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A coherent approach to analysing heterogeneity in network data

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.

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2021-COG

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Host institution

FONDATION JEAN JACQUES LAFFONT,TOULOUSE SCIENCES ECONOMIQUES
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 654 178,75
Address
5E ETAGE, 1 ESP DE L'UNIVERSITE
31080 Toulouse
France

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Region
Occitanie Midi-Pyrénées Haute-Garonne
Activity type
Research Organisations
Links
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 966 000,00

Beneficiaries (2)

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