WhoForShe? Or: tinkering with Open Data

This post is not really related to the SeNeReKo project. But using an unrelated example, it shows the benefits of Open Data: Re-combining existing information to create new insights. This is really what SeNeReKo is about.

Last week, the UN Women launched the campaign “HeForShe” with Emma Watson’s speech at the UN Headquaters. The campaign aims at including men into a “solidarity movement for gender equality”. In order to make this support visible, the campaign website allows men to commit to the campaign’s idea by submitting a web form. Based on the submitted data, the website displays a map of the world, showing (on click) the number of signers.

Map of HeForShe signers

Above the number of total signers (which quickly surpassed the initial target of 100’000), the sentence “Join them and move your country to the lead” struck me. Not only does the gender equality campaign subscribe to the stereotype of men being competitive. It also is a quite unequal race, given the vast differences in population size. If one wants to compare countries, one should compare the proportion of men who sign.

In order to get better numbers, I looked for the source data. Fortunately, the raw data are loaded from a JSON file. This already allowed me to have a look at the countries with most signers:

  1. United States
  2. United Kingdom
  3. Canada
  4. Australia
  5. Iceland
  6. Sweden
  7. France
  8. Germany
  9. Mexico
  10. India

But where could I easily get population data for each country, without looking at the Wikipedia page for each? I found out that the World Bank publishes lots of data through its Open Data portal. This includes a collection of gender-related data. There is actually an R package called WDI that allows to access the World Bank data directly from within R. But I couldn’t make it work for the variables I needed, so I downloaded the population data as a CSV file from the website.

Using these data, I could then calculate the proportion of signers to each country’s male population aged above 14. This results in a very different list:

  1. Iceland
  2. Sweden
  3. Australia
  4. United Kingdom
  5. New Zealand
  6. Ireland
  7. Canada
  8. Denmark
  9. Norway
  10. Singapore

It was already striking that Iceland, as a really small country, is #5 in absolute numbers, so it is probably not a big surprise that this earns it the top place in the proportional list. But a plot of the top 50 countries reveals how exceptional Iceland’s position is:

Barplot of signer’s proportion by countrySo the share of Iceland’s men who signed the campaign is more than 35 times larger than the one of the second country, Sweden. And 512 times as large as Germany’s share, which ranks 31st.So much for moving my country into the lead … But this is in line with an announcement I just read today: Iceland plans to hold a conference on gender equality open only to men, to foster their inclusion in this matter. Looks like Iceland’s men do really take this seriously.

Now how would a map look that is based on the proportional support for the campaign? Using DataMaps and rMaps, this is quite an easy task:

Map of the share of signers per country

Click the image for an interactive version.

All the data are as of September 29th, 2014. If you want to explore the code, you can get it from GitHub.


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