Extracting the structures of economies

In the early 1960s two American geography professors, John Nystuen and Michael Dacy, were working on a way to make sense of a huge database of telephone records in Washington state. Clearly the majority of calls were being made either to or from Seattle, the state’s largest city, but they suspected there was more underlying structure to the pattern of calls. They pioneered a simple, but powerful, way of treating the calls going in and out of each urban area in the state as a network, then using this network representation to extract patterns from the data.

An extract from the original 1961 paper shows how simple the idea is, and also how old the typewritten paper looks now!

The data on how many calls were made from one city to another is arranged in a grid. This is exactly the same idea as those tables of distances you get in road atlases. (Look! These things still exist!) You look up your “from city” in the column, and go across to the “to city”. The number you arrive at is the number of calls made from the “from city” to the “to city”. Nystuen and Dacy simply looked at the largest call flows out of each city. Where a city’s largest flow was to a city smaller than itself, that city was deemed to be a node: a kind of ultimate ‘destination’ of calls.

They used this data to produce a simple network diagram of all the calls in the area, boiling a whole table of numbers down to a few simple relationships drawn with arrows between cities:

They went on to extend this idea to include indirect as well as direct flows. For example, if people in Bray, tend to call people in Maidenhead who tend to call people in London, (see this map for an illustration of this made-up example) then London should get some of the ‘credit’ for the Bray to Maidenhead calls too.

I’ve taken this simple idea and applied it to the enormous network of goods and services trade that we’re building here at CASA in London. The results are pretty interesting and make some kind of sense of an otherwise tangled mess of flows within and between countries.

Here’s the UK economy, where the size of the circle is given by the number of ‘in’ connections:

It’s fun to see how metals flows to machinery, which flows to vehicles, vehicle trade and finally to the hospitality industry. It’s exactly this kind of chain of relationships that Nystuen and Dacy were hoping to reveal in their original study. Also the wood/minerals to construction to financial services relationship is interesting. Overall, the UK economy can be seen to be hugely focused around hospitality (bars, hotels, tourism, cafes etc.) and financial services, with all other sectors being subservient to one or other of these two.

Here’s the same picture for the US. You can see that there are many more separate networks, suggesting that the US is less reliant on a small number of sectors. (Don’t be fooled by the small circles for fuel and chemicals: it doesn’t mean these sectors are small, just that few other sectors have these as their nodes.)

China:

Interesting to note here that leather is differently in each of the three examples we’ve seen so far. It’s used in the hospitality industry (chairs?) in the UK, in vehicles in the US and for textiles in China.

Finally, here’s Japan:

What’s also interesting is to look at the connections between sectors worldwide. Here, each country is in a different colour, and it’s clear that most countries exist in their own clusters.

In the whole galaxy of trade that flows between sectors in a country and countries in the world, there are only two clusters of inter-related countries. They are Korea and Indonesia, and Canada, Mexico and the US. By this measure at least, these latter three countries seem to act as a single country in a way that none of the countries in the EU do, for example:

There’s clearly tons to explore here, and this is just using nothing but a simple network analysis from 1961! There will be far more interesting and modern analyses on this blog in the near future, which will be more subtle in helping us pick out clusters between countries and within countries. There may even be something to say on the clusters of trade routes which are most important to global production. All this and more to follow…