The state of girl data: Why girls must count

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.


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.


"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.


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.


"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."


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.." 


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." 


"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