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Multiple outcomes of education
This paper introduces a new measure of the labor markets served by colleges and universities across the United States. About 50 percent of recent college graduates are living and working in the metro area nearest the institution they attended, with this figure climbing to 67 percent in-state. The geographic dispersion of alumni is more than twice as great for highly selective 4-year institutions as for 2-year institutions. However, more than one-quarter of 2-year institutions disperse alumni more diversely than the average public 4-year institution. In one application of these data, we find that the average strength of the labor market to which a college sends its graduates predicts college-specific intergenerational economic mobility. In a second application, we quantify the extent of “brain drain” across areas and illustrate the importance of considering migration patterns of college graduates when estimating the social return on public investment in higher education.
In spring 2020, nearly every public school in the U.S. closed at the onset of the Covid-19 pandemic. Existing evidence suggests that initial decisions to re-open schools for in-person instruction were generally unrelated to Covid case and death rates. Instead, local political partisanship and teachers union strength were better predictors of school re-opening status in fall 2020. We replicate and extend these analyses using data collected over the entire 2020-21 academic year. We demonstrate that Covid case and death rates were, in fact, meaningfully related to initial rates of in-person instruction. We also show that all three of these factors—Covid, partisanship, and teachers unions—became less predictive of in-person instruction as the school year continued. Conversely, the relationship between prior student achievement and the rate of in-person instruction increased in salience. We then leverage data from two nationally representative surveys of Americans’ attitudes toward education and identify an as-yet-undiscussed factor that predicts in-person instruction: pre-pandemic public support for increasing teacher salaries. We speculate that education leaders were better able to manage the logistical and political complexities of school reopenings in communities with greater support for educators.
Interactive, text message-based advising programs have become an increasingly common strategy to support college access and success for underrepresented student populations. Despite the proliferation of these programs, we know relatively little about how students engage in these text-based advising opportunities and whether that relates to stronger student outcomes – factors that could help explain why we’ve seen relatively mixed evidence about their efficacy to date. In this paper, we use data from a large-scale, two-way text advising experiment focused on improving college completion to explore variation in student engagement using nuanced interaction metrics and automated text analysis techniques (i.e., natural language processing). We then explore whether student engagement patterns are associated with key outcomes including persistence, GPA, credit accumulation, and degree completion. Our results reveal substantial variation in engagement measures across students, indicating the importance of analyzing engagement as a multi-dimensional construct. We moreover find that many of these nuanced engagement measures have strong correlations with student outcomes, even after controlling for student baseline characteristics and academic performance. Especially as virtual advising interventions proliferate across higher education institutions, we show the value of applying a more codified, comprehensive lens for examining student engagement in these programs and chart a path to potentially improving the efficacy of these programs in the future.
While a growing body of literature has documented the negative impacts of exclusionary punishments, such as suspensions, on academic outcomes, less is known about how teachers vary in disciplinary behaviors and the attendant impacts on students. We use administrative data from North Carolina elementary schools to examine the extent to which teachers vary in their use of referrals and investigate the impact of more punitive teachers on student attendance and achievement. We also estimate the effect of teachers' racial bias in the use of referrals on student outcomes. We find more punitive teachers increase student absenteeism and reduce student achievement. Moreover, more punitive teachers negatively affect the achievement of students who do not receive disciplinary sanctions from the teacher. Similarly, while teachers with a racial bias in the use of referrals do not negatively affect academic outcomes for White students, they significantly increase absenteeism and reduce achievement for Black students. The results suggest punitive disciplinary measures do not aid teachers in productively managing classrooms; rather, teachers taking more punitive stances may undermine student engagement and learning. Moreover, bias in teachers' referral usage contributes to inequities in student outcomes.
Student absenteeism is often conceptualized and quantified in a static, uniform manner, providing an incomplete understanding of this important phenomenon. Applying growth curve models to detailed class-attendance data, we document that secondary school students' unexcused absences grow steadily throughout a school year and over grades, while the growth of excused absences remain essentially unchanged. Importantly, students starting the school year with a high number of unexcused absences, Black and Hispanic students, and low-income students accumulate unexcused absences at a significantly faster rate than their counterparts. Lastly, students with higher growth rates in unexcused absences consistently report lower perceptions of all aspects of school culture than their peers. Interventions targeting unexcused absences and/or improving school culture can be crucial to mitigating disengagement.
There is an emerging consensus that teachers impact multiple student outcomes, but it remains unclear how to measure and summarize the multiple dimensions of teacher effectiveness into simple metrics for research or personnel decisions. We present a multidimensional empirical Bayes framework and illustrate how to use noisy estimates of teacher effectiveness to assess the dimensionality and predictive power of teachers' true effects. We find that it is possible to efficiently summarize many dimensions of effectiveness and most summary measures lead to similar teacher rankings; however, focusing on any one specific measure alone misses important dimensions of teacher quality.
We show that natural disasters affect a region’s aggregate human capital through at least four channels. In addition to causing out-migration, natural disasters reduce student achievement, lower high school graduation rates, and decrease post-secondary attendance. We estimate that disasters that cause at least $500 in per capita property damage reduce the net present value (NPV) of an affected county’s human capital by an average of $505 per person. These negative effects on human capital are not restricted to large disasters: less severe events – disasters with property damages of $100-$500 per capita – also cause significant and persistent reductions in student achievement and post-secondary attendance.
The primary goal of job training programs is to improve employment and earning outcomes of participants. However, effective job training programs may have potential secondary benefits, including in the form of reduced arrests. In this paper, we evaluate the impact of a job training program in New Orleans that was implemented using a randomized controlled trial design. We find that among those who had a prior criminal record, those assigned to the treatment group were two-fifths as likely to get arrested as those assigned to the control group at any time point after randomization. We explore several potential mechanisms for why this effect occurs and find suggestive evidence that the training program’s impact on wages, as well as peer effects from other trainees, can partially explain this effect.
Educators must balance the needs of students who start the school year behind grade level with their obligation to teach grade-appropriate content to all students. Educational software could help educators strike this balance by targeting content to students’ differing levels of mastery. Using a regression discontinuity design and detailed software log and administrative data, we compare two versions of an online mathematics program used by students in three education agencies. We find that although students assigned the modified curriculum did progress through content objectives more quickly than students assigned the default curriculum, they did not perform better on pre- and post-objective quizzes embedded in the software, and most never progressed far enough to reach the grade-level content. Furthermore, there was no statistically significant effect of the modified curriculum on formative test scores. These findings suggest policymakers and practitioners should exercise caution when assigning exclusively remedial content to students who start the school year behind grade level, even though this is a common feature of many math educational software programs.
Many teacher education researchers have expressed concerns with the lack of rigorous impact evaluations of teacher preparation practices. I summarize these various concerns as they relate to issues of internal validity, external validity, and measurement. I then assess the prevalence of these issues by reviewing 166 impact evaluations of teacher preparation practices published in peer-reviewed journals between 2002-2019. Although I find that very few studies address issues of internal validity, external validity and measurement, I highlight some innovative approaches and present a checklist of considerations to assist future researchers in designing more rigorous impact evaluations.