Showing papers by "Joshua D. Angrist published in 2019"

Breaking Ties: Regression Discontinuity Design Meets Market Design

Abstract: Centralized school assignment algorithms must distinguish between applicants with the same preferences and priorities. This is done with randomly assigned lottery numbers, non-lottery tie-breakers like test scores, or both. The New York City public high school match illustrates the latter, using test scores, grades, and interviews to rank applicants to screened schools, combined with lottery tie-breaking at unscreened schools. We show how to identify causal effects of school attendance in such settings. Our approach generalizes regression discontinuity designs to allow for multiple treatments and multiple running variables, some of which are randomly assigned. Lotteries generate assignment risk at screened as well as unscreened schools. Centralized assignment also identifies screened school effects away from screened school cutoffs. These features of centralized assignment are used to assess the predictive value of New York City’s school report cards. Grade A schools improve SAT math scores and increase the likelihood of graduating, though by less than OLS estimates suggest. Selection bias in OLS estimates is egregious for Grade A screened schools.

Replication data for: Charters without Lotteries: Testing Takeovers in New Orleans and Boston

Abstract: Lottery estimates suggest oversubscribed charter schools boost student achievement in urban districts. But these estimates needn’t capture treatment effects for students who haven’t applied to charter schools or for students attending charters where demand is weak. This paper reports estimates of the effect of charter school attendance on middle-schoolers in charter takeovers in New Orleans and Boston. Takeovers are traditional public schools that close and then re-open as charter schools. Students enrolled in the schools designated for closure are eligible for “grandfathering” into the new schools; that is, they are guaranteed seats. We use this fact to construct instrumental variables estimates of the effects of passive charter attendance: the grandfathering instrument compares students at schools designated for takeover with students who appear similar at baseline and who were attending similar schools not yet closed, while adjusting for possible violations of the exclusion restriction in such comparisons. Estimates for a large sample of takeover schools in the New Orleans Recovery School District show impressive gains. In Boston, where we can compare takeover and lottery estimates, takeover charters generate achievement gains as large or larger than the gains for students assigned seats in lotteries. ∗Our thanks to Raymond Cwiertniewicz, Alvin David, Gabriela Fighetti, and Jill Zimmerman from the Recovery School District; to Kamal Chavda and the Boston Public Schools; and to Scott Given, Ryan Knight and the staff at Unlocking Potential for graciously sharing data and answering our many questions. We’re grateful to Alonso Bucarey, Olivia Kim, and Mayara Silva for exceptional research assistance and to SEII program manager Annice Correia for invaluable administrative support. Data from the Recovery School District were made available to us through the Institute for Innovation in Public School Choice. We gratefully acknowledge financial support from the Institute for Education Sciences (under Award R305A120269), from the National Science Foundation (under award SES-1426541), and from the Arnold Foundation. Thanks also go to seminar participants at the Federal Reserve Bank of New York for helpful comments. The views expressed here are those of the authors alone. †Abdulkadiroğlu: Duke University, e-mail: atila.abdulkadiroglu@duke.edu. Angrist: MIT and NBER, e-mail: angrist@mit.edu. Hull: MIT, e-mail: hull@mit.edu. Pathak: MIT and NBER, e-mail: ppathak@mit.edu. No child’s chances in life should be determined by the luck of a lottery – President Obama (quoted in The Boston Globe, March 13, 2011)

Maimonides Rule Redux

Abstract: We use the discontinuous function of enrollment known as Maimonides Rule as an instrument for class size in large Israeli samples from 2002-2011. As in the 1991 data analyzed by Angrist and Lavy (1999), Maimonides Rule still has a strong first stage. In contrast with the earlier Israeli estimates, however, Maimonides-based instrumental variables estimates using more recent data show no effect of class size on achievement. The new data also reveal substantial enrollment sorting near Maimonides cutoffs, with too many schools having enrollment values that just barely produce an extra class. A modified rule that uses data on students’ birthdays to compute statutory enrollment in the absence of enrollment manipulation also generates a precisely estimated zero. In older data, the original Maimonides Rule is unrelated to socioeconomic characteristics, while in more recent data, the original rule is unrelated to socioeconomic characteristics conditional on a few controls. Enrollment manipulation therefore appears to be innocuous: neither the original negative effects nor the recent data zeros seem likely to be manipulation artifacts.

Choice and Consequence: Assessing Mismatch at Chicago Exam Schools

Abstract: The educational mismatch hypothesis asserts that students are hurt by affirmative action policies that place them in selective schools for which they wouldn't otherwise qualify. We evaluate mismatch in Chicago's selective public exam schools, which admit students using neighborhood-based diversity criteria as well as test scores. Regression discontinuity estimates for applicants favored by affirmative action indeed show no gains in reading and negative effects of exam school attendance on math scores. But these results are similar for more- and less-selective schools and for applicants unlikely to benefit from affirmative-action, a pattern inconsistent with mismatch. We show that Chicago exam school effects are explained by the schools attended by applicants who are not offered an exam school seat. Specifically, mismatch arises because exam school admission diverts many applicants from high-performing Noble Network charter schools, where they would have done well. Consistent with these findings, exam schools reduce Math scores for applicants applying from charter schools in another large urban district. Exam school applicants' previous achievement, race, and other characteristics that are sometimes said to mediate student-school matching play no role in this story.

Breaking Ties: Regression Discontinuity Design Meets Market Design

Abstract: Many schools in large urban districts have more applicants than seats. Centralized school assignment algorithms ration seats at over-subscribed schools using randomly assigned lottery numbers, non-lottery tie-breakers like test scores, or both. The New York City public high school match illustrates the latter, using test scores and other criteria to rank applicants at ``screened'' schools, combined with lottery tie-breaking at unscreened ``lottery'' schools. We show how to identify causal effects of school attendance in such settings. Our approach generalizes regression discontinuity methods to allow for multiple treatments and multiple running variables, some of which are randomly assigned. The key to this generalization is a local propensity score that quantifies the school assignment probabilities induced by lottery and non-lottery tie-breakers. The local propensity score is applied in an empirical assessment of the predictive value of New York City's school report cards. Schools that receive a high grade indeed improve SAT math scores and increase graduation rates, though by much less than OLS estimates suggest. Selection bias in OLS estimates is egregious for screened schools.