We present a new identification condition for regression discontinuity designs. We replace the local randomization of Lee (2008) with two restrictions on its threat, namely, the manipulation of the running variable. Furthermore, we provide the first auxiliary assumption of McCrary's (2008) diagnostic test to detect manipulation. Based on our auxiliary assumption, we derive a novel expression of moments that immediately implies the worst-case bounds of Gerard, Rokkanen, and Rothe (2020) and an enhanced interpretation of their target parameters. We highlight two issues: an overlooked source of identification failure, and a missing auxiliary assumption to detect manipulation. In the case studies, we illustrate our solution to these issues using institutional details and economic theories.

翻译：我们为回归断点设计提出了一种新的识别条件。我们将Lee（2008）的局部随机化假设替换为其威胁来源——即运行变量的操纵行为——上的两项约束。进一步地，我们提供了McCrary（2008）诊断性检验中用于检测操纵行为的首个辅助假设。基于该辅助假设，我们推导出矩条件的新表达式，该表达式直接蕴含了Gerard、Rokkanen和Rothe（2020）的最坏情形边界，并深化了对目标参数的解读。我们强调两个关键问题：一个被忽视的识别失效来源，以及一个缺失的操纵检测辅助假设。在案例研究中，我们借助制度细节与经济理论阐明了针对这些问题的解决方案。