Project: FREE policy brief

Economic Perspectives on Domestic Violence | Insights from the FROGEE Webinar | Part 1

Posted on October 26, 2020 | Policy Brief

Broken mirror with a man's hand representing domestic violence during COVID-19 pandemic frogee

The COVID-19 pandemic and the resulting lockdown restrictions have amplified the academic and policy interest in the causes and consequences of domestic violence. With this in mind, the FREE Network invited academic researchers to participate in an online workshop entitled “Economic perspectives on domestic violence“. This policy brief is the first in a series of two briefs summarizing the papers presented at the workshop. The current brief addresses the presentations that had a more general focus on domestic violence. The second brief will discuss the papers devoted to the domestic violence implications of the pandemic.

Introduction

Domestic violence (DV), as well as one of its main forms – intimate partner violence (IPV) – are societal issues of massive proportion. The World Health Organization estimates that 1 in 3 women across 80 countries worldwide are victims of IPV during their lifetime (WHO, 2013). IPV imposes huge costs on society: its victims, for instance, are estimated to be twice as susceptible to depression and alcohol abuse, and 16% more likely to give birth to a low birth-weight child (WHO, 2013).

IPV separates itself from other types of violent offenses in several aspects. To start with, the intimate victim-perpetrator relationship causes IPV to be vastly underreported. The victim may have feelings of shame, guilt, and self-blame, which could deter her from seeking support.  Further, IPV and more generally DV cases also have high rates of attrition within the justice system. These distinct characteristics highlight the level of difficulty in developing policies aimed at helping victims of intimate partner abuse. The fact that the prevalence of IPV is widespread and at the same time vastly under-reported, casts doubt on the policy measures and legislation in place today.

This policy brief is the first in a series of two that summarizes the recent economic research on IPV presented in the workshop entitled “Economic Perspectives on Domestic Violence”. The workshop was organized as a part of the Forum for Research on Gender Economics (FROGEE) supported by the Swedish International Development Cooperation Agency (SIDA).

Economic Determinants of Domestic Violence

A number of presentations in the workshop were devoted to the economic determinants of domestic violence.

Andreas Kotsadam presented a paper on the relationship between women’s employment and IPV in Ethiopia. The link between the two is twofold: employment could increase women’s empowerment and, thereby, decrease IPV; however, the boost in empowerment could threaten the man’s status in (male-female) relationships, and lead to violent retaliation. Violence could also be used to extract economic resources from working women. To study which of these mechanisms prevail, the authors conducted an extensive field experiment collaborating with shoe and garment factories in Ethiopia. From a list of qualified job-candidates provided by employers, they randomly assigned 1500 equally qualified women living with partners to either getting a job (treatment group) or not (control group). Prior to treatment, women from both groups were interviewed and asked to answer various questions regarding intimate partner abuse. They were also called to a follow-up survey 6 months later. The statistical analysis of these answers fails to establish a causal link between employment status and the incidence of IPV.

Taking a more theoretical approach, Paul Seabright‘s preliminary work on the determinants of IPV offered a dynamic framework modeling how (unpredictable) economic circumstances and (predictable) individuals’ traits influence domestic violence, as well as formation and dissolution of partnerships. The model distinguishes individuals in their ability to control resources within relationships without the use of violence (“skills”), and in their costs of engaging in violence (“temperament”). The model assumes that individuals with more violent temperaments are on average endowed with lower skills. It predicts women’s income and their risk of IPV should be negatively correlated cross-sectionally, but that positive shocks in income should increase IPV for married women while decreasing it for women with easier exit options. The authors test the model on survey data from Brazil and data on randomized expansions of a food-program in Ecuador. The results support the cross-sectional prediction and confirm that the effect of income shocks depends on exit options, though does not support the prediction of an increase for married women.

Sonia Bhalotra’s presentation addressed the DV consequences of another type of economic shock, namely female and male unemployment, and also considered the role of unemployment benefits as a mitigating factor. By exploiting an extensive dataset covering every court case in Brazil between 2009 and 2017, and information on mass layoffs at the local level, the study finds that the probability of a male being prosecuted for a DV crime increases by 32% when he loses his job and persists at similar levels 4 years after. For female job-loss, the corresponding effect is significantly larger and amounts to 52%. Bhalotra and her co-authors argue that the fact that unemployment of either the man or the woman leads to an increase in domestic violence is consistent with unemployment constituting a negative shock to income and a positive shock to time spent at home. They further argue that the larger impact of female relative to male unemployment is potentially consistent with the “household bargaining model”, which encapsulates the idea that it becomes more difficult for a woman to leave a violent relationship when she is more economically dependent on her partner. Additional analysis shows that eligibility for unemployment insurance increases DV once benefits expire and that this is in turn a result of unemployment benefits increasing peoples’ time in unemployment.

The Role of Police

Part of the workshop was dedicated to the role of the criminal justice system. A fact that stresses the importance of studying police behavior is that domestic abuse cases generally suffer from high legal attrition and most of them are dropped before reaching the court. Variation in the characteristics of law enforcement could likely play a role in explaining differences in DV across contexts.

In this vein, Sofia Amaral introduced a study on the relationship between gender diversity of the police force and domestic violence in the UK. The gender-distribution within law enforcement is believed to directly influence DV in two ways: First, gender-based differences in attitudes and norms may influence police-handling in DV cases. Second, if the gender of the victim aligns with that of the officer, the victim may be more willing to cooperate and disclose evidence. The data shows that the total share of women in the police force is almost equal to that of men, but the tasks performed differ systematically across genders. Women are found to be overrepresented among call-handlers and underrepresented among first-response teams. For each position, Amaral and her co-authors investigate whether changes in gender-distribution influence the rate of legal attrition, rate of repeat victimization, and the amount of time spent at a scene (response duration). By analyzing police force and crime data the study shows that there are substantial efficiency gains from increasing gender diversity, particularly in first-response teams. An increase in the share of females in first-response teams increases response duration, reduces legal attrition, and decreases repeat victimization. There is an even larger effect when a female is the most experienced officer in the team. The gender of the call-handler has no significant effect on the outcomes of interest.

