Network Data

TL;DR

This paper reviews econometric methods for analyzing network-embedded economic activities, focusing on dyadic regression, network summary parameters, and strategic formation models, highlighting current challenges and open questions.

Contribution

It introduces and discusses advanced econometric techniques for analyzing network structures and strategic formation in economic contexts, addressing unobserved heterogeneity and causal inference.

Findings

Dyadic regression with unobserved heterogeneity supports causal inference.

Methods for estimating network summary parameters like degree distribution.

Models of strategic network formation with interdependent preferences.

Abstract

Many economic activities are embedded in networks: sets of agents and the (often) rivalrous relationships connecting them to one another. Input sourcing by firms, interbank lending, scientific research, and job search are four examples, among many, of networked economic activities. Motivated by the premise that networks' structures are consequential, this chapter describes econometric methods for analyzing them. I emphasize (i) dyadic regression analysis incorporating unobserved agent-specific heterogeneity and supporting causal inference, (ii) techniques for estimating, and conducting inference on, summary network parameters (e.g., the degree distribution or transitivity index); and (iii) empirical models of strategic network formation admitting interdependencies in preferences. Current research challenges and open questions are also discussed.