Systematic Risk Profiling A Novel Approach With Applications To Kenya Rwanda And Malawi
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Systematic risk profiling: A novel approach with applications to Kenya, Rwanda, and Malawi
This paper uses machine learning, simulation, and data mining methods to develop Systematic Risk Profiles of three developing economies: Kenya, Rwanda, and Malawi. We focus on three exogenous shocks with implications for economic performance: world market prices, capital flows, and climate-driven sectoral productivity. In these and other developing countries, recent decades have been characterized by increased risks associated with all these factors, and there is a demand for instruments that can help to disentangle them. For each country, we utilize historical data to develop multi-variate distributions of shocks. We then sample from these distributions to obtain a series of shock vectors, which we label economic uncertainty scenarios. These scenarios are then entered into economywide computable general equilibrium (CGE) simulation models for the three countries, which allow us to quantify the impact of increased uncertainty on major economic indicators. Finally, we utilize importance metrics from the random forest machine learning algorithm and relative importance metrics from multiple linear regression models to quantify the importance of country-specific risk factors for country performance. We find that Malawi and Rwanda are more vulnerable to sectoral productivity shocks, and Kenya is more exposed to external risks. These findings suggest that a country’s level of development and integration into the global economy are key driving forces defining their risk profiles. The methodology of Systematic Risk Profiling can be applied to many other countries, delineating country-specific risks and vulnerabilities.
Rwanda: Systematic analysis of domestic production and world market shocks
Author: Mukashov, Askar
language: en
Publisher: Intl Food Policy Res Inst
Release Date: 2025-04-29
This study explores Rwanda’s vulnerability to economic shocks and identifies those contributing most to economic uncertainty. The Rwandan Computable General Equilibrium (CGE) model was employed to simulate a range of potential economic outcomes under various sampled shock scenarios developed using historical data to capture domestic agricultural yield volatilities and world market prices uncertainty for traded goods. Data mining and machine learning methods were applied to quantify the contribution of each shock to the uncertainty of economic outcomes (gross domestic product [GDP], private consumption, poverty, and undernourishment). Key findings suggest that domestic root and cereal yield volatility risks are the most important for GDP, poverty, and undernourishment outcomes, while external factors like world energy prices pose the most significant risks to high-income households’ consumption. Understanding how possible shocks would impact various segments of the Rwandan economy and population is a critical first step in facilitating discussions on relevant risk mitigation strategies, such as increasing average crop yields, adopting technologies and practices that narrow yield uncertainties, or diversifying production away from risky crops and sectors.
Kenya: Systematic analysis of domestic production and world market shocks
Author: Mukashov, Askar
language: en
Publisher: Intl Food Policy Res Inst
Release Date: 2025-04-29
This study explores Kenya’s vulnerability to economic shocks and identifies those contributing most to economic uncertainty. The Kenyan Computable General Equilibrium (CGE) model was employed to simulate a range of po-tential economic outcomes under various sampled shock scenarios developed using historical data to capture do-mestic agricultural yield volatilities and world market prices uncertainty for traded goods. Data mining and machine learning methods were applied to quantify the contribution of each shock to the uncertainty of economic outcomes (gross domestic product [GDP], private consumption, poverty, and undernourishment). Key findings suggest that domestic yield volatility is the key risk factor for GDP, urban consumption and poverty, while external risks, partic-ularly world beverage crop prices, are more significant for rural consumption and poverty. As the majority of those below the poverty line are rural farmers, world beverage price volatility is the top risk for national poverty levels. Finally, for undernourishment outcomes, domestic cereal yield volatility is the dominant risk factor for all household types. Understanding how possible shocks would impact various segments of the Kenyan economy and population is a critical first step in facilitating discussions on relevant risk mitigation strategies, such as increasing average crop yields, adopting technologies and practices that narrow yield uncertainties, or diversifying production away from risky crops and sectors.