Regional economic status inference from information flow and talent mobility

Jun Wang, Jian Gao, Jin-Hu Liu, Dan Yang, Tao Zhou

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.