🤖 AI Summary
This study addresses the lack of theoretical guidance in specifying the functional form—such as neighborhood radius—of spillover exposure measures in estimating economic policy spillover effects. Building on an experimental design framework that leverages the randomness in treatment assignment, the paper jointly identifies both the functional form and its parameters through orthogonal moment conditions. It further develops the first asymptotic theory tailored to spatial and network-dependent structures. The proposed design-based inference approach enables data-driven selection of the exposure function and is validated through two large-scale poverty alleviation programs. In these applications, several pre-specified radii are formally rejected, and adjusting the exposure specification leads to substantively different estimates of policy effects.
📝 Abstract
Economic policies rarely affect only their direct targets. To study these spillovers, researchers summarize who else was treated with a simple exposure measure, such as the share of treated neighbors within a radius. But for many settings, economic theory provides little guidance on choosing the functional form (e.g., ring) of that measure or its parameters (e.g., radius). We show that the data can inform both choices. Correctly specified exposure measures imply orthogonality conditions that can be used for both estimation and testing. We establish consistency and asymptotic normality of the resulting estimator under spatial and network dependence in a design-based framework, with all randomness arising from treatment assignment. We then characterize the efficient moment conditions. Applied to two large-scale anti-poverty programs, the framework supports some prior radius estimates but rejects others. In the latter case, the revised radius yields substantively different policy-effect estimates.