Working Paper: CEPR ID: DP11048
Authors: Francois Gerard; Miikka Rokkanen; Christoph Rothe
Abstract: A key assumption in regression discontinuity analysis is that units cannot manipulate the value of their running variable in a way that guarantees or avoids assignment to the treatment. Standard identification arguments break down if this condition is violated. This paper shows that treatment effects remain partially identified in this case. We derive sharp bounds on the treatment effects, show how to estimate them, and propose ways to construct valid confidence intervals. Our results apply to both sharp and fuzzy regression discontinuity designs. We illustrate our methods by studying the effect of unemployment insurance on unemployment duration in Brazil, where we find strong evidence of manipulation at eligibility cutoffs.
Keywords: bounds; manipulation; regression discontinuity
JEL Codes: C2
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Treatment effects become partially identified (C22) | Sharp bounds on treatment effects (C22) |
| Identification of treatment effects among potentially assigned units (C90) | Importance of distinguishing between assigned units (C90) |
| Manipulation of running variable (C29) | Treatment effects become partially identified (C22) |
| UI take-up (J65) | Time to return to formal employment increases (J29) |
| Manipulation at eligibility cutoffs (C24) | Treatment effects can be inferred (C22) |