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Prior research has found that financial investments in North Carolina’s early childhood education programs—Smart Start and NC Pre-K—generated positive effects on student achievement in reading and mathematics through eighth grade (Bai et al., 2020). The current study examined if these effects were moderated by two dimensions of educational opportunity in NC public school districts, as measured by (1) the average academic achievement level in third grade and (2) the rate of growth in academic achievement from third to eighth grade. The Smart Start effect on eighth grade reading achievement was larger in school districts with higher levels of average achievement. Also, the NC Pre-K effect on eighth grade reading achievement was smaller in school districts with higher rates of achievement growth.
Starting in 2009, the U.S. public education system undertook a massive effort to institute new high-stakes teacher evaluation systems. We examine the effects of these reforms on student achievement and attainment at a national scale by exploiting the staggered timing of implementation across states. We find precisely estimated null effects, on average, that rule out impacts as small as 1.5 percent of a standard deviation for achievement and 1 percentage point for high school graduation and college enrollment. We also find little evidence of heterogeneous effects across an index measuring system design rigor, specific design features, and district characteristics.
In 2009, the federal government passed the American Recovery and Reinvestment Act (ARRA) to combat the effects of the Great Recession and state revenue shortfalls, directing over $97 billion to school districts. In this chapter, we draw lessons from this distribution of fiscal stimulus funding to inform future federal intervention in school finance during periods of economic downturn. We find that district spending declined by $945 per pupil per year following the Great Recession, particularly after a stimulus funding cliff when ARRA funding declined. Spending declines varied more within than across states, while stimulus funding was directed to districts through pre-Recession state funding formulae which varied in their relative progressivity. Spending losses were greater in districts serving fewer shares of students qualifying for free or reduced-price lunch or special education services, in districts with higher-achieving students, and in districts with greater levels of spending prior to the Great Recession; declines were unassociated with district’s racial/ethnic composition, the share of English language learners, or a district’s reliance on state aid. We conclude by identifying different stimulus policy targets and with recommendations regarding the magnitude and distribution of future federal fiscal stimulus funding, lessons relevant to the COVID-19-induced recession and beyond.
Policy debate on refugee resettlement focuses on perceived adverse effects on local communities, with sparse credible evidence to ascertain its impact. This paper examines whether attending school with refugees affects the academic outcomes of non-refugee students. Leveraging variation in the share of refugees within schools and across grades, I find that increasing the share of grade-level refugees by 1 pp results in a 0.01 sd increase in average math scores. While I find no effect on average English Language Arts scores, using nonlinear-in-means specifications I estimate negative spillovers in ELA performance among low-achieving students and positive spillovers among high-achieving students.
We consider the case in which the number of seats in a program is limited, such as a job training program or a supplemental tutoring program, and explore the implications that peer effects have for which individuals should be assigned to the limited seats. In the frequently-studied case in which all applicants are assigned to a group, the average outcome is not changed by shuffling the group assignments if the peer effect is linear in the average composition of peers. However, when there are fewer seats than applicants, the presence of linear-in-means peer effects can dramatically influence the optimal choice of who gets to participate. We illustrate how peer effects impact optimal seat assignment, both under a general social welfare function and under two commonly used social welfare functions. We next use data from a recent job training RCT to provide evidence of large peer effects in the context of job training for disadvantaged adults. Finally, we combine the two results to show that the program's effectiveness varies greatly depending on whether the assignment choices account for or ignore peer effects.
In a randomized trial that collects text as an outcome, traditional approaches for assessing treatment impact require that each document first be manually coded for constructs of interest by human raters. An impact analysis can then be conducted to compare treatment and control groups, using the hand-coded scores as a measured outcome. This process is both time and labor-intensive, which creates a persistent barrier for large-scale assessments of text. Furthermore, enriching ones understanding of a found impact on text outcomes via secondary analyses can be difficult without additional scoring efforts. Machine-based text analytic and data mining tools offer one potential avenue to help facilitate research in this domain. For instance, we could augment a traditional impact analysis that examines a single human-coded outcome with a suite of automatically generated secondary outcomes. By analyzing impacts across a wide array of text-based features, we can then explore what an overall change signifies, in terms of how the text has evolved due to treatment. In this paper, we propose several different methods for supplementary analysis in this spirit. We then present a case study of using these methods to enrich an evaluation of a classroom intervention on young children’s writing. We argue that our rich array of findings move us from “it worked” to “it worked because” by revealing how observed improvements in writing were likely due, in part, to the students having learned to marshal evidence and speak with more authority. Relying exclusively on human scoring, by contrast, is a lost opportunity.
High school Career and Technical Education (CTE) has received an increase in attention from both policymakers and researchers in recent years. This study fills a needed gap in the growing research base by examining heterogeneity within the wide range of programs falling under the broader CTE umbrella, and highlights the need for greater nuance in research and policy conversations that often consider CTE as monolithic. Examining multiple possible outcomes, including earnings, postsecondary education, and poverty avoidance, we find substantial differences in outcomes for students in fields as diverse as healthcare, IT, and construction. We also highlight heterogeneity for student populations historically overrepresented in CTE, and find large differences in outcomes for CTE students, particularly by gender.
We examine the effects of disseminating academic performance data—either status, growth, or both—on parents’ school choices and their implications for racial, ethnic, and economic segregation. We conduct an online survey experiment featuring a nationally representative sample of parents and caretakers of children age 0-12. Participants choose between three randomly sampled elementary schools drawn from the same school district. Only growth information—alone and not in concert with status information—has clear and consistent desegregating consequences. Because states that include growth in their school accountability systems have generally done so as a supplement to and not a replacement for status, there is little reason to expect that this development will influence choice behavior in a manner that meaningfully reduces school segregation.
Levels of governance (the nation, states, and districts), student subgroups (racially and ethnically minoritized and economically disadvantaged students), and types of resources (expenditures, class sizes, and teacher quality) intersect to represent a complex and comprehensive picture of K-12 educational resource inequality. Drawing on multiple sources of the most recently available data, we describe inequality in multiple dimensions. At the national level, racially and ethnically minoritized and economically disadvantaged students receive between $30 and $800 less in K-12 expenditures per pupil than White and economically advantaged students. At the state and district levels, per-pupil expenditures generally favor racially and ethnically minoritized and economically disadvantaged students compared to White and economically advantaged students. Looking at nonpecuniary resources, minoritized and economically disadvantaged students have smaller class sizes than their subgroup counterparts in the average district, but these students also have greater exposure to inexperienced teachers. We see no evidence that district-level spending in favor of traditionally disadvantaged subgroups is explained by district size, average district spending, teacher turnover, or expenditures on auxiliary staff, but Black and Hispanic spending advantage is correlated with the relative size of the Black and Hispanic special education population.