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Problems and Progress in the Historiography of the USSR: Robert W. Davies and his Pioneering Research
This essay highlights the advancement of studies on the Soviet Union since the 1980s, as reflected in the grand research project of the British economic historian Robert W. Davies. In 7 volumes and over 3.000 pages of dense information, “The Industrialisation of Soviet Russia” stands out as almost an encyclopedia of the dramatic and eventful period from the late 1920s to 1939.
After the Second World War, the British authorities recognized that before 1939 their knowledge of the USSR was insufficient and misleading as to the accomplishments of the Soviet leadership. This fact hampered British assessments in the initial period of the German-Soviet war. As the Swedish economic historian Martin Kahn explained, London had underestimated the military-industrial strength of the USSR, and in 1941 projected that a Nazi victory on the Eastern front was probably only a matter of months.
Consequently, given the unexpected Soviet army’s victory, and its mobilized economy outperforming the German military industry, British authorities during the Cold War spurred their scholars in social and economic sciences for more solid research of the USSR. A pioneer was Alexander Baykov (1899–1963) who was active at the well-known institute in Prague, where S.N. Prokopovich (1871–1955) and other émigré Russians had published surveys of Soviet economic development. After the Nazi occupation of the Czech Republic in spring 1939, Baykov fled to Britain. After the war, Baykov published The Development of the Soviet economic system, a standard handbook at Anglo-Saxon universities that was republished in numerous editions from 1946 till 1988. He was appointed professor at Birmingham University and founded a one-man Department of Economics and Institutions of the USSR. One of his Ph.D. students was Robert W. Davies (b. 1925) who defended a thesis on the Soviet budgetary system. As the “Thaw” had changed Soviet-Western relations in the late 1950s, Baykov actively proposed a broadening of studies on the USSR. One result was the foundation of the Centre for Russian and East European Studies (CREES) at Birmingham University in 1963.
As director at CREES, Robert Davies established valuable exchanges of study visits, conferences and seminars with Soviet institutions. Among the first scholars from CREES to spend long research visits in Moscow and Leningrad were Robert Davies, Julian Cooper and other Ph.D. students. The research program at CREES on Soviet technology produced several fundamental studies by Julian Cooper, Ronald Annan and Robert Lewis. Soviet economists were invited for study visits at CREES. Among the more prominent can be noted Vasilii Nemchinov (1894–1964) and Nikolai Fedorenko (1917–2006) who were both engaged in the reform debates in the 1960s and applied mathematical and cybernetic methods.
A common problem in those days was that for the 1920s only printed sources were available. However, for the New Economic Policy (NEP) years, these were considered as reliable. On the other hand, the hardening censorship of the 1930s hindered objective research by Western observers. Such was the conclusion of the British historian Edward H. Carr (1892–1982) who decided to stop his study of Soviet history by 1929. However, his 14 (!) volumes A History of Soviet Russia bear witness to how much research could be done with merely printed sources. As explained by his biographer Jonathan Haslam, Carr’s legacy is disputed concerning his political theory, but not his impressive History of Soviet Russia. Even Soviet-time critics of “bourgeois falsifiers” recognized Carr’s contribution as outstanding.
For the volumes on the Soviet economy in the final years of the NEP period, Carr invited Robert Davies as his co-author. Their two volumes in Foundations of a Planned Economy, 1926–1929 (1969) treated the debates among the Soviet leadership on how to replace the mixed-market economy with long-term economic planning.
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Based on the experience from the above-mentioned joint project with Carr, Davies decided to continue research on the industrialization of Soviet Russia. His first volumes in the new project, The Industrialisation of Soviet Russia, published in 1980, are in-depth studies, based on printed sources from the USSR, concerning the collectivization of agriculture and the formal statutes and real conditions of the new collective farms. A few years earlier, at the Sorbonne, the Russian-born scholar Moshe Lewin (1921–2010) had presented his doctoral thesis La Paysannierie et le Pouvoir Soviétique, 1928–1930. This was one of the more important forerunners to Davies’ own research of the topic. Jonathan Haslam has studied the correspondence between Lewin and Carr concerning the collectivization of the peasantry. Carr raised numerous objections and questions to Lewin’s interpretations. Between 1968 and 1978 Lewin joined CREES as researcher and lecturer. Lewin gave many impulses for a broader social and economic history of the USSR. In particular, Lewin approached the debates among Bolshevik leaders in the 1920s and much later, in post-Stalin era, of reformers in the 1960s, with a keen eye for the fine print or allusions in the heavily censored printed sources. The telling title of his research project is Political undercurrents in Soviet economic debates (1974).
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Davies’ third volume on industrialization was published in 1989. He there analyzes the launching of the first five-year plan – for 1928–32, and successive upscaling towards more unrealistic final planning targets. Although the French economist Eugène Zaleski and others had earlier treated this most disputed Soviet planning effort, Davies managed to add a lot of detailed information based on a careful reading of newspapers, statistical reports and memoirs.
With glasnost and perestroika merely a few years later, conditions for studying the Soviet era changed radically. Robert Davies keenly observed the changes in the Russian information sphere in his surveys Soviet History in the Gorbachev Revolution (1989) and Soviet History in the Yeltsin Era (1997). These two surveys are a good introduction to the latest historiographical changes in Russia, the struggle against a conservative heritage and for an objective and complex historiography of the Soviet period.
The opening of formerly closed archives favored a radical broadening of Davies’ project. In the fourth volume Crisis and progress in the Soviet Economy, 1931–1933 (1996) the primary sources from archives give a better understanding of how the first 5-year plan actually proceeded and what the real accomplishments were. Davies gives concise and pertinent commentaries on numerous Soviet leaders, managers, planners, and economists, even far below the well-known top brass in the Communist Party, adding understanding of the decision-makers’ backgrounds and the otherwise often anonymous bureaucracy.
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The fifth volume The Years of Hunger, Soviet agriculture, 1931–1933 (2004) contains analyses of the multiple causes of the famines in various parts of the Soviet Union in the early 1930s. Davies wrote this volume together with Stephen G. Wheatcroft, an eminent specialist on Russian agriculture and Soviet-era statistics. In 1930, the grain harvest from the forcibly established collective farms had surpassed the expectations of the authorities. Between 1932 and 1933, on the contrary, the countryside was struck by widespread famine.
This volume concerns a topic that is hotly debated by Russian and Ukrainian historians. Consequently, there was a demand for a Russian translation: Gody goloda. Selskoe khoziaistva SSSR, 1931–1933. Davies and Wheatcroft discern a multitude of causes and separate several forms of the famines in the early 1930s – in Kazakhstan, Ukraine, and certain regions of Russia. The detailed statistics provided by Davies and Wheatcroft as well as a methodological appendix to the volume may serve as basis for any discussion of the various interpretations of the causes of the 1932 – 33 famine, and how this issue has been politicized in certain countries. They emphasize the fundamental mistakes made by the regime. They also argue that there can hardly have been a genocidal intent from Stalin, Kaganovich and other leaders. The British historian Robert Conquest had argued, in his Harvest of Sorrow in the mid-1980s, that the Soviet leaders intentionally committed a genocidal action against the Ukrainian peasantry. After reading Years of Hunger, Conquest changed his mind and frankly declared that the famine was unintentional albeit possibly avoidable with other policies.
