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      Productive Ecosystems and the arrow of development

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          Abstract

          Economic growth is associated with the diversification of economic activities, which can be observed via the evolution of product export baskets. Exporting a new product is dependent on having, and acquiring, a specific set of capabilities, making the diversification process path-dependent. Taking an agnostic view on the identity of the capabilities, here we derive a probabilistic model for the directed dynamical process of capability accumulation and product diversification of countries. Using international trade data, we identify the set of pre-existing products, the product Ecosystem, that enables a product to be exported competitively. We construct a directed network of products, the Eco Space, where the edge weight corresponds to capability overlap. We uncover a modular structure, and show that low- and middle-income countries move from product communities dominated by small Ecosystem products to advanced (large Ecosystem) product clusters over time. Finally, we show that our network model is predictive of product appearances.

          Abstract

          As countries experience economic growth, diversification of economic activities may occur. Here, the authors develop a probabilistic model to examine the diversification of economic activities and how countries may move from small ecosystem products to advanced product clusters over time.

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          Most cited references36

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          Endogenous Technological Change

          Paul Romer (1990)
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            A Contribution to the Theory of Economic Growth

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              Community structure in social and biological networks.

              A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known--a collaboration network and a food web--and find that it detects significant and informative community divisions in both cases.
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                Author and article information

                Contributors
                n.oclery@ucl.ac.uk
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                5 March 2021
                5 March 2021
                2021
                : 12
                : 1479
                Affiliations
                [1 ]GRID grid.83440.3b, ISNI 0000000121901201, Centre for Advanced Spatial Analysis, , University College London, ; London, UK
                [2 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Mathematical Institute, , University of Oxford, ; Oxford, UK
                [3 ]GRID grid.38142.3c, ISNI 000000041936754X, Growth Lab, Center for International Development, , Harvard University, ; Cambridge, MA USA
                [4 ]GRID grid.15876.3d, ISNI 0000000106887552, Koç University, ; Sarıyer/İstanbul, Turkey
                [5 ]GRID grid.38142.3c, ISNI 000000041936754X, Harvard Kennedy School, ; Cambridge, MA USA
                [6 ]GRID grid.209665.e, ISNI 0000 0001 1941 1940, Santa Fe Institute, ; Santa Fe, NM USA
                Author information
                http://orcid.org/0000-0003-1876-8002
                http://orcid.org/0000-0001-9284-5745
                Article
                21689
                10.1038/s41467-021-21689-0
                7977064
                33674606
                e4d6ff6e-0961-42d8-b741-28e633794cbc
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 7 October 2018
                : 2 February 2021
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                environmental economics,industry,developing world,economics
                Uncategorized
                environmental economics, industry, developing world, economics

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