IDEAS home Printed from https://ideas.repec.org/a/eee/ehbiol/v52y2024ics1570677x24000054.html
   My bibliography  Save this article

Assessing the effectiveness of international government responses to the COVID-19 pandemic

Author

Listed:
  • López-Mendoza, Héctor
  • González-Álvarez, María A.
  • Montañés, Antonio

Abstract

This paper examines the effectiveness of non-pharmaceutical measures adopted by governments to control the evolution of the COVID-19 pandemic. Using a Panel VAR model for the OECD countries, we test for Granger causality between the 7-day cumulative incidence, mortality rate, and government response indexes. Granger-type statistics reveal evidence that the evolution of the COVID-19 pandemic influenced the measures taken by governments. However, limited or nonexistent evidence supports the reverse situation. This suggests that government measures were not highly effective in controlling the pandemic. While not implying total ineffectiveness, our results indicate a considerable lack of efficacy, emphasizing a lesson for governments to learn from and correct in preparation for similar events in the future.

Suggested Citation

  • López-Mendoza, Héctor & González-Álvarez, María A. & Montañés, Antonio, 2024. "Assessing the effectiveness of international government responses to the COVID-19 pandemic," Economics & Human Biology, Elsevier, vol. 52(C).
  • Handle: RePEc:eee:ehbiol:v:52:y:2024:i:c:s1570677x24000054
    DOI: 10.1016/j.ehb.2024.101353
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1570677X24000054
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ehb.2024.101353?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    2. Pesaran, M. Hashem & Vanessa Smith, L. & Yamagata, Takashi, 2013. "Panel unit root tests in the presence of a multifactor error structure," Journal of Econometrics, Elsevier, vol. 175(2), pages 94-115.
    3. Thomas Hale & Noam Angrist & Rafael Goldszmidt & Beatriz Kira & Anna Petherick & Toby Phillips & Samuel Webster & Emily Cameron-Blake & Laura Hallas & Saptarshi Majumdar & Helen Tatlow, 2021. "A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker)," Nature Human Behaviour, Nature, vol. 5(4), pages 529-538, April.
    4. Herby, Jonas & Jonung, Lars & Hanke, Steve, 2022. "A Literature Review and Meta-Analysis of the Effects of Lockdowns on COVID-19 Mortality – II," Studies in Applied Economics 210, The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise.
    5. Dergiades, Theologos & Milas, Costas & Panagiotidis, Theodore, 2022. "Unemployment claims during COVID-19 and economic support measures in the U.S," Economic Modelling, Elsevier, vol. 113(C).
    6. Abel Brodeur & David Gray & Anik Islam & Suraiya Bhuiyan, 2021. "A literature review of the economics of COVID‐19," Journal of Economic Surveys, Wiley Blackwell, vol. 35(4), pages 1007-1044, September.
    7. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    8. David Meenagh & Patrick Minford, 2021. "A structural model of coronavirus behaviour for testing on data behaviour," Applied Economics, Taylor & Francis Journals, vol. 53(30), pages 3515-3534, June.
    9. Michael R. M. Abrigo & Inessa Love, 2016. "Estimation of panel vector autoregression in Stata," Stata Journal, StataCorp LP, vol. 16(3), pages 778-804, September.
    10. Alfano, Vincenzo & Ercolano, Salvatore & Cicatiello, Lorenzo, 2021. "School openings and the COVID-19 outbreak in Italy. A provincial-level analysis using the synthetic control method," Health Policy, Elsevier, vol. 125(9), pages 1200-1207.
    11. Andrews, Donald W. K. & Lu, Biao, 2001. "Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models," Journal of Econometrics, Elsevier, vol. 101(1), pages 123-164, March.
    12. Michael R.M. Abrigo & Inessa Love, 2016. "Estimation of Panel Vector Autoregression in Stata: a Package of Programs," Working Papers 201602, University of Hawaii at Manoa, Department of Economics.
    13. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
    14. M. Hashem Pesaran, 2021. "General diagnostic tests for cross-sectional dependence in panels," Empirical Economics, Springer, vol. 60(1), pages 13-50, January.
