A Structural Model of Business Card Exchange Networks

TL;DR

This paper develops a scalable structural model to analyze how professional networks form through business card exchanges, revealing homophily effects and heterogeneity among users in a large dataset.

Contribution

It introduces a novel, scalable estimation method for a structural network formation model with heterogeneity and strategic interactions, applied to a large real-world dataset.

Findings

Users exhibit homophily based on location and unobservable traits.

The model captures strategic interactions influencing network formation.

Estimation method is efficient for massive networks.

Abstract

Social and professional networks affect labor market dynamics, knowledge diffusion and new business creation. To understand the determinants of how these networks are formed in the first place, we analyze a unique dataset of business cards exchanges among a sample of over 240,000 users of the multi-platform contact management and professional social networking tool for individuals Eight. We develop a structural model of network formation with strategic interactions, and we estimate users' payoffs that depend on the composition of business relationships, as well as indirect business interactions. We allow heterogeneity of users in both observable and unobservable characteristics to affect how relationships form and are maintained. The model's stationary equilibrium delivers a likelihood that is a mixture of exponential random graph models that we can characterize in closed-form. We overcome several econometric and computational challenges in estimation, by exploiting a two-step estimation procedure, variational approximations and minorization-maximization methods. Our algorithm is scalable, highly parallelizable and makes efficient use of computer memory to allow estimation in massive networks. We show that users payoffs display homophily in several dimensions, e.g. location; furthermore, users unobservable characteristics also display homophily.