An important aspect of Soviet-era historiography has been the publication of source and documentary volumes. At CREES, the historian Arfon Rees had published several monographs on the legendary Bolshevik manager Lazar Kaganovich, the people’s commissar of transport and politburo member since the 1930s. As the very informative correspondence between Stalin during his summer vacation at the Black Sea, and his colleagues in Moscow revealed much on the deliberations among the leaders, viewpoints that were not seen in the final resolutions, Davies and Rees edited two volumes. One in Russian that gives the complete collection of all letters sent by courier to and from Stalin; the other in English but abridged with explanatory introduction and comments by the editors.
The sixth volume The Years of Progress: The Soviet Economy, 1934–1936 (2014) covers in detail the advance of industry, capital investment, domestic and foreign trade. Davies places special emphasis on the dual threat of war, in the east from Japan, especially after their occupation of Manchuria in 1931, and in the west from Germany after Hitler’s takeover of power. The Soviet defense industry got higher priorities given these threat assessments. Davies frames the latter part of the 1930s as consisting of two distinct periods. Hard lessons were learned from misjudged efforts during the first five-year period. It was a period when the dominant drive to set up heavy industry was revised in favor of a more balanced attempt to promote the growth of consumer-oriented branches. Investment calculations and development targets were thereafter set with a better grasp of what managers, engineers, and workers in various enterprises could eventually handle.
Davies again collaborated with Wheatcroft, a specialist on Soviet agriculture, but also with Oleg Khlevniuk, one of Russia’s best experts on the history of Stalinism. Khlevniuk contributed to the sections concerning the Gulag camp system and its role in the economy. For a short period, there was also a certain relaxation of repressive measures, particularly those that targeted specialists who had been persecuted previously.
Davies’ panorama of all Soviet industrial branches underscores the undeniable high growth rates in industry and the accompanying indicators of a more evenly distributed advancement of the economy as a whole. The book has a well-organized structure and a straightforward chronological layout that makes reading this exhaustive study fascinating: first comes a lucid introduction of Soviet forecasts and plans; second the problems of quarterly or even monthly implementation of those plans; and finally an analysis of each year’s achievements “in retrospect”.
This highlights how the decision-making processes actually were egalitarian, even at a time when Joseph Stalin, as general secretary of the Communist Party, was considered the undisputed leader. An appendix clearly illustrates this thesis by a detailed scheme of how the collection of grain was decreed for peasants throughout 1936.
While a theoretical approach to the Soviet economic system may start with the concepts of a totalitarian system, the rich empirical evidence of conflicting Soviet realities and a mix of economic viewpoints suggests that until recently we held oversimplified views of the system. The fact that Soviet leaders in the mid-1930s meticulously scrutinized their own failures—more often casting such failures in concrete, technical terms than attributing them to “sabotage” by “enemies of the people”—indicates the need for multiple frameworks of interpretation. The contrast could hardly be greater than between the proclaimed triumph of socialism in 1936, and the staged show-trials of Party members as well as mass-scale deportations or execution of millions of ordinary citizens.
In each volume of Industrialization of Soviet Russia the reader will find plenty of hints for further research, reflections on debates among specialists on the USSR as well as discussion on the source base. Davies also edited and contributed to shorter articles in two textbooks with articles by Western specialists on the Tsarist, NEP and Stalinist period economics. In less than one hundred pages he also skillfully explained the main problems in Soviet economic development from Lenin to Khrushchev (1998).
The first volumes of Carr’s History of Soviet Russia were published when the Cold War was intensive and ideological confrontations were reflected even in academic historiography. They had been received critically by a number of Western specialists, who were opposed to Carr’s detached, non-moralizing but strictly analytical approach, as he explained in his famous lectures What is History? As his History of Soviet Russia expanded to over a dozen solid and well-researched volumes, admiration predominated for Carr’s outstanding grasp of an enormous basis of sources. In comparison, Davies’ Industrialization has been received positively in the academic communities and in particular in those countries where an empiricist approach is appreciated. Japanese scholars have even coined the term “the Birmingham school of Soviet studies”, with respect to the standards set by Baykov, Carr and Davies and their followers at CREES.
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The final volume The Soviet economy and the Approach of war, 1937–1939 (2018) covers one of the darkest times in Soviet history. The economic changes must be contextualized in different ways here. As before but more urgently, the assessments of a future war became more acute with the advances of Japan in occupied China, the civil war in Spain and the outspoken revanchist policy of Nazi Germany. In 1937–38, repressions widened from the Communist party and industry captains to hundreds of thousands of ordinary citizens. On dubious ethnic or social criteria, they were convicted to forced labour in camps or executed. The authors analyze in detail how the high-level and also mass repressions paralyzed the functioning of the state administration. The growing role of the Gulag system for the economy in various regions is set out clearly.
An important contribution is the chapter on how two population censuses were carried out; the results of the first census of 1937 were unacceptable to Stalin as they clearly showed the devastating effects of collectivization and famine. The next census in 1939 tried to fix the data and embellish the statistics. The real demographic outcome of the 1930s was only discerned in the post-Soviet period, when the primary data of the first census was declassified and published in documentary volumes.
The main aspect of the volume is reflected in the title; how the growing threat of a major war influenced a particular industry. The investments in defense enterprises set the basis for a much more militarized economy. The special aspect of Soviet planning were the so-called mobilization plans that were based on carefully assessed maximum production capabilities in case of war. The modernization of Soviet artillery, tanks and aircraft and the preparedness for mass production in wartime had become the main goal by 1939.
The final chapter of volume 7 sets the whole project of Soviet industrialization in historical perspective, given the Tsarist background, on the one hand, and the outcome, the collapse of the system in 1991, on the other hand. The authors reflect on the forced industrialization and the lack of incentives in the system. The statistical system was basically professional, however, the political goals tended to distort the result presentation. In the end, even the leadership would lack a reliable data basis for their planning. The militarization of the economy that received its definite form in the late 1930s proved capable of outperforming even the German war economy. The foundation of this war preparedness had been outlined already in the late 1920s, as various development strategies were discussed. Its basic structure would remain more or less reformed till the end of the Soviet period. As mentioned above, the special discipline of Soviet studies was institutionalized in Great Britain right after the Second World War. The Soviet economic performance formed a part of so-called development economics from the 1950s onwards. The Soviet model of development was used as textbook reference for comparative studies of industrialized and less-developed countries in the Third World. This final chapter carefully discerns the undisputable success performance of the Soviet economy up to 1939, but likewise underlines all the negative or even disastrous aspects in the break-neck social and economic transformation. In an afterword, alas far too brief, Davies himself reflects on how his own view of Soviet history has changed, from the 1950s and 1960s when he wrote Foundations of a planned economy.
The seven volumes of The Industrialisation of Soviet Russia by Robert Davies, and for the four last volumes in cooperation with eminent specialists on various aspects of the Soviet economy, Stephen G. Wheatcroft, Oleg Khlevniuk and Mark Harrison, will stand out as foundations for any further research on this period. Given their empirical richness, strict chronological pattern and thematic clarity, as well as the massive amount of tables with pertinent source evaluations, they may even serve as an encyclopedia on a crucial period, 1929–1939, in Russia’s modern history.
