# Regional economic status inference from information flow and talent mobility

## TL;DR

This study demonstrates that analyzing online social media relations and offline talent mobility networks can effectively predict regional economic status, with talent mobility data showing particularly strong predictive power.

## Contribution

The paper introduces a novel approach combining social media and talent mobility networks to estimate regional economic status, highlighting the effectiveness of talent mobility data.

## Key findings

- Talent mobility network predicts GDP with high accuracy.
- Structural features of networks explain up to 84% of GDP variance.
- Talent mobility data is a cost-effective indicator for socioeconomic analysis.

## Abstract

Novel data has been leveraged to estimate socioeconomic status in a timely manner, however, direct comparison on the use of social relations and talent movements remains rare. In this letter, we estimate the regional economic status based on the structural features of the two networks. One is the online information flow network built on the following relations on social media, and the other is the offline talent mobility network built on the anonymized resume data of job seekers with higher education. We find that while the structural features of both networks are relevant to economic status, the talent mobility network in a relatively smaller size exhibits a stronger predictive power for the gross domestic product (GDP). In particular, a composite index of structural features can explain up to about 84% of the variance in GDP. The result suggests future socioeconomic studies to pay more attention to the cost-effective talent mobility data.