Project: FREE policy brief
Governance in the Times of Corona: Preliminary Policy Lessons from Scandinavia
This policy brief summarizes the key points discussed in the webinar entitled “How did we end up here? Governance lessons from the Covid-19 pandemic” which was organized by CEPR, LSE IGA, SPP and SITE on June 18, 2020. The main insights concern the relationship between science and expert authorities on the one hand and elected and democratically accountable political institutions on the other hand. The Covid-19 pandemic has illustrated the need to strike a balance between being prepared and having a plan, and at the same time being able to take in new information and learn as new challenges unfold. This requires drawing on expertise from multiple fields as well as keeping an open mind to reevaluate chosen strategies when necessary.
Introduction
Economists have long reflected upon the potential benefits from separating the short-run decision making and implementation of policies from the overarching long-run goals. Central bank independence is probably the most prominent example, but the general idea of elected politicians transferring decisions to technocrats is widespread and, in different forms and to a different extent, part of the governance structure of all countries.
In the context of the corona crisis, governance issues have also been discussed, and the pros and cons of different systems are under debate: China, with its authoritarian system, has found it easier to control its population’s movements than many hard-hit European countries. In the US, the duality between the federal government and strong states has caused a lot of tensions. In Brazil, strong mayors and state governments have partly succeeded in counterbalancing the federal policy by imposing lockdown measures at the local level. The Covid-19 crisis is special: as a global health crisis, it certainly requires more coordination and expert knowledge than most other types of crises. Hence, in all countries, epidemiologists have received particular attention, but even internationally the Swedish state epidemiologist Anders Tegnell stands out with regards to this.
In the webinar entitled “How did we end up here? Governance lessons from the Covid-19 pandemic” which was organized by CEPR, LSE IGA, SPP and SITE on June 18, economists Karolina Ekholm and Bengt Holmström discussed governance issues within the Covid-19 crisis with a special focus on the Nordic countries. Ekholm is a professor at Stockholm University, former deputy governor of the Swedish Central Bank and served as a state secretary at the Swedish Ministry of Finance until 2019. Holmström, professor at the MIT and Nobel prize laureate, has been part of the Finnish commission on corona. Finland’s approach to the Covid-19 crisis has been widely approved of: the country imposed an early lock-down which seems to have successfully contained the spread of the virus. Sweden, by contrast, has made headlines all over the world due to its relatively loose policy approach, and more recently, due to the high death toll the country has recorded so far. How have governance issues contributed to these very different outcomes and what can we learn from this for the larger picture?
A Transdisciplinary Approach for a Multidimensional Crisis
Holmström contributed with an instructive account of his experience advising the Finnish government. The initial forecast turned out to be overly pessimistic, according to him, partly because epidemiologists underestimated a driving force behind people´s behavior: fear. If people had not been so afraid of the virus, compliance with the restrictions may have been much lower. This is not to blame epidemiologists: economists have struggled for decades to understand people’s behavior better and to integrate it into their models, which is everything but an easy exercise. But what policymakers can certainly learn from the first wave of Covid-19 is that the societal appreciation of the urgency of the pandemic can make a crucial difference and will determine whether policies fail or succeed. This may be of vital importance if a second wave of the virus is to follow. Moreover, scientists need to remember to update their models. What has worked for the swine flu may not work for Covid-19. As noted by one of the webinar participants: what is needed now is a forward-looking approach to science.
The Pitfalls of Technocratic Rule
Economists tend to focus on the benefits of technocratic rule in opposition to government corruption. This may be true in certain contexts, but technocratic rule is not a panacea. A priori, health experts are better informed than politicians during a health crisis. The Swedish, as well as the Finnish and the UK governments, were following their health agencies’ advice at the beginning of the Covid-19 outbreak. Yet, the governments in Helsinki and London departed from this policy quite early. According to Ekholm, the Finnish government soon overruled expert advice because they expected that voters would punish politicians who did not prioritize saving lives. A reason which is often invoked to explain why the Swedish government has not followed the Finnish example is that the Swedish constitution does not allow ministerial rule. Yet, this is unlikely to be decisive in the comparison to Finland, which also has a tradition of autonomous government agencies. Ekholm thinks that the evaluation of the health agencies in Scandinavia made at the outset of the crisis did not differ much from each other – with the exception of the Swedish health agency being more pessimistic with regards to the possibility of suppressing the spread of the virus by going into lock-down. The Swedish health agency also still enjoys high approval and confidence both from politicians and the general public. However, why it took so long for the health agency to push for more testing capacity remains a mystery to the webinar speakers.
Holmström mentioned another reason for exercising caution: just as economists, epidemiologists tend to fall for their standard models and may not question them enough. Scientists are trained to reason along their disciplines’ main paradigms and models and this can limit their intellectual flexibility and ability to analyze new phenomena. In this sense, having a lot of experience can sometimes lead to being overly confident in solutions which have been “proven before” as for instance, the idea of “herd immunity”.
The Use of Scientific Evidence
Science is supposed to be objective and transparent, but from an epistemological point of view, things are ambiguous. Holmström named the example of face masks, which have become the symbol of the Covid-19 pandemic elsewhere, but which are still rare on the streets of Stockholm and Helsinki. The Swedish and Finnish health authorities have hesitated to endorse the use of face masks, mainly because there is little evidence of their efficiency. Yet, other countries have endorsed them, following the very argument that there is little evidence of their harmfulness. Which question you are asking – whether masks help fight the spread of the virus or whether they may cause any collateral damage – determines which conclusion you come to. While a priori this may appear mostly as a philosophical question, the stakes are high in a health crisis and the dimensions of the current pandemic may very well justify adherence to the principle of precaution, according to Holmström.
Efficiency vs. Resilience
Economists’ workhorse model by contrast tends to be that of optimization: minimizing costs and maximizing efficiency or welfare. Particularly in the context of healthcare, this approach has been subject to criticism, though. Ekholm confirmed that the health sector in Sweden has been slimmed down, partly following extensive privatizations. In Sweden, another issue has been the lack of coordination between the national, the regional (largely responsible of healthcare) and the local level (responsible of nursing homes). Ekholm believes that there are many lessons to be learned from the numerous failures in vertical and horizontal cooperation between different Swedish governance institutions. Conferring more responsibilities to the European level in the domain of health could be efficient but both speakers agree that, despite generally high approval of the European Union, the Swedish and the Finnish public are unlikely to agree to such measures.
Conclusions
All conclusions we draw at this point must necessarily be preliminary. First, the Covid-19 crisis has challenged local, regional, national and supranational governance more than any previous crisis. The reasons for this are manifold: Covid-19 has grown from a health emergency to becoming an economic, social, political and potentially financial crisis. Second, the merits and pitfalls of technocratic rule must be evaluated. No single expert authority can – or should – claim the sole power of interpretation when facing a multidimensional crisis such as the current one. Considering this, it seems advisable that scientists with different expertise be included in a transparent decision-making process that then is clearly and openly communicated to the public. Crucially, all decisions and rules must be updated constantly, as new evidence arises; there is no room for dogmatism. Finally, there is no doubt that society has to become more resilient in the future. Whether this is to be achieved via supranational integration, investments in research and healthcare, more efficient crisis management mechanisms, or a combination of all these, is to be evaluated.
List of Speakers
Karolina Ekholm, Professor, Stockholm University and Fellow, CEPR
Bengt Holmström, Paul A. Samuelson Professor of Economics, MIT
Chair and Moderator:
Erik Berglöf, Director, Institute of Global Affairs, LSE School of Public Policy and Fellow, CEPR
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Ahead of Future Waves of Covid-19: A Regional Perspective on Health Risks and Healthcare Resources in Germany and Poland
Drawing on the most fundamental conclusions from the early research on the Covid-19 pandemic, in this policy paper we examine the regional prevalence of a number of risk factors related to severe consequences of Covid-19. Using the examples of Germany and Poland, two neighbouring countries which have generally dealt relatively well with the outbreak in recent months, we show that there is significant regional variation both in the distribution of health status and healthcare resources. Highly differentiated demographic and epidemiological risks related to the pandemic between as well as within Germany and Poland call for a decentralised evaluation of risks and point out the need to consider an application of regionally focused policy reactions such as lockdowns and social distancing regulations. The cross-country regional perspective adds a valuable angle to the analysis of challenges raised by the Covid-19 pandemic and should urgently be considered regarding any possible consequences of future outbreaks of the virus.
Introduction
In the first five months of 2020 the Covid-19 crisis has grown from a local epidemic outbreak in the Chinese city of Wuhan to a global pandemic, which by the end of May, according to official statistics, took the lives of over 370 thousand people and has been detected in nearly all countries around the world. In the initial phase of the pandemic, the healthcare systems of many countries were pushed to the brink of collapse, and in the severely hit regions even the need of “prioritizing” patients with a high chance of survival became reality. In most European countries the total number of identified cases has continued to grow throughout the month of May, but the rate of growth generally decreased, and in some countries, such as Austria or Slovenia, only a handful of cases were identified in the last two weeks of May. As a result, countries eased the social and economic lockdown, and in many parts of Europe life is beginning to portray a certain restricted semblance of pre-Covid-19 normality. At least in this part of the world, it seems that the first wave of the pandemic is behind us: the “hammer” is over, the “dance” has begun. Thus now that the spread of the virus is slowing down and we are in a phase of smaller local outbreaks, it is time to take a step back and use the information available to draw lessons before the arrival of a potential second wave, which according to many epidemiologists is likely to happen later this year.
Drawing on the most fundamental conclusions from the early research on the Covid-19 pandemic and taking a cross-country perspective, in this policy paper we examine the prevalence of a number of risk factors related to severe consequences of Covid-19 from a regional perspective. In our analysis we focus on Germany and Poland — two neighbouring countries which differ in the demographic structure of their populations as well as with respect to their healthcare infrastructure. Epidemiological research suggests that the risk of serious health complications as well as the risk of dying as a result of Covid-19 grows rapidly with age and is much higher among people with pre-existing health conditions such as cardiovascular conditions, diabetes, hypertension, chronic pulmonary disease and malignancy (Emami et al. 2020). Thus, the prevalence of these risk factors might serve as an indicator for the need of (in-hospital) health care in times of larger outbreaks. We then extend the analysis by a discussion of regional statistics on systemic features of healthcare resources reflecting the potential for addressing the pandemic. One can generally say that both in Germany and Poland the first wave of the pandemic, while placing additional heavy strain on healthcare in some regions, has not led to the collapse of healthcare provision. Yet, regions with lower level of service are at greater risk of healthcare rationing, thus further raising the likelihood of severe consequences to the local populations in the future.
We begin this policy paper with a discussion of the key demographic and epidemiological risk factors related to severe health consequences of Covid-19 (Section 1), which is followed by a presentation of the regional distribution of Covid-19 cases in Germany and Poland, as reflected in official statistics at the end of May 2020 (Section 2). We then discuss regional differences in the proportion of people aged 65+ and in the rates of the relevant comorbidities by showing regional statistics on the main causes of death (Section 3). This is complemented in Section 4 by a discussion of the regional distribution of healthcare resources as indicated by the number of hospital beds and the number of doctors. All aspects of our analysis are presented at the level of “powiat” for Poland and “Kreise” for Germany, referred to below as “counties”. There are 380 counties in Poland (including township with county status) and 401 counties in Germany, which in the international Nomenclature of Territorial Units for Statistics (NUTS) correspond to the former NUTS level 4 (former LAU 1) and NUTS level 3 respectively.
As we demonstrate, there are significant differences both across and within the two countries with respect to the relevant demographic and epidemiological risk factors. At the same time there is high heterogeneity across Germany and Poland in the resources of the respective healthcare systems. We show that the cross-country regional perspective adds an additional valuable angle to the analysis of challenges raised by the Covid-19 pandemic. Epidemiologists have modelled various scenarios of future Covid-19 waves including recurring small outbreaks, a new “monster wave” or even a persistent crisis (Moore et al. 2020). Whatever the shape of future outbreaks, the pandemic is expected to persist until “herd immunity” is reached, be it through successful vaccination or through developing immunity in response to illness. Thus, regions potentially facing more serious consequences of the pandemic need to be brought to the attention of central governments as they prepare to address the challenge of future outbreaks of the Covid-19.
1. Macro-Level Determinants of the Health-Related Consequences of Covid-19
At the initial stage of the pandemic, the WHO estimated the fatality rate of the Covid-19 disease at 3-4% (WHO 2020a). As the public health crisis developed, this general conclusion has been challenged given a high number of asymptomatic infections, low testing capacities in most countries and relatively low test accuracy for antibodies as well as PCR testing (Ghandi et al. 2020, Kandel et al. 2020, Manski & Molinari 2020). The available statistics should thus be treated more as “fatality-case” ratios, i.e. the ratios of deaths resulting from Covid-19 to the number of individuals tested positive. According to the most recent studies, this ratio differs substantially between countries, from as low as 0.04% in Qatar and 0.08% in Singapore to over 15% in Belgium or France (Oke & Heneghan 2020). Such high variation is unlikely to reflect “real” differences in the way the virus affects people in different countries, but is more likely to be a consequence of specific factors as the testing strategies, the demographic structure of the population, the characteristics of the part of the population affected (e.g. young holiday makers vs. patients of care institutions), as well as the ability of the healthcare system to deal with a sudden surge in the number of hospitalisations.
There is mounting evidence that the probability of developing severe symptoms of the infection, of hospitalisation and finally of dying, increases significantly with age. According to some early estimates the fatality-case rates grow from 1.8-3.6% among people aged 60-69, through 4.8-12.8% among those aged 70-79, up to 13-20.2% among those 80+ (Roser et al. 2020). Higher hospitalization and fatality rates are also strongly correlated with underlying health conditions, in particular with cardiac disorders, chronic lung diseases, diabetes and cancer (ECDC 2020). This further puts older individuals, among whom these health conditions are most prevalent, at much greater risk as compared to the younger population.
While the risk of severe consequences of Covid-19 substantially increases at older ages, several competing mechanisms are at play with regard to the role of the demographic structure for a potential spread of the virus. On the one hand, since levels of economic activity are generally lower among older people, their compliance with self-isolation rules is likely to be less sensitive to the intensity of economic activity at regional or country level. On the other hand, however, as social life now returns to a higher level of interaction, different forms of living arrangements of older individuals place certain groups at a particular risk. The first months of the pandemic in Europe have revealed high vulnerability of people living in long-term care facilities, many of which became Covid-19 clusters with high rates of mortality among their residents (Comas-Herrera et al. 2020; Gardner et al. 2020; McMichael et al. 2020). On the other hand, in countries characterised by low rates of institutionalization, older individuals are more likely to co-reside in households with children and younger adults (Myck et al. 2020), i.e. groups which in conditions of lifted lockdown restrictions will be exposed to the risk of infection. Studies at the early stages of the epidemic showed that intra-household transmission of the virus may be responsible for the majority of clusters (WHO 2020b). This implies that while the strategies to protect the most vulnerable groups may differ depending on the specific living arrangements, regions with a higher proportion of older people face an increased risk of severe health consequences of Covid-19 outbreaks.
Similar arguments apply to the regions where incidence of the relevant comorbidities is particularly high. Systemic constraints related to healthcare played an important role at the height of the recent Covid-19 crisis in countries such as Italy or Spain where the number of patients in need of in-hospital treatment exceeded the capacities of the healthcare systems (Pasquariello & Stranges 2020, Remuzzi & Remuzzi 2020, Verelst et al. 2020). We thus argue that regions with populations facing highest risks related to the Covid-19 pandemic ought to be particularly vigilant to the spread of the disease and ensure that their healthcare infrastructure can respond adequately to future outbreaks.
