Monitoring Digital Gender Inequality using Facebook and Google Advertising Audience Estimates Ridhi Kashyap · Masoomali Fatehkia · Reham Al Tamime · Ingmar Weber Abstract Gender equality in internet access and digital skills are important targets within the United Nations (UN) Sustainable Development Goals (SDGs). Gender-disaggregated data to measure progress in these domains are limited. In this paper, we leverage anony- mous, aggregate data from the online advertising platforms of Google and Facebook to measure global digital gender inequality. Building on previous work that has used Facebook data, we assess the potential of another novel data source Google’s advertisement impres- sion estimates (AdWords) that provides estimates of the times an advertisement is shown on a search result page or another site on the Google Display Network. These estimates can be filtered based on targeting criteria such as age and gender. We generate gender gap indica- tors using both AdWords and Facebook data, and find that these online indicators are highly correlated with gender gaps in internet use and low-level digital skills computed using sur- vey data from the International Telecommunications Union (ITU) when available. Although Facebook gender gap indicators independently perform better at predicting ITU internet gender gaps than AdWords indicators, the best performing models are those that combine Facebook and Google online indicators with a country’s offline development indicators such as the Human Development Index. Facebook indicators combined with offline development indicators also provide the best performance for predicting low-level digital skills gender gaps. Our work highlights how appropriate regression models built on anonymous, aggre- gate, real-time data from online advertising platforms, can be used to complement existing data sources to monitor important global development indicators. Keywords Gender inequality · Novel digital data sources · Sustainable Development Goals (SDGs) · digital divide · big data · Development indicators R. Kashyap University of Oxford Address correspondence to ridhi.kashyap@nuffield.ox.ac.uk Nuffield College, New Road, Oxford OX1 1NF, UK M. Fatehkia Qatar Computing Research Institute, Qatar, mfatehkia@hbku.edu.qa R. Al Tamime University of Southampton, UK, rat1g15@soton.ac.uk I. Weber Qatar Computing Research Institute, Qatar, iweber@hbku.edu.qa