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Several K-12 and university systems have adopted race-neutral affirmative action in place of race-based alternatives. This paper explores whether these plans are effective substitutes for racial quotas in Chicago Public Schools (CPS), which now employs a race-neutral, place-based affirmative action system at its selective exam high schools. The CPS plan is ineffective compared to plans that explicitly consider race: about three-quarters of the reduction in average entrance scores at the top schools could have been avoided with the same level of racial diversity. Moreover, the CPS plan is less effective at adding low-income students than was the previous system of racial quotas. We develop a theoretical framework that motivates quantifying the inefficiency of race-neutral policies based on the distortion in student preparedness they create for a given level of diversity and use it to evaluate several alternatives. The CPS plan can be improved in several ways, but no race-neutral policy restores minority representation to prior levels without substantially greater distortions, implying significant efficiency costs from prohibitions on the explicit use of race.

Coordinated single-offer school assignment systems are a popular education reform. We show that uncoordinated offers in NYC’s school assignment mechanism generated mismatches. One-third of applicants were unassigned after the main round and later administratively placed at less desirable schools. We evaluate the effects of the new coordinated mechanism based on deferred acceptance using estimated student preferences. The new mechanism achieves 80 percent of the possible gains from a no-choice neighborhood extreme to a utilitarian benchmark. Coordinating offers dominates the effects of further algorithm modifications. Students most likely to be previously administratively assigned experienced the largest gains in welfare and subsequent achievement.

A number of labor markets and student placement systems can be modeled as many-to-one matching markets. We analyze the scope for manipulation in many-to-one matching markets under the student-optimal stable mechanism when the number of participants is large. Under some regularity conditions, we show that the fraction of participants with incentives to misrepresent their preferences when others are truthful approaches zero as the market becomes large. With an additional condition, truthful reporting by every participant is an approximate equilibrium under the student-optimal stable mechanism in large markets.

Empirical and experimental evidence suggests different levels of sophistication among families in the Boston Public School student assignment plan. We analyze the preference revelation game induced by the Boston mechanism with sincere players who report their true preferences and sophisticated players who play a best response. We characterize the set of Nash equilibrium outcomes as the set of stable matchings of a modified economy, where sincere students lose priority to sophisticated students. Any sophisticated student weakly prefers her assignment under the Pareto-dominant Nash equilibrium of the Boston mechanism to her assignment under the recently adopted student-optimal stable mechanism. (JEL D82, I21)

A Boston Globe article, \"School Choice: A Mechanism Design Approach\" (2003) highlighted the difficulties that the Boston Public School (BPS) system may give parents in strategizing about applying to schools. Briefly, Boston tries to give students their first-choice school. But a student who fails to get her first choice may find her later choices filled by students who chose them first. So there is a risk in ranking a school first if there is a chance of not being admitted; other schools that would have been possible had they been listed first may also be filled. Valerie Edwards, then Strategic Planning Manager at BPS, and her colleague Carleton Jones invited the authors to a meeting in October 2003. BPS agreed to a study of their assignment system and provided them with micro-level data sets on choices and characteristics of students in the grades at which school choices are made (K, 1, 6, 9), and school characteristics. Based on the pending results of this study, the Superintendent has asked for the authors' advice on the design of a new assignment mechanism. This paper describes some of the difficulties with the current mechanism and some elements of the design and evaluation of possible replacement mechanisms.

School choice systems aspire to delink residential location and school assignments by allowing children to apply to schools outside of their neighborhood. However, choice programs also affect incentives to live in certain neighborhoods, and this feedback may undermine the goals of choice. We investigate this possibility by developing a model of public school and residential choice. School choice narrows the range between the highest and lowest quality schools compared to neighborhood assignment rules, and these changes in school quality are capitalized into equilibrium housing prices. This compressed distribution generates an ends-against-the-middle trade-off with school choice compared to neighborhood assignment. Paradoxically, even when choice results in improvement in the lowest-performing schools, the lowest type residents need not benefit.

School choice may lead to improvements in school productivity if parents’ choices reward effective schools and punish ineffective ones. This mechanism requires parents to choose schools based on causal effectiveness rather than peer characteristics. We study relationships among parent preferences, peer quality, and causal effects on outcomes for applicants to New York City’s centralized high school assignment mechanism. We use applicants’ rank-ordered choice lists to measure preferences and to construct selection-corrected estimates of treatment effects on test scores, high school graduation, college attendance, and college quality. Parents prefer schools that enroll high-achieving peers, and these schools generate larger improvements in short- and long-run student outcomes. Preferences are unrelated to school effectiveness and academic match quality after controlling for peer quality.

In Fall 2009, Chicago authorities abandoned a school assignment mechanism midstream, citing concerns about its vulnerability to manipulation. Nonetheless, they asked thousands of applicants to re-rank schools in a new mechanism that is also manipulable. This paper introduces a method to compare mechanisms by their vulnerability to manipulation. Our methodology formalizes how the old mechanism is at least as manipulable as any other plausible mechanism, including the new one. A number of similar transitions took place in England after the widely popular Boston mechanism was ruled illegal in 2007. Our approach provides support for these and other recent policy changes.

A growing number of school districts use centralized assignment mechanisms to allocate school seats in a manner that reflects student preferences and school priorities. Many of these assignment schemes use lotteries to ration seats when schools are oversubscribed. The resulting random assignment opens the door to credible quasi-experimental research designs for the evaluation of school effectiveness. Yet the question of how best to separate the lottery-generated randomization integral to such designs from non-random preferences and priorities remains open. This paper develops easily-implemented empirical strategies that fully exploit the random assignment embedded in a wide class of mechanisms, while also revealing why seats are randomized at one school but not another. We use these methods to evaluate charter schools in Denver, one of a growing number of districts that combine charter and traditional public schools in a unified assignment system. The resulting estimates show large achievement gains from charter school attendance. Our approach generates efficiency gains over ad hoc methods, such as those that focus on schools ranked first, while also identifying a more representative average causal effect. We also show how to use centralized assignment mechanisms to identify causal effects in models with multiple school sectors.