Along somewhat similar lines, Victoria Endl-Geyer presented research on the link between the quality of police response and DV in the UK. More specifically, the research explores how increased police response times, caused by police station closures in 2012, affected the rate of repeat victimization in DV cases. Faster police response times are believed to improve the victim’s cooperation: If the police are quick to arrive at the scene, the victim gets less time to revise the initial assessment that she needed support. The results show that faster police responses are associated with a higher conviction rate. However, they also increase the likelihood of repeat victimization. A potential explanation could be the so-called “reprisal effect” – the perpetrator retaliates with more violence as a response to being reported by his partner.

Criminalization

Many studies on IPV, including some that were presented at the workshop, highlight that an inherently good policy such as improving police response, sometimes leads to unintended negative consequences to victims. In the keynote speech, Leigh Goodmark addressed this topic by critically discussing the history, consequences, and alternatives to criminalization of IPV in the US. As suggested by her recent book, domestic violence has fallen in the US since the introduction of criminalization and mandatory arrest of IPV crimes. However, historical trends show that the overall crime rate has fallen to a greater extent. Goodmark provided several reasons why criminalization has likely been unsuccessful in deterring IPV.  Some studies emphasize that it is the accountability and monitoring of perpetrators (even after incarceration) that has been effective in deterring IPV crimes and not the punishment itself. In fact, there are vast costs of DV criminalization occurring to victims of domestic abuse, such as financial instability caused by unemployment of (in many cases) the primary breadwinner in a household. Also, criminalization has been shown to exacerbate other correlates of IPV such as aggressive and hostile tendencies of the perpetrator. Goodmark proposed alternatives to DV criminalization that avoid such costs and thereby, are potentially more effective in reducing domestic abuse. First, there are solutions rooted in economics such as cash-transfer programs, employment training, and micro-financing. These types of measures can help to reduce the economic penalties of seeking support and strengthen the victim’s financial independence. Also, more social solutions were suggested such as community organizing, restorative justice, and community accountability. Moreover, Goodmark underlined the fact that individuals with adverse childhood experiences, often involving violence, are significantly more likely to commit violent crimes such as IPV. Identifying and intervening at an early age to educate these individuals about intimate relationships has been shown to be effective in dealing with the problem.  In a nutshell, Goodmark stressed the importance of constructing a balanced policy approach that targets the origins of DV and argued that the time has come to reconsider punishing violence with more violence.

Reporting

Problems related to IPV misreporting were a recurring subject of discussion at the workshop. A lot of the previous research on IPV relies on direct surveys asking women whether they were a victim of different instances of IPV. The main problem associated with such surveys relates to accuracy: social factors such as stigma, shame, and/or self-blame, as well as privacy concerns, are likely to influence respondents’ answers. A practice that has proven successful for sensitive questions is the use of an indirect method called list experiments, where the structure of the survey mitigates much of the above concerns on the respondent’s side (see, e.g., https://blogs.worldbank.org/ impactevaluation/list-experiments-sensitive-questions-methods-bleg).

Veronica Frisancho presented a study on the gap in reporting originating from direct questionnaires vs. list experiments based on experimental evidence from Peru. The experiment considers two groups of 500 women each. Women in the first group participate in a survey that uses direct questionnaires, whereas those in the second group answer a survey using indirect questionnaires. Based on the answers, the authors obtain an IPV prevalence rate for each group and define under-reporting as the difference in prevalence between them, under the assumption that the rate of under-reporting in the presence of indirect questionnaires is minor. Unexpectedly, yet encouraging, they find no evidence of misreporting in the direct-questions method. However, when looking closer at different education levels, they find that under-reporting is significantly more prevalent for highly educated women. In other words, less educated women are more truthful when answering questions about IPV. Frisancho emphasized that these types of patterns make it more difficult to identify the most vulnerable groups, implying that direct methods could increase the risk of mistargeted policies.

More generally, there are several reasons why respondents may be less truthful when answering questions related to IPV. On the one hand, individuals may be aware that they are victims of abuse, but perhaps are unwilling to confess due to stigma. On the other hand, it could be that individuals fail to identify themselves as victims of abuse at all, and do not consider their relationship unhealthy. Against this background, Nishith Prakash presented preliminary results of an ongoing study on behavioral barriers to the demand for DV-support services. The baseline results of the survey indicate belief gaps among women who scored high on levels of abuse: a significant majority of abuse victims rated their relationship as healthy. While 46.43% of respondents report some form of physical, emotional, or sexual violence, the portion of those with the prior belief that they are in an abusive relationship is only 1%. The study also finds that stress about Covid-19 correlates with higher levels of self-blame, abuse, and lower levels of understanding of what abusive behaviors are.

The covid-19 pandemic and its massive repercussions on determinants of DV such as mobility, economic insecurity, and social isolation have offered new possibilities for researchers to study the underlying causes of DV, while also making DV research ever more important. The next policy brief in this series will summarize the presentations which were specifically devoted to the consequences of the pandemic on DV. On behalf of FROGEE and SITE, we would like to thank the speakers for their contributions to the understanding of this topic, which will be indispensable both to the academic community and to policymakers in their efforts to design more effective policies for the future. We would also like to thank SIDA for generous financial support.

