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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 quasi-experimental 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 that the 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 develop new quasi-experimental tools to measure racial discrimination, due to either racial bias or statistical discrimination, in the context of bail decisions. We show that the omitted variables bias in observational release rate comparisons can be purged by using the quasi-random assignment of judges to estimate average race-specific misconduct risk. We find that approximately two-thirds of the average release rate disparity between white and Black defendants in New York City is due to racial discrimination. We then develop a hierarchical marginal treatment effects model to study the drivers of discrimination, finding evidence of both racial bias and statistical discrimination. Outcome-based tests of racial bias therefore omit an important source of racial discrimination in bail decisions, and cannot be used to rule out all possible violations of U.S. anti-discrimination law.

There is growing concern that the rise of algorithmic decision-making can lead to discrimination against legally protected groups, but measuring such algorithmic discrimination is often hampered by a fundamental selection challenge. We develop new quasi-experimental tools to overcome this challenge and measure algorithmic discrimination in the setting of pretrial bail decisions. We first show that the selection challenge reduces to the challenge of measuring four moments: the mean latent qualification of white and Black individuals and the race-specific covariance between qualification and the algorithm’s treatment recommendation. We then show how these four moments can be estimated by extrapolating quasi-experimental variation across as-good-as-randomly assigned decision-makers. Estimates from New York City show that a sophisticated machine learning algorithm discriminates against Black defendants, even though defendant race and ethnicity are not included in the training data. The algorithm recommends releasing white defendants before trial at an 8 percentage point (11 percent) higher rate than Black defendants with identical potential for pretrial misconduct, with this unwarranted disparity explaining 77 percent of the observed racial disparity in algorithmic recommendations. We find a similar level of algorithmic discrimination with regression-based recommendations, using a model inspired by a widely used pretrial risk assessment tool. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Credit reports are used in nearly all consumer lending decisions and, increasingly, in hiring decisions in the labor market, but the impact of a bad credit report is largely unknown. We study the effects of credit reports on financial and labor market outcomes using a difference-in-differences research design that compares changes in outcomes over time for Chapter 13 filers, whose personal bankruptcy flags are removed from credit reports after 7 years, to changes for Chapter 7 filers, whose personal bankruptcy flags are removed from credit reports after 10 years. Using credit bureau data, we show that the removal of a Chapter 13 bankruptcy flag leads to a large increase in credit scores, and an economically significant increase in credit card balances and mortgage borrowing. We study labor market effects using administrative tax records linked to personal bankruptcy records. In sharp contrast to the credit market effects, we estimate a precise zero effect of flag removal on employment and earnings outcomes. We conclude that credit reports are important for credit market outcomes, where they are the primary source of information used to screen applicants, but are of limited consequence for labor market outcomes, where employers rely on a much broader set of screening mechanisms.

In this paper, we show that the design and decentralized, school-based scoring of New York’s high school exit exams – the Regents Examinations – led to the systematic manipulation of test sores just below important proficiency cutoffs. Our estimates suggest that teachers inflate approximately 40 percent of test scores near the proficiency cutoffs. Teachers are more likely to inflate the scores of high-achieving students on the margin, but low-achieving students benefit more from manipulation in aggregate due to the greater density of these students near the proficiency cutoffs. Exploiting a series of reforms that eliminated score manipulation, we find that inflating a student’s score to fall just above a cutoff increases his or her probability of graduating from high school by 27 percent. These results have important implications for educational attainment of marginal high school graduates. For example, we estimate that the black-white graduation gap is about 5 percent larger in the absence of test score manipulation.

We experimentally test several approaches to increasing the demand for workers with a criminal record on a nationwide staffing platform by addressing potential downside risk and productivity concerns. The staffing platform asked hiring managers to make a series of hypothetical hiring decisions that affected whether workers with a criminal record could accept their jobs in the future. We find that 39% of businesses in our sample are willing to work with individuals with a criminal record at baseline, which rises to over 50% when businesses are offered crime and safety insurance, a single performance review, or a limited background check covering just the past year. Wage subsidies can achieve similar increases but at a substantially higher cost. Based on our findings, the staffing platform relaxed the criminal background check requirement and offered crime and safety insurance to interested businesses.

