This study uses a welfare state lens to examine disparities in Covid-19 infections and mortality rates between countries in Eastern Europe compared to West European democracies. Expanding on Esping-Andersen’s typology of welfare regimes, the authors compare six country groups to conduct a multivariate statistical analysis that, when controlling for economic and health differences, shows the number of cases and deaths per 100,000 to be significantly higher for Eastern Europe. In comparing First, Second, and Third Wave data, the difference in Covid-19 infections and mortality rates can be explained through stricter lockdown measures implemented in the East at the start of the First Wave. Overall higher numbers in the East reflect comparatively looser state measures in response to the Second and Third Waves as well as the lack of trust in government and the weak implementation of public health measures.
Introduction and Context
State social insurance was first legislated into German social policy in 1881, when chancellor Otto von Bismarck introduced sickness and accident coverage as well as old age and invalidity insurance as social benefits. By 1911, Britain caught up to Germany and established old-age pensions, health insurance, workers’ compensation, and unemployment insurance (Kuhnle and Sander 2010). The nineteenth-century social insurance packages were the first forms of welfare provision undertaken by modern states. Until World War I welfare was provided through social insurance; but after the Great Depression, both as a consequence of the war and a means to resist the perceived communist threat, welfare in the Western world was delivered based on the idea of universal coverage: social rights, rather than the capacity to have previously contributed to the system, determined access (Lightman and Lightman 2017).
It is important to note that Western welfarism was always envisioned as complementing capitalism. The welfarist perspective accepts that societal resources will be primarily distributed through the private market, yet this sort of distribution is to be evened out via the implementation of redistributive measures aimed at achieving a less unequal playing field, hence via intervention through what social policy analysts have called a secondary distribution. In short, the purpose of the welfare state is to undo the inequalities produced through the primary distribution of resources in the private market. Through several interlocking programs, such as sick pay, pensions, maternity leave, educational leave, unemployment support, and social assistance, the welfare state aims to de-commodify its citizens from the market—to reduce, in other words, individual dependency on the private realm. The larger this de-commodification, some would say, the stronger a country’s welfare regime (Esping-Andersen 1990). By contrast, looking at Eastern Europe in the post-1945 communist era, the state became the primary stratification agent in societal resource allocation given the absence of a private market. With the fall of communism in 1989, the Eastern European states transitioned from a state-based primary distribution to a system granting private markets primacy, with the state assigned merely to manage the negative effects produced by this brand of distribution.
In The Three Worlds of Welfare Capitalism, the Danish sociologist Gøsta Esping-Andersen (1990) established a welfare state typology that would guide analyses within the social policy field for years to come. Focusing on a diverse set of criteria related to social rights, social stratification, and distribution across the market, along with the state’s assistance in the delivery of well-being in relation to health care, old age, or social security, Esping-Andersen divided most Western nations into three types of welfare states: Liberal, Social Democratic, and Conservative/Corporatist. The Liberal group includes the Anglosphere states of Canada, the United States (US), Ireland, the United Kingdom (UK), Australia, and New Zealand, as well as Japan, the only non-Western state grouped under this rubric. Sweden, Norway, Denmark, Finland, Iceland, and the Netherlands form the Social Democratic cluster. Continental Europe includes what Esping-Andersen called the Conservative or Corporatist states: Italy, Greece, France, Germany, Austria, Switzerland, and Belgium.
Staple characteristics of the Liberal states include minimalist governmental intervention, the provision of modest benefits and social insurance plans, guaranteed support for the market, and a pronounced emphasis on individual rather than collective responsibility. In contrast, universal coverage for health and benefit provisions, a strong focus on governmental intervention and social equality in welfare distribution are characteristic of the Social Democratic states. The Conservative/Corporatist states feature a minimum, usually means-tested level of welfare provision that have a generally negligible impact on status differentials, and the distribution of welfare according to actuarial principles that reward former contributions (Asatiani and Verulava 2017; Esping-Andersen 1990; Van Voorhis 2002). Social Democratic regimes envision welfare distribution as aligned with the population’s needs; whereas Conservative models are based on social insurance principles and the Liberal states’ policies are grounded in the notion of minimum public intervention.
Esping-Andersen’s typology has been criticized for its single-minded orientation toward Western democracies and for its neglect of the newly established democracies within the former Eastern bloc. Indeed, for Esping-Andersen (1990) any unorthodox state not easily conforming to his classification is unstable. The steady regimes were those tailored to the Liberal, Social Democratic, and Conservative types. However, the new democracies that emerged out of the fall of communism have also been assessed as hybrid welfare states. Scholars have argued that the introduction of Perestroika in the Soviet Union, the liberalization of trade, and the transition toward the private marketization of healthcare transformed the former Soviet republics into Liberal or Social Democratic regimes (Asatiani and Verulava 2017; Cook 2007). The concomitant incorporation of state and market elements and the fusion of a social democratic universalism in terms of social coverage with stratified provisions particular to the corporatist models have transformed the former Eastern bloc countries into unorthodox replicas of their Western counterparts (Cook 2007; Soede et al. 2004).
Fitting the East European states into a welfare state typology is not a straightforward task. The academic literature on the topic has not been decisive. Some scholars group these countries under the one-size-fits-all “postcommunist” welfare rubric (Adăscăliței 2012) or under a single Central-East European cluster (McMenamin 2004). Others question the amalgamation of Central European states with those from the Eastern part of the continent due to various regional differences. Central European states hew close to the corporatist continental model, whereas the East European states are envisioned as unstable actors, unable to match what the traditional welfare regimes can do (Laužadytė-Tutliene, Baležentis, and Goculenko 2018). Therefore, some propose that distinctions be drawn among the former USSR countries, the most economically developed states from East Central Europe such as Poland and the Czech Republic, and the less economically developed countries such as Romania, Moldova, and Georgia (Fenger 2007). Differences in post-1989 reform trajectories ought to be considered in such classifications as well. The most economically developed countries from East Central Europe had a smoother transition, and most are now European Union (EU) members. In contrast, the post-USSR republics saw a gradual withdrawal of state protectionism (Adăscăliței 2012; Cook 2007; Laužadytė-Tutliene, Baležentis, and Goculenko 2018). While Poland and Hungary, for instance, moved toward a “market-conforming social liberal model” (Cook 2007, 22), Russia and Kazakhstan chose an informalized system of welfare provision with weak coverage and unregulated social security markets (Cook 2007).
Consider, too, the popular attachments to statist welfare ideals. In Russia, liberalizing reforms intended to replace public programs with social insurance markets were publicly blocked for most of the 1990s. Social programs inherited from the Soviet Union endured throughout the transition period until the beginning of the 2000s. Popular opposition to the reforms came from the former communist stakeholders, who created new electoral parties aiming to re-implement a system of welfare provision reminiscent of what had been in place under the communist regime (Cook 2007). Market-based economic restructuring was also delayed in Belarus and Ukraine (Adăscăliței 2012). In contrast, in Central European and in other East European countries the political right has regularly been in power, promoting a small-state/big-market agenda—think of the right-leaning Croatian Democratic Union (Hrvatska demokratska zajednica, HDZ), Fidesz in Hungary, the Citizens for European Development of Bulgaria (Grazhdani za Evropeysko Razvitie na Bŭlgaria, GERB), or the Law and Justice party in Poland (Prawo i Sprawiedliwość, PiS) (Bejan 2016). Even where the electorate alternated between left and right, as in Romania, where the Social Democratic Party was regularly voted into power, major welfare reforms have been avoided. As the mainstream approach involved pushing a liberal model of minimal state protectionism, welfare provisions have mainly been used as a carrot dangled in front of voters to secure electoral gains (Adăscăliței 2012). In the social policy literature on welfare provision, the successor states of Yugoslavia have largely been left out of such classification attempts. Some have deemed these countries, envisioned as a combination of corporatist and liberal elements, unfit even for the hybrid welfare models that have been proposed for the other Central and Eastern European states, due to the former’s recent history of ethnonational warfare (Stambolieva 2015).
