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Methodology, measurement and data
This paper studies the causal impacts of public universities on the outcomes of their marginally admitted students. I use administrative admission records spanning all 35 public universities in Texas, which collectively enroll 10 percent of American public university students, to systematically identify and employ decentralized cutoffs in SAT/ACT scores that generate discontinuities in admission and enrollment. The typical marginally admitted student completes an additional year of education in the four-year sector, is 12 percentage points more likely to earn a bachelor's degree, and eventually earns 5-10 percent more than their marginally rejected but otherwise identical counterpart. Marginally admitted students pay no additional tuition costs thanks to offsetting grant aid; cost-benefit calculations show internal rates of return of 19-23 percent for the marginal students themselves, 10-12 percent for society (which must pay for the additional education), and 3-4 percent for the government budget. Finally, I develop a method to disentangle separate effects for students on the extensive margin of the four-year sector versus those who would fall back to another four-year school if rejected. Substantially larger extensive margin effects drive the results.
While learning outcomes in low- and middle-income countries are generally at low levels, the degree to which students and schools more broadly within education systems lag behind grade-level proficiency can vary significantly. A substantial portion of existing literature advocates for aligning curricula closer to the proficiency level of the “median child” within each system. Yet, amidst considerable between-school heterogeneity in learning outcomes, choosing a single instructional level for the entire system may still leave behind those students in schools far from this level. Hence, establishing system-wide curriculum expectations in the presence of significant between-school heterogeneity poses a significant challenge for policymakers — especially as the issue of between-school heterogeneity has been relatively unexplored by researchers so far. This paper addresses the gap by leveraging a unique dataset on foundational literacy and numeracy outcomes, representative of six public educational systems encompassing over 900,000 enrolled children in South Asia and West Africa. With this dataset, we examine the current extent of between-school heterogeneity in learning outcomes, the potential predictors of this heterogeneity, and explore its potential implications for setting national curricula for different grade levels and subjects. Our findings reveal that between-school heterogeneity can indeed present both a severe pedagogical hindrance and challenges for policymakers, particularly in contexts with relatively higher levels of performance and in the higher grades. In response to meaningful between-system heterogeneity, we also demonstrate through simulation that a more nuanced, data-driven targeting of curricular expectations for different schools within a system could empower policymakers to effectively reach a broader spectrum of students through classroom instruction.
To understand the causes and consequences of polarized demand for government expenditure, we conduct three field experiments in the context of public higher education. The first two experiments study polarization in taxpayer demand. We provide information to shape beliefs about social returns on investment. Our treatments narrow the political partisan gap in ideal policies---a reduction in ideological polarization---by up to 32%, with differences in partisan reasoning as a key mechanism. Providing information also affects how people communicate their ideal policies to elected officials, increasing their propensity to write a (positive) letter to an official of the other party---a reduction in affective polarization. In the third experiment, we send these letters to a randomized subset of elected officials to study how policymakers respond to constituent demand. We find that officials who receive their constituents' demands engage more with higher education issues in our correspondences.
Educational researchers often report effect sizes in standard deviation units (SD), but SD effects are hard to interpret. Effects are easier to interpret in percentile points, but converting SDs to percentile points involves a calculation that is not transparent to educational stakeholders. We show that if the outcome variable is normally distributed, we can approximate the percentile-point effect simply by multiplying the SD effect by 37 (or, equivalently, dividing the SD effect by 0.027). For students in the middle three-fifths of a normal distribution, this rule of thumb is always accurate to within 1.6 percentile points for effect sizes of up to 0.8 SD. Two examples show that the rule can be just as accurate for empirical effects from real studies. Applying the rule to empirical benchmarks, we find that the least effective third of educational interventions raise scores by 0 to 2 percentile points; the middle third raise scores by 2 to 7 percentile points; and the most effective third raise scores by more than 7 percentile points.
Children's approaches to learning (AtL) are widely recognized as a critical predictor of educational outcomes, especially in early childhood. Nevertheless, there remains a dearth of understanding regarding the dimensionality of AtL, the reciprocal dynamics between AtL and learning outcomes, and how AtL operates in non-Western contexts. This paper aims to extend the existing AtL literature by both conceptually and empirically investigating the dimensionality of the AtL scale of the International Development and Early Learning Assessment (IDELA) – a globally used measure of early childhood development – based on data from Ghanaian children newly enrolled in formal schooling. Additionally, our research explores reciprocal relationships between AtL subconstructs and academic skills over time. Our analysis identifies two dimensions within the IDELA AtL scale: Self-Regulation (SR) and Motivation. We found that children with higher levels of SR early in schooling demonstrated better literacy and numeracy skills in later grades compared to their peers with low early SR, whereas children's motivation did not predict subsequent literacy and numeracy skills. This study enhances understanding of AtL in non-Western contexts, with implications for culturally appropriate support for children’s engagement in learning.
We study how colleges shape their students' voting habits by linking millions of SAT takers to their college-enrollment and voting histories. To begin, we show that the fraction of students from a particular college who vote varies systematically by the college's attributes (e.g. increasing with selectivity) but also that seemingly similar colleges can have markedly different voting rates. Next, after controlling for students' college application portfolios and pre-college voting behavior, we find that attending a college with a 10 percentage-point higher voting rate increases entrants' probability of voting by 4 percentage points (10 percent). This effect arises during college, persists after college, and is almost entirely driven by higher voting-rate colleges making new voters. College peers' initial voting propensity plays no discernible role.
We use a marginal treatment effect (MTE) representation of a fuzzy regression discontinuity setting to propose a novel estimator. The estimator can be thought of as extrapolating the traditional fuzzy regression discontinuity estimate or as an observational study that adjusts for endogenous selection into treatment using information at the discontinuity. We show in a frequentest framework that it is consistent under weaker assumptions than existing approaches and then discuss conditions in a Bayesian framework under which it can be considered the posterior mean given the observed conditional moments. We then use this approach to examine the effects of early grade retention. We show that the benefits of early grade retention policies are larger for students with lower baseline achievement and smaller for low-performing students who are exempt from retention. These findings imply that (1) the benefits of early grade retention policies are larger than have been estimated using traditional fuzzy regression discontinuity designs but that (2) retaining additional students would have a limited effect on student outcomes.
Providing ample opportunities for students to express their thinking is pivotal to their learning of mathematical concepts. We introduce the Talk Meter, which provides in-the-moment automated feedback on student-teacher talk ratios. We conduct a randomized controlled trial on a virtual math tutoring platform (n=742 tutors) to evaluate the effectiveness of the Talk Meter at increasing student talk. In one treatment arm, we show the Talk Meter only to the tutor, while in the other arm we show it to both the student and the tutor. We find that the Talk Meter increases student talk ratios in both treatment conditions by 13-14%; this trend is driven by the tutor talking less in the tutor-facing condition, whereas in the student-facing condition it is driven by the student expressing significantly more mathematical thinking. Through interviews with tutors, we find the student-facing Talk Meter was more motivating to students, especially those with introverted personalities, and was effective at encouraging joint effort towards balanced talk time. These results demonstrate the promise of in-the-moment joint talk time feedback to both teachers and students as a low cost, engaging, and scalable way to increase students' mathematical reasoning.