Hidden Workforce: Analysis on Recognizing Unpaid Domestic Work Through Social Determinants

Abstract

The United Nations collects data at gigabyte scale from the member countries for sustainable development goals, and recognizes the impact big data collection has on opportunities and risks for sustainable development. The United Nations (UN) Sustainable Development Goals (SDG) open data hub provided a dataset that details the "proportion of time spent on unpaid domestic chores and care work" world wide. This primary dataset is only a portion of the data available related to the research area. This data contains many related variables, such as the population's age, sex, and location metrics. In an effort to better understand the impact of unpaid domestic work, the dataset was analyzed in conjunction with another dataset from the UN Statistics Division that details the rate of divorce/separation in the world population. Our analysis included methods such as principal component analysis, the k-means clustering algorithm, the random forest clustering algorithm, and implementing a neural network. The principal components were clustered using the k-means algorithm to cluster the variables in a manner that explains the variance present in the dataset. The analysis found that age, sex, and location demographics are key variables that explain the diverse variation between countries and the percentage of time spent on unpaid domestic work.Machine learning algorithms enabled the confirmation of this relationship. Using the key variables identified, a random forest of decision trees and a neural network were generated to classify the percentage of time spent on unpaid domestic work. Similarly, the random forest algorithm and a neural network were also implemented to classify geographical regions. These models were compared to determine the strength of the relationship between age, sex, location metrics, the percentage of time spent, and geographical regions.The analysis detailed in this work strives to identify the social factors that classify the percentage of time spent on unpaid domestic work in accordance with the UN SDG 5.4, which is to "recognize and value unpaid care and domestic work through the provision of public services, infrastructure, and social protection policies and the promotion of shared responsibility within the household and the family as nationally appropriate."