Economic Complexity is a new line of research which portrays economic growth as an evolutive process of ecosystems of technologies and industrial capabilities. Complex systems analysis, simulation, systems science methods, and big data capabilities offer new opportunities to empirically map technology and capability ecosystems of countries and industrial sectors, analyse their structure, understand their dynamics and measure economic complexity. This approach provides a new perspective for data-driven fundamental economics in a strongly connected, globalised world.
In particular here we discuss how it is possible to assess the competitiveness of country and complexity of products starting from the archival data on export flows that is the COMTRADE dataset which provides the matrix of countries and their exported products. According to the standard economic theory the specialization of countries towards certain specific products should be optimal. The observed data show that this is not the case and that diversification is actually more important. Specialization may be the leading effect in a static situation but the strongly dynamic and globalized world market suggests instead that flexibility and adaptability are essential elements of competitiveness as in bio-systems.
The crucial challenge is therefore how these qualitative observations can be turned into quantitative variables. We have introduced a new metrics for the Fitness of countries and the Complexity of products which corresponds to the fixed point of the iteration of two nonlinear coupled equations. The nonlinearity is a key feature because it translates in mathematical terms the fact that the upper bound on the Complexity of a product must be mainly given by the less developed country able to produce it. The information provided by the new metrics can be used in several ways. As an example, the direct comparison of the Fitness with the country GDP per capita (Fitness-Income Plane) gives an assessment of the non-expressed potential of growth of a country. This can be used as a predictor of GDP evolution or stock index and sectors perfomances.
The global dynamic in the Fitness-Income Plane reveals, however, a large degree of heterogeneity which implies that countries can evolve with different level of predictability according to the specific zone of the Fitness-Income plane they belong to. This heterogeneous dynamics is often disregarded in usual economic analysis. When dealing with heterogeneous systems, in fact, the usual tools of linear regressions become inappropriate. Making reliable predictions of growth in the context of economic complexity will then require a paradigm shift in order to catch the information contained in the complex dynamic patterns observed.
These methods and concepts can give concrete contributions, as other possible applications, to risk analysis, investment opportunities analysis, policy-modelling of country growth and industrial planning.