Height conditions salary expectations: Evidence from large-scale data in China

Xiao Yang, Jian Gao*, Jin Hu Liu, Tao Zhou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Height premium has been revealed by extensive literature, however, evidence from China based on large-scale data remains still lacking. In this paper, we study how height conditions salary expectations by exploring a dataset covering over 140,000 Chinese job seekers. By using graphical and regression models, we find evidence in support of height premium that tall people expect a significantly higher salary in career development. In particular, regression results suggest stronger effects of height premium on female than on male, however, the gender differences decrease as the education level increases and become insignificant after holding all control variables fixed. Further, results from graphical models suggest three promising ways in helping short people: (i) to accumulate more working experiences, since one year seniority can respectively make up about 3 cm and 7 cm shortness for female and male; (ii) to increase the level of education, since one higher academic degree may eliminate all disadvantages that brought by shortness; (iii) to target jobs in regions with a higher level of development. Our work provides a cross-culture supportive evidence of height premium and contributes two novel features to the literature: the compensation story in helping short people, and the focus on salary expectations in isolation from discrimination channels.

Original languageEnglish (US)
Pages (from-to)86-97
Number of pages12
JournalPhysica A: Statistical Mechanics and its Applications
Volume501
DOIs
StatePublished - Jul 1 2018

Keywords

  • Data analysis
  • Height premium
  • Regression model
  • Salary expectation
  • Statistical method

ASJC Scopus subject areas

  • Statistics and Probability
  • Condensed Matter Physics

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