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Improving the Economic Complexity Index

Author

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  • Saleh Albeaik
  • Mary Kaltenberg
  • Mansour Alsaleh
  • Cesar A. Hidalgo

Abstract

How much knowledge is there in an economy? In recent years, data on the mix of products that countries export has been used to construct measures of economic complexity that estimate the knowledge available in an economy and predict future economic growth. Here we introduce a new and simpler metric of economic complexity (ECI+) that measures the total exports of an economy corrected by how difficult it is to export each product. We use data from 1973 to 2013 to compare the ability of ECI+, the Economic Complexity Index (ECI), and Fitness complexity, to predict future economic growth using 5, 10, and 20-year panels in a pooled OLS, a random effects model, and a fixed effects model. We find that ECI+ outperforms ECI and Fitness in its ability to predict economic growth and in the consistency of its estimators across most econometric specifications. On average, one standard deviation increase in ECI+ is associated with an increase in annualized growth of about 4% to 5%. We then combine ECI+ with measures of physical capital, human capital, and institutions, to find a robust model of economic growth. The ability of ECI+ to predict growth, and the value of its coefficient, is robust to these controls. Also, we find that human capital, political stability, and control of corruption; are positively associated with future economic growth, and that income is negatively associated with growth, in agreement with the traditional growth literature. Finally, we use ECI+ to generate economic growth predictions for the next 20 years and compare these predictions with the ones obtained using ECI and Fitness. These findings improve the methods available to estimate the knowledge intensity of economies and predict future economic growth.

Suggested Citation

  • Saleh Albeaik & Mary Kaltenberg & Mansour Alsaleh & Cesar A. Hidalgo, 2017. "Improving the Economic Complexity Index," Papers 1707.05826, arXiv.org, revised Jul 2017.
  • Handle: RePEc:arx:papers:1707.05826
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    File URL: http://arxiv.org/pdf/1707.05826
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    References listed on IDEAS

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    Cited by:

    1. Charles D. Brummitt & Andres Gomez-Lievano & Ricardo Hausmann & Matthew H. Bonds, 2018. "Machine-learned patterns suggest that diversification drives economic development," Papers 1812.03534, arXiv.org.
    2. Saleh Albeaik & Mary Kaltenberg & Mansour Alsaleh & C'esar A. Hidalgo, 2017. "729 new measures of economic complexity (Addendum to Improving the Economic Complexity Index)," Papers 1708.04107, arXiv.org.

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