References

  • WHO, Department of Reproductive Health and Research, London School of Hygiene and Tropical Medicine, South African Medical Research Council. “Global and regional estimates of violence against women”. Reference No. 978 92 4 156462 5. 2013.

List of Participants

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.

Belarus Economic Outlook

Posted on October 19, 2020 | Policy Brief

20201019 Belarus Economic Outlook. FREE Network Policy Brief Image of dark streets in Minsk representing Belarusian economic outlook

The Belarus economy was already struggling to generate growth before both the corona pandemic and the political protests following the August presidential election. The lack of growth was the result of an incomplete transition process to modernize the economy combined with a strong reliance on the Russian economy and its dependence on international commodity prices that have not paid off in recent years. With the added political turmoil and, so far, lack of a new political and economic strategy, the economic outlook for Belarus looks grim. Even if a full-blown crisis may be avoided by restrictive economic policies, stagnation will nevertheless be the most likely outcome without fundamental reforms.

Introduction

The Belarus economy was for many years doing very well under president Lukashenko, but since the global financial crisis in 2008/09, this course has been reversed. The downward growth trend has been exacerbated by both slumps in international oil prices (particularly important because of linkages with Russia, see Becker 2016a, 2016b, 2018, 2020), and the COVID-19 pandemic. This is clearly illustrated in Figure 1, which shows how the average growth rate has fallen all the way to a negative one percent in the years since 2015, while the period before the global financial crisis generated an average growth of 8 percent.

The lack of growth in Belarus and its causes has been analyzed in several papers long before the current developments. Akulava (2015) discusses how the government already five years ago understood that it needs to stimulate the private sector to generate growth; Kruk and Bornukova (2016) in turn describe how growth in the boom years was driven by capital accumulation but not improvements in productivity (TFP) that could have sustained growth in more recent years. As for policies to generate growth, Kruk (2014) argues that Belarus should focus on institutional changes that create the right incentives for firms and lead to a more efficient resource allocation rather than simply spend money on new equipment for existing firms. The need for productivity-enhancing reforms is further stressed in Kruk (2019) who points out that there is limited space to stimulate growth by expansionary macroeconomic policies.

Although the political situation after the election is strongly linked to the lack of democracy and freedom, the citizens’ willingness to protest is most likely enforced by the very poor economic performance of recent years. And while the importance of economic developments is sometimes glossed over in the current reporting and narrative of Belarus it will be an important factor in the popularity of any future government in Belarus as well as the current one.

Figure 1. Real GDP growth

Source: IMF World Economic Outlook April 2020.

 Note: The chart is based on the April 2020 version of the IMF’s World Economic Outlook and in the just-released October edition, the 2020 forecast is less negative due to global economic developments. However, this does not change the general downward growth trend Belarus has experienced.

Background

On the structural side, the economy of Belarus is heavily connected to Russian economic developments, which in turn depends on international oil prices (Becker 2016a, 2016b). In the group of FSU countries, Belarus stands out as the country that has the largest share of its exports going to Russia and the largest share of its FDI coming from Russia. On top of that, Belarus enjoys subsidized prices on oil and gas from Russia that benefits not only its exporting refineries but also other energy-intensive industries that are important for generating export revenues.

Figure 2. Exports and FDI shares with Russia and Rest of the World

Source: IMF directions of trade, World Bank development indicators and Central Bank of Russia data on FDI

As a final background note, the importance of SOEs in terms of employment has gone down in recent years but SOEs are still an important provider of jobs in Belarus and another sign of an unfinished transition agenda.

Figure 3. Importance of SOEs

Source: Belstat

To improve growth prospects, this is clearly a sector in need of reforms, including some privatizations, to make it more competitive and less of a drain on government finances. However, this process will need to deal with sensitive employment issue regardless of who is in charge politically.

Furthermore, Marozau, Aginskaya, and Akulava (2020) discuss how the corona pandemic may threaten the jobs of the over 1 million people that are employed by SMEs. The financial constraints of the government make it hard to offer widespread support to SMEs, and the authors argue that the government should target future winners among SMEs rather than the big losers in the crisis.

The challenge of increased unemployment is further exacerbated by the lack of an unemployment benefit system with extensive coverage (Bornukova, 2017). The lack of a well-targeted social security system could lead to a new increase in poverty rates. Mazol (2019) shows how past crises had a negative impact on poverty with absolute poverty increasing almost twofold in 2015/2016.

Recent Developments

The economy in Belarus was facing challenges (like much of the world) this year due to the COVID-19 pandemic well before the political crisis following the August election triggered additional problems. The IMF growth forecast for the year was well into negative numbers and given the (not always stable) links to Russia and thus to oil prices, the longer-term outlook was cloudy as well. Although the IMF’s October forecast shows less negative growth for 2020 (from minus 6 to minus 3 percent as the world is expected to see less of a contraction due to the COVID-19 pandemic), the longer-term outlook is one of stagnation with annual growth of around 1 percent.

For 2020, the economic and political difficulties can be seen in exchange rate developments as well as in the evolution of foreign exchange reserves (Figures 4 and 5).  In some ways, the 25-30 percent depreciation of the currency viz the dollar and euro is not the full story on the currency, since the exchange rate viz the Russian ruble has been much more stable. Given the close links to the Russian economy, this is quite important to note. Indeed, foreign currency reserves (the more liquid part of international reserves) have gone down by some 40% this year but are still at around 3 billion USD.

Additional pressure on the financial system in the past months came from significant withdrawals and people moving their savings to hard currencies after the August election. Krug and Lvovskiy (2020) discuss how this development is driven by political turmoil and also how the lack of trust that is currently generated in the system will lead to further stagnation of the economy. This line of reasoning is supported by Mazol (2018), who shows how financial stress in the past has contributed to costly economic contractions.

