The corona crisis hit the different European countries differently and government responses were imposed at different speeds. The reasons for these observed divergent trends are not yet clear. Since the goal of government action in response to the corona pandemic – reducing the number of deaths – should be the same in each country, political institutions should be particularly important in explaining the differences in the speed and extent of responses and restrictions. Aiko Wagner (FU Berlin and WZB) and Sascha Kneip (WZB Berlin) test this assumption for its empirical validation. They combine data on the separation of powers and the number of corona-related infections and show that institutional veto points do not explain the differences in the speed and extent of government responses. Instead, they find that the extent of problem pressure in the different countries studied has much more explanatory power.
Since February 2020, the corona pandemic has affected all European countries to varying degrees. Also, the response of governments to this pandemic has varied between countries, regarding the closing of schools, suspension of sports events, curtailment of civil liberties and general lockdown of public life. While in Britain the government long denied a serious threat from the pandemic and refused to take drastic measures, France took strong measures to slow down the spread of coronavirus quite early and rapidly. In Germany, too, the measures to contain the virus were implemented very quickly after initial hesitation, while in Sweden only moderate measures were taken for a long time. The reasons for these divergent trends observed in Europe are not yet clear. Two explanations seem worth investigating: first, the different reactions of governments can simply be a function of problem pressure in relation of the scale of the outbreak. In this case, government’s responses to the crisis are explained by the external shock or ‘treatment’ with Covid-19 itself. This scope hypothesis is a straightforward application of the stimulus-response model: the earlier and larger the outbreak, the earlier and more severe the reactions of governments.
Secondly, the different reactions can also be the result of different institutional structures in the respective countries. In comparative political science, veto point theory is a dominant approach to explaining differences in public policies (Immergut 1992). Veto points are institutions that are necessary for the creation of laws. These institutions can hinder a change of policy and thus stabilize the status quo if the governing parties do not have a majority in these arenas (Immergut 2010). Since the goal of government action in response to the corona pandemic should be identical across different countries – reducing the number of deaths – political institutions should be of particular importance in explaining differences in the restrictions.
Veto point hypothesis and horizontal and vertical checks and balances
Two aspects are relevant here. First, the dimension of vertical separation of powers: Federalism can severely limit the power of the national executive or slow down the implementation of policies, as long as the subnational units have competences in the policy area concerned. Second, the specification of horizontal checks and balances, i.e. the balance of powers between the executive, the legislative and the judicial branches. Legislative and judicial interventions determine how quickly and extensively the government can react. Accordingly, a strong federal system of government with a pronounced separation of powers should react more slowly to new challenges than a unitary system of government without a strong jurisdiction. The veto point hypothesis is therefore quite simple: more and stronger veto points are associated with slower and less strict restrictions.
For our analysis, we conceptualize the corona pandemic as a natural experiment. Our assumption is that the occurrence of the virus as an external shock acts like a ‘treatment’ leading to political reactions. The question is whether these reactions are determined by the treatment itself or by institutional settings. The answer to this question gives us new insights into how democratic politics reacts to unexpected challenges.
Timing, steepness, and extent of reactions
We measure these restrictions as the mean value of six indicators from the Oxford Coronavirus Government Response Tracker project (Hale et al. 2020). The six variables represent the closing of schools, the closing of workplaces, the cancelation of public events, the shutdown of public transportation, restrictions on domestic mobility, and international travel controls. Thus, the variables represent restrictions on civil liberties and public life in general, but do not include measures for economic recovery. These restrictions were recoded from 0 (no restrictions) to 1 (restrictions in all six areas). Figure 1 shows the different trajectories in our 23 European countries from 1 February to 15 April.
It is noticeable that the reactions of the various countries differ considerably in terms of the speed and extent of the restrictions. While Italy, for example, responded to the pandemic comparatively early and increased the intensity of interventions quickly, Sweden reacted very hesitantly and with only moderate interventions. Great Britain also responded late to the challenges and even then only with below-average intensity. With regard to the slope or ‘steepness’ of the curve in Figure 1, Italy is by no means the forerunner: reactions from 0 to 1 in a short time show countries as different as Austria, Denmark, Spain, or Slovenia. Spain in particular reacts very late, but then particularly intensively. What Figure 1 shows, therefore, are clear differences between countries in three points: the speed in terms of a) timing and b) steepness of the reactions, and c) the extent of the reaction.
Since we want to explain the speed and extent of governmental responses to the crisis in terms of restrictions on public life, our first dependent variable is the date on which a country put the restrictions into force – between the end of February and mid-March. Our second dependent variable is the steepness of the curves from the date of the first reactions to the country-specific maximum. Our third dependent variable is the extent of restrictions, measured as the maximum of the index presented in Figure 1. While some restrictions were imposed in all countries, some countries reacted more strictly than others did (see Figure 2).
