Michele Coscia

As reported in different parts of this website, the group I am currently working in is called “Center for International Development”. The mission of this group, in the words of its head Ricardo Hausmann, is quite trivial and unambitious: to eradicate poverty from the world. Knowing how to do it is far from easy and there are different schools of thought about it. The one that Hausmann chose starts with understanding how production and economic growth work, i.e. why some industries are successful in a country and not in another.

To address this question, Hausmann (together with César A. Hidalgo, Bailey Klinger and Albert-Laslo Barabasi) developed the Product Space. The original idea has been published in this paper in 2007, far before I joined the group, but my (overestimated) expertise was heavily exploited to generate its current implementation (the Atlas of Economic Complexity, a book freely available in electronic format). For this reason I feel no shame in writing a post about it in this blog (and to share the Product Space itself in my Dataset page).

The fundamental assumption of the Product Space is the following: countries are able to have a comparative advantage in exporting a given product because they have the capabilities to export it. In abstract, it means: “I do this, because I can“. Quite reasonable. In pictures, with the awesome “A capability is a piece of LEGO” metaphor created by Hidalgo:

From this assumption, it follows that if a country can export two different products, it is because it has the capabilities to export both. Also this step is quite easy. The conclusion is immediate: a country’s development success is lead by its capabilities. The more capabilities the country has (meaning that its LEGO box is big and it contains a lot of pieces), the more capabilities it will be able to acquire, the faster it will grow.

There is a small problem with this conclusion: we can’t observe the capabilities. Of course, if we could then they would be blatantly obvious, so every country could employ its growth strategy based on them. (There is actually another problem: capabilities are tacit knowledge, as Nonaka and Takeuchi would say, so you really can’t teach them, but we will come to this problem later)

What we can observe is simply which countries export what:

But remember: if two products are exported by the same country, then there may be common capabilities needed for their productions; while if two countries export the same products, then they share at least part of the same capabilities. In mathematical terms, it means that the observed country export picture is actually the multiplication of the two halves of the second picture of the post, or:

This is nice because it means that we can collapse the country-product relationships into product-product relationships and then mapping which product is related to which other product, because it requires the same capabilities. The advice for countries is then: if you are exporting product x, then you are likely to have most of the capabilities to export all the products that are connected to x. And this is how the Product Space was born, a single picture expressing all these relationships:

(click on the picture for a higher resolution, or just browse the Atlas website, that is also dynamic).

In the picture the nodes are colored according to the community they belong to (for more information about communities, see a previous post). The communities make sense because they group products that intuitively require the same extended set of capabilities: in cyan we have the electronic products, light blue is machinery, green is garments and so on and so forth.

Is the structure of the Product Space telling us something reliable? Yes, countries are way more likely to start export products that are close, in the Product Space, to the products they already export. Also, it is important to know where the export products of a country are in the Product Space. The more present a country is in the denser cores of the Product Space, the more complex it is said to be. This measure of complexity is a better predictor of GDP growth than classical measures used in political economy like average years of schooling.

Is the structure of the Product Space telling us something interesting? Hell yeah, although it’s not nice to hear. The Product Space has communities, so if you export a product belonging to a community then you have a lot of options to expand inside the community. However, many products are outside the communities, and they are very weakly connected with the rest, often through long chains. The meaning? If you are only exporting those products, you are doomed to not grow, because there is no way that you’ll suddenly start exporting products of a community from nothing (because this would require tacit knowledge that you cannot learn and is not close to what you know). And guess what products the poorest countries are currently exporting.

To conclude, a couple of pictures to provide one proof of the reasoning above. In 1970, Peru had more average years of schooling, more land and twice the GDP per capita of South Korea. Traditional political economy would say that Peru was strong and there was no way that South Korea could catch up. Where South Korea is today is evident. What was the difference between the two countries in terms of Product Space?

(again, click for higher resolution. The black square border indicates which products the country is exporting). There is not a lot of difference in quantities (and this explains why South Korea was poorer). However, South Korea had those two or three products in a very valuable position, while Peru had only products in the branches: a long way to the core. In 2003, this was the result:

Peru is still mainly on the edges, South Korea occupies the center. And that’s all.

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When thinking about our social life, we instinctively recall the reason why we know the people we know. There is my sister, there’s the friend with whom I shared all my experiences – from stealing snacks at the primary school to the bachelor thesis we wrote together – and finally there’s that obscure guy who I don’t really know but he added me on Facebook and I don’t want to reduce my friend counter. A long time ago a very nice app called Nexus allowed Facebook users to visualize this concept. Nexus then died, it was replaced by Social Graph, which died too. Now you can use Touch Graph, but it is not nearly as good and, besides this, it’s a different story from the one I want to tell.

Nexus’ visualizations looked more or less like this:

The grey dots are my friends on Facebook and they are connected if they are friends on Facebook too. It’s clear that people you know for the same reason are densely connected to each other, because they are very likely to know each other too. The structure is pretty spectacular, I have to admit. It also seems to make a lot of sense and these different dense areas (“communities”) do not look too hard to be extracted automatically by an algorithm.

It’s on the shoulders of this gigantic positivist naïvety that countless authors decided to write such algorithm. Soon enough, a new branch of complex network analysis was born, called Community Discovery. The aim of  community discovery is to group nodes that are densely connected to each other. The groups are then called “communities” and nodes are said to be “members” of a community. Community discovery has a long, long history made by initial successes, coups de théâtre, drama and romance, but it is a complicated story and not in the scope of this post. Basically, many authors realized that they were dealing with a larger mess than previously thought. The main reason is that most, if not all, real world networks do not look like the above example. At. All. They look like this:

An ugly and meaningless hairball. Most people scratched their heads and decided to stick with their definition of community. This is the wrong way because it implies that we just give up in analyzing the second case and we consider the two examples as completely different phenomena. Guess what: they are not. The second network, too, is a sample of the Facebook friendship network and it contains the first. The sole difference between the two is the scale: the first only considers the immediate neighborhood of a node (me), the second just samples 15,000 users and the connections between them.

It’s on the basis of these considerations that I wrote a paper on community discovery, together with my co-authors at the KDDLab in Italy. We developed an algorithm that exploits the order at the local level around a node to find a sense of the connective mess at the global scale. It is called DEMON: Democratic Estimate of the Modular Organization of a Network.

The details of the implementation are included in the paper accepted at the SIGKDD 2012 conference on Data Mining. It was recently presented there by my co-author Giulio Rossetti who also happens to have written its implementation, freely available (and Open Source!). In this post, I’ll just give you a quick idea about how DEMON works.

Let’s consider a simple, messy, network:

It looks more like the messy hairball, doesn’t it? Let’s now select a node:

And then all the other nodes that are directly connected to it:

Now, let us ignore all the other nodes of the network, included the first selected. We just create a network only using the green nodes and the connections between them. What does this network look like? Like this:

Surprise! We’ve fallen back into the first, neatly divided, example. Now what we need to do is to just apply a very simple, old-school, community discovery algorithm to this sample and we have an idea about the communities surrounding the yellow node. We apply this operation to all the nodes of the network and then we merge together the communities that share an extensive portion of their members. I won’t bother you with the details and the proofs about how awesome this method is and how it outperforms the current state-of-the-art community discovery algorithms because everything is in the paper and I don’t like to brag (twice).

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