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Short-term certificate (STC) programs at community colleges represent a longstanding policy priority to align accelerated postsecondary credentials with job opportunities in local labor markets. Despite large investments in developing STCs, little evidence exists about where and when STCs are opened and whether community colleges open new programs of study in coordination with labor market trends. Using public workforce and postsecondary data, I examine health and manufacturing STC program openings to understand alignment with labor market activity in related industries. I find that STCs are spatially aligned across labor markets within a state, but not necessarily temporally aligned with county-specific trends. One additional STC per college is associated with labor markets that had 2-3 percentage points more total employment and new hires in related industries. Large launches of clustered STC programs occurred before periods of growth in health employment but declines in manufacturing. Large launches preceded earnings growth of 2-5 percentage points in both sectors.
Using data from the National Longitudinal Study of Youth 1997, we examine differences in educational experiences and in social and economic mobility for youths experiencing poverty relative to their more affluent peers. We also explore the extent to which different educational experiences are associated with greater mobility for students experiencing poverty. We find that youths from poverty are less than half as likely as their more affluent peers to earn a living wage, reach the top quartile of income, or attain a high level of economic wellbeing and stability. They also have less educational opportunity in their youth, particularly when it comes to academic experiences. Meanwhile, the educational experiences where there are the largest inequities are also the ones that are most predictive of long-term mobility for students from poverty, suggesting that having the opportunity to do well in school may help young people improve their economic standing and achieve broader levels of wellbeing later in life. At the same time, students experiencing poverty who have exceptional academic outcomes on average still do not manage to exceed the average adult income of the typical student not coming from poverty. Altogether, our findings point to both the importance and inadequacy of academic experiences for breaking the cycle of intergenerational poverty.
We use data from the applications North Carolina public school districts and charter schools submitted for Elementary and Secondary School Emergency Relief (ESSER) to investigate the sense that educational leaders made of the pandemic as it unfolded. LEAs understood the pandemic as a multifaceted problem. Nearly all applications addressed four problems: (1) public health, (2) academics and learning loss, (3) student and community well-being, and (4) instructional access. However, we document considerable variation in problem emphasis over time, across LEAs, and across organizational sector. The pandemic was not a single organizational problem, but many simultaneous problems posed in varying and shifting combinations. We argue this multi-faceted organizational view should be a starting point for assessments of LEAs’ pandemic response.
There is a large and growing number of non-degree credential offerings between a high school diploma and a bachelor's degree, as well as degree programs beyond a bachelor’s degree. Nevertheless, research on the financial returns to non-degree credentials and degree-granting programs is often narrow and siloed. To fill this gap, we leverage a national sample of individuals across nine MSAs and four industries to examine the relative financial returns to a variety of non-degree credentials and degree programs. Leveraging two-way fixed-effect models, we explore the relationship between completing a credential or degree and earnings premiums. We find that an associate’s, bachelor’s, master’s and doctorate degree follows a similar model of returns in which the number of schooling years is linearly related to proportional earnings premiums. However, students completing sub-baccalaureate certificates and earning non-school credentials appear to get larger financial returns for less time. Additionally, while we noticed subtle differences across degree programs, we noticed substantial differences in non-school credentials: only women experienced a significant earnings premium from a non-degree credential. Finally, in terms of race/ethnicity, students of color often experienced larger economic returns to undergraduate certificates and degree programs.
Children spend most of their time at home in their early years, yet efforts to promote human capital at home in many low- and middle-income settings remain limited. We conduct a randomized controlled trial to evaluate an intervention which encourages parents and caregivers to foster human capital accumulation among their children between ages 3 and 5, with a focus on math and phonics skills. Children gain 0.52 and 0.51 standard deviations relative to the control group on math and phonics tests, respectively (p<0.001). A year later effects persist, but math gains dissipate to 0.15 (p=0.06) and phonics to 0.13 (p=0.12). Effects appear to be mediated largely through instructional support by parents and not other parent investment mechanisms, such as more positive parent-child interactions or additional time spent on education at home beyond the intervention. Our results show that parents can be effective conduits of educational instruction even in low-resource settings.
This article provides a review of prior empirical work exploring whether and to what extent school district racial composition affects the costs associated with providing equal educational opportunity to achieve a common set of outcomes. This prior work mainly involves education cost function modeling, on several specific states and in an earlier version of our national education cost model. Here, we update the national education cost model and apply a series of tests for selecting the optimal cost model and determining a) whether it is necessary to retain measures of racial composition in the model and b) the effect those measures have on the estimated costs to achieve common outcomes. We find that the optimal model includes an interaction term between % enrollment that is black and population density and that for majority Black enrollment urban districts, the predicted costs per pupil are 20 to 50% higher when using models with this measure than when using models with race neutral alternatives. While changes in cost estimates for these districts are large, aggregate national cost increases from including racial composition are 1.3 to 2.7% in most years.
Over the past decade, there has been a significant increase in the number of U.S. institutions offering STEM-eligible degree programs in economics. This paper documents the trends in STEM-degree offerings across degree levels and examines the share of foreign students and other characteristics of institutions that offer STEM-eligible programs. Using a difference-in-differences design, this paper finds that departments with a proportion of foreign students above the sample median are 6 and 9 percentage points more likely to offer a STEM-eligible degree program at the bachelor's and master's levels, respectively, after the STEM designation in 2013. Additionally, the tobit regression results suggest that early adopters of STEM-eligible programs are associated with a higher share of foreign students, private institutions and doctoral and research institutions.
