在线数据揭示预期薪金的影响因素

Translated title of the contribution: Online Data Reveal Key Factors on Salary Expectation

Jun Wang, Jian Gao, Xiao Yang, Jin Hu Liu, Tao Zhou

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

The enrichment of data resources and the innovation of analytic methods are gradually facilitating the transformation of socioeconomics into a data-driven and quantitative discipline. As a part of quantitative human resources, the investigation of salary has a significant role on social and economic development. However, previous studies are mainly based on census data with limited sizes and lack of considerations in a different economic and cultural background. Based on large-scale resume data that were crawled from websites of Chinese human resource service providers, this paper analyzes key factors on job seekers' salary expectation. Results suggest that height, working experiences, and educational degree affect salary expectation, and there are significant gender differences. In particular, females have lower salary expectation on average and lag behind males for five years' working experience or one educational degree. Finally, the robustness of the analytical results is checked using the multivariate regression method.

Translated title of the contributionOnline Data Reveal Key Factors on Salary Expectation
Original languageChinese (Traditional)
Pages (from-to)307-314
Number of pages8
JournalDianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China
Volume48
Issue number2
DOIs
StatePublished - Mar 30 2019

Keywords

  • Big data
  • Computational socioeconomics
  • Data-driven
  • Multivariate regression
  • Salary expectation

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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