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, 115 (27), 6958-6963

Analyzing Gender Inequality Through Large-Scale Facebook Advertising Data

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Analyzing Gender Inequality Through Large-Scale Facebook Advertising Data

David Garcia et al. Proc Natl Acad Sci U S A.

Abstract

Online social media are information resources that can have a transformative power in society. While the Web was envisioned as an equalizing force that allows everyone to access information, the digital divide prevents large amounts of people from being present online. Online social media, in particular, are prone to gender inequality, an important issue given the link between social media use and employment. Understanding gender inequality in social media is a challenging task due to the necessity of data sources that can provide large-scale measurements across multiple countries. Here, we show how the Facebook Gender Divide (FGD), a metric based on aggregated statistics of more than 1.4 billion users in 217 countries, explains various aspects of worldwide gender inequality. Our analysis shows that the FGD encodes gender equality indices in education, health, and economic opportunity. We find gender differences in network externalities that suggest that using social media has an added value for women. Furthermore, we find that low values of the FGD are associated with increases in economic gender equality. Our results suggest that online social networks, while suffering evident gender imbalance, may lower the barriers that women have to access to informational resources and help to narrow the economic gender gap.

Keywords: Facebook; development; gender divide; inequality; social media.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
The FGD across 217 countries. Countries are colored according their FGD from highly skewed toward males (red) and balanced (blue) to highly skewed toward females (green; not visible). Left Inset shows the scatterplot of male and female activity ratios across all countries, revealing a spread along the diagonal. Right Inset shows the histogram of FGD values in bins of width 0.2. While the mode of countries is slightly below zero, there is significant skewness toward high FGD values. An online interactive version of this figure can be found at https://dgarcia-eu.github.io/FacebookGenderDivide/Visualization.html.
Fig. 2.
Fig. 2.
Regression results of FGD as a function of gender equality. (A) Model predictions vs. rank of FGD, where rank 1 is the country with the highest FGD. The model achieves a high R2 above 0.74, explaining the majority of the variance of the FGD ranking. Some countries are labeled, from high FGD [Liberia (LR), India (IN), and Saudi Arabia (SA)] to low FGD [Finland (FI), Norway (NO), and Uruguay (UY)], as well as some outliers [Dominican Republic (DO), Austria (AT), and Sri Lanka (LK)]. (B) Coefficient estimates and 95% CIs of the terms of the regression fit (excluding intercept). Education (Edu), health (Heal), and economic gender equality (Eco) are significantly and negatively associated with the FGD, but political gender equality (Pol) is not. From the control variables, Internet penetration (IP) is negatively associated with FGD, but the rest are not. The main role of education equality in FGD can be observed in A, where dots are colored according to the rank of education gender equality, showing that countries with low FGD are ranked high on education gender equality. An online interactive version of this figure can be found in https://dgarcia-eu.github.io/FacebookGenderDivide/Visualization.html. FBP, Facebook penetration; Ineq, income inequality; Pop, total population.
Fig. 3.
Fig. 3.
Gender differences in network externalities on Facebook. Scaling of the Facebook activity ratio per gender vs. total Facebook penetration. Solid lines show fit results, and shaded areas show their 95% CIs. Both male and female activity ratios grow superlinearly with Facebook penetration (α>1), indicating positive network externalities. These network externalities are stronger for female than for male users (αF>αM).
Fig. 4.
Fig. 4.
Analysis of changes in economic gender equality and FGD. Coefficient estimates of the regression model of changes in economic gender equality as a function of FGD and control terms (excluding intercept; Upper Left) and of the model of changes in FGD as a function of economic gender equality and control terms (excluding intercept; Lower Left). Right shows the bootstrap distributions of partial R2 of FGD2015 in the first model and of Eco2015 in the second one, with dashed vertical lines showing the median R2 values: 0.027 (Upper Right) in the first model and 0.002 (Lower Right) in the second one. The FGD explains changes in economic gender equality much better than economic gender equality explains changes in the FGD. GDP, gross domestic product.

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