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Analysis of unpaid care work patterns using K-Means: A Machine Learning application on ENUT 2021 data
Keywords:
care economy, time use, unpaid work, machine learning, k-means, intersectionalityAbstract
This study applies machine learning and cluster analysis to identify sociodemographic profiles of women in Colombia, based on time dedicated to unpaid care work. Using data from the 2021 National Time Use Survey (ENUT), synthetic variables were constructed for domestic work, unpaid care, self-care, leisure, and a traditionalism index. The k-means algorithm, combined with principal component analysis (PCA) and validation criteria such as the elbow method and silhouette index, identified six profiles with contrasting care organization patterns. Results confirm the persistence of the sexual division of labor, with key implications for public policy and the care economy. They also highlight the importance of an intersectional approach to expose structural inequalities in the distribution of unpaid work burdens.
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