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K-12 Education
A substantial body of experimental evidence demonstrates that in-person tutoring programs can have large impacts on K-12 student achievement. However, such programs typically are costly and constrained by a limited local supply of tutors. In partnership with CovEducation (CovEd), we conduct a pilot program that has potential to ease both of these concerns. We conduct an experiment where volunteer tutors from all over the country meet 1-on-1 with middle school students online during the school day. We find that the program produces consistently positive (0.07σ for math and 0.04σ for reading) but statistically insignificant effects on student achievement. While these estimates are notably smaller than those found in many higher-dosage in-person tutoring programs, they are from a significantly lower-cost program that was delivered within the challenging context of the COVID-19 pandemic. We provide evidence that is consistent with a dosage model of tutoring where additional hours result in larger effects.
Economic downturns can cause major funding shortfalls for U.S. public schools, often forcing districts to make difficult budget cuts including teacher layoffs. In this brief, we synthesize the empirical literature on the widespread teacher layoffs caused by the Great Recession. Studies find that teacher layoffs harmed student achievement and were inequitably distributed across schools, teachers, and students. Research suggests that specific elements of the layoff process can exacerbate these negative effects. Seniority-based policies disproportionately concentrate layoffs among teachers of color who are more likely to be early career teachers. These “last-in first-out” policies also disproportionately affect disadvantaged students because these students are more likely to be taught by early career teachers. The common practice of widely distributing pink slips warning about a potential job loss also appears to increase teacher churn and negatively impact teacher performance. Drawing on this evidence, we outline a set of policy recommendations to minimize the need for teacher layoffs during economic downturns and ensure that the burden of any unavoidable job cuts does not continue to be borne by students of color and students from low-income backgrounds.
There is an emerging consensus that teachers impact multiple student outcomes, but it remains unclear how to measure and summarize the multiple dimensions of teacher effectiveness into simple metrics for research or personnel decisions. We present a multidimensional empirical Bayes framework and illustrate how to use noisy estimates of teacher effectiveness to assess the dimensionality and predictive power of teachers' true effects. We find that it is possible to efficiently summarize many dimensions of effectiveness and most summary measures lead to similar teacher rankings; however, focusing on any one specific measure alone misses important dimensions of teacher quality.
Over the last two decades, twenty-two states have moved away from traditional defined benefit (DB) pension systems and toward pension plan structures like the defined contribution (DC) plans now prevalent in the private sector. Others are considering such a reform as it is seen as a means of limiting future pension funding risk. It is important to understand the implications of such reforms for end-of-career exit patterns and workforce composition. Empirical evidence on the relationship between pension plan structure and retirement timing is currently limited, primarily because, most state pension reforms are so new that few employees enrolled in those alternative plans have reached retirement age. An exception, and the subject of our analysis, is the teacher retirement system in Washington State, which introduced a hybrid DB-DC plan in 1996 and allowed employees in its traditional DB plan to transfer into the new plan. Our analysis focuses on a years-of-service threshold, the crossing of which grants employees early retirement eligibility and, in many cases, a large upward shift in retirement wealth. The financial implications of crossing this threshold are far greater under the state’s traditional DB plan than under the hybrid plan. We find that employees are responsive to crossing the years-of-service threshold, but we fail to find significant evidence that the propensity to exit the workforce varies according to plan enrollment.
A growing literature uses value-added (VA) models to quantify principals' contributions to improving student outcomes. Principal VA is typically estimated using a connected networks model that includes both principal and school fixed effects (FE) to isolate principal effectiveness from fixed school factors that principals cannot control. While conceptually appealing, high-dimensional FE regression models require sufficient variation to produce accurate VA estimates. Using simulation methods applied to administrative data from Tennessee and New York City, we show that limited mobility of principals among schools yields connected networks that are extremely sparse, where VA estimates are either highly localized or statistically unreliable. Employing a random effects shrinkage estimator, however, can alleviate estimation error to increase the reliability of principal VA.
In this paper we estimate the effect of charter schools on the diversity of nearby traditional public schools (TPSs) and neighborhoods in New York City. We employ a difference-in-differences approach that exploits the differences in the expansion of the charter sector between grades in the same school. This approach allows us to isolate the effect of charter schools from other neighborhood demographic changes. Our results show small positive effects of charter school expansion on TPS diversity as measured by the entropy score. This change is explained by small increases in the number of White students attending nearby TPSs and larger reductions in the number of Black and Hispanic students in these schools. We also find descriptive evidence that while both neighborhoods and TPSs are slightly more diverse following charter school expansion, schools are changing faster than their surrounding neighborhoods.
Black and Latinx students are under-represented in Advanced Placement (AP) and Dual Enrollment (DE), and implicit bias of educators has been discussed as one potential contributing factor. In this study, I test whether implicit and explicit racial bias are related to AP and DE participation and racial/ethnic gaps in participation, controlling for various observable contextual factors. I find a small relationship between implicit racial bias and disparate AP participation for Black students relative to White students, and suggestive evidence of a relationship between explicit racial bias and disparate DE participation for Black students relative to White students. Further, more explicitly-biased communities tend to have lower AP participation rates overall. Implications for school leaders regarding implicit bias training and other ways to address systemic inequities in access are discussed.
We use publicly available, longitudinal data from Washington state to study the extent to which three interrelated processes—teacher attrition from the state teaching workforce, teacher mobility between teaching positions, and teacher hiring for open positions—contribute to “teacher quality gaps” (TQGs) between students of color and other students in K–12 public schools. Specifically, we develop and implement an agent-based model simulation of decisions about attrition, mobility, and hiring to assess the extent to which each process contributes to observed TQGs. We find that eliminating inequities in teacher mobility and hiring across different schools would close TQGs within 5 years, while just eliminating inequities in teacher hiring would close gaps within 10 years. On the other hand, eliminating inequities in teacher attrition without addressing mobility and hiring does little to close gaps.
English learner (EL) education is widely conceived as services for immigrant-origin students, however nearly one in ten American Indian, Alaska Native, and Native Hawaiian students are classified in school as ELs. Title III of the Every Student Succeeds Act (2015) defines EL eligibility differently for Indigenous, compared to non-Indigenous, students with implications for who is identified as an EL and how best to serve their academic and linguistic interests. This study presents findings from a 50-state review of Indigenous EL identification policy. We find that states fall into four categories ranging from no differentiation in Indigenous EL identification to clear differentiation. We describe each of these four categories and conclude with reflections on how this wide variation in state policies has implications for Indigenous students’ educational resources and experiences.
We synthesize and critique federal fiscal policy during the Great Recession and Covid-19 pandemic. First, the amount of aid during both crises was inadequate to meet policy goals. Second, the mechanisms used to distribute funds was disconnected from policy goals and provided different levels of aid to districts with equivalent levels of economic disadvantage. Third, data tools are missing making it difficult to understand whether funds were used to meet policy goals. Details for these results are provided along with policy recommendations.