be generated, linking each product to all other products. The result of this complex web of production interactions can be represented in a network based on proximity data.

In this context, Hausmann and Klinger (2006) propose a measure of proximity that is based on observed patterns of diversification across countries. More specifically, they use the concept of conditional probabilities of co‐exporting pairs of products as the building block in the construction of a square matrix of proximities for all products according to a comprehensive classification, in their case all 4‐digit products in the Standard International Trade Classification (SITC Rev 2). Global trade data is used in the definition of proximities due to the lack of comparable global production statistics for goods (or services).<sup>3</sup> Hausmann and Klinger (2006) also propose concepts from network theory for the analysis of this matrix of proximities. Among other things, using a minimum‐spanning tree algorithm with proximities across all 784 products in the global trade database, they construct a product‐space map that clusters products into categories with similar production inputs. Moreover, they show that products exported by stagnant countries tend to be isolated, located in the sparsest and less connected parts of the PS map, while products exported by countries with higher long‐run GDP growth rates tend to be in the densest part. In the PS framework, this provides initial support to the idea that producers in more dynamic economies have a larger set of capabilities, and that they can use their capabilities and knowledge more freely to recombine available production inputs.

The location of a country’s export mix in PS matters for growth. For example, a country may be “stuck” in low value added industries or in products with declining world export markets, or both. Ideally, a country wants to be moving into higher value industries (moving up the value chain) and towards expanding export markets; however, this is difficult because it crucially depends on many factors, including unknown unknowns that influence the future developments of world exports and products. Hidalgo and Hausmann (2009) provide evidence that a country’s position in PS is strongly correlated with its level of GDP per capita. Moreover, they show that there is a large heterogeneity in the distribution of capabilities across countries and that this distribution does not have linear consequences. For example, countries with very few capabilities will experience small diversification gains if just a small number of capabilities are added whereas a country that starts out with many capabilities will experience much larger gains if the same number of capabilities is added. The empirical evidence also suggests that the structure of the PS has important implications for technology spillovers across regions. To exemplify, the probability that a product is added to a country’s export basket is, on average, 65 percent higher if a neighboring country is a successful exporter of that same product (Bahar, Hausmann, and Hidalgo 2012). Moreover, this technology diffusion is stronger at shorter geographical distances, weaker for more knowledge‐intensive products, and accelerating over time.

> 3 In recent years, technological upgrading is making exports of services more important as these become increasingly tradable. PS metrics based exclusively on merchandise trade data cannot capture this effect. In order to mitigate this shortcoming, our analysis includes a less detailed discussion of service exports, based on IMF Balance of Payment statistics.

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