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Recent public discussions and legal decisions suggest that school segregation will remain persistent in the United States, but increased transparency may help monitor spending across schools. These circumstances revive an old question: is it possible to achieve an educational system that is separate but equal—or better—in terms of spending? This question motivates further understanding the measurement of spending progressivity and its association with segregation. Focusing on economic disadvantage, we compare two commonly-used measures of spending progressivity: exposure-based and slope-based. We show that each measure is predicated on different assumptions about the progressivity of within-school resource allocations, and that they are theoretically linked through segregation. We empirically examine school spending progressivity and its properties using nationwide school spending data from the 2018-19 school year. Consistent with our theory, the exposure-based measure is the slope-based measure shrunk inversely by economic school segregation. This property makes more segregated school districts look more progressive on the exposure-based measure, representing a seemingly “separate but better” relationship. However, we show that this provocative pattern may be reversed by relatively modest poor-versus-nonpoor differences in unobserved parental contributions. We discuss implications for the measurement of progressivity, and for theory on public educational investments broadly.
This simulation study examines the characteristics of the Explanatory Item Response Model (EIRM) when estimating treatment effects when compared to classical test theory (CTT) sum and mean scores and item response theory (IRT)-based theta scores. Results show that the EIRM and IRT theta scores provide generally equivalent bias and false positive rates compared to CTT scores and superior calibration of standard errors under model misspecification. Analysis of the statistical power of each method reveals that the EIRM and IRT theta scores provide a marginal benefit to power and are more robust to missing data than other methods when parametric assumptions are met and provide a substantial benefit to power under heteroskedasticity, but their performance is mixed under other conditions. The methods are illustrated with an empirical data application examining the causal effect of an elementary school literacy intervention on reading comprehension test scores and demonstrates that the EIRM provides a more precise estimate of the average treatment effect than the CTT or IRT theta score approaches. Tradeoffs of model selection and interpretation are discussed.
Districts nationwide have revised their educator evaluation systems, increasing the frequency with which administrators observe and evaluate teacher instruction. Yet, limited insight exists on the role of evaluator feedback for instructional improvement. Relying on unique observation-level data, we examine the alignment between evaluator and teacher assessments of teacher instruction and the potential consequences for teacher productivity and mobility. We show that teachers and evaluators typically rate teacher performance similarly during classroom observations, but with significant variability in teacher-evaluator ratings. While teacher performance improves across multiple classroom observations, evaluator ratings likely overstate productivity improvements among the lowest-performing teachers. Evaluators, but not teachers, systematically rate teacher performance lower in classrooms serving higher concentrations of economically disadvantaged students. And while teacher performance improves when evaluators provide more critical feedback about teacher instruction, teachers receiving critical feedback may seek alternative teaching assignments in schools with less critical evaluation settings. We discuss the implications of these findings for the design, implementation and impact of educator evaluation systems.
Current public pension funding policy has arguably failed on both theoretical and empirical grounds. The traditional actuarial approach elides the risk-return tradeoff at the heart of finance economics and has resulted in steadily rising contribution rates, instead of a sustainable steady state. We propose an economic reformulation of funding policy based on steady-state analysis of the fundamental equations of motion for pension asset and liability growth, incorporating both an expected return on risky assets and a low-risk discount rate for liabilities. Our steady-state result simultaneously conveys the benefit of risky investment and the cost of the associated risk. We integrate our analysis into a simple social welfare function to re-examine the basis for pre-funding and elucidate the net benefits of using risky assets to defray contributions. We also formally derive a family of transition policies for convergence to the expected steady state. We illustrate how the parameters of our proposed policy can be adjusted to manage the tradeoff between long-run contribution rate risk and short-term responsiveness. We believe our analysis provides the basis for reformulating contribution policy in a way that better supports sustainability and coherently conveys the tradeoffs consistent with finance economics.
Billions of dollars are invested in opt-in, educational resources to accelerate students’ learning. Although advertised to support struggling, marginalized students, there is no guarantee these students will opt in. We report results from a school system’s implementation of on-demand tutoring. The take up was low. At baseline, only 19% of students ever accessed the platform, and struggling students were far less likely to opt in than their more engaged and higher achieving peers. We conducted a randomized controlled trial (N=4,763) testing behaviorally-informed approaches to increase take-up. Communications to parents and students together increase the likelihood students access tutoring by 46%, which led to a four-percentage point decrease in course failures. Nonetheless, take-up remained low, showing concerns that opt-in resources can increase—instead of reduce—inequality are valid. Without targeted investments, opt-in educational resources are unlikely to reach many students who could benefit.