In exploring how the Covid-19 pandemic unfolded across various welfare regimes, this paper starts from Esping-Andersen’s typology and adds former communist states to the analysis. When we started to collect data, at the beginning of the First Wave of the pandemic, we expected that different welfare trajectories could explain differences in Covid-19 infection and mortality rates and that such differences would be observable along East-vs.-West geopolitical lines. This logic rested on the supposition that the Liberal and Corporatist Western states would have higher Covid-19 mortality rates than the democracies in the East, which have a legacy of decades-long statist systems of social and healthcare provision. While the former communist countries lag behind the older European democracies in addressing citizens’ needs (Adăscăliței 2012; Soede et al. 2004), these same states also inherited an institutional legacy of public social policies and universal systems of social service provision (Cook 2007; Fenger 2007; Nagy, Király, and Géring 2016) that could have exerted a protective effect on Covid-19 infection and mortality rates. Consider healthcare. In the former Soviet Union, health provision was heavily centralized, so that by the 1970s many of the republics had surpassed the wealthier West European states in their numbers of hospital beds and healthcare staff (Asatiani and Verulava 2017) a trend that continued to the present day (WHO 2020a). Adequate numbers of hospital beds and hospital staff, especially during a pandemic, represent a form of welfare provision that reflects the care of the state for its most vulnerable citizens.
This analysis follows Esping-Andersen’s division of Liberal, Social Democratic, and Corporatist states since this grouping has been the most influential paradigm within the field of social policy. Indeed, most complementary welfare state classifications have been derived from Esping-Andersen and are mere variations of his typology. For instance, Leibfried (1993) divided the Conservative states into “Bismarck” and “Latin rim” types. Ferrera (1996) included a distinct southern category (i.e., Greece, Italy, Portugal, Spain) and omitted Australia, Canada, and the US from the Anglosphere grouping. Navarro and Shi (2001), labeling the Conservative states as Christian Democrat, apportioned some of them into an ex-fascist group containing Spain, Greece, and Portugal. Bambra (2005) formed a subgroup amongst the Liberal states which included Ireland, the UK, and New Zealand but limited the “main liberal group” to Australia, Japan, and the US. Soede et al. (2004) labeled the Liberal states as Anglo-Saxon, grouped Greece, Spain, Portugal, and Italy into a Mediterranean cluster, and extended Esping-Andersen’s taxonomy to include as a stand-alone subset the Visegrád countries of Poland, the Czech Republic, Hungary, and Slovakia.
In our study, we do something similar: We add former communist states to Esping-Andersen’s classification. Yet to reflect geopolitical regional variations, we divide them into a) former USSR republics; b) Central and East European states; and c) Southeast European states. The former USSR republics include Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Latvia, Lithuania, Moldova, Russia, and Ukraine. While other scholars differentiated the welfare regimes in Central Europe from those from the eastern part of the continent (Fenger 2007; Laužadytė-Tutliene, Baležentis, and Goculenko 2018), we group them into one category due to methodological considerations related to sample size. The Central and East European countries for this paper’s sample include Bulgaria, Estonia, Hungary, Poland, Romania, Slovakia, and the Czech Republic (Table 1).
|Liberal||Social Democratic||Conservative/Corporatist||Central and Eastern Europe||Southeastern Europe||Former USSR|
The Southeast European sample group, then, includes those countries with their origins in the dissolution of the Socialist Federal Republic of Yugoslavia: Bosnia-Herzegovina, Croatia, Kosovo, Montenegro, North Macedonia, Serbia, and Slovenia. To this list, we add Albania. Yugoslavia was different from the rest of the communist world because it rejected the Soviet model of welfare provision yet created a version of its own that included strong benefit protection and comprehensive benefit coverage (Stambolieva 2015). Welfare was provided in the Socialist Federal Republic of Yugoslavia and continued to be provided in the postsocialist transition period on actuarial principles, hence based on contributable amounts paid into a plan that later allowed one to benefit from such a plan, and not on state budgets as was the case with the other former communist states (Bartlett, Božikov, and Rechel 2012; Bredenkamp, Mendola, and Gragnolati 2011). This is not to say that welfare provision has been homogenous across all the successor states of Yugoslavia. For instance, the state kept its monopoly in Slovenia, although the country adopted certain corporatist reforms, such as an individualized pension plan or voluntary health insurance, while North Macedonia, in contrast, has turned toward a residualized model of welfare provision during the transition period (Stambolieva 2015).
Albania is difficult to classify from a welfare state perspective, as it embodies characteristics common with the former USSR and displays more recent similarities to the other Southeast European states. On the one hand, Albania is different from the former Yugoslav states, as most of its communist-era welfare provision was centralized. For example, its health system, based on the Soviet model, was guided by a central government allocation of expenditures (Bredenkamp, Mendola, and Gragnolati 2011). Yet in the transition period, after 1995, Albania started to rely on health insurance, although on a smaller scale than in the successor states of Yugoslavia (Bredenkamp, Mendola, and Gragnolati 2011).
Social epidemiology has long used welfare state variables to analyze cross-national differences in population health and the distribution of the social determinants of health (Bambra 2007, 2011). Research has shown that welfare provision is effective in targeting health-related aspects and equalizing health outcomes by leveling the socioeconomic playing field and decreasing social marginalization (Kaplan 2007; Olafsdottir 2007). Welfare states with universal health care systems tend to show a less marked relationship between class stratification and health disparities and are generally associated with lower mortality rates (Chung and Muntaner 2006; Kaplan 2007; Lundberg 2010; Olafsdottir 2007).
Adopting a welfare state approach can yield insights into Covid-19 infection and mortality rates. When the pandemic’s first wave arrived in March 2020, many governments were hesitant to implement infection-restrictive measures. The UK, for instance, delayed lockdown and failed to impose timely restrictions on freedom of movement. The country quickly saw some of the highest rates of infection, hospitalization, and death in Europe (Burn-Murdoch and Giles 2020; Siddique 2020). By contrast, countries that acted rapidly, such as Greece, Australia, and New Zeeland, were able to contain, to a certain extent, the pandemic (Bejan and Nikolova 2021).
Lockdowns continued to be implemented through 2021, and policymakers kept trying to find the best strategies to fight Covid-19 community transmission. Yet not solely the lockdowns, but also the distribution of healthcare provisions—universal coverage, national health expenditures—were the pillars that strong welfare democracies depended on to manage pandemic-related health outcomes (Bejan and Nikolova 2021, 2022). In fact, most developed states were quick to mobilize state resources to address the pandemic’s health and socioeconomic effects. A welfare state is a stratifying structure (Esping-Andersen 1990) and thus any welfare regime would inevitably affect the Covid-19 situation in a given country. For instance, the Social Democratic states, strong on the provision of social rights and ideologically committed to values of altruism, caring, and a sense of solidarity, have developed, for years, programs aimed at de-commodifying workers and freeing them from reliance on the private market. It might well be assumed that Social Democratic welfare regimes might have been more inclined than their Liberal or Corporatist counterparts to implement physical distancing policies, as well as pandemic-related benefit measures such as wage subsidies, caregiving benefits, or sickness benefits that would better protect their residents. Take paid sick days as an example: Covid-19 is an occupational disease, likely spread on the job among those performing precarious yet essential work (Bejan et al. 2021). Lack of paid sick leave will force precarious workers to perform their jobs even when sick or feeling unwell, hence increasing the likelihood of viral spread. In contrast, paid sick days would help reduce the spread of Covid-19 through the mediation of what Esping-Andersen (1990) termed the decommodification of the individual from the labor market. Such decommodification indirectly supports a reduction in Covid-19 epidemiological counts, yet varies vis-à-vis the respective Social Democratic, Conservative/Corporatist, and Liberal states, and also vis-à-vis the add-on Eastern European sample.
