BARCELONA — When it comes to data, organizations must look beyond averages to uncover and address intersectional inequalities, said Papa Seck, UN Women’s chief statistician. This will help to ensure no one is left behind in efforts to achieve the Sustainable Development Goals, he added.
“What we've found is that, essentially, a country may do well on average, but once you start drilling down and looking at who was most likely to be left behind, you can have the same groups essentially experiencing the same levels of quite a lot of deprivation in multiple areas,” Seck said, adding that gender tends to be at the center of intersectional inequalities.
“If you're talking about intersectional inequalities without actually looking at it from a gender perspective, we honestly think that, from the evidence we've seen, we're really missing the point.”
For example, a 2018 report by UN Women found that while the average primary school attendance in Nigeria was 66% among girls, that figure dropped to 12% for Hausa girls from rural areas and poor households. “[The findings] truly brought home the fact that you really need to go beyond averages and start looking at specific groups of women and girls most likely to be deprived and left behind,” Seck said.
Speaking to Devex, Seck discussed the difference that disaggregated data can make in identifying the needs of the most marginalized, how this data should be used, and the promise and perils that new data sources can bring.
This conversation has been edited for length and clarity.
How can disaggregated data be used to acquire a better understanding of the needs of the most marginalized people?
One problem with disaggregation is that if you take it just from a technical point of view, essentially you can disaggregate to no end. But if disaggregated data is really something to be looked at from a policy point of view, first look at, essentially, why do we need this information, and have a dialogue with civil society organizations or policymakers who really understand what information is needed in order to make a dent in policy.
Subnational information, group-level information, and so on really comes from the dialogue between producers and users in order to see exactly what data is needed for what policies and how to produce that data. Sometimes, it's not just disaggregation — it may be having to to collect brand new data. For instance, if you're looking at homelessness and housing policies, a household survey will not give you the information that you need.
Later this month, UN Women and the U.N. Statistics Division are organizing a global conference precisely on these issues, looking at it from a normative point of view. So what are the principles we should use for data disaggregation and, looking at intersectional inequalities, who should produce that data, what partnerships are needed, and how that data should be used?
The way that data should be used, obviously, first, I think there has to be some key human rights principles that need to be addressed. What are the human rights principles needed so that data does no harm? But also, who needs that data? What partnerships are needed between national statistical offices, nongovernmental organizations, and policymakers so that whatever data is produced can be taken and used for advocacy to inform policies? I think this should be ingrained in the policy apparatus of countries.
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This article was originally published on Devex. UN Women is partnering with Devex to explore how data is being used to inform policy and advocacy to advance gender equality. Gender data is crucial to make every woman and girl count. Visit the Focus on: Gender Data page for more. Disclaimer: the views in this article do not necessarily represent the views of UN Women.
