Identification and Inference in Regression Discontinuity Designs with a Manipulated Running Variable

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: IZA Discussion Papers ; No. 9604

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions ; Single Equation Models: Single Variables: Instrumental Variables (IV) Estimation ; Model Construction and Estimation
• Thema: regression discontinuity ; treatment effects ; manipulation ; partial identification

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Gerard, Francois ; Rokkanen, Miikka ; Rothe, Christoph
• Ereignis: Veröffentlichung
• (wer): Institute for the Study of Labor (IZA)
• (wo): Bonn
• (wann): 2015

Weitere Informationen

• Handle: http://hdl.handle.net/10419/127565
• Letzte Aktualisierung: 10.03.2025, 11:42 MEZ