ArticleJournal of the Royal Society, Interface2020
Machine-learned patterns suggest that diversification drives economic development.
Article in Journal of the Royal Society, Interface, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Bridging the short-term and long-term dynamics of economic structural change.Nature communications · 2025Article
- The new paradigm of economic complexity.Research policy · 2022Article
- Estimating the drivers of urban economic complexity and their connection to economic performance.Royal Society open science · 2021Article
- Reconciling contrasting views on economic complexity.Nature communications · 2020Article
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Authors and funding
4 authors.
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Abstract
We combine a sequence of machine-learning techniques, together called Principal Smooth-Dynamics Analysis (PriSDA), to identify patterns in the dynamics of complex systems. Here, we deploy this method on the task of automating the development of new theory of economic growth. Traditionally, economic growth is modelled with a few aggregate quantities derived from simplified theoretical models. PriSDA, by contrast, identifies important quantities. Applied to 55 years of data on countries' exports, PriSDA finds that what most distinguishes countries' export baskets is their diversity, with extra weight assigned to more sophisticated products. The weights are consistent with previous measures of product complexity. The second dimension of variation is proficiency in machinery relative to agriculture. PriSDA then infers the dynamics of these two quantities and of
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