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Big Data Is Filling Gender Data Gaps—And Pushing Us Closer to Gender Equality

by Guest Blogger for the Women and Foreign Policy Program
April 12, 2017

Maasai girls watch a video on a mobile phone in Imbirikani, Kenya, April 21, 2016. In Kenya, as in many countries around the world, national statistical systems have been ill-equipped and under-funded to collect data on ethnic minorities, rural communities, and indigenous populations. REUTERS/Siegfried Modola

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Voices from the Field features contributions from scholars and practitioners highlighting new research, thinking, and approaches to development challenges. This article is authored by Emily Courey Pryor, executive director of Data2X.

Imagine you are a government official in Nairobi, working to deploy resources to close educational achievement gaps throughout Kenya. You believe that the literacy rate varies widely in your country, but the available survey data for Kenya doesn’t include enough data about the country’s northern regions. You want to know where to direct programmatic resources, and you know you need detailed information to drive your decisions.

But you face a major challenge—the information does not exist.

Decision-makers want to use good data to inform policy and programs, but in many scenarios, quality, complete data is not available. And though this is true for large swaths of people around the world, this lack of information acutely impacts girls and women, who are often overlooked in data collection even when traditional surveys count their households. If we do not increase the availability and use of gender data, policymakers will not be able to make headway on national and global development agendas.

Gender data gaps are multiple and intersectional, and although some are closing, many persist despite the simultaneous explosion of new data sources emerging from new technologies. So, what if there was a way to utilize these new data sources to count those women and girls, and men and boys, who are left out by traditional surveys and other conventional data collection methods?

Big Data Meets Gender Data

“Big data” refers to large amounts of data collected passively from digital interactions with great variety and at a high rate of velocity. Cell phone use, credit card transactions, and social media posts all generate big data, as does satellite imagery which captures geospatial data.

In recent years, researchers have been examining the potential of big data to complement traditional data sources, but Data2X entered this space in 2014 because we observed that no one was investigating how big data could help increase the scope, scale, and quality of data about the lives and women and girls.

Data2X is a collaborative technical and advocacy platform that works with UN agencies, governments, civil society, academics, and the private sector to close gender data gaps, promote expanded and unbiased gender data collection, and use gender data to improve policies, strategies, and decision-making. We host partnerships which draw upon technical expertise, in-country knowledge, and advocacy insight to tackle and rectify gender data gaps. Across partnerships, this work necessitates experimental approaches.

And so, with this experimental approach in-hand, and with support from our funders, the William and Flora Hewlett Foundation and the Bill & Melinda Gates Foundation, Data2X launched four research pilots to build the evidence base for big data’s possible contributions to filling gender data gaps.

Think back to the hypothetical government official in Kenya trying to determine literacy rates in northern Kenya. This time, a researcher tells her that it’s possible – that by using satellite imagery to identify correlations between geospatial elements and well-being outcomes, the researcher can map the literacy rate for women across the entire country.

This is precisely what Flowminder Foundation, one of the four partner organizations in Data2X’s pilot research, was able to do. Researchers harnessed satellite imagery to fill data gaps, finding correlations between geospatial elements–such as accessibility, elevation, or distance to roads–and social and health outcomes for girls and women (as reported in traditional surveys) – such as literacy, access to contraception, and child stunting rates. Flowminder then mapped these phenomena, displaying continuous landscapes of gender inequality which can provide policymakers with timely information on regions with greatest inequality of outcomes and highest need for resources.

This finding, and many others, are outlined in a new Data2X report, “Big Data and the Well-Being of Women and Girls,” which for the first time showcases how big data sources can fill gender data gaps and inform policy on girls’ and women’s lives. In addition to the individual pilot research findings outlined in the report, there are four high-level takeaways from this first phase of our work:

Country Context is Key: The report affirms that in developing and implementing approaches to filling gender gaps, country context is paramount – and demands flexible experimentation. In the satellite imagery project, researchers’ success with models varied by country: models for modern contraceptive use performed strongly in Tanzania and Nigeria, whereas models for girls’ stunting rates were inadequate for all but one pilot country.

To Be Useful, Data Must Be Actionable: Even with effective data collection tools in place, data must be demand-driven and actionable for policymakers and in-country partners. Collaborating with National Statistics Offices, policymakers must articulate what information they need to make decisions and deploy resources to resolve gender inequalities, as well as their capacity to act on highly detailed data.

One Size Doesn’t Fit All: In filling gender data gaps, there is no one-size-fits-all solution. Researchers may find that in one setting, a combination of official census data and datasets made available through mobile operators sufficiently fills data gaps and provides information which meets policymakers’ needs. In another context, satellite imagery may be most effective at highlighting under-captured dimensions of girls’ and women’s lives in under-surveyed or resource-poor areas.

Ground Truth: Big data cannot stand alone. Researchers must “ground truth,” using conventional data sources to ensure that digital data enhances, but does not replace, information gathered from household surveys or official census reviews. We can never rely solely on data sources which carry implicit biases towards women and girls who experience fewer barriers to using technology and higher rates of literacy, leaving out populations with fewer resources.

Big data offers great promise to complement information captured in conventional data sources and provide new insights into potentially overlooked populations. There is significant potential for future, inventive applications of these data sources, opening up opportunities for researchers and data practitioners to apply big data to pressing gender-focused challenges.

When actionable, context-specific, and used in tandem with existing data, big data can strengthen policymakers’ evidence base for action, fill gender data gaps, and advance efforts to improve outcomes for girls and women.

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