Gender equality in access to the internet and mobile phones has become increasingly recognised as a development goal. Monitoring progress towards this goal however is challenging due to the limited availability of gender-disaggregated data, particularly in low-income countries. In this data sparse context, we examine the potential of a source of digital trace `big data' -- Facebook's advertisement audience estimates -- that provides aggregate data on Facebook users by demographic characteristics covering the platform's over 2 billion users to measure and `nowcast' digital gender gaps. We generate a unique country-level dataset combining `online' indicators of Facebook users by gender, age and device type, `offline' indicators related to a country's overall development and gender gaps, and official data on gender gaps in internet and mobile access where available. Using this dataset, we predict internet and mobile phone gender gaps from official data using online indicators, as well as online and offline indicators. We find that the online Facebook gender gap indicators are highly correlated with official statistics on internet and mobile phone gender gaps. For internet gender gaps, models using Facebook data do better than those using offline indicators alone. Models combining online and offline variables however have the highest predictive power. Our approach demonstrates the feasibility of using Facebook data for real-time tracking of digital gender gaps. It enables us to improve geographical coverage for an important development indicator, with the biggest gains made for low-income countries for which existing data are most limited.
Using Facebook Ad Data to Track the Global Digital Gender Gap