This context is thus salient to this article’s examination of whether strong welfare democracies, possessing a universalist model of healthcare provision or a statist system of inherited social protectionism, constitute proxies for the mediation of adverse public health outcomes during the pandemic, compared to states that have historically been reliant on a stratified model of welfare provision. Theoretically speaking, we hypothesize that (1) the Social Democratic welfare regimes provided high pandemic benefit levels according to need and irrespective of former participation in the labor market, and these contributions, coupled with strong systems of healthcare access and distribution, helped reduce the spread of Covid-19; (2) the Conservative/Corporatist states, heavily based on social insurance principles, issued benefits based on former contributions, thereby forcing many to venture into the labor market and risk exposure to the virus—here we anticipate higher Covid-19 infection numbers and mortality rates than in their Social Democratic counterparts; (3) the Liberal states, whose distributive policies tend to be based on the idea of deserving versus undeserving recipients, left many residents unprotected and hence their infection rates were even higher; and (4) the former communist states, despite regional variation, most likely capitalized on the residue and memory of their former statist approaches to healthcare delivery and benefit provision and had somewhat lower rates of Covid-19 infections and mortality.
The outcome variables include the number of confirmed Covid-19 cases per 100,000 people and the number of Covid-19 deaths per 100,000 people during the first three waves of the pandemic. To account for possible underreporting of Covid-related cases and deaths, the excess deaths per 100,000 are also included as an outcome variable (Our World in Data 2020). Excess deaths represent the difference between the number of reported deaths in a country from all causes and the number of deaths that would have been expected had there been no pandemic. Using this metric, we account for pandemic deaths that might be attributed to non-pandemic causes as well as non-Covid-19 deaths occurring during the pandemic due to overstretched healthcare services. The coronavirus statistics reported by the World Health Organization (WHO) from 1 January 2020 to 30 June 2021 are used in conjunction with the latest available population estimates for each country to calculate the number of deaths per 100,000 and the number of cases per 100,000. Wave One comprises all cases/deaths that occurred between 1 January 2020 and 31 July 2020. Wave Two comprises the Covid-19 numbers between 1 August 2020 and 31 January 2021. Wave Three refers to the numbers counted between 1 February 2021 and 30 June 2021. Some countries have less clear delineations between the infection waves. Variations also exist among countries in each wave. The wave timelines were based on the average start/end dates of each wave for all the countries in our sample.
Economic, public health, and policy covariates were included to account for current differences among countries in the availability of healthcare resources (i.e., the number of doctors, nurses, and hospital beds) as well as the healthcare capacity and health coverage (i.e., Covid vaccinations, number of Covid tests, universal healthcare, etc.). Policy variables included public trust in government, stringency of government policies (i.e., lockdowns), income relief, and the running of electoral events during the pandemic. Measures of economic equality included the Gini Index, the inequality-adjusted Human Development Index (HDI), and Purchasing Power Parity (PPP), to control for differences among countries in terms of their economic status and income distribution, as these factors would affect healthcare capacity and coverage in any welfare democracy. The complete list of economic, public health, and policy variables is included in Table 2.
|Excess deaths per 100,000||The number of deaths from any cause compared to how many deaths would be expected had there been no pandemic (Our World in Data 2020)|
|Universal health coverage (UHC)||Coverage of essential health services on a scale of 0–100 based on 14 indicators of health service coverage including financial risk protection from health care expenses. Latest data available for 2017 (WHO 2020a)|
|Covid vaccination rate||Percent of population fully vaccinated against Covid at the conclusion of Wave 3 (Our World in Data 2021d)|
|Nurses per 10,000 people||Density of nursing and midwifery personnel per 10,000 people. Latest available data varies by country 2014–18 (WHO 2020b)|
|Doctors per 10,000 people||Density of medical doctors per 10,000 people. Latest data varies by country 2015–18 (WHO 2020c)|
|Hospital beds per 10,000 people||The number of hospital beds available for every 10,000 people. Latest available data varies by country 2013–18 (WHO 2020d)|
|Health expenditure as % of GDP||Current health expenditure by government as % of GDP. Latest available data 2017 (WHO 2020e)|
|Out-of-pocket expenditure as % of total expenditure on health||Out-of-pocket payments for health as percentage of total expenditure on health. Latest data 2014 (WHO 2020f)|
|More than 10% of household income on out-of-pocket healthcare||Proportion of population spending more than 10% of household income on out-of-pocket healthcare expenses (The World Bank 2020a)|
|Population over 65||Proportion of the population over the age of 65 (The World Bank 2020a)|
|Gini Index||An equality measure indicating the extent to which the distribution of income deviates from a perfectly equal distribution (0 represents perfect equality, 100 represents perfect inequality) (The World Bank 2020b)|
|Inequality adjusted Human Development Index (iHDI)||Represents average achievements in health, education, and income for each country, with 1 representing perfect equality and 0 representing perfect inequality (United Nations Development Programme 2020)|
|Purchasing Power Parity (PPP)||Gross domestic product (GDP) expressed as PPP, eliminates the effects due to variation in price levels between countries (The World Bank 2020d)|
|Income by lowest 20% of households||Share of income held by lowest 20% of households (The World Bank 2020c)|
|Government Stringency Index||A composite measure ranging from 0 to 100 based on strictness of closures (work, schools, borders, etc.). If intranational variation is present, measure is based on strictest subregion. Value taken two weeks from the start of each wave to allow for government response to health indicators (Our World in Data 2021a).|
|Mask policies||Based on strictest subregion in a country at the peak of each wave, dichotomized to no policy/recommended only or required in some or all public spaces (Our World in Data 2021b)|
|Income support||Coverage of salaries or universal basic income for those who have lost their jobs or were not able to work at the peak of each wave, dichotomized to no income support/covers less than 50% of salary and covers more than 50% of salary (Our World in Data 2021c)|
|Debt or contract relief||Government freezing of financial obligations, such as loan repayments, service stoppage, or evictions at the peak of each wave, dichotomized to no relief/narrow relief and broad relief (Our World in Data 2021d)|
|National elections||National election occurred in the country during Waves One to Three, yes/no (Politico 2021).|
|Trust in national government||Percentage of population reporting confidence in the national government (Ortiz-Ospina and Roser 2016).|
Pearson chi-square analyses examined differences in public health Covid-19 policy response by type of welfare state, with Fisher’s Exact test applied when expected cell counts were less than five. Bivariate correlations examined the relationships between Covid-19 outcomes and the potential continuous confounding variables related to health and economic factors. Repeated measures Multivariate Analysis of Variance (MANOVA) tests compared the average Covid-19 cases, mortality rates, and the number of excess deaths across the three waves and by the type of welfare state. A repeated measures MANOVA analysis is a mixed factorial design that accounts for the intercorrelations among a set of related continuous dependent variables; it also examines the change in these relationships across time. In other words, MANOVA can account for how the number of Covid-19 cases impacts the number of Covid-19 deaths, which, in turn, impacts the number of excess deaths reported by each country at each wave of the pandemic. A repeated measures Multivariate Analysis of Covariance (MANCOVA) was used to examine whether controlling for economic and health variables changes the relationship between the type of welfare state and confirmed cases, Covid-19 deaths, and excess deaths per 100,000 for Wave One, Wave Two, and Wave Three. Bonferroni adjustments were used in all bivariate tests, post hoc tests, and estimated marginal means calculations to control for experiment-wise error due to the large number of variables. All analyses were completed in Statistical Package for the Social Sciences (SPSS) version 27.
