The COVID-19 pandemic upended the U.S. education system in ways that dramatically affected the jobs of K–12 employees. However, there remains considerable uncertainty about the nature and degree of staffing challenges during the pandemic. We draw on data from the Bureau of Labor Statistics (BLS) and State Education Agencies (SEA) to describe patterns in K–12 education employment and to highlight the limitations of available data. Data from the BLS suggest overall employment in the K–12 labor market declined by 9 percent at the onset of the pandemic and remained well below pre-pandemic levels more than two years later. SEA data suggest that teachers did not leave the profession en masse as many predicted, with turnover decreasing in the summer of 2020 and then increasing modestly in 2021 back to pre-pandemic levels. We explore possible explanations for these patterns including weak hiring through the summer of 2020 and high attrition among K–12 instructional support and noninstructional staff. State vacancy data also suggest that schools faced substantial challenges filling open positions during the 2021–22 academic year. Our analyses illustrate the imperative to build nationally representative, detailed, and timely data systems on the K–12 education labor market to better inform policy.

We study the adoption and implementation of a new mobile communication application (app) among a sample of 132 New York City public schools. The app provides a platform for sharing general announcements and news, as well as engaging in personalized two-way communication with individual parents. We provide participating schools with free access to the app and randomize schools to receive intensive support (training, guidance, monitoring, and encouragement) for maximizing the efficacy of the app. Although user supports led to higher levels of communication within the app in the treatment year, overall usage remained low and declined in the following year when treatment schools no longer received intensive supports. We find few subsequent effects on perceptions of communication quality or student outcomes. We leverage rich internal user data to explore how take-up and usage patterns varied across staff and school characteristics. These analyses help to identify early adopters and reluctant users, revealing both opportunities and obstacles to engaging parents through new communication technology.

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.

Most teacher layoffs during the Great Recession were implemented following inverse-seniority policies. In this paper, I examine the implementation of a discretionary layoff policy in Charlotte Mecklenburg Schools. Administrators did not uniformly lay off the most or least senior teachers but instead selected teachers who were previously retired, late-hired, unlicensed, low-performing, or nontenured. Using quasi-experimental variation within schools across grades, I then estimate the differential effects of teacher layoffs on student achievement based on teacher seniority and effectiveness. Mathematics achievement in grades that lost an effective teacher, as measured by principal evaluations or value-added scores, decreased 0.05 to 0.11 standard deviations more than in grades that lost an ineffective teacher. In contrast, teacher seniority has limited predictive power on the effects of layoffs. Simulation analyses show that the district selected teachers who were, on average, less effective than those teachers identified under an inverse-seniority policy, and also reduced job losses.

Evidence on the effect of extending the school day is decidedly mixed because of the stark differences in how schools use additional time. In this paper, I focus narrowly on the effect of additional time used for individualized tutorials. In 2005, MATCH Charter Public High School integrated two hours of tutorials throughout an extended day. The unanticipated implementation of this initiative and the school's admissions lottery allow me to use two complementary quasi-experimental methods to estimate program effects. I find that providing students with daily tutorials that are integrated into the school day and taught by full-time, recent college graduates increased achievement on tenth-grade English language arts exams by 0.15–0.25 standard deviations per year. I find no average effect in mathematics beyond the large gains students were already achieving, although quantile regression estimates suggest the tutorials raised the lowest end of the achievement distribution in mathematics.