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We use administrative panel data from seven states covering nearly 3 million students to document and explore variation in "academic mobility," a term we use to describe the extent to which students' ranks in the distribution of academic performance change during their public schooling careers. On average, we show that student ranks are highly persistent during elementary and secondary education--that is, academic mobility is limited in U.S. schools as a whole. Still, there is non-negligible variation in the degree of upward mobility across some student subgroups as well as individual school districts. On average, districts that exhibit the greatest upward academic mobility serve more socioeconomically advantaged populations and have higher value-added to student achievement.

There is empirical evidence of substantial heterogeneity in economic mobility across geographic areas and the efficacy of schools has been suggested as an explanatory factor. Using administrative microdata from seven states covering nearly 3 million students, we explore the potential role of schools in promoting economic mobility by estimating cross-district variation in "academic mobility"--a term we use to describe the extent to which students' ranks in the distribution of academic performance change during their schooling careers. We show that there exists considerable heterogeneity in academic mobility across school districts. However, after aggregating our district-level measures of academic mobility to the commuting-zone level and merging them with geographically matched external estimates of economic mobility, we find little scope for geographic differences in academic mobility to meaningfully account for differences in economic mobility.

We evaluate the feasibility of estimating test-score growth with a gap year in testing data, informing the scenario when state testing resumes after the 2020 COVID-19-induced test stoppage. Our research design is to simulate a gap year in testing using pre-COVID-19 data--when a true test gap did not occur--which facilitates comparisons of district- and school-level growth measures that are estimated with and without a gap year. We find that growth estimates based on the full data and gap-year data are generally similar, and our results highlight an advantage of using comprehensive growth models with rich controls for student and schooling circumstances. With the caveat that there is looming uncertainty about which students will be tested in public schools when testing resumes, and how they will be tested, our findings establish the potential for estimating useful growth measures with a gap year in testing.

When the COVID-19 pandemic hit the U.S. last spring, schools nationwide shut their doors and states cancelled annual standardized tests. Now federal and state policymakers are debating whether to cancel testing again in 2021. One factor they should consider is whether a two-year gap in testing will make it impossible to measure student-level achievement growth during this historic period. Using administrative data from Missouri from the pre-pandemic years of 2016 to 2019, a team of researchers in the department of economics at the University of Missouri--Ishtiaque Fazlul, Cory Koedel, Eric Parsons, and Cheng Qian--evaluated the efficacy and implications of growth models that reflected one-year versus two-year gaps in testing. Among their key findings: (1) District and school growth estimates based on a single-year gap convey similar information to growth estimates based on data with no gap year; (2) Student subgroup growth estimates based on gap-year data can also be reliably estimated; and (3) However, just 27 percent of students attend schools that can generate growth measures if two consecutive years of test scores are missing. These findings suggest that calculating student growth measures from 2019 to 2021 is feasible even with the gap in testing last year, and that the results are likely to be quite accurate--so long as states test students this year. But if states cancel testing again in 2021, it will be extremely difficult to determine how effective individual schools were during this challenging period in American education. That's because few students will be in the same school in 2022 as they were in 2019 (elementary schoolers will have moved onto middle school, and middle schoolers will have moved onto high school). Still, the next few months are hard to predict. If spring testing is still not possible for some states in 2021, they might consider administering assessments when schools reopen in fall 2021, although such a scenario would pose its own logistical, analytic, and political challenges. [Foreword written by Michael J. Petrilli.]

Improving public sector workforce quality is challenging in sectors such as education where worker productivity is difficult to assess and manager incentives are muted by political and bureaucratic constraints. In this paper, we study how providing improved information to principals about teacher effectiveness and encouraging them to use the information in personnel decisions affects the composition of teacher turnovers. Our setting is the Houston Independent School District, which recently implemented a rigorous teacher evaluation system. Prior to the new system, teacher effectiveness was negatively correlated with district exit and we show that the policy significantly strengthened this relationship, primarily by increasing the relative likelihood of exit for teachers in the bottom quintile of the quality distribution. Low-performing teachers working in low-achieving schools were especially likely to leave. However, despite the success, the implied change to the quality of the workforce overall is too small to have a detectable impact on student achievement.

Improving public sector workforce quality is challenging in sectors such as education where worker productivity is difficult to assess and manager incentives are muted by political and bureaucratic constraints. In this paper, we study how providing information to principals about teacher effectiveness and encouraging them to use the information in personnel decisions affects the composition of teacher turnovers. Our setting is the Houston Independent School District, which recently implemented a rigorous teacher evaluation system. Prior to the new system teacher effectiveness was negatively correlated with district exit and we show that the policy significantly strengthened this relationship, primarily by increasing the relative likelihood of exit for teachers in the bottom quintile of the quality distribution. Low-performing teachers working in low-achieving schools were especially likely to leave. However, despite the success, the implied change to the quality of the workforce overall is too small to have a detectable impact on student achievement. Supplementary tables are appended.

The specifics of how growth models should be constructed and used to evaluate schools and teachers is a topic of lively policy debate in states and school districts nationwide. In this paper we take up the question of model choice and examine three competing approaches. The first approach, reflected in the popular student growth percentiles (SGPs) framework, eschews all controls for student covariates and schooling environments. The second approach, typically associated with value-added models (VAMs), controls for student background characteristics and aims to identify the causal effects of schools and teachers. The third approach, also VAM-based, fully levels the playing field so that the correlation between school- and teacher-level growth measures and student demographics is essentially zero. We argue that the third approach is the most desirable for use in educational evaluation systems. Our case rests on personnel economics, incentive-design theory, and the potential role that growth measures can play in improving instruction in K-12 schools. Data Description is appended. (Contains 4 tables, 3 figures and 23 footnotes.)