全球智库信息集成服务系统(G2TT): DP12792 Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition

G2TT

来源类型	Discussion paper
规范类型	论文
来源ID	DP12792
	DP12792 Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition
	Aureo de Paula; Imran Rasul; Pedro Souza
发表日期	2018-03-14
出版年	2018
语种	英语
摘要	Social interactions determine many economic behaviors, but information on social ties does not exist in most publicly available and widely used datasets. We present results on the identification of social networks from observational panel data that contains no information on social ties between agents. In the context of a canonical social interactions model, we provide sufficient conditions under which the social interactions matrix, endogenous and exogenous social effect parameters are all globally identified. While this result is relevant across different estimation strategies, we then describe how high-dimensional estimation techniques can be used to estimate the interactions model based on the Adaptive Elastic Net GMM method. We employ the method to study tax competition across US states. We find the identified social interactions matrix implies tax competition differs markedly from the common assumption of competition between geographically neighboring states, providing further insights for the long-standing debate on the relative roles of factor mobility and yardstick competition in driving tax setting behavior across states. Most broadly, our identification and application show the analysis of social interactions can be extended to economic realms where no network data exists.
主题	Development Economics ; Industrial Organization ; Public Economics
URL	https://cepr.org/publications/dp12792-2
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/541601
推荐引用方式 GB/T 7714	Aureo de Paula,Imran Rasul,Pedro Souza. DP12792 Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition. 2018.