This article 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 model of racial bias, which predicts that rates of pretrial misconduct will be identical for marginal white and marginal black defendants if bail judges are racially unbiased. In contrast, marginal white defendants will have higher rates of misconduct than marginal black defendants if bail judges are racially biased, whether that bias is driven by racial animus, inaccurate racial stereotypes, or any other form of bias. To test the model, we use 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 find suggestive evidence that this racial bias is driven by bail judges relying on inaccurate stereotypes that exaggerate the relative danger of releasing black defendants.

Over 20 percent of prison and jail inmates in the United States are currently awaiting trial, but little is known about the impact of pretrial detention on defendants. This paper uses the detention tendencies of quasi-randomly assigned bail judges to estimate the causal effects of pretrial detention on subsequent defendant outcomes. Using data from administrative court and tax records, we find that pretrial detention significantly increases the probability of conviction, primarily through an increase in guilty pleas. Pretrial detention has no net effect on future crime, but decreases formal sector employment and the receipt of employment- and tax-related government benefits. These results are consistent with (i) pretrial detention weakening defendants' bargaining positions during plea negotiations and (ii) a criminal conviction lowering defendants' prospects in the formal labor market. (JEL J23, J31, J65, K41, K42)

This paper argues that the debt forgiveness provided by the U.S. consumer bankruptcy system helped stabilize employment levels during the Great Recession. We document that over this period, states with more generous bankruptcy exemptions had significantly smaller declines in non-tradable employment and larger increases in unsecured debt write-downs compared to states with less generous exemptions. We interpret these reduced form estimates as the relative effect of debt relief across states, and develop a general equilibrium model to recover the aggregate employment effect. The model yields three key results. First, substantial nominal rigidities are required to rationalize our reduced form estimates. Second, with monetary policy at the zero lower bound, traded good demand spillovers across states boosted employment everywhere. Finally, the ex-post debt forgiveness provided by the consumer bankruptcy system during the Great Recession increased aggregate employment by almost two percent.

This paper tests for bias in consumer lending decisions using administrative data from a high-cost lender in the United Kingdom. We motivate our analysis using a simple model of bias in lending, which predicts that profits should be identical for loan applicants from different groups at the margin if loan examiners are unbiased. We identify the profitability of marginal loan applicants by exploiting variation from the quasi-random assignment of loan examiners. We find significant bias against both immigrant and older loan applicants when using the firm's preferred measure of long-run profits. In contrast, there is no evidence of bias when using a short-run measure used to evaluate examiner performance, suggesting that the bias in our setting is due to the misalignment of firm and examiner incentives. We conclude by showing that a decision rule based on machine learning predictions of long-run profitability can simultaneously increase profits and eliminate bias.

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 non-race 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 non-race 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 non-race correlates such as employment and education. In the second part of the paper, we challenge the mainstream legal position that the use of a protected characteristic always violates the Equal Protection Clause. We first 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 legal solution requires exclusion of race and all potential non-race 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 blacks relative to whites. Both of our proposed algorithms substantially reduce the number of black defendants detained compared to these 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.

We study the drivers of financial distress using a large-scale field experiment that offered randomly selected borrowers a combination of (i) immediate payment reductions to target short-run liquidity constraints and (ii) delayed debt write-downs to target long-run debt constraints. We identify the separate effects of the payment reductions and debt write-downs using variation from both the experiment and cross-sectional differences in treatment intensity. We find that the debt write-downs significantly improved both financial and labor market outcomes despite not taking effect for three to five years. In sharp contrast, there were no positive effects of the more immediate payment reductions. These results run counter to the widespread view that financial distress is largely the result of short-run constraints.

This paper tests for bias in consumer lending using administrative data from a high-cost lender in the United Kingdom. We motivate our analysis using a new principal-agent model of bias where loan examiners are incentivized to maximize a short-term outcome, not long-term profits, leading to bias against illiquid applicants at the margin of loan decisions. We identify the profitability of marginal applicants using the quasi-random assignment of loan examiners, finding significant bias against immigrant and older applicants when using the firm’s preferred measure of long-run profits but not when using the short-run measure used to evaluate examiner performance. In this case, market incentives based on characteristics that vary across groups lead to inefficient group-based bias.

