| 来源类型 | Discussion paper
|
| 规范类型 | 论文
|
| 来源ID | DP11356
|
| DP11356 The Informational Theory of Legislative Committees: An Experimental Analysis |
| Marco Battaglini
|
| 发表日期 | 2016-06-27
|
| 出版年 | 2016
|
| 语种 | 英语
|
| 摘要 | We experimentally investigate the informational theory of legislative committees first proposed by Gilligan and Krehbiel [1987, 1989]. Two committees provide policy-relevant information to a legislature under two different procedural rules. Under the open rule, the
legislature is free to make any decision; under the closed rule, the legislature is constrained to choose between a committee's proposal and an exogenous status quo. Our experiment shows that even in the presence of conflicts of interests, legislative committees help improve the legislature's decision by providing useful information. We further obtain evidence in support of three theoretical predictions: the Outlier Principle, according to which more extreme preferences of the committees reduce the extent of information transmission; the Distributional Principle, according to which the open rule is more distributionally eefficient than the closed rule; and the Restrictive-rule Principle, according to which the closed rule better facilitates the informational role of legislative committees. We, however, obtain mixed evidence for the Heterogeneity Principle, according to which more information can be extracted in the presence of multiple committees with heterogeneous preferences. Our experimental findings provide overall support for the equilibrium predictions of Gilligan and Krehbiel [1989], some of which have been controversial in the literature. |
| 主题 | Public Economics
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| 关键词 | Legislative committees
Information transmission
Laboratory experiment
|
| URL | https://cepr.org/publications/dp11356
|
| 来源智库 | Centre for Economic Policy Research (United Kingdom)
|
| 资源类型 | 智库出版物
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| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/540171
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推荐引用方式
GB/T 7714 |
Marco Battaglini. DP11356 The Informational Theory of Legislative Committees: An Experimental Analysis. 2016.
|
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