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Personnel evaluation systems have historically failed to identify and remediate low-performing teachers. In 2012, Chicago Public Schools implemented an evaluation system that incorporated remediation and dismissal plans for low-rated teachers. Regression discontinuity estimates indicate that the evaluation reform increased the exit of low-rated tenured teachers by 50 percent. The teacher labor supply available to replace low-rated teachers was higher performing on multiple dimensions, and instrumental variables estimates indicate that policy-induced exit of low-rated teachers significantly improved teacher quality in subsequent years. Policy simulations show that the teacher labor supply in Chicago is sufficient to remove significantly more low-performing teachers.
Human-capital formation in school depends largely on the selection and retention of teachers. I conduct a discrete-choice experiment with responses linked to administrative teacher and student records to examine teacher preferences for compensation structure and working conditions. I calculate willingness-to-pay for a rich set of work attributes. High-performing teachers have similar preferences to other teachers, but they have stronger preferences for performance pay. Taking the preference estimates at face value I explore how schools should structure compensation to meet various objectives. Under each objective, schools appear to underpay in salary and performance pay while overpaying in retirement. Restructuring compensation can increase both teacher welfare and student achievement.
Economic downturns can cause major funding shortfalls for U.S. public schools, often forcing districts to make difficult budget cuts including teacher layoffs. In this brief, we synthesize the empirical literature on the widespread teacher layoffs caused by the Great Recession. Studies find that teacher layoffs harmed student achievement and were inequitably distributed across schools, teachers, and students. Research suggests that specific elements of the layoff process can exacerbate these negative effects. Seniority-based policies disproportionately concentrate layoffs among teachers of color who are more likely to be early career teachers. These “last-in first-out” policies also disproportionately affect disadvantaged students because these students are more likely to be taught by early career teachers. The common practice of widely distributing pink slips warning about a potential job loss also appears to increase teacher churn and negatively impact teacher performance. Drawing on this evidence, we outline a set of policy recommendations to minimize the need for teacher layoffs during economic downturns and ensure that the burden of any unavoidable job cuts does not continue to be borne by students of color and students from low-income backgrounds.
Providing consistent, individualized feedback to teachers is essential for improving instruction but can be prohibitively resource intensive in most educational contexts. We develop an automated tool based on natural language processing to give teachers feedback on their uptake of student contributions, a high-leverage teaching practice that supports dialogic instruction and makes students feel heard. We conduct a randomized controlled trial as part of an online computer science course, Code in Place (n=1,136 instructors), to evaluate the effectiveness of the feedback tool. We find that the tool improves instructors’ uptake of student contributions by 24% and present suggestive evidence that our tool also improves students’ satisfaction with the course. These results demonstrate the promise of our tool to complement existing efforts in teachers’ professional development.
Principals shape the academic setting of schools. Yet, there is limited evidence on whether principal professional development improves schooling outcomes. Beginning in 2008-09, Pennsylvania’s Inspired Leadership (PIL) induction program required that newly hired principals complete targeted in-service professional development tied to newly established state leadership standards within five years of employment. Using panel data on all Pennsylvania students, teachers, and principals, we leverage variation in the timing of PIL induction across principal-school cells and employ difference-in-differences and event study strategies to estimate the impact of PIL induction on teacher and student outcomes. We find that PIL induction increased student math achievement through improvements in teacher effectiveness, and that the effects of PIL induction on teacher effectiveness were concentrated among the most economically disadvantaged and urban schools in Pennsylvania. Principal professional development had the greatest impact on teacher effectiveness when principals completed PIL induction during their first two years in the principalship. We also find evidence that teacher turnover declined in the years following the completion of PIL induction. We discuss the implications of our findings for principal induction efforts.
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.
Teacher strikes have gained national attention with the “#RedforEd” movement. Such strikes are polarizing events that could serve to elevate education as a political priority or cast education politics in a negative light. We investigate this empirically by collecting original panel data on U.S. teacher strikes, which we link to congressional election campaign advertisements. Election ads provide a useful window into political discourse because they are costly to sponsors, consequential for voter behavior, and predictive of future legislative agendas. Using a differences-in-differences framework, we find that teacher strikes dramatically increase education issue salience, with impacts concentrated among positively-framed ads. Effects are driven by strikes lasting only a few days and occurring in battleground areas with highly-contested elections.
We combine a large multi-site randomized control trial with administrative and survey data to demonstrate that intensive advising during high school and college leads to large increases in bachelor's degree attainment. Novel causal forest methods suggest that these increases are driven primarily by improvements in the quality of initial enrollment. Program effects are consistent across sites, cohorts, advisors, and student characteristics, suggesting the model is scalable. While current and proposed investments in postsecondary education focus on cutting costs, our result suggest that investment in advising is likely to be a more efficient route to promote bachelor's degree attainment.
Traditional public schools in the United States must comply with a variety of regulations on educational inputs like teacher certification, maximum class sizes, and restrictions on staff contracts. Absent regulations, policymakers fear that troubled districts would make inappropriate decisions that would harm students. However, it is also possible that strict regulations hinder schools from optimizing student learning. This paper tests the salience of these two hypotheses within the context of a widespread deregulation effort in Texas which allows traditional public school districts to claim District of Innovation status and opt out of regulations not related to health, safety, and civil rights. Using a novel dataset of administration data merged with implementation information scraped from district websites, I estimate the impact of District of Innovation status with a difference-in-differences strategy where later implementers act as the comparison group for early implementers. I find that, despite the breadth of regulations exempted, regulatory autonomy does not significantly impact either math or reading achievement nor does it impact hiring or class sizes. Together, the results offer strong evidence against the hypothesis that regulation hinders school improvement and suggests that state input regulations play only a limited role in determining school decision-making or student achievement.
If school closures and social-distancing experiences during the Covid-19 pandemic impeded children’s skill development, they may leave a lasting legacy in human capital. To understand the pandemic’s effects on school children, this paper combines a review of the emerging international literature with new evidence from German longitudinal time-use surveys. Based on the conceptual framework of an education production function, we cover evidence on child, parent, and school inputs and students’ cognitive and socio-emotional development. The German panel evidence shows that children’s learning time decreased severely during the first school closures, particularly for low-achieving students, and increased only slightly one year later. In a value-added model, learning time increases with daily online class instruction, but not with other school activities. The review shows substantial losses in cognitive skills on achievement tests, particularly for students from disadvantaged backgrounds. Socio-emotional wellbeing also declined in the short run. Structural models and reduced-form projections suggest that unless remediated, the school closures will persistently reduce skill development, lifetime income, and economic growth and increase inequality.