Improving the Economic Complexity Index

1 Improving the Economic Complexity Index Saleh Albeaik1, Mary Kaltenberg2,3, Mansour Alsaleh1, Cesar A. Hidalgo2 1 Center for Complex Engineering Systems, King Abdulaziz City for Science and Technology 2 Collective Learning Group, The MIT Media Lab, Massachusetts Institute of Technology 3 UNU-MERIT, Maastricht University 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 initial 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. KEYWORDS: Economic Complexity, Knowledge Intensity, Economic Growth, International Development

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Introduction For decades, the theory and empirics of economic growth has attempted to understand why some economies grow faster than others. The early literature focused on the accumulation of simple productive factors such as labor and physical capital 1. But soon enough, the literature turned into more nuanced factors, such as human capital 2,3, institutions 4,5, social capital 6–8, and technological change 9,10. Yet, even when taken together, these factors have been unable to fully explain economic growth. As a result, economic growth still poses questions, embodied in the idea of Total Factor Productivity (TFP), a measure of the output of an economy that is not explained by the availability of factors. That is, a measure of how much output an economy can produce per unit of input.
In the last decade, the search to understand TFP gave rise to a new literature on economic complexity 11–21, which does not aim to identify individual factors, but to measure combinations of them indirectly. The assumption is that, if growth depends on having combinations of factors, and on the ability to use them productively, then, it should be possible to measure the combinations of factors that predict growth—whatever these may be—by looking at the expression of these factors in the diversity and sophistication of the products that countries produce and export.
Consider exporting fresh fish. Fresh fish is a product that requires specific physical capital inputs, such as a reliable power grid and cold storage, but also, that requires specific institutional factors, such as navigating international phytosanitary standards. Producing and exporting fresh fish, however, also requires specific knowledge on aquiculture and on the global fish market. This means that an observation as simple as seeing a country export fresh fish can tell us about the presence of specific technological, human, and institutional factors, in an economy. Measures of economic complexity have been validated by studying their ability to predict future economic growth. Economic complexity is highly predictive of future economic growth once we control for a country’s initial level of income 11,22, and this observation is robust to controlling for a large number of factors, from human capital factors, to measures of

3 competitiveness and institutions 22. That is, countries with an income that is below what we expect based on its productive structure grow faster than those with an income that is too high. Moreover, recent work has also shown that countries with relatively high levels of economic complexity tend to have lower levels of income inequality, even after controlling for measures of education, income, and institutions 14.
The ability of

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