© Book cover illustrations reproduced with permission of Palgrave Macmillan.
References
Carr, E.H., What is History?: Trevelyan Lectures in the University of Cambridge, London 1961, and numerous later editions.
Carr, E.H. & R.W. Davies, Foundations of a Planned Economy, 1926 – 1929, vol. 1: part 1–2, London 1969.
Cox, M. (ed.) E.H. Carr: A critical appraisal, Basingstoke 2000.
Cooper, J. & R. Amman, Industrial Innovation in the Soviet Union, London, 1982.
Cooper, J. & R. Amman (eds.), Technical Progress and Soviet Economic Development, Blackwell, Oxford, 1986.
Davies, R.W., The Industrialisation of Soviet Russia, vol. 1. The Socialist Offensive: The collectivization of Soviet agriculture, 1929–1930, vol. 2. The Soviet Collective Farm, 1929–1930, vol. 3. The Soviet economy in turmoil, 1929–1930, vol. 4. Crisis and progress in the Soviet economy, 1931 – 1933, vol. 5. The Years of hunger, 1931–1933, vol. 6. The Years of progress: The Soviet economy, 1934–1936, vol. 7. The Soviet economy and the approach of war, 1937–1939 (London: Macmillan/Palgrave 1980–2018).
Davies, R.W., Soviet economic development from Lenin to Khrushchev, Cambridge 1998.
Davies, R.W. & O.V. Khlevniuk & E.A. Rees & Kosheleva, L.P. & Rogovaya, L.A., The Stalin–Kaganovich Correspondence, 1931–1936, New Haven, 2008 (abridged translation of Stalin i Kaganovich Perepiska, 1931–1936 gg. Moscow 2001).
Davies, R.W., ‘Carr’s Changing Views of the Soviet Union’, pp. 91–108 in E.H. Carr: A Critical Appraisal, ed. Michael Cox, London, 2000.
Haslam, J., The Vices of Integrity: E.H. Carr 1892–1982, London 2000.
Kahn, M., Measuring Stalin’s strength during total war : U.S. and British intelligence on the economic and military potential of the Soviet Union during the Second World War, 1939–45, Gothenburg University 2004.
Lewin, M., La Paysannerie et le Pouvoir Soviétique, 1928–1930, Paris 1966, (transl. Russian peasants and Soviet power: A study of collectivization, London 1968).
Lewin, M., Political undercurrents in Soviet economic debates: From Bukharin to the Modern reformers, Princeton 1974.
Zaleski, E., Planning for economic growth in the Soviet Union, 1918–1932, Chapel Hill, 1971 (transl. Planification de la croissance et fluctuations économiques en URSS. T. 1, 1918-1932, Paris 1962.
COVID19 | FREE Network Project
The Covid-19 pandemic is affecting all the inhabited continents of this planet and leaves none of us untouched. It has already killed thousands of people across the globe, closed down cities, borders and businesses and most countries are still just in the initial phase of this crisis. Although there is 24/7 reporting on the pandemic, much of the focus in international media has been on the most affected countries and richer countries in Eastern Asia, the EU and the US. Much less attention has been given to countries around the Baltics, in Eastern Europe and the Caucasus.
However, these countries are home to more than 200 million people and to the institutes that form the Forum for Research on Eastern Europe and Emerging Economies, i.e. the FREE network. We have therefore started to collect data on this region from official sources with the ambition to offer a regularly updated, comprehensive and easily comparable overview of the health impact of the Covid-19 pandemics, as well as the policies and practices countries in the region adopt to deal with it.
The countries in the network and the region we include are Belarus, Georgia, Latvia, Poland, Russia, Sweden, and Ukraine. For comparison, we also include Italy as a point of comparison since it is a country that has been particularly badly affected and we have several people in our faculties that know Italian and follow these developments closely. In addition to FREE Network countries in our reporting, we partially cover Armenia, Estonia, Lithuania, Moldova and Germany due to close links with economists and researchers specialised in these countries, therefore extending our covered region.
The quality of the health data will by necessity vary between countries and this also affects the comparability of numbers. For example, the ability and willingness to test the population for the virus differs significantly between countries and will obviously affect the number of infections that is reported to the European Centre for Disease Prevention and Control (ECDC), the main source of data on health outcomes in our tables and graphs. Other data that we report, such as border or school closures, are easier to compare, but there may still be differences in how these policies are implemented on the national level. However, we still believe that it is useful to compile this data for our region in one place as a starting point for discussions on how the virus is spreading and governments respond to the crisis.
Since the FREE Network focuses on economic issues, we put particular emphasis on high-frequency indicators in this area and on measures governments have taken to deal with the economic consequences of the pandemic. In the initial phase of this collaborative project, the focus will be on providing a descriptive picture of the state of the situation using the best data we can find, but over time, this will be complemented by more in-depth policy analysis of the measures and implications for the economies in the region.
Country Reports
The main data is presented in a summary page that facilitates comparisons between countries, and this is complemented with more detailed country reports.
| Belarus country report |
| Georgia country report |
| Italy country report |
| Latvia country report |
| Poland country report |
| Sweden country report |
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.
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.
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- 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.
Whistleblowers During the Covid-19 Pandemic
Numerous stories have emerged about whistleblowers being silenced and retaliated against during the Covid-19 pandemic. In this policy brief we consider some cases of retaliation against whistleblowers and cases illustrating the significance of the information they bring forward. Two facts about Covid-19 and whistleblowers become salient. First, it is hard to externally monitor behavior within care homes due to the risk of contagion (auditor-patient/patient-auditor). Second, it is hard to infer from outcomes (e.g. number of deaths) that management misbehaved, due to the high uncertainty and the many possible factors involved in the spread of Covid-19. Adequate whistleblower protections and confidential reporting channels are therefore essential to ensure transparency, compliance with safety rules, and more generally public accountability in the management of this crisis.
Whistleblowers are Silenced and Suffer Retaliation
The Covid-19 crisis has created pressure on governments, hospitals and secondary health institutions – in particular elderly care homes – to control the narrative on the spread of the virus and their response to it. As a result, we have already seen several whistleblowers being silenced and retaliated against.
The most (in)famous case is probably that of Li Wenliang, the Chinese doctor at Wuhan Central Hospital who warned his colleagues about a new SARS-like virus to on the 30th of December 2019. Four days later he was summoned to the Public Security Bureau where he was ordered to sign a letter in which he was accused of “making false comments” and “severely disturbed the social order”. Another seven persons were also arrested on suspicion of “spreading rumors”.
China is perhaps not the first country that comes to mind when considering adequate whistleblower protections, but the problem is a broad one.
In the US, several doctors and nurses have been fired and disciplined for expressing worries about their work conditions, also in relation to a lack of personal protective equipment (PPE). Nor is the issue of retaliation against whistleblowers localized to healthcare. The Vice President of Amazon’s cloud computing arm, Tim Brady, quit his job “in dismay at Amazon firing whistleblowers who were making noise about warehouse employees frightened of Covid-19”. Nine US senators also sent Amazon a request to explain its policy for firing workers after several employees who had expressed concerns over working conditions were laid off.
