Predicting Unpaid Care Work in India Using Random Forest: An Analysis of Socioeconomic and Demographic Factors

Abstract

Given the complexity of unpaid care work in the Indian context, this study employs advanced machine learning techniques to unveil hidden patterns within the 2019 time-use survey dataset. The study pursues a dual objective: (1) assessing the superior predictive capability of machine learning over traditional statistical methods in estimating unpaid care work time, and (2) unveiling the sociodemographic determinants of extended unpaid care work durations. The results emphasise the exceptional predictive performance of machine learning, notably the random forest analysis, with a noteworthy 9 per cent improvement in forecast accuracy. Key determinants influencing unpaid care work time encompass gender, employment status, marital situation, and age. Findings underscore the heightened vulnerability of young married women without employment, who face amplified unpaid care work demands, exacerbating related challenges and risks. It further highlights the country’s imperative for a comprehensive care framework to mitigate caregiving constraints hindering women’s equitable participation in evolving economic paradigms.