Economic Complexity, Relatedness and Industrial Policy
economic complexity. relatedness. industrial policy. Productive diversification. product space.
Industrial policy has experienced a resurgence, evolving from a once-marginalized concept to a critical component of economic debate. This dissertation applies the Economic Complexity and Relatedness framework to address informational challenges in industrial policy, providing tools to guide more effective, targeted strategies. The first paper, The Paradox of Relatedness, examines why economies often favor established products over new opportunities. We introduce a measure to identify sectors with disproportionately high returns compared to related products, which can discourage diversification and potentially lead to development traps. The second paper, Less is More, proposes a refinement of relatedness metrics by applying network filtering to the Product Space. Our findings show that filtered relatedness improves forecasts of productive upgrading, offering a more effective tool for industrial policy design. The final paper, Back to Leontief, uses input-output data to develop complexity measures that explain per capita income differences and reveal potential productive pathways. This approach complements the trade-based complexity framework and integrates input-output and trade data for a more comprehensive foundation for industrial policy. Together, these studies contribute to the Economic Complexity and Relatedness literature, providing valuable insights for designing effective industrial policies across diverse economic contexts.