An Overview of Geographically Discontinuous Treatment Assignments with an Application to Children’s Health Insurance ^☆

Abstract

We study research designs where a binary treatment changes discontinuously at the border between administrative units such as states, counties, or municipalities, creating a treated and a control area. This type of geographically discontinuous treatment assignment can be analyzed in a standard regression discontinuity (RD) framework if the exact geographic location of each unit in the dataset is known. Such data, however, is often unavailable due to privacy considerations or measurement limitations. In the absence of geo-referenced individual-level data, two scenarios can arise depending on what kind of geographic information is available. If researchers have information about each observation’s location within aggregate but small geographic units, a modified RD framework can be applied, where the running variable is treated as discrete instead of continuous. If researchers lack this type of information and instead only have access to the location of units within coarse aggregate geographic units that are too large to be considered in an RD framework, the available coarse geographic information can be used to create a band or buffer around the border, only including in the analysis observations that fall within this band. We characterize each scenario, and also discuss several methodological challenges that are common to all research designs based on geographically discontinuous treatment assignments. We illustrate these issues with an original geographic application that studies the effect of introducing copayments for the use of the Children’s Health Insurance Program in the United States, focusing on the border between Illinois and Wisconsin.

Citation

Keele, L., Lorch, S., Passarella, M., Small, D. and Titiunik, R. (2017), "An Overview of Geographically Discontinuous Treatment Assignments with an Application to Children’s Health Insurance ^☆ ", Regression Discontinuity Designs (Advances in Econometrics, Vol. 38), Emerald Publishing Limited, Leeds, pp. 147-194. https://doi.org/10.1108/S0731-905320170000038007