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Evidence on educational returns and the factors that determine the demand for schooling in developing countries is extremely scarce. We use two surveys from Tanzania to estimate both the actual and perceived schooling returns and subsequently examine what factors drive individual misperceptions regarding actual returns. Using ordinary least squares and instrumental variable methods, we find that each additional year of schooling in Tanzania increases earnings, on average, by 9 to 11 percent. We find that on average, individuals underestimate returns to schooling by 74 to 79 percent, and three factors are associated with these misperceptions: income, asset poverty, and educational attainment. Shedding light on what factors relate to individual beliefs about educational returns can inform policy on how to structure effective interventions to correct individuals' misperceptions.
Classroom teachers in the US are absent on average approximately six percent of a school year. Despite the prevalence of teacher absences, surprisingly little research has assessed the key source of replacement instruction: substitute teachers. Using detailed administrative and survey data from a large urban school district, we document the prevalence, predictors, and variation of substitute coverage across schools. Less advantaged schools systematically exhibit lower rates of substitute coverage compared with peer institutions. Observed school, teacher, and absence characteristics account for only part of this school variation. In contrast, substitute teachers’ preferences for specific schools, mainly driven by student behavior and support from teachers and school administrators, explain a sizable share of the unequal distribution of coverage rates above and beyond standard measures in administrative data.
Performance-based funding models for higher education, which tie state support for institutions to performance on student outcomes, have proliferated in recent decades. Some states have designed these policies to also address educational attainment gaps by including bonus payments for traditionally low-performing groups. Using a Synthetic Control Method research design, we examine the impact of these funding regimes on race-based completion gaps in Tennessee and Ohio. We find no evidence that performance-based funding narrowed race-based completion gaps. In fact, contrary to their intended purpose, we find that performance-based funding widened existing gaps in certificate completion in Tennessee. Across both states, the estimated impacts on associate degree outcomes are also directionally consistent with performance-based funding exacerbating racial inequities in associate degree attainment.
We use high frequency internet search data to study in real time how US households sought out online learning resources as schools closed due to the Covid-19 pandemic. By April 2020, nationwide search intensity for both school- and parent-centered online learning resources had roughly doubled relative to baseline. Areas of the country with higher income, better internet access and fewer rural schools saw substantially larger increases in search intensity. The pandemic will likely widen achievement gaps along these dimensions given schools' and parents' differing engagement with online resources to compensate for lost school-based learning time. Accounting for such differences and promoting more equitable access to online learning could improve the effectiveness of education policy responses to the pandemic. The public availability of internet search data allows our analyses to be updated when schools reopen and to be replicated in other countries.
COVID-19 has forced essentially all schools in the country to close their doors to inperson activities. In this study, we provide new evidence about variation in school responses across school types. We focus on five main constructs of school activity during COVID-19: personalization and engagement in instruction, personalization and engagement in other school communication with students, progress monitoring (especially assignment grading), breadth of services (e.g., counseling and meals), and equitable access (to technology and services for students with special needs). We find that the strongest predictor of the extent of school activities was the education level of parents and other adults in schools’ neighborhoods. Internet access also predicts school responses. Race, parent/adult income, and school spending do not predict school responses. Private schools shifted to remote learning several days faster than traditional public schools, though others eventually caught up. On some measures, charter schools exceeded the responses of other schools; in other cases, traditional public schools had the highest overall measures. States in the Midwest responded more aggressively than those in other regions, especially the South, even after controlling for the full set of additional covariates. Learning management systems were reported by a large majority of schools, followed by video communication tools and tutorial/assessment programs. Several methods are proposed and implemented to address differential website use. We discuss potential implications of these findings for policy and effects on student outcomes.
We investigate the determinants and consequences of increased school choice by analyzing a 22-year school panel matched to county-level demographic, economic, and political data. Using an event-study design exploiting the precise timing of charter school enrollment change, we provide robust evidence that charter enrollment growth increases racial and especially socioeconomic school segregation, a finding that is partially explained by non-poor students’ transition from the private to public sector. Charter growth drives public sector incorporation, while also increasing within-public sector segregation. To assess the effects of disparate choice policies on segregation, we replicate this analysis for magnet schools, which have admissions practices intended to increase diversity, and find no evidence that magnet enrollment growth increases segregation.
Researchers decompose test score “gaps” and gap-changes into within- and between-school portions to generate evidence on the role that schools play in shaping educational inequality. However, existing decomposition methods (a) assume an equal-interval test scale and (b) are a poor fit to coarsened data such as proficiency categories. We develop two decomposition approaches that overcome these limitations: an extension of V, an ordinal gap statistic (Ho, 2009), and an extension of ordered probit models (Reardon et al., 2017). Simulations show V decompositions have negligible bias with small within-school samples. Ordered probit decompositions have negligible bias with large within-school samples but more serious bias with small within-school samples. These methods are applicable to decomposing any ordinal outcome by any categorical grouping variable.
Many kindergarten teachers place students in higher and lower “ability groups” to learn math and reading. Ability group placement should depend on student achievement, but critics charge that placement is biased by socioeconomic status (SES), gender, and race/ethnicity. We predict group placement in the Early Childhood Longitudinal Study of the Kindergarten class of 2010-11, using linear and ordinal regression models with classroom fixed effects. The best predictors of group placement are test scores, but girls, high-SES students, and Asian Americans receive higher placements than their test scores alone would predict. One third of students move groups during kindergarten, and some movement is predicted by changes in test scores, but high-SES students move up more than score gains would predict, and Hispanic children move up less. Net of SES and test scores, there is no bias in the placement of African American children. Differences in teacher-reported behaviors explain the higher placement of girls, but do little to explain the higher or lower placement of other groups. Although achievement is the best predictor of ability group placement, there are signs of bias.
New York City’s universal pre-kindergarten program, which increased full-day enrollment from 19,000 to almost 70,000 children, is ambitious in both scale and implementation speed. We provide new evidence on the distribution of pre-K quality in NYC by student race/ethnicity, and investigate the extent to which observed differences are associated with the spatial distribution of higher-quality providers. Relative to other jurisdictions, we find the average quality of public pre-K providers is high. However, we identify large disparities in the average quality of providers experienced by black and white students, which is partially explained by differential proximity to higher-quality providers. Taken together, current racial disparities in the quality of pre-K providers may limit the program’s ability to reduce racial achievement gaps.