In Russia three doctors, two of which had protested their working conditions during the crisis suspiciously fell out of hospital windows, allegedly due to excessive work pressure. Two of the doctors died, and one doctor was threatened with criminal charges for spreading “fake news” about Covid-19. We do not know whether these cases were accidents, suicides, or retaliation for speaking up, but an investigation is currently ongoing.
The problem has been particularly severe in residential elderly care homes, where in many countries there have been extreme rates of contagion and deaths.
In Italy, complaints from caring personnel about lack of PPE and safety procedures in terms of restrictions in visits of relatives from ‘red zones’ and transfer of personnel and patients across departments, emerged already in the end of February. At the private elderly care home Trivulzio in Milan, caretakers claim that their early complaints were ignored by management, who allegedly also harassed those wearing face masks on the ground that they would scare guests. An investigation is currently ongoing, but if the allegations turn out to be true, numerous deaths in Lombardy could potentially be attributed to this negligence and could have been avoided.
In the UK, a country that had much more time than others to prepare for the arrival of the virus, a recent report by the whistleblower hotline Compassion in Care registers a dramatic increase in calls to their hotline: over 170 since the Covid-19 outbreak, while they normally receive no more than 30 cases per month. These whistleblowers at residential care homes, care agencies, and nursing homes continue to detail a widespread lack of protective equipment, and retaliation for raising these concerns. Five lost their jobs and are considering taking legal action.
In some countries there is instead a noticeable absence of whistleblowers at nursing homes and the like. Germany is one example. While this can be due to the country’s fast and apparently adequate response to Covid-19, the country also has an infamous history of mistreating whistleblowers, and the country´s protections are some of the weakest in the EU, which may have deterred potential whistleblower from reporting. A well-known example related exactly to nursing homes is the case of Heinisch vs. Germany, where a nurse was fired for reporting improper working conditions in 2005, and then lost her case for reinstatement at all levels of German labor courts, even though it was recognized that her claims were correct. She had to turn to the European Court of Human Rights to be vindicated, only after six years of legal hassle, though.
These are just some examples of the systemic issue of silencing and retaliation that is now emerging. Watchdog organizations are warning about a widespread and extensive mistreatment of whistleblowers worldwide during this pandemic. The Government Accountability Project details several cases of maltreatment of whistleblowers, describing the situation created by the Covid-19 crisis as “the largest attack on whistleblowers in the world”.
Other cases of whistleblowing, absent retaliation, further illustrate the crucial value of the information they bring forward. In Sweden for example, a country that should have been particularly careful given its softer approach to contain the virus, whistleblowers still reported a lack of PPE and poor safety routines in elderly care institutions. At one home, employees detailed how they went from caring for Covid-19 patients to caring for non-Covid patients while wearing inadequate safety protections. At that same home, it is estimated that more than 35 persons died from Covid-19: over a third of all residents.
Fighting Misinformation and Uncovering Wrongdoing
Protecting whistleblowers is also crucial to fight misinformation and fraud related to Covid-19. For example, a whistleblower recently alleged that the founder of JetBlue, who previously had argued against lockdowns, helped fund an influential yet controversial study which found that the infection rate in Santa Clara County, California, was 85 times higher than believed – which would have driven down the local fatality rate to flu levels at 0.12% – 0.2%. The whistleblower complaint also contained emails suggesting the authors of the study disregarded warnings raised by two other Stanford professors who attempted to verify the accuracy of the antibody tests used in the study.
Worries about abuse and fraud related to stimulus packages linked to Covid-19 have also been mounting. And indeed, the US Securities and Exchange Commission has seen a 35% increase in whistleblower claims received between mid-March and mid-May compared to the previous year.
There is already strong public support for whistleblower protections with respect to important matters like healthcare and elderly care (Butler et al., 2019), and as we have argued elsewhere (Nyreröd and Spagnolo, 2020a, 2020b), whistleblowers are currently not adequately protected or incentivized in the EU: they do not speak up to the degree desirable from a law enforcement/public interest point of view. The negative consequences of speaking up are often significant: blacklisting from the industry, harassment, and social and economic uncertainty are frequently associated with whistleblowing. This is not different with Covid-19 whistleblowers.
What Can Be Done
The state of whistleblower protection in Europe has been rather poor and uneven (Wolfe et al., 2014). In 2013, Transparency International rated a disappointing four countries in Europe as having “advanced” legal protection for whistleblowers. In recent years, several countries have enacted legislation to remedy the issue. France enacted Sapin II in 2017, which prohibits retaliation against whistleblowers; Sweden improved its protection in 2016 (Proposition 2015/16:128); and since November 2017, whistleblower protection in Italy, which was previously limited to the public sector, has been extended to the private sector.
It is only now, however, with the new EU Directive on Whistleblowing that we will see even protection levels for whistleblowers throughout the EU. Among other things the Directive would require firms with more than 50 employees to establish confidential internal whistleblower channels. The deadline for transposing the directive (implementing it into national law) is December 17, 2021.
EU member states should try to transpose the directive as soon as possible, as whistleblower protections are not only needed at nursing homes, but also at firms who may choose to put employees at excessive risk of infection when faced with high cost of compliance with safety measures. This is important, because monitoring compliance with safety measures externally will likely be difficult and costly, while the new directive contains several articles that would improve the informational flow within organizations but also externally to supervisory agencies.
To conclude, the Covid-19 crisis has created pressure to silence whistleblowers to control reputational risks for governments and private firms. If whistleblowers are successfully silenced, we risk ending up with an incomplete picture of the spread of the virus, a lack of public accountability, unnecessary deaths, and several good faith whistleblowers being retaliated against without adequate protection. Hastening the implementation of the new Whistleblower Directive is one way to ensure some level of protection for whistleblowers throughout the EU.
References
Nyreröd, T; and G Spagnolo, 2020a. “Myths and Numbers on Whistleblower Reward”, Regulation and Governance, forthcoming.
Nyreröd, T; and G Spagnolo, 2020b. “Financial Incentives for Whistleblowers: A Short Survey”, forthcoming in Cambridge Handbook of Compliance. Sokol, D., van Rooij, B. (Eds). Cambridge University Press.
Butler, J; D Serra; G Spagnolo, 2019. ”Motivating Whistleblowers”, Management Science, 66(2), 605-621.
Wolfe, S; M Worth; S Dreyfu; A Brown, 2014. “Whistleblower Protection Laws in G20 Countries, Priorities for Action.” Blueprint for Free Speech, The University of Melbourne, Griffith University, Transparency International Australia.
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.
Addressing the Covid-19 Pandemic in Eastern Europe
The Covid-19 pandemic is affecting everyone around the globe and leaves none untouched. However, much of the focus in international media has been on the most affected countries and richer countries in East Asia, the European Union and the United States with less attention given to countries around the Baltic Sea, Eastern Europe and the Caucasus.
Since the FREE Network includes research and policy institutes in Belarus (BEROC), Latvia (BICEPS), Russia (CEFIR@NES), Poland (CenEA), Georgia (ISET), Ukraine (KSE) and Sweden (SITE), experts from the FREE Network institutes discuss the regional perspective on the pandemic with examples of very different strategies implemented in the countries concerned.
