(or “the incredible shrinking treemap” HT @albertocairo).
Treemaps are a really useful way to understand hierarchical data, but they are not well-suited to side by side comparison.
Recently I’ve been working on the World Bank’s Atlas of the Sustainable Development Goals 2017 (which was a large team effort). One highlight that I worked on was a comparative treemap / cartogram of people living in extreme poverty, which is a bit different from the typical treemap.
Here’s one version, an animated GIF that Tariq Khokhar captured (you can see the interactive timeline here):
Which of the following choropleth class boundaries (and so, legend styles) would you use?
Here’s my answer, via github.
Many economic statistics seem conceptually simple, but are rather intricate and technical as actually measured (or more accurately, constructed). You can’t just line up dollars of GDP and count them. Price indices are similarly fiendish.
Conceptually, the purchasing power parity exchange rate between two countries is simply the relative cost, in local currency units, of buying the same basket of goods in each country. This can be very different from the market exchange rate, but is generally a better way to convert, say, GDP per capita, if you’re interested in cross-country comparisons of welfare (GDP? welfare? we’re already on shaky ground…)
Last week another Australian I know in DC was surprised to see that the International Comparison Program’s (ICP) 2015 PPP exchange rate for Australia is 1.487. Are things really 50% more “expensive” (dollar for dollar) in Australia, he wondered?
In the last week I have found myself arguing both that murder statistics should be expressed as population rates and that doing so can be quite misleading. That kind of apparently inconsistency deserves a quick explanation.
We are entering, it is said, a post-fact era. Populist politicians dismiss data and expertise out of hand, and the UK and US publics seem to nod along. Michael Gove, a UK MP campaigning for Brexit, summed this up simply: “people in this country have had enough of experts.”
This comes at a time when good-quality expertise and data have never been easier to access. Fact-checkers, explainer websites, data journalism and gigabytes of open data are all a click away.
And yet despite all this, in 2016 so far, numbers themselves seem to be under assault by a populist insurrection. We could blame populist anti-elitism, innumeracy, racism and xenophobia – and indeed we should. But economists (including economic journalists) must also consider our role.
Some “large green plantains”, courtesy of Wikipedia user Daegis.
A colleague asked me about fuzzy matching of string data, which is a problem that can come up when linking datasets. I figured I might as well reproduce my comments here since this is such a common problem, and many of the built-in algorithms are well suited to word matching but not to multiword strings. (more…)
I now live in the United States, and amongst the differences from the otherwise-similar places I’ve lived before, Australia and the UK, is the adoption of domestic technologies here. The contrast between America and the UK seems greatest, with Australia – like in so many respects – sitting somewhere in between.
Americans living in Britain famously bemoan the lack of mixer taps on bathroom sinks, forcing one to choose between freezing and scalding, or mix the two in the basin. But it goes well beyond this. Based on my experiences so far, certain amenities seem to be more or less standard in medium-grade-and-above housing in America: dishwashers, garbage disposal units and air-conditioning being three particular examples. (more…)
(Back to the overview post)
This is much more speculative, but perhaps within-country divergence and convergence explains more of the time-variation in national growth rates than we realise.
One of the very useful reminders of William Easterly’s Tyranny of Experts is that the nation-state is often not the appropriate unit at which to consider big questions of growth and development. For example, if we look at per capita gross world product, there is no slowdown to explain. In fact growth seems to accelerate in the final quarter of the 20th century. The growth-pessimists will respond that this is merely ‘catch-up’, countries far from the technology- and productivity- frontier moving closer to it, rather than a pushing-forward of the frontier itself.
Of course they’re correct, but if that reasoning applies to the late-20th and early-21st century world, why should it not apply within the mid-20th century United States?
(Back to the overview post)
The invention of modern national accounting, which includes the construction of GDP estimates, is credited to Kuznets and others, who did pioneering work on measuring output in the 1930s and 1940s. Ever since it was invented, thoughtful economists have cautioned against using GDP as a measure of welfare; all the while most other economists do just that.The standard criticisms of national output are outlined in clarity by the Stiglitz-Sen-Fitoussi commission, or more poetically by Robert Kennedy:
I would express these specific criticisms in a common way:
GDP measures the things that seemed important in 1940.
(Back to the overview post)
Income growth is not a neat story, but technology pessimists would like you to think it is. Robert Gordon provides the following neat picture of ‘Growth in real GDP-per-capita, 1300-2100’ (here or unpaywalled summary, here).
The end-date alone should be enough to make us realise that this is a stylised representation, at best (and the 2008 onwards data is purely imagined, a ‘provocative fantasy’ in Gordon’s words). But even with that caveat it is a highly selective and misleading representation of growth.