This paper demonstrates a method for estimating treatment effects in spatial tests, utilizing a second control group to measure unexplained spatial phenomena. The technique is implemented on two innovations in Ugandan microfinance, and we measure the ways in which concurrent shocks such as an Ebola outbreak and a contentious presidential election altered outcomes differentially across regions. By correcting for this spatial heterogeneity, we measure the impact of the policies; a program that increased borrowers' control over the terms of their loans improved outcomes, while the results of a program that bundled health insurance into the lending contract were more mixed.

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