The timeline of Covid-19 policies was obtained through searches of governmental websites and newscasts for each country regarding nationwide Covid-19 restrictions implemented between 1 March and 31 December 2020. Local regulations for a single city, county, or region were excluded. Only national, large-scale measures were collected. The timeline of these policies was superimposed onto trendlines of the number of new cases per million. Trendlines were developed to limit the variation in testing rates between weekends/holidays and weekdays using a seven-day average of new cases per million (all cases declared from Monday to Sunday were added together and then divided by seven).
The average Covid-19 cases and deaths per 100,000 by type of welfare state are presented in Table 3. During Wave One, the lowest numbers of cases were within the Central and East European states with 133.9 cases per 100,000, and the highest numbers of cases were within the former USSR states at more than 430 cases per 100,000. The lowest number of deaths were within the Central and East European countries with 5.3 deaths per 100,000, and the highest number of deaths were within the Western Corporatist countries with 27.7 deaths per 100,000. Excess deaths were also lowest within Central and East European countries with less than 1 excess death per 100,000, while Corporatist and former USSR states were reporting 30 and 33 excess deaths per 100,000, respectively.
|Type of Welfare State||Wave One||Wave Two||Wave Three|
|Cases per 100,000 (SD)||Deaths per 100,000 (SD)||Excess deaths per 100,000 (SD)||Cases per 100,000 (SD)||Deaths per 100,000 (SD)||Excess deaths per 100,000 (SD)||Cases per 100,000 (SD)||Deaths per 100,000 (SD)||Excess deaths per 100,000 (SD)|
|Liberal||342.4 (453.6)||21.0 (24.2)||17.9 (40.3)||2152.8(2563.9)||31.7 (39.3)||45.6 (86.5)||928.1 (897.7)||17.4 (18.2)||49.2 (93.5)|
|Social Democratic||360.0 (243.6)||19.4 (22.1)||13.8 (29.4)||2720.7(2066.9)||25.4 (24.4)||39.4 (52.8)||2216.8(1963.6)||10.7 (10.2)||34.1 (58.6)|
|Conservative/Corporatist||372.6 (244.4)||27.7 (28.1)||29.7 (38.2)||4424.9(1727.8)||71.5 (23.6)||108.0 (67.2)||2724.9(732.2)||39.5 (16.6)||122.0 (69.2)|
|Central and Eastern Europe||133.9 (74.5)||5.3 (3.5)||0.01 (7.4)||4415.5(2105.6)||98.4 (40.5)||188.8 (71.1)||4064.7(1,8443.2)||118.0 (39.9)||321.7 (103.4)|
|Southeastern Europe||310.8 (163.0)||9.1 (6.1)||18.1 (19.3)||4943.6(2445.7)||101.9 (47.7)||226.8 (44.9)||3442.4(1348.6)||80.5 (47.9)||370.1 (106.9)|
|Former USSR||430.8 (394.8)||7.6 (8.1)||33.0 (34.2)||3356.3(1961.2)||52.6 (30.2)||205.2 (88.0)||2194.5(938.5)||46.7 (20.9)||290.6 (96.2)|
|Total||335.3 (297.2)||15.7 (20.0)||20.8 (32.2)||3738.5(2243.1)||64.6 (43.1)||137.2 (99.4)||2575.7(1533.6)||51.3 (43.4)||196.2 (153.5)|
During Wave Two, all countries experienced a sharp increase in the number of cases and deaths, with Corporatist, Central and East European as well as Southeast European countries all reporting more than 4000 cases per 100,000 and almost 100 deaths per 100,000 by the end of the wave. Excess deaths in Southeast European and the former USSR states were particularly high, with more than 200 deaths per 100,000 people. By the end of Wave Three, Central and East European states had more than four times the number of cases than Liberal states and ten times the number of Covid-19 deaths than Social Democratic states.
A selection of the economic and health predictors by type of welfare state is presented in Table 4. The number of doctors and hospital beds per 10,000 was highest in the former USSR states, while the number of nurses per 10,000 people was highest in the Liberal states. Universal health coverage and percent of GDP spent on health were also highest in Liberal states (85% and 10.2%, respectively) and lowest within the Southeast European (69% and 7.7%, respectively) and Central and East European states (72% and 6.7%, respectively). The highest out-of-pocket spending on health, at 49.44%, was within the former USSR republics. Vaccination rates and trust in national government were highest in Social Democratic states (37% and 67%, respectively) and considerably lower in all East European states.
|Type of Welfare State||Doctors per 10,000||Nurses per 10,000||Hospital beds per 10,000||Universal health coverage||% GDP for health||% out of pocket on health||iHDI||PPP||Wave One GSI||Wave Two GSI||Wave Three GSI||Trust in national govern-ment||Wave Three tests per 1000||% fully vaccinated at Wave Threea|
|Liberal||28.86 (5.63)||116.52 (30.20)||53.30 (45.75)||84.88 (4.05)||10.17 (3.07)||16.46 (730)||0.84 (0.03)||52,321 (14,937)||74.3 (5.6)||58.6 (4.6)||66.7 (14.4)||58.3 (10.5)||983.9 (859.6)||25.6 (17.8)|
|Social Democratic||37.34 (4.35)||137.48 (31.34)||29.80 (5.67)||83.67 (3.50)||9.87 (0.96)||15.12 (3.06)||0.88 (0.01)||57,306 (5997)||69.1 (4.3)||43.8 (3.7)||62.0 (13.5)||67.0 (11.8)||2055.3(2257)||37.1 (12.6)|
|Conservative/Corporatist||38.60 (10.72)||92.43 (57.25)||47.74 (16.19)||81.00 (2.80)||9.32 (1.98)||23.85 (10.82)||0.81 (0.04)||52,179 (22,987)||82.6 (1.4)||52.0 (2.7)||70.2 (10.6)||52.1 (19.3)||2687.0(2784)||35.4 (12.4)|
|Central and Eastern Europe||32.55 (10.63)||61.92 (32.70)||59.59 (15.31)||72.00 (6.23)||6.72 (0.89)||28.01 (13.02)||0.77 (0.05)||29,745 (8305)||79.1 (1.8)||44.0 (6.6)||65.5 (12.7)||43.6 (12.0)||1754.1(2756)||30.7 (11.9)|
|Southeastern Europe||28.32 (3.54)||64.90 (22.10)||45.36 (8.69)||69.33 (6.22)||7.68 (1.20)||28.16 (13.86)||0.73 (0.08)||21,177 (9444)||92.1 (2.2)||47.8 (4.6)||57.2 (10.1)||37.9 (9.5)||455.2 (154.9)||18.7 (12.6)|
|Former USSR||43.63 (14.22)||70.29 (21.68)||60.93 (21.50)||70.80 (4.05)||6.54 (1.82)||49.44 (20.14)||0.72 (0.05)||20,462 (9154)||78.1 (9.1)||47.8 (4.6)||58.0 (9.4)||48.9 (24.7)||814.4 (513.3)||11.9 (12.8)|
|Total||35.70 (10.86)||89.09 (43.95)||50.73 (24.12)||77.06 (7.51)||8.40 (2.34)||27.92 (17.12)||0.79 (0.07)||38,811 (20,583)||64.0 (12.2)||50.8 (18.0)||1480.0(1953)||25.8 (15.6)|
aVaccination numbers are taken from the end of Wave Three and reflect only the first few months of vaccine administration. GDP, Gross Domestic Product; iHDI, inequality adjusted Human Development Index; PPP, purchasing power parity; GSI, Government Stringency Index.