We use data from the Democracy Barometer project (Merkel and Bochsler 2018) to measure the strength and number of institutional points (vertical and horizontal checks and balances). In the horizontal dimension, the Democracy Barometer (DB) looks at the balance of power between the executive, legislative and judicial branches and at the extent of judicial review. As vertical checks of power, the DB includes the degree of federalism and the subnational fiscal autonomy.
It’s problem pressure, stupid!
As figure 2 shows, there is a large variance between the countries studied with regard to horizontal and vertical veto points. Now the question is: Did those institutional veto points affect the restrictions that seemed necessary to fight the corona pandemic? To answer the question, we regressed the three dependent variables on the horizontal and vertical veto points as measures of the DB project. We model the external shock of the coronavirus using the logarithm of the number of confirmed cases at the time when a country introduced the measures (“log. cases”) and the timing of the outbreak of the infection in each country (date when the number of confirmed cases reached 100) (again using data from Hale et al. (2020)).
|Model 1||Model 2||Model 3|
|Dependent Variable:||1st day||Slope||Extent|
|Checks btw three Powers||-0.011||0.001||0.008*|
|Vertical Power Sharing||-0.017||0.000||-0.001|
|Date when Infections = 100||0.713***||-0.003*||-0.011|
Note: Results of OLS regressions, standard errors in parentheses. We re-estimated model 2 using a logged dependent variable and calculated truncated and tobit regressions (right-truncation/upper limit at 1) for model 3. The results, however, are the same. * p<0.05; ** p<0.01; *** p<0.001.
Regarding our first dependent variable (when did the restrictions start), neither vertical nor horizontal power sharing institutions make a difference: institutional veto points do not explain why some countries reacted to the pandemic sooner or later than others. The results of model 1 speak in favor of the first hypothesis: If the 100th positive case of corona was identified at a later point in time, the respective country started with the restrictions later. At the same time, countries with later reactions were those with more positive cases. As R² indicates, the timing of the starting point of a country’s response can very well be explained by the timing of the outbreak.
Regarding our second dependent variable, again, neither checks between the three powers nor vertical power sharing had an effect. However, the more cases a country was confronted with at the beginning of the pandemic, the steeper the path a country took from no restrictions to full restrictions. This steepness of the curve also correlates with the date on which the 100th positive case was identified: Countries that were affected earlier put restrictions in place more quickly. This means that the ‘treatment’ with corona explains government’s reactions – institutional veto points again had no effects.
However, the severity of the restrictions is not a function of the severity of the crisis itself (third dependent variable). Neither the number of confirmed cases nor the date of the 100th positive case correlates significantly with the extent of restrictions (model 3), leading to a rejection of the scope hypothesis. Interestingly, horizontal power sharing has a positive correlation with the extent of restrictions, both with and without controlling for the number of cases: more checks and balances between the political branches are associated with stronger restrictions (model 3; see Figure 3). This is in complete contradiction with the predictions of the institutional veto point perspective. One explanation for this may be that in more closely intertwined institutional settings, blame-sharing is easier and leads to more strict reactions. Conversely, blame attribution is easier when a government is subject to less control. Under these circumstances, governments may be more cautious in their actions, as they do not want to take the blame for negative consequences of the restrictions. Another explanation could be that at the beginning of the pandemic, governments acted through executive decrees rather than formal legislation. Veto points were therefore initially absorbed and ineffective. But these post-hoc speculations need to be further elaborated in future research.
The overall result is that in times of extraordinary challenges such as the corona pandemic, veto point theory obviously does not help to explain reactions of governments – neither how quickly a country reacts nor how intense the reaction is. Problem pressure, on the other hand, explains the speed of the restrictions in Europe well.
Aiko Wagner ist wissenschaftlicher Mitarbeiter der Abteilung „Demokratie und Demokratisierung“ am Wissenschaftszentrum Berlin für Sozialforschung (WZB) und an der Freien Universität Berlin. Er ist Mitglied der German Longitudinal Election Study (GLES).
Dr. Sascha Kneip ist wissenschaftlicher Mitarbeiter der Abteilung Demokratie und Demokratisierung des WZB. Seine Forschungsgebiete umfassen Rechts- und Verfassungspolitikforschung, empirische Gerichtsforschung sowie theoretische und empirische Demokratieforschung.
Hale, Thomas, Sam Webster, Anna Petherick, Toby Phillips, and Beatriz Kira. 2020. Oxford COVID-19 Government Response Tracker. Blavatnik School of Government.
Immergut, Ellen M. 1992. Health Politics. Interests and Institutions in Western Europe. New York: Cambridge University Press.
Immergut, Ellen M. 2010. “Political Institutions.” In The Oxford Handbook of the Welfare State, ed. Frank G. Castles, Stefan Leibfried, Jane Lewis, Herbert Obinger & Christopher Pierson. Oxford: Oxford University Press: 235–245.
Merkel, Wolfgang and Daniel Bochsler. 2018. Democracy Barometer. Aarau: Zentrum für Demokratie.
 These results hold even if we exclude Sweden as a potentially influential outlier.