We estimate the causal effects of parental incarceration on children’s short- and long-run out-comes using administrative data from Sweden. Our empirical strategy exploits exogenous varia-tion in parental incarceration from the random assignment of criminal defendants to judges with different incarceration tendencies. We find that the incarceration of a parent in childhood leads to a significant increase in teen crime and significant decreases in educational attainment and adult employment. The effects are concentrated among children from the most disadvantaged families, where criminal convictions increase by 10 percentage points, high school graduation decreases by 25 percentage points, and employment at age 25 decreases by 29 percentage points. In contrast, there are no detectable effects among children from more advantaged families. These results suggest that the incarceration of parents with young children may significantly increase the intergenerational persistence of poverty and criminal behavior, even in affluent countries with extensive social safety nets.

Using survey data from the Promise Academy in the Harlem Children’s Zone, we estimate the effects of high-performing charter schools on human capital, risky behaviors, and health outcomes. Six year...

This paper provides causal estimates of the impact of service programs on those who serve, using data from a web-based survey of former Teach For America (TFA) applicants. We estimate the effect of voluntary youth service using a discontinuity in the TFA application process. Participating in TFA increases racial tolerance, makes individuals more optimistic about the life prospects of poor children, and makes them more likely to work in education.

Consumer bankruptcy is one of the largest social insurance programs in the United States, but little is known about its impact on debtors. We use 500,000 bankruptcy filings matched to administrative tax and foreclosure data to estimate the impact of Chapter 13 bankruptcy protection on subsequent outcomes. Exploiting the random assignment of bankruptcy filings to judges, we find that Chapter 13 protection increases annual earnings by $5,562, decreases five-year mortality by 1.2 percentage points, and decreases five-year foreclo-sure rates by 19.1 percentage points. These results come primarily from the deterioration of outcomes among dismissed filers, not gains by granted filers. (JEL D14, I12, J22, J31, K35)

Credit reports are used in nearly all consumer lending decisions and, increasingly, in hiring decisions in the labor market, but the impact of a bad credit report is largely unknown. We study the effects of credit reports on financial and labor market outcomes using a difference-in-differences research design that compares changes in outcomes over time for Chapter 13 filers, whose personal bankruptcy flags are removed from credit reports after 7 years, to changes for Chapter 7 filers, whose personal bankruptcy flags are removed from credit reports after 10 years. Using credit bureau data, we show that the removal of a Chapter 13 bankruptcy flag leads to a large increase in credit scores, and an economically significant increase in credit card balances and mortgage borrowing. We study labor market effects using administrative tax records linked to personal bankruptcy records. In sharp contrast to the credit market effects, we estimate a precise zero effect of flag removal on employment and earnings outcomes. We conclude that credit reports are important for credit market outcomes, where they are the primary source of information used to screen applicants, but are of limited consequence for labor market outcomes, where employers rely on a much broader set of screening mechanisms.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

This paper uses data from three prominent exam high schools in New York City to estimate the impact of attending a school with high-achieving peers on college enrollment and graduation. Our identification strategy exploits sharp discontinuities in the admissions process. Applicants just eligible for an exam school have peers that score 0.17 to 0.36 standard deviations higher on eighth grade state tests and that are 6.4 to 9.5 percentage points less likely to be black or Hispanic. However, exposure to these higher-achieving and more homogeneous peers has little impact on college enrollment, college graduation, or college quality. (JEL H75, I21, J15)

Information asymmetries are prominent in theory but difficult to estimate. This paper exploits discontinuities in loan eligibility to test for moral hazard and adverse selection in the payday loan market. Regression discontinuity and regression kink approaches suggest that payday borrowers are less likely to default on larger loans. A $50 larger payday loan leads to a 17 to 33 percent drop in the probability of default. Conversely, there is economically and statistically significant adverse selection into larger payday loans when loan eligibility is held constant. Payday borrowers who choose a $50 larger loan are 16 to 47 percent more likely to default. (JEL D14, D82, G21)

In this paper, we collect data on the inner-workings of 39 charter schools and correlate these data with school effectiveness. We find that traditionally collected input measures—class size, per-pupil expenditure, teacher certification, and teacher training—are not correlated with school effectiveness. In stark contrast, we show that an index of five policies suggested by qualitative research—frequent teacher feedback, the use of data to guide instruction, high-dosage tutoring, increased instructional time, and high expectations—explains approximately 45 percent of the variation in school effectiveness. The same index provides similar results in a separate sample of charter schools. (JEL H75, I21, I28)

We estimate the impact of charter schools on early-life labor market outcomes in Texas. We find that, at the mean, charter schools have no impact on test scores and a negative impact on earnings. N...

