Volume 125, Number 6, March 2019
|Number of page(s)||7|
|Section||Interdisciplinary Physics and Related Areas of Science and Technology|
|Published online||26 April 2019|
Regional economic status inference from information flow and talent mobility
1 Big Data Research Center, University of Electronic Science and Technology of China - Chengdu 611731, PRC
2 Institution of New Economic Development - Chengdu 610049, PRC
Received: 15 January 2019
Accepted: 14 March 2019
Novel data has been leveraged to estimate the 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 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 résumé data of job seekers with higher education. We find that while the structural features of both networks are relevant to the 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 that future socioeconomic studies should pay more attention to the cost-effective talent mobility data.
PACS: 89.65.-s – Social and economic systems / 89.75.Fb – Structures and organization in complex systems / 87.23.Ge – Dynamics of social systems
© EPLA, 2019
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