Inferring personal economic status from social network location

Area of Science:

• Socioeconomics
• Network Science
• Computational Social Science

Background:

• Social ties are widely believed to impact individual economic status.
• Quantifying this relationship at a network level requires operational definitions and large-scale data analysis.

Purpose of the Study:

• To develop a network-level measure of social influence to infer economic well-being.
• To analyze the correlation between social network position and economic status using real-world data.
• To validate the utility of social network metrics for targeted interventions.

Main Methods:

• Analysis of large-scale telecommunications and financial datasets from a national population.
• Development of a metric for individual location and influence within the social network structure.
• Implementation of a marketing campaign to compare targeted vs. random strategies.

Main Results:

• Individual network location, defined by collective influence, strongly correlates with personal economic status.
• Patterns of social influence within the network mirror economic inequality distributions.
• Targeting individuals based on social network metrics in a marketing campaign yielded a threefold increase in response rate compared to random targeting.

Conclusions:

• Social network structure provides a powerful tool for understanding and potentially influencing economic status.
• Network-based metrics can effectively identify individuals for targeted strategies, outperforming random approaches.
• This methodology has implications for economic policy, marketing, and understanding societal economic dynamics.