Identifying network ties from panel data: theory and an application to tax competition

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 identi…cation 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 su¢cient conditions under which the social interactions matrix, endogenous and exogenous social e¤ect parameters are all globally identi…ed. While this result is relevant across di¤erent 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 …nd the identi…ed social interactions matrix implies tax competition di¤ers 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 identi…cation and application show the analysis of social interactions can be extended to economic realms where no network data exists. JEL Codes: C31, D85, H71

The network structure of mutual support links: Evidence from rural Tanzania

This paper takes a network perspective t oinvestigate how rural households in developing countries form the links through which they provide and get economic support. I test the hypothesis that indirect contacts (e.g. friends of friends) matter for link formation. An estimation procedure of a network formation model à la Jackson and Wolinsky (1996) is proposed and applied to data on a single village in Tanzania. Results show that when agents evaluate the net advantage of forming a link they also consider the wealth and the position of indirect contacts. The network externalities from indirect contacts are negative, which suggests a mechanism of competition over scarce resources. This paper proposes the first structural estimation of an endogenous network formation model, and also contributes to the development literature by overcoming the dyadic regression approach and providing evidence that village-level network structure has an explanatory value disregarded by all previous studies.mutual support ; network formation ; structural estimation ; indirect contacts