The Covid-19 disease and policy response to mitigate the economic impact in the EU
Abstract
This study aims to understand how Covid-19 contagious disease and the EU’s policy response may affect macroeconomic performance. Previous studies on this topic have used historical data sets on “rare macroeconomic disasters” such as Great Influenza to assess the impact of the current pandemic on the global economy. The authors examine the main channels of transmission and targeted policy response to mitigate crisis qualitatively. The authors use heuristics and apply qualitative trend-based analysis because the current pandemic is a unique event for which accurate quantitative data are not currently available. Researchers first identify a set of eight variables based on previous academic theories. They then express each variable as a trend: ascending, descending, and constant. The numerical calculations consist of 17 scenarios, supplemented by 24 transitions and a transition graph. Besides, the article proposes a graphical solution to examine the change in GDP that is too small. The results of the study should be understood as a reference point to allow both private and public stakeholders to understand better the relationship between the observed variables and their dynamics. The research provides a comprehensive list of future events to examine further the implications for the economy as a whole and each individual.
First published online 26 April 2021
Keyword : Covid-19, economic impact, GDP, qualitative trend-based analysis, model, scenarios
How to Cite
Zinecker, M., Doubravský, K., Balcerzak, A. P., Pietrzak, M. B., & Dohnal, M. (2021). The Covid-19 disease and policy response to mitigate the economic impact in the EU. Technological and Economic Development of Economy, 27(3), 742-762. https://doi.org/10.3846/tede.2021.14585
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Barro, R. J., & Ursúa, J. F. (2008). Macroeconomic crises since 1870. Brookings Papers on Economic Activity, 2008, 255–335. https://doi.org/10.1353/eca.0.0000
Barro, R. J., Ursúa, J., F., & Weng, J. (2020). The coronavirus and the great influenza pandemic: Lessons from the “Spanish Flu” for the coronavirus’s potential effects on mortality and economic activity. (Working paper). National Bureau of Economic Research. https://doi.org/10.3386/w26866
Boissay, F., & Rungcharoenkitkul, P. (2020). Macroeconomic effects of Covid-19: an early review. Bank for International Settlements, Basel. https://www.bis.org/publ/bisbull07.htm
Bredeweg, B., Liem, J., & Nicolaou, C. (2016). Assessing learner-constructed conceptual models and simulations of dynamic systems. In K. Verbert, M. Sharples, & T. Klobučar (Eds.), Lecture notes in computer science: Vol. 9891. Adaptive and adaptable learning (pp. 357–362). Springer Verlag. https://doi.org/10.1007/978-3-319-45153-4_27
Cahuc, P. (2019). Short-time work compensation schemes and employment. IZA World of Labor, 11(2), 1. https://doi.org/10.15185/izawol.11.v2
Correia, S., Luck, S., & Verner, E. (2020). Pandemics depress the economy, public health interventions do not: Evidence from the 1918 flu. SSRN. https://doi.org/10.2139/ssrn.3561560
Dias, R., Teixeira, N., Machova, V., Pardal, P., Horak, J., & Vochozka, M. (2020). Random walks and market efficiency tests: evidence on US, Chinese and European capital markets within the context of the global Covid-19 pandemic. Oeconomia Copernicana, 11(4), 585–608. https://doi.org/10.24136/oc.2020.024
Dohnal, M. (1991). A methodology for common-sense model development. Computers in Industry, 16(2), 141–158. https://doi.org/10.1016/0166-3615(91)90086-O
Dosi, G., Napoletano, M., Roventini, A., Stiglitz, J. E., & Treibich, T. (2020). Rational heuristics? Expectations and behaviors in evolving economies with heterogeneous interacting agents. Economic Inquiry, 58(3), 1487–1516. https://doi.org/10.1111/ecin.12897
Doubravský, K., & Dohnal, M. (2015). Reconciliation of decision-making heuristics based on decision trees topologies and incomplete fuzzy probabilities sets. Plos One, 7(10), 1–18. https://doi.org/10.1371/journal.pone.0131590
Doubravský, K., & Dohnal, M. (2018). Qualitative equationless macroeconomic models as generators of all possible forecasts based on three trend values – Increasing, constant, decreasing. Structural Change and Economic Dynamics, 45, 30–36. https://doi.org/10.1016/j.strueco.2018.01.001
Doubravský, K., Doskocil, R., & Dohnal, M. (2020). A dynamic knowledge model of project time-cost analysis based on trend modelling. Periodica Povlytechnica Social and Management Sciences, 28(1), 18–28. https://doi.org/10.3311/PPso.13318
Eichenbaum, M. S., Rebelo, S., & Trabandt, M. (2020). The macroeconomics of epidemics. (NBER Working Paper Series). https://doi.org/10.3386/w26882
Eriksson von Allmen, U., Jeasakul, P., Kang, H., & Khera, P. (2020). Macrofinancial considerations for assessing the impact of the COVID-19 pandemic. International Monetary Fund
.
