Faculty Bibliography

A longstanding and influential view in U.S. correctional policy is that "nothing works" when it comes to rehabilitating incarcerated individuals. We re-examine this hypothesis by studying an innovative law-enforcement-led program recently launched in the county jail of Flint, Michigan: Inmate Growth Naturally and Intentionally Through Education (IGNITE). We develop a new instrumental variable approach to estimate the effects of IGNITE exposure, leveraging quasi-random court delays that cause individuals to spend more time in jail both before and after the program's launch. We find that IGNITE exposure dramatically reduces both within-jail misconduct and post-release recidivism. Qualitative evidence suggests a cultural change within the jail as a key mechanism.

We study the labor market impacts of retroactively reducing felonies to misdemeanors in San Joaquin County, CA, where criminal justice agencies implemented Proposition 47 reductions in a quasi-random order, without requiring input or action from affected individuals. Linking records of reductions to administrative tax data, we find employment benefits for individuals who (likely) requested their reduction, consistent with selection, but no benefits among the larger subset of individuals whose records were reduced proactively. A field experiment notifying a subset of individuals about their proactive reduction also shows null results, implying that lack of awareness is unlikely to explain our findings.

The U.S. Constitution does not guarantee a right to health care. Yet since 1976, the Supreme Court has held that deliberate indifference to the serious medical needs of incarcerated people — a population that is disproportionately sick, poor, and from marginalized racial and ethnic groups — violates the Eighth Amendment’s prohibition against cruel and unusual punishment. What this right means in practice, however, is far from settled, given that the standards for “deliberate indifference” and “serious medical need” are subject to judicial interpretation. Lacking quality standards, robust monitoring, and funding from public medical insurance programs, correctional administrators must provide health care for incarcerated people with limited guidance and often scarce resources. Incarcerated people have little recourse for woefully inadequate medical care except litigation, but they face multiple barriers to accessing the legal system and counsel, and rare wins yield only incremental relief. In the wake of Dobbs v. Jackson Women’s Health Organization, it is particularly important to elucidate the relevant legal landscape and explore mechanisms for safeguarding the constitutional right to health care in correctional facilities.

Human decision-makers frequently override the recommendations generated by predictive algorithms, but it is unclear whether these discretionary overrides add valuable private information or reintroduce the human biases and mistakes that motivated the adoption of the algorithms in the first place. We develop new quasi-experimental tools to measure the impact of human discretion over an algorithm, even when the outcome of interest is only selectively observed, in the context of bail decisions. We find that 90% of the judges in our setting generally underperform the algorithm when making a discretionary override, with most judges making override decisions that are no better than random. Yet the remaining 10% of judges outperform the algorithm in terms of both accuracy and fairness when making a discretionary override. We provide suggestive evidence on the behavior underlying these differences in judge performance, showing that the high-performing judges are more likely to use relevant private information and less likely to overreact to highly-salient events compared to the low-performing judges.

In this article, we review a growing empirical literature on the effectiveness and fairness of the US pretrial system and discuss its policy implications. Despite the importance of this stage of the criminal legal process, researchers have only recently begun to explore how the pretrial system balances individual rights and public interests. We describe the empirical challenges that have prevented progress in this area and how recent work has made use of new data sources and quasiexperimental approaches to credibly estimate both the individual harms (such as loss of employment or government assistance) and public benefits (such as preventing non-appearance at court and new crimes) of cash bail and pretrial detention. These new data and approaches show that the current pretrial system imposes substantial short- and long-term economic harms on detained defendants in terms of lost earnings and government assistance, while providing little in the way of decreased criminal activity for the public interest. Non-appearances at court do significantly decrease for detained defendants, but the magnitudes cannot justify the economic harms to individuals observed in the data. A second set of studies shows that these costs of cash bail and pretrial detention are disproportionately borne by Black and Hispanic individuals, giving rise to large and unfair racial differences in cash bail and detention that cannot be explained by underlying differences in pretrial misconduct risk. We then turn to policy implications and describe areas of future work that would enable a deeper understanding of what drives these undesirable outcomes.

