Gender mobility in the labor market with skills-based matching models
Ajaya Adhikari Steven Vethman Daan Vos Marc Lenz Ioana Cocu Ioannis Tolios Cor J. Veenman
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.