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Why data can be sexist

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Melinda Gates: Data can be sexist
When I worked at Microsoft, we were passionate about connecting everybody to computers and software. To do it effectively and efficiently, we scrutinized data every single day. We had access to so many numbers, and we looked at them backward and forward to make sure there was solid evidence behind every decision.

When we started our foundation, we learned right away that everyone working there was just as passionate, maybe even more passionate, about making sure every child in the world has the chance to lead a healthy, productive life. But when it came time to turn that passion into evidence-based decisions, I was surprised by how much of the data we needed was simply lacking, especially when it came to the lives of women and girls.

Bill and I just published our Annual Letter about what has surprised us the most over the years, and one of the topics I take on is these data gaps—and what the world needs to do to fill them in.


H ow much income did women in developing countries earn last year? How much property do they own? How many more hours do girls spend on household chores than boys?

I don’t know. Neither does anyone else. The data just doesn’t exist.

Bill and I could easily spend our whole annual letter talking about the role data plays in driving progress for the world’s poorest people. Data leads to better decisions and better policies. It helps us create goals and measure progress. It enables advocacy and accountability.

That’s why the missing data about women and girls’ lives is so harmful. It gets in the way of helping them make their lives better.

The problem isn’t only that some women are missing from the record altogether. It’s also that the data we do have—data that policymakers depend on—is bad. You might even call it sexist. We like to think of data as being objective, but the answers we get are often shaped by the questions we ask. When those questions are biased, the data is too.

That’s why the missing data about women and girls’ lives is so harmful. It gets in the way of helping them make their lives better.
For example, what little data we do have about women in developing countries is mostly about their reproductive health—because in places where women’s primary role in society is being a wife and mother, that’s what researchers tend to focus on. But we have no idea how much most of these women earn or what they own, because, in many countries, income and assets are counted by household. Since the husband is considered the head of the household, everything a married woman brings in is credited to him.

When such flawed data is all you have to go on, it’s easy to undervalue women’s economic activity—and difficult to measure whether women’s economic condition is improving.

Three years ago, our foundation made a big investment to start filling some of these data gaps. We are part of a network of organizations working to accelerate a gender data revolution—from empowering data collectors with new tools and training to breaking down existing datasets by gender to mine them for insights.

This work to collect and analyze data can sound—let’s face it—boring. But what’s not boring is using data to empower millions of women and girls.

When I was in Kenya a few years ago, a data collector named Christine let me accompany her as she went door to door surveying women in one of the poorest parts of Nairobi. She told me that many of the women she meets through this work have never been asked questions about their lives before. Christine says that when she knocks on a woman’s door and explains that she’s there to learn more about her, it sends a message to that woman that she matters—that someone cares about her.
What we choose to measure is a reflection of what society values.
I think her point is a powerful one. What we choose to measure is a reflection of what society values. That’s why when it comes to understanding the lives of women and girls, the world can’t accept “I don’t know” as an answer.

Read the rest of our Annual Letter at gatesletter.com.

Posted: March 5, 2019
Originally published: LinkedIn on February 24, 2019
Edition: Forward