European Commission. (2020). Coordinated economic response to the COVID-19 Outbreak. COM(2020) 112 final. Brussels. https://ec.europa.eu/commission/presscorner/detail/en/ip_20_459
Gigerenzer, G., & Gaissmaier, G. (2011). Heuristic decision making. Annual Review of Psychology, 62, 451–482. https://doi.org/10.1146/annurev-psych-120709-145346
Gopinath, G. (2020, April 14). The Great Lockdown: Worst economic downturn since the Great Depression. IMFBlog. International Monetary Fund, New York. https://blogs.imf.org/2020/04/14/the-great-lockdown-worst-economic-downturn-since-the-great-depression/
Guerrieri, V., Lorenzoni, G., Straub, L., & Werning, I. (2020). Macroeconomic implications of Covid-19: Can negative supply shocks cause demand shortages? (NBER Working Paper Series). https://doi.org/10.3386/w26918
Huterski, R., Huterska, A. A., Łapińska, J., & Zdunek-Rosa, E. (2020). The problem of savings exclusion and gross savings in the new European Union member states. Entrepreneurship and Sustainability Issues, 7(3), 2470–2480. https://doi.org/10.9770/jesi.2020.7.3(67)
Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London, series A 115(772), 700–721. https://doi.org/10.1098/rspa.1927.0118
Korzeb, Z., & Niedziółka, P. (2020). Resistance of commercial banks to the crisis caused by the COVID-19 pandemic: the case of Poland. Equilibrium. Quarterly Journal of Economics and Economic Policy, 15(2), 205–234. https://doi.org/10.24136/eq.2020.010
Kufel, T. (2020). ARIMA-based forecasting of the dynamics of confirmed Covid-19 cases for selected European countries. Equilibrium. Quarterly Journal of Economics and Economic Policy, 15(2), 181– 204. https://doi.org/10.24136/eq.2020.009
Kuc-Czarnecka, M. (2020). COVID-19 and digital deprivation in Poland. Oeconomia Copernicana, 11(3), 415–431. https://doi.org/10.24136/oc.2020.017
Lagarde, C. (2020, April 9). How the ECB is helping firms and households. The ECB Blog. https://www.ecb.europa.eu/press/blog/date/2020/html/ecb.blog200409~3aa2815720.en.html
McKibbin, W. J., & Fernando, R. (2020). The global macroeconomic impacts of COVID-19: Seven scenarios. SSRN. https://doi.org/10.2139/ssrn.3547729
Nakamura, E., Steinsson, J., Barro, R., & Ursúa, J. (2010). Crises and recoveries in an empirical model of consumption disasters (NBER Working Paper Series, 15920). https://doi.org/10.3386/w15920
OECD. (2020). Evaluating the initial impact of COVID-19 containment measures on economic activity. https://read.oecd-ilibrary.org/view/?ref=126_126496-evgsi2gmqj&title=Evaluating_the_initial_impact_of_COVID-19_containment_measures_on_economic_activity
Pardal, P., Dias, R., Šuleř, P., Teixeira, N., & Krulický, T. (2020). Integration in Central European capital markets in the context of the global COVID-19 pandemic. Equilibrium. Quarterly Journal of Economics and Economic Policy, 15(4), 627–650. https://doi.org/10.24136/eq.2020.027
Ramsden, D. (2020). The potential long-term economic effects of Covid. Institute for Policy and Engagement, University of Nottingham. http://www.bankofengland.co.uk/news/speeches
UNCTAD. (2020). Global trade impact of the Corona Virus (Covid-19) epidemic. https://unctad.org/en/PublicationsLibrary/ditcinf2020d1.pdf
Vicha, T., & Dohnal, M. (2008). Qualitative identification of chaotic systems behaviours. Chaos, Solutions and Fractals, 38(1), 70–78. https://doi.org/10.1016/j.chaos.2008.01.027
