| Peer-Reviewed

COVID-19 Deaths Linked to Restrictions Stringency Lag: A G7 and Global Analysis, Implications for Public Policy

Received: 29 June 2021     Accepted: 12 July 2021     Published: 2 August 2021
Views:       Downloads:
Abstract

This study focuses on the results of the G7 countries from the analysis of daily data from 184 countries of the world during the COVID-19 epidemic. After an increase in restrictions, there is an increase in new COVID-19 deaths. To understand the influences on number of deaths by country, the analysis reveals that per capita income is significantly positively correlated with mortality from COVID-19. This suggests that the epidemic first hit rich countries the hardest through the correlation to the human development index. This finding was contrary to what was predicted by the Global Health Security Index on pre-pandemic preparedness. Within affluent countries, deaths and cases were higher among socio-economic challenged populations. This was supported by the number of deaths that are positively influenced by the GINI index that is an indicator of disparity of income and wealth. The research indicates that after an increase in restrictions, there is an increase in new COVID-19 deaths and cases. This along with the finding on the stringency index, correlated with the stringency lag, point to the effectiveness of policies being negatively correlated due to a lag in implementation and partial application. Moreover, the uncertainty or the variability of the stringency index has a negative impact on mortality. The “Power Distance” by was used to understand individual’s reaction to restrictions indicated by the stringency index and the stringency lag, COVID-19 death numbers were also found to be positively influenced by a countries “Power Distance”. These findings are key to the improve policy management of the virus. The Delta plus and Lambda variant’s increased transmissibility and potential vaccine resistance increases the urgency for policy makers to understand and immediately enforce the stringency of regulations in consideration of their countries Power Balance index, and to reduce the stringency lag of their policies to increase the effectiveness in reducing the transmission of COVID-19.

Published in International Journal of Economics, Finance and Management Sciences (Volume 9, Issue 4)
DOI 10.11648/j.ijefm.20210904.12
Page(s) 134-158
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

COVID-19, Variant, Nonfinancial Risk Management, Public Policy, Mathematics, Spread of Viral Disease, GRAFT, Finance