2. The Regional Spread of Covid-19 infections in Germany and Poland
The first official case of the disease in Germany was confirmed on 27 January, while the first infection in Poland dates to 4 March. Since then 183 thousand Covid-19 infections have been identified in Germany and 23 thousand in Poland by the end of May 2020. The corresponding fatality-case ratio at that point stood at the average country levels of 4.69% and 4.47% respectively. The difference in the overall number of cases relates both to the greater spread of the virus and the more extensive testing conducted in Germany as well as to a simple difference in the size of population (83 vs. 38 million inhabitants). Importantly, when we take a regional perspective on the pandemic, as we can see in Figure 1, the distribution of the infection rate is far from homogenous. In Germany, the level of infection rates is much higher in some of the southern and western regions (Bavaria, Baden-Württemberg and North Rhine Westphalia), while in Poland the region of Silesia is a clear local “hot-spot” of the pandemic.
Figure 1. COVID-19 infections per 100 thousand inhabitants by county
(as of 31 May 2020)
Source: own compilation based on data from Robert Koch Institute (RKI) and Federal Agency for Cartography and Geodesy (BKG) in case of Germany and data collected individually by Michał Rogalski (https://www.micalrg.pl/) from Voivodeship Offices, Voivodeship and Powiat Epidemiological-Sanitary Stations, media and materials sent on request and Head Office of Geodesy and Cartography (GUGiK) in case of Poland.
Note: Class 3, 4 and 5 cover 20% of the observations, the other classes 10% each.
In Germany, the first outbreaks were attributed to business travel and skiing tourism and the spread within certain communities went on via close contacts during large gatherings such as those at the time of carnival festivities and at church services, and also as a result of specific economic activities (e.g. delivery services or workers in slaughterhouses). Numerous cases have also been reported in institutionalised accommodation such as nursing and refugee homes. As Figure 1 shows, the counties with the highest rates of infections were located in Bavaria. By the end of May one of the Bavarian counties (Tirschenreuth) had an infection rate far higher than any other county – 1,568 infections per 100,000 inhabitants, when this rate was 891 and 890 in the next highest scoring counties of Straubing and Wunsiedel. At the same time the counties of Uckermark and Prignitz (in the region of Brandenburg), Friesland and Wilhelmshaven (Niedersachsen), Ostholstein (Schleswig-Holstein) and Rostock (Mecklenburg-Vorpommern) recorded infections rates of below 35 per 100,000 inhabitants.
The origins of the first reported cases in Poland were also directly related to international travel – to Germany and Italy. Further local outbreaks were reported in hospitals and social welfare homes. The virus often spread between such institutions due to a transmission via medical and care personnel working in several institutions in parallel. Initially, only Warsaw and neighbouring counties stood out with regard to the infection rate, which could be due to higher mobility and population density in the first case, and local outbreaks in social welfare homes in the latter. However, about two months after the beginning of the pandemic, a major surge in new cases was recorded in the region of Silesia where the bulk of infections concentrated among mine workers. Often asymptomatic, infections were identified as a result of extensive screening of miners and their families. By the end of May, about one third of Poland’s total infections were found in Silesia alone. Together with the cases reported in the Mazovian region (with Warsaw as capital), these two regions represented about half of the total number of infections in Poland. The highest infection rate in Poland exceeding 500 infections per 100,000 inhabitants was observed in the counties of Silesia (Bytom, Jastrzębie-Zdrój and powiat lubliniecki), Mazovia (powiat białobrzeski) and Greater Poland voivodship (powiat kępiński), while a handful of counties located throughout Poland (powiaty: bartoszycki, bieszczadzki, drawski, gołdapski, kolski, lidzbarski, międzyrzecki, sejneński, żuromiński) have not recorded any infections.
Figure 2 provides another angle on the aftermath of the epidemic in both countries – regional case fatality rates, calculated as a ratio of deaths to recorded infections and presented at a higher level of aggregation – the level of Bundesländer in Germany and Voivodship in Poland (due to the lack of comparable data on county level in Poland). Even though, as mentioned above, the country average death rates are very similar, the within-country regional differences are striking. As compared to Poland, the regional death ratios in Germany do not deviate much from the country average (4.7), with the lowest rate in the region of Mecklenburg-Vorpommern (2.6) and the highest one in the region of Saarland (6.0). On the other hand, the differences between Polish regions are substantial, with no deaths per 120 infections in the lubuskie region and the fatality rate exceeding 9.0 in the podkarpackie region. At this early stage of the pandemic such differences might reflect a number of factors and may not be systematically related to specific risks. However, as we show below, the most clearly identified risk factors are far from evenly distributed both between and within the two countries, which in cases of broader outbreaks is likely to lead to significant systematic differentiation of risks at the regional level.
Figure 2. Covid-19 death rates by region (DE: Bundesländer, PL: Voivodeships) (as of 31 May 2020)
Source: own compilation based on data from Robert Koch Institute (RKI) and Federal Agency for Cartography and Geodesy (BKG) in case of Germany and data collected individually by Michał Rogalski (https://www.micalrg.pl/) from Voivodeship Offices, Voivodeship and Powiat Epidemiological-Sanitary Stations, media and materials sent on request and Head Office of Geodesy and Cartography (GUGiK) in case of Poland.
Note: Class 3, 4 and 5 cover 20% of the observations, the other classes 10% each.
3. Demographic and Epidemiological Variation at Regional Level in Germany and Poland
There are significant differences in the age structure of the population with a substantially higher proportion of individuals in older age groups in Germany. While 17.5% of the Polish population is over 65 years old and 2.1% is aged 85+, the corresponding proportions in Germany amount to 21.4% and 2.7%. These average differences, however, conceal significant within country variation in the demographic composition, which – as we argue – is very relevant against the background of the potential consequences of the Covid-19 pandemic.
In Figure 3 we present shares of people aged 65+ in the general population by county in 2018. The counties with highest proportions of older individuals in Germany are concentrated in the east of the country. The variation in the proportion of those aged 65+ ranges between 15.7% in Frankfurt am Main (region Hessen) and Freising (region Bavaria) and 31.5% in Suhl (region Thüringen). The ‘youngest’ of German counties resemble some of the oldest ones in Poland, where we find counties with the proportion of people aged 65+ as low as 11.2% or 12.1% (powiats kartuski and gdański, region Pomerania). Only in 15 counties in Poland (less than 4% of counties), the proportion of those aged 65+ exceeds 21% – which we find in about two thirds of counties in Germany. Similar differences are found regarding the proportion of those aged 85+ (not shown here), with a distinct concentration of the “oldest-old” in the eastern parts in both countries. However, while in Poland less than half of counties have a proportion of the 85+ population higher than 2%, this is the case in all but one county in Germany.
Figure 3. Share of people aged 65+ by county, 2018
Source: own compilation based on data from Eurostat and Federal Agency for Cartography and Geodesy (BKG) in case of Germany and Central Statistical Office (GUS) and Head Office of Geodesy and Cartography (GUGiK) in case of Poland.
Note: Class 3, 4 and 5 cover 20% of the observations, the other classes 10% each.
When we compare the regional variation in the number of Covid-19 infections with the population’s age structure, it seems that the pandemic in both countries has so far affected the ‘younger’ regions. The spread of the virus has been relatively slow both in the eastern part of Germany and in the east of Poland. Thus, there is a negative correlation between the within-country spread of Covid-19 and the proportion of older age groups at the county level. This might be due to a higher level of travel and economic activity in younger regions of the two countries which – at least in the initial phase – limited further spread of the virus to the parts with higher proportions of older individuals.
Apart from older age several pre-existing medical conditions have also been identified as risk factors for severe consequences of Covid-19. Figure 4 displays the ratio of deaths due to a selected group of diseases in the total number of deaths among people aged 65+ to proxy the incidence of these health conditions among the living population. The causes of death are coded according to the diagnostic criteria of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) compiled by the WHO. Deaths caused by external factors such as traffic accidents are excluded from the total of fatalities due to different reporting practise in Poland and Germany. Since no clear deviations in reporting deaths due to internal causes has been found, we assume this data is comparable between the two countries and we use deaths due to internal causes as a measure of total deaths in Figure 4. Causes that are especially relevant against the background of Covid-19 include deaths due to circulatory diseases, neoplasms and respiratory diseases (the level of data aggregation does not allow to single out deaths due to diabetes). In contrast to Figure 3, which showed much higher proportions of older people in Germany than in Poland, when it comes to health risks due to the specified conditions, the country picture is reversed. While the rate of deaths resulting from the selected conditions exceeds 90% of all deaths in the 65+ population in multiple counties across Poland (over 8% of all), it does not surpass 84% anywhere in Germany. Importantly, the regional distribution of death ratios in Germany due to the chosen conditions closely reflects the proportion of the older population and is concentrated in eastern parts of the country, in particular in the southern regions of the former East Germany. Epidemiological risks related to Covid-19 seem to be lower in the more prosperous regions in southern and western Germany, as well as in bigger cities such as Hamburg. In Poland there is no apparent relation between the selected health risks and the demographic structure of the regions. The highest proportion of deaths due to the selected conditions is found in the north-western regions and in the south-east, leaving central Poland with somewhat lower incidence rates of death due to these causes – at similar levels observed in many parts of Germany. Moreover, the within-country variation in the proportion of these deaths is much higher in Poland, where in sztumski county (Pomerania region) as many as 94.5% of deaths among 65+ can be attributed to the selected conditions, while in ełcki county (Warmia-Masuria region) this number was only 66.6%.
Figure 4. Share of deaths due to neoplasms, circulatory and respiratory diseases among people aged 65+ by county, 2016
Source: own compilation based on data from European Data Portal and Federal Agency for Cartography and Geodesy (BKG) in case of Germany and Central Statistical Office (GUS) and Head Office of Geodesy and Cartography (GUGiK) in case of Poland.
Note: Class 3, 4 and 5 cover 20% of the observations, the other classes 10% each.
4. Healthcare Resources at the Regional Level in Germany and Poland
The initial wave of the Covid-19 pandemic in several most affected countries resulted in a significant overburden of their healthcare capacities with a sudden wave of patients in need of in-hospital intensive care. While in some hospitals in Germany and Poland the first inflow of patients placed a heavy burden on the available resources, both healthcare systems have so far not been overwhelmed to the extent that was experienced in Italy, Spain, or some states of the USA. However, there are significant differences between the healthcare resources available in Germany and Poland and these differences might become apparent if the next waves of the pandemic result in much higher rates of infections. Health expenditure accounted for 11.3% of Germany’s gross domestic product (GDP) in 2017, with an expenditure of 4,459€ per inhabitant. The spending in Poland was much lower and amounted to 6.5% of the GDP and an expenditure of 731€ per inhabitant (Eurostat 2020a). The differences are not as high in the absolute values of traditional healthcare indicators such as the number of hospital beds per 1,000 people (601.5 in Germany and 485.1 in Poland; Eurostat 2020b) or the number of doctors per 100.000 inhabitants (424.9 in Germany and 237.8 in Poland; Eurostat 2020c), but they are still notable.
We show the regional distribution of hospital beds and practising doctors in Figures 5 and 6. As in the case of the demographic structure and epidemiological conditions, there are significant regional differences in the capacity of healthcare as measured by these indicators. In the latter case the data do not allow for a direct cross-country comparison as the data in Germany only covers medical doctors who provide health services to patients with social health insurance in outpatient clinics. In Poland the data is limited to the medical doctors working directly with patients conditional on their primary workplace / main employer in case of multiple assignments (excluded if private practice is reported as such). This means that the data at hand only covers a proportion of all medical doctors – in Germany it captures 37% of all those with an active medical license (according to the German Medical Association) and in Poland 60% of licensed doctors as reported by the Polish Supreme Medical Chamber. As this data is not directly comparable across countries, the proportions in Figure 6 are presented in shades of blue and green for Germany and Poland respectively. However, the key dimension of the data we present is the high within-country variation in the level of medical staff across regions.
In both countries there is an urban-rural divide of the healthcare capacities that is most pronounced in Poland and in the south-western regions of Germany. In Poland this originates partly from the task division at consecutive levels of local administration. Although county authorities are responsible for the broad network of hospitals, the major clinical hospitals are located in the biggest cities. The north-south difference that we observe in Germany is related to the fact that in northern regions many populated cities compose a county together with neighbouring municipalities, while in the southern and central parts they constitute an independent county. This brings out the contrast between cities and the localities around them, which is also noticeable in the case of Poland. For many areas this means that their inhabitants have to travel or be transported relatively long distances when in need for medical treatment, in particular in cases of specialised interventions. In 2016 there were four counties in Germany and as many as 24 counties in Poland with no hospitals.
Figure 5. Number of hospital beds per 1,000 inhabitants by county, 2016
Source: own compilation based on data from Federal Statistical Office and Statistical Offices of the Länder and Federal Agency for Cartography and Geodesy (BKG) in case of Germany and Central Statistical Office (GUS) and Head Office of Geodesy and Cartography (GUGiK) in case of Poland.
Note: Class 3, 4 and 5 cover 20% of the observations, the other classes 10% each.
The rural-urban divide is even more evident in Poland when we look at the number of medical doctors, as doctors are clustered in the biggest cities or counties with clinical hospitals (Figure 6). In 2018, three counties had 20 or less medical doctors per 100,000 inhabitants (powiat łomżyński in Podlaskie region, średzki in Lower Silesia and siedlecki in Mazovia), and in 30% of counties this number was below 100. Almost 10% of counties (all big cities and regional capitals) had at the same time 400 or more doctors per 100,000 inhabitants, two counties in South-East Poland – Lublin (Lubelskie region) and Rzeszów (Podkarpackie region) reported over 770 doctors. Thus, the striking feature of several regions in Poland is that besides a strong medical centre, there is a high number of municipalities around them with very low number of doctors. This is the case for example in Olsztyn in the north-east of Poland (region Warmia-Masuria) or Poznań in the west (Greater Poland region).
Since for Germany we only considered doctors working in outpatient clinics and excluded doctors working solely in hospitals and thus concentrated in major regional cities, the medical workforce seems spread out more equally (Figure 6) compared to the availability of hospital beds (Figure 5). However, in particular since in Germany the data covers a much lower proportion of medical doctors compared to Poland, even in the German counties with lowest statistics, the numbers of doctors are still much higher than in many rural areas throughout Poland.
Figure 6. Number of doctors per 100,000 inhabitants by county, 2018
A) in Germany: doctors working in outpatient clinics B) in Poland: doctors working directly with patients in primary workplace
Source: own compilation based on data from Federal Medical Registry (KBV) and Federal Agency for Cartography and Geodesy (BKG) in case of Germany and Central Statistical Office (GUS) and Head Office of Geodesy and Cartography (GUGiK) in case of Poland.
Note: Class 3, 4 and 5 cover 20% of the observations, the other classes 10% each.
Conclusion
The early evidence suggests that people over the age of 65 and those with pre-existing health conditions such as cardiovascular conditions, diabetes, hypertension, chronic pulmonary disease and cancer are at the highest risk of severe consequences of Covid-19. A well-equipped healthcare system is required to respond appropriately to increases in demand for healthcare in order to safeguard the population against the worst outcomes of the disease in potential future waves of the pandemic. This regards the issue of preventing Covid-19 related fatalities, but it also refers to the continued need to provide other general types of healthcare which are constantly required alongside the cases directly related to the pandemic.
Such a combination of health risks related to demographic, epidemiological and systemic factors results in potentially high regional variation of the scale of consequences of the spread of the Covid-19 pandemic. Using the example of Germany and Poland, two neighbouring countries which have generally dealt relatively well with the outbreak of Covid-19 in recent months, this policy paper shows that there is significant regional variation both in the distribution of health risks and healthcare resources. These regional inequalities should be considered regarding the consequences of future outbreaks of the virus. The regional analysis of the first wave of the pandemic – with data until 31 May 2020 – suggests that in both countries the virus spread mainly in ‘younger’ regions (with low proportions of people aged 65+) with lower incidence of the relevant comorbidities. At the same time the number of cases in the two countries was low enough so that both the German and the Polish healthcare systems, notwithstanding the differences between them, were not overwhelmed by the inflow of Covid-19 patients.
