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EdWorkingPapers

Takako Nomi, Michael Podgursky, Darrin DeChane.

This paper investigates patterns of racial/ethnic and gender gaps in post-secondary degree attainment trajectories by the levels of students’ pre-college academic preparation. We follow four cohorts of Missouri public high school freshmen for five years beyond on-time graduation among White, Black, and Hispanic male and female students. A composite measure of pre-college academic preparation is constructed based on test scores, GPA, attendance, and advanced course enrollment, which we label Academic Index (AI) and split students into AI Quintiles for analysis. We find large racial/ethnic gaps in AI, with the largest difference for Black male students, who are heavily concentrated in the lowest quintile. Gender gaps in academic readiness widen during high school. College enrollment is higher for Black male and female students near the average AI and below, but this advantage completely disappears for degree completion. Hispanic-White gaps emerge earlier than that of Black-White gaps as Hispanic students are less likely to graduate from high school and attend college. An important finding is that top-performing Black and Hispanic male students have much lower rates of degree completion than other top-performing students. Also, Black-White gaps are much wider in any degree attainment than in bachelor’s degree attainment, suggesting lower likelihood of completing a sub-bachelor degree among Black students. After controlling for academic preparation and the FRL status, bachelor’s degree attainment among Black and Hispanic female students are similar to, or higher than, that of White female students.

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Sarah Ruth Morris, Andy Parra-Martinez, Jonathan Wai, Robert Maranto.

This mixed-methods study synthesizes Standards-Based Grading (SBG) literature, analyzes 249 Arkansas administrators' survey responses using OLS regressions, and identifies themes through in-vivo coding of qualitative feedback. Results show more SBG support among liberal, elementary-level administrators in larger, economically diverse districts. Qualitative insights highlight structural barriers and mindsets against SBG, emphasizing its importance for mastery-focused assessment and grading alignment. These findings underscore the influence of principals' beliefs on SBG support and suggest researching the contextual and ideological factors influencing SBG's implementation.

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D'Wayne Bell, John B. Holbein, Samuel Imlay, Jonathan Smith.

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.

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Matthew A. Kraft, Sarah Novicoff.

We examine the fundamental and complex role that time plays in the learning process. We begin by developing a conceptual framework to elucidate the multiple obstacles schools face in converting total time in school into active learning time. We then synthesize the causal research and document a clear positive effect of time on student achievement of small to medium magnitude, but also with likely diminishing marginal returns. Further descriptive analyses reveal how large differences in the length of the school day and year across public schools are an underappreciated dimension of educational inequality in the United States. Finally, our case study of time loss in one urban district demonstrates the potential to substantially increase learning time within existing constraints.

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Joshua Bleiberg, Eric Brunner, Erica Harbatkin, Matthew A. Kraft, Matthew Springer.

Federal incentives and requirements under the Obama administration spurred states to adopt major reforms to their teacher evaluation systems. We examine the effects of these reforms on student achievement and attainment at a national scale by exploiting their staggered implementation across states. We find precisely estimated null effects, on average, that rule out impacts as small as 0.017 standard deviations for achievement and 1.2 percentage points for high school graduation and college enrollment. We highlight five factors that likely limited the efficacy of teacher evaluation at scale: political opposition, decentralization, capacity constraints, limited generalizability, and the absence of compensating wages.

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Blake Heller.

In 2016, the GED® introduced college readiness benchmarks designed to identify testers who are academically prepared for credit-bearing college coursework. The benchmarks are promoted as awarding college credits or exempting “college-ready” GED® graduates from remedial coursework. I show descriptive evidence that those identified as college-ready by these benchmarks enroll and persist in college at significantly higher rates than others who pass the GED® exam, but at lower rates than recent graduates with traditional high school diplomas. Regression discontinuity estimates show that crossing a college readiness threshold does not substantially influence testers' college enrollment or persistence during the two years following their first test attempt. Relatedly, I observe little exam retaking by those who fall narrowly short of the minimum college readiness score thresholds. This contrasts strongly with retaking behavior near the lower GED® passing threshold that determines eligibility for a high school equivalency credential. Those who narrowly fail a GED® subject test are over 100 times more likely to retest than those who fall just short of a college readiness benchmark in the same subject. GED® college readiness benchmarks do not currently appear to promote better college outcomes, but in the absence of more detailed test score information they offer a simple heuristic to predict short-run college enrollment and persistence among GED® graduates, particularly for those who identify educational gain as a primary reason for testing. The results highlight the promise and challenges associated with building pathways for non-traditional students to earn credit for prior learning.

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Isaac M. Opper, Umut Özek.

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.

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Deven Carlson, Adam Shepardson.

As students are exposed to extreme temperatures with ever-increasing frequency, it is important to understand how such exposure affects student learning. In this paper we draw upon detailed student achievement data, combined with high-resolution weather records, to paint a clear portrait of the effect of temperature on student learning across a six-year period for students in Tulsa, Oklahoma. The detailed, longitudinal nature of our data allows us to estimate the effects of both test-day and longer-term temperature on student test performance, and to examine how the effects of both temperature measures vary across seasons, student background, and the distribution of student achievement. Our results show that test-day temperature has no significant effect on student test performance in fall or winter, but a clear negative effect on students’ spring performance, particularly in math. Second, we find that summer temperature has a positive, statistically significant, and substantively meaningful effect on student performance on the fall MAP assessment—these effects appear in both math and reading. The results also illustrate that 90-day temperature affects math performance in winter and spring, but these estimates are modest in substantive magnitude.

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Kathleen Lynch, Monica Lee, Susanna Loeb.

The COVID-19 pandemic’s impact on preschool children’s school readiness skills remains understudied. This research investigates Head Start preschool children’s early numeracy, literacy, and executive function outcomes during a pandemic-affected school year. Study children (N = 336 assessed at fall baseline; N = 237-250 assessed in spring depending on outcome; fall baseline sample: mean age = 51 months; 46% Hispanic; 36% Black Non-Hispanic; 52% female) in a network of Head Start centers in four states (Nevada, New Jersey, Pennsylvania, and Wisconsin) experienced low in-person preschool exposure compared to national pre-pandemic norms. Children experienced fall to spring score gains during the pandemic-affected year of 0.05 SD in executive function, 0.27 SD in print knowledge, and 0.45-0.71 SD in early numeracy skills. Descriptively, for two of the three early numeracy domains measured, spring test score outcomes were stronger among children who attended more in-person preschool. We discuss implications for future research and policy.

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Joshua B. Gilbert.
When analyzing treatment effects on test scores, researchers face many choices and competing guidance for scoring tests and modeling results. This study examines the impact of scoring choices through simulation and an empirical application. Results show that estimates from multiple methods applied to the same data will vary because two-step models using sum or factor scores provide attenuated standardized treatment effects compared to latent variable models. This bias dominates any other differences between models or features of the data generating process, such as the use of scoring weights. An errors-in-variables (EIV) correction removes the bias from two-step models. An empirical application to data from a randomized controlled trial demonstrates the sensitivity of the results to model selection. This study shows that the psychometric principles most consequential in causal inference are related to attenuation bias rather than optimal scoring weights.

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