We measure the economic costs of the US pretrial system using several complementary approaches and data sources. The pretrial system operates as one of the earliest points of entry in the criminal justice system. It typically represents an individual’s first opportunity to be incarcerated, potentially leading to subsequent long-term damage in the form of family separation, work interruption, loss of housing, and so on. We find that individuals lose almost $30,000 in forgone earnings and social benefits when detained in jail while awaiting the resolution of their criminal cases. These adverse consequences are also present in aggregate measures of economic well-being, with increases in county pretrial detention rates associated with increases in poverty rates and decreases in employment rates. Counties with high levels of pretrial detention also exhibit significantly lower levels of intergenerational mobility among children, consistent with pretrial detention having an adverse impact on young children who may be the dependents of individuals affected by the pretrial system.

In this paper, we provide a new statistical and legal framework to understand the legality and fairness of predictive algorithms under the Equal Protection Clause. We begin by reviewing the main legal concerns regarding the use of protected characteristics such as race and the correlates of protected characteristics such as criminal history. The use of race and nonrace correlates in predictive algorithms generates direct and proxy effects of race, respectively, that can lead to racial disparities that many view as unwarranted and discriminatory. These effects have led to the mainstream legal consensus that the use of race and nonrace correlates in predictive algorithms is both problematic and potentially unconstitutional under the Equal Protection Clause. This mainstream position is also reflected in practice, with all commonly used predictive algorithms excluding race and many excluding nonrace correlates such as employment and education. Next, we challenge the mainstream legal position that the use of a protected characteristic always violates the Equal Protection Clause. We develop a statistical framework that formalizes exactly how the direct and proxy effects of race can lead to algorithmic predictions that disadvantage minorities relative to non-minorities. While an overly formalistic solution requires exclusion of race and all potential nonrace correlates, we show that this type of algorithm is unlikely to work in practice because nearly all algorithmic inputs are correlated with race. We then show that there are two simple statistical solutions that can eliminate the direct and proxy effects of race, and which are implementable even when all inputs are correlated with race. We argue that our proposed algorithms uphold the principles of the equal protection doctrine because they ensure that individuals are not treated differently on the basis of membership in a protected class, in stark contrast to commonly used algorithms that unfairly disadvantage minorities despite the exclusion of race. We conclude by empirically testing our proposed algorithms in the context of the New York City pretrial system. We show that nearly all commonly used algorithms violate certain principles underlying the Equal Protection Clause by including variables that are correlated with race, generating substantial proxy effects that unfairly disadvantage Black individuals relative to white individuals. Both of our proposed algorithms substantially reduce the number of Black defendants detained compared to commonly used algorithms by eliminating these proxy effects. These findings suggest a fundamental rethinking of the equal protection doctrine as it applies to predictive algorithms and the folly of relying on commonly used algorithms.

In Arnold, Dobbie, and Yang (2018, ADY), we find that marginally released white defendants have higher rates of pre-trial misconduct than marginally released black defendants. We interpret these findings as evidence of racial bias against black defendants through the lens of the marginal outcome test originally developed by Becker (1957). Canay, Mogstad, and Mountjoy (2020, CMM) question the interpretation of our empirical findings and the logical validity of the marginal outcome test. However, CMM’s conclusions are based on an incomplete definition of racial bias that is different from the one used in ADY. Under ADY’s definition of bias, the marginal outcome test is logically valid and a useful tool for studying discrimination in real-world settings.

There are economically large costs of pretrial detention—and, by extension, the use of cash bail—due to the significant collateral consequences of having a criminal conviction on labor market outcomes as well as the direct costs of pretrial detention. In contrast, there are relatively small benefits to pretrial detention due to the relatively low costs of apprehending defendants who fail to appear in court. The existing evidence also suggests that the current pretrial system contribute to inequalities and inefficiencies in the criminal justice system.

