Reconciling contrasting views on economic complexity.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
03 07 2020
03 07 2020
Historique:
received:
25
10
2019
accepted:
08
06
2020
entrez:
5
7
2020
pubmed:
6
7
2020
medline:
6
7
2020
Statut:
epublish
Résumé
Summarising the complexity of a country's economy in a single number is the holy grail for scholars engaging in data-based economics. In a field where the Gross Domestic Product remains the preferred indicator for many, economic complexity measures, aiming at uncovering the productive knowledge of countries, have been stirring the pot in the past few years. The commonly used methodologies to measure economic complexity produce contrasting results, undermining their acceptance and applications. Here we show that these methodologies - apparently conflicting on fundamental aspects - can be reconciled by adopting a neat mathematical perspective based on linear-algebra tools within a bipartite-networks framework. The obtained results shed new light on the potential of economic complexity to trace and forecast countries' innovation potential and to interpret the temporal dynamics of economic growth, possibly paving the way to a micro-foundation of the field.
Identifiants
pubmed: 32620815
doi: 10.1038/s41467-020-16992-1
pii: 10.1038/s41467-020-16992-1
pmc: PMC7335174
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3352Références
Brillat-Savarin, J. A. Physiologie du goût (Charpentier, 1841).
Hausmann, R., Hwang, J. & Rodrik, D. What you export matters. J. Economic Growth 12, 1–25 (2007).
doi: 10.1007/s10887-006-9009-4
Schumpeter, J. The theory of economic development (Transaction Publishers, 1934).
Aghion, P., & Howitt, P. A model of growth through creative destruction. Tech. Rep. No. w3223 (National Bureau of Economic Research, 1990).
Romer, P. M. Endogenous technological change. J. Political Econ. 98, S71–S102 (1990).
doi: 10.1086/261725
Rodrik, D. Goodbye Washington consensus, hello Washington confusion? A review of the World Bank’s economic growth in the 1990s: learning from a decade of reform. J. Econ. Lit. 44, 973–987 (2006).
doi: 10.1257/jel.44.4.973
Rodrik, D. One economics, many recipes: Globalization, institutions, and economic growth (Princeton University Press, 2008).
Nelson, R. R. An evolutionary theory of economic change (Harvard University Press, 2009).
Easterly, W. & Levine, R. What have we learned from a decade of empirical research on growth? It’s not factor accumulation: stylized facts and growth models. World Bank Econ. Rev. 15, 177–219 (2001).
doi: 10.1093/wber/15.2.177
Hulten, C. R. in New developments in productivity analysis. 1–54 (University of Chicago Press, 2001).
Hausmann, R. et al. The Atlas of Economic Complexity: Mapping paths to prosperity (MIT Press, 2014).
Hidalgo, C. A. & Hausmann, R. The building blocks of economic complexity. Proc. Natl Acad. Sci. USA 106, 10570–10575 (2009).
doi: 10.1073/pnas.0900943106
Inoua, S. A simple measure of economic complexity. arXiv. preprint arXiv:1601.05012 (2016).
Mariani, M. S., Vidmer, A., Medo, M. & Zhang, Y.-C. Measuring economic complexity of countries and products: which metric to use? Eur. Phys. J. B 88, 293 (2015).
doi: 10.1140/epjb/e2015-60298-7
Tacchella, A., Cristelli, M., Caldarelli, G., Gabrielli, A. & Pietronero, L. A new metrics for countries’ fitness and products’ complexity. Sci. Rep. 2, 723 (2012).
Caldarelli, G. et al. A network analysis of countries’ export flows: firm grounds for the building blocks of the economy. PLoS ONE 7, 47278 (2012).
Cristelli, M., Gabrielli, A., Tacchella, A., Caldarelli, G. & Pietronero, L. Measuring the intangibles: a metrics for the economic complexity of countries and products. PLoS ONE 8, e70726 (2013).
doi: 10.1371/journal.pone.0070726
Kemp-Benedict, E. An interpretation and critique of the Method of Reflections. MPRA Paper No. 60705 (MPRA, 2014).
Morrison, G. et al. On economic complexity and the fitness of nations. Sci. Rep. 7, 15332 (2017).
doi: 10.1038/s41598-017-14603-6
Felipe, J., Kumar, U., Abdon, A. & Bacate, M. Product complexity and economic development. Struct. Change Econ. Dyn. 23, 36–68 (2012).
doi: 10.1016/j.strueco.2011.08.003
Poncet, S. & de Waldemar, F. S. Export upgrading and growth: the prerequisite of domestic embeddedness. World Dev. 51, 104–118 (2013).
doi: 10.1016/j.worlddev.2013.05.010
Alsén, A. et al. National strategy for Sweden: From wealth to well-being. Tech. Rep. (The Boston Consulting Group, 2013).
Cristelli, M., Tacchella, A., Cader, M., Roster, K. & Pietronero, L. On the predictability of growth. Policy Research Working Paper No. WPS8117 (The World Bank, 2017).
Brito, S., Magud, N. E. & Sosa, S. Real exchange rates, economic complexity, and investment. Working Paper No. 18/107 (International Monetary Fund, 2018).
Balassa, B. Trade liberalisation and “Revealed” Comparative Advantage. Manch. Sch. 33, 99–123 (1965).
doi: 10.1111/j.1467-9957.1965.tb00050.x
Newman, M. E. Network - An Introduction (Oxford University Press, 2010).
Golub, G. H. & Van Loan, C. F. Matrix Computations. Vol. 3 (JHU Press, 2012).
Sciarra, C., Chiarotti, G., Laio, F. & Ridolfi, L. A change of perspective in network centrality. Sci. Rep. 8,15269 (2018).
