AI 中文总结
研究存在溢出效应的（准）实验中的检验与推断问题，利用数据为暴露度量的函数形式及参数选择提供信息，通过建立相关条件和性质，应用于扶贫项目得出不同半径估计及政策效应估计。
AI 中文摘要
经济政策很少仅影响其直接目标。为研究这些溢出效应，研究者用简单的暴露度量（如一定半径内被治疗邻居的比例）来总结其他受影响者。但在许多情况下，经济理论在选择该度量的函数形式（如环）及其参数（如半径）方面几乎没有指导作用。我们表明数据可以为这两种选择提供信息。正确设定的暴露度量意味着可用于估计和检验的正交性条件。我们在基于设计的框架中，在空间和网络依赖性下建立了所得估计量的一致性和渐近正态性，所有随机性都源于处理分配。然后我们刻画了有效矩条件。应用于两个大规模扶贫项目时，该框架支持一些先前的半径估计，但拒绝了其他估计。在后一种情况下，修正后的半径产生了实质上不同的政策效应估计。
英文摘要
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.
