For over three decades debt statistics published by the World Bank have provided the authoritative accounting of the external debt of developing countries. Governments, investors and bankers, academics, and journalists have relied on them to identify financial trends and vulnerabilities.
It’s raining data. Financial inclusion data, that is. The Development Research Group has published the complete micro dataset of the Global Financial Inclusion (Global Findex) dataset on the Open Data Microdata Library. We’re talking over 150,000 individual-level observations, representing adults in 148 economies and 97 percent of the world’s adult population. Users can download the complete worldwide dataset, or datasets by country.
Update: the event will be webstreamed on World Bank Live.
This is an exciting time to be in the data business. There have never been more groups, from such different backgrounds, with a passion for producing and using data for the public good.
Let’s say you are in the middle of what others may call ‘nowhere’ and need information on the Bank’s work in the vicinity before an upcoming meeting with local officials. Or you are a civil society organization rep and want to make sure that the numbers you have about a particular project are the same as what the Bank reports (and if not, you want to know why not).
Your laptop is no good because - it is the middle of nowhere after all! - and you can only rue your decision to leave your stack of papers behind.
What do you do? Well, the answer might be in your pocket.
It took longer than we'd hoped but it's finally here - the new World Bank Finances app answers many of the questions you asked after the release of the first version last year (click here to download the new version for Android; an updated iOS version will be out soon).
The UNDP just launched open.undp.org. The site details information on their 6,000+ projects in 177 countries and territories worldwide and lets you search and browse by location, funding source, and focus areas.
I think it’s really good and I’m most impressed by three features:
We’ve recently released an Open Government Data Toolkit (OGD Toolkit), designed to provide staff at the World Bank and in country governments a basic set of resources for initiating and developing an open data program. The toolkit is a “work in progress” which we expect to revise and improve as we receive your feedback and real-world experience.
We developed the toolkit based on questions we’ve frequently heard from countries considering open data programs:
Speaking at the World Bank on Wednesday, musician and activist Bono made the call for “open data and transparency” to “turbocharge the fight against poverty.”
When asked what the World Bank could do, he responded: “We need better data.”
It was awesome.
Here are seven things I learned:
1) Iteration is the path to perfection
By now you’ve heard of Nate Silver - the statistician behind FiveThirtyEight and a near-perfect prediction of the 2012 US elections. What you may have missed is the best interactive graphic of the year - the New York Times’ “Paths to the White House” built with Mike Bostock’s D3:
Shan Carter from the NYT graphics team showed how newspapers have struggled to represent the potential scenarios and actual outcomes of US elections ever since the late 19th century. His team eventually came up with the graphic above, but see how many revisions they went through to get there:
That’s 257 revisions. As early as version 15, you can see the core idea. At version 81, it looks almost done, but it takes another 157 revisions and that extra attention to detail, high production values and pride in your work to be at the top of your game like this.
Lesson: Iterate and aim high: editors are your friends, they’ll make your work stand out. Also: this is the benchmark for what a good data visualization looks like - if you can’t honestly say what you’re doing is at least this good, iterate.
So much data, so little evidence. So much information, yet unsatisfactory decisions...can big data change it, and how?
This post originally appeared on Let's Talk Development.
The World Bank’s classification of economies as low-, lower-middle-, upper-middle-, or high-income has a long history. Over the years these groupings have provided a useful way of summarizing trends across a wide array of development indicators. Although the income classification is sometimes confused with the World Bank’s operational guidelines, which set lending terms and are determined only in part by average income, the classification is provided purely for analytical convenience and has no official status.