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Hackathon: Exposing Gaps in Data on Women’s Empowerment

Here’s Challenge #1 for today’s Hackathon:

New research shows that even the most advanced current efforts to gauge women’s empowerment still miss critical elements of what it takes for women to be empowered in the developing world. Far too many of the indicators used to measure women’s empowerment, for far too many countries, are based on data that is largely unreliable, old, or inconsistent. This compromises the accuracy and integrity of the assessments and makes them less reliable for policy makers who base decisions on them. In its upcoming 2015 Hunger Report, Bread for the World Institute will identify key missing data and explain why better data are essential to continued progress.

Data and background information can be found in the “Challenge 1″ in this Google drive folder.

25 thoughts on “Hackathon: Exposing Gaps in Data on Women’s Empowerment

  1. How do you visualize data that don’t exist? Ideas we’re throwing around: small multiples of countries; dot matrices with shares of indicators that exist and don’t. These are countries…we have to map them!

  2. Over the last decade or so, international organizations have gathered 52 different indicators on women’s empowerment. But, for too many countries the data are missing on every indicator.

    It’s these huge gaps that are most important, and what an interesting design challenge. How do you emphasize nothing?

    Have to show empty/white space. Set up a traditional visualization where you’d expect to have data, and instead you can show space.

    It’s an inversion of the normal process, data-wise and for the viewer.

  3. This is what a small-multiples visualization of missing data could look like. This chart is Algeria: the x-axis is years; the y-axis is whether the indicator exists. Then we could array each of 172 countries’ charts next to each other for a big-picture view.

  4. Women’s empowerment in the developing world is a huge topic; there are already 52 indicators that people are currently gathering. With some data for 172 countries, 52 indicators, and data broken out by region, development status and other indicators, one visualization would have to achieve a lot.

    One thought is to group indicators by type: health, food access, economic development, transportation. From there you could array each country’s data availability in the same way…

  5. The earliest year for which we have some data is 1990. If every country had data collected on each of 52 indicators for 23 years, each country would have 1196 data points.

    In reality, some countries will have at most a few dozen data points.

  6. We can lead this interactive feature with a loading-gif… then segue into some kind of tagline “Missing Women” followed by feature content and data visuals about what information we’re missing by gender across econ/public health/human rights/education.

  7. Before we settle on a final design or product (or several final products) we have to know what our goal is.

    Are we making a call to action? (Fund Bread’s research and data-analysis capacities?)

    Are we hoping that showing these data, and their holes, will lead to new and interesting research questions? (Why are economic indicators missing in this region of the world, but health indicators are missing in this other part?)

    Perhaps we’re solely on an information campaign…

  8. One of our designers (see above) came up with a visualization title: “Missing Women” – from a policy and economic perspective, if you can’t measure something, often, it’s by default not there. These women are missing, because we can’t, or haven’t, measured them.

    But our Bread Institute hosts tell us that “Missing Women” is also another, bigger, darker phenomenon. Across the developing world, female children die at higher rates than male children. Twenty years later, a generation grows up and is missing women – how would that generation, its country, its economy, its culture, be different with all of its women?

    Might we tie our visualization project into that theme and issue?

  9. Sometimes the best digital hack starts with a gorgeous and creative analog hack. #Missingwomen #missingdata

  10. Update on the “small multiples” idea. Each little chart is a country, years are arrayed across the x-axis, and indicators are along the y-axis. Black space: data DO exist. White space: data are missing.

    Note on those white spaces: do they represent data that have never been gathered or won’t be gathered? Or might there be studies in the field or data from local sources at different points in time during the year?

    Working on adding a third dimension about if perhaps those data could be found.

  11. This is Xan Gregg’s version of the small multiples visualization. Xan is a coder in North Carolina. (Thanks!)

  12. Another visualization: array each indicator’s series, and opacity correlates with missingness…less data mean more opacity, blocking out the picture of the woman behind. The more data, the clearer and more visible the woman.

  13. Here’s an update of the country-by-year heatmap with 2013 removed and non-countries (like North America) removed. And I made it tall enough to see all the names.

  14. Thanks for chiming in, Xan! Really good to have you involved!

    My only hesitation about the heatmap is that it’s really hard to find a specific country. Maybe color-coding the country labels by region? This came up several times today…..

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