Such a situation is by and large not guaranteed in the case of future waves of the pandemic. The virus is likely to spread beyond the best connected and most mobile regional populations, which has been the case so far in Germany and Poland. With respect to the demographic structure of the population, the places most at risk for severe health consequences due to Covid-19 are the counties of the former East Germany and those in the east of Poland, where we observe an outstandingly large proportion of people aged 65+. Similarly – looking at the incidence of relevant comorbidities, the northern and southern counties clearly stand out in Poland, and in this respect the health of the German 65+ population presents a much lower risk compared to the health status of the Polish counterparts.
How these two critical risk factors translate into health outcomes in future waves of Covid-19 depends on the readiness of the local healthcare system to provide support to patients requiring in-hospital and intensive care. Using regional data on the number of beds and medical doctors we have shown that in both countries there is a significant variation in healthcare resources. This variation is particularly visible in Poland with a substantial urban-rural divide and high concentration of healthcare resources and staff in larger cities. A rapid spread of the disease in future months could be devastating in Polish rural areas with poor medical infrastructure and high proportions of the population at risk.
The differences between and within the countries regarding the healthcare infrastructure lead to two crucial conclusions with regard to the potential consequences of future waves of Covid-19. First of all, it is clear that the German healthcare system – with the better hospital infrastructure and higher number of doctors, is overall better prepared to face a surge in Covid-19 cases. Secondly, there is a much higher proportion of counties in Germany with high level of medical resources and few localities standing out with much lower levels of hospital capacity or doctors compared to those with the highest values. This is not the case in Poland where the majority of counties have very low capacities of both hospital beds and doctors. While such inequalities in medical resources may be of less concern in ‘normal times’ when individuals from areas with poorer infrastructure might find a place in their nearest relevant hospital, in the case of a sudden increase in demand for hospitalisations such local medical centres might rapidly become overwhelmed. Additionally, moving patients to distant hospitals would place significant additional demand on medical transportation. In cases of rapid increases in the numbers of infected people problems are also likely to occur at the level of the basic diagnosis before the patients are classified for hospitalisation.
As shown in this policy paper the variance in the demographic structure of the population as well as in the main causes of death at older ages between Germany and Poland and within each of the two countries is substantial. In many regions these underlying demographic and epidemiological factors overlap with relatively low general capacities of the healthcare system to deal with a sudden surge of hospitalisations (Kandel et al. 2020). Thus, the analysis presented in this policy paper points towards the need for a disaggregated regional level risk-management approach to future waves of the Covid-19 pandemic. Highly differentiated demographic and epidemiological risks related to the pandemic between as well as within Germany and Poland call for a decentralised evaluation of risks and point out the need to consider an application of regionally focused policy reactions such as lockdowns and social distancing regulations. If risks and the ability to respond to them vary significantly at the regional level, policies should consider and account for such variation to prepare for potential next outbreaks later this year or next year.
Acknowledgement
The authors wish to acknowledge the support of the German Science Foundation (DFG, project no: BR 38.6816-1) and the Polish National Science Centre (NCN, project no: 2018/31/G/HS4/01511) in the Beethoven Classic 3 funding scheme. We are grateful to Vera Birgel for research assistance.
References
- Comas-Herrera, A., Zalakaín, J., Litwin, C., Hsu, A.T., Lane, N., Fernández, J.-L. (2020) Mortality associated with COVID19 outbreaks in care homes: early international evidence. LTCcovid.org, CPEC-LSE. https://ltccovid.org/wp-content/uploads/2020/05/Mortality-associated-with-COVID-21-May-6.pdf
- ECDC – European Centre for Disease Prevention and Control (2020) Disease background of COVID-19. https://www.ecdc.europa.eu/en/2019-ncov-background-disease
- Eurostat (2020a): Healthcare expenditure statistics. https://ec.europa.eu/eurostat/statistics-explained/index.php/Healthcare_expenditure_statistics
- Eurostat (2020b): Healthcare resource statistics – beds. https://ec.europa.eu/eurostat/statistics-explained/index.php/Healthcare_resource_statistics_-_beds
- Eurostat (2020c): Health care personnel statistics – physicians. https://ec.europa.eu/eurostat/statistics-explained/index.php/Healthcare_personnel_statistics_-_physicians#Healthcare_personnel
- Emami, A., Javanmardi, F., Pirbonyeh, N., Akbari, A. (2020) Prevalence of underlying diseases in hospitalized patients with COVID-19: a systematic review and meta-analysis. Arch Acad Emerg Med, 8, e35. https://www.ncbi.nlm.nih.gov/pubmed/32232218
- Gardner, W., States, D., Bagley, N. (2020) The Coronavirus and the Risks to the Elderly in Long-Term Care. J Aging Soc Policy, 1‐6. https://pubmed.ncbi.nlm.nih.gov/32245346/
- Ghandi, M., Yokoe, D. S., Havlir, D. V. (2020) Asymptomatic transmission – the achilles’ heel of current strategies to control Covid-19. N Engl J Med, 382, 2158-2160. https://www.nejm.org/doi/full/10.1056/NEJMe2009758
- Kandel, N., Chungong, S., Omaar, A., Xing, J. (2020) Health security capacities in the context of COVID-19 outbreak: an analysis of International Health Regulations annual report data from 182 countries. Lancet, 395, 1047-1053. https://www.sciencedirect.com/science/article/pii/S0140673620305535
- Manski, C. F., Molinari, F. (2020) Estimating the COVID-19 infection rate: Anatomy of an inference problem. JoE (Online first). https://www.sciencedirect.com/science/article/pii/S0304407620301676
- McMichael, T., Currie, D., Clark, S., Pogosjans, S., Kay, M., Schwartz, N., Lewis, J., Baer, A., Kawakami, V., Lukoff, M., Ferro, J., Brostrom-Smith, C., Rea, T., Sayre, M., Riedo, F., Russell, D., Hiatt, B., Montgomery, P., Rao, A., Chow, E., Tobolowsky, F., Hughes, M., Bardossy, A., Oakley, L., Jacobs, J., Stone, N., Reddy, S., Jernigan, J., Honein, M., Clark, T., Duchin J. (2020) Epidemiology of Covid-19 in a long-term care facility in king county, Washington. N Engl J Med. https://www.ncbi.nlm.nih.gov/pubmed/32220208
- Moore, K. A., Lipsitch, M., Barry, J. M., Osterholm, M. T. (2020) COVID-19: The CIDRAP viewpoint. Part 1: The future of the COVID-10 pandemic: Lessons learned from pandemic influenza. https://www.cidrap.umn.edu/sites/default/files/public/downloads/cidrap-covid19-viewpoint-part1_0.pdf
- Myck M., Oczkowska M., Trzciński K. (2020) Safety of older people during the Covid-19 pandemic: Co-residence of people aged 65+ in poland compared to other European countries. FREE Policy Paper. https://freepolicybriefs.org/2020/05/18/safety-older-people-covid-19/
- Oke, J., Heneghan, C. (2020) Global Covid-19 case fatality rates. https://www.cebm.net/covid-19/global-covid-19-case-fatality-rates/
- Pasquariello, P., Stranges, S. (2020) Excess mortality from COVID-19: Lessons learned from the italian experience. Preprints. https://www.preprints.org/manuscript/202004.0065/v1
- Remuzzi, A. & Remuzzi, G. (2020) COVID-19 and Italy: what next? Lancet, 395, 1225-1228. https://www.thelancet.com/article/S0140-6736(20)30627-9/fulltext
- Roser, M., Ritchie, H., Ortiz-Ospina, E., Hasell, J. (2020) Mortality risk of COVID-19. https://ourworldindata.org/mortality-risk-covid#case-fatality-rate-of-covid-19-by-age
- Verelst, F, Kuylen, E & Beutels, P (2020) Indications for healthcare surge capacity in European countries facing an exponential increase in coronavirus disease (COVID-19) cases, March 2020. Euro Surveill, 25. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7140594/pdf/eurosurv-25-13-3.pdf
- Roser, M., Ritchie, H., Ortiz-Ospina, E., Hasell, J. (2020) Mortality risk of COVID-19. https://ourworldindata.org/mortality-risk-covid#case-fatality-rate-of-covid-19-by-age
- WHO (2020a) “Coronavirus disease 2019 (COVID-19)”. Situation Report – 46.
- WHO (2020b) Report of the WHO-China joint mission on coronavirus disease 2019 (COVID-19). https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Can Central Banks Always Influence Financial Markets? Evidence from Russia
In many financial markets, including the UK and US, central banks are able to influence asset prices through unexpected interest rate changes (so-called indirect channel of monetary policy). In our paper (Shibanov and Slyusar 2019) we study the Russian market in 2013-2019 and measure policy shocks by the difference between the key rate and analysts’ median forecast. We show that in the short-term, the Central Bank of Russia does not significantly influence the general stock market or the ruble exchange rate outside December 2014 and January 2015, while some sectoral stock indices react to the changes opposite to what theoretical models predict. Overall, the Russian case is more similar to the ECB and the case of the German economy than to results from the UK or the US. This may mean that the Bank of Russia has more influence through the direct channel on the interest rates of credits and deposits.
Asset Price Reaction to Policy Changes
What should we expect from a general stock market or a national currency reaction to the central bank interest rate policy? This indirect effect may lead to changes in the collateral available in the economy, or in imports and exports of a country. Theoretical models predict that an expected decrease in the key rate would have no impact on asset prices, while unexpected increases in the key rate may have a negative impact on asset prices (Kontonikas et al. 2013). If the interest rate increases more than the markets or analysts expect, we would see prices decrease as discount rates most probably increase; the opposite happens when the interest rate decreases more than expected.
The results of testing this presumption on different countries are not uniform. While in the US (Kontonikas et al. 2013) and in the UK (Bredin et al. 2009) the impacts of key rate policy surprises are significant, the ECB influences neither the UK nor the German stock markets (Breidin et al. 2009).
Regarding the exchange rate (Hausman and Wongswan, 2011), there is evidence that unexpected changes in the US interest rate have a strong impact on floating currencies.
The Case of Russia
Russian monetary policy has changed a lot since 2013. The introduction of the “key rate” as the main policy tool, switch to the floating ruble and inflation targeting in November 2014 all lead to a new framework used by the Bank of Russia. Therefore, it is of interest to check what happens with the indirect channel of policy transmission (through asset prices and financial markets).
There is at least one paper that precedes our research. Kuznetsova and Ulyanova (2016) study the impact of verbal interventions by the Bank of Russia (Central Bank of Russia) on both the returns and the volatility of the Russian stock market index (RTS) in 2014-2015. Their findings suggest that returns do react to the Bank of Russia communications, while volatility does not.
In our paper (Shibanov and Slyusar 2019) we study the period of 2013-2019, that is the time of Elvira Nabiullina as governor of the Bank of Russia. Our approach is based on the assumption that news are incorporated in the stock market reasonably fast, no later than 4 trading days after the day of announcement. For the exchange rate we take short-term movements 30 minutes before and after the time of publication (like in Hausman and Wongswan 2011). Monetary policy surprise is measured as the difference between the realized key rate and the median expectations of analysts in Thomson Reuters. Abnormal returns are computed using an index model.
Figure 1 shows that the surprises are close to zero except for two dates: December 2014 and January 2015. In the first period the key rate was increased to 17%, while in the second it was reduced to 15%. In the paper we show that these two days are clear outliers that bias the results, so we study the relationship without them.
Results for the Stock Market
The stock market reaction in the symmetric window of four days before the announcement and four days after is muted (see Table 1). While the main index (MICEX) does not react significantly, two sectors (MM – metals and mining, and chemistry) react positively to the unexpected increase in the key rate. This result seems to contradict what we would expect from the market. The bond index does not significantly react to the changes.
Table 1. Cumulative effect, sample with no shocks (days from -4 to +4).
| Sector | Estimate | t-statistic | P-value | Significance | |
| MICEX | 1.6192 | 0.6803 | 0.4999 | 0.041 | |
| OG | 0.2511 | 1.125 | 0.2668 | 0.005 | |
| Finance | -1.2933 | -1.080 | 0.2860 | 0.024 | |
| Energy | -0.4513 | -0.7145 | 0.4787 | 0.004 | |
| MM | 2.2876 | 3.326 | 0.0018 | *** | 0.113 |
| Telecom | -0.2534 | -0.2844 | 0.7774 | 0.001 | |
| Consum. | 0.2178 | 0.4191 | 0.6772 | 0.001 | |
| Chemistry | 2.9787 | 2.642 | 0.0114 | ** | 0.132 |
| Transport | 0.3200 | 0.1548 | 0.8777 | 0.001 | |
| Bonds | 1.4080 | 1.048 | 0.3002 | 0.037 |
Source: Shibanov and Slyusar (2019), Thomson Reuters, Moscow Stock Exchange and Bank of Russia data.
Results for the Ruble Exchange Rate
The exchange rate should react with a depreciation to the unexpected key rate decrease. If there is an unexpected increase, the return on the ruble-denominated bonds rises and so the currency becomes more attractive to the international investors.
However, we do not observe any significant difference between the cases of expected and unexpected changes (see Table 2). All the movements are quite noisy and do not show any stable pattern.
Table 2. Exchange rate reaction to the key rate changes.
| Key rate increase | Key rate decrease | |
| Unexpected | -1.05% | -0.04% |
| Expected | 0.65% | 0.003% |
Source: Shibanov and Slyusar (2019), Thomson Reuters and Bank of Russia data.
Figure 1. Deviations of the actual key rate from median expectations (key rate surprises), percentage points.
Source: Shibanov and Slyusar (2019), Thomson Reuters and Bank of Russia data.
Conclusion
As we see from our analysis, the Bank of Russia’s impact on financial markets is similar to the one observed in Germany after ECB policy changes. There is almost no sizeable and stable effect neither on asset prices nor on the exchange rate.
The results do not mean, however, that monetary policy in Russia is irrelevant. The direct channel – i.e. the impact of the central bank’s decisions on the interest rates of credits and deposits works well. Moreover, we only consider short-term effects concentrated around the announcement date. Longer-term effects may be more pronounced.
References
- Bredin, D. et al. (2009) ‘European monetary policy surprises: the aggregate and sectoral stock market response’, International Journal of Finance & Economics. Wiley Online Library, 14(2), pp. 156–171.
- Hausman, J. and Wongswan, J. (2011) ‘Global asset prices and FOMC announcements’, Journal of International Money and Finance. Elsevier Ltd, 30(3), pp. 547–571. doi: 10.1016/j.jimonfin.2011.01.008.
- Kontonikas, A., MacDonald, R. and Saggu, A. (2013) ‘Stock market reaction to fed funds rate surprises: State dependence and the financial crisis’, Journal of Banking and Finance, 37(11), pp. 4025–4037. doi: 10.1016/j.jbankfin.2013.06.010.
- Kuznetsova, O. and Ulyanova, S. (2016) ‘The Impact of Central Bank’s Verbal Interventions on Stock Exchange Indices in a Resource Based Economy: The Evidence from Russia’, Working Paper, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2876617.
- Shibanov, O. and Slyusar A. (2019) ‘Interest rate surprises, analyst expectations and stock market returns: case of Russia’, Working Paper.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
How to Liberalise EU-Ukraine Trade under DCFTA: Tariff Rate Quotas
This policy brief focuses on trade relations between Ukraine and the EU amid preparations for the review of the Deep and Comprehensive Free Trade Agreement (DCFTA) due in 2021. In particular, it analyses Ukraine’s utilization of the DCFTA tariff rate quotas (TRQs) over 2016-2019. According to the results, Ukraine has been steadily increasing the level of TRQs usage – in terms of the number of utilized TRQs and export volumes within and beyond TRQs. For some DCFTA TRQs, total exports to the EU far outweigh quota volumes, while for other TRQs supply is limited by quota volume. The brief provides arguments and recommendations for the DCFTA TRQs update to increase Ukraine’s duty-free access to the EU market.