Figure 4. Exchange rate indices

(Jan 2020=100)

Source: National Bank of Belarus

 

Figure 5. Foreign exchange reserves

Source: National Bank of Belarus

Outlook and Policy Conclusions

The current economic policy will not generate growth in the short or long term by itself and the current political situation is clearly affecting growth negatively. The current political leadership could of course once again turn to Russia to ask for economic assistance in various forms, including loans, subsidies, or investments. Given the situation in Belarus, this will clearly come at a high political cost that will not necessarily be immediately transparent to people in Belarus or the outside world. Further, a sufficient level of assistance is not bulletproof either – Russia is itself facing difficult economic times ahead, both because of the COVID-19 pandemic and its impact on oil prices but also because of its own inability to generate sustainable growth that is not based on oil, gas and minerals (Becker, 2018, 2020).

How long the political and economic repression can go on without triggering a full-blown meltdown of the financial system in Belarus is anyone’s guess. Unfortunately, a policy mix of more restrictions on financial and exchange transactions in combination with accepting stagnation has been shown to be a model that has “worked” from Cuba, to Iran, Venezuela and North Korea for very long periods of time, so there are no given deadlines for such regimes.

Regardless of short-term policy changes, Russia will remain an important economic player in Belarus for a long time unless something dramatic changes. If there is a transition of political power in Belarus, any new political leadership will have to make careful choices with regard to its relationship with Russia. Quickly cutting ties to its big eastern neighbor could turn out to be very costly for Belarus from an economic perspective given the structure of trade, subsidies, and investments between the two countries.

If the EU (or the West more generally) wants to provide Belarus with a realistic economic alternative to Russia in the short run, it will need to provide substantial funding and strongly support a wide-ranging economic reform program that will need to address transition issues that most of its neighbors did many years ago. This will involve not only selling state assets to foreign investors but also changing the economic system from the ground up, including institutions and management practices. Another important part of the needed change is modern Western education. The importance of higher education institutions (HEI) to generate growth in Belarus is stressed by Marozau (2019), who discusses the role of HEIs in improving productivity and how the universities in Belarus fail to stimulate innovation and entrepreneurship.

The support package may not be cheap for the EU financially but helping the people in Belarus to finally make the transition to a modern, democratic market economy on the doorstep of the EU would certainly be worth it. The question is if the EU will manage to unite around such a policy in a time of COVID-19 lockdowns and economic hardship within its current boundaries. Patience may be required among those that fight for their freedom and a new economic model in Belarus.

References

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.

Domestic Violence in the Time of Covid-19

Posted on October 12, 2020 | Policy Brief

20201012 Domestic Violence in the Time of Covid-19 Policy Brief image 01

 Since the outbreak of Covid-19 in the spring of 2020, media outlets around the world have reported increases in domestic violence. United Nations secretary-general António Guterres has even referred to it as a “shadow pandemic”. Besides news outlets, academic researchers have also taken an interest in the issue, which is crucial if we are to draw the right conclusions from the patterns we see in the statistics. Preliminary evidence shows that the incidence of intimate partner violence has also increased in Sweden, notwithstanding the absence of a strict lockdown. This is likely related to the socio-economic changes brought about by the pandemic.

A Shadow Pandemic?

In response to the Covid-19 pandemic, governments around the world introduced a variety of measures aimed to stave off the contagion, and billions of worried people adapted their behavior and lifestyle. But did the pandemic, and the changes brought by it, also lead to an increase in domestic violence?

Were we to simply look at the number of domestic violence offenses reported over time, we would not be able to answer this question. Historical trends and seasonal patterns in domestic violence would confound this observation, while the crisis might affect the reporting of crimes independently of their occurrence. More rigorous statistical analysis is needed for understanding not only the true situation with domestic violence under the pandemic, but also the reasons behind it. Investigating the driving factors is crucial for informing policy reactions already in the short run — is it a loss of income that generates violence, or could it simply be increased exposure? Do we need more unemployment benefits or shelters for victims?  ­­Moreover, the rather special conditions created by the pandemic can contribute to our general understanding of how domestic violence occurs in relation to other societal dynamics, unveil some of the causal mechanisms that are still open questions in the literature and help to fight this issue further, even after the pandemic is over.

Socio-economic Theories of Violence

Within social science research, studies that focus on the relationship between domestic violence and factors at a societal level can be divided into several different branches. A large corpus of theories interprets violence as a result of power imbalance within households. This perspective is associated with explanations such as bargaining power, exit options, and status, theoretical concepts that are often embodied and approximated by observable factors such as (relative) education, income or employment status. For example, Aizer (2010) provides results in line with the bargaining power hypothesis showing that a decrease in the gender wage gap in the US is associated with a decrease in domestic violence against women. Along the same lines, Anderberg et al. (2016) use UK data to show that an increase in unemployment among men reduces the incidence of intimate partner violence (IPV) while an increase in unemployment among women increases it. In contrast, a study from Spain documents the opposite relationship in provinces characterized by stronger traditional gender roles (Tur-Prats, 2019). It finds that a decrease in female relative to male unemployment causes an increase in violence, which is more in line with the “backlash explanation” — when a woman improves her economic position and independence, the man in the household feels that his identity as breadwinner is threatened and retaliates with violence as a result. Studies such as Iyer et al. (2012) and Miller and Segal (2018) highlight the importance of improving the position of women in society, which can be achieved, for example, through role models and female representation in critical positions. They associate the proportion of women among elected politicians and among the police, in India and the United States respectively, with a significant increase in reports of crimes against women and at the same time a significant decrease in the incidence of such crimes.

