Economic Fitness and Complexity

The Institute of Economics will hold a meeting of its Seminar Series on Thursday, December 20, 2018: Luciano Pietronero, from the Sapienza University of Rome, will present the paper Economic Fitness and Complexity.

Abstract:

Economic Fitness (EF) is a novel iteration of Complexity Science applied to Economics which evolves this approach into a systematic and mathematically sound and testable framework. It i) forecasts long-term or structural growth better than the IMF WEO process; ii) characterizes diversification strategy better than existing measures; and iii) identifies complexity of goods and services helping governments and private sector understand constraints to sustainable growth, upgrading, and diversification. EF describes economics as evolutionary process of ecosystems made of industrial and financial technologies that are all globally interconnected. This offers new opportunities to constructively describe technological ecosystems, analyse their structures, understand their internal dynamics, as well as to introduce new economic metrics. This approach provides a new paradigm for a fundamental economic science based on data and not on ideologies or interpretations. One characteristics is to go from the many parameters of the standard economic analysis to a new methodology with zero parameters. This dimensional reduction is essential for a novel approach to Big Data and for the analysis and forecasting beyond the standard regressions [1,2]. EF is a general algorithm. It has been applied to both export and import data (as a proxy for globally-consistent and comparable disaggregated production data) to understand the dynamics of economic growth - for example that many fast-growing countries have a sustained build-up of EF or accruing mutually-reinforcing capabilities prior to the fast growth stage. When the fitness algorithm is applied to patents, it treats each patent as a bundle of technologies which provides information and indicators of innovation strategy and dynamics associated with future product development investment and likelihood of eventual global competitiveness. The Fitness algorithm when combined with machine learning has also characterized the interrelations between products, technologies and science allowing analysis of the core elements of the innovation process in a systematic and coherent way. The Fitness methodology has been extensively adopted by IFC-WB, the private sector arm of the World Bank Group which analysed more than 70 countries and developed private sector applications of EF including assessing development impact of individual project investments. In a recent collaboration with IFC-WB we presented a detailed comparison of the GDP forecasting based on the Fitness methodology with the standard IMF forecasting. According to a recent report by Bloomberg [3]: The new Fitness method, “systematically outperforms standard methods, despite requiring much less data”

.