Four-day school weeks are used in over 1,600 schools across 24 states, but little is known about adoption and implementation of these types of school calendars. Through examinations of school calendars and correspondence with school districts, we have compiled the most complete four-day school week dataset to date. We use this unique database to conduct a comprehensive analysis of four-day school week policy adoption and implementation. We find adoption of four-day school weeks is often financially-motivated and has generally remained a small, rural district phenomenon. These schedules feature a day off once a week – often Friday – with increased time in school on each of the remaining four school days that, on average, is nearly an hour longer than the national average among five-day schools. Four-day school week schedules average only 148 yearly school days, yielding yearly time in school that is below the national average for five-day schools despite the longer school days. Substantial heterogeneity exists in the structure of these schedules across states, which may help explain differential four-day school week effects on student outcomes across institutional settings in the previous literature.

Empirical Bayes's (EB) estimation has become a popular procedure used to calculate teacher value added, often as a way to make imprecise estimates more reliable. In this article, we review the theory of EB estimation and use simulated and real student achievement data to study the ability of EB estimators to properly rank teachers. We compare the performance of EB estimators with that of other widely used value-added estimators under different teacher assignment scenarios. We find that, although EB estimators generally perform well under random assignment (RA) of teachers to classrooms, their performance suffers under nonrandom teacher assignment. Under non-RA, estimators that explicitly (if imperfectly) control for the teacher assignment mechanism perform the best out of all the estimators we examine. We also find that shrinking the estimates, as in EB estimation, does not itself substantially boost performance.

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