Manipulation-Robust Regression Discontinuity Designs

Regression discontinuity (RD) design in a practical context is often contaminated by units' behavior to manipulate their treatment assignment. However, we have no formal justification for point identification in such a contaminated RD design. Diagnostic tests have been proposed to detect manipulations, but they do not guarantee identification without some auxiliary assumptions, and the auxiliary assumptions have not been proposed. This study proposes a set of restrictions for possibly manipulated RD designs to validate point identification and diagnostic tests. The same restrictions simultaneously validate worst-case bounds when the diagnostic tests are validated. Therefore, our designs are manipulation robust in testing and identification. The worst-case bounds have two shorter bounds as special cases, and we apply special-case bounds to a controversy regarding the incumbency margin study of the U.S. House of Representatives elections studied in Lee (2008).