Project description
Econometric tools to analyse heterogeneity in social network models
Given the plethora of social networking applications, analysing social interactions has emerged as a key area of research interest. People’s interaction patterns provide insight into how people influence each other. Agent-based models are widely used for analysis of such social networks. The network is built from the bottom up, assigning attributes to autonomous decision-making entities called agents which are, by nature, heterogeneous. Accounting for unobserved heterogeneity is challenging, frustrated by issues of identification, estimation and computation. The EU-funded NETWORK project will develop a new nonparametric approach that addresses these three issues. Its econometric tools will describe how a change in one attribute of an individual affects attributes in other individuals, taking into account unobserved heterogeneity via a random effects approach.
Objective
The overarching goal of this project is to develop a coherent set of econometric methods to deal with unobserved heterogeneity in the analysis of social interactions between agents. Such heterogeneity is well recognized to be important. It is often of great interest to document the degree of heterogeneity, evaluate its impact, and uncover the existence and form of any complementarities that may exist between agents. With the growing availability of network data, questions of this kind are increasingly being asked in applied work. The development of appropriate econometric tools to answer them has, however, not followed suit. If anything, recent theoretical work has pointed at substantial difficulties with the so called fixed-effect approach currently serving as the workhorse tool.
This project recognizes the potential of taking a random-effect view. For settings where agents interact in pairs, such a view has received some attention in the literature. However, to date, it struggles with issues of identification, estimation, and computation. We will develop a new nonparametric approach that provides a solution to each of these three issues. We will next venture forward and extend this framework to situations where agents interact in larger groups. Both collaborative and non-collaborative settings will be considered, thereby covering team production, competition, and peer effects. Special attention will be given to recovering treatment effects in the presence of social interactions, where interference on unobservable confounders is an issue. For situations where data limitations prevent a fully nonparametric approach, instrumental-variable methods that build on flexible functional form restrictions will be developed. The statistical properties of the proposed estimators will be derived, software implementation will be provided, and empirical illustrations will be presented to highlight the usefulness of the methods.
Fields of science
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
31080 Toulouse
France