[Paper Review] Measuring the Knowledge Intensity of Economies with an Improved Measure of Economic Complexity

Saleh Albeaik, Mary Kaltenberg+2 more|arXiv (Cornell University)|Jul 18, 2017

Economic and Technological Innovation21 citations

TL;DR

This paper introduces ECI+, a simplified yet more accurate measure of economic complexity that adjusts total exports by the difficulty of exporting each product. ECI+ outperforms existing metrics like ECI and Fitness in predicting long-term economic growth, with a one-standard-deviation increase linked to 4–5% higher annualized growth, and remains robust when controlling for physical capital, human capital, and institutions.

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.

Motivation & Objective

• To develop a more accurate and simpler measure of economic complexity that reflects the knowledge intensity of economies.
• To improve the prediction of future economic growth by refining existing complexity metrics using export data.
• To assess the robustness of the new metric (ECI+) against established measures like ECI and Fitness across multiple econometric models.
• To integrate ECI+ with traditional growth determinants—physical capital, human capital, and institutions—to build a comprehensive growth prediction model.

Proposed method

• Propose ECI+, a new economic complexity index that corrects total exports by the difficulty of exporting each product, using a simplified algorithm based on export data.
• Apply ECI+ to annual export data from 1973 to 2013 across multiple countries to compute time-series measures of complexity.
• Compare ECI+’s predictive power against ECI and Fitness using pooled OLS, random effects, and fixed effects models across 5-, 10-, and 20-year growth panels.
• Conduct robustness checks by including controls for physical capital, human capital, and institutional quality (e.g., political stability, control of corruption).
• Use ECI+ to project economic growth for the next 20 years and compare forecasts with those from ECI and Fitness.
• Employ standard econometric techniques to assess estimator consistency and coefficient stability across models.

Experimental results

Research questions

• RQ1 Does ECI+ provide a more accurate and consistent prediction of future economic growth than existing complexity measures like ECI and Fitness?
• RQ2 How does the inclusion of ECI+ alongside traditional growth factors (e.g., human capital, institutions) affect the explanatory power of growth models?
• RQ3 What is the magnitude of the effect of a one-standard-deviation increase in ECI+ on annualized economic growth?
• RQ4 How robust is the coefficient of ECI+ across different econometric specifications and model controls?
• RQ5 How do ECI+-based growth forecasts for the next 20 years compare to those generated by ECI and Fitness?

Key findings

• ECI+ outperforms both ECI and Fitness in predicting future economic growth across all model specifications and time horizons.
• A one-standard-deviation increase in ECI+ is associated with an annualized growth increase of approximately 4% to 5%.
• The coefficient of ECI+ remains robust and statistically significant even after controlling for physical capital, human capital, and institutional quality.
• Human capital, political stability, and control of corruption are positively associated with future economic growth, while income is negatively associated with growth, consistent with the traditional growth literature.
• ECI+ generates more consistent and reliable long-term growth forecasts over the next 20 years compared to ECI and Fitness.
• The estimator for ECI+ demonstrates greater consistency across pooled OLS, random effects, and fixed effects models than the alternatives.