Kenya: Systematic analysis of domestic production and world market shocks
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 hi...
| Autores principales: | , , , , |
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| Formato: | Brief |
| Lenguaje: | Inglés |
| Publicado: |
International Food Policy Research Institute
2024
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/168180 |
| _version_ | 1855526269306798080 |
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| author | Mukashov, Askar Mbuthia, Juneweenex Omune, Lensa Jones, Eleanor Thurlow, James |
| author_browse | Jones, Eleanor Mbuthia, Juneweenex Mukashov, Askar Omune, Lensa Thurlow, James |
| author_facet | Mukashov, Askar Mbuthia, Juneweenex Omune, Lensa Jones, Eleanor Thurlow, James |
| author_sort | Mukashov, Askar |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Brief |
| id | CGSpace168180 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | International Food Policy Research Institute |
| publisherStr | International Food Policy Research Institute |
| record_format | dspace |
| spelling | CGSpace1681802025-11-06T04:33:45Z Kenya: Systematic analysis of domestic production and world market shocks Mukashov, Askar Mbuthia, Juneweenex Omune, Lensa Jones, Eleanor Thurlow, James climate shock market prices computable general equilibrium models agriculture crop yield poverty nutrition machine learning risk assessment 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. 2024-12-20 2024-12-20T19:08:14Z 2024-12-20T19:08:14Z Brief https://hdl.handle.net/10568/168180 en https://hdl.handle.net/10568/158180 Open Access application/pdf International Food Policy Research Institute Mukashov, Askar; Mbuthia, Juneweenex; Omune, Lensa; Jones, Eleanor; and Thurlow, James. 2024. Kenya: Systematic analysis of domestic production and world market shocks. Economywide Risk Assessment Country Brief 2. Washington, DC: International Food Policy Research Institute. https://hdl.handle.net/10568/168180 |
| spellingShingle | climate shock market prices computable general equilibrium models agriculture crop yield poverty nutrition machine learning risk assessment Mukashov, Askar Mbuthia, Juneweenex Omune, Lensa Jones, Eleanor Thurlow, James Kenya: Systematic analysis of domestic production and world market shocks |
| title | Kenya: Systematic analysis of domestic production and world market shocks |
| title_full | Kenya: Systematic analysis of domestic production and world market shocks |
| title_fullStr | Kenya: Systematic analysis of domestic production and world market shocks |
| title_full_unstemmed | Kenya: Systematic analysis of domestic production and world market shocks |
| title_short | Kenya: Systematic analysis of domestic production and world market shocks |
| title_sort | kenya systematic analysis of domestic production and world market shocks |
| topic | climate shock market prices computable general equilibrium models agriculture crop yield poverty nutrition machine learning risk assessment |
| url | https://hdl.handle.net/10568/168180 |
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