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Covid-19 Response Models and Divergences Within the EU: A Health Dis-Union

  • Matilde Ceron ORCID logo EMAIL logo , Carlo Maria Palermo und Daniele Grechi
Veröffentlicht/Copyright: 9. Juli 2021
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Statistics, Politics and Policy
Aus der Zeitschrift Statistics, Politics and Policy Band 12 Heft 2

Abstract

The symmetric shock of the Covid-19 pandemic has come with heterogeneous consequences across the world. Within the common institutional framework of the European Union, the outbreak has put under extreme stress governance and interplay between the national and supranational level. Under some coordination, responses have remained largely in the hands and on the shoulders of the Member States. In this context, the article classifies pandemic outbreaks and responses along the containment and fiscal support dimensions to uncover whether a common model for Covid-19 crisis management arises across the EU27 or rather different policy choices patterns emerge within the continent. Based on indicators covering the three dimensions derived from the Oxford Covid Government response tracker, the John Hopkins CSSE Covid-19 database and the European Commission Autumn Forecasts, the paper employs hierarchical cluster analysis to uncover response group across countries and characterize them by the outbreak, containment and fiscal support strengths, delineating as well the geographical distribution across and within the clusters. The findings present the heterogeneity of response models, robust to alternative specifications and timeframes across the first and the second wave, deriving broader implications for the outlook for the vaccine-roll out and exit from the crisis. The dynamics in 2020 are also considered in the context of the shortcomings of supranational governance within the EU and the current policy reform debate, highlighting the high stakes for the upcoming Conference on the Future of Europe. The contribution of the work is furthered by offering a systematic methodology and framework to study heterogeneities of pandemic responses within the EU paving the way for further analysis of contributing factors explaining decision-makers policy choices as well as performance concerning political, social and economic outcomes across the models.

Keywords: COVID-19; European Union; pandemic response; cluster analysis; comparative politics
Corresponding author: Matilde Ceron, Department of Social and Political Sciences, Università degli Studi di Milano, Milan, Italy, E-mail:
Appendix

Variables Description

Description Stringency Containment
Stringency Index aggregating all eight restrictions (e.g. schools, stay-at-home, workplace closures, travel limitations, etc.) indicators and information campaigns
Containment Index aggregating all contained in stringency plus additional health measures indicators (e.g. testing, tracing, facial covering; vaccination, etc.)
Schools C1 Coding: 0 – no measure; 1 – recommended; 2 – required some; 3 required all x x
Workplace closing C2 Coding: 0 – no measure; 1 – recommended; 2 – required some; 3 required all x x
Cancel public events C3 Coding: 0 – no measure; 1 – recommended; 2 – required x x
Restrictions on gatherings C4 Coding: 0 – no measure; 1– >1000; 2 – >101; 3 – >11; 4 < 10 x x
Close public transport C5 Coding: 0 – no measure; 1 – recommended; 2 – required x x
Stay at home requirements C6 Coding: 0 – no measure; 1 – recommended; 2 – required some exceptions; 3 required minimal exceptions x x
Restrictions on internal movement C7 Coding: 0 – no measure; 1 – recommended; 2 – required x x
International travel controls C8 Coding: 0 – no measure; 1 – screening; 2 – quarantine; 3 – ban some; 4 – ban all x x
Public info campaigns H1 Coding: 0 – no measure; 1 – public officials; 2 – coordinated campaign x x
Testing policy H2 Coding: 0 – no testing; 1 – symptoms & target group (e.g. known case, travel); 2 – symptoms; 3 – also asymptomatic x
Contact tracing H3 Coding: 0 no tracing; 1 – limited tracing; 2 – comprehensive tracing x
Cases Cumulative cases per million people at the indicated cutoffs
Deaths Cumulative deaths per million people at the indicated cutoff

Sensitivity Analysis

A straightforward candidate for sensitivity analysis relates to the two Oxford pandemic containment indices: the Stringency Index and the Containment and Health Index. The indices are hardly used jointly except for developing a synthetic heatmap as they substantially overlap, given that the first captures containment measures along with public information campaigns and the second the same together with most of the indicators within the health category. Regardless of the overlap, characterizing the pandemic response by one or the other alone leads to substantial changes to the clusters, as shown respectively in Figures 15 and 17 below. The respective heatmaps in Figures 16 and 18 show the variable included for clustering and the respective stringency\strength of measures for each indicator. One notable example is that while containment alone singles out as the leftward branch jointly all countries with high severity in their outbreaks (including Belgium), the same is not the case once health measures such as testing and tracing enter the picture with the second index, alike in the main analysis.

