SPECIAL FEATURE
GENDER DATA
SHORT-TERM FIXES BUT A LONG-TERM CHALLENGE
“If I was to summarize the gender data issue into one number, it would be 12. We have 54 gender-related Sustainable Development Goals indicators, but we only have regular data for 12 of them. The problems we have are a
result of weak national statistical systems that are not really equipped to produce the data that we need. Gender is not necessarily taken into account in national strategies for statistics. When countries are developing their plans for statistical production, they might prioritize economic or education statistics. But gender rarely figures. If you don’t include gender statistics in your national plans and these plans are designed to monitor development, then the statistics don’t work.
A lot of people understand gender data as just disaggregating by sex – male and female. But it’s not as simple as that; gender data tells us about women’s conditions. Gender data can be deeply rooted in gender inequalities. There’s a lack
of knowledge, and people just figure that if they desegregate by sex, they’ll bypass challenges. But that’s definitely not the case. We also have technical challenges. Sometimes, when it comes to gender, what we want to measure is not well defined; we don’t have the methodologies. For example, we use household surveys a lot. As the name suggests, these household surveys don’t collect data at the individual level. The data tells us that in the household, resources are shared; that a household is a homogenous unit. But we know this is not true. To understand better what happens within the household, we need individual-level data. Household surveys are not designed this way, and national statistics offices may not have the skills or the resources to change them. Let’s take another example: maternal mortality. We have estimates for all countries. But the data is not as accurate as we would like. Generally, this kind of data should be derived from vital hospital
records. But if a hospital system is not well equipped to diagnose the cause of death, we may not have the data. Or what we will have will be an estimation – somewhere between semi-accurate and guesswork. The consequences of not
including women in the data can be absolutely dire. The first and most fundamental consequence is that if we don’t know how women are faring, we cannot devise solutions to address their problems. The solutions that we end up with in the absence of data are really down to guesswork. For example, we know from
surveys that a lot women spend more time than men working in the home, and that this affects their labor market participation. We know this has a link to poverty, too, but we don’t have the data to prove it. So we have an assumption. But policies shouldn’t be based on assumptions; they should be based on hard facts. What if we were to provide solutions – let’s say in terms of
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