Few health and economic variables were significantly correlated with Covid-19 cases, deaths, and excess deaths (see Table 5). There were no significant correlates of Covid-19 cases during Waves One, Two, or Three. Wave One deaths were related to the Wave Three Government Stringency Index (r = 0.369, p < 0.01), such that higher deaths at Wave One were associated with more stringent policy measures implemented during Wave Three. Wave Two and Three Covid-19 deaths were negatively correlated with trust in national government (r = −0.43, p < 0.01 and r = −0.51, p < 0.001), with higher trust in government being associated with moderately lower numbers of Covid-19 deaths. The same relationship was seen between Wave Three excess deaths and trust in national government (r = −0.44, p < 0.001). The number of nurses per 10,000 was also negatively correlated with Wave Three deaths (r = −0.51, p < 0.001), Wave Two excess deaths (r = −0.42, p < 0.01), and Wave Three excess deaths (r = −0.53, p < 0.001). The numbers of doctors, hospital beds, Covid-19 tests, and Covid-19 vaccination rates were not at any point correlated to Covid-19 cases, deaths, or excess deaths. Universal health coverage was associated with lower numbers of Wave Three deaths (r = −0.53, p < 0.001), Wave Two excess deaths (r = −0.56, p < 0.001), and Wave Three excess deaths (r = −72, p < 0.001). The HDI and PPP were also strongly correlated with decreased Wave Three Covid-19 deaths, Wave Two excess deaths, and Wave Three excess deaths. Higher percentage of out-of-pocket health expenditures were associated with higher numbers of excess deaths during Wave One (r = 0.37, p < 0.01) and Wave Three (r = 0.44, p < 0.001).
|Wave Three Government Stringency Index||Trust in National Government||Nurses per 10,000||Universal Health Coverage||Percent of GDP for Health||Percent of Out-of-Pocket Health Expenditures||Inequality Adjusted Human Development Index||Purchasing Power Parity|
|Wave One deaths||0.369**||0.434**|
|Wave Two deaths||−0.432**|
|Wave Three deaths||−0.506***||−0.507***||−0.527***||−0.451***||−0.386**|
|Wave One excess deaths||0.371**|
|Wave Two excess deaths||−0.421**||−0.558***||−0.618***||−0.544***|
|Wave Three excess deaths||−0.439***||−0.528***||−0.716***||0.435**||−0.724***||−0.654***|
**p < 0.01, ***p < 0.001. Non-significant relationships are excluded and there were no significant correlates of Wave One, Two, or Three cases. Other non-significant variables include vaccination rates at Wave Three; Wave One and Two Government Stringency Index; Wave One, Two, and Three tests per 1000; doctors per 10,000; hospital beds per 10,000; GINI index; income share by lowest 20%; percent of population over 65; and poverty gap.
The relationships between the type of welfare state and categorical policy variables are presented in Table 6. Only mask policies during Wave One and income support policies during Wave One and Two significantly differed by type of welfare state. During Wave One Social Democratic and Liberal states were less likely than the Corporatist states and East European countries to require face coverings in public places. By Waves Two and Three, all countries implemented similar face-covering requirements. While income supports and debt relief were similar across the board for Wave One, income supports were more prevalent in Social Democratic, Corporatist, and Central and East European states, and were relatively rare in Southeast European and the former USSR states during the Second and Third waves. There was no relationship between electoral events during the pandemic and the type of welfare state. There was also no relationship between electoral events at any point during the pandemic and the level of stringency adopted for lockdowns, mask mandates, or Covid-19 cases or deaths during Wave One, Wave Two, or Wave Three.
|Variable||Liberal||Social Democratic||Conservative/Corporatist||Central and Eastern Europe||Southeastern Europe||Former USSR||Pearson chi-square (Cramer’s V)|
|Election during pandemic|
|No||6 (75.5%)||5 (83.3%)||10 (83.3%)||4 (57.1%)||4 (50%)||3 (30%)||9.02 (0.42)|
|Yes||2 (25%)||1 (16.7%)||2 (16.7%)||3 (42.9%)||4 (50%)||7 (70%)|
|Wave One mask policy|
|No policy||7 (87.5%)||6 (100%)||3 (25%)||3 (42.9%)||4 (66.7%)||6 (66.7%)||13.83** (0.54)|
|Required||1 (12.5%)||0 (0%)||9 (75%)||4 (57.1%)||2 (33.3%)||3 (33.3%)|
|Wave Two mask policy|
|No policy||2 (25%)||2 (33.3%)||0 (0%)||0 (0%)||0 (0%)||0 (0%)||10.91 (0.48)|
|Required||6 (75%)||4 (66.7%)||12 (100%)||7 (100%)||6 (100%)||9 (100%)|
|Wave Three mask policy|
|No policy||1 (12.5%)||0 (0%)||0 (0%)||0 (0%)||0 (0%)||0 (0%)||5.11 (0.33)|
|Required||7 (87.5%)||6 (100%)||12 (100%)||7 (100%)||6 (100%)||9 (100%)|
|Wave One debt relief|
|No or narrow relief||4 (50%)||3 (50%)||5 (41.7%)||1 (14.3%)||3 (50%)||7 (77.8%)||6.61 (0.37)|
|Broad relief||4 (50%)||3 (50%)||7 (58.3%)||6 (85.7%)||3 (50%)||2 (22.2%)|
|Wave Two debt relief|
|No or narrow relief||3 (37.5%)||4 (66.7%)||4 (33.3%)||4 (57.1%)||5 (83.3%)||7 (77.8%)||7.45 (0.39)|
|Broad relief||5 (62.5%)||2 (33.3%)||8 (66.7%)||3 (42.9%)||1 (16.7%)||2 (22.2%)|
|Wave Three debt relief|
|No or narrow relief||4 (50%)||5 (83.3%)||5 (41.7%)||4 (57.1%)||5 (83.3%)||7 (77.8%)||5.96 (0.35)|
|Broad relief||4 (50%)||1 (16.7%)||7 (58.3%)||3 (42.9%)||1 (16.7%)||2 (22.2%)|
|Wave One income support|
|None or <50% of lost salary||3 (37.5%)||0 (0%)||3 (25%)||1 (14.3%)||4 (66.7%)||6 (66.7%)||11.6 (0.49)|
|Covers >50% of lost salary||5 (62.5%)||6 (100%)||9 (75%)||6 (85.7%)||2 (33.3%)||3 (33.3%)|
|Wave Two income support|
|None or <50% of lost salary||3 (37.5%)||0 (0%)||2 (16.7%)||2 (28.6%)||4 (66.7%)||7 (77.8%)||14.47** (0.55)|
|Covers >50% of lost salary||5 (62.5%)||6 (100%)||10 (83.3%)||5 (71.4%)||2 (33.3%)||2 (22.2%)|
|Wave Three income support|
|None or <50% of lost salary||3 (37.5%)||0 (0%)||3 (25%)||1 (14.3%)||4 (66.7%)||7 (77.8%)||14.42** (0.55)|
|Covers >50% of lost salary||5 (62.5%)||6 (100%)||9 (75%)||6 (85.7%)||2 (33.3%)||2 (22.2%)|
**p < 0.01, ***p < 0.001. Cramer’s V measures the effect size between two categorical variables. A Cramer’s V of 0.10 indicates a small effect, 0.30 is a medium effect, and 0.50 is a large effect. Effect sizes are independent of sample size and statistical significance.