Harlem Children's Zone (HCZ), an ambitious social experiment, combines community programs with charter schools. We provide the first empirical test of the causal impact of HCZ charters on educational outcomes. Both lottery and instrumental variable identification strategies suggest that the effects of attending an HCZ middle school are enough to close the black-white achievement gap in mathematics. The effects in elementary school are large enough to close the racial achievement gap in both mathematics and ELA. We conclude with evidence that suggests high-quality schools are enough to significantly increase academic achievement among the poor. Community programs appear neither necessary nor sufficient. (JEL H75, I21, I28, J13, R23)

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.

High-performing charter schools can significantly increase the test scores of poor urban students. It is unclear whether these test score gains translate into improved outcomes later in life. We estimate the effects of high-performing charter schools on human capital, risky behaviors, and health outcomes using survey data from the Promise Academy in the Harlem Children's Zone. Six years after the random admissions lottery, youth offered admission to the Promise Academy middle school score 0.283 standard deviations higher on a nationally-normed math achievement test and are 14.1 percentage points more likely to enroll in college. Admitted females are 12.1 percentage points less likely to be pregnant in their teens, and males are 4.3 percentage points less likely to be incarcerated. We find little impact of the Promise Academy on self-reported health. We conclude with speculative evidence that high-performing schools may be sufficient to significantly improve human capital and reduce certain risky behaviors among the poor.

High-performing charter schools can significantly increase the test scores of poor urban students. It is unclear whether these test score gains translate into improved outcomes later in life. We estimate the effects of high-performing charter schools on human capital, risky behaviors, and health outcomes using survey data from the Promise Academy in the Harlem Children's Zone. Six years after the random admissions lottery, youth offered admission to the Promise Academy middle school score 0.283 standard deviations higher on a nationally-normed math achievement test and are 14.1 percentage points more likely to enroll in college. Admitted females are 12.1 percentage points less likely to be pregnant in their teens, and males are 4.3 percentage points less likely to be incarcerated. We find little impact of the Promise Academy on self-reported health. We conclude with speculative evidence that high-performing schools may be sufficient to significantly improve human capital and reduce certain risky behaviors among the poor.

Charter schools were developed, in part, to serve as an R&D engine for traditional public schools, resulting in a wide variety of school strategies and outcomes. In this paper, we collect unparalleled data on the inner-workings of 35 charter schools and correlate these data with credible estimates of each school's effectiveness. We find that traditionally collected input measures -- class size, per pupil expenditure, the fraction of teachers with no certification, and the fraction of teachers with an advanced degree -- are not correlated with school effectiveness. In stark contrast, we show that an index of five policies suggested by over forty years of qualitative research -- frequent teacher feedback, the use of data to guide instruction, high-dosage tutoring, increased instructional time, and high expectations -- explains approximately 50 percent of the variation in school effectiveness. Our results are robust to controls for three alternative theories of schooling: a model emphasizing the provision of wrap-around services, a model focused on teacher selection and retention, and the "No Excuses'' model of education. We conclude by showing that our index provides similar results in a separate sample of charter schools.

Nearly one million American youth have participated in service programs such as Peace Corps and Teach For America. This paper provides the first causal estimate of the impact of service programs on those who serve, using data from a web-based survey of former Teach For America applicants. We estimate the effect of voluntary youth service using a sharp discontinuity in the Teach For America application process. Participating in Teach For America increases racial tolerance, makes individuals more optimistic about the life chances of poor children, and makes them more likely to work in education. We argue that these facts are broadly consistent with the "Contact Hypothesis," which states that, under appropriate conditions, interpersonal contact can reduce prejudice.

Nearly one million American youth have participated in service programs such as Peace Corps and Teach For America. This paper provides the first causal estimate of the impact of service programs on those who serve, using data from a web-based survey of former Teach For America applicants. We estimate the effect of voluntary youth service using a sharp discontinuity in the Teach For America application process. Participating in Teach For America increases racial tolerance, makes individuals more optimistic about the life chances of poor children, and makes them more likely to work in education. We argue that these facts are broadly consistent with the "Contact Hypothesis," which states that, under appropriate conditions, interpersonal contact can reduce prejudice.

