Dynamic indexing and clustering of government strategies to mitigate Covid-19
Abstract
Objective: The objective of the article is to identify the reference group of countries with similar Covid strategies and other groups with their performance success, and to construct a composite Covid Mitigation Index for comparative purposes, thus, implying how to redesign the strategic policies.
Research Design & Methods: Gaussian Mixture Modelling and Factor Analysis: the main design is quantitative, using Gaussian Mixture Modelling to find the optimal number of country clusters, and Factor Analysis with Principal Axis Factoring (FA-PAF) to build a composite index of governmental policies. Data includes eight mitigation policy variables and three supporting economic policy variables. Data are aggregated to form three periods and the cluster changes are identified by Gaussian Mixture Modelling. Then, the Covid Mitigation Index (CMI) is constructed by FA-PAF to obtain a comparative measure over the periods and the country clusters. The results were obtained by means of R studio and SPSS.
Findings: The dynamic clustering leads to a decreasing number of clusters from nine clusters in the first period (Janunary-February 2020), four clusters in the second period (March-April 2020), and two clusters in the third period (May-June 2020). In the first period, China (with CMI=48) took serious actions forming its own cluster, while 11 other countries (with CMI>10), e.g., early affected European countries such as Italy and Spain and large Asian countries such India and Indonesia, took moderate actions. In the second period all cluster averages were greater than China’s in the first period, i.e., most world countries were dedicated to fight Covid-19. In Europe, Italy, San Marino and France showed the highest CMI values, similarly to Iraq and Palestine in the Middle East, Peru and Honduras in the Latin America, and China, India and Indonesia in Asia. In the third period, cluster averages showed even tighter policies even though 42 countries had lower CMI values than previously.
Implications & Recommendations: The approach provided a big picture for decision makers both in business and in governments. The key idea was to reveal reference groups of countries which help governmental actors to design and adapt their strategies over time by learning by their own experience and the results of the better performing clusters. It was suggested that a multi-criteria approach accounting for individual government’s preferences over health and economy is used along with the presented approach.
Contribution & Value Added: Clustering with Gaussian Mixture Models and factor analysis based on Principal Axis Factoring for composite-index building were used. The methods are well-established, but they were applied in a novel way dynamically over time and for the composite CMI. CMI was built on two factors which identified the structure of mitigation policies and economic policies. The development of governmental polices over the first cycle of Covid-19 pandemic was described.
Keywords
clustering; composite index; pandemic; public policy; Covid-19 mitigation
References
Andersen, N., Bramness, J.G., & Lund, I.O. (2020). The emerging COVID-19 research: dynamic and regularly up-dated science mapsand analyses. BMC Medical Informatics and Decision Making, 20(1), 1-7. https://doi.org/10.1186/s12911-020-01321-9
Androniceanu, A. (2020). Major structural changes in the EU policies due to the problems and risks caused by COVID-19. Administratie si Management Public, 34, 137-149. https://doi.org/10.24818/amp/2020.34-08
Androniceanu, A., Kinnunen, J., & Georgescu, I. (2020). E-Government clusters in the EU based on the Gaussian Mixture Models. Administratie si Management Public, 35, 6-20. https://doi.org /10.24818/amp/2020.35-01
Androniceanu, A., & Marton, D.- M., (2021). The psychosocial impact of the Romanian government measures on the population during the COVID-19 pandemic. Central European Public Administration Review, 19(1), 7–32.
Androniceanu, A.-M., Georgescu, I., Dobrin, C., & Dragulanescu I.V. (2020a). Multifactorial components analysis of the renewable energy sector in the OECD countries and managerial implications. Polish Journal of Man-agement Studies, 22 (2), 36-49. https://doi.org/10.17512/pjms.2020.22.2.03
Androniceanu, A.-M., Georgescu, I., Tvaronavičiene, M., & Androniceanu, A.(2020b). Canonical Correlation Analysis and a New Composite Index on Digitalization and Labor Force in the Context of the Industrial Revo-lution 4.0. Sustainability, 12(17), 6812. https://doi.org/10.3390/su12176812
Biernacki, G., Celeux, G., & Govaert, G. (2000). Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(7), 719–725. https://doi.org/10.1109/34.865189
Bouveyron, C., Celeux, G., Murphy, T.B., & Raftery, A.E. (2019). Model-based clustering and classification for data science. Cambridge University Press.
