Evaluating Gender Wage Inequality in Academia using Causal Inference Methods for Observational Data

[2505.24078] Evaluating Gender Wage Inequality in Academia using Causal Inference Methods for Observational Data Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Statistics > Applications arXiv:2505.24078 (stat) [Submitted on 29 May 2025 ( v1 ), last revised 10 Apr 2026 (this version, v3)] Title: Evaluating Gender Wage Inequality in Academia using Causal Inference Methods for Observational Data Authors: Zihan Zhang , Jan Hannig View a PDF of the paper titled Evaluating Gender Wage Inequality in Academia using Causal Inference Methods for Observational Data, by Zihan Zhang and Jan Hannig View PDF HTML (experimental) Abstract: Observational studies often present challenges for causal inference due to confounding and heterogeneity. In this paper, we illustrate how modern causal inference methods can be applied to large-scale academic salary data. Using records from 12,039 tenure-track faculty in the University of North Carolina system, linked with bibliometric indicators and institutional classifications, we estimate the causal effect of gender on faculty salaries. Our analysis combines propensity score matching with causal forests to adjust for rank, discipline, research productivity, and career experience. Results indicate that female faculty earn approximately 6% less than comparable male colleagues, with variation in the gap across career stages and levels of research productivity. This case study demonstrates how causal inference methods for observational data can provide insight into structural disparities in complex social systems. Subjects: Applications (stat.AP) ; General Economics (econ.GN) Cite as: arXiv:2505.24078 [stat.AP] (or arXiv:2505.24078v3 [stat.AP] for this version) https://doi.org/10.48550/arXiv.2505.24078 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zihan Zhang [ view email ] [v1] Thu, 29 May 2025 23:51:17 UTC (1,213 KB) [v2] Mon, 1 Dec 2025 03:40:19 UTC (1,092 KB) [v3] Fri, 10 Apr 2026 02:56:45 UTC (1,137 KB) Full-text links: Access Paper: View a PDF of the paper titled Evaluating Gender Wage Inequality in Academia using Causal Inference Methods for Observational Data, by Zihan Zhang and Jan Hannig View PDF HTML (experimental) TeX Source view license Current browse context: stat.AP < prev | next > new | recent | 2025-05 Change to browse by: econ econ.GN q-fin q-fin.EC stat References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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