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)
Chair/Moderator
- Torbjörn Becker, Director of the Stockholm Institute of Transition Economics (SITE / Sweden)
The Social Impacts of Covid-19 – Case for a Universal Support Scheme?
Beyond its impact on the healthcare system, the Covid-19 pandemic has already reached labor markets throughout every economy via economic shocks. As of 1 April 2020, ILO estimates indicate a substantial rise in global unemployment, leading to a 6.7% decline in working hours in the second quarter of 2020, which is equivalent to 195 million full-time workers.[1] In this policy note we will draw the reader’s attention to the potential scale of the impact on the labor market and the respective social consequences in Georgia. We will identify a wide variety of groups affected by the Covid-19 crisis, with a special emphasis on the labor market, and provide our judgement on the possible extent of the repercussions. The current crisis affects almost every segment of the population, including members of the following large social groups:
- Labor market participants face high risk of job loss. Fewer employment opportunities and broad scale layoffs force a large section of self-employed and salaried workers into challenging circumstances.
- Recipients of Targeted Social Assistance (TSA) are at great risk of slipping deeper into poverty. While members of this group mostly rely on social assistance layouts, the supplementary income that they receive, often from informal sources, could be cut. In addition, the increased prices on food and other essential goods could be particularly detrimental to this group of people.
- Senior citizens are extremely exposed to the danger of the virus and struggle with greater health risks.
Our analysis starts with an overview of the Georgian labor market and the short-term impacts of Covid-19 on workforce displacement throughout the various sectors. The impact is not gender neutral, as it affects men and women differently depending on the sector. Therefore, we will further provide the decomposition of the impacts on the labor market and propose gender-responsive solutions to the pandemic. To mitigate adverse effects across various vulnerable groups, we will review the existing theoretical and practical evidence on targeted and universal support schemes. An overview of international social support programs is moreover provided in this note. We will further analyze the relative merits and drawbacks of our pre-defined policy options based on a multi-criteria assessment in the context of the Covid-19 crisis and thereafter provide recommendations for policy implementation.
Covid-19 – Impact Across Sectors and an Overview of the Labor Market
Unemployment in Georgia is expected to experience a large-scale increase in the short-term, leading to massive social problems. Workers have been told to remain at home because of the broad virus containment measures taken during the outbreak. Those with the opportunity to work from home are relatively well-off, unlike the large variety of vulnerable groups affected by the lockdown. Low levels of economic activity impact almost all industries, and the most vulnerable sectors include accommodation and food services, most wholesale and retail trade and entertainment and recreation. These difficulties place hundreds of thousands at risk, either by downward adjustments to income or working hours, or by completely losing their jobs.
In order to evaluate Covid-19’s potential short-run effect on employment across various economic sectors, we have qualitatively assessed the strength of the impact at the sub-sectoral level,[2] taking into account the following: (1) list and scale of economic activities prohibited during the ‘lockdown’; (2) restrictions imposed on transportation; (3) drop in consumer demand; (4) fall in intermediate input use.
In Table 1 we present our assessment of the Covid-19 impact across sectors, coupled with the corresponding labor market statistics.[3]
Table 1: Covid-19 impact on possible workforce displacement across sectors.
Source: Authors’ sectoral assessments and calculations based on Geostat Labor Force Survey (LFS 2018).
The key findings from the labor market assessment include:
- Close to 30 percent of hired workers face a high risk of job displacement, mostly driven by an expected fall in economic activity in the trade, construction, manufacturing, and accommodation and food services sectors;
- The least impacted industries are projected to be education, public administration and defense, utilities, and health;
- The majority of self-employed are active in the agricultural sector, which faces a moderate impact for several reasons: the closedown of open food markets, restrictions on transportation, and a partial decline in demand (mostly from the food service sector). Although agriculture is not projected to be severely affected, a substantial number of the self-employed (mostly subsistence farmers) in this sector, considering their significantly lower than average baseline earnings, may require special policy emphasis within this group.
Finally, it should be emphasized that the severity of impacts across sectors will further depend on the longevity of the lockdown measures and the sequence in which they may be lifted for different economic activities.
In addition to the assessments in Table 1, Annex 1 presents a correlation between our estimates weighted by sub-sectors and the ILO’s assessment of the current global impact of the crisis on economic output across the sectors. It should be further noted that, in most cases, the scale of impacts coincide, and the remaining differences are due to: (1) our approach being based on more detailed sub-sectoral data; (2) the ILO looks at the global impact, whereas we focus solely on Georgia.
Short-term Workforce Displacement Risks in Vulnerable Sectors
To alleviate social problems stemming from the labor market shock during the strict, short-term quarantine measures, the clear need for safety net programs has raised the important questions of how they should be designed and who the recipients of support should be.
The discussion of social program designs requires a thorough analysis of the potential target groups. As mentioned in the previous section, after drastic quarantine and lockdown measures, many people in Georgia are at risk of finding themselves without jobs or with decreased salaries and earnings, which, in turn, is a main cause of social problems, like the inability to provide food and other necessities. The highly affected groups, as outlined in Table 1, can be clustered across the following sectors of economy:
- The accommodation and food service sector is currently the most directly and highly affected sector. Hotels and restaurants are completely closed for an uncertain period, except for the food delivery business. However, even this is constrained to certain periods of the day, since according to the state’s emergency rules after 21:00 all movement, including delivery, is forbidden.
Most people hired within accommodation businesses face temporary job loss. This group includes hotel administration staff, housekeeping staff, people working in hotel restaurants, etc. Similarly affected are employees in restaurants and cafés, faced with cutbacks in salaries, if not complete job loss.
Another significant group within this sector are the self-employed. Owners of small family hotels and restaurants, typically dependent on tourism expenditure, now find themselves without any cashflow.
- A significant portion of the wholesale and retail trade sector also faces major shutdowns. To begin with, employees of trade centers and individual stores are now out of work for an indefinite period. These include consultants in clothing stores, hardware stores, household appliance stores, etc. A very limited number of shops that continue to work via online sales have retained several employees on decreased salaries.
The reality is also harsh for the self-employed in retail trade. Open marketplaces, including construction materials shops and farmers’ markets have been shut, and such people are left without a vital income source. It should also be noted that most of these workers are members of a lower social strata and are less likely to have enough, if any, savings for the quarantine period.
- As for the relatively small, but equally affected, arts, entertainment and recreation sector, art galleries, museums, night clubs, theatres, movies, and sports and spa facilities, have all been closed down due to their ‘non-vital’ function. Salaried as well as self-employed workers in these sectors found themselves without employment soon after the state emergency was announced.
- Additional highly affected groups are those hired and self-employed in the transportation sub-sectors. The closing of public transportation has left hired bus, metro, and minibus drivers entirely without work.
Other than hired employees, self-employed drivers for intercity transportation are now left without work since intercity commuting is now forbidden under the state of emergency. Comparatively less affected are self-employed taxi drivers, who are still allowed to work, however only between 06:00-21:00. The fact that many drivers previously worked night shifts, combined with declined daytime demand, results in significant cutbacks in daily earnings for taxi drivers.
