Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition

Aureo de Paula, Imran Rasul and Pedro Souza ()

No 12792, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: 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.

Date: 2018-03
New Economics Papers: this item is included in nep-dcm, nep-soc and nep-ure
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Related works:
Working Paper: Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition (2023)
Working Paper: Identifying network ties from panel data: theory and an application to tax competition (2023)
Working Paper: Identifying network ties from panel data: Theory and an application to tax competition (2023)
Working Paper: Identifying Network Ties from Panel Data: Theory and an application to tax competition (2023)
Working Paper: Identifying network ties from panel data: theory and an application to tax competition (2023)
Working Paper: Identifying network ties from panel data: theory and an application to tax competition (2019)
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