Some data from Gapminder
Main link here. Some of the data you can find at the link:
i. Have you ever wondered how the covariation between alcohol consumption and GDP looks like on an international level? Now you know (…more about it than you did before):
Each colour represents a different region (look at the map at the top right part of the image); for instance, orange is Europe + Russia/central Eurasia, blue is Africa, green is Middle Eastern countries etc.. Each dot is a country and the population size of the country determines the size of the dot. In general, it looks very messy and there’s a lot of weird stuff going on here! Not too surprising to me, it turns out that the most hard-drinking countries are Eastern European countries; the top 5 countries in terms of alcohol consumption are: Moldova, South Korea (though that one came as a huge surprise to me!), Belarus, Ukraine and Estonia. A little further down you have Czech Republic, Russia, Romania, Lithuania, Hungary as well as Uganda (also did not see that one coming!). Denmark is at roughly 12 liters/year/person, but long-time Danish readers of this blog already knew that.
ii. What about BMI and income? Are poor countries full of skinny people and rich countries full of fat people? Here’s the data:
In general, it seems that national BMI tends to grow with national income – this is the case whether you remove really poor regions like Africa and the Indian subcontinent or not, though the strength of the association goes down a lot if you do. This is best seen if you look at the direct association between income and BMI, not the association between log(income) and BMI (which is what is displayed above) – then it looks like this:
Remember that these are snapshots from 2008. At gapminder you can also look at the data over time, and when I did that it seemed quite likely to me that the most important weight-income effect is the one occurring at the point where most people start being able to consistently afford enough food to not go hungry at any point in time, and most countries fortunately have incomes which are well above that level. Compared to what happens after that, BMI seems to go up a lot before a country hits the $10.000/capita-mark.
But do remember to be aware of the fact that there’s a lot of variation here – for example, Jordan has a GDP/capita of ~5k and a mean BMI of 27, which is higher than that of, say, Sweden (~34k GDP/capita). Iraq has a mean BMI of 27. There are a lot of countries with mean BMIs above 25 and far from all of them are ‘Western countries’. A mean BMI above 25 is not the same thing as saying that half of the population is overweight (for that you’d need the median; the BMI-measure is for natural reasons skewed to the right), but it probably does mean that a lot of countries which are not normally thought of as ‘filthy rich countries’ will have to look forward to some perhaps quite significant issues with lifestyle diseases a few decades down the line. I know that I often mention this in these sorts of contexts, but it’s important also to note that a lot of variation is not captured by data which is gathered at the national level, because of aggregation; Much of Western China is still more or less purely agricultural and there are a lot of poor people driving down the average, but the developed parts look a lot more like the rest of the world than people are perhaps aware – you can google ‘China obesity epidemic’ or similar terms if you want to know more about this particular subject.
iii. What about broadband subscription and income? Well…
Looking at this variable is, I believe, a relatively good way to have a feel for some of that intra-country heterogeneity which is often hidden in other measures. According to these numbers (which are, perhaps, subject to gaming, but let’s pretend they’re not completely unreasonable), 6.3% of the Chinese had broadband access in 2008. This number was higher than that of Brazil (5.3%) and it’s comparable to Russia (6.5%). Mexico’s number was 7.1% and the number for Turkey was 7.8%. The Chinese 2008-number is higher than the broadband subscribing rates of France and the UK in 2003 (at which point the highest numbers were those of South Korea and Hong Kong).
iv. What about something like forest coverage? Here’s the data:
It looks very messy! And looking at the development over time doesn’t really help – in specific cases you can probably read more about the country in question and try to figure out what was going on (Uganda experienced income growth and forest coverage losses in most years during the 1990-2005 time period – the country seems to have lost something like one third of it’s forest area during the last two decades), but on the international level there’s no clear pattern here, as far as I can tell; a lot of the variation looks quite random. However I thought that the actual forest coverage numbers on their own were quite interesting.
Gapminder has a small ‘how to use’ menu/video you can watch if it’s not obvious to you how you do stuff you’d like to do, and it’s very easy to use this ressource. I encourage you to go have a look at some data yourself – there’s a lot of interesting stuff here.
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