A further distinction – given the divergence in the ranking across the two measures – is worth making for the outbreak characterization itself by cases or by deaths per million inhabitants. Clusters do differ running the full analysis (including fiscal measures) by cases or deaths alone. In the first instance, as shown in Figure 19, the high outbreak strength group is homogenously clustered in the leftward branch, prevailing over the limited fiscal response. Looking at the heatmap in Figure 20 along with the clusters characterization in Table 3 shows how the cases-only cluster homogeneously position all geographically eastwards countries on the rightward branch, regardless of their ranges of high-low fiscal responses. The east-west divide remains in considering deaths alone as shown in Figure 21. However, sub-branches do vary across the two clusters. Nonetheless – as shown in Figure 22 – characterization of the emerging clusters is at first glance more problematic as countries like Greece and Sweden, differing both for the severity of the pandemic, containment policy mix and fiscal response are lower-branch grouped together. While the exercise supports the importance of considering both dimensions of the outbreak as in the main analysis, the key intended takeaway regards the instability of groupings depending on the variables considered.

Figure 10: Outbreak and containment clusters in the first wave.
Figure 10:

Outbreak and containment clusters in the first wave.

Figure 10 shows the clusters considering the timeframe up to the end of July. While the broad clustering within the heavy outbreak group remains on the left, the Czech Republic is not yet a member of this nefarious club. Moreover, sub-branches change somehow splitting more closely across the death-cases divide (Belgium, Italy, Spain) than the intensity of response measures as in the analysis on 2020 as a whole. Interestingly, the so-far stable couple of Estonia and Finland is split up in the early phase. Finland joins the Czech Republic, one of the subsequently most heavily hit countries (see Figure 11).

Figure 11: Heatmap of outbreak and containment measures in the first wave.
Figure 11:

Heatmap of outbreak and containment measures in the first wave.

Short of fully detailing the clusters, four broad groups emerge, the main branch on the left with the heavily impacted Member States and three groups within the branch on the left. As shown in Figure 10, starting from the less sizable group, Hungary, Slovakia, Croatia and Malta are among the most spared by the pandemic, while deploying nonetheless a fairly stringent containment response. The branch to their left is likewise largely spared, but in parallel deploying more modest containment measures. While the remaining branch captures a sizable middle-ground. Heterogeneous responses remain also in the first phase, considering that also in the heavily hit group measures deployed by Italy differ substantially, for example, from the more modest response in Belgium and Spain, among the severely hit countries in the early phase.

Figure 12: Outbreak and containment clusters in the second wave.
Figure 12:

Outbreak and containment clusters in the second wave.

Switching to the post-July period, clusters change substantially. Firstly, some of the usual suspects such as Italy, leave altogether the high incidence group. More in general, the singling out of heavily hit countries is less straightforward. The more moderately hit leftward branch still groups, however, countries such as Italy, Lithuania and Romania with higher responses – especially in terms of school closures – while the heavily hit Czech Republic and Slovenia display limited containment measures, as shown in Figure 12. All to say that regardless of the changes in where countries fall according to outbreak and response, it is not the case that homogeneity emerges in the second wave across the severity of the pandemic and stringency of the deployed measures. Additionally, as well displayed in the heatmap in Figure 12, substantial variation in the chosen policy mix across countries remains even in the later stage. Interestingly, full convergence emerges for information campaigns (hence excluded in the heatmap below) and limited convergence at the extremes emerges for contract tracing. Testing, however, remains heterogeneous and with somewhat limited matching to the severity of the outbreak (see Figure 14).

