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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.
Disparate turnover among teachers of color remains a persistent educational challenge, yet little research explores the link between school leadership, peer teaching staff, and turnover disparities. This study explores whether principal and peer teacher demographics predict teacher turnover in New York City, and whether they do so differently for teachers of color. We find teachers are less likely to exit when their principal and a higher share of peer teachers are of the same race/ethnicity, with Black teachers having especially lower transfer rates with a higher share of Black peer teachers. However, results suggest school leadership style and positive teacher relationships are not differentially associated with turnover for teachers of color. We conclude with a discussion of implications.
This article reviews the development of my thesis that the California Supreme Court's Serrano decisions, which began in 1971 and sought to disconnect district school spending with local property taxes, led to the fiscal conditions that caused California voters to embrace Proposition 13 in 1978, which radically undermined the local property tax system. I submit that my thesis is most likely true because of Proposition 13’s durability and the absence of alternative explanations that account for its longstanding power over California politics. The article then circles back to John Serrano himself. I want to respectfully suggest that John’s views about the role of public education and my own have more in common than might be suspected. At the very least I want to correct the impression that John supported Proposition 13, which was suggested by the title of my last full article about this topic.
‘QuantCrit’ (Quantitative Critical Race Theory) is a rapidly developing approach that seeks to challenge and improve the use of statistical data in social research by applying the insights of Critical Race Theory. As originally formulated, QuantCrit rests on five principles; 1) the centrality of racism; 2) numbers are not neutral; 3) categories are not natural; 4) voice and insight (data cannot ‘speak for itself); and 5) a social justice/equity orientation (Gillborn et al, 2018). The approach has quickly developed an international and interdisciplinary character, including applications in medicine (Gerido, 2020) and literature (Hammond, 2019). Simultaneously, there has been ferocious criticism from detractors outraged by the suggestion that numbers are anything other than objective and scientific (Airaksinen, 2018). In this context it is vital that the approach establishes some common understandings about good practice; in order to sustain rigor, make QuantCrit accessible to academics, practioners, and policymakers alike, and resist widespread attempts to over-simplify and pillory. This paper is intended to advance an iterative process of expanding and clarifying how to ‘QuantCrit’.
The COVID-19 pandemic upended the U.S. education system and the economy in ways that dramatically affected the jobs of K-12 educators. However, data limitations have led to considerable uncertainty and conflicting reports about the nature of staffing challenges in schools. We draw on education employment data from the Bureau of Labor Statistics (BLS) and State Education Agencies (SEA) to describe patterns in K-12 education employment and to highlight the limitations of available data. Data from the BLS suggest overall employment in the K-12 labor market declined by 9.3 percent at the onset of the pandemic and remains well below pre-pandemic levels. SEA data suggest that teachers have not (yet) left the profession in mass as many predicted, but that turnover decreased in the summer of 2020. We explore possible explanations for these patterns including (1) weak hiring through the summer of 2020 and (2) high attrition among K-12 instructional support staff. State vacancy data also suggest that schools are facing substantial challenges filling open positions during the 2021-22 academic year. Our analyses illustrate the imperative to build more timely, detailed, and nationally representative data systems on the K-12 education labor market to better inform policy.
In very low-income settings, how much does family demand matter for child learning? In rural Gambia, caregivers with high aspirations for their children’s future education and career, measured before children start school, invest substantially more than other families in their children’s education. Despite this, essentially no children are literate or numerate three years later. In contrast, in villages receiving a highly impactful, teacher-focused supply-side intervention, children of high-aspirations caregivers are 25 percent more likely to achieve literacy and numeracy than other children. In such settings, greater demand can map onto developmentally meaningful learning differences, but only with adequate complementary inputs.