Integrated Demand Analysis Platform: Economic Disruptions Caused by COVID-19
Swift response models are vital tools for emergency assistance agencies. The COVID-19 pandemic revealed the lack of economic models for short-run policy relevant research to anticipate local impacts and design effective policy responses. This project developed and implemented a new micro general-equilibrium (GE) modeling approach to quickly simulate impacts of the pandemic and lockdowns on poor and non-poor rural and urban households across sub-Saharan Africa.
Monte Carlo bootstrapping was used to construct four stylized regional GE models from 34 existing local economy-wide impact evaluation (LEWIE) models. Simulations revealed that the pandemic and policy responses to curtail its spread were likely to affect rural households at least as severely as urban households. Simulated income losses are greater in poor households in both urban and rural settings. These findings are relatively consistent across models spanning sub-Saharan Africa. Because COVID-19 impacts are so far-reaching, all types of economies experience downturns.
Our research underlines the importance of modeling assumptions. We find total annualized impacts of around a 6-percent loss of GDP, smaller than estimates from single-country models that ignore price effects, such as SAM-multiplier models, but in line with The World Bank’s baseline forecast of a 5.2% contraction in global GDP in 2020. The largest negative impacts are on poor rural households.
The modeling builds on an extensive body of research on general-equilibrium impacts on heterogeneous household groups, and it derives micro-econometric estimates from a number of countries in which local economy-wide impact evaluation (LEWIE) models already existed prior to the pandemic. Estimates are aggregated to represent different groups of countries with certain common features, including food-import dependency, trade openness, strength of local supply chains and income level, optimally classified using a clustering algorithm programmed in Python, an open-source platform. We explore the use of remote continuous food security and market monitoring data to inform scenarios simulated in the model as the COVID-19 situation evolves in different countries. The model was built to be informative enough to support decision making but lean enough to require no additional microdata besides what the WFP normally collects. The latter is to ensure scalability.
Funding Agency: United Nations World Food Programme (WFP) and Food and Agriculture Organization (FAO)
Publications:
Filipski, M., Gupta, A., Kagin, J., Husain, A., Grinspun, A., Caccavale, O.M., Daidone, S., Giuffrida, V., Greb, F., Hooker, J. and Sandström, S., 2022. A local general‐equilibrium emergency response modeling approach for sub‐Saharan Africa. Agricultural Economics, 53(1), pp.72-89. https://onlinelibrary.wiley.com/doi/full/10.1111/agec.12687