Debt Relief and Debtor Outcomes: Measuring the Effects of Consumer Bankruptcy Protection: Dissertation Summary

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 $6,747, decreases five-year mortality by 1.1 percentage points, and decreases five-year foreclosure rates by 19.0 percentage points. These results come primarily from the deterioration of outcomes among dismissed filers, not gains by granted filers

Getting Beneath the Veil of Effective Schools: Evidence from New York City

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.

Equal Protection Under Algorithms: A New Statistical and Legal Framework

In this Article, 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 nonminorities. 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

Information Asymmetries in Consumer Credit Markets: Evidence from Payday Lending

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, G2

Essays in Labor Economics

This dissertation consists of three essays in the area of Labor Economics

Are High Quality Schools Enough to Close the Achievement Gap? Evidence from a Social Experiment in Harlem

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.

The Impact of Youth Service on Future Outcomes: Evidence from Teach For America

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.

Exam High Schools and Academic Achievement: Evidence from New York City

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.

The Impact of Attending a School with HighAchieving Peers: Evidence from the New York City Exam Schools

Abstract 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