Hello! I am a labor economist studying the relationship between human capital accumulation & inequality and how social ties influence decision-making, especially in the contexts of crime and education. I recently completed my PhD in Economics at the University of Michigan. Prior to the PhD, I worked on projects involving macroeconomics and applied econometrics.
I am currently a Postdoctoral Fellow in the Economics of Crime at the National Bureau of Economic Research.
Email: nikhrao [at] umich [dot] edu
The Socioeconomic Consequences of Misdemeanor Conviction [Draft]
with James Reeves
Supported by Arnold Ventures and Policy Impacts
Misdemeanors account for three-quarters of criminal cases filed annually in the United States, yet causal evidence on the impacts of misdemeanor conviction is limited. We combine administrative and survey data with two complementary research designs to provide novel evidence of how misdemeanor conviction affects the socioeconomic outcomes of defendants and their families. Event studies around case filing from 11 states show that conviction is associated with persistently worse earnings trajectories than dismissal. We estimate the causal effect of a conviction relative to a charge using seven county reforms that sharply reduced conviction rates. Regression discontinuity design estimates yield precise null average effects of the reforms on criminal and labor market outcomes. Defendants dismissed by the reform are least likely to be harmed by conviction---marginal defendants are older and more likely to be charged with minor crimes than the average defendant. Heterogeneity analyses and cross-reform variation in marginal defendants' characteristics suggest that conviction harms young and first-time defendants. These findings suggest that interventions targeting dismissal toward these groups would be beneficial. Finally, our causal estimates imply a negative Marginal Value of Public Funds of misdemeanor conviction, even under plausible estimates of general deterrence effects.
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 to measure discrimination after adjusting for group differences in unobserved potential outcomes, with extensions to adjust for treatment effects or multiple potential outcomes instead. 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 expands where researchers can measure such discrimination since existing tools need randomly assigned decision-makers. We use our method to study racial discrimination in misdemeanor prosecution, the most common form of contact with the United States criminal court system. 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 potential recidivism. We find suggestive evidence that this pattern is driven by prosecutors dismissing lower quality, resource-intensive cases. Such cases were more prevalent among minority defendants, perhaps due to disparities created in criminal legal decisions before prosecution.
Network Spillovers and the Social Returns to Crime-Reduction Interventions [Draft undergoing disclosure review]
with Ashley Craig, Sara Heller, and Kenneth Hofmeister
Supported by NSF, Russell Sage Foundation, J-PAL, and University of Michigan
This paper estimates how changes in individual criminal behavior spread through social networks. To overcome identification challenges, we combine four existing randomized controlled trials (RCTs) in Chicago that all decreased violence (N=12,042) with multiple administrative measures of social networks prior to randomization (N>2 million). The resulting multiplex network captures co-arrest, co-victimization, shared classes, and shared households. We describe the network, estimate causal spillover effects, and study which social ties matter for criminal behavior. Both the direct effect of treatment and the full social impacts, net of spillovers, are considerably greater than those implied by the original intent-to-treat effects. The results improve our understanding of a set of influential experiments and clarify what the social nature of crime means for how we estimate the effectiveness of crime prevention.
Parental Resources and College Major Choice [Draft]
with Zsigmond Pálvölgyi and Tyler Radler
If parental resources insure children against negative labor market shocks, access to such informal safety nets might influence children’s decisions of what to study in college. This paper examines how unexpected increases in housing prices affect children’s college major choices. We do this by combining a large survey of United States first-year undergraduates with spatial variation in housing demand growth during the 2000s housing boom. Using variation in the size of the structural break in house prices as an instrument for price changes, we find that overall, unexpected increases in house prices induce first-year students to choose majors that earn more. These effects are concentrated among students from areas with low home-ownership rates, consistent with these students primarily facing a cost of living increase. In contrast, an unexpected increase in house prices induces students from areas with high home-ownership rates to choose majors associated with lower earnings. This pattern is consistent with these students responding to a wealth increase by enrolling in majors that lead to lower-paying careers but potentially better amenities.
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
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]
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]