Villez, K. (2015). Qualitative path estimation: A fast and reliable algorithm for qualitative trend analysis. AIChE Journal, 61(5), 1535–1546. https://doi.org/10.1002/aic.14736
Yan, X., Zhou, Y., Wen, Y., & Chai, X. (2013). Qualitative and quantitative integrated modeling for stochastic simulation and optimization. Journal of Applied Mathematics, 2013, 831273. https://doi.org/10.1155/2013/831273
Barro, R. J., Ursúa, J., F., & Weng, J. (2020). The coronavirus and the great influenza pandemic: Lessons from the “Spanish Flu” for the coronavirus’s potential effects on mortality and economic activity. (Working paper). National Bureau of Economic Research. https://doi.org/10.3386/w26866
Boissay, F., & Rungcharoenkitkul, P. (2020). Macroeconomic effects of Covid-19: an early review. Bank for International Settlements, Basel. https://www.bis.org/publ/bisbull07.htm
Bredeweg, B., Liem, J., & Nicolaou, C. (2016). Assessing learner-constructed conceptual models and simulations of dynamic systems. In K. Verbert, M. Sharples, & T. Klobučar (Eds.), Lecture notes in computer science: Vol. 9891. Adaptive and adaptable learning (pp. 357–362). Springer Verlag. https://doi.org/10.1007/978-3-319-45153-4_27
Cahuc, P. (2019). Short-time work compensation schemes and employment. IZA World of Labor, 11(2), 1. https://doi.org/10.15185/izawol.11.v2
Correia, S., Luck, S., & Verner, E. (2020). Pandemics depress the economy, public health interventions do not: Evidence from the 1918 flu. SSRN. https://doi.org/10.2139/ssrn.3561560
Dias, R., Teixeira, N., Machova, V., Pardal, P., Horak, J., & Vochozka, M. (2020). Random walks and market efficiency tests: evidence on US, Chinese and European capital markets within the context of the global Covid-19 pandemic. Oeconomia Copernicana, 11(4), 585–608. https://doi.org/10.24136/oc.2020.024
Dohnal, M. (1991). A methodology for common-sense model development. Computers in Industry, 16(2), 141–158. https://doi.org/10.1016/0166-3615(91)90086-O
Dosi, G., Napoletano, M., Roventini, A., Stiglitz, J. E., & Treibich, T. (2020). Rational heuristics? Expectations and behaviors in evolving economies with heterogeneous interacting agents. Economic Inquiry, 58(3), 1487–1516. https://doi.org/10.1111/ecin.12897
Doubravský, K., & Dohnal, M. (2015). Reconciliation of decision-making heuristics based on decision trees topologies and incomplete fuzzy probabilities sets. Plos One, 7(10), 1–18. https://doi.org/10.1371/journal.pone.0131590
Doubravský, K., & Dohnal, M. (2018). Qualitative equationless macroeconomic models as generators of all possible forecasts based on three trend values – Increasing, constant, decreasing. Structural Change and Economic Dynamics, 45, 30–36. https://doi.org/10.1016/j.strueco.2018.01.001
Doubravský, K., Doskocil, R., & Dohnal, M. (2020). A dynamic knowledge model of project time-cost analysis based on trend modelling. Periodica Povlytechnica Social and Management Sciences, 28(1), 18–28. https://doi.org/10.3311/PPso.13318
Eichenbaum, M. S., Rebelo, S., & Trabandt, M. (2020). The macroeconomics of epidemics. (NBER Working Paper Series). https://doi.org/10.3386/w26882
Eriksson von Allmen, U., Jeasakul, P., Kang, H., & Khera, P. (2020). Macrofinancial considerations for assessing the impact of the COVID-19 pandemic. International Monetary Fund
.
