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

Abstract

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

Analysing global professional gender gaps using LinkedIn advertising data

“…The rapid uptake of social media sites such as LinkedIn over the 21st century has generated large and diverse online user populations across different platforms, and these online populations have in turn generated new types of social data through the 'digital traces' they leave behind on these platforms. A growing body of work in the area of digital demography [13][14][15][16] has sought to understand the demographic characteristics of users on different social media platforms, analyse their demographic representativeness, and examine their potential to study gender inequality [17][18][19][20][21][22]. In a complementary vein, with the call for a 'Data Revolution' in the context of the post-2015 global development agenda [23,24], a growing body of research has sought to examine the potential of non-traditional big data sources for measuring different indicators and social processes linked to the SDGs.…”

“…[27]), web and social media data (e.g. [18,22,[28][29][30]), to assess their utility for providing cost-efficient, timely, and more granular coverage to complement traditional data sources such as surveys or censuses. These data sources offer added value in the context of low-and middle-income countries where traditional data sources are often lacking, incomplete or outdated [31].…”

“…These data sources offer added value in the context of low-and middle-income countries where traditional data sources are often lacking, incomplete or outdated [31]. In relation to SDG 5 on gender inequality, recent work has used social media advertising data from Facebook and Google to model indicators linked to internet and mobile gender gaps [18,22]. Our study builds on this work by examining social media advertising data from LinkedIn for gender inequalities in the professional domain.…”

“…More broadly in the research on social network sites, studies of LinkedIn are considerably fewer than those of Twitter or Facebook [48]. Similarly, while the growing body of research in digital demography has often drawn on aggregate audience estimates from social media advertising platforms to analyse these online populations, most of these studies have used Facebook (e.g [30,51,52]) or more recently, Google advertising (Ad-Words) audience estimates [22]. In contrast, LinkedIn's advertising audience estimates have so far only been used to study professional gender gaps for a selection of information and communication technology (ICT) industries [17], and for a selection of cities in the United States [21].…”

How reliable is Facebook’s advertising data for use in social science research? Insights from a cross-national online survey

“…A first set of studies have employed the audience estimates that the FAM provides prior to launching a campaign for obtaining digital censuses of the user population across geographic regions. The resulting information was then used to make inferences about specific social groups and the general population (e.g., Alexander et al, 2019;Kashyap et al, 2020;Rama et al, 2020;Rampazzo et al, 2018;Ribeiro et al, 2020;Zagheni et al, 2017). For example, Zagheni et al (2017) used audience estimates to assess the share of foreign-born people living in the United States, comparing these numbers with data from the 2014 round of the American Community Survey (ACS).…”