Interrupciones laborales y cuidados infantiles: Resultados con técnicas de Machine Learning

Felipe Calvo, David (2025): Interrupciones laborales y cuidados infantiles: Resultados con técnicas de Machine Learning.

Abstract

Este trabajo investiga los factores que predicen las interrupciones laborales de corta duración debidas al cuidado infantil, un mecanismo diario que subyace a la ‘penalización por hijos’ en el mercado laboral. Utilizando datos de la American Time Use Survey (ATUS) para el periodo 2010-2019, se aplica una metodología innovadora que combina el Análisis de Supervivencia con técnicas de Machine Learning, como los Bosques Aleatorios de Supervivencia. Se propone una nueva tipología de interrupciones (‘puras’ y ‘mixtas’) para capturar con mayor precisión el conflicto trabajo-familia. Los resultados revelan que los predictores más potentes del riesgo de interrupción son la edad del hijo menor y el género del progenitor, superando a variables económicas como los ingresos. Se identifica una ‘paradoja del privilegio’, donde un mayor nivel educativo y de ingresos se asocia con un mayor riesgo de interrupción, fenómeno explicado por la combinación de un estilo de ‘crianza intensiva’ y la mayor autonomía de los ‘trabajos codiciosos’. Los principales resultados arrojan que el riesgo de interrupción diario está determinado principalmente por factores sociodemográficos y normas de género, más que por consideraciones puramente económicas, ofreciendo una micro fundamentación empírica a la persistente brecha de género.

• Item Type: MPRA Paper
• English Title: Work interruptions and childcare: Results using machine learning techniques
• Language: Spanish
• Keywords: Cuidados infantiles, desigualdad de género, interrupciones laborales, Machine Learning
• Subjects: J - Labor and Demographic Economics > J1 - Demographic Economics > J13 - Fertility ; Family Planning ; Child Care ; Children ; Youth; J - Labor and Demographic Economics > J1 - Demographic Economics > J16 - Economics of Gender ; Non-labor Discrimination

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