# Gender differences in resume language and gender gaps in salary expectations

## TL;DR

This study finds gender differences in resume language and how these differences relate to salary expectations in the job market.

## Contribution

The study introduces a new approach to understanding gender differences through resume language and salary expectations.

## Key findings

- Women and men show distinct patterns in resume language and semantic structures.
- Neural networks can predict gender from resumes with 80% accuracy.
- Better language skills correlate with higher salary expectations, but this link is stronger for men in women-dominated fields.

## Abstract

How men and women present themselves in their resumes may affect their opportunity in job seeking. To investigate gender differences in resume writing and how they are associated with gender gaps in the labour market, we analysed 6.9 million resumes of Chinese job applicants in this study. Results reveal substantial gender resume differences, where women and men show distinct patterns in both simple language features and high-level semantic structures in the word embedding space of resumes. In particular, women tend to use shorter resumes, longer sentences and a more diverse set of unique words. Neural network models trained on resumes can predict gender with 80% accuracy, and the accuracy decreases with education levels and text standardization requirements. Moreover, while better language skills are associated with higher salary expectations, this positive relationship is magnified for men but weakened for women in women-dominated occupations. This study presents a new venue for the understanding of gender differences and provides empirical findings on how men and women are different in self-portraying and job seeking.