    15. Christian Bjørnskov, 2021. "Did Lockdown Work? An Economist’s Cross-Country Comparison," CESifo Economic Studies, CESifo Group, vol. 67(3), pages 318-331.
    16. M. Hashem Pesaran, 2007. "A simple panel unit root test in the presence of cross-section dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(2), pages 265-312.
    17. M. Hashem Pesaran, 2015. "Testing Weak Cross-Sectional Dependence in Large Panels," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 1089-1117, December.
    18. David E. Bloom & Michael Kuhn & Klaus Prettner, 2022. "Modern Infectious Diseases: Macroeconomic Impacts and Policy Responses," Journal of Economic Literature, American Economic Association, vol. 60(1), pages 85-131, March.
    19. Alfano, Vincenzo & Capasso, Salvatore & Ercolano, Salvatore & Goel, Rajeev K., 2022. "Death takes no bribes: Impact of perceived corruption on the effectiveness of non-pharmaceutical interventions at combating COVID-19," Social Science & Medicine, Elsevier, vol. 301(C).
    20. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    21. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    22. Mark Pingle, 2022. "Addressing threats like Covid: why we will tend to over-react and how we can do better," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 21(1), pages 9-23, June.
    23. Holtz-Eakin, Douglas & Newey, Whitney & Rosen, Harvey S, 1988. "Estimating Vector Autoregressions with Panel Data," Econometrica, Econometric Society, vol. 56(6), pages 1371-1395, November.
    24. Vincenzo Alfano & Salvatore Ercolano, 2022. "Stay at Home! Governance Quality and Effectiveness of Lockdown," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 159(1), pages 101-123, January.
    25. Douglas W. Allen, 2022. "Covid-19 Lockdown Cost/Benefits: A Critical Assessment of the Literature," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 29(1), pages 1-32, January.
    26. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    27. Alfano, Vincenzo & Ercolano, Salvatore & Pinto, Mauro, 2022. "Fighting the COVID pandemic: National policy choices in non-pharmaceutical interventions," Journal of Policy Modeling, Elsevier, vol. 44(1), pages 22-40.
    28. John Gibson, 2022. "Hard, not early: putting the New Zealand Covid-19 response in context," New Zealand Economic Papers, Taylor & Francis Journals, vol. 56(1), pages 1-8, January.
    29. Yang, Qi-Cheng & Chen, Xia & Chang, Chun-Ping & Chen, Di & Hao, Yu, 2021. "What is the relationship between government response and COVID-19 pandemics? Global evidence of 118 countries," Structural Change and Economic Dynamics, Elsevier, vol. 59(C), pages 98-107.
    30. Nina Haug & Lukas Geyrhofer & Alessandro Londei & Elma Dervic & Amélie Desvars-Larrive & Vittorio Loreto & Beate Pinior & Stefan Thurner & Peter Klimek, 2020. "Ranking the effectiveness of worldwide COVID-19 government interventions," Nature Human Behaviour, Nature, vol. 4(12), pages 1303-1312, December.
    31. Vincenzo Alfano & Salvatore Ercolano, 2020. "The Efficacy of Lockdown Against COVID-19: A Cross-Country Panel Analysis," Applied Health Economics and Health Policy, Springer, vol. 18(4), pages 509-517, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ryan H. Murphy & Colin O’Reilly, 2019. "Applying panel vector autoregression to institutions, human capital, and output," Empirical Economics, Springer, vol. 57(5), pages 1633-1652, November.
    2. MAÏ ASSAN CHEDI, Maman, 2022. "Does Defence Expenditure Affect Education and Health expenditures in Saharan Africa?," African Journal of Economic Review, African Journal of Economic Review, vol. 10(4), September.
    3. Hertweck, Matthias & Brey, Bjoern, 2017. "The Persistent Effects of Monsoon Rainfall Shocks in India: A Nonlinear VAR Approach," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168256, Verein für Socialpolitik / German Economic Association.
    4. Kacou Yves Thierry Kacou & Yacouba Kassouri & Andrew Adewale Alola & Mehmet Altuntaş, 2022. "Examining the sustainable development approach of migrants' remittances and financial development in sub‐Saharan African countries," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(5), pages 804-816, October.
