Difference-in-differences (DID) strategies are particularly useful for evaluating policy effects in natural experiments in which, for example, a policy affects some schools and students but not others. However, the standard DID method may produce biased estimation of the policy effect if the confounding effect of concurrent events varies by…

When using time series accountability data to evaluate system-wide education policies, concurrent changes often pose threats to internal validity. The standard difference-in-differences (DID) method resorts to a non-equivalent comparison group whose average outcome change is due to such confounding. This strategy relies on the strong assumption…