Why Update DCFTA TRQs for Ukraine?
EU-Ukraine trade under the Deep and Comprehensive Free Trade Agreement (DCFTA, in effect since January 1, 2016) progressed considerably. Ukraine’s exports of goods to the EU reached $20.8 billion in 2019 – a 54% increase compared to 2016 and a 24% increase compared to pre-crisis 2013.
According to the EU-Ukraine Association Agreement/DCFTA, the parties may initiate a review of its provisions in five years from its implementation – in 2021. So far, both governments confirmed their readiness to start such negotiations next year.
Ukraine advocates for further trade liberalisation with the EU through reducing the existing tariff and, most importantly, non-tariff barriers. This is an imperative for maintaining positive trade dynamics and providing new impetus to deepening bilateral economic integration.
Updating duty-free tariff rate quotas (TRQs) under the DCFTA is at the top of the EU-Ukraine 2021 negotiations agenda. Current quota volumes are based on outdated statistics, as it has been 10 years since the DCFTA negotiations (2008-2011).
Many TRQs are too low in terms of Ukraine’s current export and production capacities. For example, Ukraine’s total exports of grains (annual averages) increased from 19 million tons in 2008-2010 to 42.3 million tons in 2016-2018. Honey exports increased from 5.9 thousand tons in 2008-2010 to 58 thousand tons in 2016-2018. As a result, some TRQs are fully exhausted in the first days or months of the year.
High competition for access to duty-free quota volumes is a barrier first of all for SMEs that cannot compete effectively for it with large companies, while out-off-quota tariffs may be too restrictive for them.
Ukraine’s TRQs Utilisation During 2016-2019
DCFTA TRQs grant partial liberalisation of market access to the EU. Zero tariff rates are only applied to a specified quantity of imported goods inside a TRQ, while beyond TRQ imports to the EU are dutiable on a regular basis (subject to third-country tariff rates).
The EU applies TRQs for 36 groups of agro-food products originated in Ukraine plus 4 additional TRQs for certain product groups (in total 40 TRQs under DCFTA) – see Table 1. Ukraine applies TRQs for 3 groups of products plus 2 additional TRQs.
By the level of utilisation, TRQs fall into three groups: 1) fully utilised. They, in turn, can be divided into TRQs with and without over-quota supply; 2) partially utilised; and 3) not utilised.
The data indicate a general upward trend in Ukraine’s utilisation of TRQs under the DCFTA. In general, Ukrainian exporters utilised 32 TRQs in 2019 (80%) comparing to 26 TRQs in 2016 (65%).
Figure 1. Number of DCFTA TRQs utilized by Ukraine during 2016-2019.
Table 1 shows Ukraine’s utilization of 40 DCFTA TRQs over 2016-2019 – in tons and %. The main findings include:
The number of fully exhausted TRQs has been increasing. In 2019, Ukraine filled up 12 TRQs including honey; processed tomatoes; wheat; maize; poultry meat; barley groats and flour, other cereal grains; sugars; grape and apple juice; butter and dairy spreads starches; starch processed; as well as malt-starch processed products. For 9 of them, Ukraine’s supplies exceeded TRQs volumes.
The number of partially utilized TRQs increased from 16 in 2016 to 20 in 2019. In 2018-2019, Ukraine began using new TRQs such as fermented-milk processed products; malt-starch processed products; sugar syrups. High TRQs utilization rates (over 80%) in 2019 were observed for malt and wheat gluten; cereal processed products; eggs (main); barley, barley flour and pellets.
Moreover, Ukraine increased utilisation of TRQs for processed products. For example, utilisation of a TRQ for cereal processed products increased from 2.7% in 2016 to 99.5% in 2019. This signifies the growing ability of Ukrainian producers to comply with the EU food safety requirements and standards for processed products. Exports of processed starch increased significantly in 2019 and exceeded TRQ volume by a lot.
Ukraine’s utilisation of some TRQs has decreased. For example, a TRQ for oats gradually decreased from 100% in 2016 to 31% in 2019 due to a decrease in total exports and domestic production of oats in Ukraine during this period. Low utilisation of other TRQs may also be attributed to high price competition and quality requirements in the EU, complex quota allocation procedure, etc.
The number of not utilized TRQs decreased from 14 in 2016 to 8 in 2019. For instance, no exports within TRQs were observed for beef, pork, sheep meat, as Ukraine has not yet been authorized to export these meat products to the EU.
Moreover, since October 2017, Ukraine has been able to use provisional TRQs that were granted by the EU as autonomous trade measures (ATM) for 3 years. They increased duty-free access for 8 groups of Ukrainian products – in addition to the relevant DCFTA TRQs. So far, Ukraine fully utilises 5 ATM TRQs including honey; processed tomatoes; barley groats and meal, cereal grains otherwise worked; wheat, flour and pellets; maize, flour and pellets.
Total Exports to the EU vs Duty-Free Exports Within TRQs
For most fully utilized DCFTA TRQs, Ukraine’s total exports of the covered products exceeded TRQ volumes during 2016-2019. Considerable over-quota supply occurred for: honey; processed tomatoes; barley groats and meal, cereal grains; apple and grape juice; maize, flour and pellets; poultry meat; wheat, flour and pellets; sugars; butter and dairy spreads; starch processed.
For instance, over-quota exports of processed tomatoes from Ukraine to the EU in 2019 (31.2 thousand t) more than doubled the quota volumes (10,000 t of the DCFTA TRQ and 3,000 t of the provisional ATM TRQ). See Figure 2 for more examples.
Figure 2. Ukraine’s exports to the EU within and beyond certain TRQs, 2016-2019.
Increasing exports beyond TRQs indicate significant demand for these Ukrainian products in the EU, and their competitiveness in terms of price and quality on the EU market.
It also signifies that volumes of these fully utilised DCFTA TRQs with increasing over quota exports are rather low in terms of Ukraine’s export and production potential. Therefore, these TRQs are the primary candidates for updating.
At the same time, for certain DCFTA TRQs (malt-starch processed products; starch, malt and wheat gluten), exports to the EU were about 100% of TRQ volume but did not go far beyond. This may indicate a significant restrictive impact of those TRQs and out-of-quota tariffs for Ukrainian exports. These TRQs also need to be further analysed and revised.
Тable 1. Utilisation of DCFTA tariff rate quotas by Ukraine, 2016-2019.
| 2016 | 2019 | |||||
| Quota name | Quota volume | Utilised | Quota volume | Utilised | ||
| t | t | % | t | t | % | |
| “First-come, first-served” method for TRQ allocation | ||||||
| Sheep meat | 1500 | 0 | 0,0% | 1950 | 0 | 0,0% |
| Honey | 5000 | 5000 | 100% | 5600 | 5600 | 100% |
| Garlic | 500 | 49 | 9,8% | 500 | 393 | 78,6% |
| Oats | 4000 | 4000 | 100% | 4000 | 1239 | 31,0% |
| Sugars | 20070 | 20070 | 100% | 20070 | 20070 | 100% |
| Other sugars | 10000 | 5929 | 59,3% | 16000 | 1006 | 6,3% |
| Sugar syrups | 2000 | 0 | 0,0% | 2000 | 7 | 0,4% |
| Barley groats and meal, cereal grains otherwise worked | 6300 | 6300 | 100% | 7200 | 7200 | 100% |
| Malt and wheat gluten | 7000 | 7000 | 100% | 7000 | 6319 | 90,3% |
| Starches | 10000 | 1898 | 19,0% | 10000 | 10000 | 100% |
| Starch processed | 1000 | 0 | 0,0% | 1600 | 1600 | 100% |
| Bran, wastes and residues | 17000 | 7286 | 42,9% | 20000 | 14467 | 72,3% |
| Mushrooms main | 500 | 0 | 0,1% | 500 | 0 | 0,0% |
| Mushrooms additional | 500 | 0 | 0,0% | 500 | 0 | 0,0% |
| Processed tomatoes | 10000 | 10000 | 100% | 10000 | 10000 | 100% |
| Grape and apple juice | 10000 | 10000 | 100% | 16000 | 16000 | 100% |
| Fermented-milk processed products | 2000 | 0 | 0,0% | 2000 | 866 | 43,3% |
| Processed butter products | 250 | 0 | 0,0% | 250 | 0 | 0,0% |
| Sweetcorn | 1500 | 13 | 0,9% | 1500 | 23 | 1,5% |
| Sugar processed products | 2000 | 340 | 17,0% | 2600 | 417 | 16,0% |
| Cereal processed products | 2000 | 55 | 2,7% | 2000 | 1989 | 99,5% |
| Milk-cream processed products | 300 | 73 | 24,4% | 420 | 9 | 2,2% |
| Food preparations | 2000 | 5 | 0,3% | 2000 | 65 | 3,2% |
| Ethanol | 27000 | 1889 | 7,0% | 70800 | 6083 | 8,6% |
| Cigars and cigarettes | 2500 | 0 | 0,0% | 2500 | 0 | 0,002% |
| Mannitol-sorbitol | 100 | 0 | 0,0% | 100 | 0 | 0,0% |
| Malt-starch processed products | 2000 | 0 | 0,0% | 2000 | 1998 | 99,9%* |
| Import licensing method for TRQ allocation | ||||||
| Beef meat | 12000 | 0 | 0,0% | 12000 | 0 | 0,0% |
| Pork meat main | 20000 | 0 | 0,0% | 20000 | 0 | 0,0% |
| Pork meat additional | 20000 | 0 | 0,0% | 20000 | 0 | 0,0% |
| Poultry meat and preparations main | 16000 | 16000 | 100% | 18400 | 18400 | 100% |
| Poultry meat and preparations additional | 20000 | 8552 | 42,8% | 20000 | 9174 | 45,9% |
| Eggs and albumins main | 1500 | 232 | 15,5% | 2400 | 2027 | 84,5% |
| Eggs and albumins additional | 3000 | 0 | 0,0% | 3000 | 1891 | 63,0% |
| Wheat, flours, and pellets | 950000 | 950000 | 100% | 980000 | 980000 | 100% |
| Barley, flour and pellets | 250000 | 249460 | 99,8% | 310000 | 249250 | 80,4% |
| Maize, flour and pellets | 400000 | 400000 | 100% | 550000 | 550000 | 100% |
| Milk, cream, condensed milk and yogurts | 8000 | 0 | 0,0% | 9200 | 250 | 2,7% |
| Milk powder | 1500 | 450 | 30,0% | 3600 | 560 | 15,6% |
| Butter and dairy spreads | 1500 | 690 | 46,0% | 2400 | 2400 | 100% |
Source: European Commission, own calculations * Note: We consider 99.9% usage rate as fully utilized TRQ.
Conclusion
The EU and Ukraine confirmed their readiness to initiate the update of the DCFTA due in 2021. Ukraine is interested in increasing duty-free trade under DCFTA with the EU in line with the current state of Ukraine’s production and export capacities, as well as EU-Ukraine bilateral trade developments.
Although many DCFTA TRQs did not limit over-quota exports, Ukraine wants to revise DCFTA TRQs to secure permanent broader duty-free access to the EU market and reduce access barriers for SMEs (as SMEs are more affected by TRQs and other non-tariff barriers). So far, the EU temporarily increased certain TRQs in 2017 for three years as autonomous trade preferences for Ukraine. The primary candidates for the update should include DCFTA TRQs demonstrating high utilization rates, with or without over-quota supply (honey; processed tomatoes; barley groats and meal, cereal grains; apple juice; sugars; butter and dairy spreads; starch processed, etc.).
Amid future DCFTA update negotiations, Ukraine should conduct a detailed analysis for each DCFTA TRQ (taking into account temporary ATM quotas) to prepare its suggestions how and to what extent to liberalise them. It is worth considering different options of such liberalisation – by either increasing TRQs’ volumes or setting up preferential tariff rates for Ukraine instead, etc.
In the framework of the future negotiations with the EU, a special emphasis should be placed on increasing duty-free access for Ukrainian processed goods to promote their exports to the EU – as stipulated in the Export Strategy of Ukraine. For this purpose, Ukraine may explore possibilities for modifying the structure of certain TRQs (such as wheat, flour and pellets; maize, flour and pellets; barley, flour and pellets) to separate primary and processed products and to ensure more duty-free volumes for processed products.
References
- European Commission, 21.04.2020. DG Agriculture and Rural Development. “AGRI TRQs – Allocation Coefficients and Decisions”.
- European Commission, 12.02.2020. Remarks by Commissioner Várhelyi at a press conference with Prime Minister of Ukraine, Oleksiy Honcharuk.
- European Commission, DG Taxation and Customs Union, 21.04.2020. Tariff quota consultation.
- European Commission, 21.04.2020. “Trade Helpdesk Statistics.”
- OECD, 2018. “Fostering greater SME participation in a globally integrated economy”.
- Official Journal of the European Union, 2014. “EU-Ukraine Association Agreement”.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Addressing the Covid-19 Pandemic: Policy Responses Across Eastern Europe
The world holds its breath as Covid-19 continues to spread and challenge local health care systems as well as local economies. The focus of international media has mostly been on China and then Western Europe and the US. However, countries around the Baltic Sea, Eastern Europe and the Caucasus differ from the West with respect to their socio-economic development, trade integration, and political systems. The webinar “Addressing the Covid-19 Pandemic in Eastern Europe: Policy Responses Across Eastern Europe” hosted by the the Forum for Research on Eastern Europe and Emerging Economies (FREE) Network on May 28, 2020 aimed to fill this gap in the current discourse and give voice to experts from Latvia, Russia, Georgia, Belarus, Poland, Ukraine as well as Sweden, in order to contextualize their countries’ policy choices and experiences in the crisis. Policy recommendations can only be of preliminary nature at this point of time. Yet, it becomes clear that even though transition countries have fared relatively well during the health crisis, they will not be spared from the ensuing economic crisis and will require policy tools which are adapted to the local context.
Introduction
Less than six months after the outbreak of the Covid-19 crisis in China, the pandemic has spread across the globe. The epicenter has moved from Asia to Europe and the US, and in late May 2020 some voices are warning that it is now shifting towards Latin America. While the world´s eyes have been on Milan and Paris, little has been said about how the new EU member states and countries to the East of the European Union cope with the pandemic. Some observers have claimed the emergence of a new “iron curtain” in the corona crisis; Eastern Europe, the Baltic States and the Caucasus having been relatively unscathed compared to the West. Persisting differences in trade and travel patterns, demographic and socio-economic differences, as well as differences in trust levels could account for such an observation.
Yet, the most recent statistics suggest that this may be a premature interpretation and the overall picture is much more heterogeneous. Infections in Russia seem to be rising quickly, Georgia by contrast has turned out to be one of the top students.
Figure 1: Total confirmed Covid-19 cases vs. deaths per million.
Source: Our World in Data, 2020. • CC BYa.
Note: Data includes the most recent numbers as of May 25, 2020. Both measures are expressed per million people of the country’s population. The confirmed counts are lower than the totals. The main reason for this is limited testing.
On May 28, 2020, the Forum for Research on Eastern Europe and Emerging Economies (FREE) Network hosted a webinar with its member institutes: BEROC in Belarus, BICEPS in Latvia, CEFIR@NES in Russia, CenEA in Poland, ISET-PI in Georgia, KSE in Ukraine, and SITE in Sweden to discuss how their countries have fared in the corona crisis so far. The webinar provided an opportunity to share experiences and to add some interpretations and insights to the crude statistics, which often become unintelligible in the current overflow of information.
Figure 2: FREE Network Countries.
Source: SITE 2020.
The webinar started with Torbjörn Becker, director of SITE, introducing recent developments in terms of health statistics in the region and the research being done within the framework of the FREE Network.
