Making Time Count : A Machine Learning Approach to Predict Time Use in Low-Income Countries from Physical Activity Tracking Data

Understanding men’s and women’s time use is a key factor in addressing issues and formulating policies related to division of labor, domestic work, and related gender disparities. However, obtaining data on individuals’ time use can be difficult and costly in the context of household surveys. Leveraging unique survey data collected in rural Malawi, this study investigates the possibility of predicting men’s and women’s time allocation to an extensive set of activities, using sensor signal data captured by accelerometers. Using machine learning techniques, the study builds a supervised classification model that is trained on the accelerometer data and a random subset of the time use survey data to predict individuals’ time allocation to 12 broad activity groups. The model can correctly classify each performed activity in 76 percent of the cases. The analysis shows that with 40 percent of the training data, this method can achieve 90 percent of the maximum level of predictive accuracy reached in the analysis. The findings prove the feasibility of this methodology and offer insights for enhancing both survey and accelerometer data collection processes to build better models. Using the method can improve the quality of costly and difficult to obtain time use surveys with cheaper, yet accurate, modeled estimates, obtained by combining objective data from wearable devices with time use data collected on smaller samples.