Carrasco Sierra, A., Cobos Flores, M.J., Fuentes Duarte, B., & Hernández Comi, B.I. (2020). Successful Manage-ment System by a Metalworking Mexican Company During Covid-19 Situation. Analysis Through a New Index (Case Study). International Journal of Entrepreneurial Knowledge, 8(2), 42-55. https://doi.org/10.37335 /ijek.v8i2.116
Celeux, G., & Govaert, G. (1995). Gaussian parsimonious clustering models. Pattern Recognition, 28(5), 781-93. https://doi.org/10.1016/0031-3203(94)00125-6
Cheng, C., Barceló, J., Hartnett, A.S., Kubinec, R., & Messerschmidt, L. (2020). COVID-19 government response event dataset (CoronaNet v. 1.0). Nature Human Behaviour, 4(7), 756-768. https://doi.org/10.1038/s41562-020-0909-7
Davulis, T., Gasparėnienė, L., & Raistenskis, E. (2021). Assessment of the situation concerning psychological sup-port to the public and business in the extreme conditions: case of Covid-19. Entrepreneurship and Sustaina-bility Issues, 8(3), 308-322. https://doi.org/10.9770/jesi.2021.8.3(19)
Fabrigar, L.R., Wegener, D.T., Maccallum, R.C., & Strahan, E.J. (1999). The use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272-299. https://doi.org/10.1037/1082-989X.4.3.272
Fetzer, T.R., Witte, M., Hensel, L., Jachimowicz, J., Haushofer, J., Ivchenko, A., Caria, S., Reutskaya, E., Roth, C.P., Fiorin, S., Gómez, M., Kraft-Todd, G., Götz, F.M., & Yoeli, E. (2020). Global behaviors and perceptions at the onset of the COVID-19 pandemic (No. w27082). National Bureau of Economic Research.
Hale, T., Petherick, A., Phillips, T., & Webster, S. (2020a). Variation in government responses to COVID-19. Blavatnik School of Government Working Paper, 31, 2020-11.
Hale, T., Webster, S., Petherick, A., Phillips, T., & Kira, B. (2020b). Stringency Index (OXBS). Oxford COVID-19 Government Response Tracker. Blavatnik School of Government Working Paper. Retrieved from https://www.bsg.ox.ac.uk/research/research-projects/oxford-covid-19-government-response-tracker on February 20, 2021.
Haleem, A., Javaid, M., & Vaishya, R. (2020). Effects of COVID-19 pandemic in daily life. Current Medicine Research and Practice, 78-79. https://doi.org/10.1016 /j.cmrp.2020.03.011
James, N., & Menzies, M. (2020). Cluster-based dual evolution for multivariate time series: Analyzing COVID-19. Chaos: An Interdisciplinary Journal of Nonlinear Science, 30(6), 061108. https://doi.org/10.1063/5.0013156
Joliffe, I.T. (2002). Principal component analysis. Springer. 2nd edition
Kinnunen, J., & Georgescu, I. (2020). Disruptive pandemic as a driver towards digital coaching in OECD countries. Revista Românească pentru Educaţie Multidimensională, 12(2Sup1), 55-61. https://doi.org/10.18662/rrem/12.2Sup1/289
Kinnunen, J., Georgescu, I., & Androniceanu, A.-M. (2020). Evaluating governmental responses to Covid-19 and the implications for tourism industry. In Proceedings of 14th International Management Conference: Manag-ing Sustainable Organizations, Bucharest, Romania, (pp. 585-594). https://doi.org/10.24818/IMC/2020/03.11
Kosach, I., Duka, A., Starchenko, G., Myhaylovska, O., & Zhavoronok, A. (2020). Socioeconomic viability of public management in the context of European integration processes. Administratie si Management Public, 35, 139-152. https://doi.org/10.24818/amp/2020.35-09
Marona, B., & Tomal, M. (2020). The COVID-19 pandemic impact upon housing brokers’ workflow and their cli-ents’ attitude: Real estate market in Krakow. Entrepreneurial Business and Economics Review, 8(4), 221-232. https://doi.org/10.15678/EBER.2020.080412
McLachlan, G., & Peel, D. (2000). Finite mixture models. Wiley Series in Probability and Statistics. John Wiley & Sons, Inc.