SITE on Sweden
Jesper Roine, Professor at the Stockholm School of Economics and SITE, then presented the case of Sweden, the country which – with regards to death rates – has surpassed all other FREE Network countries by far. The Swedish case has been very controversially discussed in international media throughout the pandemic. Yet, the common claim that in Sweden everything was “business as usual” is not true, according to Roine. Compared to its direct neighboring countries Finland, Denmark and Norway, Sweden has chosen a relatively lenient approach to Covid-19, but high schools and universities have moved to distance learning since March and working from home is highly encouraged. Mobility reports show that Swedes have reduced their movement a lot, but less so than their Scandinavian neighbors. Roine confirmed that the Swedish health policy has been dominated by the public health agency, Folkhälsomyndigheten. Even though this is the default option in Swedish law, Roine stressed that this does not mean that the government’s hands are tied.
He presented two preliminary conclusions regarding the impact of the Swedish strategy: first, Sweden’s mitigation strategy has worked relatively well; the public health system is seriously strained but not overwhelmed. Yet, Roine said that the “lack of testing [remained] a mystery”, even for advocates of the current mitigation strategy. Second, in Roine’s opinion the attempt to protect the elderly has failed. The virus has spread to numerous nursing homes and excess death rates indicate that mortality has increased sharply for citizens above 65 years of age, much less for other age groups. Geographically, Stockholm has been the center of the epidemic. Other parts of the country have been affected to a much lesser degree.
BICEPS on Latvia
Sergejs Gubins, Research Fellow at the Baltic International Centre for Economic Policy Studies (BICEPS) presented the Latvian experience of the corona crisis. A small country of about 2 million inhabitants, Latvia currently presents the second lowest Covid-19 mortality rate within the EU. Gubins related this to the Latvian government’s quick and determined policy reaction. After the first cases were reported in early March, schools and universities were closed, public gatherings forbidden, international travel halted, and a two-meter social distance rule imposed. Given the success of this strategy, Latvia has started to loosen its restrictions. A “Baltic Schengen area” was announced very recently and travel among the Baltic states is now possible again. The economic support package announced by the government amounts to 45 percent of GDP and includes a large equity investment in the airline airBaltic as well as important investments in infrastructure. According to Gubins, the current policy discussion focuses on the accessibility and size of help funds, widely deemed insufficient. Furthermore, the economic outlook of the country in terms of unemployment rates and GDP growth is bleak despite its success in containing the virus.
CEFIR on Russia
According to Natalia Volchkova, Director of the Centre for Economic and Financial Research (CEFIR) at the New Economic School in Moscow, Russia has pursued a “standard European strategy” in its fight against Covid-19. Two new hospitals exclusively for Covid-19 patients were created in Moscow, the current epicenter of the pandemic, and nearby. Most money spent on health care went to these new facilities, less was transferred as bonuses to medical workers. Russia has emphasized testing: around 10 million tests were performed; close to 400,000 cases of Covid-19 were confirmed. On May 27, free antibody testing was started in Moscow and is to be extended to other parts of the country. State-financed testing will serve to measure the potential degree of immunization of the population. While cases have started to decline in Moscow, other regions of Russia lag behind and are still expected to peak.
Volchkova stressed the role of the Russian shadow economy, which has been severely hit by the crisis. The size of the informal sector makes it difficult for the Kremlin to pass efficient support packages for the economy. Another policy problem lies in the weakness of the social security net, particularly unemployment benefits are hard to obtain. Therefore, most policy measures have focused on companies. Family allowances are the government’s second heavily used tool, which to Volchkova’s mind is an efficient policy choice. She concluded that the current help measures may already amount to 3 percent of GDP.
ISET-PI on Georgia
As of May 28, 2020, Georgia had only reported 12 corona deaths. According to Yaroslava V. Babych, Lead Economist at ISET Policy Institute in Tbilisi, the key explanation for Georgia’s relative success in the corona crisis is that, as in Latvia, testing started very early. She explained that even before Georgia’s neighbor Iran confirmed an outbreak of Covid-19, passengers’ temperatures were taken at the border crossing. The government in Tbilisi then soon imposed harsh quarantine measures, local quarantines in regional hotspots, a shutdown of public transport, an evening curfew and very high fines. Compliance with the measures was very high. Orthodox Easter celebrations were allowed to take place under strict hygiene measures and did not result in a spike in infection rates.
The country, largely reliant on tourism and agriculture, is now focusing on the economic consequences of the crisis. According to Babych, Georgia holds the ambition to become the first European country to open up to international tourism again from July 1, 2020. The government is also determined to avoid another meltdown of the important construction sector, as happened in 2008 – 2009. However, similar to the Russian case, Babych identified two factors which crucially weaken the Georgian economy: the lack of automatic stabilizers in the form of unemployment benefits and the large informal sector. Policymakers have therefore resorted to monthly cash payments to those who stopped paying income tax around March and fixing prices for specific food products. While the effectiveness of these measures still has to be evaluated, the policy discourse in Georgia has moved on to the socio-economic consequences of the crisis.
BEROC on Belarus
Lev Lvovskiy, Senior Research Fellow at the Belarusian Economic Research and Outreach Center (BEROC), provided an overview of the Belarusian policy measures. According to Lvovskiy, Belarus has a high number of nurses and doctors and a relatively efficient “Soviet style of fighting pandemics”. There have been hardly any restrictions to public gatherings and events, both the Orthodox and the Catholic Easter festivities were maintained, as were soccer games and the national Victory parade. Initially, the official policy was to trace and isolate cases, but this did not prove to be very efficient, supposedly due to poor enforcement. Lvovskiy said that testing is rare which is why statistics on the spread of the virus and its effects remain of questionable quality.
While Belarus disposed of a solid health care system, it was not well prepared economically, which explains why the government has not been very proactive in Lvovskiy’s opinion. The Belarusian industrial production decreased by 7 percent in April 2020 compared to the same month the year before; unemployment has started to increase, yet, there are no significant unemployment benefits. Increasing the height of unemployment pay is the key policy issue under discussion in Minsk but in the absence of international loans, the government´s hands are tied. The issue is urgent: the most recent BEROC survey suggests that 46% of individuals living in urban areas have already seen their income decrease. Lvovskiy’s preliminary conclusion is that the Belarusian policy response to the Covid-19 crisis was not as bad as expected by many international observers: the health crisis has mostly been contained. But like in the Georgian case, the socio-economic implications of the crisis are becoming more pressing now.
CenEA on Poland
Michal Myck, Director of the Centre for Economic Analysis (CenEA) in Szczecin, explained that Poland also successfully avoided a spike in infection rates thanks to a quick policy response. Poland was one of the first countries to impose international travel restrictions and very harsh social distancing measures, yet, infection rates remain higher than in other FREE Network countries. Since the second half of April, most measures have been lifted and the spread of the virus seems under control and concentrated in the region of Silesia.
All limitations were implemented without invoking a state of emergency. Myck suggested that the government may have made this choice because the presidential elections would have been automatically postponed otherwise, an outcome the government wanted to avoid. The elections were eventually postponed, but doubts persist with regards to the constitutional validity of the way this decision was taken. Myck stressed the persisting political uncertainty. Economic policy in Poland has focused on protecting jobs and providing liquidity to enterprises. State loans have been primarily directed to SMEs and will be partly written off, conditional on continued activity and employment. In Myck’s opinion, the economic outcome for Poland will depend on whether investments from and exports to Western Europe quickly resume or not.
KSE on Ukraine
Tymofiy Mylovanov, President of the Kyiv School of Economics and former Minister of Economic Development, Trade and Agriculture, stressed that in the first few weeks of the pandemic, Ukraine enforced harsher policy measures than its neighbors. The lock down was almost complete, with only grocery stores and pharmacies allowed to open. Compliance was high during the first few weeks but then started to decline.
The government allocated 3 percent of GDP to a Covid-19 support fund, there has been a lot of deregulation on the labor market, but the central bank’s key interest rate remains at 8 percent. Pressure for a looser monetary policy increases according to Mylovanov, as GDP has fallen by 1.2 percent and unemployment is expected to reach up to 10 percent by the end of the year.
Mylovanov’s thoughts about Ukraine’s economic prospects are mixed: average salaries continue to grow during the crisis which may be explained by the fact that low-skilled employees get laid off first, suggesting a potentially long-lasting change of the composition of the workforce. At the same time, the political situation is volatile with local elections coming up in October 2020 and public pressure mounting. As Poland, Ukraine did not declare a state of emergency. While Mylovanov thinks that the policy response could have been better, he is optimistic that Ukraine was better prepared to Covid-19 than to previous crises and will not have to resort to international loans.
Preliminary Conclusions
It is too early to draw any definite conclusions, but undoubtedly, a lot can be learned from the very diverse experiences of the corona crisis in the region. The former Soviet countries have a different historical and political legacy than Western European countries and accordingly, have found different ways of handling the crisis. Some have been more successful than their Western neighbors. But even those countries which have not faced a large health crisis have been severely hit economically and are likely to suffer economic hardship in the future.
The lack of a strong tradition of unemployment benefits and automatic stabilizers renders countries like Georgia, Belarus and Russia particularly vulnerable to the economic crisis which will inevitably follow the Covid-19 outbreak. In some countries, the corona shock may also accelerate or trigger political changes. In the view of this, the FREE Network will organize a series of follow-up webinars and briefs on more specific corona-related topics, with the aim of contextualizing statistics and providing a wider picture of the socio-economic consequences and policy implications of the crisis.
Please find a full recording of the webinar below. Updates on further events will be posted on the FREE website and on social media channels (Facebook, Twitter).
List of Speakers
- Jesper Roine, Professor at the Stockholm Institute of Transition Economics (SITE / Sweden)
- Sergejs Gubins, Research Fellow at the Baltic International Centre for Economic Policy Studies (BICEPS / Latvia)
- Natalia Volchkova, Director of the Centre for Economic and Financial Research at New Economic School (CEFIR@NES / Russia)
- Yaroslava V. Babych, Lead Economist at ISET Policy Institute (ISET / Georgia)
- Tymofiy Mylovanov, President at the Kyiv School of Economics (KSE / Ukraine)
- Lev Lvovskiy, Senior Research Fellow at the Belarusian Economic Research and Outreach Center (BEROC / Belarus)
- Michal Myck, Director of the Centre for Economic Analysis (CenEA / Poland)
- Torbjörn Becker, Director of the Stockholm Institute of Transition Economics (SITE)
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Supporting Measures for Belarusian SMEs: the Context of the Covid-19 Pandemic
In the context of the evolving global economic crisis, governments are “competing” with each other in the complexity and scale of measures to support the economy and, in particular, small and medium-sized enterprise (hereafter SMEs). The main goal of these measures is, on the one hand, to prevent a significant increase in unemployment and a consequent social strain, and, on the other hand, to ensure economic recovery driven by the most efficient enterprises.
Belarusian SMEs, which currently employ more than 1.3 million people, usually respond faster and more extensively than the state companies to the downturn in the economy by laying off employees. At the same time, they are also expected to be more sensitive reacting to governmental support policies. In this regard, the policy brief discusses the role and response of SMEs in the period of crises and delineates short- and medium-term measures.
Why are SMEs in the Focus During Economic Downturns?
SMEs often become the focus of state policy in a period of adverse and unstable economic situations and the recent pandemic is not an exception. This special attention can be motivated by the following basic assumptions:
1) SMEs are more flexible and respond faster to both negative and positive trends in the economy (Muller at al., 2018);
2) the activity of SMEs is more labor-intensive compared to large enterprises (Beck et al., 2005; Cravo et al., 2012);
3) a period of economic uncertainty creates new opportunities (new niches, exits of competitors from the market) that can be used by the most proactive SMEs (Cowling et al., 2015).
Based on these assumptions, a large share of SMEs on the one hand makes the economy more resilient in crises and, on the other hand, contributes to the volatility of unemployment. As a result, governments try to support SMEs to prevent a rapid increase in unemployment due to staff cuts and bankruptcy and, simultaneously, to maintain a competitive environment that creates incentives for innovation.
Typically, governments have substantial experience and proven tools to uphold large public and too-big-to-fail private enterprises, while supporting a heterogeneous population of SMEs requires additional study and field tests.
At the same time, the design, the scope, and the coverage of support policies should be introduced having in mind the possible reactions of various types of SMEs to the economic hardship. Indeed, during an economic decline even in the worst hit sectors, businesses and SMEs in particular may react by implementing three basic strategies:
1) reducing costs by firing employees, cutting wages and by increasing productivity;
2) increasing revenue by introducing innovations (product, process, organizational, marketing), diversification, and entering new markets;
3) suspension of activities or liquidation of an enterprise (OECD, 2009).
Definitely, any government aims for the largest possible share of enterprises that pursue the second strategy leading to job creation and significant added value.
Policy Responses in the Period of the Pandemic
Due to the urgency of adoption and the weak predictability of the epidemiological situation, most of the proposed SME-support packages around the world are designed for the short term and are poorly targeted. Based on the study of already announced measures, the OECD (2020) has developed a comprehensive classification and sequence of SME-support measures undertaken by governments:
1. Health measures, and information for SMEs on how to adhere to them;
2. Measures to address liquidity by deferring payments (taxes, social security & pension contribution, rental, utilities);
3. Measures to provide extra and more easily available credit to strengthen SME resilience;
4. Measures to mitigate the consequences of lay-offs by extending possibilities for temporary redundancies and wage subsidies;
5. Structural policies (digitalization, training and education for SMEs, support in finding and entering new markets etc.).
Unfortunately, the government of Belarus has started discussing and implementing some of these measures only partially and in a rather non-specific way. Instead of this, we argue that all the measures should be targeted and adjusted to different sectors. To further expand and analyze our point, BEROC developed and commissioned an express random-sample survey of 100 Belarusian SMEs on April 13-27 in order to elaborate and justify relevant support measures (BEROC, 2020).
Belarusian SMEs in the Pandemic
The financial situation of Belarusian SMEs by sector and their response to the crisis manifested in implementing innovative approaches and new business models are illustrated in Figure 1.
Figure 1. Decrease of revenues and response of SMEs
Note: Area of circles is proportional to the number of SME employees in a sector.
Source: Own elaboration based on the survey.
SMEs operating in hotels, restaurants, catering (HoReCa), education, sport & leisure as well as transportation (the right lower rectangle) are characterized by a substantial decrease of revenues and low adaptability. At the same time SMEs in the communication and IT sector and scientific, technological and consulting sectors demonstrate a high degree of adaptability that may be related to some extend to managerial competencies and human capital in general which is concentrated in these sectors.
As an implication for policy makers and SMEs’ leaders, possible support measures (based on OECD classification) and business strategies are summed up in Table 1.
Table 1. Support measures and business strategies for Belarusian SMEs
| Group | Sectors | Recommended strategy | Relevant Measure (number in the OECD classification) |
| A. Decrease of revenues + slow adaptation | Construction,
wholesale trade & retail manufacturing |
Re-configuring supply chains, entering new niches, business process optimization | 2,3,5 |
| B. Decrease of revenues + active adaptation | Communication & IT
Scientific, technological, consulting services |
Focusing on development of anti-crisis solutions in B2B and B2C segments | 2,4 |
| C. Substantial decrease of revenues + slow adaptation | Transportation
HoReCa Education Leisure, beauty & sport |
«Conservation» or liquidation of a business | 2,3,5 |
| D. Substantial decrease of revenues + active adaptation | Not identified in the survey | Diversification to adjacent market segments | 2,4,5 |
| E. No changes or growth of revenue | Agriculture & Forestry
E-commerce, pharmacy, online services, online games… |
Expansion to new markets while competitors are on quarantine. | 5 |
Source: Own elaboration based on the survey.
The main measure to support SMEs in the short term (items 2-4 in the OECD classification) can be:
- Deferral, reduction or suspension of contributions to the social security fund (groups B, C) – this will save jobs in the short term;
- Wage subsidies that will allow paying minimum wages and keeping staff (groups A, C)
- Rent and utility deferrals or at least payment in arrears – for groups A, C – combined with the support of building owners. This will significantly reduce costs in the face of falling revenues instead of reducing labor costs;
- Loan holidays and preferential conditions for SMEs (group D). This will provide liquidity for enterprises that according to banks’estimates will be able to develop in the medium term;
- Temporary repeal of fines for late payment of taxes and contribution to the social security fund (groups A-D).
