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

Author(s): François Gerard, Miikka Rokkanen and Christoph Rothe

Publication Date: January 2016

Keyword(s): bounds, manipulation and regression discontinuity

Programme Area(s): Public Economics

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.

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Bibliographic Reference

Gerard, F, Rokkanen, M and Rothe, C. 2016. 'Identification and Inference in Regression Discontinuity Designs with a Manipulated Running Variable'. London, Centre for Economic Policy Research. https://cepr.org/active/publications/discussion_papers/dp.php?dpno=11048