Socioeconomic correlations and stratification in social-communication networks

TL;DR

This study analyzes large-scale mobile and banking data to reveal how wealth influences social connections, mobility, and stratification, confirming longstanding hypotheses about socioeconomic segregation in society.

Contribution

It provides the first large-scale empirical analysis linking social networks and economic status using combined mobile and banking datasets.

Findings

Wealth and debt follow Pareto distribution.

Social networks are stratified with same-class connections.

Rich individuals have wider mobility ranges.

Abstract

The uneven distribution of wealth and individual economic capacities are among the main forces which shape modern societies and arguably bias the emerging social structures. However, the study of correlations between the social network and economic status of individuals is difficult due to the lack of large-scale multimodal data disclosing both the social ties and economic indicators of the same population. Here, we close this gap through the analysis of coupled datasets recording the mobile phone communications and bank transaction history of one million anonymised individuals living in a Latin American country. We show that wealth and debt are unevenly distributed among people in agreement with the Pareto principle; the observed social structure is strongly stratified, with people being better connected to others of their own socioeconomic class rather than to others of different classes; the social network appears with assortative socioeconomic correlations and tightly connected "rich clubs"; and that egos from the same class live closer to each other but commute further if they are wealthier. These results are based on a representative, society-large population, and empirically demonstrate some long-lasting hypotheses on socioeconomic correlations which potentially lay behind social segregation, and induce differences in human mobility.