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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.
Colleges can send signals about their quality by adopting new, more alluring names. We study how this affects college choice and labor market performance of college graduates. Administrative data show name-changing colleges enroll higher-aptitude students, with larger effects for alluring-but-misleading name changes and among students with less information. A large resume audit study suggests a small premium for new college names in most jobs, and a significant penalty in lower-status jobs. We characterize student and employer beliefs using web-scraped text, surveys, and other data. Our study shows signals designed to change beliefs can have real, lasting impacts on market outcomes.
A pervasive issue in the school choice literature is whether schools of choice cream-skim students by enrolling high-achieving, less challenging, or less costly students. Similarly, schools of choice may “pushout” low-achieving, more challenging, or more costly students. Using longitudinal student-level data from Indiana, we created multiple measures to examine whether there is evidence consistent with the claims of voucher-participating private schools cream skimming the best students from public schools or pushing out voucher-receiving students. We do not find evidence consistent the claim of cream skimming. However, we find evidence consistent with the claim of private schools pushing out the lowest achieving voucher students.
Counselors are a common school resource for students navigating complicated and con- sequential education choices. I estimate counselors’ causal effects using quasi-random assignment policies in Massachusetts. Counselors vary substantially in their effectiveness at increasing high school graduation and college attendance, selectivity, and persistence. Counselor effects on educational attainment are similar in magnitude to teacher effects, but they flow through improved information and assistance more than cognitive or non-cognitive skill development. Counselor effectiveness is most important for low-income and low-achieving students, so improving access to effective counseling may be a promising way to increase educational attainment and close socioeconomic gaps in education.
In this paper, I study the causal relationship between violence and human capital accumulation. Due to a power vacuum left in conflict zones of Colombia after the 2016 peace agreement, large spikes in violence were reported in the municipalities of the country dominated by the rebel group FARC. I compare student test scores in municipalities that experienced the increase in violence to the ones that did not, before and after the national peace agreement. I find that a 10 percent increase in the homicide rate reduces average high school test scores by approximately 0.03 standard deviations. However, this impact is greater in the case of poor students who suffered a reduction of about 0.1 standard deviations per subject area, equivalent to 3.3 percentage points out of the final score. I also consider heterogeneity by gender finding a slightly larger negative impact on female students. This disparate effect on women and on the poorest students adds new evidence to the literature on the effects of armed conflict on learning outcomes.