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  • Girl data: Making the numbers add up

    Feature

    Quality data makes our work smarter

    Visibility

    Targeted, effective programming: Sara Posada, from the Nike Foundation, on what data can do for development

    Quality data helps us target effective, economically sound projects to lift adolescent girls out of poverty - it's that simple. Sara Posada, from the Nike Foundation, on what data can do for development.

    Data makes our money go further

    One of the major challenges development programmes face is finding the people in the greatest need. With limited funds, our approach needs to be targeted, so not a single penny is wasted. That's where data comes in. Accurate statistics shine a light on the specific groups we need to be working with and show us exactly where they are. 

    
Data proves our point

    Girls need unique, targeted programmes, centred around them. But if we haven't got the right kind of data to prove this, those programmes don't get funding - and therefore they don't happen. Take the example of maternal mortality: we know childbirth is the leading cause of death for adolescent girls, but maternal mortality statistics aren't split by age, so it's hard to make the argument to policymakers and other programmers that prevention strategies should be targeted at girls. With disaggregated data we would win that argument hands down.

    
Data tells our stories

    We took the first step on the journey towards better data for girls with the Girls Discovered project. Driven by a desire to tell the world what we do and don't know about adolescent girls, members of the Coalition for Adolescent Girls and a team of advisers from the United Nations Foundation and the Nike Foundation joined forces with data mapping specialists Maplecroft. We brought existing global data onto one platform, made it visible and shared it. 

Girls Discovered is the go-to resource on global data for the Nike Foundation. In some cases we have shifted our geographic focus for an investment because of data we found on Girls Discovered. But it's a first step. For programmers and many policymakers, we need to see data at a sub-national level.

    Data saves lives

    If the data gaps were filled, we could have more targeted and, as a result, more impactful programming. For example; if we understood where the HIV hotspots are, we could make a difference for young girls who are infected with HIV through sexual contact and for girls who are at risk for dying in childbirth.

    
So… what next?

    Data that is split by both sex and age, from age 10 - that's the goal. Without data on girls in different age groups, the lines get blurred and we don't get the true picture of what adolescence for girls is really like. And we need that picture to be geographically specific. We need to focus in on the state, the county and even the village.
    With extra investment we can find the girls who need us most, and work with them to end poverty - for good.

     

     

    Sara Posada
    Feature

    The state of girl data: Why girls must count

    Visibility

    Gaps in girl data mean missed opportunities to target or track development work, says the data expert

    We asked Alyson Warhurst, of data analysis experts Maplecroft, how and why we should count girls better.

    The invisible girls

    It is estimated that there are 250 million adolescent girls living in poverty in the developing world. But really, when it comes to data, many of those girls are invisible to us. Thanks to Girls Discovered a landmark data mapping project by Maplecroft and the Coalition for Adolescent Girls, we have a better handle on the state of girl data than ever before - but it's also clear there's a lot of work still to do.

    Who's even counting girls?

     "What we discovered was that there are 600 million adolescent girls worldwide, but actually trying to find data that's broken down by gender and that's broken down particularly in the age groups: 10-12, 12-14, 14-16 and 16-18; was very, very difficult;" says Maplecroft CEO Alyson Warhurst.

    That disaggregated data is absolutely crucial. We need it to find girls, reach girls and measure the impact of projects that target them - in short, to track the girl effect.

    Data gaps are opportunities

    Girls Discovered features an interactive global map with more than 200 datasets users can combine to create bespoke maps according to their interests and specialties. As well as being a groundbreaking tool, it shows where there are still huge data holes to fill.

     "What's good about data is that it shows the shortcomings and the absences, and we've always said from the beginning that the absences speak volumes in their own right," says Alyson. "[available data] varies from topic to topic: some education data has very good coverage, data on contraceptive use and HIV/AIDS is now getting better. What we've really got to understand better is youth… everyone's interested in that - what can girls do and what are they doing? What are the risks and opportunities there?"

    How do we go after girl data? There are two jobs to be done; first make sure data collectors are asking the right questions and second, extract girl-specific information from the data that's already there.

    Step one: think girls

     "The data for girls is much worse than the data for either boys or women," says Alyson. Putting that right starts at the beginning of the process, with good data collection. "There's a challenge about making sure the data is accurate and disaggregated properly. One of the things that is obvious is that if it's collected better, then it will be disaggregated better. It takes more time at the point of collection, but it's impossible to do retrospectively."

    Step two: invest

    The good news is, there's a lot of girl data out there -but getting it means money. Funding for a data project in India let Maplecroft drill down to detail and made the country one of the most extensively data-mapped places in the world. "The biggest challenge is subnational data, because you really need the subnational picture; it's money, really, which prevents us getting it." Says Alyson "What we discovered was there's actually a lot of subnational data out there, but you have to go looking for it. You have to make local networks, but it is out there.." 

    Today's global hot-spots

    So where do you put your money? If you want to reach girls, there are four countries Maplecroft say are in desperate need of closer inspection. "We've picked out some countries that we thought could really benefit from a subnational analysis like we did for India - Ethiopia, Malawi, Liberia and Guatemala," says Alyson. "These countries represent the most vulnerable groups of young women and adolescent girls from our measurements. It's basically practising what we preach - using the data analysis to highlight the priorities."

    When those priorities are highlighted, we're in a better position to target efforts and track the girl effect. "What would be really helpful for us is to see companies and organisations developing projects using this data as an entry point, or as a baseline and starting to measure what they're able to contribute." 

    Go get it

    "Unless we invest in data collection and data depiction, we're not going to get it all and unless we do that, we can't really understand what development efforts need to be prioritised and where."

    The next step is clear. There must be a commitment to gathering properly disaggregated data. This means creating the right criteria at the point of collection and investing time and money into extracting girl data from existing sources. That will give us the real picture of the world for girls - then we can start to shape it.

    Read Bill Gates on the importance of data

     

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