We study the econometric properties of so-called donut regression discontinuity (RD) designs, a robustness exercise which involves repeating estimation and inference without the data points in some area around the treatment threshold. This approach is often motivated by concerns that possible systematic sorting of units, or similar data issues, in some neighborhood of the treatment threshold might distort estimation and inference of RD treatment effects. We show that donut RD estimators can have substantially larger bias and variance than contentional RD estimators, and that the corresponding confidence intervals can be substantially longer. We also provide a formal testing framework for comparing donut and conventional RD estimation results.

翻译：我们研究了所谓“甜甜圈断点回归设计”（donut RD designs）的计量经济学性质。该设计是一种稳健性检验方法，涉及剔除处理阈值附近某个区域内的数据点，并重新进行估计与推断。采用此方法的常见动机是担忧处理阈值邻近区域可能存在单位系统性排序或类似的数据问题，从而扭曲对处理效应的估计与推断。研究表明，甜甜圈断点回归估计量的偏差与方差可能显著大于传统断点回归估计量，其对应的置信区间也可能大幅延长。此外，本文还提供了一个正式的检验框架，用于比较甜甜圈断点回归与传统断点回归的估计结果。