Sometimes a treatment, such as receiving a high school diploma, is assigned to students if their scores on two inputs (e.g., math and English test scores) are above established cutoffs. This forms a multidimensional regression discontinuity design (RDD) to analyze the effect of the educational treatment where there are two running variables instead of one. Present methods for estimating such designs either collapse the two running variables into a single running variable, estimate two separate one-dimensional RDDs, or jointly model the entire response surface. The first two approaches may lose valuable information, while the third approach can be very sensitive to model misspecification. We examine an alternative approach, developed in the context of geographic RDDs, which uses Gaussian processes to flexibly model the response surfaces and estimate the impact of treatment along the full range of students that were on the margin of receiving treatment. We demonstrate theoretically, in simulation, and in an applied example, that this approach has several advantages over current approaches, including over another nonparametric surface response method. In particular, using Gaussian process regression in two-dimensional RDDs shows strong coverage and standard error estimation, and allows for easy examination of treatment effect variation for students with different patterns of running variables and outcomes. As these nonparametric approaches are new in education-specific RDDs, we also provide an R package for users to estimate treatment effects using Gaussian process regression.
Criminal activity is seasonal, peaking in the summer and declining through the winter. We provide the first evidence that arrests of children and reported crimes involving children follow a different pattern: peaking during the school year and declining in the summer. We use a regression discontinuity design surrounding school start dates and an excess crime calculation to show that the school environment increases reported crimes involving children by roughly 50% annually. School exacerbates preexisting inequality in criminal interactions, increasing the Black-white and male-female gaps in reported juvenile crime and arrest rates by more than 40%.
Whole-school reforms have received widespread attention, but a critical limitation of the current literature is the lack of evidence around whether these extensive and costly interventions improve students’ long-term outcomes after they leave reform schools. Leveraging Tennessee’s statewide turnaround reforms, we use difference-in-differences models to estimate the effect of attending a turnaround middle school on student outcomes in high school, including test scores, attendance, chronic absenteeism, disciplinary actions, drop out, and high school graduation. We find little evidence to support improved long-run student outcomes – mostly null effects that are nearly zero in magnitude. Our results contribute to a broad call for educational researchers to examine whether school reforms meaningfully affect student outcomes beyond short-term improvements in test scores.
The path to becoming a school principal is characterized by a variety of trajectories that reflect the diverse experiences and backgrounds of aspiring leaders. While ideally the road to the principalship would result in a proportional and representative body of principals, research has shown this is rarely the case. To gain a better understanding of where sorting mechanisms may occur along the principal pipeline, this paper longitudinally analyzes the full, start-to-finish career trajectories of over 1.6 million educators in Texas for 30 years. Using social sequence analysis and discrete-time hazard modeling, we find that (1) emergent principals tend to stay in their first teaching position longer than other educators and most often take a direct pathway towards the principalship; (2) proportionally, more principals emerge from elementary, ELA, Social Studies, or STEM fields, while fewer come from Special Education; (3) holding other features constant, male and Black educators are more likely to become a principal while female and Hispanic educators are less likely; and (4) educators are more likely to first become principals when transitioning to a smaller school with more Black and/or Hispanic students. While the pipeline does result in a balanced principal market in some areas, increasing efforts to encourage a more diverse content area representation as well as representation for Hispanic educators in Texas will be particularly important.
Students’ postsecondary course-taking is of interest to researchers, yet has been difficult to study at large scale because administrative transcript data are rarely standardized across institutions or state systems. This paper uses machine learning and natural language processing to standardize college transcripts at scale. We demonstrate the approach’s utility by showing how the disciplinary orientation of students’ courses and majors align and diverge at 18 diverse four-year institutions in the College and Beyond II dataset. Our findings complicate narratives that student participation in the liberal arts is in great decline. Both professional and liberal arts majors enroll in a large amount of liberal arts coursework, and in three of the four core liberal arts disciplines, the share of course-taking in those fields is meaningfully higher than the share of majors in those fields. To advance the study of student postsecondary pathways, we release the classification models for public use.
This study examined the impact of COVID-19 on academic "redshirting" in kindergarten, the practice of holding a child back for a year and enrolling them in kindergarten at age 6, using student-level data on all Delaware kindergarten students from fall 2014 through fall 2022. The rate of redshirting declined by 40% in fall 2020, then increased by 44% (relative to pre-pandemic baseline) in fall 2021, and more for some subgroups of children traditionally less likely to redshirt. Further, redshirting was not restricted to children with summer birthdays, as in previous years, with growth seen across the age distribution. Redshirting had not returned to pre-pandemic baseline by fall 2022. These findings point to changes in the motivations for redshirting kindergarten students since the pandemic.
Mastery learning – the process by which students must demonstrate proficiency with a single topic before moving on – is well recognized as one of the best ways to learn, yet many teachers struggle or remain unsure about how to implement it into a classroom setting. This study leverages two field experiments to test the efficacy of a program designed to encourage greater mastery learning through technology and proactive continuous teacher support. Focusing on elementary and middle school mathematics, teachers receive weekly coaching in how to use Computer Assisted Learning (CAL) for students to follow a customized roadmap of incremental progress. Results indicate significant intent-to-treat effects on math performance of 0.12-0.22 standard deviations. Further analysis shows that these gains are concentrated among students in classrooms with at least an average of 35 minutes of practice per week. Teachers able to achieve high-dosage practice have a high degree of initial buy-in, a clear implementation strategy for when practice occurs, and a willingness to closely monitor progress and follow-up with struggling students.