As for the medium-term measures, the most relevant ones are as follows:
- Expanding the coverage and improving the quality of business education (including digitalization of business) by means of providing vouchers and/or grants;
- Subsidies to unemployed people for starting up a business combined with basic training on entrepreneurship;
- Export support by developing infrastructure for certification and international marketing as well as providing export loans (Marozau et. al., 2020).
Conclusion
The Belarusian government is substantially restricted in terms of financial resources, fiscal and external debt opportunities to extensively support businesses suffering from the economic crisis. Therefore, formal and economically justified criteria for selecting sectors, as well as individual businesses and individual entrepreneurs should be developed. Meanwhile, the beneficiaries of the state support should not be the most affected businesses, but rather the most forward-looking ones. This so-called “picking winners” approach (Gonzalez-Pernia et al., 2018) would conduce to faster economic recovery and job creation driven by the private sector and, particularly, by SMEs. This is probably the main argument in favor of supporting small and medium-sized businesses in the crisis.
References
- Beck, T., Demirguc-Kunt, A., Levine, R. (2005). “SMEs, Growth and Poverty: Cross- country evidence.” Journal of Economic Growth, 10(3), 199-229.
- BEROC. (2020). “SME Survey Results”, Access mode http://covideconomy.by/business. Access date: May 19, 2020).
- Cowling, M., Liu, W., Ledger, A., & Zhang, N. (2015). “What really happens to small and medium-sized enterprises in a global economic recession? UK evidence on sales and job dynamics.” International Small Business Journal, 33(5), 488-513.
- Cravo, T.A., Gourlay, A., Becker, B. (2012). “SMEs and Regional Economic Growth in Brazil.” Small Business Economics, 38 (2), 217-230.
- González-Pernía, J. L., Guerrero, M., Jung, A., & Pena-Legazkue. (2018). “Economic recession shake-out and entrepreneurship: Evidence from Spain.” BRQ Business Research Quarterly, 21(3), 153-167.
- Marozau, R., Akulava, M., Aginskaya, H., (2020). “Measures to support small and medium-sized businesses in Belarus in the context of the pandemic and global recession.” BEROC Policy Paper Series, PP no.89.
- Muller, P., Mattes, A., Klitou, D., Lonkeu, O., Ramada, P., Ruiz, F.A., Devnani, S., Farrenkopf, J., Makowska, A., Mankovska, N., Robonn, N., Steigertahl, I. (2018). Annual report on European SMEs 2017/2018. The 10th Anniversary of the Small Business Act. European Commission.
- OECD. (2020). “COVID-19: SME Policy Responses.” OECD Centre for Entrepreneurship, SMEs, Regions and Cities (CFE). Access mode https://read.oecd-ilibrary.org/view/?ref=119_119680-di6h3qgi4x&title=Covid-19_SME_Policy_Responses. Access date: May 19, 2020.
- OECD. (2009). “The Impact of the Global Crisis on SME and Entrepreneurship Financing and Policy Responses.” OECD – Centre for Entrepreneurship, SMEs and Local Development, Paris.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Covid-19 and Gender Inequality in Russia
Gender inequality is a complex phenomenon characterized by significant and persistent differences in social and economic indicators for women and men. In connection with the Covid-19 pandemic and unprecedented quarantine measures around the world, economists are thinking not only about the obvious global consequences for the global economy but also about the indirect effects, including those through gender-related changes in the labor market. What will the consequences of this crisis on the labor market be in the long run, especially on its gender and family-related components? In this brief, we look at the potential effects of the Covid-19 epidemic and the associated quarantine on gender inequality in Russia.
Introduction
Gender inequality is a complex phenomenon characterized by significant and persistent differences in social and economic indicators for women and men. These may be differences in access to education and medicine, labor market participation, wages, entrepreneurship, participation in politics and public administration, and the distribution of domestic unpaid labor within the family. Reducing gender inequality (like any other form of inequality) correlates with increases in GDP.
The prevalence and scale of gender inequality is, on average, lower in developed countries than in developing countries, and although there is a general tendency for gender gaps to narrow over time, this does not happen simultaneously and equally in all countries. According to the Global Gender Gap Index (2020), which ranks more than 150 countries, the five countries with the best indicators include Iceland, Norway, Finland, Sweden, and Nicaragua, while Congo, Syria, Pakistan, Iraq, and Yemen are in the very bottom. As of 2020, Russia is located approximately in the middle, being the 81st, right between El Salvador and Ethiopia.
In connection with the Covid-19 pandemic and unprecedented quarantine measures around the world, economists are thinking not only about the obvious global consequences for the global economy but also about the indirect effects, including those through gender-related changes in the labor market. A study of World War II, for example, shows that even short-term gender differences in the labor market can have long-term consequences (Goldin and Olivetti, 2013). What will the consequences of this crisis on the labor market be in the long run, especially on its gender and family-related components? In this brief, we look at the potential effects of the Covid-19 epidemic and the associated quarantine on gender inequality in Russia.
Heterogeneous Cross-Sectoral Effects
Economists are now discussing two main channels that can influence gender inequality (Alon et al., 2020). The first one works through differential risk of losing jobs and salaries for women and men due to the disproportionate impact of the epidemic and quarantine on sectors which predominantly employ each gender. The direction of this effect is not easy to predict. On the one hand, the current crisis differs from ordinary recessions in that the service sector, where more women are traditionally employed, is now suffering more than usual. However, it is very important to emphasize what kind of services we are talking about: restaurants and salons are not the whole of the Russian economy. According to the Russian Statistical Agency (Rosstat) 49% of all employed women in 2019 worked in three sectors – trade, healthcare, and education. At the same time, hotels, restaurants, and other services (which include hair and beauty salons) provided less than 8% of women’s employment.
Therefore, from the point of view of assessing the risk of job loss, it makes sense to consider state-financed sectors, where employees are likely to be retained, separately. Among the private businesses, two (non-mutually exclusive) types of sectors are likely to suffer the least. First, the critical ones that do not stop their activity during quarantine (for example, food retail, private medical centers). And second, those that are characterized both by a high ability to work “remotely” and continue to have sufficient demand for their goods and services – either directly or through value chains (see e.g. Volchkova, 2020). For example, agriculture, manufacturing and hotels are worse off in this combination than the financial sector, science, administration, and some types of online education. At the level of the individual characteristics of the employee, even when comparing the same occupations, the possibility of remote work positively correlates with the level of education, wealth, working for a company (rather than self-employment), and being female (according to Saltiel, 2020, for developing countries).
According to the same data from Rosstat, it turns out that about 49% of all women and 40% of all men worked in the “state-financed” and “remote-work” sectors (or 69% against 52%, if we add the trade sector). This is of course an overestimate, since not every job within a sector is characterized by state-financing or remoteness, but it likely represents the relative propensity across genders, which is of our interest. This relative propensity is mostly due to the much higher employment of women compared to men in health and education (approximately 4 to 1 in both sectors). In general, this may mean that the risk of job loss is now higher for men, and not for women as was predicted using US data by Alon et al. (2020), given the gender structure of employment by industry in the US. This rough assessment does not account for different opportunities for women and men to quickly find a new job, especially in the areas of high demand. For example, if the need for delivery workers has increased, and men are more likely to take this job, then it may be easier for them to quickly find a new job. This adaptive effect would unlikely overturn the original difference, because the number of such jobs is also limited.
The Effect of Childcare Facilities Closure
The second channel, likely having a multiplicative effect on the first, operates through the unexpected closure of children’s educational institutions (kindergartens and schools). These effects may be different depending on family composition. While before the pandemic, working parents could send their children to kindergarten and school, this opportunity is now completely unavailable. In the case of online education, not all children are independent enough to learn at home, especially primary school students. At the same time, other childcare support (e.g. from nannies, grandparents and other relatives, etc.) can also be significantly limited due to social distancing and self-isolation, although Russia is in a better position in this regard compared to many developed countries because grandparents traditionally help more in raising children. (It is interesting that in developed countries, the possibility of outsourcing household chores – childcare, cleaning, etc. – is one of the important explanatory factors for higher fertility among more educated women, compared with less educated ones, (see Hazan and Zoabi, 2015)).
Naturally, the situation with closed childcare and educational institutions will not affect the productivity of people without young children. According to the latest census in 2010, about 88 million people, which is as much as 75% of the total adult population of the country, do not live together with children under 18 years old. Also, most likely there will not be a big negative effect on families with children where one of the parents (most often the mother) or another individual in the household (a grandparent) took care of the child at home before the quarantine.
For all other families, the critical problem is juggling childcare with work. The most vulnerable categories of the population here are single mothers and single fathers (and there are about 5 and 0.6 million in Russia, respectively), especially those who do not have any outside help.
Among families with small children where both parents work, several important factors can be identified. On the one hand, according to developed countries, even in families where both parents work, women spend more time on household chores and childcare than men (Doepke and Kindermann, 2019). If one believes that the initial factors that affected this distribution of domestic work (such as traditional norms and role models or the relative income of spouses) have not disappeared, then the sharply increased burden of household chores will disproportionately fall on women. This can lead to a decrease in the relative productivity of women compared to men in the labor market and a greater risk of dismissal. In the long run, this can also negatively affect gender inequality, as even a temporary exit from the labor market may be accompanied by human capital losses and a worse career path in the future.
The Interaction of Both Effects
On the other hand, the opposite situation is also possible. If, due to the disproportionate effect of quarantine on various sectors of the economy, which has been discussed above, women have a lower risk of losing their jobs, then it is possible that at least temporarily, a significant part of the childcare will fall on men. This situation can also happen in families where the woman works in critical sectors of the economy (especially in healthcare) and the man works remotely from home.
Economists have suggested several mechanisms for the effect of short-term additional interaction between fathers and children on long-term participation in their upbringing: there is more information about children’s needs, learning-by-doing, and greater attachment to children. For example, the data from Canada shows that the introduction of 5 weeks of parental leave for fathers led to a more even distribution of domestic labor in households and a greater likelihood of the mother’s participation in the labor market, even 1-3 years after the fact (Patnaik, 2019). Moreover, even if there are not many families like this in the country, the new social norms can gradually spread in society through so-called “peer effects”. Dahl et al. (2014), for example, show using Norwegian data that the brothers and colleagues of men who took parental leave were 11-15% more likely to take it in the future, relative to brothers and colleagues of men who did not take such leave.
Other Hypotheses
Another major consequence of the epidemic and quarantine is the potential upsurge in domestic violence. Several European countries have already noticed an increase in such crimes (European Parliament, 2020), and some crisis centers in Russia have also reported an increase in calls to helplines. Economists identify different triggers for this behavior (Peterman et al., 2020). This may be a direct consequence of quarantine, which increases the time spent by the potential victim and abuser in a closed space, and the inability to seek immediate help, both psychological and medical. Indirect effects can also work through an increased risk of depression and post-traumatic stress syndrome, which were well documented for previous epidemics such as SARS and swine flu. and that may happen due to job loss, reduced income, general economic uncertainty, or a direct fear of getting sick.
These effects disproportionately affect women (and children); therefore, additional resources should be dedicated to identifying such crimes, strengthening support structures for women, and increasing the availability of reporting options without attracting the attention of an abuser (for example, such a warning system may be installed in pharmacies – a place where a woman can go to alone).
Economists have yet to accurately measure and test all these mechanisms, which interact with each other in complex combinations, but it is now clear that very different scenarios are possible, including the positive ones – of a long-run decrease in gender inequality.
References
- Alon T., Doepke M., Olmstead-Rumsey J., and Tertilt M. “The impact of Covid-19 on gender equality”, Covid Economics, Issue 4, 14 April 2020.
- Dahl G.B., Løken K.V., Mogstad M. “Peer Effects in Program Participation”, American Economic Review 104(7): 2049–2074 (2014).
- Doepke M. and Kindermann F. “Bargaining over Babies: Theory, Evidence, and Policy Implications”, American Economic Review, 109(9): 3264–3306 (2019).
- Goldin C. and Olivetti C. “Shocking Labor Supply: A Reassessment of the Role of World War II on Women’s Labor Supply”, American Economic Review, 103(3): 257-262 (2013).
- Hazan M. and Zoabi H. “Do highly educated women choose smaller families?” Economic Journal, 125(587): 1191-1226 (2015).
- Patnaik A. “Reserving Time for Daddy: The Consequences of Fathers’ Quotas”, Journal of Labor Economics, 37(4): 1009-1059 (2019).
- Peterman A., Potts A., O’Donnell M., Thompson K., Shah N., Oertelt-Prigione S., and van Gelder N. “Pandemics and Violence Against Women and Children”, Center for Global Development working paper, 1 April 2020.
- Saltiel F. “Who can work from home in developing countries?” Covid Economics, Issue 6, 17 April 2020.
- Volchkova N. “Who should receive government support during Covid-19 crisis”, in “Economic Policy during Covid-19”, April 2020.
- European Parliament. “COVID-19: Stopping the rise in domestic violence during lockdown”, Press Release 7 April 2020.
- Rosstat, “Russian census 2010”.
- Rosstat, “Russian labor force survey 2019”.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Covid-19: News for Europe’s Energy Security
While there has been a lot of attention on the effect of Covid-19-related developments in the oil market, the effect on the natural gas market has almost evaded media attention. For the EU, however, the gas market and especially the impact of the pandemic on the gas relationship with its largest gas supplier, Russia, is of high relevance. This brief discusses the potential implications of Covid-19 on this relationship both under the pandemic and during the expected slow economic recovery. We argue that, while in the short run the security of Russian gas supply is likely to improve, this is unlikely to be the case in the aftermath of the pandemic. To ensure gas supply security in post-pandemic markets, the EU may need to finally implement the long-awaited “speaking with one-voice” energy policy.
Introduction
The ongoing coronavirus pandemic will not only affect human lives, but also bring new economic and political challenges. The energy sector, and in particular the dramatic decrease of oil prices, has been in the news since the beginning of the Covid-19 crisis. But discussions have so far rarely touched the natural gas market, despite the pandemic taking its toll also on this market. As for oil, the demand and price have been negatively affected by the economic slowdown. While not as drastic as for oil, the price of natural gas in the EU has declined by approximately 40% since the beginning of 2020 (World Bank, 2020). However, the impact of the pandemic is likely to be quite different in oil and gas markets. There are multiple reasons for that; for example, oil and oil products are predominantly consumed by the transport sector while natural gas is mostly used in the power sector, the industry and households, and these sectors were differently affected by the Covid-19 pandemic.
Understanding the impact of the pandemic on the gas market is especially interesting from the European point of view, given that natural gas accounts for 25% of total energy consumption and two thirds of this gas is imported. The imports are also very concentrated, with the main supplier Russia providing around 40% of the gas, compared to 25% of the crude oil. This dependency, as well as a long history of tensions with third parties (Ukraine and Belarus) on the Russian gas transit routes, has made the EU’s concerns about the security of Russian gas supply much more pronounced than for oil (see Le Coq and Paltseva, 2012). The combination of these factors – i.e. the importance of natural gas for the EU and the long-standing concern about gas supply security warrant an analysis of the short and mid-term effect of the Covid-19 pandemic on the gas market, and, specifically, on the EU-Russia gas relationship. This brief discusses how the pandemic-driven decline in gas demand, and the potential shift in the balance of power between the parties may affect both the dependency on, and the transit of, Russian gas.