- Another significantly affected group are those workers employed in households. These include housemaids, nannies, private tutors, handymen, etc. Since everyone is being cautious and following social distancing instructions, many households have dismissed their hired help for an indeterminate period, and even those still employed have a hard time getting to work due to the suspension of public transport, and are therefore left without vital daily income.
- The agriculture, forestry, and fishing sector, the largest in terms of employment, remains less affected relatively, though it is facing restrictions since restaurants and cafés require fewer agricultural products than before. Moreover, as farmers’ markets have closed, their access to marketplaces has become significantly constrained. Farmers are now supplying only supermarket chains and restaurants with delivery services, a significant economic decrease compared to the normal environment. It should also be noted that self-employed small farmers are in the majority in the sector. Such workers are likely without strong links to supermarket chains or restaurants, and therefore, they will be more noticeably affected by the economic impact.
An important specificity of self-employed and domestic workers is that many are also informally employed, thus their identification by official sources (i.e. in tax returns or small business registers) is extremely problematic. Thus, the existence of a large variety of potentially affected groups, as well the inability to correctly estimate the severity of impacts across groups, highlights the need for a temporary social protection mechanism that will cover all affected parties, particularly since the people included in the groups above are not typically the main recipients of social assistance programs.
Decomposition of Labor Market Impacts by Gender
In this section, we present the gender decomposition of labor market impacts, and conclude that unemployment-driven assistance may benefit men considerably more than women.
Chart 1 summarizes the distribution of self-employed and salaried men and women across low, medium, and highly affected industries, based on the sub-sectoral assessments previously described and using gender-disaggregated employment data.
Figure 1: COVID-19 impact on possible workforce displacement, by gender
Source: Authors’ sectoral assessments and calculations based on Geostat’s Labor Force Survey (LFS, 2018).
It is evident that the proportion of employed men is significantly higher in the most vulnerable sub-sectors. Such a picture is highlighted by the high male-employment ratios in construction, transportation, and parts of manufacturing, as well as the high female-employment in the minimally affected education and healthcare industries[4].
To summarize, during the current crisis men are more susceptible to job displacement, and if a social assistance policy is solely based on labor market outcomes, they will yield higher benefits. Such social support mechanisms will deepen existing gender inequalities[5] in the country as women face disproportionate and increasing burden of care work (in situation of lockdown).
Social Assistance Policy Objectives in a Crisis
Considering the diversity of groups influenced by the lockdown, any assistance program should have several main policy objectives:
- Maximizing the reach of a policy to those in need and minimizing their risk of impoverishment – a large part of the population is affected by the lockdown, thus there is a substantial risk of increasing poverty directly from job loss and indirectly via job losses within families. Social assistance should, in a best-case scenario, reach the maximum number of disadvantaged people, while avoiding providing assistance to the affluent.
- Minimizing fiscal pressure – social assistance can create substantial pressure on the budget, especially in the current situation as revenues have decreased due to the lockdown. Furthermore, people who do not require support should not receive assistance, thus, decreasing unjustified pressure on the budget.
- Progressivity and gender responsiveness – an assistance program should provide proportionally larger support to those in greater need and aim to balance support by gender.
To mitigate the negative social impact of the economic lockdown, the government will have to provide significant and effective social assistance. And this is where all governments face a key dilemma, as they decide between providing targeted versus universal assistance.
An Overview of Targeted vs. Unconditional Universal Assistance
Targeted assistance is based on the methodology to define target groups, this could be under a points-based system (similar to current targeted social assistance available in Georgia) or a certain criterion defining affected groups. Under any targeting approach, two major challenges exist: (i) missing certain affected people (exclusion error), where defining an ideal criterion is impossible; and (ii) supporting those who do not require any assistance (inclusion error). Hanna and Olken (2018)[6] show that targeted programs have the potential to maximize welfare, however, they require a substantial amount of data and effort to minimize errors in the inclusion and exclusion of recipients. They further illustrate that, under normal circumstances, to reach 80% of poor people, the inclusion error will be around 22-31%. Deciding on a targeting methodology can also be costly and time consuming. Klasen and Lange (2016)[7] highlight that there is little difference between simple targets, such as demography or geography, and more complex asset-based measures, and both make poor proxies as they do not capture poverty effects in great enough detail.
In contrast, a universal support scheme can also be considered; defined as an unconditional transfer to every member of society. From the administrative perspective it is substantially easier to organize and administer, as it will not require the formation of targeting methodology or identification of target groups. Compared to targeted assistance, universal support will simply not have exclusion errors. However, the universality of the scheme would be associated with large inclusion errors. Nevertheless, considering the current situation in Georgia, with a large variety of affected groups, the inclusion error need not be as high as in normal circumstances. As previously noted, due to the lockdown, the number of vulnerable groups will have increased substantially.
Unlike targeted support schemes, there is limited practical evidence behind the implementation of universal programs (Banerjee et al., 2019).[8] However, some of the impacts can be identified from existing pilot case studies, impact assessments of existing targeting schemes, and an analysis of theoretical knowledge. The key here is that the expected impacts depend substantially on the duration and type of the support scheme (i.e. direct cash transfers, provision of vouchers or coupons, tax credit).
For our purposes we assume that the duration of the support scheme will be relatively short-term (related to the length of the lockdown). Furthermore, there is nearly no practical evidence on the impact of the long-lasting universal support schemes (Banerjee et al. 2019). Theoretically, long-lasting universal support can have a negative impact on labor force participation. Moreover, Banerjee et al. (2017)[9] finds no evidence that unconditional transfers discourage work. Considering the characteristics of the crisis, labor market participation is already limited because of the lockdown.
In addition, direct unconditional cash transfers could serve the progressivity purpose well, as households in greater need will receive a larger portion of their income, compared to those who require less assistance. Progressivity will depend on whether the recipient of a cash transfer is a household or an individual. Providing a cash transfer to households might have a disproportionate impact on larger households, requiring them to sustain themselves with less money per capita. Another important point to consider is whether money should be provided to everyone or only to the working age population (those above 15 years of age).
Coupons and Vouchers vs. Direct Cash Transfer
The type of support scheme can have a substantial influence on its impacts from the welfare and macroeconomic perspectives. One form of support scheme is the provision of vouchers or coupons to help households with utility payments or to purchase essential goods. Utility vouchers will disproportionally support more well-off households that use more appliances. The universality of such vouchers is also questionable, as some households are not connected to the utility networks (for instance the natural gas network), and thus will not benefit at all from vouchers. Considering the situation, the positive impact of vouchers is that during such a lockdown utility companies will not face liquidity problems that may otherwise arise from increased delinquency rates.
On the other hand, cash transfers allow recipients to rationalize between the consumption of different types of goods. As opposed to the provision of coupons and vouchers, transfers could further increase welfare by allowing individuals to self-rationalize (Ghatak & Maniquet, 2019).[10]
A Review of Social Support Programs Internationally
In this section, we discuss various governments’ (Table 2) social protection measures during the Covid-19 crisis. The actions taken cover the different functions of social protection, such as unemployment benefits; special social assistance or direct cash transfers; wage subsidies; deferrals of tax payments; pensions and pension fund adjustments; sickness and childcare benefits; etc.
