Optimizing Social Assistance Strategies in Response to the COVID-19 Crisis

Arian Daneshmanda (), Ali Mazyaki, Mohammad Reza Farzanegan () and Mohammad Javad Gheidari
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Arian Daneshmanda: Faculty of Economics, Allameh Tabataba’i University, Tehran, Iran
Ali Mazyaki: Faculty of Economics, Allameh Tabataba’i University, Tehran, Iran
Mohammad Javad Gheidari: Faculty of Economics, Allameh Tabataba’i University, Tehran, Iran
Authors registered in the RePEc Author Service: Arian Daneshmand

MAGKS Papers on Economics from Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung)

Abstract: The COVID-19 pandemic highlighted significant challenges in designing social assistance strategies for crisis management. This study investigates optimal approaches using theoretical modeling and multinomial logit analysis of data from 47 countries during the pre-vaccination phase of 2020. The findings underscore the importance of combining conditional (targeted) and unconditional (universal) social assistance measures, with unconditional assistance prioritized in severe crises due to its rapid implementation and broad reach. By addressing the complexities of resource allocation and policy implementation under crisis conditions, this study provides actionable insights for public policy design, emphasizing the need for robust budgetary systems to sustain multifaceted strategies, mitigate immediate impacts, and build resilience against future disruptions.

Keywords: COVID-19; disaster mitigation; social assistance strategies; conditional vs. unconditional support. (search for similar items in EconPapers)
JEL-codes: C54 H12 H53 I31 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:mar:magkse:202422

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