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

Authors: Áureo de Paula , Imran Rasul and Pedro CL Souza
Date: 21 October 2019
Type: cemmap Working Paper, CWP55/19
DOI: 10.1920/wp.cem.2019.5519

Abstract

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.

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Previous version:
Áureo de Paula, Imran Rasul and Pedro CL Souza October 2018, Recovering social networks from panel data: identification, simulations and an application, cemmap Working Paper, The IFS

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