Large-scale randomized experiments are important for determining how policy interventions change average outcomes. Researchers have begun developing methods to improve the external validity of these experiments. One new approach is a balanced sampling method for site selection, which does not require random sampling and takes into account the practicalities of site recruitment including high nonresponse.

The goal of balanced sampling is to develop a strategic sample selection plan that results in a sample that is compositionally similar to a well-defined inference population. To do so, a population frame is created and then divided into strata, which "focuses" recruiters on specific subpopulations. Units within these strata are then ranked, thus identifying "replacements" similar to sites that can be recruited when the ideal site refuses to participate in the experiment.

In this article, we consider how a balanced sample strategic site selection method might be implemented in a welfare policy evaluation.

We find that simply developing a population frame can be challenging, with three possible and reasonable options arising in the welfare policy arena. Using relevant study-specific contextual variables, we craft a recruitment plan that considers nonresponse.