The concept of proximity formalizes the intuitive idea that the ability of a country to produce a product depends on its ability to produce other ones. For example, a country with the ability to export apples will probably have most of the conditions suitable to export pears. Unfortunately this intuitive definition of proximity is, very cumbersome to measure. It requires quantifying the overlap between the set of markets related to each product. On the paper "The Product Space Conditions the Development of Nations", published at Science magazine, the authors explain how they've measured proximity by using an outcome based method founded on the assumption that similar products are more likely to be exported in tandem.
The authors generated a network representation of the original proximity matrix to help them develop intuition about its structure as well as to visualize and study the dynamics of countries on it. The matrix representing the product space has many small values which represent weak connections between products. That is why a network representation becomes an adequate way to layout the products, giving the researchers a quick visual way to show the relevant links and to determine where countries are located and where they could be headed.
Another advantage of using a network representation is that we can simultaneously look at the structure of the space and other covariates. In this case, the authors painted the network using the product classifications performed by Edward E. Leamer, and made the size of the nodes proportional to the money moved by that particular industry or World Trade. To give a sense of the proximity of the links involved in our network representation we color coded them by using dark red and blue for strong links; and yellow and light blue for weaker ones.
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