An alternative interpretation of domestic violence puts more emphasis on its emotional and irrational nature. In this case, particular events or negative emotional shocks, such as an unexpected negative result of an important football match (Card and Dahl, 2011), are believed to trigger violent reactions in the heat of the moment. The likelihood of such incidents is exacerbated by stress and emotional climate within a household, which in turn are influenced by economic conditions or financial uncertainty. For example, several studies from developing countries associate improvements in general economic conditions with a reduction in domestic violence (Hidrobo et al., 2016; Kim et al., 2007; Haushofer et al., 2019).

Finally, there is a common perception that domestic violence increases during holidays and weekends as families spend more time together and potential victims are more isolated from their social networks, in line with the so-called exposure model in criminology. So far, research on this hypothesis is limited and incomplete. However, it is precisely one of the areas where studies from the recent months may fill the knowledge gap: the fact that lockdowns and work from home  forced many families to spend more time together at home while retaining full wages, gives a unique opportunity to examine exposure in isolation from other economic factors.

The opposite of exposure is known as (self-) incapacitation theory: no aggression will occur while a (potentially violent) partner is occupied with something else, whether imposed or self-chosen. Several studies focusing on this hypothesis have documented that the incidence of violent crimes declines, on the street or in the home environment, when potential perpetrators are in school (Jacob and Lefgren, 2003), in prison (Levitt, 1996), at the cinema (Dahl and DellaVigna, 2009) and when they have access to a legal prostitution market (Cunningham and Shah, 2018; Ciacci and Sviatschi, 2018; Berlin et al., 2019). During a lockdown, the availability of such activities is restricted, both to violent people as well as potential victims.

Research on Domestic Violence During Covid-19

The list of studies analyzing data from the past few months is growing by the day. Although full consensus is yet to be reached, the results that have emerged point towards a few patterns: spikes in domestic violence can be credibly connected to strict limitations of movement, at least in some contexts (India, Ravindran and Shah, 2020; Peru, Agüero, 2020; 15 large US cities, Leslie and Wilson, 2020);  unemployment could be an important mechanism (Bhalotra et al., 2020; in Canada, Beland et al., 2020 find no impact of unemployment or work arrangements per se, but do associate spikes in violence to financial difficulties); alcohol does not seem to amplify domestic violence during the pandemic, at least in some context (Silverio-Murillo and Balmori de la Miyar do not find any effect of the prohibition to sell alcohol in parts of Mexico City); and by and large barriers to reporting might be a serious issue (Spencer et al, 2020).

A selection of studies on domestic violence during the Covid-19 crisis, many of which are as yet unpublished, were presented at the recent FROGEE Workshop “Economic Perspectives on Domestic Violence”. Two FREE Policy Briefs summarizing the event are forthcoming.

Domestic Violence in Sweden During Covid-19

Studying Sweden against this background can be particularly interesting for at least two reasons. Sweden regularly occupies the top positions in international rankings of gender equality in many dimensions and is seen as having advanced progressive norms and attitudes in this area. As pointed out by the literature on the economic determinants of domestic violence, underlying norms and attitudes can play a significant role in shaping the impact of other factors, such as unemployment (Tur-Prats, 2019). Therefore, the Swedish case can offer a valuable comparison to studies focusing on countries that have different attitudes and norms.

According to estimates by the National Council for Crime Prevention (BRÅ), at least 7% of the Swedish population is exposed yearly to domestic violence, both men and women in roughly equal parts. However, women are much more likely to report recurring violence and to end up hospitalized.

When it comes to the particular situation of the Covid-19 crisis, Sweden is also close to unique in its contagion-management strategy. Swedish policy relied much more than elsewhere on voluntary participation and individual responsibility rather than coercion. Certainly, working from home when possible was encouraged, the use of public transport discouraged, and indoor events with more than 500, and thereafter 50 participants were forbidden, which included many sports and cultural events. In fact, the Google mobility index, based on location data from Google Account users, shows patterns of clear deviation from the baseline since week 11 of 2020, when the authorities declared a very high risk of community spread.

Figure 1. Mobility patterns in Sweden during Covid-19

Source: Author’s aggregation of Google mobility index. The lines show the deviation from baseline, in percentual terms, of total user presence in different urban areas by category.

The plots in Figure 1 show that the presence of Google Account users was about 10% higher in residential areas (the pink line) and much lower in workplaces, despite some variation over the period: the initial decline was roughly half as large as the impact of summer vacation, as shown by the blue line. Also, visits to retail centers and grocery stores, recreation places (such as restaurants, cinemas, and theaters), and transit stations decreased, especially during the beginning of the period. Mobility in parks and green areas, shown separately, follow to a larger extent a seasonal pattern.

Nevertheless, the general population was never forbidden or even discouraged from leaving their homes, which clearly makes a stark difference for many of the mechanisms that, based on the literature, we think could play a role in explaining domestic violence.

According to BRÅ, during the first half of 2020, there was a 1% increase in total reported crime compared to the same period of the previous year. However, there is wide variation among the crime categories: 9% more violent assaults against women were reported, and 4% more against men, but 6% fewer rapes of women and 9% fewer rapes of men. As discussed above, it is not straightforward to draw conclusions from simple comparisons over time. Preliminary analysis utilizing the variation in mobility patterns over weeks and municipalities reveals that a 10% increase in residential mobility is associated with a (lower bound) increase in reported non-battery crimes against women committed by an intimate partner by 0.015 crimes per 10,000 individuals (a sixth of the mean). The corresponding figure for a 10% reduction in mobility in retail and recreation areas and transit mobility is around 0.0025 additional crimes (3% of the mean) (see Figure 2). Crime categories include attempted or planned homicides; sexual molestations, sexual assaults, and rapes; violations of integrity and privacy (including limitation of freedom, coercion, threats, persecutions; battery crimes are not included for the time being because of a coding mistake in the police system pertaining this particular category).

