Research

Lower performance and mismatch — a situation where students would be better off if not enrolled in an elite university or prestigious major — is one of the main concerns surrounding affirmative action (AA) policies in higher education. Misperception about the major and lack of proper preparation are appointed as the main causes of these problems. However, what if other causes explain the affirmative action students’ lower performance? Using two rich administrative data sets and a rule of admission at one top university in Brazil that splits students into two classes, we apply a regression discontinuity design to study the effect of class allocation on academic performance and labor market outcomes. The last student of the first class will have higher-ability peers but a lower ordinal rank than the first student of the second class. These effects usually play in different directions. The main results suggest that affirmative action students in technology and health sciences majors are negatively impacted by being the last student in the first class.

Affirmative action in higher education can lead to mismatch, where students admitted through preferential treatment struggle academically due to inadequate preparation before college. Although some students may face initial challenges, by providing access to quality education for talented individuals who might have otherwise been overlooked due to systemic disadvantages, these programs may enable students to bridge the gap and catch up to their peers. In this study, we examine the effects of a quota-type affirmative action policy on gaps in college outcomes between potential beneficiaries and non-beneficiaries. Using comprehensive administrative data from a leading Brazilian university which implemented affirmative action in 2005, we find that compared to their non-quota peers, potential quota beneficiaries are less likely to progress smoothly through college and less likely to graduate, a result that is mostly driven by those who would not be admitted to the university otherwise. Notably, however, most of these differences shrink as the students progress through college, suggesting a catch-up effect between those groups. While potential quota students initially face challenges, resulting in a reduced course load in their early college years, they compensate by taking more credits in later years to ultimately graduate.

The relationship between schooling and crime is well documented in the economics literature: more schooling means less crime. Yet the mechanisms behind this relationship remain an open question, especially in developing countries. We ask whether full-time schooling lowers crime by building human capital or simply by keeping students supervised, exploiting the staggered rollout of São Paulo’s Full-Time Education Program (Programa de Ensino Integral) across public schools between 2012 and 2024. Using Callaway and Sant’Anna’s (2021) doubly-robust difference-in-differences estimator applied to geocoded crime records, we find that adopting the full-time model reduces property crime in the surrounding area by about 7% (18.8 fewer offenses per hexagon-year), an effect driven by robbery. The reductions are sharply concentrated in time and by offender: theft falls by 26% during school hours but is, if anything, slightly higher outside them; effects are strongest on school days; and crimes committed by juveniles fall disproportionately — roughly 65% for theft and 23% for robbery. Because the effects emerge precisely when, and for whom, school supervision is most binding, they are hard to reconcile with longer-run human-capital or opportunity-cost channels and instead point to incapacitation. Full-time schooling thus yields immediate public-safety gains in a middle-income setting, which is relevant to both education and crime-prevention policy.