Institutions in economic complexity: enhancing growth predictions and theoretical understanding

This study enhances economic growth forecasting by incorporating for the first time insights from Institutional Economics into the Economic Complexity (EC) framework. We augment the traditional Velocity Bootstrapped Selective Predictability Scheme GDP prediction models by adding Polity - a metric capturing institutional information - alongside existing metrics like Fitness (export sophistication) and Technological Fitness (innovation capacity). We systematically compare prediction accuracy across different model configurations to gather insight about each of the metrics. Our findings are robust across multiple complementary analyses. We find each metric excels in predicting growth for different country groups: Polity for early-stage developing countries, Technological Fitness for advanced economies, and Fitness for middle-stage developing countries. We additionally identify a clearly-defined set of countries for which using more than two metrics improves predictions, challenging EC's conventional two-dimensional approach. We explain why the cases of China and India constitute outliers, analyse them separately. These findings imply that the underlying system driving economic development is more complex than previously thought, while establishing a methodological framework for expanding Economic Complexity theory.