Fraym Partners with Facebook’s Project 17: Layering Sex-Disaggregated Data Sets Expose Gender Inequalities
In order to successfully combat issues like food insecurity, programming must take into account gender disparities by both generating more data and applying advanced analytical techniques to better understand them
About the Project
In collaboration with Facebook’s Project 17, Fraym explored the use of the Facebook Survey on Gender Equality at Home and high-resolution population data to unearth subnational differences in the male and female food security. Project 17 is Facebook’s partnerships-first approach to accelerating progress on the Sustainable Development Goals (SDGs). Its current area of focus is increasing the availability and use of gender data.
Hyperlocal Targeting for Gender Disparity in Food Insecurity
Our proof of concept largely focused on SDG 2: Zero Hunger, with a focus on the sex disaggregated responses to the question of “during the last 30 days, was there a time when you were worried about not having enough food because of lack of money or other resources?” in Nigeria. The data shows there was already a 7-percentage point difference in self-reported food security between men (79%) and women (86%). Were we to just apply this national average to the Facebook’s high resolution male and female population density maps, subnational differences would be constant at 7-percentage points. But again, our hypothesis was that this gender gap will likely differ by community based on the spatial differences we have seen in hundreds of subnational analyses.
In an effort to weight these national averages, our team of analysts reviewed Fraym’s database of over 100 spatially precise community level indicators produced from secondary data sources like the Demographic and Health Survey (DHS) to identify the most comparable measure of food security that could be sex-disaggregated. The team determined a measure of chronic food insecurity, like child undernutrition, was the most viable candidate for this pilot effort. Now equipped with three publicly available data inputs, our team weighted the female food insecure population with the girls undernutrition dataset and male food insecure population with the boys undernutrition dataset. The result was a hotspot map of where the gender gap in food insecurity is very likely the largest.
This exercise identified that Imo, Rivers, Abia, Federal Capital Territory (FCT) and Plateau are states with the largest gender gaps in food security where women are more food insecure than men. Our model indicates that there may be communities facing 25+ percentage point differences in food security representing enormous gender inequality in measuring SDG 2.
Gender Differences in Local Communities
Model Gender Disaggregated Food Insecurity
Weighted Female Food Insecurity
Of Men Reported Food Insecurity
Of Women Reported Food Insecurity
Gender Differences in Hotspot Communities
Understanding Spatial Differences Between Communities
In order to successfully measure the SDGs, we must take into account gender disparities by both generating more data and applying advanced analytical techniques to better understand them. At Fraym, we advocate for the use of hyperlocal data for decision-making because years of work has unearthed the stark spatial differences between communities.
“Facebook’s publicly available gender datasets are only meaningful when they are used to expose and address unseen gender gaps. By layering these insights with other data sources, Fraym has helped to reveal how this gender data can advance equality across the SDGs”