High-quality data on rural women’s and men’s labor are imperative for tracking progress on gender equality and women’s empowerment, as well as for evaluating development interventions aimed at these outcomes. Yet, there remains a general lack of sex-disaggregated data on unpaid work, earnings, informal employment, and entrepreneurship.
Researchers are increasingly looking to digital technologies, such as mobile phones, as an emerging data source with significant potential for closing these data gaps. In this paper, we illustrate how digital trace data, which includes call detail records (CDRs) and are produced every time mobile phones are actively used, e.g., to make calls, send text messages, browse the internet, or pay using mobile money, can be used with machine learning models to predict gendered indicators related to work, employment, and time use. We provide preliminary evidence based on digital trace data and phone survey data for a large sample of mobile phone users in Ghana. In complement to traditional labor force surveys, such models could be used at-scale by governments and/or international organizations as an early warning system to monitor labor market shocks and their consequences for women and men with high temporal and spatial resolution.
Lendie Follet is an Assistant Professor of Business Analytics at Drake University. She received her Ph.D. in statistics from Iowa State University in 2016.
Greg Seymour is a research fellow with the Environment and Production Technology Division of the International Food Policy Research Institute (IFPRI). He has a Ph.D. in economics from American University.