Dependence matters: Statistical models to identify the drivers of tie formation in economic networks

TL;DR

This paper introduces two statistical models, ERGM and AME, for empirically analyzing network formation in economic contexts, accounting for interdependencies and enabling testing of endogenous effects.

Contribution

It presents a comprehensive framework for modeling economic networks, comparing ERGM and AME, and demonstrates their application with real-world trade and foreign exchange networks.

Findings

ERGM explicitly tests network structure effects

AME captures actor-specific latent effects

Both models are demonstrated with empirical data

Abstract

Networks are ubiquitous in economic research on organizations, trade, and many other areas. However, while economic theory extensively considers networks, no general framework for their empirical modeling has yet emerged. We thus introduce two different statistical models for this purpose -- the Exponential Random Graph Model (ERGM) and the Additive and Multiplicative Effects network model (AME). Both model classes can account for network interdependencies between observations, but differ in how they do so. The ERGM allows one to explicitly specify and test the influence of particular network structures, making it a natural choice if one is substantively interested in estimating endogenous network effects. In contrast, AME captures these effects by introducing actor-specific latent variables affecting their propensity to form ties. This makes the latter a good choice if the researcher is interested in capturing the effect of exogenous covariates on tie formation without having a specific theory on the endogenous dependence structures at play. After introducing the two model classes, we showcase them through real-world applications to networks stemming from international arms trade and foreign exchange activity. We further provide full replication materials to facilitate the adoption of these methods in empirical economic research.