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EdWorkingPapers

Andrew Camp, Gema Zamarro, Josh B. McGee.

The COVID-19 pandemic has been a trying period for teachers. Teachers had to adapt to unexpected conditions, teaching in unprecedented ways. As a result, teachers' levels of stress and burnout have been high throughout the pandemic, raising concerns about a potential increase in teacher turnover and future teacher shortages. We use administrative data for the state of Arkansas to document the effects of the COVID-19 pandemic on teachers’ mobility and attrition during the years 2018-19 to 2021-2022. We find stable turnover rates during the first year of the pandemic (2020-2021) but an increase in teacher mobility and attrition in the second year (2021-2022). Teacher mobility and attrition increased by 2 percentage points (10% relative increase) this second year but with heterogeneous effects across regions and depending on the teacher and school characteristics. Our results raise concerns about increased strain in areas already experiencing teacher shortages and a potential reduction in the diversity of the Arkansas teacher labor force.

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Christopher A. Candelaria, Shelby M. McNeill, Kenneth A. Shores.

School finance reforms are not well defined and are likely more prevalent than the current literature has documented. Using a Bayesian changepoint estimator, we quantitatively identify the years when state education revenues abruptly increased for each state between 1960 and 2008 and then document the state-specific events that gave rise to these changes. We find 108 instances of abrupt increases in state education revenues across 43 states; about one-quarter of these changes had been undocumented. Half of the abrupt increases that occurred post-1990 were preceded by litigation-prompted legislative activity, and Democrat-party control of a state increases the probability of a changepoint occurring by 8 percentage points.

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Todd Pugatch, Paul Thompson.

Can public university honors programs deliver the benefits of selective undergraduate education within otherwise nonselective institutions? We evaluate the impact of admission to the Honors College at Oregon State University, a large nonselective public university. Admission to the Honors College depends heavily on a numerical application score. Nonlinearities in admissions probabilities as a function of this score allow us to compare applicants with similar scores, but different admissions outcomes, via a fuzzy regression kink design. The first stage is strong, with takeup of Honors College programming closely following nonlinearities in admissions probabilities. To estimate the causal effect of Honors College admission on human capital formation, we use these nonlinearities in the admissions function as instruments, combined with course-section fixed effects to account for strategic course selection. Honors College admission increases course grades by 0.10 grade points on the 0-4 scale, or 0.14 standard deviations. Effects are concentrated at the top of the course grade distribution. Previous exposure to Honors sections of courses in the same subject is a leading potential channel for increased grades. However, course grades of first-generation students decrease in response to Honors admission, driven by low performance in natural science courses. Results suggest that selective Honors programs can accelerate skill acquisition for high-achieving students at public universities, but not all students benefit from Honors admission.

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Daniel Rodriguez-Segura, Beth E. Schueler.

A significant share of education and development research uses data collected by workers called “enumerators.” It is well-documented that “enumerator effects”—or inconsistent practices between the individual people who administer measurement tools— can be a key source of error in survey data collection. However, it is less understood whether this is a problem for academic assessments or performance tasks. We leverage a remote phone-based mathematics assessment of primary school students and survey of their parents in Kenya. Enumerators were randomized to students to study the presence of enumerator effects. We find that both the academic assessment and survey was prone to enumerator effects and use simulation to show that these effects were large enough to lead to spurious results at a troubling rate in the context of impact evaluation. We therefore recommend assessment administrators randomize enumerators at the student level and focus on training enumerators to minimize bias.

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Min Sun, Christopher A. Candelaria, David Knight, Zachary LeClair, Sarah E. Kabourek, Katherine Chang.

Knowing how policy-induced salary schedule changes affect teacher recruitment and retention will significantly advance our understanding of how resources matter for K-12 student learning. This study sheds light on this issue by estimating how legislative funding changes in Washington state in 2018-19—induced by the McCleary court-ordered reform—affected teacher salaries and labor market outcomes. By embedding a simulated instrumental variables approach in a mixed methods design, we observed that local collective bargaining negotiations directed new state-level funding allocations toward certificated base salaries, particularly among more senior teachers. Variability in political power, priorities, and interests of both districts and unions led to greater heterogeneity in teacher salary schedules. Teacher mobility rate was reduced in the first year of the reform, and subsequently new hiring rate was reduced in the second year. Suggestive evidence indicates that a $1,000 salary increase would have larger effects on junior teachers’ hiring and their transfers between districts to a greater extent than late-career teachers.

