Time: 13:00-15:00 (UK Time), Wednesday, 23 March 2022
Presenters: Prof. Pasquale Scaramozzino, SOAS University of London; Prof. Luciano Pietronero, Centro Ricerche Enrico Fermi, Rome; & Dr. Andrea Zaccaria, Istituto dei Sistemi Complessi, Consiglio Nazionale delle Ricerche, Rome
Chair: Prof. Victor Murinde, SOAS University of London
Online venue: Click here to join the seminar on Microsoft Teams (For any inquiry about how to join the online seminar, please contact Dr Meng Xie: xm1@soas.ac.uk)
Abstract
The comparative analysis of the economic performance of countries or regions is usually carried out in terms of mainstream models of economic growth. We employ recent developments from the theory of complexity to analyse the underlying factors behind the wide and persistent economic disparities across different economies on the basis of their underlying productive capabilities. The Economic Fitness and Complexity (EFC) approach enables us to obtain indicators of the relative competitiveness of an economy from the diversification of its production structure, weighted by the complexity of the products. The evolution of the fitness indicator specific to each product is then evaluated with methods of Machine Learning. Growth predictions obtained from EFC tend to significantly outperform those from more conventional statistical methods.
Presenters
Prof. Pasquale Scaramozzino
Pasquale Scaramozzino is a Professor of Economics in the School of Finance and Management at SOAS University of London. He received his Laurea from Università di Roma La Sapienza and both his MSc and PhD from the London School of Economics. He has taught at the University of Bristol, University College London and Università di Roma Tor Vergata. He has published in a number of academic journals including Economica, Economic Journal, Economic Modelling, Journal of Comparative Economics, Journal of Development Economics, Journal of Environmental Economics and Management, Journal of Population Economics, Oxford Bulletin of Economics and Statistics, Oxford Economic Papers, Oxford Review of Economic Policy, and World Economy. He has been a consultant for the World Bank and for the Asian Development Bank.
Prof. Luciano Pietronero
Luciano Pietronero is the President of the Enrico Fermi Research Centre, Rome. He has been a Full Professor of Condensed Matter Theory at the University of Groningen and of Condensed Matter Physics at the University of Roma “La Sapienza”. After graduated in Physics from University of Rome ”La Sapienza” he worked at the Xerox Webster Research Center, N.Y. (USA) and at the Brown Boveri Research Center, Baden (CH). Professor Pietronero was the founding Director of the CNR Institute of Complex Systems in Italy, which included about 200 scientists. He was a Visiting Professor at the University of S. Barbara (USA), Harvard University (USA), the International Centre for Theoretical Physics, Trieste (Italy), and Vanderbilt University (USA). Professor Pietronero is a Fellow of the American Physical Society since 1990. He has been a consultant for Boston Consulting Group, Royal Dutch Shell, the Institute of New Economic Thinking, the Institute for Public Policy Research (UK), and a member of Stiglitz’s Task Force on Industrialization. He is currently Senior Advisor to the IFC World Bank for the implementation and adoption of the methodology of Economic Fitness.
Dr. Andrea Zaccaria
Andrea Zaccaria is a researcher at the Institute for Complex Systems (ISC)-CNR and a consultant at the International Finance Corporation (IFC), World Bank Group. He received his PhD in Physics at the "Sapienza" University of Rome, where he applied concepts and methods borrowed from Statistical Physics and the Physics of Complex Systems to the study of financial markets. As a member of the research and development group of SoSE Spa, one of the main Italian fiscal agencies, he developed a machine learning algorithm to classify taxpayers and detect tax evasion. His present work at ISC and IFC is focused on the Economic Complexity approach, and particularly on the application of complex networks methodologies, algorithmic tools, and machine learning to development economics.