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When an employee expects repeated evaluation and performance incentives over time, the potential future rewards create an incentive to invest in building relevant skills. Because new skills benefit job performance, the effects of an evaluation program can persist after the rewards end or even anticipate the start of rewards. I test for persistence and anticipation effects, along with more conventional predictions, using a quasi-experiment in Tennessee schools. Performance improves with new evaluation measures, but gains are larger when the teacher expects future rewards linked to future scores. Performance rises further when incentives start and remains higher even after incentives end.
We study teachers’ choices about how to allocate class time across different instructional activities, for example, lecturing, open discussion, or individual practice. Our data come from secondary schools in England, specifically classes preceding GCSE exams. Students score higher in math when their teacher devotes more class time to individual practice and assessment. In contrast, students score higher in English if there is more discussion and work with classmates. Class time allocation predicts test scores separate from the quality of the teacher’s instruction during the activities. These results suggest opportunities to improve student achievement without changes in teachers’ skills.
We examine the state of the U.S. K-12 teaching profession over the last half century by compiling nationally representative time-series data on four interrelated constructs: professional prestige, interest among students, preparation for entry, and job satisfaction. We find a consistent and dynamic pattern across every measure: a rapid decline in the 1970s, a swift rise in the 1980s, relative stability for two decades, and a sustained drop beginning around 2010. The current state of the teaching profession is at or near its lowest levels in 50 years. We identify and explore a range of factors that might explain these historical patterns including education funding, teacher pay, outside opportunities, unionism, barriers to entry, working conditions, accountability, autonomy, and school shootings.
We develop a unifying conceptual framework for understanding and predicting teacher shortages at the state, region, district, and school levels. We then generate and test hypotheses about geographic, grade level, and subject variation in teacher shortages using data on teaching vacancies in Tennessee during the fall of 2019. We find that teacher staffing challenges are highly localized, causing shortages and surpluses to coexist. Aggregate descriptions of staffing challenges mask considerable variation between schools and subjects within districts. Schools with fewer local early-career teachers, smaller district salary increases, worse working conditions, and higher historical attrition rates have higher vacancy rates. Our findings illustrate why viewpoints about, and solutions to, shortages depend critically on whether one takes an aggregate or local perspective.
Policy makers periodically consider using student assignment policies to improve educational outcomes by altering the socio-economic and academic skill composition of schools. We exploit the quasi-random reassignment of students across schools in the Wake County Public School System to estimate the academic and behavioral effects of being reassigned to a different school and, separately, of shifts in peer characteristics. We rule out all but substantively small effects of transitioning to a different school as a result of reassignment on test scores, course grades and chronic absenteeism. In contrast, increasing the achievement levels of students' peers improves students' math and ELA test scores but harms their ELA course grades. Test score benefits accrue primarily to students from higher-income families, though students with lower family income or lower prior performance still benefit. Our results suggest that student assignment policies that relocate students to avoid the over-concentration of lower-achieving students or those from lower-income families can accomplish equity goals (despite important caveats), although these reassignments may reduce achievement for students from higher-income backgrounds.
Classroom discourse is a core medium of instruction --- analyzing it can provide a window into teaching and learning as well as driving the development of new tools for improving instruction. We introduce the largest dataset of mathematics classroom transcripts available to researchers, and demonstrate how this data can help improve instruction. The dataset consists of 1,660 45-60 minute long 4th and 5th grade elementary mathematics observations collected by the National Center for Teacher Effectiveness (NCTE) between 2010-2013. The anonymized transcripts represent data from 317 teachers across 4 school districts that serve largely historically marginalized students. The transcripts come with rich metadata, including turn-level annotations for dialogic discourse moves, classroom observation scores, demographic information, survey responses and student test scores. We demonstrate that our natural language processing model, trained on our turn-level annotations, can learn to identify dialogic discourse moves and these moves are correlated with better classroom observation scores and learning outcomes. This dataset opens up several possibilities for researchers, educators and policymakers to learn about and improve K-12 instruction.
Education is one of the most important public goods provided by modern governments. Yet governments worldwide seldom perform well in the sector. This raises the question: why do governments preside over poor education quality? This article answers this question with evidence from Tanzania. Using data from surveys, administrative reports, and policy documents, it analyzes changing goals of education policy and associated impacts on access and learning over time. The main finding is that learn- ing has not always been the goal of schooling in Tanzania. Furthermore, for decades the government rationed access to both primary and secondary schooling for ideological reasons. These past policy choices partially explain contemporary poor outcomes in education. This article increase our understanding of the politics of education in low-income states. It also provides a corrective against the common assumption that governments always seek to maximize the provision of public goods and services for political gain.
An increasing share of new teachers enter the profession through alternative certification programs. While these programs increase teacher supply in areas facing critical shortages, they also increase instability in local teacher labor markets via high teacher turnover. A fundamental question is what effect these programs have on student achievement over the long run. To address this question, I study Teach For America (TFA) teachers working in New York City (NYC) between 2012 and 2019. This research brief reports on three key findings. First, I document five-year cumulative retention rates and find that, as expected, retention is lower for TFA teachers (25%) than for other NYC teachers working in similar schools (43%). Second, I estimate within-teacher returns to experience using a teacher fixed effects strategy and find that TFA teachers who continue teaching through year five improve at double the rate of the average NYC teacher. Third, I model the joint relationship between turnover and performance over time and find that the TFA performance advantage is large enough to offset turnover costs. I conclude that the net effect of TFA hiring on student achievement is positive in the short and long run.
We examine the labor supply decisions of substitute teachers – a large, on-demand market with broad shortages and inequitable supply. In 2018, Chicago Public Schools implemented a targeted bonus program designed to reduce unfilled teacher absences in largely segregated Black schools with historically low substitute coverage rates. Using a regression discontinuity design, we find that incentive pay substantially improved coverage equity and raised student achievement. Changes in labor supply were concentrated among Black and Hispanic substitutes from nearby neighborhoods with experience in incentive schools. Wage elasticity estimates suggest incentives would need to be 50% of daily wages to close fill-rate gaps.
This simulation study examines the characteristics of the Explanatory Item Response Model (EIRM) when estimating treatment effects when compared to classical test theory (CTT) sum and mean scores and item response theory (IRT)-based theta scores. Results show that the EIRM and IRT theta scores provide generally equivalent bias and false positive rates compared to CTT scores and superior calibration of standard errors under model misspecification. Analysis of the statistical power of each method reveals that the EIRM and IRT theta scores provide a marginal benefit to power and are more robust to missing data than other methods when parametric assumptions are met and provide a substantial benefit to power under heteroskedasticity, but their performance is mixed under other conditions. The methods are illustrated with an empirical data application examining the causal effect of an elementary school literacy intervention on reading comprehension test scores and demonstrates that the EIRM provides a more precise estimate of the average treatment effect than the CTT or IRT theta score approaches. Tradeoffs of model selection and interpretation are discussed.