Manipulation-Robust Regression Discontinuity Designs

TL;DR

This paper introduces low-level conditions for identifying manipulation-robust regression discontinuity designs, emphasizing the importance of the running variable's density and providing diagnostic tools to detect manipulation.

Contribution

It offers a new potential outcome framework with simple restrictions for identification and highlights the role of the running variable's density in ensuring robustness.

Findings

Established low-level conditions for identification in RDDs

Highlighted the importance of the running variable's continuous density

Provided a diagnostic density test for manipulation detection

Abstract

We present simple low-level conditions for identification in regression discontinuity designs using a potential outcome framework for the manipulation of the running variable. Using this framework, we replace the existing identification statement with two restrictions on manipulation. Our framework highlights the critical role of the continuous density of the running variable in identification. In particular, we establish the low-level auxiliary assumption of the diagnostic density test under which the design may detect manipulation against identification and hence is manipulation-robust.