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David Blazar, Beth Schueler.

What guidance does research provide school districts about how to improve system performance and increase equity? Despite over 30 years of inquiry on the topic of effective districts, existing frameworks are relatively narrow in terms of disciplinary focus (primarily educational leadership perspectives) and research design (primarily qualitative case studies). To bridge this gap, we first review the theoretical literatures on how districts are thought to affect student outcomes, arguing that an expanded set of disciplinary perspectives—organizational behavior, political science, and economics—have distinct theories about why districts matter. Next, we conduct a systematic review of quantitative studies that estimate the relationship between district-level inputs and performance outcomes. This review reveals benefits of district-level policies that cross disciplinary perspectives, including higher teacher salaries and strategic hiring, lower student-teacher ratios, and data use. One implication is that future research on district-level policymaking needs to consider multiple disciplinary perspectives. Our review also reveals the need for significant additional causal evidence and provides a multidisciplinary map of theorized pathways through which districts could influence student outcomes that are ripe for rigorous testing.

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Daniela Alvarez-Vargas, Sirui Wan, Lynn S. Fuchs, Alice Klein, Drew H. Bailey.

Despite policy relevance, longer-term evaluations of educational interventions are relatively rare. A common approach to this problem has been to rely on longitudinal research to determine targets for intervention by looking at the correlation between children’s early skills (e.g., preschool numeracy) and medium-term outcomes (e.g., first-grade math achievement). However, this approach has sometimes over—or under—predicted the long-term effects (e.g., 5th-grade math achievement) of successfully improving early math skills. Using a within-study comparison design, we assess various approaches to forecasting medium-term impacts of early math skill-building interventions. The most accurate forecasts were obtained when including comprehensive baseline controls and using a combination of conceptually proximal and distal short-term outcomes (in the nonexperimental longitudinal data). Researchers can use our approach to establish a set of designs and analyses to predict the impacts of their interventions up to two years post-treatment. The approach can also be applied to power analyses, model checking, and theory revisions to understand mechanisms contributing to medium-term outcomes.

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Edward J. Kim.

This study introduces the signal weighted teacher value-added model (SW VAM), a value-added model that weights student-level observations based on each student’s capacity to signal their assigned teacher’s quality. Specifically, the model leverages the repeated appearance of a given student to estimate student reliability and sensitivity parameters, whereas traditional VAMs represent a special case where all students exhibit identical parameters. Simulation study results indicate that SW VAMs outperform traditional VAMs at recovering true teacher quality when the assumption of student parameter invariance is met but have mixed performance under alternative assumptions of the true data generating process depending on data availability and the choice of priors. Evidence using an empirical data set suggests that SW VAM and traditional VAM results may disagree meaningfully in practice. These findings suggest that SW VAMs have promising potential to recover true teacher value-added in practical applications and, as a version of value-added models that attends to student differences, can be used to test the validity of traditional VAM assumptions in empirical contexts.

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Cory Koedel, Trang Pham.

We study the conditional gender wage gap among faculty at public research universities in the U.S. We begin by using a cross-sectional dataset from 2016 to replicate the long-standing finding in research that conditional on rich controls, female faculty earn less than their male colleagues. Next, we construct a data panel to track the evolution of the wage gap through 2021. We show that the gap is narrowing. It declined by more than 50 percent over the course of our data panel to the point where by 2021, it is no longer detectable at conventional levels of statistical significance.

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Johnathan G. Conzelmann, Steven W. Hemelt, Brad J. Hershbein, Shawn Martin, Andrew Simon, Kevin Stange.

This paper introduces a new measure of the labor markets served by colleges and universities across the United States. About 50 percent of recent college graduates are living and working in the metro area nearest the institution they attended, with this figure climbing to 67 percent in-state. The geographic dispersion of alumni is more than twice as great for highly selective 4-year institutions as for 2-year institutions. However, more than one-quarter of 2-year institutions disperse alumni more diversely than the average public 4-year institution. In one application of these data, we find that the average strength of the labor market to which a college sends its graduates predicts college-specific intergenerational economic mobility. In a second application, we quantify the extent of “brain drain” across areas and illustrate the importance of considering migration patterns of college graduates when estimating the social return on public investment in higher education.

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