Economic Complexity Index
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Through Matrix (mathematics), Economic growth and Income distribution
Higher economic complexity as compared to country's income level drives economic development.
Background
The ECI was developed by Cesar A. Hidalgo, from the MIT Media Lab and Ricardo Hausmann, from Harvard University's Kennedy School of Government. ECI data is available in The Observatory of Economic Complexity. The original formulation of the Economic Complexity Index was published in PNAS in 2009.
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Formulation
In its strict mathematical definition, the ECI is defined in terms of an eigenvalue of a matrix connecting countries to countries, which is a projection of the matrix connecting countries to the products they export. Since the ECI considers information on the diversity of countries and the ubiquity of products, it is able to produce a measure of economic complexity containing information about both the diversity of a country's exports and their sophistication. For example, Japan or Germany, with high ECIs, export many goods that are less common and that are produced by highly diversified countries, indicating that these are diverse and sophisticated economies. Countries with low ECI, like Angola or Botswana, export only a few products, which are of relatively high ubiquity and which are exported by countries that are not necessarily very diversified, indicating that these are countries that have little diversity and that the products that they export are not very sophisticated.
Utility
Hidalgo and Hausmann propose the concept of ECI not only as a descriptive measure, but also as a predictive tool for economic growth and income inequality. According to the statistics models presented in their Atlas of Economic Complexity (2011), the ECI is a more accurate predictor of GDP per capita growth than traditional measures of governance, competitiveness (World Economic Forum's Global Competitiveness Index) and human capital (as measured in terms of educational attainment). ECI also shows a strong negative correlation with income inequality, suggesting that more knowledge intense productive structures are more inclusive in terms of income distribution, and providing a statistically more powerful explanation of cross-national variations in income inequality than Kuznets Curve.
Economic development requires the accumulation of productive knowledge and its use in both more and more complex industries. According to this metric, many low-income countries, including Bangladesh, Venezuela, and Angola have failed to diversify their knowhow and face low growth prospects while those like India, Turkey, and the Philippines have added productive capabilities to enter new sectors and are expected by some drive growth over the coming years.