EU Dependency on Russian Gas Under the Covid-19 Pandemic
As is well known, Covid-19 and the associated lockdowns imposed by many EU Member States, have caused a slowdown in most economies and a decline in energy demand. However, for natural gas, the effect is likely to be significantly smaller than for oil. While we do not yet have statistics for the EU’s gas demand in recent months, the Norwegian energy consultancy Rystad Energy has predicted the decline of gas demand to be around 4% for March and April 2020. This forecast was given quite early in the course of the pandemic, and is very likely an underestimation; still, it is very different from the one for oil, with the demand drop estimated to be a whopping 34% in April.
One reason why we do not observe a sizable decrease in gas demand is that the natural gas is used in electricity generation, especially as a base-load fuel to compensate for the intermittency of green energy sources, such as sun and wind. With the reduced electricity demand, renewable power generation has become relatively more important in the electricity supply in many countries. Since mid-March 2020, the share of renewable power generation across the EU is 46%, nine percent higher than during the same period last year (Energy Transition Lab, 2020). Interestingly, in France, Germany, Belgium, the Netherlands, the Czech Republic, Poland and Hungary, the absolute volume of electricity generation by renewable sources even increased relative to the same period in 2019, despite declining energy demand. One potential channel, anecdotally recorded for Germany could be higher solar generation due to cleaner skies resulting from the decline in emissions because of lower fossil energy consumption. A higher volume of a renewable generation often requires more back-up power to maintain grid stability. While natural gas is not the only back-up source, this need might still limit the decline in gas demand (or even increase it like e.g. in the Czech Republic). Of course, cheaper gas prices may also play a role: for example, Slovakia and Romania experienced an increase in gas-based generation, but a drop in the renewable generation since mid-March 2020 relative to the same period in 2019. Finally, another reason for the moderate gas demand decline is its residential use – which is likely to be sustained due to the lockdown regime introduced by many countries.
When it comes to Russian gas imports, the official statistics since mid-March – roughly the beginning of lockdown policies across the EU – are not available yet. However, we can with some reservation look at the evolution of the volume of gas sales to the EU disclosed by Gazprom (2020). There was a very sizable decrease in Russian gas imports by the EU – of more than 21% – as compared to the same period last year but it started before the lockdown: January 2020 recorded a drop of 34% and February of 20%). This suggests that the current decrease in Russian gas imports is only marginally related to the pandemic, and more related to the overall gas market situation (such as relatively full gas storage in the EU in 2020, a warm winter, an increase in LNG imports, etc.).
It is, however, likely that the negative effect of the pandemic on Russian gas imports by the EU will be noticeably higher than it currently appears in the Gazprom data, thereby further decreasing the EU’s dependency on Russian gas. Moreover, since demand and prices decrease, substituting for Russian gas, were there a supply interruption, should be relatively easy and cheap with the current excess capacity of the natural gas market and the substantial storage in the EU.
Another reason for the improvement in the security of Russian gas supply to the EU is the observation that Russia’s dependency on oil and gas exports in combination with pandemic-associated factors may lead to a substantial economic downturn in Russia (Becker, 2020). In these dire circumstances, Russia is unlikely to further risk its gas export revenues by pursuing geopolitical goals through the means of gas supply and gas transit. For all these reasons, one may expect the security of Russian gas supply to the EU to improve during the pandemic.
However, the EU dependency on Russian gas may still be a concern due to medium-run effects of Covid-19. First of all, while the gas prices have been in decline for roughly a year now, the recent decrease in natural gas prices has accelerated the negative impact on the unconventional natural gas industry. For example, the US natural gas rig count has declined by 20% since mid-March 2020, which accounts for more than a third of the 54% year-to-year decline (Ycharts.com, 2020). Similarly, nearly 42% of Australian gas resources could be uneconomic under the current gas prices, according to Rystad Energy. While gas prices are unlikely to stay low forever, the industry will need time to recover even if/when the natural gas demand rises again. Moreover, the East-Asian markets are likely to be served first, as they are expected to recover from the pandemic shock before Europe. This dynamic, coupled with historically higher LNG prices in Asia may delay the LNG flows to Europe. A shortage of LNG in Europe, in turn, is likely to hinder any diversification strategy from Russian gas, weakening the EU’s bargaining power. The new Russia-China gas pipeline, “Power of Siberia”, operational since the end of 2019, will also be used to satisfy the post-Covid-19 Chinese gas demand which is likely to recover before demand picks up in the EU. Its use will then allow Russia to be less reliant on exporting gas to the EU, further contributing to the EU’s gas security concerns.
Transit of Russian Gas to the EU: Covid-19 Effect
The EU’s energy security also depends on the reliability of Russian gas transit to the EU. There are currently 5 transit routes connecting Russia to the EU (plus the routes that are serving the Baltic states and Finland without further transit), see Figure 1. Three onshore routes connect Russia to the EU via Ukraine and Belarus. There has been a history of gas transit disputes associated with these routes, at times threatening the Russian gas supply to the EU. Two newer offshore pipelines, Nord Stream 1 (in operation since 2011) and TurkStream (in operation since 2020) connect Russia directly to Germany, and to the South-East of Europe via Turkey. Further, one more offshore route to Germany, Nord Stream 2, is currently underway, with the operations announced to start in the first quarter of 2021. All three offshore projects are expected to not suffer from geopolitical transit issues.
In relation to the Covid-19 pandemic, there are likely to be two major effects on Russian gas transit. First, the inauguration of Nord Stream 2 is likely to be further delayed. Nord Stream 2 is 50% financed by Gazprom, and this financing scheme may be difficult to sustain after the fall in oil and gas prices and a significant decrease of Gazprom’s export revenues. Indeed, while the statistics for March and April 2020 are not yet available, the Russian customs statistics suggests that the USD value of gas exports from Russia in January-February 2020 has decreased by 45% relative to the same period last year. Because Nord Stream 2 could facilitate gas delivery to the EU in case of a transit conflicts, its expected delay may negatively impact the EU’s gas security.
Additionally, the Covid-19 related demand drop may impact the utilization of Russia-EU gas routes, driven by the current agreements between Russia and the transit countries. Russia and Ukraine have just signed a transit agreement for the next 5 years. This agreement was widely perceived as a diplomatic success of the EU (that facilitated the deal), given the historically difficult geopolitical relation between Ukraine and Russia. One of the new features of this agreement is of particular interest within the Covid-19 context. Unlike for previous deals, Russia agreed to prepay a fixed volume of gas transit, 178.1 mcm/day for 2020, and 110 mcm/day units for 2021-24 (Pirani et al., 2020). So, underutilization of this route is costly for Russia.
Figure 1. Gas supply Routes to the EU.
Source: Ukrainian Liaison Office in Brussels
With decreased demand due to Covid-19, warmer weather in the coming months and almost full gas storages in the EU, this contractual feature may affect how Russia allocates its gas exports across the routes. At least, in the short term, it may undermine Russian gas transit via the Belarus-Poland route. The concern about the utilization of this route in relation to the new Russia-Ukraine transit agreement has already been raised by Pirani et al. (2020). The Covid-19-associated decrease in gas demand is likely to make this concern much more real. Russia may use the Belarus-Poland pipeline sporadically, e.g. to adjust for the seasonal spikes in demand, without long-term capacity booking. Recent gas tensions between Russia and Poland (e.g. Poland winning in the arbitration court against Gazprom (RFE/RL, 2020), and Poland repeatedly expressing opinions and exercising legislative effort restricting the usage of Nord Stream 1 and construction of Nord Stream 2) may further exacerbate the issue.
In the medium term, however, when the EU gas demand has recovered but Nord Stream 2 is not yet in place, the Belarus-Poland route is likely to prove useful for Russia, at least starting from 2021 (when prepaid volumes of Russian gas transit via Ukraine will decline according to their agreement).
The transit contract between Russia and Poland is to be renewed in mid-May 2020, and as of now, it is unclear if, and how it will be written and whether the Belarus-Poland transit route will be used to a substantial degree or only marginally. If transit through the Belarus-Poland route is limited, it will imply poorer route diversification for a major part of European consumers of Russian gas, thereby lowering their security of Russian gas supply. This may also put another strain on the bargaining power allocation within the EU and the EU’s intended common energy policy of “speaking with one voice” with external energy suppliers like Russia.
Conclusion
Summing up, the decrease in demand of natural gas, as well as other factors associated with the ongoing Covid-19 pandemic, such as economic recession and turbulence in stock markets, are likely to have noticeable implications for the security of Russian gas supplies to the EU in the short term. On the one hand, even if the current pandemic-associated decrease in demand of gas from Russia seems rather moderate, the ultimate negative effect on Russian gas imports by the EU is likely to be larger. Lower imports from Russia are likely to improve the security of supply, both through lower import dependency of the EU, and through improved market opportunities due to the current market’s overcapacity. On the other hand, in the medium run, lower demand also negatively affects the non-conventional gas industry, undermining the diversification opportunities to LNG, and, consequently, natural gas energy security. Further, a fall in the gas demand by the EU coupled with the newly signed transit agreement between Russia and Ukraine may potentially cause underusage of the Belarus-Poland transit route, thereby putting a strain on the diversification of Russian gas import routes to the EU and on the power balance within the EU.
Energy security might be even more of a concern in the post coronavirus period when the economy is slowly recovering, and cheap and guaranteed energy supply is crucial. To ensure this supply, national efforts combined with an EU-wide policy coordination would be required. The long-discussed “speaking with one voice” common energy policy may finally need to materialize in order to facilitate reliable access to natural gas.
References
- Becker, Torbjörn, 2020. “Russia Economic Update — Brace for the Covid-19 Impact!”, FREE Policy Brief.
- Energy Transition Lab, Wärtsilä, 2020, retrieved April 27, 2020
- Gazprom, 2020. REMIT RSS, retrieved April 26, 2020.
- Le Coq, Chloé and Elena Paltseva, 2012. “Buyer Power as a Tool for EU Energy Security”, FREE Policy Brief.
- Pirani, Simon; Jack Sharples, Katja Yafimava, Vitaly Yermakov, 2020. “Implications of the Russia-Ukraine gas transit deal for alternative pipeline routes and the Ukrainian and European markets”, Oxford Institute for Energy Studies.
- World Bank, 2020. “Commodity Price Data (The Pink Sheet)”, retrieved April 26, 2020.
- Ycharts.com, 2020. “US Natural Gas Rig Count: 85.00 for Wk of Apr 24 2020”, retrieved April 27, 2020.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Quota or not Quota? On Increasing Women’s Representation in Politics
All over the world, politics remains one of the most male-dominated spheres in society, in spite of the substantial progress made in achieving more gender balance in the last decades. A large number of countries worldwide have adopted some form of electoral gender quotas to accelerate this progress, but the empirical evidence on the effectiveness of such policy tools is mixed.
In this policy brief, we first discuss the potential impacts of gender quotas. Quotas may (a) increase women’s representation in political positions, or decrease it, if there are backlash effects; (b) improve or worsen the quality of selected politicians; and (c) bring about important policy changes, given the wealth of empirical evidence of gender differences in policy preferences, with, for instance, women appearing more concerned about health and the health system than men. We then provide an overview of the empirical evidence on quota impacts in the economics literature, and contextualize these findings with a special focus on the countries of the FREE (Forum for Eastern Europe and Emerging Economies) network. We end with policy advice on the design of gender quotas in the domain of politics.
Quotas in the World and in the FREE Network Region
According to the International Institute for Democracy and Electoral Assistance (IDEA), 127 countries worldwide currently use quotas with the goal of increasing the presence of women in governmental institutions. Broadly speaking electoral gender quotas can be classified into seat reservation and candidate lists quotas. The former limit the competition for a governmental seat to women, whereas the latter prescribe a minimum representation of women in electoral lists. Candidate quotas can be legislated, i.e. they constitute a legal requirement, or voluntary, whereby parties adopt quotas in their internal statute.
Table 1: Share of women in national parliaments (in %) FREE Network countries
Source: World Bank Data (2020).
The popularity of gender quotas is, however, not uniformly distributed across the globe. For example, while political gender representation is far from equal in most countries of FREE network region (see e.g. table 1), out of these countries only Armenia, Poland and Sweden dispose of electoral gender quotas (see figure 1).
Figure 1: Gender quotas in the FREE Network region
Note: the FREE Network region is marked in light red, the countries in the region with quota are marked in dark red. Source: SITE, 2020.
Since 2011, Armenia has had a legislated candidate quota of 40% for its National Assembly. This quota replaced a previous quota of 15%, passed in 2005 – one of the requirements to enter the Council of Europe (Itano 2007). Poland has also had a legislated candidate quota of 35% for the Lower House (the Sejm) as well as for subnational elections since 2011 (IDEA 2020; World Bank 2019). Sweden, the fourth most gender equal country worldwide according to the 2020 ranking of the World Economic Forum, and ninth in the women’s political empowerment sub-index, does not have legislated quotas. However, political parties themselves have decided to adopt voluntary quotas: the ruling Social Democrats use a zipper system in which the two sexes alternate on party lists; the Left Party has a minimum 50% quota for women, while the Green Party has a 50% gender quota (IDEA 2020). The Swedish Moderates, Liberals, Center parties and the extreme-right Swedish Democrats currently do not have gender quotas. The Swedish Democrats entered the parliamentary elections in 2018 with the highest share of male candidates observed among the Swedish parties – 70% (SVT 2020; SVT 2018).
In spite of their popularity among policy-makers worldwide, the merits of quotas are still largely debated. Opponents of gender quotas are often concerned about their effects on the meritocratic selection of politicians. Another common criticism is that nominating more female candidates may not automatically translate into more women in powerful positions. For instance, the shares of women in the Armenian and the Polish Parliament are 24 and 29% respectively (World Bank 2019), well below the national legislated candidate quota (it bears noting, however, that these shares have been growing over the last ten years, as shown in Figure 3). The respective shares of female ministers are 7% and 23% (Government of the Republic of Armenia 2020; OECD 2020,).
Figure 2: Share of women in national parliaments (in %)
Source: The authors’ own rendering of World Bank Data (2020).
Why is increasing women’s political participation considered a policy objective of utmost importance in many countries worldwide, and how can gender quotas help achieving it? In this brief we contribute to the ongoing debate on the merits of gender quotas, by offering an overview of their potential effects and by critically reviewing the empirical evidence from the most recent academic literature.
Which Effects Can We Expect From Quotas?
The primary objective of electoral quotas is to reduce gender gaps in representation in electoral lists and in the targeted representative institutions. Quotas can also activate trickle-up mechanisms, whereby gender gaps decrease in positions that are not directly targeted by the quota. The trickle up effect occurs, for instance, if women’s networks within parties or in governmental organizations help the promotion of female leaders. Furthermore, gender quotas may help to improve the quality of politicians. As noted by, among others, Bertrand (2018), a society likely improves the quality of its leaders when it enlarges the pool where those leaders are chosen from. A critical underlying assumption in this line of argument is that there are no major differences in the distribution of “political talent” between women and men. However, even with equal distribution of political talent, if the supply of women willing to enter politics is very limited and there are not enough qualified women to fill the quota positions, the average quality of a “quota” politician may end up being lower than that of her colleagues – and quotas may have the unintended consequence of reinforcing stereotypes against female politicians. This, in turn, may ultimately imply lower promotion rates of women to key positions and/or worse electoral support of female politicians, thereby undermining women’s political empowerment at various levels.