Racial and gender disparities are prevalent in the criminal justice system, but the sources of these disparities remain largely unknown. This paper investigates whether judge political affiliation contributes to these disparities using data on over 500,000 federal defendants linked to sentencing judge. Exploiting random case assignment, we find that Republican appointed judges sentence black defendants to 3.0 more months than similar non-blacks and female defendants to 2.1 fewer months than similar males, compared to Democratic appointed judges. Disparities by judge political affiliation cannot be explained by other judge characteristics and grow substantially larger when judges are granted more discretion.

This paper develops a new test for identifying racial bias in the context of bail decisions – a high-stakes setting with large disparities between white and black defendants. We motivate our analysis using Becker's (1957) model of racial bias, which predicts that rates of pre-trial misconduct will be identical for marginal white and marginal black defendants if bail judges are racially unbiased. In contrast, marginal white defendants will have a higher probability of misconduct than marginal black defendants if bail judges are racially biased against blacks. To test the model, we develop a new estimator that uses the release tendencies of quasi-randomly assigned bail judges to identify the relevant race-specific misconduct rates. Estimates from Miami and Philadelphia show that bail judges are racially biased against black defendants, with substantially more racial bias among both inexperienced and part-time judges. We also find that both black and white judges are biased against black defendants. We argue that these results are consistent with bail judges making racially biased prediction errors, rather than being racially prejudiced per se.

This paper explores the impact of fear on the incomplete take-up of safety net programs in the United States. We exploit changes in deportation fear due to the roll-out and intensity of Secure Communities (SC), an immigration enforcement program that empowers the federal government to check the immigration status of anyone arrested by local police, leading to the forcible removal of approximately 380,000 immigrants. We estimate the spillover effect of SC on the take-up of federal means-tested programs by Hispanic citizens. Though not at personal risk of deportation, Hispanic citizens may fear their participation could expose non-citizens in their network to immigration authorities. We find significant declines in SNAP and ACA enrollment, particularly among mixed-citizenship status households and in areas where deportation fear is highest. The response is muted for Hispanic households residing in sanctuary cities. Our results are most consistent with network effects that perpetuate fear rather than lack of benefit information or stigma.

Few decisions in the criminal justice process are as consequential as the determination of bail. Indeed, recent empirical research finds that pre-trial detention imposes substantial long-term costs on defendants and society. Defendants who are detained before trial are more likely to plead guilty, less likely to be employed, and less likely to access social safety net programs for several years after arrest. Spurred in part by these concerns, critics of the bail system have urged numerous jurisdictions to adopt bail reforms, which have led to growing momentum for a large-scale transformation of the bail system. Yet supporters of the current system counter that pre-trial detention reduces flight and pre-trial crime—recognized benefits to society—by incapacitating defendants. Despite empirical evidence in support of both positions, however, advocates and critics of the current bail system have generally ignored the real trade-offs associated with detention. This Article provides a broad conceptual framework for how policymakers can design a better bail system by weighing both the costs and benefits of pre-trial detention—trade-offs that are historically grounded in law, but often disregarded in practice. I begin by presenting a simple taxonomy of the major categories of costs and benefits that stem from pre-trial detention. Building from this taxonomy, I conduct a partial cost-benefit analysis that incorporates the existing evidence, finding that the current state of pre-trial detention is generating large social losses. Next, I formally present a framework that accounts for heterogeneity in both costs and benefits across defendants, illustrating that detention on the basis of “risk” alone can lead to socially suboptimal outcomes. In the next part of the Article, I present new empirical evidence showing that a cost-benefit framework has the potential to improve accuracy and equity in bail decision-making, where currently bail judges are left to their own heuristics and biases. Using data on criminal defendants and bail judges in two urban jurisdictions, and exploiting variation from the random assignment of cases to judges, I find significant judge differences in pre-trial release rates, the assignment of money bail, and racial gaps in release rates. While there are any number of reasons why judges within the same jurisdiction may vary in their bail decisions, these results indicate that judges may not be all setting bail at the socially optimal level. The conceptual framework developed in this Article also sheds light on the ability of recent bail reforms to increase social welfare. While the empirical evidence is scant, electronic monitoring holds promise as a welfare-enhancing alternative to pre-trial detention. In contrast, application of the conceptual framework cautions against the expanding use of risk-assessment instruments. These instruments, by recommending the detention of high-risk defendants, overlook the possibility that these high-risk defendants may also be “high-harm” such that they are most adversely affected by a stay in jail. Instead, I recommend that jurisdictions develop “net benefit” assessment instruments by predicting both risk and harm for each defendant in order to move closer toward a bail system that maximizes social welfare.