Books shape how children learn about society and norms, in part through representation of different characters. We introduce new artificial intelligence methods for systematically converting images into data and apply them, along with text analysis methods, to measure the representation of race, gender, and age in award-winning children’s books from the past century. We find that more characters with darker skin color appear over time, but the most influential books persistently depict a greater proportion of light-skinned characters than other books, even after conditioning on race; we also find that children are depicted with lighter skin than adults. Relative to their growing share of the U.S. population, Black and Latinx people are underrepresented in these same books, while White males are overrepresented. Over time, females are increasingly present but appear less often in text than in images, suggesting greater symbolic inclusion in pictures than substantive inclusion in stories. We then report empirical evidence for predictions about the supply of and demand for representation that would generate these patterns. On the demand side, we show that people consume books that center their own identities. On the supply side, we document higher prices for books that center non-dominant social identities and fewer copies of these books in libraries that serve predominantly White communities. Lastly, we show that the types of children’s books purchased in a neighborhood are related to local political beliefs.
The disruption of in-person schooling during the Covid-19 pandemic has affected students’ learning, development, and well-being. Students in Latin America and the Caribbean have been hit particularly hard because schools in the region have stayed closed for longer than anywhere else, with long-term expected adverse consequences. Little is known about which factors are associated with the slow in-person return to school in the region and how these factors have had differential effects based on students’ socio-economic status. Combining a longitudinal national survey of the Chilean school system and administrative datasets, we study the supply and demand factors associated with students’ resuming in-person instruction and the socio-economic gaps in school reopening in Chile in 2021. We defined socio-economic status based on parents’ education and household income. Our results show that in-person learning in 2021 was limited mainly by supply factors (i.e., sanitary, administrative, and infrastructure restrictions). However, once the supply restrictions decreased, many low-income students and their families did not resume in-person instruction. We found vast inequalities in face-to-face instruction by school’s socio-economic characteristics. On average, schools in the highest 10% of the socio-economic distribution had three times higher attendance rates than the remaining 90%. We found no significant differences between schools in the lowest 90% of the distribution. After exceptionally long school closures, most school authorities, students, and their families did not return to in-person instruction, particularly those of low socio-economic status. These inequalities in in-person instruction will expand existing disparities in students’ learning and educational opportunities.
Youth voter turnout remains stubbornly low and unresponsive to civic education. Rigorous evaluations of the adoption of civic tests for high school graduation by some states on youth voter turnout remain limited. We estimate the impact of a recent, state-mandated civics test policy—the Civics Education Initiative (CEI)—on youth voter turnout by exploiting spatial and temporal variation in the adoption of CEI across states. Using nationally-representative data from the 1996-2020 Current Population Survey and a Difference-in-Differences analysis, we find that CEI does not significantly affect youth voter turnout. Our null results, largely insensitive to a variety of alternative specifications and robustness checks, provide evidence regarding the lack of efficacy of civic test policies when it comes to youth voter participation.
Community schools are an increasingly popular strategy used to improve the performance of students whose learning may be disrupted by non-academic challenges related to poverty. Community schools partner with community based organizations (CBOs) to provide integrated supports such as health and social services, family education, and extended learning opportunities. With over 300 community schools, the New York City Community Schools Initiative (NYC-CS) is the largest of these programs in the country. Using a novel method that combines multiple rating regression discontinuity design (MRRDD) with machine learning (ML) techniques, we estimate the causal effect of NYC-CS on elementary and middle school student attendance and academic achievement. We find an immediate reduction in chronic absenteeism of 5.6 percentage points, which persists over the following three years. We also find large improvements in math and ELA test scores – an increase of 0.26 and 0.16 standard deviations by the third year after implementation – although these effects took longer to manifest than the effects on attendance. Our findings suggest that improved attendance is a leading indicator of success of this model and may be followed by longer-run improvements in academic achievement, which has important implications for how community school programs should be evaluated.
How much does family demand matter for child learning in settings of extreme poverty? In rural Gambia, families with high aspirations for their children’s future education and career, measured before children start school, go on to invest substantially more than other families in the early years of their children’s education. Despite this, essentially no children are literate or numerate three years later. When villages receive a highly-impactful, teacher-focused supply-side intervention, however, children of these families are 25 percent more likely to achieve literacy and numeracy than other children in the same village. Furthermore, improved supply enables these children to acquire other higher-level skills necessary for later learning and child development. We also document patterns of substitutability and complementarity between demand and supply in generating learning at varying levels of skill difficulty. Our analysis shows that greater demand can map onto developmentally meaningful learning differences in such settings, but only with adequate complementary inputs on the supply side.