The Product Space Conditions the Development of Nations

Abstract

Economies grow by upgrading the type of products they produce and export. The technology, capital, institutions and skills needed to make such new products are more easily adapted from some products than others. We study the network of relatedness between products, or 'product space', finding that most upscale products are located in a densely connected core while lower income products occupy a less connected periphery. We show that countries tend to move to goods close to those they are currently specialized in, allowing nations located in more connected parts of the product space to upgrade their exports basket more quickly. Most countries can reach the core only if they "jump" over empirically infrequent distances in the product space. This may help explain why poor countries have trouble developing more competitive exports, failing to converge to the income levels of rich countries.

Identification of influential spreaders in complex networks

“…S13). Examples of real networks that are similar to a random structure are the network of product space of economic goods 22 and the Internet at the AS level (analysed in Supplementary Section SV).…”

Prediction in complex systems: The case of the international trade network

“…In Ref. [6] the proximity φ αβ of products α and β was introduced as the symmetrized probability that a country exports product α, given that it exports product β. (2) where C i is the set of products exported by country i, P(α ∈ C i |β ∈ C i ) is the probability of exporting product α given that β is being exported, and φ αβ is the proximity of α and β.…”

“…The countries' future exports are particularly complex to predict, as their evolution depends on many external factors, such as geographical position, diplomatic relations between countries, available natural resources, and technologies. Nevertheless, previous studies [6,13] showed that it is possible to measure the competitiveness of a country, estimate its future growth and even predict its future exports using solely the international trade data. The last aspect, personalized prediction of future exports for each country, further suffers from the lack of a traditional approach with conventional metrics and renowned prediction algorithms.…”

“…To improve the recommendation accuracy, we adopt a temporal approach to devise a new definition of mutual distance between products, and couple it with the diffusion method in order to enhance the prediction of the links. This approach is closely related to the proximity of products [6], defined as a distance between each couple of products depending on the number of co-occurrences in countries' export baskets. Based on this distance, it was shown that the closer a product is to one of the products the country is currently exporting, the more likely it is to be added to the country's export basket in the future.…”

“…Recently, not only the countries, but also their exports have been analyzed under the complex network framework. The Product Space attempts to explain how the nations develop by projecting their exports on a 2D map, and observing how they diffuse in the Product Space [6]. The Economic Complexity aims to rank products by the technological requirements needed for a country to be able to manufacture a product, and to rank the countries by their competitiveness [7,8].…”