Search EdWorkingPapers
Search EdWorkingPapers by author, title, or keywords.
Search
EdWorkingPapers
Why do employers offer pensions? We empirically explore two theoretical rationales, namely that pensions may improve worker effort and worker selection. We examine these hypotheses using administrative measures on effort and output in public schools around the pension-eligibility notch. Worker effort and output do not fall as workers cross the eligibility threshold, implying that pensions may not elicit additional effort. As for selection, we find that pensions retain low-value-added and high-value-added workers at the same rate, suggesting pensions have little or no influence on selection.
As states incorporate measures of college readiness into their accountability systems, school and district leaders need effective strategies to identify and support students at risk of not enrolling in college. Although there is an abundant literature on early warning indicators for high school dropout, fewer studies focus on indicators for college enrollment, especially those that are simple to calculate and easy for practitioners to use. This study explores three potential indicators of college readiness that educational leaders may consider using as part of an early warning system for college enrollment. Using district administrative data, our analysis shows that an indicator based on attendance, grades, and advanced course-taking is slightly more effective at predicting college enrollment than indicators based on course failures or standardized test scores. However, the performance of these indicators varies across different student demographic and socioeconomic subgroups, highlighting the limitations of these measures and pointing to areas where they may need to be supplemented with contextual information. Through event history analysis, we demonstrate that the ninth grade is a particularly challenging year for students, especially those who are male, Black, Hispanic, or economically disadvantaged. These results suggest that educational leaders ought to consider identifying and targeting students at risk of not attending college with additional resources and support during the freshman year of high school.
Each year, over a thousand public schools in the US close due to declining enrollments and chronic low performance, displacing hundreds of thousands of students. Using Texas administrative data and empirical strategies that use within-student across-time and within-school across-cohort variation, I explore the impact of school closures on students' educational and labor market outcomes. The findings indicate that experiencing school closures results in disruptions in both test scores and behavior. While the drop in test scores is recovered within three years, behavioral issues persist. This study further finds decreases in post-secondary education attainment, employment, and earnings at ages 25–27. These impacts are particularly pronounced among students in secondary education, Hispanic students, and those from originally low-performing schools and economically disadvantaged families.
Teachers’ professional identities are the foundation of their practice. Previous scholarship has largely overlooked the extent to which the broader reform culture shapes teachers’ professional identities. In this study, I draw on survey data from 950 teachers across four US states (California, New York, Florida, and Texas) to examine the extent to which teachers’ professional identities are associated with what I term “institutionalized conceptions” of their roles. Across diverse state policy contexts, I find that teachers draw upon a shared set of institutionalized conceptions of their roles, which are associated with their professional identities. The findings suggest that the taken-for-granted ways society frames teaching may be associated with dimensions of teachers’ professional identity, such as self-efficacy and professional commitment.
Home-visitation programs have improved child development in low- and middle-income countries, but they are costly to scale due to their reliance on trained workers. We evaluated an inexpensive and low-tech alternative with 2,433 caregivers of children aged 6 to 30 months served by 250 public childcare centers in Uttarakhand, India: automated phone calls offering parenting advice. The intervention was implemented largely as intended, with more than two-thirds of caregivers completing at least 10 calls. Yet, counter to expectations, it had negative but statistically insignificant effects on caregivers’ knowledge and interactions with their children, reduced their self-efficacy (by 0.11 standard deviations), and increased their anxiety (by 0.10 standard deviations). Consistent with this pattern, it had precisely estimated null effects on children’s development and language. An analysis of program materials suggests four reasons why the program may not have had the desired effects.
Colleges have increasingly turned to data science applications to improve student outcomes. One prominent application is to predict students’ risk of failing a course. In this paper, we investigate whether incorporating data from learning management systems (LMS)--which captures detailed information on students’ engagement in course activities--increases the accuracy of predicting student success beyond using just administrative data alone. We use data from the Virginia Community College System to build random forest models based on student type (new versus returning) and data source (administrative-only, LMS-only, or full data). We find that among returning college students, models that use administrative-only outperform models that use LMSonly. Combining the two types of data results in minimal increased accuracy. Among new students, LMS-only models outperform administrative-only models, and accuracy is significantly higher when both types of predictors are used. This pattern of results reflects the fact that community college administrative data contains little information about new students. Within the LMS data, we find that LMS data pertaining to students’ engagement during the first part of the course has the most predictive value.
Teachers are the most important school-specific factor in student learning. Yet, little evidence exists linking teacher professional development programs and the strategies or activities that comprise them to student achievement. In this paper, we examine a fellowship model for professional development designed and implemented by Leading Educators, a national nonprofit organization that aims to bridge research and practice to improve instructional quality and accelerate learning across school systems. During the 2015-16 and 2016-17 school years, Leading Educators conducted its fellowship program for two cohorts of instructional leaders, such as department chairs, mentor teachers, instructional coaches, and assistant principals, to provide these educators ongoing, collaborative, job-embedded professional development and to improve student achievement. Relying on quasi-experimental methods, we find that a school’s participation in the fellowship program significantly increased student proficiency rates in English language arts and math on state achievement exams. The positive impact was concentrated in the first cohort and in just one of three regions, and approximately 80 percent of treated schools were charters. Student achievement benefitted from a more sustained duration of participation in the fellowship program, varied depending on the share of a school’s educators who participated in the fellowship, and differed based on whether fellows independently selected into the program or were appointed to participate by their school leaders. Taken together, findings from this paper should inform professional learning organizations, schools, and policymakers on the design, implementation, and impact of educator professional development.
College students make job decisions without complete information. As a result, they may rely on misleading heuristics (“interesting jobs pay badly”) and pursue options misaligned with their goals. We test whether highlighting job characteristics changes decision making. We find increasing the salience of a job’s monetary benefits increases the likelihood college students apply by 196%. In contrast, emphasizing prosocial, career, or social benefits has no effect, despite students identifying these benefits as primary motivators for applying. The study highlights the detrimental incongruencies in students’ decision making alongside a simple strategy for recruiting college students to jobs that offer enriching experiences.
Many decentralized matching markets experience high rates of instability due to information frictions. This paper analyzes these frictions in a particularly unstable U.S. market, the labor market for first-year school teachers. We develop and estimate a dynamic, partial equilibrium model of labor mobility that incorporates non-pecuniary information frictions for school climate and teacher workload. In terms of reducing turnover, a policy that improves information outperforms each alternative considered, including targeted wage premiums at hard-to-staff schools, large retention bonuses, and relaxed tenure requirements. Replicating the gains made through information revelation requires retention bonuses valued at 35% of teachers’ current salaries.
In-person tutoring has been shown to improve academic achievement. Though less well-researched, virtual tutoring has also shown a positive effect on achievement but has only been studied in grade five or above. We present findings from the first randomized controlled trial of virtual tutoring for young children (grades K-2). Students were assigned to 1:1 tutoring, 2:1 tutoring, or a control group. Assignment to any virtual tutoring increased early literacy skills by 0.05-0.08 SD with the largest effects for 1:1 tutoring (0.07-0.12 SD). Students initially scoring well below benchmark and first graders experienced the largest gains from 1:1 tutoring (0.15 and 0.20 SD, respectively). Effects are smaller than typically seen from in-person early literacy tutoring programs but still positive and statistically significant, suggesting promise particularly in communities with in-person staffing challenges.