Using Facebook ad data to track the global digital gender gap. (July 2018) - Fatehkia, Masoomali; Kashyap, Ridhi; Weber, Ingmar
Highlights: Gender gaps in internet and mobile phone access are difficult to measure due to data gaps, especially in low-income countries. We use data from Facebook on users by age and gender to predict digital gender gaps for 150+ countries. Facebook data are highly correlated with available official data on digital gender gaps. Predictive power of Facebook data is improved when combined with country-level development and gender gap indicators. Our approach shows how web data can be used to expand coverage for an important development indicator, with biggest gains for low-income countries. Abstract: 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.