One of the most popular arguments in favor of the adoption of gender quotas is that women’s political preferences may not be adequately represented by male-dominated political bodies. Gender quotas, by increasing female representation among politicians (and possibly among voters), can thus help closing a potential gap in substantial representation. A large body of literature has documented gender differences in policy preferences, by considering, e.g. the size and composition of government spending after the expansion of suffrage to women (Kenny and Lott 1999), voting records in referenda (Funk and Gathmann 2015), survey data (see, e.g. Bagues and Campa 2020), or women’s contributions to legislative amendments (Lippmann 2020). In this historical moment when the world is plagued by a pandemic, the most important gender difference to emphasize seems to be in the area of health. Exploiting the federal referenda held between 1981 and 2001 in Switzerland, Funk and Gathmann (2015) show that Swiss women are more likely to be in favor of health, unemployment and social security spending than men, and less likely to be in favor of military spending. Similarly, based on survey data from a sample of nearly 60,000 Spanish residents, Bagues and Campa (2020) find that women are significantly more likely than men to report that the health system is one of the problems that affects them the most. Likewise, Lippmann (2020) analyzes the contribution of French legislators to amendments and finds that women are 25% more likely than men to initiate at least one amendment related to health issues. This gender difference regarding health policy is also visible in the European Social Survey (ESS), which covers a representative sample of the population of 19 European countries. When asked to give a general opinion on the current state of health services in their country, female respondents turn out to be significantly less satisfied than male respondents on average. The difference is statistically significant, albeit not particularly large (12% of a standard deviation) and holds in most of the countries included in the ESS. One potential reason behind this noticeable difference in satisfaction with health services is that women also report lower health status than men (10% of a standard deviation and statistically significant).
Figure 3: Self-reported satisfaction with the current state of national health services
Source: The authors’ own rendering of the ESS (2018).
A natural question to ask in spring 2020 is whether a world with more women among political leaders would have had health systems better equipped to face a pandemic. While we will never have a definite answer to this question, studies of the impacts of gender quotas can help assessing whether the gender of political leaders matters for policy decisions.
What is the Empirical Evidence on the Effects of Quotas?
Quotas increase women’s representation in electoral lists, but only when they are binding and appropriately enforced (i.e. the cost for parties of not complying with the quota must be high enough). Yet, when quotas are limited to the composition of electoral lists, the strategic positioning of female candidates in “not-winning” positions tends to undermine the quota effect on the election of women (see Esteve-Volart and Bagues, 2012, and Bagues and Campa, 2020). This seems to be the case of Poland: According to Gwiazda (2017), the lack of a placement mandate obliging parties to put women in the top positions of a party list, is indeed one reason why the Polish quota has not translated into a higher share of female representatives.
The evidence on the spill-over of quotas to higher positions is mixed. Two studies find that candidate quotas in Italy and Sweden increased the probability that women reach leadership positions, above and beyond the quota mandate (De Paola et al. 2010, O’Brien and Rickne, 2016). Bagues and Campa (2020), however, fail to establish similar evidence in Spain.
In studies of developing countries, Beaman et al. (2009) find that seat reservation in India improved male voters’ perception of female leaders, as well as women’s probability of being elected once the reservation was removed. Conversely, experimental evidence from Lesotho suggests that, if anything, a quota-mandated female representative reduces women’s self-reported engagement with local politics (see Clayton, 2015).
An increasing number of studies also examine the quota impact on the quality of the elected politicians, proxied by different measures. Baltrunaite et al. (2014) find that a gender quota improved the average education of elected politicians in Italy, and Besley et al. (2017) provide similar evidence looking at a measure of labor market performance in Sweden. Bagues and Campa (2020), studying candidate quotas in Spain, fail to find an improvement in the quality of politicians, measured by their education and electoral performance; however, their assessment is that the quota did not decrease quality either, contrary to the expectation of many quota opponents. However, Chattopadhyay and Duflo (2004) find that, in the context of seat reservations in rural India, quota candidates are less educated.
Finally, the evidence on whether gender quotas bring about policy change is scarce. Chattopadhyay and Duflo (2004) show that the reservation of the most important seat in Indian villages brought policy choices closer to women’s preferences. In Spanish municipalities, Bagues and Campa (2020) fail to find significant increases in the share of “female expenditures” (issues women have been found to care more about than men, based on surveys) over two legislatures when candidate quotas were used.
Conclusion
Gender quotas are a popular policy tool used to close existing gender gaps in political empowerment, which are large in many countries in the FREE Network. A growing economics literature on the impacts of gender quotas helps assessing what objectives policy-makers may be pursuing when they adopt them, and under which conditions these objectives can be achieved. There is a number of lessons to be learned from this literature.
First, the design of the quota is crucial for it to achieve its primary objective, which is to increase women’s presence in the targeted political positions. Placement mandates, for instance, are particularly important in the design of candidate quotas to avoid that women are strategically placed at the end of the ballot. Second, policy-makers need to take the local context into account. Whether a candidate quota can generate spill-overs to higher-level positions likely depends on the degree of centralization of political parties for instance; where party leaders are very powerful, we may be less likely to see an increase in the share of female leaders following the adoption of a candidate quota. Third, the question when gender quotas successfully bring about policy change needs additional investigation. Different factors likely play a role, such as: the type of position targeted by the quota (legislative or executive, local or national, etc.); the extent of the increase in representation achieved; the magnitude of the gender difference in preferences; the type of decision-making process prevailing (majority voting or unanimity); how the selection of politicians is affected by the quota; and how women’s influence on policy is measured. Studies that systematically vary some of these factors will improve our understanding of this area of research. Fourth, there is no overwhelming evidence of negative effects of gender quotas in a number of dimensions, at least over a medium-term horizon.
The case for adopting and testing different forms of gender quotas, perhaps in combination with additional measures, is therefore relatively strong. Overall, our assessment is that quotas will have to remain in policy-makers’ toolbox for some time if the worldwide effort to close the persisting gender gaps in political empowerment is to continue.
References
- Bagues, Manuel; and Pamela Campa, 2020. “Can Gender Quotas in Candidate Lists Empower Women? Evidence from a Regression Discontinuity Design.” CEPR Discussion Paper No. 12149.
- Bagues, Manuel; Mauro Sylos-Labini; and Natalia Zinovyeva, 2017. “Does the Gender Composition of Scientific Committees Matter?”. American Economic Review, 107(4), pp. 1207–1238.
- Baltrunaite, Audinga; Piera Bello, Alessandra Casarico; and Paola Profeta, 2014. “Gender Quotas and the Quality of Politicians”, Journal of Public Economics, 118, pp. 62-74.
- Beaman, Lori; Raghabendra Chattopadhyay; Esther Duflo; Rohini Pande; and Petia Topalova, 2009. “Powerful Women: Does Exposure Reduce Bias?”. Quarterly Journal of Economics, 124(4), pp. 1497–1540.
- Besley, Timothy; Olle Folke; Torsten Persson; and Johanna Rickne, 2017. “Gender Quotas and the Crisis of the Mediocre Man: Theory and Evidence from Sweden”, American Economic Association, 107(8), pp. 2204-2242.
- Bertrand, Marianne, 2018. “Coase Lecture – The Glass Ceiling”. Econometrica 85, pp. 205-231.
- Chattopadhyay, Raghabendra and Esther Duflo, 2004. “Women as Policy Makers: Evidence from a Randomized Experiment in India”. Econometrica, 72, pp. 1409-1443.
- Clayton, Amanda, 2015. “Women’s Political Engagement Under Quota-Mandated Female Representation: Evidence From a Randomized Policy Experiment“. Comparative Political Studies, 48(3), pp.333 –369.
- Dahlerup, Drude (Ed.), 2006. “Women, Quotas and Politics”. Routledge, Taylor & Francis Group.
- De Paola, Maria; Vincenzo Scoppa; and Rosetta Lombardo, 2010. “Can gender quotas break down negative stereotypes? Evidence from changes in electoral rules”. Journal of Public Economics 94 (5), pp.344-353.
- Esteve-Volart, Berta; and Manuel Bagues, 2015. “Politicians’ Luck of the Draw: Evidence from the Spanish Christmas Lottery”, Journal of Political Economy, 124(5), pp. 1269-1294.
- Funk, Patricia; and Christina Gathmann, 2015. “Gender gaps in policy making: evidence from direct democracy in Switzerland”. Economic Policy, 30 (81), pp. 141–181.
- Government of the Republic of Armenia, 2020. Structure.
- Gwiazda, Anna, 2017. “Women in parliament: assessing the effectiveness of gender quotas in Poland”. Journal of Legislative Studies, 23(3), pp. 326-347.
- IDEA (Institute for Democracy and Electoral Assistance), 2020. Gender Quota Database.
- Itano, Nicole, 2007. “Quota Law Puts More Women in Armenia’s Election“. Women’s eNews.
- Kenny, Lawrence W. and John R. Lott, 1999. “Did Women’s Suffrage Change the Size and Scope of Government?”. Journal of Political Economy, 207, pp. 1163- 1198.
- Lippmann, Quentin, 2020. “Gender and Lawmaking in Times of Quotas.”
- O’Brien, Diana and Johanna Rickne, 2016. “Gender Quotas and Women’s Political Leadership”. American Political Science Review. 110(1), pp. 112-126.
- OECD (2020), Women in politics (indicator).
- SVT, 2018. “Här är partierna som har högst och lägst andel kvinnor bland kandidaterna”.
- SVT, 2020. “Ny mätning: SD Sveriges största parti“.
- World Bank Data, 2019. “Proportion of seats held by women in national parliaments (%)”.
- World Economic Forum, 2019. “Global Gender Gap Report 2020”. ISBN-13: 978-2-940631-03-2.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
The Swedish Exceptions: Early Lessons From Sweden’s Different Approach to COVID-19 – Insights From a SITE-LSE Webinar
Sweden’s policy in the Corona crisis has been subject to a lot of discussion in international media recently. Some point to the country portraying “the Swedish way” as a valid policy alternative to the forced lock-down of society, others criticize the Swedish government for being imprudent. Given the pace with which the virus spreads and considering the volatility of current events, it is pre-mature to draw any definite conclusions. But it is certainly time to start an informed policy discussion. The webinar “The Swedish Exceptions: Early Lessons from Sweden’s different approach to COVID-19, jointly organized by the Stockholm Institute of Transition Economics (SITE) and the London School of Economics (LSE) on April 22, 2020” brought together academics from different relevant disciplines from Scandinavia, the UK and the US . The webinar allowed to discern a few of the motivations behind the Swedish policy choices as well as a number of criteria which will serve to measure the success of governments’ responses to the Covid-19 pandemic in the future.
Understanding the Swedish Approach to Covid-19
Much has been written and said about the Swedish reluctance to impose a strict lock-down on the country: the Swedish government has so far relied mostly on expert recommendations, avoiding from more stringent policies such as the strict lock-downs imposed by for instance Sweden’s neighboring countries Norway and Denmark (more on Sweden in the Covid-19 crisis here). The majority of the speakers in the webinar agree that the Swedish policy in the Corona crisis has been an outlier, even with respect to traditional Swedish policy: Peter Baldwin, historian and professor at the University of New York and the University of California, Los Angeles, argued that Sweden has had an interventionist tradition with respect to social and health policy in the past. “Native policy traditions” therefore do not explain why Sweden has chosen this policy course in his view.
While it seems difficult to pin down historical or ideological reasons behind the Swedish policy stance with respect to Covid-19, Lars Trägårdh, professor of social history at Ersta Sköndal Bräcke University College in Stockholm, pointed out that even though the legal differences may seem stark, the difference in the policy impact may be smaller than expected, the crucial factor being the degree of compliance with a certain measure or recommendation and not its legal force. Trägårdh further argued that, since it may take many months to develop a vaccine, the sustainability of a given policy strategy is essential. According to him, a policy relying on voluntary compliance as the Swedish one rather than legal obligation, may therefore yield comparable effects in the short and medium run and could even turn out to be more successful in the long run.
Trägårdh argued that the true exceptionality of the Swedish response to the global pandemic has been the choice to not close elementary schools. This policy choice can be explained above all by the concern for children’s rights: for smaller children, digital learning simply is not a valid option. As declared by the government on several occasions, another reason is that parents working in professions such as healthcare may be induced to stay at home if schools are closed. Finally, Trägårdh cited a recent study from Iceland which suggests that the effect of closing schools on limiting the spread of the virus may be relatively small.
Later in the discussion, another potential argument in favor of the Swedish strategy emerged: Professor Sara Hagemann from the LSE School of Public Policy described the difficulty of leaving a lock-down, which Denmark is currently experiencing. The question which measures are to be lifted and which sectors of the economy are to be opened first has caused considerably more controversy than imposing the initial lock-down. In contrast, the public debate in Sweden can immediately focus on dealing with the long-term consequences of the crisis according to Trägårdh.
The significance of the concept of “herd immunity” (meaning the protection from disease arising from large percentage of the population having developed immunity) for the Swedish strategy is unclear. Baldwin pointed out that even though Swedish authorities have declared not targeting herd immunity, many measures implicitly seem to be aiming for this outcome.
Results of the Swedish Approach Until Today
Tom Britton, professor of mathematics at Stockholm University, agreed that the Swedish response to the Covid-19 crisis came late and that there has been too little testing. However, he argued that the government’s policy has been consistent, focusing on reducing the spread of the virus and protecting risk groups and especially the elderly. Whether Sweden has achieved the latter goal is still up to discussion, though. As of April 2020, reported infections and deaths in nursing homes had increased, which according to Trägårdh has been the major failure of the Swedish policy response up until today. Yet, the speakers agreed that the Swedish government’s measures have received a lot of public support within Sweden so far, which is a non-negligible factor for the long-term success of the strategy.
General Policy Conclusions
Professor Ole Petter Ottersen, president of the Karolinska Institute in Stockholm, Sweden’s largest centre of medical research, stressed the speed with which the virus has been spreading: the rapid development forces policymakers to quickly take decisions based on limited information. Given the lack of data, Ottersen called for politicians to practice humility and acknowledge the uncertainty surrounding policy choices. According to him, it will take years to evaluate whether the Swedish model or the Norwegian model of a quick and strict lock-down is better suited to fight the pandemic.
Policymakers around the globe face a dilemma: for sustainable crisis management and given countries’ interdependency, measures meant to fight the spread of Covid-19 should be aligned internationally and taken cooperatively. Yet, as Hagemann pointed out, it is clear that one policy cannot fit all: countries differ for instance with respect to their socio-economic structure, health care quality and availability, demographics, and with respect to the point in time when they were hit by the virus. This is not only the case between countries, but even within countries, which could justify a differentiated approach between rural and urban areas in some instances. In other words, all models and policy recommendations have to be adapted to the specific local setting. A strategy which allows for making local adjustments while maintaining a global perspective will be a major challenge for policymakers in the coming months and, likely, years.
Britton stressed the importance of understanding the limits of the models being used. Their predictions depend on a lot of assumptions regarding for instance how individuals behave and to what extent rules and regulations are being respected. Anti-body tests will soon provide more data on the actual spread of the virus, but even then, major questions, such as how to treat a potential trade-off between preventing deaths from Covid-19 vs. the socio-economic and health costs caused by a lock-down, will remain unanswered. This trade-off is country specific as well: Hagemann argued that Sweden and the other Nordic countries have quite successfully implemented remote working and learning options. This, however, will not be feasible in most developing countries, for instance, which necessarily affects the cost-benefit analysis of the available policy options.
Further, data collection and availability undoubtedly need to improve. As long as no better instruments of analysis are available, both scientists and politicians should be transparent about the simplifying assumptions and models they base their policy recommendations and decisions on.
Finally, despite their different academic backgrounds, all experts agreed on the need to take into account the indirect consequences of both the spread of the virus and the policy measures implemented to fight it. Covid-19 is likely to reinforce social inequities. For instance, it has been shown that in Stockholm, immigrant communities have been hit the hardest. As soon as the imminent health crisis is under control, the policy focus, therefore, has to shift towards the socio-economic consequences of the crisis.
Acknowledgements
The Stockholm Institute of Transition Economics wishes to express its appreciation to the speakers for their contributions to the policy debate, to the London School of Economics for the successful cooperation in organizing the event, and to the audience for its engaging questions and interest in the topic.
List of Speakers:
- Peter Baldwin, New York University and University of California, Los Angeles
- Tom Britton, Stockholm University
- Sara Hagemann, London School of Economics
- Ole Petter Ottersen, Karolinska Institute, Stockholm
- Lars Trägårdh, Ersta Sköndal Bräcke University College, Stockholm
- Erik Berglöf, London School of Economics (moderator)
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.