The stringency of lockdowns varied. While there was no statistically significant difference for the Liberal states among the three waves as measured by the Government Stringency Index (GSI), all other welfare regimes experienced significant decreases in the level of stringency between Waves One and Two. The Social Democratic and the former USSR states decreased on the GSI by 25 points (p < 0.05) from Wave One to Wave Two. Corporatist and Central and East European states decreased by 30 and 35 points each (p < 0.001 and p < 0.01, respectively) and Southeast European states decreased the most, by 45 points (p < 0.001). While Social Democratic and Corporatist countries significantly increased the stringency of lockdowns between Waves Two and Three (MD = 18, p < 0.05 and MD = 18, p < 0.001, respectively), none of the East European countries showed a significant change in lockdown restrictions between Waves Two and Three.
Based on the repeated measures MANOVA, there is a significant relationship among wave, type of welfare state, and the Covid-19 cases, Covid-19 deaths, and excess deaths. Pillai’s Trace indicates that there is a significant main effect of the pandemic over time (F(6, 174) = 59.20, p < 0.001) on the number of cases (F(1.6, 69.8) = 100.27, p < 0.001), the number of deaths (F(2, 87.8) = 69.94, p < 0.001), and the number of excess deaths (F(1.4, 58) = 265.53, p < 0.001). Cases increased by an average of 3396 per 100,000 from Wave One to Wave Two (p < 0.001) and by 2292 per 100,000 from Wave One to Wave Three (p < 0.001). There was a significant decrease in the number of cases between Wave Two and Wave Three (MD = −1,104, p < 0.001). Similarly, the number of deaths increased from Wave One to Wave Two (MD = 49.4, p < 0.001) and from Wave One to Wave Three (MD = 38.1, p < 0.001) but decreased between Wave Two and Wave Three (MD = −11.3, p < 0.05). Excess deaths also increased from Wave One to Wave Two (MD = 116.9, p < 0.001) and from Wave One to Wave Three (MD = 179.2, p < 0.001) but decreased between Wave Two and Wave Three (MD = −63.3, p < 0.001).
Across all waves taken together, the type of welfare state also significantly impacted the number of Covid-19 cases (F(5,44) = 3.48, p < 0.05), Covid-19 deaths (F(5, 44) = 7.56, p < 0.001), and excess deaths (F(5, 44) = 11.68, p < 0.001). Liberal states had on average 1730 fewer cases than Central and East European states (p < 0.05) and 1888 fewer cases than Southeast European countries (p < 0.05). There were no other significant differences in the number of cases. Similarly, Liberal states had on average 50 fewer deaths than Central and East European states (p < 0.001) and 43 fewer deaths than Southeast European states (p < 0.01). Social Democratic regimes had on average 55 fewer deaths than Central and East European states (p < 0.001) and 48 fewer deaths than Southeast European countries (p < 0.01). Former USSR states also reported on average 38 fewer deaths than Central and East European states (p < 0.05). Lastly, Liberal states had on average 132 fewer excess deaths than Central and East European states (p < 0.01), 167 fewer excess deaths than Southeast European states (p < 0.001), and 138 fewer excess deaths than former USSR states (p < 0.001). Social Democratic states also had fewer excess deaths than Central and East European, Southeast European, and former USSR countries (MD = −141, p < 0.01, MD = −175, p < 0.001, and MD = −147, p < 0.001 respectively). Corporatist states were only significantly different from the former USSR and Southeast European countries (MD = −89, p < 0.05 and MD = −118, p < 0.01, respectively) with regard to the number of excess deaths per 100,000.
There was a significant interaction among the three respective waves and type of welfare state on the number of Covid-19 cases (F(7.9, 69.8) = 2.86, p < 0.01), Covid-19 deaths (F(10, 87.8) = 11.11, p < 0.001), and excess deaths (F(6.6, 58) = 28.36, p < 0.001). The interaction effect indicates that the increases in cases and deaths across each wave are dependent on the type of welfare state. Figure 1 demonstrates this interaction relationship. While all countries started Wave One with statistically similar Covid-19 cases, Covid-19 deaths, and excess deaths, by Wave Two all countries from Eastern Europe were experiencing significant increases in cases and deaths compared to the Liberal and Social Democratic states. The difference among types of welfare states becomes particularly pronounced when examining Wave Three excess deaths. Eastern Europe had average excess death rates that were two to three times those seen in Western Europe.
Significant covariates in the MANCOVA model predicting the number of Covid-19 cases by type of welfare state included percentage of GDP spent on health (F(1, 35) = 5.72, p < 0.05), number of hospital beds (F(1, 35) = 4.48, p < 0.05), and out-of-pocket expenditures on health (F(1, 35) = 6.85, p < 0.05). Higher out-of-pocket expenditures on health and percentage of GDP spent on health were associated with higher confirmed cases. Significant covariates of Covid-19 deaths were percentage of GDP spent on health (F(1, 35) = 7.95, p < 0.01), stringency of lockdowns during Wave Three (F(1, 35) = 4.64, p < 0.05), and trust in government (F(1, 35) = 4.80, p < 0.05). Higher stringency during Wave Three and increased trust in government were associated with lower numbers of Covid-19 deaths. There were no significant covariates of excess deaths. Testing rates, income relief, face-covering mandates across the three waves, as well as vaccination rates, and the health, policy, and economic variables, interestingly, were not associated with the number of cases, deaths, or excess deaths, and thus proved to be statistically insignificant in our analysis.
Once the covariates were accounted for in the MANCOVA model, the differences among some of the Western countries became more pronounced. When controlling for the variables mentioned above, Liberal states had on average 1628 cases per 100,000 fewer than Corporatist states (p < 0.01). All other significant pairwise comparisons between welfare types and the number of Covid-19 cases per 100,000 remained the same. The difference among Liberal, Social Democratic and all three East European clusters in the number of Covid-19 deaths and number of excess deaths increased once the covariates were added to the model by an additional 8–15 deaths per 100,000 and 20–40 excess deaths per 100,000, but the significant differences between the former USSR and the Central and East European states disappeared. Refer to Figure 2 for a visual representation of the change in the average cases, deaths, and excess deaths by type of welfare state once the covariates are included in the model.
Differences among the First, Second and Third Covid-19 Waves across the East European States
A timeline of national public health policies related to Covid-19 and the number of cases per million by type of welfare state are presented in Figures 3 –5. Examining the timing of such policies in conjunction with the pattern of daily cases per million for the East European states demonstrates that most of these countries experienced a First Wave between 1 March 2020 and 30 June 2020. Figure 2 shows that, by early March 2020, all Central and East European states had implemented a state of emergency in response to the pandemic. This was lifted in each country sometime between mid-May and mid-June. Renewed public health responses were implemented again in September as cases began to rise. Most former USSR countries and most Southeast European countries demonstrated a similar pattern—a state of emergency was declared prior to 25 March, then lifted by mid-summer, though Moldova waited to suspend its state of emergency until the end of September.
