Abstract
Allocation of time use is important to develop appropriate policies, especially in terms of gender equality. Individual well-being depends on many factors, including how time is spent. Therefore, knowing and analysing the time use and workload of academic staff is relevant for academic policy making. We analyse the responses of 703 Spanish academic staff regarding different activities of paid work and household work (unpaid). We use an innovative machine learning technique in this field, archetype analysis, which we introduce step by step while exploring our data. We identify five profiles, and we examine gender inequalities. The findings indicate that there is a higher prevalence of women in the profiles with a greater workload in household activities and teaching-related activities, but the prevalence is the same in the profile with a greater workload in research activities.
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