🤖 AI Summary
This study addresses a critical limitation in existing design-based simulations used to evaluate inference methods, which often overstate bias induced by spatial correlation due to unrealistic data-generating mechanisms. In particular, share-shift designs that fix outcomes and resample shocks conflate true treatment effects with error dependence structures, leading to misleading assessments. To remedy this, the paper proposes an improved simulation framework that more accurately models error dependence and avoids spurious entanglement between treatment effects and error terms, thereby better approximating real-world data-generating processes. Integrating resampling techniques with share-shift analysis, the proposed approach substantially enhances the reliability of inference evaluation across multiple empirical applications, underscoring the essential role of aligning simulation designs with genuine underlying mechanisms for valid inference assessment.
📝 Abstract
Design-based simulations - procedures that hold realized outcomes fixed and generate variation by resampling treatment assignment or shocks - are widely used in both methodological and applied work to assess inference procedures. This paper studies the extent to which such simulations are informative about inference validity. Focusing on shift-share designs, we show that standard simulations that fix outcomes and resample shocks may rely on a data-generating process that is not aligned with the true one. In particular, these simulations confound true treatment effects with error dependence, potentially overstating inference distortions due to spatial correlation. We propose alternative simulation designs that circumvent this problem and illustrate their use in prominent empirical applications. Our results highlight that the usefulness of design-based simulations depends critically on how closely the simulated data-generating process aligns with the true one.