A Design-Based Approach to Testing and Inference in (Quasi-)Experiments with Spillovers
Finding the right way to measure how policies spread to nearby people
When governments run anti-poverty programs, the benefits often spread beyond the direct recipients to their neighbors and social connections. Researchers typically guess at how far these spillovers reach, but this paper shows how to let the data reveal the correct distance and shape instead. Applied to two major poverty-reduction programs, the method confirmed some previous estimates but rejected others—and the corrected distances produced substantially different estimates of how well the policies actually worked.
Policy makers rely on accurate impact estimates to decide whether programs are worth the cost. If researchers measure spillovers using the wrong distance or formula, they systematically underestimate or overestimate program effects. This framework provides a testable, data-driven way to get the measurement right, which directly changes which policies look effective and how much funding they deserve.