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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.
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.
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.
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.
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.
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.
The formula used to allocate federal funding for state and local special education programs is one of the Individual with Disabilities Act’s most critical components. The formula not only serves as the primary mechanism for dividing available federal dollars among states, it also represents policymakers’ intent to equalize educational opportunities for students with disabilities nationwide. In this study, we evaluate the distribution of IDEA Part B(611) funding in the wake of changes to the formula that were instituted at the law’s 1997 reauthorization. We find that the revised formula generated large and concerning disparities among states in federal special education dollars. We find that, on average, states with proportionally larger populations of children and children living in poverty, children identified for special education, and non-White and Black children receive fewer federal dollars, both per pupil and per student receiving special education. We present policy simulations that illustrate how changes to the existing formula might improve the fairness and efficiency with which federal IDEA Part B funding is allocated to states.
Over the past few decades, the U.S. has received a consistent and increasing influx of immigrants into the nation. Immigration poses challenges relating to diversity, inclusion and cohesion in education systems, including K-12 education. In the context of immigration, the theory of native flight argues that U.S. born populations move away from neighborhoods when an increasing number of immigrants move in. I test the theory of native flight in the context of K-12 school enrollments, by examining the impact of immigrant influx on public, private and public charter school enrollments, differentiating across U.S. born races and ethnicities. To do so, I merge yearly school enrollment measures from the common core of data (CCD) with immigration data from the American Community Survey (ACS) over the years 2005-2019. Using an instrumental variables approach (2SLS) to address potentially endogenous settlement patterns of immigrants into Metropolitan Statistical Areas (MSAs), I find that students of U.S. born race/ethnicities display heterogeneous enrollment responses to immigrant influx. Shares of White students and Black students in public non-charter schools decrease significantly in response to an increase in immigration. At the same time, the shares of Hispanic students and Asian students increase significantly in public non-charter schools. Analogous estimates for native flight into private schools lend further credence to public school estimates. Across private schools, the share of White students increases significantly in response to immigration. The share of Black students decreases across private schools as well, signaling a crowding-out effect. There are two key implications. First, significant White flight from the public-school system still exists over the past decade and a half. Second, while the increasing shares of White students in private schools might compensate for White students leaving the public school system, the shares of Black students are dropping across private and public schools.
Von Hippel & Cañedo (2021) reported that US kindergarten teachers placed girls, Asian-Americans, and children from families of high socioeconomic status (SES) into higher ability groups than their test scores alone would warrant. The results fit the view that teachers were biased.
This comment asks whether parents’ lobbying for higher placement might explain these results. The answer, for the most part, is no. Measures of parent-teacher contact explained little variation in children’s ability group placement, and did not account for the higher placement of girls, Asian-Americans, or high-SES children. In fact, Asian-American parents had less teacher contact than did white children. It appears that the biases observed by von Hippel & Cañedo resided primarily in teachers, not in parents.
We also ask whether teachers who used more objective assessment techniques were less biased in placing children into higher and lower ability groups. The answer, again, was no. Unfortunately, biases persisted in the face of objective information about students’ skill. Fortunately, the biases were not terribly large.
In spring 2020, nearly every U.S. public school closed at the onset of the Covid-19 pandemic. Existing evidence suggests that local political partisanship and teachers union strength were better predictors of fall 2020 school re-opening status than Covid case and death rates. We replicate and extend these analyses using data collected over the 2020-21 academic year. We demonstrate that Covid case and death rates were meaningfully associated with initial rates of in-person instruction. We also show that all three factors—Covid, partisanship, and teachers unions—became less predictive of in-person instruction as the school year continued. We then leverage data from two nationally representative surveys of Americans’ attitudes toward education and identify an as-yet undiscussed factor that predicts in-person instruction: public support for increasing teacher salaries. We speculate that education leaders were better able to manage the logistical and political complexities of school re-openings in communities with greater support for educators.