Under the Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA) of 1996, individuals convicted of drug-related felonies were permanently banned from receiving welfare and food stamps. Since then, over 30 states have opted out of the federal ban. In this paper, I estimate the impact of public assistance eligibility on recidivism by exploiting both the adoption of the federal ban and subsequent passage of state laws that lifted the ban. Using administrative prison records on five million offenders and a triple-differences research design, I find that public assistance eligibility for drug offenders reduces one-year recidivism rates by 10 percent.

This paper estimates the effect of Chapter 13 bankruptcy protection on post-filing financial outcomes using a new dataset linking bankruptcy filings to credit bureau records. Our empirical strategy uses the leniency of randomly-assigned judges as an instrument for Chapter 13 protection. Over the first five post-filing years, we find that Chapter 13 protection decreases an index measuring adverse financial events such as civil judgments and repossessions by 0.316 standard deviations, increases the probability of being a homeowner by 13.2 percentage points, and increases credit scores by 14.9 points. Chapter 13 protection has little impact on open unsecured debt, but decreases the amount of debt in collections by $1,315.

This paper estimates the impact of local labor market conditions on criminal recidivism using rich administrative prison records on over four million offenders released from 43 states between 2000 and 2013. Exploiting each offender’s exact date of release, I find that being released to a county with higher low-skilled employment and higher average low-skilled wages significantly decreases the risk of recidivism. The impact of higher wages on recidivism is larger for both black offenders and first-time offenders, and in sectors that report being more willing to hire ex-offenders. These results are robust to individual and county-level controls, policing and corrections activity, and do not appear to be driven by changes in the composition of released offenders during good or bad economic times.

The Federal Sentencing Guidelines were promulgated in response to concerns of widespread disparities in sentencing. After almost two decades of determinate sentencing, the Guidelines were rendered advisory in United States v. Booker. How has greater judicial discretion affected interjudge disparities, or differences in sentencing outcomes that are attributable to the mere happenstance of the sentencing judge assigned? This Article utilizes new data covering almost 400,000 criminal defendants linked to sentencing judges to undertake the first national empirical analysis of interjudge disparities after Booker. The results are striking: Interjudge sentencing disparities have doubled since the Guidelines became advisory. Some of the recent increase in disparities can be attributed to differential sentencing behavior associated with judge demographic characteristics, with Democratic and female judges being more likely to exercise their enhanced discretion after Booker. Newer judges appointed post-Booker also appear less anchored to the Guidelines than judges with experience sentencing under the mandatory Guidelines regime. Disentangling the effects of various actors on sentencing disparities, I find that prosecutorial charging is likely a prominent source of disparities. Rather than charging mandatory minimums uniformly across eligible cases, prosecutors appear to selectively apply mandatory minimums in response to the identity of the sentencing judge, potentially through superseding indictments. Drawing on this empirical evidence, this Article suggests that recent sentencing proposals calling for a reduction in judicial discretion in order to reduce disparities may overlook the substantial contribution of prosecutors.