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来源类型 | Discussion paper |
规范类型 | 论文 |
来源ID | DP11048 |
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 |
URL | https://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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