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America's decentralized system of public school governance is premised on the assumption that the interests of voters who elect school boards will be aligned with the educational needs of students. We explore the plausibility of this assumption by comparing the demographic characteristics of voters and students across four states. Using official voter turnout records and rich microtargeting data, we document considerable demographic differences between voters who participate in school board elections and the students attending the schools that boards oversee, suggesting that the assumption is unlikely to describe reality in many settings. For example, we show that most majority-nonwhite districts in our sample have a majority-white electorate and that these electoral disparities are associated with racial achievement gaps. Our novel analysis provides important political context for considering the electoral incentives facing school boards and how these incentives shape the quality of public education.
We use close tax elections to estimate the impact of school district funding increases on operational spending and student outcomes across seven states. Districts with passing levies directed new revenue toward support services and instructor salaries but did not increase teacher staffing levels. These districts eventually realized gains in student achievement and attainment. Our preferred estimates imply that increasing operational spending by $1,000 per pupil increased test scores by approximately 0.15 of a standard deviation and graduation rates by approximately 9 percentage points. There is some evidence of diminishing returns, as these effects are driven by districts below the median in spending per pupil. Based on research linking academic outcomes to earnings, we conclude that these spending increases were likely cost-effective.
We use information on the charter school choices made by North Carolina families, separately by race, who switched their child from a traditional public school (TPS) to a charter school in 2015-16 to explore how such choices affect racial segregation between schools and racial isolation within charter schools. We find that the movement of white switchers, but not minority switchers to charter schools increases racial segregation between schools. In addition, using a conditional logit model to estimate revealed preferences, we find that the value parents place on the racial composition of individual charter schools differs by the race and income of the switchers. As a result, even after we control for other valued aspects of charter schools -- such as distance from the previous traditional public school and the charter school’s mission, academic performance and services offered -- the differential preferences of the switchers leads to substantial racial isolation within charter schools.
A huge portion of what we know about how humans develop, learn, behave, and interact is based on survey data. Researchers use longitudinal growth modeling to understand the development of students on psychological and social-emotional learning constructs across elementary and middle school. In these designs, students are typically administered a consistent set of self-report survey items across multiple school years, and growth is measured either based on sum scores or scale scores produced based on item response theory (IRT) methods. While there is great deal of guidance on scaling and linking IRT-based large-scale educational assessment to facilitate the estimation of examinee growth, little of this expertise is brought to bear in the scaling of psychological and social-emotional constructs. Through a series of simulation and empirical studies, we produce scores in a single-cohort repeated measure design using sum scores as well as multiple IRT approaches and compare the recovery of growth estimates from longitudinal growth models using each set of scores. Results indicate that using scores from multidimensional IRT approaches that account for latent variable covariances over time in growth models leads to better recovery of growth parameters relative to models using sum scores and other IRT approaches.
Survey respondents use different response styles when they use the categories of the Likert scale differently despite having the same true score on the construct of interest. For example, respondents may be more likely to use the extremes of the response scale independent of their true score. Research already shows that differing response styles can create a construct-irrelevant source of bias that distorts fundamental inferences made based on survey data. While some initial studies examine the effect of response styles on survey scores in longitudinal analyses, the issue of how response styles affect estimates of growth is underexamined. In this study, we conducted empirical and simulation analyses in which we scored surveys using item response theory (IRT) models that do and do not account for response styles, and then used those different scores in growth models and compared results. Generally, we found that response styles can affect estimates of growth parameters including the slope, but that the effects vary by psychological construct, response style, and model used.
School Improvement Grants (SIG) represent one type of governments’ capacity-building investment to spur sustainable changes in America’s persistently under-performing public schools. This study examines both short- and long-run effects of the first two cohorts of SIG schools from two states and two urban districts across the country. Using dynamic event analyses, we observe that SIG showed larger effects in the second and third years of the intervention than the first year on 3-8th grade student test scores—a pattern of gradually increase over the course the intervention. These positive effects are largely sustained three or four years after the funding ended. In high schools, the SIG effects on 4-year graduation rates were steadily increasing throughout the period of six or seven years after the initial start of the intervention. These patterns of SIG effects mostly apply to each of the four locations, but the magnitude of effects varies across locations, suggesting differential implementations. Moreover, SIG effects on students of color or low-socioeconomic students are similar to, and sometimes a bit larger than, the overall SIG effects. We also conduct a variety of sensitivity and robustness checks. Lastly, we discuss the policy implications of our findings on states’ continuing efforts of transforming public organizations and building their long-term capacity for better performance.
This study examines whether county-level estimates of implicit bias predict black-white test score gaps in county schools. Data from over 1 million respondents from across the United States who completed an online version of the Race Implicit Association Test (IAT) were combined with data from the Stanford Education Data Archive covering over 300 million test scores from U.S. schoolchildren in grades 3 through 8. In both bivariate and multivariate models, counties with higher levels of racial bias had larger black-white test score disparities. This relationship was primarily explained by sorting mechanisms: The black-white test score gap was larger in counties with higher levels of implicit bias because these counties’ schools were more racially segregated and were characterized by larger racial gaps in gifted and talented assignment as well as special education placement.
The Every Student Succeeds Act of 2015 (ESSA) grants states unprecedented discretion in implementing many of the federal law’s requirements concerning the needs of the nation’s educationally disadvantaged students. This theoretical paper addresses a void in the policy implementation literature on why ESEA reform efforts have not been more effectively sustained. It synthesizes previous research on ESEA by proposing the use of multiple political science frames to guide new empirical research on ESSA’s impacts. These alternative models—ESSA’s Legal Framework, Institutional Actors, and Stakeholder Bargaining—can inform the law’s national impacts on equity for disadvantaged students and the key conditions affecting differences in state responses to the equity challenge ESSA presents.
Political parties in the U.S. are composed of networks of interest groups, according to the extended party network theory. Scholars have focused on national extended party networks. We use the case of education interest groups to explore how policy environments shape party networks on the state level. Using 145,000 campaign contributions from 2000 to 2017, we show that the alignment of education interest groups has changed over time. In 2000, teachers unions were the dominant group and aligned with Democrats. Meanwhile, Republicans lacked support from any education group. This pattern was relatively consistent across states. Over time, coalitions diverged, with some state networks polarizing, meaning unions increasingly aligned with Democrats and reform groups with Republicans, while others did not experience such polarization. We find that labor law restrictions and private school choice programs were related to these trends, suggesting that state-level policies shape the contours of state party networks.
Revealed preferences for equal college access may be due to beliefs that equal access increases societal income or income equality. To isolate preferences for those goods, we implement an online discrete choice experiment using social statistics generated from true variation among commuting zones. We find that, ceteris paribus, the average income that individuals are willing to sacrifice is (i) $4,984 dollars to increase higher education (HE) enrollment by 1 standard deviation (14%); (ii) $1,168 dollars to decrease rich/poor gaps in HE enrollment by 1 standard deviation (8%); (iii) $2,900 to decrease the 90/10 income inequality ratio by 1 standard deviation (1.66). In addition, we find that political affiliation is an important moderator of preferences for equality. While both Democrats and Republicans are willing to trade over $4,000 dollars to increase HE enrollment by 1 standard deviation, Democrats are willing to sacrifice nearly three times more income to decrease either rich/poor gaps in HE enrollment or the 90/10 income inequality ratio by 1 standard deviation.