Figure 2. Mobility patterns and IPV in Sweden during Covid-19 – non-battery crimes

Source: Author’s analysis. Crime data provided by the police, mobility index provided by Google.

We consider this a lower bound because of the voluntary nature of the Swedish ”lockdown” – if people have the freedom to choose, then it is reasonable to expect that individuals more exposed to the risk of domestic violence would decide to be less at home, which would reduce the strength of the relationship observed. In the opposite direction, we might be worried that when more people are at home, more crimes are reported by a third party, such as neighbors, and thus not implying that more crimes are being committed. However, we differentially see more reported crimes with a female victim than with a male victim, which is not necessarily easy for a third party to distinguish by the sounds. Therefore, it seems likely that, based on the changes in mobility patterns, IPV against women has increased in Sweden during the Covid-19 crisis. Other consequences of the crisis that might also play an important role in shaping IPV and domestic violence, including the huge increase in unemployment and changes in alcohol sales, remain to be investigated.

Conclusion

In conclusion, research from the past months finds some limited support for hypotheses originating from previous literature on the relationship between different socio-economic factors and domestic violence. When these factors were affected by the pandemic and the associated economic crisis, domestic violence responded as well, to a varying extent depending on the context. This can be seen as an indirect and hidden cost of the pandemic.

Preliminary evidence indicates a similar case for Sweden, notwithstanding the absence of a strict lockdown. This implies that a significant part of the changes in behavior, which in turn can be expected to affect domestic violence, have occurred as a response to the pandemic itself and not necessarily as a result of policy measures.

While the shock of the pandemic will help us to better understand some of the underlying mechanisms behind the phenomenon of domestic violence, many questions are still open, and it is important to look beyond the pandemic. Domestic violence existed before Covid-19 and will, unfortunately, remain part of our societies when the pandemic is over. Investigating and understanding its determinants is important in order to formulate proper policies to combat it during and after the crisis.

References

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.

Does Political Illegitimacy in Belarus Imply New Economic Risks?

Posted on October 5, 2020 | Policy Brief

Large group of peaceful demonstration in Belarus that represents Belarus and people who seek to avoid economic risks

Today’s political crisis in Belarus has given rise to the phenomenon classified in political science as political illegitimacy. However, this is not a pure political phenomenon. It causes adverse and severe economic adjustments. In a short-term perspective, it gives rise to numerous risks of financial destabilization. Moreover, it is likely to deepen the current recession and make it protracted. In the long-term, political illegitimacy causes adverse institutional adjustments and erosion of human capital, which is likely to lead a country into a long-lasting depression. We argue that resolving the political crisis in a way that revives trust and legitimacy is the only ‘good’ solution.

Short-term Economic Effects of Political Illegitimacy

Since August 9, 2020, Belarus has been widely discussed worldwide in mass media because of the country’s political crisis. Political scientists classify the current situation in Belarus as a case of political illegitimacy, i.e. there is no consensus in the Belarusian society concerning the recognition and acceptance of a new term for the governing regime.

In turn, the governing regime prefers to ignore the illegitimacy issue. There is an implicit assumption behind this:  illegitimacy is an intangible issue that can hardly result in any tangible threat to the sustainability of the governing regime.

We oppose this view and argue that, at least in an economic dimension, there are numerous channels through which illegitimacy transforms into tangible problems. Inasmuch as the stance of the economy affects political sustainability, it will undermine the latter.

From a short-term perspective, the issue of political illegitimacy has become part of the information accounted for in the decision-making of economic agents in Belarus. Hence, in their economic decisions they either try to struggle against it, or at least to hedge against corresponding adverse effects.

Most evident, the adjustments in decision-making has already visualized in households’ savings behavior. Directly, illegitimacy considerations gave rise to deposit withdrawals from the banking system and enlarged demand for hard currency. Consequently, this led to a rise in depreciation-/inflation-expectations and lowered public trust in the banking system, which in turn has amplified these patterns of the households’ behavior.  In August, Belarus experienced historical peaks in deposit outflows and international reserves were depleted as a result. This has substantially amplified the risks of financial turmoil.

So far, the authorities have curbed the financial stress by implementing a restrictive monetary policy. However, this does not suppress adverse patterns in households’ behavior. It only somewhat allows for a shift of adverse adjustments from financial markets towards the real economy. Moreover, it weakens but does not completely remove the threat of full-fledged financial turmoil, taking into account the systemic financial fragility in Belarus.

In addition to the illegitimacy issue itself, other adverse expectations are likely to give rise to unfavourable trends in households’ consumption behaviour as well. First, household consumption is likely to be dampened as a result of poor consumer confidence and sentiment. Second, additional losses in consumption are likely to occur due to tightening access to credit and progressing financial fragility.

Similar mechanisms are likely to be in place with respect to investment demand. First, poor confidence and sentiment undermine the investment activity of businesses. In Belarus, this channel is likely to be more powerful for private businesses, as investment plans of SOEs (due to their directive nature) are less sensitive to confidence and expectations. Second, investment activity is likely to decline due to deteriorating financial conditions and consequent contraction of credit. This linkage is especially important for the SOEs and housing investments.

The power of adverse consumption and demand trends is still questionable. However, preliminary estimates (introducing negative shocks in addition to scenarios in Kruk, 2020) show that they will reduce the output growth rate by at least 1.5-2.0 percentage points in 2020 Q3-Q4. In other words, they are expected to deepen the current recession and are likely to make it more long-lasting.

