2018/12/09 by Charles D. Brummitt, Brummitt, Charles D., Andrés Gómez-Liévano +5
Economics, Econometrics and Finance · #Economic and Technological Innovation #FOS: Economics and business #FOS: Physical sciences #General Economics (econ.GN) #Physics and Society (physics.soc-ph)
paper · pdf · doi:10.48550/arxiv.1812.03534
openalex publication_date 2018/12/09 · openalex created_date 2022/08/01 · openalex updated_date 2026/07/28
We develop a machine-learning-based method, Principal Smooth-Dynamics\nAnalysis (PriSDA), to identify patterns in economic development and to automate\nthe development of new theory of economic dynamics. Traditionally, economic\ngrowth is modeled with a few aggregate quantities derived from simplified\ntheoretical models. Here, PriSDA identifies important quantities. Applied to 55\nyears of data on countries' exports, PriSDA finds that what most distinguishes\ncountries' export baskets is their diversity, with extra weight assigned to\nmore sophisticated products. The weights are consistent with previous measures\nof product complexity in the literature. The second dimension of variation is a\nproficiency in machinery relative to agriculture. PriSDA then couples these\nquantities with per-capita income and infers the dynamics of the system over\ntime. According to PriSDA, the pattern of economic development of countries is\ndominated by a tendency toward increased diversification. Moreover, economies\nappear to become richer after they diversify (i.e., diversity precedes growth).\nThe model predicts that middle-income countries with diverse export baskets\nwill grow the fastest in the coming decades, and that countries will converge\nonto intermediate levels of income and specialization. PriSDA is generalizable\nand may illuminate dynamics of elusive quantities such as diversity and\ncomplexity in other natural and social systems.\n