全球智库信息集成服务系统(G2TT): DP15041 A Graphical Lasso Approach to Estimating Network Connections: The Case of U.S. Lawmakers

G2TT

来源类型	Discussion paper
规范类型	论文
来源ID	DP15041
	DP15041 A Graphical Lasso Approach to Estimating Network Connections: The Case of U.S. Lawmakers
	Marco Battaglini; Forrest Crawford; Eleonora Patacchini; Sida Peng
发表日期	2020-07-14
出版年	2020
语种	英语
摘要	Abstract In this paper, we propose a new approach to the estimation of social networks and we apply it to the estimation of productivity spillovers in the U.S. Congress. Social networks such as the social connections among lawmakers are not generally directly observed, they can be recovered only using the observable outcomes that they contribute to determine (such as, for example, the legislators’ effectiveness). Moreover, they are typically stable for relatively short periods of time, thus generating only short panels of observations. Our estimator has three appealing properties that allows it to work in these environments. First, it is constructed for “small” asymptotic, thus requiring only short panels of observations. Second, it requires relatively nonrestrictive sparsity assumptions for identification, thus being applicable to dense networks with (potentially) star shaped connections. Third, it allows for heterogeneous common shocks across subnetworks. The application to the U.S. Congress gives us new insights about the nature of social interactions among lawmakers. We estimate a significant decrease over time in the importance of productivity spillovers among individual lawmakers, compensated by an increase in the party level common shock over time. This suggests that the rise of partisanship is not affecting only the ideological position of legislators when they vote, but more generally how lawmakers collaborate in the U.S. Congress.
主题	Public Economics
关键词	Social Networks Legislatures Graphical lasso
URL	https://cepr.org/publications/dp15041-0
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/543992
推荐引用方式 GB/T 7714	Marco Battaglini,Forrest Crawford,Eleonora Patacchini,et al. DP15041 A Graphical Lasso Approach to Estimating Network Connections: The Case of U.S. Lawmakers. 2020.