCTs]  [Preliminary note on co-arrest spillovers in the READI Chicago RCT] 

Randomized controlled trials (RCTs) have been influential in shaping policy to address the stark racial and income disparities in the criminal legal system. Yet crime-prevention experiments typically ignore the possibility of peer spillovers, which could misrepresent interventions’ net effects in either direction. To estimate how changes in individual criminal behavior spread, we combine four existing RCTs in Chicago (N ≈ 12,000) with multiple administrative measures of pre-randomization social networks (N ≈ 2 million). These networks capture co-arrest, co-victimization, shared classes, shared households, and shared neighborhoods. We explore which social ties matter for criminal behavior, quantify intervention spillovers through these networks, and re-assess the net effects of the original interventions. Using our reduced-form estimates and information on how network formation responds to treatment, future work will build a model of crime propagation and explore optimal targeting strategies. The results, which are still in progress, will improve our understanding of a set of influential experiments (and potentially many other RCTs), expand our knowledge of how people affect each other's criminal decision-making, and provide guidance to policymakers about how to leverage peer effects to maximize future program impacts.

Parental Resources and College Major Choice 

with Zsigmond Pálvölgyi and Tyler Radler

Since parental resources have been shown to informally insure against labor market risks, resources may influence human capital investment decisions and play a role in propagating intergenerational inequality. In particular, greater parental resources might induce children to pursue fields of study or occupations with greater labor market risks, steeper earnings profiles or better amenities. We investigate how parental housing wealth affects the college major choice of their children by combining surveys of United States undergraduate students from the Higher Education Research Institute and spatial variation in housing demand growth between 2000 and 2006. We find that students entering four-year colleges during periods of booming housing demand are more likely to choose majors with greater earnings risk and higher initial investment, and are more likely to report non-pecuniary factors as key reasons for major choice. Importantly, we find limited evidence of selection into four-year college enrollment. We are working on linking these first-year student responses to their end of college responses to investigate whether the effects on major choice persist until graduation, and if they impact occupation choice.

Peer Socioeconomic Mismatch, Student Expectations, and Achievement

with Micah Y. Baum

In this paper we examine how peers' socio-economic backgrounds affect the educational outcomes, experiences, and human capital investment decisions of low-income college students. Using random assignment of first-year room-mates and administrative student records, we find that low-income first-year students randomly paired with very high-income room-mates perform worse in terms of GPA than low-income students randomly paired with other types of room-mates, even after two years in college. To understand more about these patterns, we developed and piloted a survey of first-year undergraduate students. We find that low-income students spend relatively little time with their randomly assigned high-income room-mates and are less likely to report that the room-mate has helped them socially, academically, or with their career goals. Going forward, we are expanding the survey to further investigate mechanisms.

Examining Interventions to Improve Outcomes of Justice-involved Individuals, FSRDC Project #2957

with James Reeves

Publications

Inflation Expectations and Nonlinearities in the Phillips Curve

with Alexander Doser, Ricardo Nunes and Viacheslav Sheremirov

Journal of Applied Econometrics 38:4 (2023), 453-471. [Paper] [Published version]

Online Appendix

Sectoral inflation and the Phillips curve: What has changed since the Great Recession? 

with Maria Jose Luengo-Prado and Viacheslav Sheremirov

Economics Letters 172 (2018), 63–68. [Paper] [Published version]