In order to promote income security and stimulate aggregate demand, several countries have introduced either universal or quasi-universal direct cash payments (e.g. Australia, Hong Kong, Singapore, Serbia, Greece, the US). In order to further ease liquidity constraints on individuals and enterprises, some countries have announced the deferral of certain tax payments, social security contributions, rent, and utility payments (e.g. Bulgaria, Estonia, Spain, Canada). In addition, several governments are providing grants and wage subsidies to SMEs, start-ups, and other hard-hit businesses to avoid the drop in revenues and safeguard employment. In most cases, these measures were supplemented by extended unemployment benefits.
Table 2: Covid-19 social protection measures, by country
| Central, South, and Eastern European Countries | Certain Social Protection Measures Taken
|
| Estonia |
|
| Poland |
|
| Latvia |
|
| Serbia |
|
| Bulgaria |
|
| Albania |
|
| Ukraine |
|
| Asia-Pacific | |
| Hong Kong, China |
|
| Australia |
|
| New Zealand |
|
| Singapore |
|
| Western Countries | |
| United States of America |
|
| Canada |
|
| Germany |
|
| Greece |
|
| Spain |
|
| Norway |
|
Source: Policy Responses to Covid-19, IMF policy tracker, April 2020; Social protection responses to the Covid-19 crisis, ILO, March 2020; Countries’ public announcements of Covid-19 economic responses.
Alternative Policy Options
Considering the existing social challenges, policy objectives, and possible alternatives implemented around the world, we propose the following five policy options:
Option 1 – Targeted Assistance
Considering the current situation in Georgia, the state’s capacity to implement a targeted exercise is extremely limited. This is largely due to the lockdown and the complexity of matching the current economic challenges and general characteristics of target groups. One way for the government to target different groups would be to use its administrative resources and revenue service databases to identify affected unemployed people no longer receiving salaries. However, using these resources, it will be hard to identify the majority of self-employed and informal workers who have also lost their income (fully or partially) and are facing hardships; examples of these individuals may include a small business owner working at the Eliava construction materials market, a self-employed tourism sector worker, a domestic worker – a nanny or cleaning lady, etc. Under normal circumstances, such individuals do not require any social assistance, however due to the lockdown they may not have enough cash inflow to sustain their families.
Furthermore, targeted assistance can create perverse incentives for some employees. Depending on the amount of the assistance, employees (that are still allowed to work) whose net salaries are close to the assistance threshold, might be discouraged from work. For example, if targeted assistance is 200 GEL, a grocery store worker with a gross salary of 300 GEL might prefer to leave their job temporarily (as unpaid leave for example).
Furthermore, the government could target following socially vulnerable groups that are easier to identify, such as:
- Receivers of targeted social assistance, adults – 297,094 individuals;
- Receivers of targeted social assistance, under 18 – 161,374 individuals;
- Pensioners – 765,911 individuals.
Providing additional support to these groups will mean indirectly covering some self-employed individuals and informal workers. Many of such socially vulnerable groups work informally or are self-employed. Furthermore, some individuals could potentially have family members that are either informally or self-employed.
To calculate the total number of people subject to the targeted scheme, we consider the above listed individuals and add the group of hired employees that may lose the job or may have to take unpaid leave. Based on our estimates, around 200,000 hired workers may lose their income. Adding this to the number of TSA recipients (458,468) and pensioners not receiving TSA payments (692,431) brings the total number of beneficiaries of a targeted assistance scheme to 1,350,899 individuals. Assuming, 150 GEL in assistance per adult, and 75 GEL for under 18s, this will bring the cost of targeted assistance to approximately 191 mln. GEL per month.
Option 2 – Income Tax Breaks
The second policy option to consider is a variation on a tax break (tax credit, lowering income, or other taxes)[11]. Such an assistance mechanism will not be universal and only benefit the taxpayers. Furthermore, it is not a fact that tax relief will be transferred from employers to employees. Thus, essential social assistance may not be provided to a large proportion of the population. In addition, due to the lockdown, opportunities for investments have shrunk and hence, most tax saving will not influence economic growth. Finally, a decrease in tax rates will create additional pressure on government revenues, already negatively influenced by the lockdown, which may potentially create fiscal problems.
Aside from the costs of tax breaks, one should also bear in mind that this policy option is only intended for income tax payers who managed to retain their jobs. In an optimistic scenario, about 200,000 of hired employees will be left jobless, thus, about 640 thousand people will be aided by tax breaks. If income tax for all these employees would be reimbursed, the cost of tax breaks would amount to approximately GEL 136 mln. (monthly). It should also be mentioned that if companies are not paying income tax to the government, they might fail to reimburse this money to their employees, leaving some people without any assistance.
Option 3 – Unconditional Universal Cash Transfers
The third policy option is unconditional universal cash transfers. In this case, the government would make an unconditional cash transfer to every member of society. From a practical perspective there are two important questions to be answered: (i) should cash transfers be provided to individuals or to households?; and, (ii) should cash transfers only be made to the working age population or to children as well?
To minimize the potential negative consequences stemming from the possible negative gender impacts, individual payments are the preferred system. This may be as men are more often than not considered to be heads of their households, and if assistance is household-based women may not be able to take full advantage of it.
Furthermore, to ensure the progressivity of a universal cash transfer, it should not be limited to the working age population. A common approach would be to give guardians of children a decreased amount of a standard Universal Basic Income (UBI) payment (Ghatak & Maniquet, 2019). The progressivity of such a scheme is an important advantage, as it ensures support to those people who are not participants of the labor market and dependents of employed family members. Thus, the universal system helps mitigate the substantial indirect impacts on poverty resulting from job losses.
A major drawback of the unconditional universal cash transfer is its expense. This is primarily due to the large inclusion error, which accompanies this system by its very definition. However, alternatively, in a targeted program the vast majority of the affected self-employed and domestic workers (in total, close to 50% of all employment) are nearly impossible to identify. Furthermore, due to the lockdown, the potential group under risk of impoverishment is greater than under normal conditions. Consequently, compared to a perfectly targeted system (without any inclusion or exclusion errors) an unconditional universal cash transfer would be only marginally costlier.
However, with imperfect targeting, an unconditional cash transfer would be substantially costlier compared to targeted assistance. Assuming 150 GEL assistance for all working age population (2,968,964 individuals) and 75 GEL for children (754,500 individuals), the total cost of unconditional universal cash transfers would be 502 mln. GEL per month.
Option 4 – An Opt-out/Opt-in Unconditional Universal Transfers
As previously mentioned, the significant cost of a universal support scheme is a notable challenge, particularly because budgetary fiscal pressure is already high due to decreased economic activity and tax revenues. Thus, implementing a potentially costly assistance program will be hard from a public finance perspective. To partially alleviate this problem and decrease the inclusion error of universal cash transfers, the government could implement it in the following ways:
- The government could offer unconditional transfers to all individuals whose income is impossible to identify, while providing an opt-out option in case they do not deem the assistance necessary (for example, individuals and their families with savings or those unaffected by non-labor income);
- The government may assist employed workers based on their income using the following two principles:
- Offer assistance using an opt-out option to everyone whose income is below a certain threshold (for example, 700 GEL gross salary for the month of March);
- Offer assistance using an opt-in option to everyone whose income is above the threshold.
