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Half of kindergarten teachers split children into higher and lower ability groups for reading or math. In national data, we predicted kindergarten ability group placement using linear and ordinal logistic regression with classroom fixed effects. In fall, test scores were the best predictors of group placement, but there was bias favoring girls, high-SES (socioeconomic status) children, and Asian Americans, who received higher placements than their scores alone would predict. Net of SES, there was no bias against placing black children in higher groups. By spring, one third of kindergartners moved groups, and high-SES children moved up more than their score gains alone would predict. Teacher-reported behaviors (e.g., attentiveness, approaches to learning) helped explain girls’ higher placements, but did little to explain the higher placements of Asian American and high-SES children.
Private school choice policies have been enacted and expanded across the United States since the 1990s. By January 2021, 30 states plus the District of Columbia and Puerto Rico hosted 67 distinct private school choice policies. Why have some states adopted and expanded this education reform while others have demurred? Which states are more likely to adopt specific types of private school choice initiatives in the coming years? We present the results of an exploratory empirical analysis examining which state-level political, economic, and educational factors predict past policy decisions regarding the enactment and expansion of private school choice in 49 states from 2000 to 2016. The results from our most preferred statistical model further predict which states are more and less likely to take action towards such policies in subsequent years. The political factors involving Republican control of the governorship and legislature, prevalence of minority students in the K-12 population, and share of private school enrollment in the state prove to be highly predictive factors in school choice adoption. The economic factor of a comparatively low state per-capita GDP also consistently predicts school choice policy adoption in our models.
Underrepresented minority (URM) college students have been steadily earning degrees in relatively less-lucrative fields of study since the mid-1990s. A decomposition reveals that this widening gap is principally explained by rising stratification at public research universities, many of which increasingly enforce GPA restriction policies that prohibit students with poor introductory grades from declaring popular majors. We investigate these GPA restrictions by constructing a novel 50-year dataset covering four public research universities' student transcripts and employing a staggered difference-in-difference design around the implementation of 29 restrictions. Restricted majors’ average URM enrollment share falls by 20 percent, which matches observational patterns and can be explained by URM students’ poorer average pre-college academic preparation. Using first-term course enrollments to identify students who intend to earn restricted majors, we find that major restrictions disproportionately lead URM students from their intended major toward less-lucrative fields, driving within-institution ethnic stratification and likely exacerbating labor market disparities.
Principals (policymakers) disagree as to whether U. S. student performance has changed over the past half century. To inform conversations, agents administered seven million psychometrically linked tests in math (m) and reading (rd) in 160 survey waves to national probability samples of cohorts born between 1954 and 2007. Estimated change in standard deviations (sd) per decade varies by agent (m: -0.10sd to 0.27sd, rd: -0.02sd to 0.12sd). Consistent with Flynn effects, median trends show larger gains in m (0.19sd) than rd (0.04sd), though rates of progress for cohorts born since 1990 have increased in rd but slowed in m. Greater progress is shown by students tested at younger ages (m: 0.31sd, rd: 0.08sd) than when tested in middle years of schooling (m: 0.17sd, rd: 0.03sd) or toward end of schooling (m: 0.06sd, rd: 0.02sd). Young white students progress more slowly (m: 0.28sd, rd: 0.09sd) than Asian (m: 46sd, rd: 0.28sd), black (m: 0.36sd, rd: 0.19sd) and Hispanic (m: 0.29sd, rd: 0.13sd) students. These ethnic differences generally attenuate as students age. Young students in the bottom quartile of the SES distribution show greater progress than those in the top quartile (difference in m: 0.08sd, in rd: 0.15sd), but the reverse is true for older students. Moderators likely include not only changes in families and schools but also improvements in nutrition, health care, and protection from contagious diseases and environmental risks. International data suggest that subject and age differentials may be due to moderators more general than just the United States.
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.