References
[1] Board, G. P. M. (2019). A world at risk: annual report on global preparedness for health emergencies. Geneva, Switzerland: World Health Organization.
[2] Board, G. P. M. (2020). A world in disorder. Geneva, Switzerland: World Health Organization.
[3] Buckley, C., Kirkpatrick, D., & Hernández, J. (2021). The 25 days that changed the world, Globe and Mail.
[4] Cugat, G. And Narita, F. (2020) How COVID-19 Will Increase Inequality in Emerging Markets and Developing Economies, International Monetary Fund Blog.
[5] Falk, G., Carter, J. A., Nicchitta, I. A., Nyhof, E. C, Romero, P. D. (Jan, 2021) Unemployment Rates During the COVID-19 Pandemic: In Brief, Congressional Research Service.
[6] Finch WH and Hernández Finch ME (2020) Poverty and Covid-19: Rates of Incidence and Deaths in the United States During the First 10 Weeks of the Pandemic. Front. Sociol. 5: 47. doi: 10.3389/fsoc.2020.00047.
[7] GINI index (World Bank estimate) Data: data.worldbank.org. Retrieved 2021-3-9.
[8] Haug, N., Geyrhofer, L., Londei, A., Dervic, E., Desvars-Larrive, A., Loreto, V.,... & Klimek, P. (2020). Ranking the effectiveness of worldwide COVID-19 government interventions. Nature human behaviour, 1-10.
[9] Helbing, D. (2013). Globally networked risks and how to respond. Nature, 497 (7447), 51.
[10] Hofstede, G. (2009). Power Distance Index.
[11] Jackson, J. K., Weiss, M. A, Schwarzenberg, A. B., Nelson, R. M., Sutter, K. M., Sutherland, M. D. (Feb 2021) Global Economic Effects of COVID-19, Congressional Research Service.
[12] John Hopkins University & Medicine, (2021), Cases and Mortality by Country, https://coronavirus.jhu.edu/data/mortality last accessed March 9, 2021.
[13] Leduc, S., & Liu, Z. (2020). The Uncertainty Channel of the Coronavirus. Economic Letters.
[14] OECD, Key Indicators of the Labour Market (KILM): 2001-2002, International Labour Organisation, Geneva, 2002, page 704.
[15] O’Toole, A. & Hill, V (2021). Rambaut Group, University of Edinborgh.
[16] Richter, F (Jan 2021). IMPACT OF COVID-19 PANDEMIC ON MENTAL HEALTH.
[17] Pandemic Causes Spike in Anxiety & Depression.
[18] Szymanski, B. K., Lin, X., Asztalos, A., & Sreenivasan, S. (2015). Failure dynamics of the global risk network. Scientific reports, 5, 10998.
[19] Tullo, Lois (October 2017). Global Risks and Trends Framework (GRAFT). Global Risk Institute (GRI).
[20] Tullo, Lois (March 2020). GRAFT: Covid-19 Implications. Global Risk Institute (GRI).
[21] Turchin, A. V. (2010). Structure of the global catastrophe. The risks of dying out humanity in the XX century./AV Turchin–M.
[22] WEF (2020). The Global Risk Report 2020, Insight Report 15th Edition In partnership with Marsh & McLennan and Zurich Insurance Group.
[23] World Health Organization (Jan 2021), New COVID-19 variants fuelling Africa’s second wave.
[24] Goldin, I and Muggah, R., WEF (October 2020) COVID-19 is increasing multiple kinds of inequality. Here’s what we can do about it? https://www.weforum.org/agenda/2020/10/covid-19-is-increasing-multiple-kinds-of-inequality-here-s-what-we-can-do-about-it/
[25] World Bank (2018), GINI index (World Bank Estimate) https://data.worldbank.org/indicator/SI.POV.GINI?view=map last accessed March 9, 2021.
[26] World Bank. (2020). Poverty and Shared Prosperity 2020: Reversals of Fortune. The World Bank.
[27] Mukherjee Siddhartha (February 22, 2021) Why Does the Pandemic Seem to Be Hitting Some Countries Harder Than Others? The New Yorker. Coronavirus Chronicles, March 1, 2021 Issue.
[28] Schellekens, P., and Sourrouille, D. M. (2020). COVID-19 mortality in rich and poor countries: a tale of two pandemics?. World Bank Policy Research Working Paper, (9260).
[29] Lai, S., Ruktanonchai, W., Zhou, L., Prosper, O., Luo, W., Floyd, J., Wesolowski, A., Zhang, C., Du, X., Tatem, A., Effect of non-pharmaceutical interventions for containing the COVID-19 outbreak: an observational and modelling study. medRxiv2020.03.03.20029843; doi: https://doi.org/10.1101/2020.03.03.20029843.
Cite This Article
  • APA Style

    Marcella Lucchetta, Lois Tullo. (2021). COVID-19 Deaths Linked to Restrictions Stringency Lag: A G7 and Global Analysis, Implications for Public Policy. International Journal of Economics, Finance and Management Sciences, 9(4), 134-158. https://doi.org/10.11648/j.ijefm.20210904.12

    Copy | Download

    ACS Style

    Marcella Lucchetta; Lois Tullo. COVID-19 Deaths Linked to Restrictions Stringency Lag: A G7 and Global Analysis, Implications for Public Policy. Int. J. Econ. Finance Manag. Sci. 2021, 9(4), 134-158. doi: 10.11648/j.ijefm.20210904.12

    Copy | Download

    AMA Style

    Marcella Lucchetta, Lois Tullo. COVID-19 Deaths Linked to Restrictions Stringency Lag: A G7 and Global Analysis, Implications for Public Policy. Int J Econ Finance Manag Sci. 2021;9(4):134-158. doi: 10.11648/j.ijefm.20210904.12

