# Gender mobility in the labor market with skills-based matching models

arXiv: https://arxiv.org/abs/2307.08368 · HTML: https://mlsanity.com/paper/2307.08368/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 17 Jul 2023.

## Builds on (most cited first, 5 of 5)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Intrinsic Bias Metrics Do Not Correlate with Application Bias](https://mlsanity.com/paper/2012.15859/) (2020)
- [metric-learn: Metric Learning Algorithms in Python](https://mlsanity.com/paper/1908.04710/) (2019)

## Abstract (arXiv)

Skills-based matching promises mobility of workers between different sectors and occupations in the labor market. In this case, job seekers can look for jobs they do not yet have experience in, but for which they do have relevant skills. Currently, there are multiple occupations with a skewed gender distribution. For skills-based matching, it is unclear if and how a shift in the gender distribution, which we call gender mobility, between occupations will be effected. It is expected that the skills-based matching approach will likely be data-driven, including computational language models and supervised learning methods. This work, first, shows the presence of gender segregation in language model-based skills representation of occupations. Second, we assess the use of these representations in a potential application based on simulated data, and show that the gender segregation is propagated by various data-driven skills-based matching models.These models are based on different language representations (bag of words, word2vec, and BERT), and distance metrics (static and machine learning-based). Accordingly, we show how skills-based matching approaches can be evaluated and compared on matching performance as well as on the risk of gender segregation. Making the gender segregation bias of models more explicit can help in generating healthy trust in the use of these models in practice.