  During the past decades the importance of soft skills for labour market
outcomes has grown substantially. This carries implications for labour market
inequality, since previous research shows that soft skills are not valued
equally across race and gender. This work explores the role of soft skills in
job advertisements by drawing on methods from computational science as well as
on theoretical and empirical insights from economics, sociology and psychology.
We present a semi-automatic approach based on crowdsourcing and text mining for
extracting a list of soft skills. We find that soft skills are a crucial
component of job ads, especially of low-paid jobs and jobs in female-dominated
professions. Our work shows that soft skills can serve as partial predictors of
the gender composition in job categories and that not all soft skills receive
equal wage returns at the labour market. Especially "female" skills are
frequently associated with wage penalties. Our results expand the growing
literature on the association of soft skills on wage inequality and highlight
their importance for occupational gender segregation at labour markets.
