Systematic risk profiling: Assessing compounding economic risks in developing countries
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Elsevier
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This paper presents a systematic risk profiling (SRP) framework to identify the most critical economic risks facing developing countries. Integrating computable general equilibrium (CGE) models with historical shock data and machine-learning tools, we examine how compound shocks affect development outcomes. We apply this method to Kenya, Rwanda, and Malawi, simulating thousands of plausible combinations of world price, capital flow, and productivity exogenous shocks and their impacts on countries' GDP, household consumption, poverty, and undernourishment. The results reveal distinct risk profiles driven by structural differences: Kenya’s primary vulnerability is the volatility in global beverage crop prices, whereas Rwanda and Malawi face the highest risks from domestic root crop and cereal yields, respectively. These findings underscore that vulnerability is not just a function of shock magnitude, but of the specific structure of each economy. Specifically, the high economic volatility in Malawi and Rwanda is driven by the larger role of subsistence agriculture and more volatile domestic yields, whereas Kenya’s agricultural sector is more export-oriented. Unlike standard ad hoc scenario analysis, SRP quantifies both the likelihood of compound events and the relative importance of their drivers. This transparent, scalable framework provides policymakers a new tool to move beyond reactive measures and design targeted, country-specific resilience strategies for an increasingly volatile world.
Palabras clave
climate, computable general equilibrium models, machine learning, risk, uncertainty