Overview

Our approach leverages online big data from the Facebook marketing API in combination with different development indicators from sources such as the World Bank and the UN to estimate global digital gender gaps in internet and mobile phone use. The full details of the data and methods used to generate the estimates shown in the maps on this website are described in Using facebook ad data to track the global digital gender gap.

Digital Gender Gap Indicators

We provide estimates of two measures – 1) Internet Gender Gap (GG), 2) Mobile Gender Gap. Both measures are female-to-male ratios of gender-specific internet penetration, and lie in the range of 0 to 1. Values of 1 or close to 1 show that the gender gap has closed. For example, a value of 0.75 could be interpreted as a 25% gap between male and female internet use, with 75 women online for roughly 100 men who are.

For each measure – 1) Internet GG and 2) Mobile Phone GG – we provide three estimates.

Internet Gender Gaps

The ITU Internet GG shows the ratio of female-to-male internet use from ITU statistics. These are available for the fewest countries and are infrequently updated.

The Internet GG – Online shows the ratio of female-to-male internet use estimated using the Facebook Gender Gap Index (see below for more information) by country. These estimates are available for the largest number of countries and are available with daily frequency.

The Internet GG – Combined shows the ratio of female-to-male internet use estimated using the Facebook Gender Gap Index by country combined with other offline indicators on the country’s development status such as its Human Development Index. This model is our best-performing model in terms of its fit with ITU statistics, and these estimates are available for more countries than ITU statistics. Their geographical coverage is not as wide as that of the Online Model Prediction and the underlying information used to generate these predictions is only annually updated.

The Internet GG – Offline shows the ratio of female-to-male internet use estimated using only offline indicators on the country’s development status such as its Human Development Index. Estimates from these models have the worst fit with ITU statistics on the internet GG, and the underlying data used to generate these are updated with an annual or longer frequency.

Mobile Gender Gaps

The Mobile GG – GSMA shows the ratio of female-to-male internet use from published GSMA reports. These are available for the fewest countries and are infrequently updated.

The Mobile GG – Online shows the ratio of female-to-male mobile use estimated using the Facebook Gender Gap Index (see below for more information) by country. These estimates are available for the largest number of countries and are available with daily frequency, although their fit with GSMA statistics is worse than the other two models.

The Mobile GG – Combined shows the ratio of female-to-male mobile use estimated using the Facebook Gender Gap Index by country combined with other indicators on the country’s development status such as its Human Development Index. Estimates from these models are closer to GSMA statistics on the mobile GG, and available for more countries than GSMA statistics, but not as many as those from the Online Model Prediction.

The Mobile GG – Offline shows the ratio of female-to-male mobile use estimated using only offline indicators on the country’s development status such as its Human Development Index. The underlying data used to generate these are updated with an annual or longer frequency.

 

Data Informing our Estimates

The Facebook Gender Gap Index

We track the global aggregate numbers of female and male users of Facebook every day available through the marketing API. Using these aggregate numbers, we generate the “Facebook Gender Gap Index”, an indicator of the number of female-to-male Facebook users in a country. While the Facebook Gender Gap Index reflects gender gaps in who has Facebook accounts and not internet use per se, we have found the Facebook Gender Gap Index is highly correlated with statistics on internet (from the International Telecommunications Union or ITU) and mobile phone gender gaps (from the GSMA) collected via surveys, for the countries for which these data are available.

Looking at statistics of gender gaps in Facebook users can help us predict gender gaps in internet use, but as data on Facebook users are available for many more countries than statistics from the ITU, we can expand geographical coverage of this indicator by using the online data.

Offline Indicators

Our dataset uses a number of country-level development indicators (e.g Human Development Index, GDP per capita) and global gender gap indicators in other domains (e.g literacy). For more information see our paper.