    Copy | Download

  • @article{10.11648/j.ijefm.20210904.12,
      author = {Marcella Lucchetta and Lois Tullo},
      title = {COVID-19 Deaths Linked to Restrictions Stringency Lag: A G7 and Global Analysis, Implications for Public Policy},
      journal = {International Journal of Economics, Finance and Management Sciences},
      volume = {9},
      number = {4},
      pages = {134-158},
      doi = {10.11648/j.ijefm.20210904.12},
      url = {https://doi.org/10.11648/j.ijefm.20210904.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijefm.20210904.12},
      abstract = {This study focuses on the results of the G7 countries from the analysis of daily data from 184 countries of the world during the COVID-19 epidemic. After an increase in restrictions, there is an increase in new COVID-19 deaths. To understand the influences on number of deaths by country, the analysis reveals that per capita income is significantly positively correlated with mortality from COVID-19. This suggests that the epidemic first hit rich countries the hardest through the correlation to the human development index. This finding was contrary to what was predicted by the Global Health Security Index on pre-pandemic preparedness. Within affluent countries, deaths and cases were higher among socio-economic challenged populations. This was supported by the number of deaths that are positively influenced by the GINI index that is an indicator of disparity of income and wealth. The research indicates that after an increase in restrictions, there is an increase in new COVID-19 deaths and cases. This along with the finding on the stringency index, correlated with the stringency lag, point to the effectiveness of policies being negatively correlated due to a lag in implementation and partial application. Moreover, the uncertainty or the variability of the stringency index has a negative impact on mortality. The “Power Distance” by was used to understand individual’s reaction to restrictions indicated by the stringency index and the stringency lag, COVID-19 death numbers were also found to be positively influenced by a countries “Power Distance”. These findings are key to the improve policy management of the virus. The Delta plus and Lambda variant’s increased transmissibility and potential vaccine resistance increases the urgency for policy makers to understand and immediately enforce the stringency of regulations in consideration of their countries Power Balance index, and to reduce the stringency lag of their policies to increase the effectiveness in reducing the transmission of COVID-19.},
     year = {2021}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - COVID-19 Deaths Linked to Restrictions Stringency Lag: A G7 and Global Analysis, Implications for Public Policy
    AU  - Marcella Lucchetta
    AU  - Lois Tullo
    Y1  - 2021/08/02
    PY  - 2021
    N1  - https://doi.org/10.11648/j.ijefm.20210904.12
    DO  - 10.11648/j.ijefm.20210904.12
    T2  - International Journal of Economics, Finance and Management Sciences
    JF  - International Journal of Economics, Finance and Management Sciences
    JO  - International Journal of Economics, Finance and Management Sciences
    SP  - 134
    EP  - 158
    PB  - Science Publishing Group
    SN  - 2326-9561
    UR  - https://doi.org/10.11648/j.ijefm.20210904.12
    AB  - This study focuses on the results of the G7 countries from the analysis of daily data from 184 countries of the world during the COVID-19 epidemic. After an increase in restrictions, there is an increase in new COVID-19 deaths. To understand the influences on number of deaths by country, the analysis reveals that per capita income is significantly positively correlated with mortality from COVID-19. This suggests that the epidemic first hit rich countries the hardest through the correlation to the human development index. This finding was contrary to what was predicted by the Global Health Security Index on pre-pandemic preparedness. Within affluent countries, deaths and cases were higher among socio-economic challenged populations. This was supported by the number of deaths that are positively influenced by the GINI index that is an indicator of disparity of income and wealth. The research indicates that after an increase in restrictions, there is an increase in new COVID-19 deaths and cases. This along with the finding on the stringency index, correlated with the stringency lag, point to the effectiveness of policies being negatively correlated due to a lag in implementation and partial application. Moreover, the uncertainty or the variability of the stringency index has a negative impact on mortality. The “Power Distance” by was used to understand individual’s reaction to restrictions indicated by the stringency index and the stringency lag, COVID-19 death numbers were also found to be positively influenced by a countries “Power Distance”. These findings are key to the improve policy management of the virus. The Delta plus and Lambda variant’s increased transmissibility and potential vaccine resistance increases the urgency for policy makers to understand and immediately enforce the stringency of regulations in consideration of their countries Power Balance index, and to reduce the stringency lag of their policies to increase the effectiveness in reducing the transmission of COVID-19.
    VL  - 9
    IS  - 4
    ER  - 

    Copy | Download

Author Information
  • Department of Economics, University Ca’ Foscari, Venice, Italy

  • Schulich School of Business, York University, Toronto, Canada

  • Sections