Deteriorating payment discipline is one more expected outcome from political illegitimacy. Being amplified by deteriorating financial conditions and economic activity, it can turn into a full-fledged payment crisis and fiscal instability.

Adverse Institutional Adjustments and Effects on Labor Market

Human-to-human interactions based on mutual benefit and trust are the core of a modern market-based economy. Key institutions created to support this interpersonal trust are laws and law-enforcement agencies. If a person does not trust her counterpart in a deal and does not think that she can take him to court to defend her rights, no deal will be signed. When an individual observes unrightful and politically-motivated court decisions in criminal cases, the distrust is also passed on to her beliefs that she would be able to defend her economic rights in the same court. As we observe police violence, tortures, and criminal charges of protesters with no attempt to prosecute those responsible, public trust in the law-enforcement system fades away, and thus all kinds of deals previously supported by a contract-enforcement system cease to exist.

The quality of a judicial system is widely recognized as a powerful determinant to overall institutional quality and the business environment. Hence, poor trust in it would likely undermine business activity directly. Existing businesses are to re-orient towards shorter-term strategies, being reluctant to initiating long-term and risky projects. Moreover, their inclination to geographical diversification of their business activity or even full migration is likely to rise. New entrants – that are extremely important to achieve productivity gains (Foster, Haltiwanger, and Syversen, 2008) – are less likely to start business in the country.

An increase in emigration is a usual consequence of political crisis, especially if it is accompanied by violence and politically-motivated incarcerations. What is unique about the current Belarus crises is that the list of potential emigrees include not only individuals but also firms, especially those working in the IT sector. After 11 August 2020, many IT companies found their employees detained, beaten and tortured. The offices of Yandex, Google and PandaDoc were searched and four top managers working at the latter were detained on tax evasion charges which are likely to be politically-inspired. As of the 18th of September, around 200 IT companies are considering relocation from Belarus and many more are considering partial relocation of their employees to already established foreign offices (Dev.by(2020a)). Results from a recent survey show that 33% of IT specialists have already decided to leave Belarus and the rest indicated that they will leave if the situation worsens (Dev.by(2020b)).

There are several major reasons for why the IT-sector is affected more by the current crises compared to traditional sectors of the Belarusian economy. Firstly, IT companies rarely own physical capital and thus can change their location in a matter of days by simply relocating their employees and laptops. Secondly, the IT labor market is global and mobile, and companies compete for the workers. Therefore, if many workers hold similar strong views on a particular situation, employers are bound to support them to a certain extent. As a result of the latter, many IT companies have openly voiced their disagreement with the election results and the politically motivated violence following the election. High-level employees and owners of major companies have participated in various opposition initiatives and as a result, now face retribution from Lukashenko’s government.

In addition to politically-motivated emigration, we can expect an increase in economically-driven emigration rates as the economy is expected to shrink (Bornukova and Lvovskiy, 2020).

What Is the Way Forward?

The political crisis in Belarus has triggered multidimensional adverse economic adjustments. Nevertheless, the authorities prefer to ignore the links between politics and economics. Hence, they try to overcome the problems with economic policy tools only. However, the room to maneuver with these tools is considerably restricted, and in some cases completely ineffective in suppressing adverse trends.

With respect to the short-term agenda, the authorities cannot offset the adverse trends. They can just mitigate challenges in one dimension and try to re-direct it to another one. For instance, currently the authorities focus on mitigating the probability of a full-fledged financial crisis. This consideration requires restricting monetary conditions. Otherwise, the exchange rate is likely to depreciate, which would be problematic from a corporate debt sustainability perspective. Although being somewhat effective in this regard, this policy mix dampens economic activity. From a financial dimension, the challenge is being re-directed to the real economy.

A similar picture might soon emerge in a fiscal sphere as well. An economic downturn and political crisis can result in a widening income gap. At the same time, the room for maneuver on the expenditure side is constrained. The funds accumulated from the previous periods have to a large extent already been spent to support SOEs. Hence, a further expansion of expenditures is hardly possible, as it would undermine fiscal and public debt sustainability. Therefore, fiscal stimulus is likely to fade away and can gradually even become negative.

Based on estimations in Kruk (2020), before the issue of illegitimacy appeared, the economy was developing according to a scenario of about a 3% drop in GDP in 2020 and a meagre recovery (if any) in 2021. Adding the assumptions associated with adverse adjustments due to the illegitimacy issue into the Kruk (2020) estimates, we show that the recession is likely to deepen by at least 1 percentage point in 2020. In 2021, output losses are likely to expand considerably. In regard to the long-term agenda, the situation is even worse. Conceptual decisions on economic activity by firms and households are closely linked with the issues of trust and legitimacy (Bornukova et al., 2020). Having lost them, the authorities are unlikely to have any effective tools for standing against adverse institutional adjustments and the erosion of human capital. Hence, we may expect that today’s poor growth potential of the Belarusian economy – up to 2.5% of per annum growth (Kruk, 2020) – is likely to weaken further and could even become negative. This means that the stagnation over the recent decade is likely to turn into a long-term depression.

Conclusions

The political crisis and the arising issue of political illegitimacy in Belarus impose severe economic challenges for the country. In a short-term perspective, there are numerous channels that are likely to deepen the recession and make it long-lasting. Moreover, risks to financial stability are progressing rapidly. Hence, there is little room for securing macro stabilization in the near future.

In a long-term perspective, the country is likely to suffer from the disruption of productivity enhancers. It will stem from lower business initiatives and the erosion of human capital. This is a way to a long-term depression.