Figure 13: Heatmap of outbreak and containment measures in the second wave.
Figure 13:

Heatmap of outbreak and containment measures in the second wave.

Disaggregate Response Model Heatmap

Figure 14: Heatmap showing all disaggregate indices components.
Figure 14:

Heatmap showing all disaggregate indices components.

Alternative Specification: Outbreak and Stringency

Figure 15: Outbreak and stringency clusters.
Figure 15:

Outbreak and stringency clusters.

Figure 16: Outbreak and stringency heatmap.
Figure 16:

Outbreak and stringency heatmap.

Alternative Specification: Outbreak Plus Containment and Health Index Components

Figure 17: Outbreak and containment and health clusters.
Figure 17:

Outbreak and containment and health clusters.

Figure 18: Outbreak and containment and health heatmap.
Figure 18:

Outbreak and containment and health heatmap.

Alternative Specification: Response Model with Outbreak by Cases

Figure 19: Outbreak (by cases only), containment and fiscal response clusters.
Figure 19:

Outbreak (by cases only), containment and fiscal response clusters.

Figure 20: Outbreak (by cases only), containment and fiscal response heatmap.
Figure 20:

Outbreak (by cases only), containment and fiscal response heatmap.

Alternative Specification: Response Model with Outbreak by Deaths

Figure 21: Outbreak (by deaths only), containment and fiscal response clusters.
Figure 21:

Outbreak (by deaths only), containment and fiscal response clusters.

Figure 22: Outbreak (by deaths only), containment and fiscal response heatmap.
Figure 22:

Outbreak (by deaths only), containment and fiscal response heatmap.

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Received: 2021-01-31
Accepted: 2021-06-23
Published Online: 2021-07-09
Published in Print: 2021-12-20

© 2021 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Editors Note
  3. Editor’s Note
  4. Special on COVID-19 Research
  5. Covid-19 Response Models and Divergences Within the EU: A Health Dis-Union
  6. Landscape Political Ecology: Rural-Urban Pattern of COVID-19 in Nigeria
  7. The Relationship Between Poverty and COVID-19 Infection and Case-Fatality Rates in Germany during the First Wave of the Pandemic
  8. Covid-19: A Trade-off between Political Economy and Ethics
  9. Special on Inequality, Policy and Society
  10. Causes and Consequences of Income Inequality – An Overview
  11. Monetary Policy and the Top 10%: A Time-Series Analysis Using ARDL and ECM
  12. Trade Intensity, Fiscal Integration and Income Inequality in ECOWAS
  13. Empirical Analysis of the Effect of Fiscal Policy Shocks in China
  14. The Political Economy of Sectoral Credit Provisioning in India: An Empirical Analysis
  15. Perspectives on Gender Stereotypes: How Did Gender-Based Perceptions Put Hillary Clinton at an Electoral Disadvantage in the 2016 Election?
https://doi.org/10.1515/spp-2021-0003
Schlagwörter für diesen Artikel
COVID-19; European Union; pandemic response; cluster analysis; comparative politics

Artikel in diesem Heft

  1. Frontmatter
  2. Editors Note
  3. Editor’s Note
  4. Special on COVID-19 Research
  5. Covid-19 Response Models and Divergences Within the EU: A Health Dis-Union
  6. Landscape Political Ecology: Rural-Urban Pattern of COVID-19 in Nigeria
  7. The Relationship Between Poverty and COVID-19 Infection and Case-Fatality Rates in Germany during the First Wave of the Pandemic
  8. Covid-19: A Trade-off between Political Economy and Ethics
  9. Special on Inequality, Policy and Society
  10. Causes and Consequences of Income Inequality – An Overview
  11. Monetary Policy and the Top 10%: A Time-Series Analysis Using ARDL and ECM
  12. Trade Intensity, Fiscal Integration and Income Inequality in ECOWAS
  13. Empirical Analysis of the Effect of Fiscal Policy Shocks in China
  14. The Political Economy of Sectoral Credit Provisioning in India: An Empirical Analysis
  15. Perspectives on Gender Stereotypes: How Did Gender-Based Perceptions Put Hillary Clinton at an Electoral Disadvantage in the 2016 Election?
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