European Commission. (2020). Coordinated economic response to the COVID-19 Outbreak. COM(2020) 112 final. Brussels. https://ec.europa.eu/commission/presscorner/detail/en/ip_20_459
Gigerenzer, G., & Gaissmaier, G. (2011). Heuristic decision making. Annual Review of Psychology, 62, 451–482. https://doi.org/10.1146/annurev-psych-120709-145346
Gopinath, G. (2020, April 14). The Great Lockdown: Worst economic downturn since the Great Depression. IMFBlog. International Monetary Fund, New York. https://blogs.imf.org/2020/04/14/the-great-lockdown-worst-economic-downturn-since-the-great-depression/
Guerrieri, V., Lorenzoni, G., Straub, L., & Werning, I. (2020). Macroeconomic implications of Covid-19: Can negative supply shocks cause demand shortages? (NBER Working Paper Series). https://doi.org/10.3386/w26918
Huterski, R., Huterska, A. A., Łapińska, J., & Zdunek-Rosa, E. (2020). The problem of savings exclusion and gross savings in the new European Union member states. Entrepreneurship and Sustainability Issues, 7(3), 2470–2480. https://doi.org/10.9770/jesi.2020.7.3(67)
Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London, series A 115(772), 700–721. https://doi.org/10.1098/rspa.1927.0118
Korzeb, Z., & Niedziółka, P. (2020). Resistance of commercial banks to the crisis caused by the COVID-19 pandemic: the case of Poland. Equilibrium. Quarterly Journal of Economics and Economic Policy, 15(2), 205–234. https://doi.org/10.24136/eq.2020.010
Kufel, T. (2020). ARIMA-based forecasting of the dynamics of confirmed Covid-19 cases for selected European countries. Equilibrium. Quarterly Journal of Economics and Economic Policy, 15(2), 181– 204. https://doi.org/10.24136/eq.2020.009
Kuc-Czarnecka, M. (2020). COVID-19 and digital deprivation in Poland. Oeconomia Copernicana, 11(3), 415–431. https://doi.org/10.24136/oc.2020.017
Lagarde, C. (2020, April 9). How the ECB is helping firms and households. The ECB Blog. https://www.ecb.europa.eu/press/blog/date/2020/html/ecb.blog200409~3aa2815720.en.html
McKibbin, W. J., & Fernando, R. (2020). The global macroeconomic impacts of COVID-19: Seven scenarios. SSRN. https://doi.org/10.2139/ssrn.3547729
Nakamura, E., Steinsson, J., Barro, R., & Ursúa, J. (2010). Crises and recoveries in an empirical model of consumption disasters (NBER Working Paper Series, 15920). https://doi.org/10.3386/w15920
OECD. (2020). Evaluating the initial impact of COVID-19 containment measures on economic activity. https://read.oecd-ilibrary.org/view/?ref=126_126496-evgsi2gmqj&title=Evaluating_the_initial_impact_of_COVID-19_containment_measures_on_economic_activity
Pardal, P., Dias, R., Šuleř, P., Teixeira, N., & Krulický, T. (2020). Integration in Central European capital markets in the context of the global COVID-19 pandemic. Equilibrium. Quarterly Journal of Economics and Economic Policy, 15(4), 627–650. https://doi.org/10.24136/eq.2020.027
Ramsden, D. (2020). The potential long-term economic effects of Covid. Institute for Policy and Engagement, University of Nottingham. http://www.bankofengland.co.uk/news/speeches
UNCTAD. (2020). Global trade impact of the Corona Virus (Covid-19) epidemic. https://unctad.org/en/PublicationsLibrary/ditcinf2020d1.pdf
Vicha, T., & Dohnal, M. (2008). Qualitative identification of chaotic systems behaviours. Chaos, Solutions and Fractals, 38(1), 70–78. https://doi.org/10.1016/j.chaos.2008.01.027
Villez, K. (2015). Qualitative path estimation: A fast and reliable algorithm for qualitative trend analysis. AIChE Journal, 61(5), 1535–1546. https://doi.org/10.1002/aic.14736
Yan, X., Zhou, Y., Wen, Y., & Chai, X. (2013). Qualitative and quantitative integrated modeling for stochastic simulation and optimization. Journal of Applied Mathematics, 2013, 831273. https://doi.org/10.1155/2013/831273