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Article: Gender differences in resume language and gender gaps in salary expectations

TitleGender differences in resume language and gender gaps in salary expectations
Authors
Keywordscomputational social science
gender gap
neural network model
resume
written language
Issue Date4-Jun-2025
PublisherThe Royal Society
Citation
Journal of the Royal Society Interface, 2025, v. 22, n. 227 How to Cite?
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.


Persistent Identifierhttp://hdl.handle.net/10722/357649
ISSN
2023 Impact Factor: 3.7
2023 SCImago Journal Rankings: 1.101
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorQu, Qian-
dc.contributor.authorLiu, Quan Hui-
dc.contributor.authorGao, Jian-
dc.contributor.authorHuang, Shudong-
dc.contributor.authorFeng, Wentao-
dc.contributor.authorYue, Zhongtao-
dc.contributor.authorLu, Xin-
dc.contributor.authorZhou, Tao-
dc.contributor.authorLv, Jiancheng-
dc.date.accessioned2025-07-22T03:14:04Z-
dc.date.available2025-07-22T03:14:04Z-
dc.date.issued2025-06-04-
dc.identifier.citationJournal of the Royal Society Interface, 2025, v. 22, n. 227-
dc.identifier.issn1742-5689-
dc.identifier.urihttp://hdl.handle.net/10722/357649-
dc.description.abstract<p>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.</p>-
dc.languageeng-
dc.publisherThe Royal Society-
dc.relation.ispartofJournal of the Royal Society Interface-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectcomputational social science-
dc.subjectgender gap-
dc.subjectneural network model-
dc.subjectresume-
dc.subjectwritten language-
dc.titleGender differences in resume language and gender gaps in salary expectations-
dc.typeArticle-
dc.identifier.doi10.1098/rsif.2024.0784-
dc.identifier.scopuseid_2-s2.0-105007499097-
dc.identifier.volume22-
dc.identifier.issue227-
dc.identifier.eissn1742-5662-
dc.identifier.isiWOS:001501540900002-
dc.identifier.issnl1742-5662-

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