Figures 1 –3 indicate that, when cases began to climb with the Second Wave, governments were slower to impose the widescale lockdowns of the sort implemented the prior spring. Only the Czech Republic, Latvia, and Moldova declared a new state of emergency during the Second Wave. Lithuania, Azerbaijan, and Ukraine, for instance, instituted nationwide lockdowns, but for much briefer periods of time than in the First Wave (e.g., weekends only in the case of Azerbaijan and Ukraine). In addition, these orders were issued when new Covid-19 case numbers were much higher than during the First Wave that had triggered the initial response to the pandemic. During the First Wave, lockdowns were implemented before there was any firm evidence of community transmission. During the Second Wave, society remained open, despite the increase in cases. Latvia had 250 new cases per day (a rate of 4220 per million), Moldova had 1200 new cases per day (a rate of 20,300 per million), and the Czech Republic had more than 2300 new cases per day (a rate of 38,300 per million) before a second state of emergency was declared. Each of these countries saw their cases rise for another three to four weeks before the distancing measures took effect and infection rates subsequently decreased.
The East European countries that did not impose official states of emergency or lockdowns during Wave Two relied on the temporary closures of non-essential businesses and of schools, placed limits on public gatherings, and implemented mandatory nationwide mask policies. As with the second round of lockdowns, these measures were put in place after the weekly number of new cases had become much higher than it had been in the spring. For example, Poland closed restaurants and bars on 24 October 2020, when the number of new cases each day was more than 6400 per million. Schools remained open for in-person instruction until early November, when more than 18,000 new daily cases per million were recorded. In contrast, during the First Wave, Poland had closed schools on 10 March 2020, when there were only seventeen confirmed cases (0.58 cases per million). Similarly, Georgia closed its schools during the Second Wave on 1 November 2020, when there were more than 10,200 new cases per million each day. Non-essential retail stores stayed open until 28 November 2020, when there were almost 32,150 daily new cases per million. In the spring, however, Georgia had declared a state of emergency on March 21, when it recorded only seven daily infections (a rate of 12.31 per million). As evinced in Figures 1 –3, the widescale shutdowns of the First Wave were not mirrored in the Second Wave, resulting in exponential growth in the number of new cases across Eastern Europe.
Contrary to our initial presupposition, the East European states did not fare better than the Western democracies in the number of Covid-19 cases and deaths. Cumulative data from all three waves of the pandemic show that the Western democracies, comprising the Liberal, Social Democratic, and Corporatist states, had lower numbers of Covid-19 cases than the Central and East European, Southeast European, and former USSR countries, even when accounting for differences in testing and reporting rates, healthcare resources, pandemic policies, and economic factors. As it relates to the number of deaths per 100,000, the Social Democratic and Liberal types had the lowest numbers when compared to Eastern Europe.
It is scarcely surprising that the Nordic, Social Democratic states seem to have experienced the lowest Covid-19 mortality rates. They are all strong welfare democracies, with universalist models of healthcare provision, extensive state transfers, redistributive welfare policies, and generous publics services and benefit plans that have all been deemed essential for population health, particularly through the reduction of mortality (Bambra 2011; Esping-Andersen 1990; Lundberg 2010). The question going forward is not so much why the Western Liberal states did well as it is why the Southeast European states fared the worst. The initial MANOVA analysis, before including the covariates, shows that by Wave Two the Southeast European countries had the highest number of cases, even higher than the Central and East European states and the former USSR. The Western Liberal states spend the most on healthcare, about 10% of their GDP, with the US spending the most at 17% while also having the highest number of cases and deaths per million. While the Southeast European states spend less on healthcare than their Liberal counterparts, just close to 8% of their GDP, their neighboring Central and East European states and the former USSR states spend even less, close to 7% and 6%, respectively. Yet notwithstanding the lower national amount spent on health, the states in Central and East Europe had lower numbers of Covid-19 cases and excess deaths than the Southeast European and former USSR states during Wave One. An important methodological point, and one that was also emphasized by Esping-Andersen (1990), is that the measure of expenditure alone can have a chameleonic effect in disguising the welfare provision of a state. In other words, state expenditure can be high because the provision of certain benefits has a limited reach, hence the state needs to spend more to manage the stratifying effects resulting from a low distribution of such benefits. For instance, state health expenditure can be high, yet if out-of-pocket health expenditures (i.e., the direct payments to healthcare providers at time-of-service use) sustained by individuals and households are equally high, state spending on healthcare will not translate into healthcare access and use. This is the case with the US, for example, the country that spends the most on healthcare among the Liberal states yet fails to provide universal health coverage for its citizens. Our analysis indicates that the higher the portion of GDP spent on health, the higher the number of confirmed Covid-19 cases, a relation probably mediated through a country’s testing capacity and the above-average spending on health by the US alone. This finding, however, does not tell us why cases were so high among the Southeast European states during Wave Two and the highest among Central and East European countries during Wave Three. In this context, it is important to note that some of the Southeast European countries, for instance Albania and Kosovo, have been experiencing catastrophic out-of-pocket health expenditures (Bredenkamp, Mendola, and Gragnolati 2011), a threshold generally crossed when payments for health services exceed 40% of household disposable income (OECD 2009).
With regard to the Covid-19 deaths per million, in controlling for the portion of GDP spent on health and hospital beds it is again the Southeast European states that recorded the highest numbers in the whole of Eastern Europe. These countries have the lowest numbers of hospital beds out of all the other clusters and the smallest number of nurses and doctors per 10,000 people, even compared to their East European neighbors. The lack of hospital beds is one of the many symptoms of the overextended reform of the healthcare system in the region (Bartlett, Božikov, and Rechel 2012). After the dissolution of socialist Yugoslavia, the successor states introduced reforms that privatized primary healthcare and transitioned health delivery to market-based arrangements modeled on the competing hospitals approach adopted in the UK under Margaret Thatcher (Bartlett, Božikov, and Rechel 2012; Pollock 2004). The Yugoslav health system had been based on a national insurance model, subsidized through a combination of payroll taxes and state contributions. It had been much more decentralized and also in other respects quite different from the systems of the other communist states, which instead relied on central financing for healthcare (Bartlett, Božikov, and Rechel 2012), which then proved much easier to privatize. Moreover, the wars on the territory of former Yugoslavia during the 1990s not only contributed to deteriorating population health but also hindered governments from building their welfare economies and strengthening their systems of health provision and delivery (Bartlett, Božikov, and Rechel 2012; Kunitz 2004).