    5. Charles Shaaba Saba & Nicholas Ngepah, 2022. "ICT Diffusion, Industrialisation and Economic Growth Nexus: an International Cross-country Analysis," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 13(3), pages 2030-2069, September.
    6. Al-Jahwari, Salim Ahmed Said, 2021. "Does the Twin-Deficits doctrine apply to the Gulf Cooperation Council? A dynamic panel VAR-X model approach," MPRA Paper 111232, University Library of Munich, Germany.
    7. Zouaoui, Haykel & Zoghlami, Feten, 2020. "On the income diversification and bank market power nexus in the MENA countries: Evidence from a GMM panel-VAR approach," Research in International Business and Finance, Elsevier, vol. 52(C).
    8. Sigmund, Michael & Ferstl, Robert, 2021. "Panel vector autoregression in R with the package panelvar," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 693-720.
    9. Christos Kollias & Suzanna-Maria Paleologou, 2016. "Investment, growth, and defense expenditure in the EU15: Revisiting the nexus using SIPRI’s new consistent dataset," Economics of Peace and Security Journal, EPS Publishing, vol. 11(2), pages 28-37, October.
    10. Thi Hong Hanh Pham, 2022. "Shadow Economy and Poverty: What Causes What?," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(4), pages 861-891, December.
    11. Dimitrios Karamanis, 2022. "Defence partnerships, military expenditure, investment, and economic growth: an analysis in PESCO countries," GreeSE – Hellenic Observatory Papers on Greece and Southeast Europe 173, Hellenic Observatory, LSE.
    12. Joan Costa-Font & Cristina Vilaplana-Prieto, 2023. "‘Investing’ in care for old age? An examination of long-term care expenditure dynamics and its spillovers," Empirical Economics, Springer, vol. 64(1), pages 1-30, January.
    13. Binder, Michael & Hsiao, Cheng & Pesaran, M. Hashem, 2005. "Estimation And Inference In Short Panel Vector Autoregressions With Unit Roots And Cointegration," Econometric Theory, Cambridge University Press, vol. 21(4), pages 795-837, August.
    14. Oluwatosin Adeniyi & Terver T Kumeka & Samuel Orekoya & Wasiu Adekunle, 2023. "Impact of tourism development on inclusive growth: A panel vector autoregression analysis for African economies," Tourism Economics, , vol. 29(3), pages 612-642, May.
    15. Goya, Daniel, 2020. "The exchange rate and export variety: A cross-country analysis with long panel estimators," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 649-665.
    16. Charles Shaaba Saba & Nicholas Ngepah, 2022. "Nexus between telecommunication infrastructures, defence and economic growth: a global evidence," Netnomics, Springer, vol. 22(2), pages 139-177, October.
    17. Christos Kollias & Suzanna-Maria Paleologou, 2019. "Military spending, economic growth and investment: a disaggregated analysis by income group," Empirical Economics, Springer, vol. 56(3), pages 935-958, March.
    18. João Leitão & Joaquim Ferreira, 2021. "Dynamic Effects of Material Production and Environmental Sustainability on Economic Vitality Indicators: A Panel VAR Approach," JRFM, MDPI, vol. 14(2), pages 1-20, February.
    19. Li, Shengfeng & Hoque, Hafiz & Thijssen, Jacco, 2021. "Firm financial behaviour dynamics and interactions: A structural vector autoregression approach," Journal of Corporate Finance, Elsevier, vol. 69(C).
    20. Balcilar, Mehmet & Roubaud, David & Uzuner, Gizem & Wohar, Mark E., 2021. "Housing sector and economic policy uncertainty: A GMM panel VAR approach," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 114-126.

    More about this item

    Keywords

    Government response index; Stringency indexes; Granger causality; Incidence; SARS-CoV-2 infection; COVID-19;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • H0 - Public Economics - - General

    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:eee:ehbiol:v:52:y:2024:i:c:s1570677x24000054. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/622964 .

    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.