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Access and admissions
This study leverages six years of public prekindergarten (PreK) and kindergarten data (N = 22,469) from the Boston Public Schools (BPS) to examine enrollment in BPS PreK from 2012–2017 for students from different racial/ethnic, socioeconomic, and linguistic groups. The largest differences in enrollment emerged with respect to race and ethnicity—and for enrollment in programs in higher-quality schools (defined as schools scoring in the top quartile on third grade standardized tests)—with disparities increasing over time. Although there were no differences across groups in proximity to BPS PreK programs in general, Black students lived about a quarter of a mile further than their White peers from the nearest program in a higher-quality school, with gaps widening over time. Closer proximity was associated with a higher likelihood of enrollment in a program in a higher-quality school. Implications for future research and policy are discussed.
Four-year public colleges may play an important role in supporting intergenerational mobility by providing an accessible path to a bachelor’s degree and increasing students' earnings. Leveraging a midsize state’s GPA- and SAT-based admissions thresholds for the four-year public sector, I use a regression discontinuity design to estimate the effect of four-year public college admissions on earnings and college costs. For low-income students and Black, Hispanic, or Native American students, admission to four-year public colleges increases mean annual earnings by almost $8,000 eight to fourteen years after applying without increasing the private costs of college. The state recovers the cost of an additional four-year public college admission through increased lifetime tax revenue. Expanding access to four-year public colleges may be a particularly effective way to improve the economic outcomes of low-income students and Black, Hispanic, or Native American students.
As affirmative action loses political feasibility, many universities have implemented race-neutral alternatives like top percent policies and holistic review to increase enrollment among disadvantaged students. I study these policies’ application, admission, and enrollment effects using University of California administrative data. UC’s affirmative action and top percent policies increased underrepresented minority (URM) enrollment by over 20 percent and less than 4 percent, respectively. Holistic review increases implementing campuses’ URM enrollment by about 7 percent. Top percent policies and holistic review have negligible effects on lower-income enrollment, while race-based affirmative action modestly increased enrollment among very low-income students. These findings highlight the enrollment gaps between affirmative action and its most common race-neutral alternatives and reveal that available policies do not substantially affect universities’ socioeconomic composition.
Despite the growing popularity of free college proposals, countries with higher college subsidies tend to have higher enrollment rates but not higher graduation rates. To capture this evidence and evaluate potential free college policies, we rely on a dynamic model of college enrollment, performance, and graduation estimated using rich student-level data from Colombia. In the model, student effort affects class completion and mitigates the risk of performing poorly or dropping out. Among our simulated policies, universal free college expands enrollment the most but has virtually no effect on graduation rates, helping explain the cross-country evidence. Performance-based free college triggers a more modest enrollment expansion but delivers a higher graduation rate at a lower fiscal cost. While both programs lower student uncertainty relative to the baseline, performance-based free college does it to a lower extent, which in turn promotes better student outcomes. Overall, free college programs expand enrollment but have limited impacts on graduation and attainment due to their limited impact on student effort.
Prediction algorithms are used across public policy domains to aid in the identification of at-risk individuals and guide service provision or resource allocation. While growing research has investigated concerns of algorithmic bias, much less research has compared algorithmically-driven targeting to the counterfactual: human prediction. We compare algorithmic and human predictions in the context of a national college advising program, focusing in particular on predicting high-achieving, lower-income students’ college enrollment quality. College advisors slightly outperform a prediction algorithm; however, greater advisor accuracy is concentrated among students with whom advisors had more interactions. The algorithm achieved similar accuracy among students lower in the distribution of interactions, despite advisors having substantially more information. We find no evidence that the advisors or algorithm exhibit bias against vulnerable populations. Our results suggest that, especially at scale, algorithms have the potential to provide efficient, accurate, and unbiased predictions to target scarce social services and resources.
Despite recent evidence on the benefits of same-race instructor matching in K-12 and higher education, research has yet to document the incidence of same-race matching in the postsecondary sector. That is, how likely are racially minoritized college students to ever experience an instructor of the same race/ethnicity? Using administrative data from Texas on the universe of community college students, we document the rate of same-race matching overall and across racial groups, the courses in which students are more or less likely to match, the types of instructors students most commonly match to, and descriptive differences in course outcomes across matched and unmatched courses. Understanding each of these measures is critical to conceptualize the mechanisms and outcomes of same-race matching and to drive policy action concerning the diversity of the professoriate.
Education is one of the most important public goods provided by modern governments. Yet governments worldwide seldom perform well in the sector. This raises the question: why do governments preside over poor education quality? This article answers this question with evidence from Tanzania. Using data from surveys, administrative reports, and policy documents, it analyzes changing goals of education policy and associated impacts on access and learning over time. The main finding is that learn- ing has not always been the goal of schooling in Tanzania. Furthermore, for decades the government rationed access to both primary and secondary schooling for ideological reasons. These past policy choices partially explain contemporary poor outcomes in education. This article increase our understanding of the politics of education in low-income states. It also provides a corrective against the common assumption that governments always seek to maximize the provision of public goods and services for political gain.
Increasing numbers of students require internet access to pursue their undergraduate degrees, yet broadband access remains inequitable across student populations. Furthermore, surveys that currently show differences in access by student demographics or location typically do so at high levels of aggregation, thereby obscuring important variation between subpopulations within larger groups. Through the dual lenses of quantitative intersectionality and critical race spatial analysis, we use Bayesian multilevel regression and census microdata to model variation in broadband access among undergraduate populations at deeper interactions of identity. We find substantive heterogeneity in student broadband access by gender, race, and place, including between typically aggregated subpopulations. Our findings speak to inequities in students’ geographies of opportunity and suggest a range of policy prescriptions at both the institutional and federal level.
How much does family demand matter for child learning in settings of extreme poverty? In rural Gambia, families with high aspirations for their children’s future education and career, measured before children start school, go on to invest substantially more than other families in the early years of their children’s education. Despite this, essentially no children are literate or numerate three years later. When villages receive a highly-impactful, teacher-focused supply-side intervention, however, children of these families are 25 percent more likely to achieve literacy and numeracy than other children in the same village. Furthermore, improved supply enables these children to acquire other higher-level skills necessary for later learning and child development. We also document patterns of substitutability and complementarity between demand and supply in generating learning at varying levels of skill difficulty. Our analysis shows that greater demand can map onto developmentally meaningful learning differences in such settings, but only with adequate complementary inputs on the supply side.
AB705 is a landmark higher education policy that has changed approaches to developmental/remedial education in the California Community College system. We study one district that implemented reforms by placing most students in transfer-level math/English courses and encouraging enrollment in support courses based on multiple measures of academic preparation (e.g., GPA). We use regression discontinuity designs to examine the impact of these new placement procedures, finding benefits to English support course recommendations for low GPA students, but no evidence of benefits or penalties for math. We use inverse probability weighted regression adjustment to explore the relationship between support course enrollment and subsequent outcomes. While enrollment in concurrent support courses appeared beneficial, enrollment in developmental courses was associated with poorer outcomes.