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This study used college dormitory room and social group assignment data to investigate the peer effect on the probability of college students switching their major fields of study. The results revealed strong evidence of peer effects on students’ decisions to switch majors. In particular, the number of a student’s peers who have the same major significantly reduces the student’s likelihood of switching majors; however, when a same-major peer switches majors, it significantly increases a student’s probability of switching majors. This study also found that peers’ majors affected students’ choice of destination majors. Students in the same peer group are more likely to choose the same destination majors, compared to non-peers. Finally, we found that in general peer effects at the dormitory room level, both in choice and persistence of major, were stronger than were peer effects at the social group level.
Narrative accounts of classroom instruction suggest that external interruptions, such as intercom announcements and visits from staff, are a regular occurrence in U.S. public schools. We study the frequency, nature, and duration of external interruptions in the Providence Public School District (PPSD) using original data from a district-wide survey and classroom observations. We estimate that a typical classroom in PPSD is interrupted over 2,000 times per year, and that these interruptions and the disruptions they cause result in the loss of between 10 to 20 days of instructional time. Administrators appear to systematically underestimate the frequency and negative consequences of these interruptions. We propose several organizational approaches schools might adopt to reduce external interruptions to classroom instruction.
Because primary education is often conceptualized as a pro-poor redistributive policy, a common argument is that democratization increases its provision. But primary education can also serve the goals of autocrats, including redistribution, promoting loyalty, nation-building, and/or industrialization. To examine the relationship between democratization and education provision empirically, I leverage new datasets covering 109 countries and 200 years. Difference-in-differences and interrupted time series estimates find that, on average, democratization had no or little impact on primary school enrollment rates. When unpacking this average null result, I find that, consistent with median voter theories, democratization can lead to an expansion of primary schooling, but the key condition under which it does—when a majority lacked access to primary schooling before democratization—rarely holds. Around the world, state-controlled primary schooling emerged a century before democratization, and in three-fourths of countries that democratized, a majority already had access to primary education before democratization.
Teaching is often assumed to be a relatively stressful occupation and occupational stress among teachers has been linked to poor mental health, attrition from the profession, and decreased effectiveness in the classroom. Despite widespread concern about teachers’ mental health, however, little empirical evidence exists on long-run trends in teachers’ mental health or the prevalence of mental health problems in teaching relative to other professions. We address this gap in the literature using nationally representative data from the 1979 and 1997 cohorts of the National Longitudinal Survey of Youth (NLSY). In the 1979 cohort, women who become teachers have similar mental health to non-teachers prior to teaching but enjoy better mental health than their non-teaching peers, on average, while working as teachers. However, in the 1997 cohort teachers self-report worse mental health, on average, than the 1979 cohort and fare no better than their non-teaching professional peers while teaching. Overall, teachers seem to enjoy mental health outcomes that are as good or better than their peers in other professions.
Success in postsecondary education requires students to engage with their institution both academically and administratively. As with the transition to college, administrative requirements students face once enrolled can be substantial. Missteps with required processes can threaten students’ ability to persist. During the 2018-19 academic year, Georgia State University implemented an artificially intelligent text-based chatbot to provide proactive outreach and support to help undergraduates navigate administrative processes and take advantage of campus resources. A team of centralized university administrators orchestrated outreach “campaigns” to support students across three broad domains: (1) academic supports; (2) social and career supports; and (3) administrative processes. We investigate GSU’s implementation of this persistence-focused chatbot through an experimental study. Of the three message domains, outreach was most effective when focused on administrative processes, many of which were time-sensitive and for which outreach could be targeted specifically to students for whom it was relevant based on administrative data. In contrast, outreach to encourage take up of other supports had little effect on student behavior. By the end of the academic year, rates of FAFSA filing and registration for the subsequent fall semester were approximately three percentage points higher, suggesting positive effects on year-to-year college persistence. The positive effects on fall enrollment persisted into summer 2019, at which time the GSU administration judged that the study results were compelling enough to conclude the experiment and roll the chatbot system out to all students. We situate our findings in the literature on nudge-type efforts to support college access and success to draw lessons regarding their effective use.
A vast research literature documents racial bias in teachers’ evaluations of students. Theory suggests bias may be larger on grading scales with vague or overly-general criteria versus scales with clearly-specified criteria, raising the possibility that well-designed grading policies may mitigate bias. This study offers relevant evidence through a randomized web-based experiment with 1,549 teachers. On a vague grade-level evaluation scale, teachers rated a student writing sample lower when it was randomly signaled to have a Black author, versus a White author. However, there was no evidence of racial bias when teachers used a rubric with more clearly-defined evaluation criteria. Contrary to expectation, I found no evidence that the magnitude of grading bias depends on teachers’ implicit or explicit racial attitudes.
This study examines the relationship between county-level estimates of implicit racial bias and black-white test score gaps in U.S. schools. Data from over 1 million respondents from across the United States who completed an online version of the Race Implicit Association Test (IAT) were combined with data from the Stanford Education Data Archive covering over 300 million test scores from U.S. schoolchildren in grades 3 through 8. Two key findings emerged. First, in both bivariate and multivariate models, counties with higher levels of racial bias had larger black-white test score disparities. The magnitude of these associations were on par with other widely accepted predictors of racial test score gaps, including racial gaps in family income and racial gaps in single parenthood. Second, the observed relationship between collective rates of racial bias and racial test score gaps was explained by the fact that counties with higher rates of racial bias had schools that were characterized by more racial segregation and larger racial gaps in gifted and talented assignment as well as special education placement. This pattern is consistent with a theoretical model in which aggregate rates of racial bias affect educational opportunity through sorting mechanisms that operate both within and beyond schools.
We study racial bias and the persistence of first impressions in the context of education. Teachers who begin their careers in classrooms with large black-white score gaps carry negative views into evaluations of future cohorts of black students. Our evidence is based on novel data on blind evaluations and non-blind public school teacher assessments of fourth and fifth graders in North Carolina. Negative first impressions lead teachers to be significantly less likely to over-rate but not more likely to under-rate black students’ math and reading skills relative to their white classmates. Teachers' perceptions are sensitive to the lowest-performing black students in early classrooms, but non-responsive to highest-performing ones. This is consistent with the operation of confirmatory biases. Since teacher expectations can shape grading patterns and sorting into academic tracks as well as students’ own beliefs and behaviors, these findings suggest that novice teacher initial experiences may contribute to the persistence of racial gaps in educational achievement and attainment.
Valid and reliable measurements of teaching quality facilitate school-level decision-making and policies pertaining to teachers, but conventional classroom observations are costly, prone to rater bias, and hard to implement at scale. 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, objective measures of teaching to complement classroom observations. 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.
Tens of millions of Americans have lost their jobs in the wake of the COVID-19 health and economic crisis, and a sizable share of these job losses may be permanent. Unemployment rates are particularly high among adults without a college degree. Recent state policy efforts have focused on increasing re-enrollment and credentialing among adults with some college but no degree (SCND); these efforts are likely to accelerate given the COVID-19 disruptions to the U.S. economy. Yet little is actually known about the background characteristics, academic experiences, or labor market trajectories of this population. Using data from the Virginia Community College System (VCCS), we provide the first detailed profile on the academic, employment, and earnings trajectories of the SCND population, and how these compare on key measures to VCCS graduates. We also develop a framework for prioritizing which segments of the SCND population states might target for re-enrollment and completion interventions. This framework may be particularly useful to states that need to fill critical workforce shortages in healthcare and other sectors or re-train their workforce in the wake of mass unemployment and economic disruption stemming from the COVID-19 crisis.