Administrative Data Linking and Statistical Power Problems in Randomized Experiments

The increasing availability of large administrative datasets has led to a particularly exciting innovation in criminal justice research, that of the "low-cost" randomized trial in which administrative data are used to measure outcomes in lieu of costly primary data collection. In this paper, we point out that randomized experiments that make use of administrative data have an unfortunate consequence: the destruction of statistical power. Linking data from an experimental intervention to administrative records that track outcomes of interest typically requires matching datasets without a common unique identifier. In order to minimize mistaken linkages, researchers will often use "exact matching" (retaining an individual only if all their demographic variables match exactly in two or more datasets) in order to ensure that speculative matches do not lead to errors in an analytic dataset