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

Francois Gerard, Miikka Rokkanen () and Christoph Rothe

No 9604, IZA Discussion Papers from Institute of Labor Economics (IZA)

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: manipulation; treatment effects; regression discontinuity; partial identification (search for similar items in EconPapers)
JEL-codes: C14 C21 C26 C51 (search for similar items in EconPapers)
Pages: 66 pages
Date: 2015-12
New Economics Papers: this item is included in nep-ore
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Citations: View citations in EconPapers (9)

Published - published as 'Bounds on treatment effects in regression discontinuity designs with a manipulated running variable' in: Quantitative Economics, 2020, 11 (3), 839-870

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Related works:
Working Paper: Identification and Inference in Regression Discontinuity Designs with a Manipulated Running Variable (2016)
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