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Age can lead to stigmatisation, which is aggravated in groups that are already at risk of exclusion, such as women. This intersectional bias between age and gender (gendered ageism) affects so-called mature workers (aged 50 and over) in different ways, including the prejudices of employers and workers regarding their skills and competencies, as well as regarding their motivation to participate in training. In this article we analyse mature female workers’ level of training, motivation, and use of job-related skills, with the aim of providing evidence that breaks with ageist gender prejudices on this issue. For this, we conducted a descriptive analysis using ANOVA, and we applied structural equation modelling in an analysis of the PIAAC data of the OECD ( 2016 ), dividing the entire sample ( n . = 31,739) into four subsamples (women −50; women 50+; men −50; men 50+). In our descriptive analysis, female older workers achieve the highest scores in almost all the variables. Our proposed model, resulting from multigroup comparisons among the four subsamples, has a more optimal fit and structural coefficients of greater weight in mature female workers than in younger ones, especially regarding the influence of informal learning at work on the level of use of job-related skills.

Questioning gendered ageism in job-related non-formal training and informal learning