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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.
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.
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.
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.
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.
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.
The absence of federal support leaves undocumented students reliant on state policies to financially support their postsecondary education. We descriptively examine the postsecondary trajectories of tens of thousands of undocumented students newly eligible for California’s state aid program, using detailed application data to compare them to similar peers. In this context, undocumented students who apply and are eligible for the program use grant aid to attend college at rates similar to their peers. Undocumented students remain more likely to enroll in a community college at the expense of attending a broad access four-year college and have higher exit rates from two-year colleges. Yet undocumented students are equally likely to attend the more selective University of California system, and across four-year public colleges have persistence rates similar to their peers, showing that those who do attend four-year colleges perform well.
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.