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Monitoring global digital gender inequality using the online populations of Facebook and Google

journal contribution
submitted on 2024-07-03, 05:50 and posted on 2024-07-04, 07:19 authored by Ridhi Kashyap, Masoomali Fatehkia, Reham Al Tamime, Ingmar Weber

Background

In recognition of the empowering potential of digital technologies, gender equality in internet access and digital skills is an important target in the United Nations (UN) Sustainable Development Goals (SDGs). Gender-disaggregated data on internet use are limited, particularly in less developed countries.

Objective

We leverage anonymous, aggregate data on the online populations of Google and Facebook users available from their advertising platforms to fill existing data gaps and measure global digital gender inequality.

Methods

We generate indicators of country-level gender gaps on Google and Facebook. Using these online indicators independently and in combination with offline development indicators, we build regression models to predict gender gaps in internet use and digital skills computed using available survey data from the International Telecommunications Union (ITU).

Results

We find that women are significantly underrepresented in the online populations of Google and Facebook in South Asia and sub-Saharan Africa. These platform-specific gender gaps are a strong predictor that women lack internet access and basic digital skills in these populations. Comparing platforms, we find Facebook gender gap indicators perform better than Google indicators at predicting ITU internet use and low-level digital-skill gender gaps. Models using these online indicators outperform those using only offline development indicators. The best performing models, however, are those that combine Facebook and Google online indicators with a country’s development indicators such as the Human Development Index.

Other Information

Published in: Demographic Research
License: https://creativecommons.org/licenses/by/3.0/de/
See article on publisher's website: https://dx.doi.org/10.4054/demres.2020.43.27

Funding

United Nations Foundation, Data2X (UNF-17-936).

History

Language

  • English

Publisher

Max Planck Institute for Demographic Research

Publication Year

  • 2020

License statement

This Item is licensed under the Creative Commons Namensnennung 3.0 Deutschland License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • Qatar Computing Research Institute - HBKU

Geographic coverage

South Asia and sub-Saharan Africa