Aureo de Paula, Imran Rasul, Pedro Souza
arXiv 16 Oct 2019 · Econometrics · publishedThe Review of Economic Studies (2024) · 23 citations (OpenAlex)
arXiv:1910.07452 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Manski, C. F (1993) Identification of Endogenous Social Effects: the reflection problem | 1.000 | 5 | 4 | 100% |
| 2 | de Paula, A (2017) Econometrics of Network Models | 1.000 | 5 | 3 | 100% |
| 3 | Blume, L. E., W. A. Brock, S. N. Durlauf, and R. Jayaraman (2015) Linear Social Interactions Models | 0.965 | 10 | 4 | 90% |
| 4 | Bramoullé, Y., H. Djebbari, and B. Fortin (2009) Identification of Peer Effects Through Social Networks | 0.965 | 10 | 4 | 90% |
| 5 | Manresa, E (2016) Estimating the Structure of Social Interactions Using Panel Data | 0.928 | 5 | 4 | 80% |
| 6 | Caner, M. and H. H. Zhang (2014) Adaptive Elastic Net for Generalized Method of Moments | 0.902 | 15 | 4 | 73% |
| 7 | Jackson, M., B. Rogers, and Y. Zenou (2017) The Economic Consequences of Social Network Structure | 0.843 | 3 | 3 | 100% |
| 8 | Breza, E., A. G. Chandrasekhar, T. H. McCormick, and M. Pan (2020) Using aggregated relational data to feasibly identify network structure without network data | 0.811 | 4 | 2 | 100% |
| 9 | Banerjee, A., A. G. Chandrasekhar, E. Duflo, and M. O. Jackson (2013) The Diffusion of Microfinance | 0.737 | 3 | 3 | 67% |
| 10 | De Giorgi, G., M. Pellizzari, and S. Redaelli (2010) Identification of Social Interactions through Partially Overlapping Peer Groups | 0.737 | 3 | 2 | 100% |
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