In many contexts we may be interested in understanding whether direct connections between agents, such as declared friendships in a classroom or family links in a rural village, affect their outcomes. In this paper, we review the literature studying econometric methods for the analysis of linear models of social effects, a class that includes the ‘linear-in-means’ local average model, the local aggregate model, and models where network statistics affect outcomes. We provide an overview of the underlying theoretical models, before discussing conditions for identification using observational and experimental/quasi-experimental data.
Authors
![Bansi Malde](/sites/default/files/styles/square_desktop/public/2023-11/Bansi%20Malde.jpg?itok=WUW9ECXS)
Research Fellow University of Kent
Bansi is a Research Fellow of the IFS, a Senior Lecturer of Economics at the University of Kent and also a Fellow at the Global Labor Organisation.
![Arun Advani](/sites/default/files/styles/square_desktop/public/2022-05/Arun_Advani.jpg?itok=i5KiRxAs)
Research Fellow University of Warwick
Arun is a Research Fellow at IFS, an Associate Professor of Economics at the University of Warwick and a Commissioner at the Wealth Tax Commission.
Journal article details
- DOI
- 10.1186/s41937-017-0011-x
- Publisher
- Springer
- JEL
- C31 C81 Z13
- Issue
- Volume 154, Issue 12, May 2018
Suggested citation
Advani, A and Malde, B. (2018). 'Methods to identify linear network models: a review' 154(12/2018)
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