Opt-out/opt-in universal cash transfers have the potential for governmental savings. To evaluate the expected cost of this option we assume that half of all employees (i.e. 430,000) with a salary of over 700 GEL gross would opt-in into the system. In this case, the total cost of opt-out/opt-in universal cash transfers would be up to GEL 470 mln. Furthermore, in the better-case scenario, where no employees with a gross salary over 700 GEL would opt-in into the system, the total cost of the cash transfer scheme would be up to GEL 437 mln. Thus, our expected cost of the opt-out/opt-in universal cash transfer will be an average of GEL 454 mln[12].
Option 5 – Conditional Cash Transfers
To decrease the fiscal pressure associated with unconditional universal cash transfers, the government could use relatively simpler methods to minimize inclusion errors in the system. In this case, the government could potentially exclude employees who may not face an urgent need for assistance. Firstly, the government could exclude individuals who received an income of over 40,000 GEL in 2019 from the program. Secondly, those workers with an average monthly income of 1,200 GEL in 2020 could also be left outside the assistance scheme. This will allow the government to limit the inclusion error of the cash transfer system, while keeping similar overall impacts.
We evaluate the expected cost of the conditional cash transfer assuming 30% of the hired workers (258,048) having monthly income above 1,200 GEL. Based on the same population data, as for calculation of the cost of the unconditional cash transfer, the expected cost for conditional cash transfer will be roughly GEL 463 mln.
Multi-Criteria Analysis of Policy Options
To summarize these options, we have created a multi-criteria assessment of the different possibilities for social assistance using our pre-defined policy objectives. We assess each policy option on a 5-point scale, with 1 representing the worst performance, while 5 showing perfect performance. The overall efficiency of the policy option is a simple average of points in each criterion.
Table 3: Multi-Criteria Assessment of different social assistance systems during Covid-19
| Assessment Criteria | Option 1 – Targeted Assistance | Option 2 – Income Tax Break | Option 3 – Unconditional Universal Cash Transfer | Option 4 – Opt-out/opt-in Unconditional Universal Cash Transfer | Option 5 – Conditional Cash Transfer
|
| Monthly Cost of the assistance Scheme (mil. GEL) | 191 | 136 | 502 | 454 | 463 |
| 1. Minimization of Exclusion Error (minimization of impoverishment risk) | 3 | 1 | 5 | 5 | 4 |
| 2. Minimization of Inclusion Error (minimization of fiscal cost) | 4 | 2 | 2 | 3 | 3 |
| 3. Ease of implementation | 2 | 5 | 5 | 4 | 4 |
| 4. Progressivity | 4 | 1 | 4 | 5 | 5 |
| 5. Gender responsiveness | 3 | 2 | 5 | 5 | 5 |
|
Overall Efficiency |
3.2 | 2.3 | 4.2 | 4.4 | 4.2 |
Summary and Recommendations
In this policy note, we have summarized the potential social impacts of Covid-19 and the subsequent lockdown caused by the pandemic. Our assessment of the sub-categories of employment show that there is a large group of mid to highly affected individuals among the employed populace. Around 30% of hired employees will be significantly influenced, while 22% will suffer a medium impact. The impact on the self-employed will also be substantial, roughly 15% of the group will be highly affected, where 84% of self-employed individuals will feel a medium impact from the lockdown. The impacts are also disproportionate from a gender perspective, posing a risk of unemployment-driven assistance benefitting men more so than women.
Having reviewed international responses to the Covid-19 crisis from 17 selected countries, the evidence compiled has helped to form possible designs for a social assistance program. We believe that direct cash transfers to individuals are preferable to providing assistance for the purchase of specific goods or services, as individuals can self-rationalize.
Our multi-criteria assessment shows that an opt-out/opt-in unconditional universal cash transfer is marginally better compared to other universal cash transfer schemes. It has the best performance in minimizing the risk of impoverishment. Furthermore, our analysis shows that under the current conditions, the government’s ability to correctly design a targeted program that is able to reach all affected individuals is limited. This is primarily due to the relatively high percentage of self-employed on the Georgian labor market. Consequently, a targeted program would have a limited impact on minimizing the risk of impoverishment. This is even more true for possible tax breaks. The greatest merit of a targeted program is that it imposes less fiscal pressure and is thus substantially less costly compared to a universal support scheme.
Annex 1 – Comparison of Sectoral Impact Assessments by ILO (globally) and ISET-PI (for Georgia)
Annex 2 – Summary of the assumptions used for calculating costs of different support schemes
| Indicator | Amount | |
| Population | ||
| A | Working Age Population (>15) | 2,968,964 |
| B | Population Below Working Age (<15) | 754,500 |
| C | Total Population | 3,723,464 |
| Hired Workers | ||
| D | Total Hired Workers | 860,161 |
| E | Hired Workers with salary above GEL 700 | 430,081 |
| F | Share of hired workers with salary above GEL 1,200 | 30% |
| G | Total number of hired workers who lose labor income | 200,000 |
| H | TSA Recipients (>18) | 297,094 |
| I | Pension Recipients | 692,431 |
| J | TSA Recipients (<18) | 161,374 |
| Cash Transfer | ||
| K | Cash transfer per adult (GEL) | 150 |
| L | Cash transfer per child (GEL) | 75 |
- [1] ILO Monitor 2nd edition: COVID-19 and the world of work, April 2020.
- [2] NACE 2 classification system, 4-digit level
- [3] Based on the Labor Force Survey, Geostat (2018)
- [4] One has to note that the working environment for frontline health workers has changed and they are exposed to higher health risk and psychological stress, which regardless of relatively stable labor market positions makes them more vulnerable physically and psychologically.
- [5] For example, more women live in poverty as demonstrated by the fact that 55% of social assistance recipients are women.
- [6] Hanna, R. & Olken, B. (2018). Universal basic incomes vs. targeted transfers: anti-poverty programs in developing
- countries. J. Econ. Perspect. 32(4):201–26.
- [7] Klasen, S. & Lange, S. (2016). How narrowly should anti-poverty programs be targeted? Simulation evidence from Bolivia and Indonesia. Discuss. Pap. 213, Courant Res. Cent., Göttingen, Ger.
- [8] Banerjee, AV., Niehaus, P. & Suri T. (2019). Universal basic income in the developing world. Annu. Rev. Econ. 11:961–85.
- [9] Banerjee, AV., Hanna, R., Kreindler, G. & Olken B. (2017). Debunking the stereotype of the lazy welfare recipient: evidence from cash transfer programs. World Bank Res. Obs. 32:155–84
- [10] Ghatak M. & Maniquet F. (2019). Some theoretical aspects of a universal basic income proposal. Annu. Rev. Econ.11.
- [11] For the purposes of this policy option we will concentrate solely on income tax breaks.
- [12] These scenarios do not consider additional potential saving from individuals with an opt-out option utilizing this opportunity.
Disclaimer
This policy brief was first published as an ISET policy note on April 17, 2020 under the title “The Social Impacts of COVID-19 – Case for a Universal Support Scheme?”.
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.