Mollenkopf, D.A., Ozanne, L.K., & Stolze, H.J. (2020). A transformative supply chain response to COVID-19. Jour-nal of Service Management, ahead-of-print. https://doi.org/10.1108/JOSM-05-2020-0143
Moyo, N. (2020). Antecendents of employee disengagement amid COVID-19 pandemic. Polish Journal of Man-agement Studies, 22(1), 323-334. https://doi.org/10.17512/pjms.2020.22.1.21
Nowiński, W., Haddoud, M., Wach, K., & Schaefer, R. (2020). Perceived public support and entrepreneurship attitudes: A little reciprocity can go a long way! Journal of Vocational Behavior, 121, 103474. https://doi.org/10.1016/j.jvb.2020.103474
Pantano, E., Pizzi, G., Scarpi, D., & Dennis, C. (2020). Competing during a pandemic? Retailers’ ups
and downs during the COVID-19 outbreak. Journal of Business Research, 116, 209-213. https://doi.org/10.1016/j.jbusres.2020.05.036
Rahman, M.A., Zaman, N., Asyhari, A.T., Al-Turjman, F., Bhuiyan, M.Z.A., & Zolkipli, M.F. (2020). Data-driven dy-namic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices. Sustainable Cities and Society, 62, 102372. https://doi.org /10.1016/j.scs.2020.102372
Sawangchai, A., Prasarnkarn, H., Kasuma, J., Polyakova, A.G., & Qasim, S. (2020). Effects of COVID-19 on digital learning of entrepreneurs. Polish Journal of Management Studies, 22(2), 502-517. https://doi.org/10.17512/pjms.2020.22.2.33
Wach, K., & Bilan, S. (2021). Public support and administration barriers towards entrepreneurial intentions of students in Poland. Administratie si Management Public, 36(1).
Zandi, G., Shahzad, I., Farrukh, M., & Kot, S. (2020). Supporting role of society and firms to COVID-19 manage-ment among medical practitioners. International Journal of Environmental Research and Public Health, 17(21), 1-2. 7961. https://doi.org/10.3390/ijerph17217961
Zarikas, V., Poulopoulos, S.G., Gareiou, Z., & Zervas, E. (2020). Clustering analysis of countries using the COVID-19 cases dataset. Data in Brief, 31, 105787. https://doi.org/10.1016/j.dib.2020.105787
Åbo Akademi University, Turku, Finland Finland
http://orcid.org/0000-0002-0154-6617
Doctoral Researcher at Information Systems, Åbo Akademi, Turku, Finland. His research interests include public policy analytics, machine learning, fuzzy modelling, and investment analysis.
Correspondence to: Jani Kinnunen, Savitehtaankatu 2 A 4, 20540, Turku, Finland, email: jani.kinnunen@abo.fi
Bucharest University of economics Romania
http://orcid.org/0000-0002-8536-5636
PhD Lecturer at Department of Informatics and Economic Cybernetics (Bucharest University of Economic Studies, Bucharest, Romania). Her research interests include time series analysis and data mining applications.
Correspondence to: Irina Georgescu, PhD, Bucharest University of Economics, Calea Dorobantilor 15-17, Sector 1, Bucharest, 010552, Romania, e-mail: irina.georgescu@csie.ase.ro
Tampere University Finland
http://orcid.org/0000-0003-0423-8529
PhD in Educational Technology (2013, University of Malaya, Kuala Lumpur, Malaysia); PhD researcher in Communication and Media Department, Tampere University, Finland; Her research interests include TPACK, technology integration into teaching and learning, Technology Acceptance Models.
Correspondence to: Tampere University, Kalevantie 4, 33100 Tampere, Finland, email: zahra.hosseini@tuni.fi
Management Doctoral School, Bucharest University of Economic Studies, Bucharest, Romania Romania
http://orcid.org/0000-0002-1441-4496
PhD Student at the Management Doctoral School, Bucharest University of Economic Studies, Bucharest, Romania. Her research interests include: economic growth, digitalisation, circular economy, international business and entrepreneurship.
Correspondence to: ane.androniceanu.drd@gmail.com
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a CC BY-ND licence that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are asked to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) only the final version of the article, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access). We advise using any of the following research society portals:
