Bridging the Gender Gap

How big data can improve the lives of a billion women and girls.


In the field of development, there are too many reports to count, but last month’s U.N. report on the Post-2015 Development Agenda stands out because it contains a big idea that could change the future for billions of people. It says that investments in the world’s poorest people won’t generate the biggest possible return until we learn how to make sure women and girls benefit from them equally.

Investing in women and girls should justify itself. They make up half the population (and the majority of the poor), yet they’ve been neglected by the development community. Moreover, advocates and experts have known for years that when women and girls have the power to make basic household decisions, they prioritize education, food, and health care — the stuff of broad-based economic and social development. In short, when we invest in women and girls, we are investing in the people who invest in everybody else.

Unfortunately, this fact hasn’t always influenced the official development agenda. Take the example of the eight Millennium Development Goals (MDGs), which have served as a charter for the development field since the U.N. adopted them in 2000.

The MDGs have been a big success because they narrow down a potentially endless list of priorities to eight discreet goals. My husband calls the MDGs the "world’s report card." Since leaders know they’re being "graded" on specific goals and targets (such as the child mortality rate), they use their resources more strategically. That’s why the world has made impressive progress on many of the goals, starting with the first one (cutting global poverty in half), which was achieved five years early.

The negative corollary to the success of the MDGs is that the priorities not enshrined in the world’s report card tend to get less attention. In some ways, that’s what happened to women and girls. One of the current goals is specifically devoted to gender equity, but it includes only one target: to eliminate gender disparities in education. That’s an important work in progress, but it’s just one among many gender-related issues that matter in development.

Which brings us back to the U.N. panel’s recommendations for what should replace the MDGs when they lapse in 2015. The proposed gender equity goal for post-2015 is much stronger than its predecessor. It includes targets for limiting gender-based violence and child marriage and for promoting property rights for women. It’s tricky to strike the right balance between the concrete specificity needed to make the goals actionable and the complex reality of women’s lives. (Indeed, this is a defining challenge across the development field.) The list of proposed targets in the report is a promising start.

However, the real breakthrough is the panel’s recommendation that data on every single goal and target be broken out by gender (and also by other key categories like income or where people live). Disaggregating the data will tell us whether the progress we’re making applies to women and men equally (or to slum dwellers and rural villagers equally, for example). This has not always been the case, and our inability to disaggregate this data leads to solutions biased toward men.

You can see the gender bias inherent in development by looking closely at the recent history of agricultural development in sub-Saharan Africa. Women do a majority of the farm work in that part of the world, but many agricultural programs are instead designed to reach the minority of male farmers. For example, the government employees who train farmers tend to be men who in some cases are not allowed to train women and in many cases simply prefer working with men. As a result, women farmers are significantly less productive than their male counterparts.

Since women are far more likely to be in charge of feeding their families, the fact that they grow less food has disastrous consequences for children across the continent — and for development at large. If we disaggregate the data, however, we will know immediately whether particular groups are being left out. And then in-country programs can be redesigned to address the problem.

We know from experience that a rising tide in development does not necessarily lift all boats. By the same token, progress toward the MDGs does not necessarily include all people. It’s possible to reduce poverty for certain types of people but not for other types. Disaggregation will go a long way toward repairing this shortcoming in the next set of global goals.

The U.N. panel points out one giant barrier to reaping the rewards of disaggregation: As it stands right now, the world doesn’t have the ability to gather the necessary data or analyze it properly. Some countries simply don’t collect enough data, or it’s not accurate. Other countries with better data don’t track it in ways that make country to country comparisons possible, which makes the data difficult to use. The report calls for a "data revolution" that takes advantage of the new digital tools at our disposal. It will take significant investment over the next two years to ensure that governments in developing countries have the capability to gather and analyze data when the new generation of MDGs comes into effect in 2015.

I know that disaggregating data sounds mundane. Stats aren’t sexy, even if you try to dress them up with a slogan like "big data revolution." However, as much as development depends on very human motivations like a mother’s desire to give her child a better life, it also relies on the sound technical basis of smart incentive structures, efficient logistics, and other details. The MDGs are the perfect example of this yin and yang. They are based on ambitious principles about the quality of life that the poorest deserve, but they work because of management truisms like "what gets measured gets done."

I recently traveled to Senegal, to follow up on the progress that country is making in delivering contraceptives to the women who want them. At a health clinic in Dakar, I met Monique, a 23-year-old woman who uses a contraceptive implant and was seeing a health worker for her six-month checkup. I asked her why she started using a contraceptive, and she said that she sees women in her community struggling to take care of unhealthy children who were born one right after the other. She doesn’t want to go through that, she said.

Behind Monique’s drive to change the future for her family, however, is a family planning infrastructure in Senegal that is now well-funded and soundly managed, not to mention based on real-time data about how many of which types of contraceptives are in stock at various health clinics. This system educates women about their options, makes sure the contraceptives they prefer are always available, and guarantees that they get excellent follow-up care.

As we’ve seen, the mere fact that women and girls can drive development isn’t enough. What is needed is a system designed to put them in the driver’s seat. And one linchpin of that system is data we can use to monitor, evaluate, and constantly improve development programs.

There are still two years before the next-generation MDGs are signed, sealed, and delivered. I hope that when they are, the theory underlying the U.N. panel’s report — that women are not just a development constituency but a powerful source of development — is still at the heart of the agenda. In the meantime, it is up to us to invest in the systems that can turn this theory into a reality. If we have both the will and the way to count women and girls, then we can count on them to help communities and societies around the world flourish.

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