Pugliese, E., Zaccaria, A. & Pietronero, L. On the convergence of the Fitness-Complexity Algorithm. Eur. Phys. J. Spec. Top. 225, 1893–1911 (2016).
doi: 10.1140/epjst/e2015-50118-1
Baudena, M. et al. Revealing patterns of local species richness along environmental gradients with a novel network tool. Sci. Rep. 5, 11561 (2015).
doi: 10.1038/srep11561
Domínguez-García, V. & Munoz, M. A. Ranking species in mutualistic networks. Sci. Rep. 5, 8182 (2015).
doi: 10.1038/srep08182
Tu, C., Carr, J. & Suweis, S. A data driven network approach to rank countries production diversity and food specialization. PLoS ONE 11, e0165941 (2016).
doi: 10.1371/journal.pone.0165941
Lin, J.-H., Tessone, C. & Mariani, M. Nestedness maximization in complex networks through the fitness-complexity algorithm. Entropy 20, 768 (2018).
doi: 10.3390/e20100768
Albeaik, S., Kaltenberg, M., Mansour, A. & Hidalgo, C. Improving the economic complexity index. arXiv. preprint arXiv:1707.05826 (2017).
Gabrielli, A. et al. Why we like the ECI+ algorithm. arXiv. preprint arXiv:1708.01161 (2017).
Gaulier, G. & Zignago, S. BACI: International trade database at the product-level. Working Paper No. 2010 – 23 (CEPII, 2010).
Soete, L., Schneegans, S., Eröcal, D., Angathevar, B. & Rasiah, R. UNESCO Science Report: Towards 2030 (UNESCO, 2015).
Randall, J. E. & Ironside, R. G. Communities on the edge: an economic geography of resource-dependent communities in Canada. Can. Geographer/Le. Géographe Canadien 40, 17–35 (1996).
doi: 10.1111/j.1541-0064.1996.tb00430.x
Battersby, B. et al. International trade performance: The gravity of Australia’s remoteness. Treasury Working Paper No 2005 – 03 (The Treasury Australian Government, 2005).
Guttmann, S. & Richards, A. Trade openness: an Australian perspective. Aust. Econ. Pap. 45, 188–203 (2006).
doi: 10.1111/j.1467-8454.2006.00287.x
Mealy, P., Farmer, J. D. & Teytelboym, A. Interpreting economic complexity. Sci. Adv. 5, 1705 (2019).
Tacchella, A., Mazzilli, D. & Pietronero, L. A dynamical systems approach to gross domestic product forecasting. Nat. Phys. 14, 861 (2018).
doi: 10.1038/s41567-018-0204-y
Allen, F., Qian, J. & Qian, M. Law, finance, and economic growth in China. J. Financial Econ. 77, 57–116 (2005).
doi: 10.1016/j.jfineco.2004.06.010
Cetorelli, N. & Goldberg, L. S. Global banks and international shock transmission: evidence from the crisis. IMF Econ. Rev. 59, 41–76 (2011).
doi: 10.1057/imfer.2010.9
Bendini, R. Exceptional measures: The Shanghai stock market crash and the future of the Chinese economy. In-depth Analysis September 2015-PE 549.067 (European Union, 2015).
Pritchett, L. & Summers, L. H. Asiaphoria meets regression to the mean. Tech. Rep. No. w20573 (National Bureau of Economic Research, 2014).
Dobbs, R. et al. Urban world: Cities and the rise of the consuming class (McKinsey & Company, 2012).
UNDESA. World Population Prospects: the 2012 revision, volume II, Demographic Profiles (st/esa/ser.a/345) (United Nations, 2013).
Brin, S. & Page, L. The anatomy of a large-scale hypertextual Web search engine. Comput. Netw. 30, 101–117 (1998).
Everett, M. & Borgatti, S. the dual-projection approach for two-mode networks. Soc. Netw. 35, 204–210 (2013).
doi: 10.1016/j.socnet.2012.05.004
Pugliese, E., Chiarotti, G. L., Zaccaria, A. & Pietronero, L. Complex economies have a lateral escape from the poverty trap. PLoS ONE 12, e0168540 (2017).
doi: 10.1371/journal.pone.0168540
Liao, H. & Vidmer, A. A comparative analysis of the predictive abilities of economic complexity metrics using international trade network. Complexity 2018, 1–12 (2018).
Brummitt, C. D., Gomez-Lievano, A., Hausmann, R. & Bonds, M. H. Machine-learned patterns suggest that diversification drives economic development. arXiv. preprint arXiv:1812.03534 (2018).
Tacchella, A., Cristelli, M., Caldarelli, G., Gabrielli, A. & Pietronero, L. Economic complexity: conceptual grounding of a new metrics for global competitiveness. J. Econ. Dyn. Control 37, 1683–1691 (2013).
doi: 10.1016/j.jedc.2013.04.006
Smith, R. L., Smith, T. M., Hickman, G. C. & Hickman, S. M. Elements of Ecology (Benjamin Cummings Menlo Parie, CA, 1998).
Frankel, J. A. & Romer, D. H. Does trade cause growth? Am. Econ. Rev. 89, 379–399 (1999).
doi: 10.1257/aer.89.3.379
Albeaik, S., Kaltenberg, M., Alsaleh, M. & Hidalgo, C. A. 729 new measures of economic complexity (addendum to Improving the Economic Complexity Index). arXiv. preprint arXiv:1708.04107 (2017).
Bonacich, P. Factoring and weighting approaches to status scores and clique identification. J. Math. Sociol. 2, 113–120 (1972).
doi: 10.1080/0022250X.1972.9989806