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We present results from a meta-analysis of 37 experimental and quasi-experimental studies of summer programs in mathematics for children in grades pre-K-12, examining what resources and characteristics relate to stronger student achievement, attainment, and social-emotional and behavioral outcomes. Compared to control group children, children who participated in summer programs that included mathematics lessons and activities enjoyed significant improvements in mathematics learning as well as social-behavioral outcomes. We find an average weighted impact estimate of +0.09 standard deviations on mathematics achievement outcomes. In a parallel meta-analysis, we found similar positive impacts of summer programs on socialemotional and behavioral outcomes, Programs conducted in both high- and lower-poverty settings saw similar positive impacts. The results highlight the potential for summer programs to strengthen children’s mathematical ability and improve learning outcomes in both mixed-poverty and high-poverty settings.
The segregation of students by socioeconomic status has been on the rise in American public education between schools during the past several decades. Recent work has demonstrated that segregation is also increasing within schools at the classroom level. In this paper, we contribute to our understanding of the determinants of this increase in socioeconomic segregation within schools. We assess whether growth in the presence and number of nearby charter schools have affected the segregation of socioeconomically disadvantaged students by classroom in traditional public schools (TPS). Using data from North Carolina, we estimate a series of models exploit variation in the number and location of charter schools over time between 2007 and 2014 to estimate the impact of charter school penetration and proximity on levels of within school segregation in TPS classrooms serving grades 3-8. We find that socioeconomic segregation in math and English language arts increase in grades 3-6 when additional charter schools open within large urban districts. We find the largest impacts on schools that are closest to the new charter schools. We estimate that the impact of charter schools can account for almost half of the overall growth in socioeconomic segregation we see over the course of the panel within grades 3-6 in large urban districts.
Mixed evidence on the relationship between school closure and COVID-19 prevalence could reflect focus on large-scale levels of geography, limited ability to address endogeneity, and demographic variation. Using county-level CDC COVID-19 data through June 15, 2020, two matching strategies address potential heterogeneity: nearest geographic neighbor and propensity scores. Within nearest neighboring pairs in different states with different school closure timing, each additional day from a county’s first case until state-ordered school closure is related to 1.5%-2.4% higher cumulative COVID-19 deaths per capita (1,227-1,972 deaths for a county with median population and deaths/capita). Results are consistent using propensity score matching, COVID-19 data from two alternative sources, and additional sensitivity analyses. School closure is more strongly related to COVID-19 deaths in counties with a high concentration of Black or poor residents, suggesting schools play an unequal role in transmission and earlier school closure is related to fewer lives lost in disadvantaged counties.
The decades-long resistance to federally imposed school desegregation entered a new phase at the turn of the new century, when federal courts stopped pushing racial balance as a remedy for past segregation, adopting in its place a color-blind approach in judging local school districts’ assignment plans. Using data that span 1998 to 2016 from North Carolina, one of the first states to come under this color-blind dictum, we examine the ways in which households and policymakers took actions that had the effect of reducing the amount of interracial contact in K-12 schools within counties. We divide these reductions in interracial contact into portions due to the private school and charter school sectors, the existence of multiple school districts, and racial disparities between schools within districts and sectors. For most counties, the last of these proves to be the biggest, though in some counties private schools, charter schools, or multiple districts played a deciding role. In addition, we decompose segregation in the state’s 13 metropolitan areas, finding that more than half can be attributed to racial disparities inside school districts. We also measure segregation by economic status, finding that it, like racial segregation, increased in the largest urban counties, but elsewhere changed little over the period.
Families and governments are the primary sources of investment in children, proving access to basic resources and other developmental opportunities. Recent research identifies significant class gaps in parental investments that contribute to high levels of inequality by family income and education and, potentially, to inequality in children’s development. State-level public investments in children and families have the potential to reduce class inequality in children’s developmental environments by affecting parents’ behavior. Using newly assembled administrative data from 1998-2014, linked to household-level data from the Consumer Expenditure Survey, we examine how public sector investment in income support, health and education is associated with the private expenditures of low and high-SES parents on developmental items for children. Are class gaps in parental investments in children narrower in contexts of higher public investment for children and families? We find that more generous public spending for children and families is associated with significantly narrower class gaps in private parental investments. Moreover, we find that equalization is driven by bottom up increases in low-SES household spending for the progressive investments of income support and health, and by top down decreases in high-SES household spending for the universal investment of public education.