Publicly funded exam schools educate many of the world's most talented students. These schools typically contain higher achieving peers, more rigorous instruction, and additional resources compared to regular public schools. This paper uses a sharp discontinuity in the admissions process at three prominent exam schools in New York City to provide the first causal estimate of the impact of attending an exam school in the United States on longer term academic outcomes. Attending an exam school increases the rigor of high school courses taken and the probability that a student graduates with an advanced high school degree. Surprisingly, however, attending an exam school has little impact on Scholastic Aptitude Test scores, college enrollment, or college graduation -- casting doubt on their ultimate long term impact.

Publicly funded exam schools educate many of the world's most talented students. These schools typically contain higher achieving peers, more rigorous instruction, and additional resources compared to regular public schools. This paper uses a sharp discontinuity in the admissions process at three prominent exam schools in New York City to provide the first causal estimate of the impact of attending an exam school in the United States on longer term academic outcomes. Attending an exam school increases the rigor of high school courses taken and the probability that a student graduates with an advanced high school degree. Surprisingly, however, attending an exam school has little impact on Scholastic Aptitude Test scores, college enrollment, or college graduation -- casting doubt on their ultimate long term impact.

This paper tests for incentive and selection effects in a subprime consumer credit market. We estimate the incentive effect of loan size on default using sharp discontinuities in loan eligibility rules. This allows us to estimate the magnitude of selection from the cross-sectional correlation between loan size and default. We find evidence of advantageous incentives and adverse selection. For a given borrower, we estimate that a $100 increase in loan size decreases the probability of default by 3.7 to 4.2 percentage points, a 20 to 23 percent decrease from the mean default rate. The incentive effect is more than o ffset by adverse selection into larger loans. Borrowers who choose $100 larger loans are 6.9 to 8.0 percentage points more likely to default than borrowers who choose smaller loans. Taken together, our results are consistent with the idea that information frictions lead to credit constraints in equilibrium.

This paper tests for incentive and selection effects in a subprime consumer credit market. We estimate the incentive effect of loan size on default using sharp discontinuities in loan eligibility rules. This allows us to estimate the magnitude of selection from the cross-sectional correlation between loan size and default. We find evidence of advantageous incentives and adverse selection. For a given borrower, we estimate that a $100 increase in loan size decreases the probability of default by 3.7 to 4.2 percentage points, a 20 to 23 percent decrease from the mean default rate. The incentive effect is more than o ffset by adverse selection into larger loans. Borrowers who choose $100 larger loans are 6.9 to 8.0 percentage points more likely to default than borrowers who choose smaller loans. Taken together, our results are consistent with the idea that information frictions lead to credit constraints in equilibrium.

Harlem Children's Zone (HCZ), which combines community investments with reform minded charter schools, is one of the most ambitious social experiments to alleviate poverty of our time. We provide the first empirical test of the causal impact of HCZ on educational outcomes, with an eye toward informing the long-standing debate whether schools alone can eliminate the achievement gap or whether the issues that poor children bring to school are too much for educators alone to overcome. Both lottery and instrumental variable identification strategies lead us to the same story: Harlem Children's Zone is effective at increasing the achievement of the poorest minority children. Taken at face value, the effects in middle school are enough to close the black-white achievement gap in mathematics and reduce it by nearly half in English Language Arts. The effects in elementary school close the racial achievement gap in both subjects. We conclude by presenting four pieces of evidence that high-quality schools or high-quality schools coupled with community investments generate the achievement gains. Community investments alone cannot explain the results.

Harlem Children's Zone (HCZ), which combines community investments with reform minded charter schools, is one of the most ambitious social experiments to alleviate poverty of our time. We provide the first empirical test of the causal impact of HCZ on educational outcomes, with an eye toward informing the long-standing debate whether schools alone can eliminate the achievement gap or whether the issues that poor children bring to school are too much for educators alone to overcome. Both lottery and instrumental variable identification strategies lead us to the same story: Harlem Children's Zone is effective at increasing the achievement of the poorest minority children. Taken at face value, the effects in middle school are enough to close the black-white achievement gap in mathematics and reduce it by nearly half in English Language Arts. The effects in elementary school close the racial achievement gap in both subjects. We conclude by presenting four pieces of evidence that high-quality schools or high-quality schools coupled with community investments generate the achievement gains. Community investments alone cannot explain the results.