Information and favoritism: The network effect on wage income in China

Yanjie Bian, Xianbi Huang, Lei Zhang

Research output: Contribution to journalArticlepeer-review

44 Scopus citations


How do social networks matter for labor market opportunities and outcomes? To fill in a gap between network theory and research evidence, we develop a theoretical explanation of how network-transmitted information and favoritism serve as causal mechanisms of wage income in China. In a large-scale Chinese survey, we find that 59% of the 4350 wage earners land on jobs through social contacts from whom the benefits of information and forms of favoritism are gained. Data analysis shows that (1) both weak ties and strong ties are used by Chinese job seekers to obtain information and favoritism to help secure job opportunities, but (2) weak ties are better able to channel job information than strong ties and strong ties are better able to mobilize forms of favoritism than weak ties, (3) information and favoritism equally promote job-worker matching, which in turn increases wage, and (4) favoritism has a stronger effect than does information on job assignment to positions of superior earning opportunity. This analysis demonstrates the non-spurious, causal effect of social networks on wage income in the Chinese context.

Original languageEnglish (US)
Pages (from-to)129-138
Number of pages10
JournalSocial Networks
StatePublished - Jan 1 2015

Bibliographical note

Funding Information:
The Chinese survey analyzed in the paper was financed by a grant from Hong Kong's Research Grants Committee ( HKUST6052/98H ) to the first author. Two research grants from China's Social Science Foundation to the first author (Project #: 11AZD022 and 13&ZD177 ), the University of Queensland Postdoctoral Fellowship and the Outside Studies Program of La Trobe University awarded to the second author, and a research grant from the Australian Research Council ( DP130100690 ) to the first two authors supported data analysis and revision of this article. We are grateful to the editor and anonymous reviewers of Social Networks for helpful comments and suggestions.

Publisher Copyright:
© 2014 Elsevier B.V.

Copyright 2020 Elsevier B.V., All rights reserved.


  • China
  • Favoritism
  • Income
  • Information
  • Networks


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