G2TT
来源类型Discussion paper
规范类型论文
来源IDDP11048
DP11048 Identification and Inference in Regression Discontinuity Designs with a Manipulated Running Variable
Francois Gerard
发表日期2016-01-17
出版年2016
语种英语
摘要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.
主题Public Economics
关键词Bounds Manipulation Regression discontinuity
URLhttps://cepr.org/publications/dp11048
来源智库Centre for Economic Policy Research (United Kingdom)
资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/539877
推荐引用方式
GB/T 7714
Francois Gerard. DP11048 Identification and Inference in Regression Discontinuity Designs with a Manipulated Running Variable. 2016.
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