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The quality of college education is hard for students and employers to observe. Knowing this, colleges often change their names to signal higher quality while leaving other features unchanged. We study how these changes affect college choice and labor market performance of college graduates. Using administrative data, we show that name-changing colleges attract more qualified applicants, with larger effects among applicants who have less information about the college. Text from web discussion boards reveals many college applicants lack important information about colleges. A resume audit study shows employers possess nearly perfect information about how college name changes affect student aptitude.
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, first under a general social planner utility function and then from both an efficiency and an equity perspective. We next use data from a recent job training RCT to provide the first 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.
COVID-19 has created acute challenges for the child care sector, potentially leading to a shortage of supply and a shrinking sector as the economy recovers. This study provides the first comprehensive, census-level evaluation of the medium-term impacts of COVID-19 on the county child care market in a large and diverse state, North Carolina. We also document the disproportionate impacts of COVID-19 on different types of providers and disadvantaged communities. We use data from two time points (February and December) from 2018 to 2020 and a difference-in-differences design to isolate the effects of COVID-19. We find that COVID- 19 reduced county-level child care enrollment by 40%, and reduced the number of providers by 2%. Heterogeneity analyses reveal that family child care providers experienced not only less severe reductions in enrollment and closures than center providers, but a small growth in the number of family providers. Declines in enrollment were most substantial for preschool-aged children. COVID-19 did not appear to further exacerbate inequities in terms of enrollment amongst low-income communities, communities with a larger share of Black residents, or rural communities, although communities with a larger share of Hispanic residents had more provider closures. Our findings underscore the importance of family child care providers in the child care sector and providing continuing and targeted support to help the sector through this crisis. Implications for future policies are discussed.
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.
Recent interest to promote and support replication efforts assume that there is well-established methodological guidance for designing and implementing these studies. However, no such consensus exists in the methodology literature. This article addresses these challenges by describing design-based approaches for planning systematic replication studies. Our general approach is derived from the Causal Replication Framework (CRF), which formalizes the assumptions under which replication success can be expected. The assumptions may be understood broadly as replication design requirements and individual study design requirements. Replication failure occurs when one or more CRF assumptions are violated. In design-based approaches to replication, CRF assumptions are systematically tested to evaluate the replicability of effects, as well as to identify sources of effect variation when replication failure is observed. In direct replication designs, replication failure is evidence of bias or incorrect reporting in individual study estimates, while in conceptual replication designs, replication failure occurs because of effect variation due to differences in treatments, outcomes, settings, and participant characteristics. The paper demonstrates how multiple research designs may be combined in systematic replication studies, as well as how diagnostic measures may be used to assess the extent to which CRF assumptions are met in field settings.
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.
Developmental education, in which college students deemed unprepared for college-level coursework enroll in non-credit bearing courses, is widespread in American higher education. The current study evaluates the effect of mobile app courseware on the college outcomes of developmental education students, using a research design which randomly assigned course sections to receive access to the apps or not. The results show that access to the apps significantly improved student performance in developmental education outcomes, marginally improved medium-term college persistence and performance, but did not effect credential attainment in the study timeframe. Despite a number of barriers to implementation, the results suggest the intervention has the potential to improve the short-term outcomes of developmental education students in addition to being low-cost and scalable.
In this paper we study the effects of three large, nearly-simultaneous coal-fired power plant closures on school absences in Chicago. We find that the closures resulted in a 7 percent reduction in absenteeism in nearby schools relative to those farther away following the closures. For the typical elementary school in our sample, this translates into around 372 fewer absence-days per year in the aggregate, or around 0.71 fewer annual absences per student. We find that reductions in absences were larger in schools where pre-closure exposure to coal-fired power plants was more intense: namely, schools with low levels of air conditioning, schools more frequently in the wind path of the plants, and non-magnet (i.e., neighborhood) schools where students were more likely to live nearby. To explore potential mechanisms responsible for these absence reductions we investigate the effects of the closures on housing values and children’s respiratory health. We do not find statistical evidence of endogenous migration into neighborhoods near the coal-fired power plants following the closures but do find declines in emergency department visits for asthma-related conditions among school-age children.
We study the effect of exposure to immigrants on the educational outcomes of US-born students, using a unique dataset combining population-level birth and school records from Florida. This research question is complicated by substantial school selection of US-born students, especially among White and comparatively affluent students, in response to the presence of immigrant students in the school. We propose a new identification strategy to partial out the unobserved non-random selection into schools, and find that the presence of immigrant students has a positive effect on the academic achievement of US-born students, especially for students from disadvantaged backgrounds. Moreover, the presence of immigrants does not affect negatively the performance of affluent US-born students, who typically show a higher academic achievement compared to immigrant students. We provide suggestive evidence on potential channels.