In analyzing the Covid-19 deaths, differences within Eastern Europe get blurred, particularly when excess deaths are considered. The Southeast European, Central and Eastern European, and former USSR states had significantly higher numbers of Covid-19 deaths than their Western counterparts, the Liberal, Social Democratic, and Corporatist welfare regimes. Yet in exploring the data from the First Wave of the pandemic, the mortality in the whole of Eastern Europe was significantly lower than in the Corporatist European states. The prompt lockdowns contributed to this gap. The number of Covid-19 infections in Eastern Europe was also lower than in Western Europe in the First Wave compared to the subsequent waves. Recent discussions in the medical field have indicated that the official numbers of Covid-19 cases and subsequent mortality rates are in fact double the official figures (Dyer 2021) or even triple, as evinced by the excess mortality numbers presented in Table 3. This difference is related to national variation in reporting and underreporting. For instance, reported excess deaths from heart disease and Alzheimer’s disease peaked equally at the height of the Covid-19 waves (Dyer 2021) and often, as with reports from Russia’s coronavirus task force established at the end of January 2020, death was primarily attributed to patients’ comorbidities rather than directly to Covid-19 (Klobucista 2021). In fact, according to Rosstat, Russia’s National Statistics Office, the country’s mortality numbers are double the official count estimated by the task force (Isachenkov 2021). Other estimates measure the Russian figures to be five times higher than the official numbers (Dyer 2021). Other parts of Eastern Europe have also been flagged for undercounting and underreporting Covid-19 numbers and deaths, with the Central and East European states estimated to only be reporting one death in three (Dyer 2021). Some of the highest undercounts have been evident in the former Soviet republics such as Belarus, Azerbaijan, and Kazakhstan (Karlinsky and Kobak 2021), but the same trend has also been seen in Central and East European states. In Bulgaria, for instance, the large difference between excess deaths (0.25%) and reported Covid-19 mortality (0.11%) spanned a period of several weeks and was directly attributed to Covid-19 yet was not publicly recorded as such (Rangachev, Marinov and Mladenov 2022). Bulgaria, North Macedonia, and Serbia are among the countries with some of the highest excess deaths (Karlinsky and Kobak 2021). While high excess mortality is a common aftereffect of low reporting (Dyer 2021), the political inclination of the East European states to hide the actual counts of Covid-19 indicates a lack of transparency and could be a reflection of a more general lack of trust in government present in the region (see Table 4).
This paper’s findings show that the initial success of the Central and East European countries early in the pandemic is mainly attributable to the rapid lockdown policies and preventative measures introduced there. While the West European states kept their economies open until the end of March 2020, many states in the East became proactive as soon as numbers started to surge in China, Italy, and Iran in the early months of 2020. Kazakhstan announced, as early as 28 January 2020 and before recording any cases, the suspension of arrival visas for Chinese citizens (Crisis24 2020b) and deferred all forms of passenger transportation to and from China by 3 February 2020 (Crisis24 2020c). Georgia suspended all flight connections with China on 20 February 2020 (Crisis24 2020a). Romania imposed, on 21 February 2020, a fourteen-day compulsory quarantine for all those arriving in the country from the affected areas in Italy (Paul 2020). By 26 February, Lithuania had introduced a preventative state of emergency (Lithuanian Radio and Television 2020). And by 29 February, Azerbaijan closed its border with Iran for two weeks (Regencia and Uras 2020). By contrast, the Corporatist states of Western Europe, which showed significant differences in the First Wave data from Eastern Europe, were slow in imposing public health restrictions. Italy, the hardest hit country on the continent, imposed a national lockdown only on 9 March 2020, at a time when the number of cases was more than 9000 (Lawler 2020). Belgium, which by early spring had recorded 2657 infections and 41 deaths, introduced lockdown measures only on 18 March 2020 (Strauss 2020). Examining Wave Two, it appears that the delayed implementation of restrictions and the lack of stringent restrictions throughout the Second Wave most likely contributed to the surge in numbers for Southeastern Europe, Central and Eastern Europe, and the former USSR states.
Widespread resistance to recommended health measures such as mask-wearing and vaccination may have also contributed to a spike of cases in Eastern Europe throughout the Second and the Third Waves. While all countries in the region had various mask mandates requiring face coverings in some or all public places, it is less clear whether these mandates were followed or enforced. Romania and Bulgaria, for instance, had the lowest vaccination rates in the European Union (23.7% and 11.7%, respectively) by the end of the Third Wave. Romania reported the highest per capita death rate from Covid-19 in the EU, and in Bulgaria the hospitals were overwhelmed in the summer of 2021 (Horowitz 2021). Widespread resistance in Eastern Europe to public health measures is likely related to the reported lack of trust in state institutions. All former communist countries have a legacy, albeit varied, of party-led state censorship based on fear, control, and the exercise of top-down power, which has left behind a generalized aura of social distrust (Hosking 2013), a “functional substitute of trust” (Khodyakov 2007; cf. Marcic 2015). As is evident from our study, there was a significant relationship between trust in the government and the number of Covid-19 deaths.
This study shows that there are significant associations between states’ economic and public health policies and their Covid-19 figures. Such findings contribute to a knowledge base that will allow further exploration of how policymaking approaches specific to various welfare states can manage pandemic health-related outcomes. The determination of specific welfare-provision factors that would change population health outcomes related to Covid-19 cases and mortality rates will require future research, which would include more covariates examining public compliance with public health measures. Public designs of social care subsidies, redistributive taxation systems, pandemic-related social transfers, and health expenditures, as well as societal ideas about protecting the most vulnerable emanate from philosophies of welfare provision and state responsibility, trickle down through health policy, determine the type of care offered to the vulnerable and the sick, and materialize into national disease statistics, from routine mortality rates to infectious disease figures.
Moreover, a state’s ability to implement lockdowns also depends on its strength and the resources available to support the population with protectionist social measures, such as pandemic-related income benefits and wage subsidies. In other words, it depends on a state’s capacity to provide welfare. Further research is needed to explore, on the one hand, the relationship between welfare states’ macroeconomic variables such as gross domestic product, employment, and poverty rates and, on the other hand, the length and potency of the lockdowns and subsequent Covid-19 infection and mortality rates. One can hypothesize that the combination of their status as newer democracies possessing a legacy of mistrust in institutions and their relatively weaker economic base may have played a role here: It is possibly that the East European states could not have afforded to extend their lockdowns and preventative public health measures during the Second and Third Waves of the Covid-19 pandemic. Workers susceptible to being furloughed and small firms forced to temporarily close down have been some of the most affected actors during the pandemic. This social vulnerability was exacerbated in the poorer East European countries, where the state proved unable to provide the billions of dollars necessary to keep furlough programs in place and economies afloat. For instance, some of Western Europe’s largest economies—Germany, the UK, France, Italy, and Spain—supported up to thirty-two million jobs through furlough programs by April 2020 (Look, Pickert, and Pogkas 2021). By contrast, Belarus did not even introduce measures of economic relief when the pandemic first hit (Olofsgård and Strömberg 2020). The lockdowns required strong state support to maintain economic activity, to compensate for employees’ lost wages, or to subsidize businesses’ payrolls to keep individuals employed. Further research is needed to explore how states’ ability to provide protectionist social programs that offset the economic impact of the pandemic may predict cross-national decisions to implement widescale lockdowns as one means to manage Covid-19 cases and mortality rates.
About the authors
Kristina Nikolova is Assistant Professor of Social Work at the University of Windsor, Windsor, Canada. She has been researching national and international gender-based violence for more than ten years. She is particularly interested in how national policies and organizational practices can exacerbate or ameliorate the risk of violence against women and children. Nikolova has also worked in child protection and in developing training for child protection workers to better meet the needs of vulnerable families who are experiencing multiple risk factors, including intimate partner violence, poverty, and trauma.
Raluca Bejan is Assistant Professor of Social Work at Dalhousie University, in Halifax, Canada. Between July 2018 and December 2019 she was Assistant Professor in Critical Social Policy at St. Thomas University, in Fredericton, Canada. She has a BA in Political Sciences from Lucian Blaga University, Sibiu, Romania, and a MSW and PhD degrees from the University of Toronto. Raluca was a former Visiting Academic at the Centre on Migration, Policy and Society (COMPAS), University of Oxford, UK, in 2016 and respectively 2018. She is currently the Book Review Editor for Refuge: Canada’s Journal on Refugees.
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