A local general‐equilibrium emergency response modeling approach for sub‐Saharan Africa. (5th December 2021) - Filipski, Mateusz; Gupta, Anubhab; Kagin, Justin; Husain, Arif; Grinspun, Alejandro; Caccavale, Oscar Maria; Daidone, Silvio; Giuffrida, Valerio; Greb, Friederike; Hooker, Joseph; Sandström, Susanna; Fletcher‐Taylor, Julian; et al.
Abstract: 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. The most direct effects of the pandemic and lockdown tended to be concentrated in urban areas; however, markets quickly transmitted impacts to rural areas as well as among poor and non‐poor households. General equilibrium modeling is a tool of choice to capture indirect, spillover effects of exogenous shocks. This article describes an unusual micro general‐equilibrium (GE) modeling approach that we developed 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.