Development encompasses many factors - economic, environmental, cultural, educational, and institutional - 
which are difficult to measure, compare, and assemble in a unified picture. The Economic Fitness approach assumes that such factors are summed up in the possible export competitiveness of countries, and algorithmically extracts this information directly from data.
Universal Economic Fitness Metric
The trade data are necessary to define a coherent network for all countries and all traded sectors (products and services). This may have some limitations for countries in which the exported products and services are not a good proxy of their industrial competitiveness. While the fitness analysis usually refers to manufacturing only, in the Universal Fitness also services are included. However, being the corresponding database less granular (i.e., more aggregated), also the products database has been aggregated and this can mildly impact on the algorithm outputs. In the final, universal dataset the relative weight of products and services reflects the respective importance in the international trade flow. A basic concept of the algorithm is the importance of diversification. This is correct at the level of countries but it becomes gradually problematic if one moves to smaller scales like regions, cities up to individual firms where specialization becomes dominant. In these cases suitable modifications should be considered. Finally, some countries have well known reporting issues in both products and services. For instance, some countries report only at some aggregation levels. Two notable examples are Ireland and China. In order to obtain a complete and more diversified database we adopt an interpolation procedure for such misreporting countries using the average declaration of similar countries and considering different aggregation levels. We can expect the final results to mildly change if export records will change in the future.
The Universal Economic Fitness (UEF) is both a measure of a country’s diversification and ability to export complex goods and services on a globally competitive basis.  Countries with the highest levels of UEF have capabilities to produce a diverse portfolio of products and services, ability to upgrade into ever-increasing complex sectors, tend to have more predictable long-term growth, and to attain good competitive position relative to other countries. Countries with low UEF levels tend to suffer from poverty, low capabilities, less predictable growth, low value-addition, and trouble upgrading and diversifying faster than other countries.  The starting data is the UN-COMTRADE list of products and the IMF-BOP list of services exported by each country. This data defines a bipartite network of countries and sectors, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Universal Economic Fitness of all countries and the Complexity of all sectors. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.
The literature of Economic Fitness uses techniques which, differently from traditional index construction approaches, do not try to average out the complexity of the system, but embraces it by explicitly building on the heterogeneity of individual actors, activities and interactions to extract relevant parameters to characterize the system.  In this way, information about production capabilities may be extracted from trade in goods and, in the case of Universal Fitness, services.  The interaction among products and services traded, and the relatively unique combinations are a precursor to future competitiveness and long-term growth.   A basic characteristic of Economic Fitness is being parameter free. The standard methods of analysis consider many elements and sum them up in some suitable way. This sum of incommensurate elements leads to a major problem of controlling noise while increasing signal. The Fitness approach starts by considering a single dataset to control noise problems.  Other data can then be added later in a controlled hierarchical framework (e.g, science, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested, and consists in two coupled equations to be iterated up to convergence. The iterations are stopped when a rank-based criterion is met, that is, when we estimate that the next change in ranking will be in a very large number of iterations. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. This novel approach to the analysis and long-term forecasting has been shown to outperform the standard methods even if it requires much less data. While the Economic Fitness is computed considering the RCA values, the Universal Economic Fitness is computed using the market shares and so while the former is intensive, the latter is extensive, that is more correlated with countries size. As a consequence, while the natural counterpart of the Economic Fitness is GDP per capita, the Universal Fitness is more comparable with GDP.