IDEAS home Printed from https://ideas.repec.org/p/mar/magkse/202422.html
   My bibliography  Save this paper

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

Author

Listed:
  • Arian Daneshmanda

    (Faculty of Economics, Allameh Tabataba’i University, Tehran, Iran)

  • Ali Mazyaki

    (Faculty of Economics, Allameh Tabataba’i University, Tehran, Iran)

  • Mohammad Reza Farzanegan

    (School of Business and Economics, Philipps-Universität Marburg)

  • Mohammad Javad Gheidari

    (Faculty of Economics, Allameh Tabataba’i University, Tehran, Iran)

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.

Suggested Citation

  • Arian Daneshmanda & Ali Mazyaki & Mohammad Reza Farzanegan & Mohammad Javad Gheidari, 2024. "Optimizing Social Assistance Strategies in Response to the COVID-19 Crisis," MAGKS Papers on Economics 202422, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
  • Handle: RePEc:mar:magkse:202422
    as

    Download full text from publisher

    File URL: https://www.uni-marburg.de/en/fb02/research-groups/economics/macroeconomics/research/magks-joint-discussion-papers-in-economics/papers/2024/22-2024-farzanegan.pdf
    File Function: First version
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    COVID-19; disaster mitigation; social assistance strategies; conditional vs. unconditional support.;
    All these keywords.

    JEL classification:

    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • H12 - Public Economics - - Structure and Scope of Government - - - Crisis Management
    • H53 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Welfare Programs
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mar:magkse:202422. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Bernd Hayo (email available below). General contact details of provider: https://edirc.repec.org/data/vamarde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.