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This paper focuses on the impact that gender segregation in the labour market exerts on the underemployment gender gap for young adult workers in Spain. In order to analyse the relative importance of segregation in this gap, we develop a methodology based on two counterfactual simulations that provides a detailed decomposition of the gap into endowments and coefficients effects as well as the interaction of these effects. To the best of our knowledge, we are the first to perform a decomposition using bivariate probit models with sample selection. Using annual samples of the Spanish Labour Force Survey 2006–2016, the results show that working in female-dominated occupations or industries hinders working as many hours as desired, especially for women. Furthermore, we conclude that the gender gap in underemployment is mainly due to the different distribution of male and female workers across occupations and industries. Additionally, the different impact by gender that working in the same gender-typing jobs exerts on the risk of underemployment contributes to widening the gap.

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This paper analyzes the education and unemployment patterns for young workers with some experience in Spain at the beginning of the current economic crisis, using the ad hoc module of the Spanish Labour Force Survey 2009. The results clearly show that educational level and field of study are crucial when explaining the instability of the first job and the difficulty in obtaining another one. Specifically, the lower is the educational level, the greater is the risk of unemployment, not only because it is less likely to keep the first job, but also because it is harder to find another one. Moreover, considering the field of study at a given educational level, it is detected that graduates from health and welfare are the best positioned in the labour market (especially university degree holders). For the rest of fields of study, and despite the differences in the risk of unemployment are small, it is observed that the lowest level of unemployment corresponds to sciences and technology, followed by social sciences and the rest of fields. The education and unemployment patterns detected in the paper may be useful to guide both policy and individual decisions.