A growing body of research and popular reporting shows racial differences in school modality choices during the COVID-19 crisis, with white students more likely to attend school in person. This in-person learning gap raises serious equity concerns. We use unique panel survey data to explore possible explanations. We find that a combination of factors may explain these differences. School districts’ offerings, political partisanship, and local COVID-19 outbreaks are all meaningfully associated with and plausibly explain the in-person learning racial gap. As schools start offering more in-person learning, significant efforts may be necessary to ensure that families and students attend those in-person learning opportunities.
This paper considers an unavoidable feature of the school environment, class rank. What are the long-run effects of a student’s ordinal rank in elementary school? Using administrative data on all public-school students in Texas, we show that students with a higher third-grade academic rank, conditional on achievement and classroom fixed effects, have higher subsequent test scores, are more likely to take AP classes, graduate from high school, enroll in and graduate from college, and ultimately have higher earnings 19 years later. We also discuss the necessary assumptions for the identification of rank effects and propose new solutions to identification challenges. The paper concludes by exploring the tradeoff between higher quality schools and higher rank in the presence of these rank-based peer effects.
Past research extensively documents inequalities in educational opportunity and achievement by students’ race/ethnicity or socioeconomic status (SES). Less scholarship focuses on how race/ethnicity and SES interact and jointly contribute to educational inequalities. We advance this burgeoning line of scholarship by charting math achievement trajectories and school socioeconomic composition by both student race/ethnicity and SES in California from 2014-15 through 2017-18. Linked administrative data allow us to operationalize student SES more richly than point-in-time free meal eligibility, a measure commonly used in education research. We find evidence of considerable racial/ethnic disparities in math achievement and school socioeconomic composition among same-SES students. White and Asian students score substantially higher on math achievement tests and attend higher-SES schools than same-SES Hispanic and Black students. Achievement and contextual inequalities are related: differential exposure to school SES by student race/ethnicity is associated with within-SES racial/ethnic achievement disparities. Our findings show that SES does not translate into the same contextual or achievement advantages for students of all racial/ethnic groups, demonstrating the importance of jointly considering student race/ethnicity and SES in future research and policy development.
A survey targeting education researchers conducted in November, 2020 provides both short- and longer-term predictions of how much achievement gaps between low- and high-income students in U.S elementary schools will change as a result of COVID-related disruptions to schooling and family life. Relative to a pre-COVID achievement gap of 1.00 SD, respondents’ median forecasts for increases in achievement gaps in elementary school by spring, 2021 were very large – from 1.00 to 1.30 and 1.25 SD, respectively, for math and reading. Researchers forecast only small reductions in gaps between spring 2021 and 2022. Although forecasts were heterogeneous, almost all respondents predicted that gaps would grow during the pandemic and would not return to pre-pandemic levels in the following school year. We discuss some implications of these predictions for strategies to reduce learning gaps exacerbated by the pandemic as well as the mental models researchers appear to employ in making their predictions.
Valid and reliable measurements of teaching quality facilitate school-level decision-making and policies pertaining to teachers. Using nearly 1,000 word-to-word transcriptions of 4th- and 5th-grade English language arts classes, we apply novel text-as-data methods to develop automated measures of teaching to complement classroom observations traditionally done by human raters. This approach is free of rater bias and enables the detection of three instructional factors that are well aligned with commonly used observation protocols: classroom management, interactive instruction, and teacher-centered instruction. The teacher-centered instruction factor is a consistent negative predictor of value-added scores, even after controlling for teachers’ average classroom observation scores. The interactive instruction factor predicts positive value-added scores. Our results suggest that the text-as-data approach has the potential to enhance existing classroom observation systems through collecting far more data on teaching with a lower cost, higher speed, and the detection of multifaceted classroom practices.