Standard economic tools are mainly ineffective against both the short-term and long-term challenges. Resolving the political crisis in a way that revives trust and legitimacy is the only ‘good’ solution.

References

  • Bornukova, K. and Lvovskiy, L. (2020). Demography as a Challenge for Economic Growth, Bankauski Vesnik, 680 (3), PP. 31-35.
  • Bornukova, K. Godes, N., and Shcherba, E. (2020). Confidence in the Economy: What is It, How it Works and Why We Need it?, Bankauski Vesnik, 680 (3), PP. 95-99.
  • Foster, L., Haltiwanger, J., and Syversen, Ch. (2008). Reallocation, Firm Turnover, and Efficiency: Selection on Productivity of Profitability? American Economic Review, 98(1), PP. 394-425.
  • Kruk, D. (2020). Short-term Perspective for the Belarusian Economy, BEROC Policy Paper No. 92.
  • Dev.by. (2020a). https://dev.by/news/pochti200-relocate
  • Dev.by. (2020b). https://dev.by/news/opros-relocate-september2020.

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.

Combating Misuse of Public Funds in COVID-19 Emergency Procurement

Posted on September 28, 2020 | Policy Brief

Image of two medical workers with face shields representing COVID-19 procurement

The Covid-19 pandemic has revealed substantial shortcomings in central governments’ and municipalities’ ability to procure items needed in the fight against Covid-19, and corruption has been rampant partially due to the increased discretion of procurement staff to award contracts. We argue that suspension of ex ante rules safeguarding accountability is essential for disaster relief, but must be compensated for by better ex post monitoring. Such monitoring can be greatly strengthened by increasing transparency of all awarded contracts and providing incentives to whistleblowers to come forward to report fraud and corruption.

Corruption in Covid-19 Procurement

The disastrous Covid-19 pandemic has revealed weaknesses in global supply chains and in national public procurement systems’ ability to secure essential Personal Protective Equipment (PPE), ICU material, and Covid tests. Several countries have been and are experiencing issues like poor quality of procured goods, extremely high prices, scams, and a general inability to source.

Examples of quality under-provision abound. The Spanish government discovered that out of 340,000 tests purchased from a Chinese manufacturer, 60,000 of them did not test accurately for Covid-19 [1], and the Dutch ministry of health issued a recall of 600,000 face masks from a Chinese supplier due to poor quality [2]. Analogous problems were common in the UK [3, 4]. Several countries have also had difficulties to procure at all, for example in terms of their desired number of tests [5, 6], or the reagents used to analyze the tests [7], as well as swabs [8].

Reports on price gouging – selling at extremely high prices – are also widespread. Examples of price gouging and investigations by competition authorities can be found throughout Europe and the US, but also in developing countries like Indonesia, Brazil, Thailand, Kenya, and South Africa (OECD 2020a), and in Ecuador and Paraguay, with corruption as the alleged cause [9].

While many reasons lie behind these procurement failures, several of them are directly traceable to the abuse of the increased discretion granted by emergency procurement rules to urgently source material and bypass time-consuming public procurement processes and legal frameworks. This important and necessary increase in discretion can easily be abused to hand out contracts to friends and/or political allies or to cash bribes.

Again, examples in the press abound. In the UK, a clearly non-urgent contract was awarded without competition to a firm owned by two long term associates of Michael Gove and Dominic Cummings [10]. In Slovenia, a gambling mogul with no public record of healthcare experience appears to have received millions in an emergency contract related to Covid-19 [11]. In Bosnia, a raspberry farm was apparently granted a contract to import 100 ventilators,paying $55,000 for each ventilator, while their price was around $7,000 to $30,000 on the international market in the relevant period [12]. In India, a Mumbai Realtor with no previous healthcare experience got a contract to supply things such as oxygen cylinder and medical beds [13]. The health minister in Bolivia was arrested in May after the country bought 179 ventilators at $27,683 each while it later was revealed that the manufacturers were offering ventilators at approximately half that price [14]. In Bangladesh, Transparency International issued a study suggesting widespread corruption in the country during Covid-19, including the purchase of substandard medical supplies at five to ten times the market price [15].

The Covid-19 crisis has exacerbated an already significant problem: according to Transparency International (2020), up to 25% of all global healthcare procurement spending is lost to corruption.

Historically, Fraud Increases During Emergencies

Disaster related fraud is frequently a problem in the western world as well. In September of 2005, in the aftermath of Hurricane Katrina in the US, the Hurricane Katrina Fraud Task Force was set up to go after frauds related to recovery funds. By August 30th, 2007, the task force had prosecuted 768 individuals for Katrina-related fraud, and additional state and local prosecutions for disaster-related fraud had been brought (DoJ 2007). The National Center for Disaster Fraud was also created within the justice department in the aftermath of several devastating hurricanes in the US, and currently houses over 80 employees.

Organizations and academics warned the public early about the risk of increased corruption in public procurement during the Covid-19 pandemic (Khasiani et al 2020, OECD 2020b). Indeed, emergency procurement and disaster relief has historically been linked to increases in corruption (Leeson and Sobel, 2008), especially where institutions are weaker (Barone and Mocetti 2014). The problems often highlighted in this context, such as using emergency authority when it is not required/warranted or using it beyond the time it is required, abuse of discretionary authority, drawing up specifications to suit the firm desired to win the contract, restricting the number of bids, and caving in to political influences (Schultz and Søreide 2008: 523), have also been on display during the Covid-19 crisis.

There are of course compelling reasons to relax stringent procurement rules in emergencies to allow for a fast response proportional to the population´s needs. But such a lessening of oversight and ex ante checks must be compensated for by much more extensive ex post checks, that should be advertised widely to deter public officials from abusing discretion. Broadly, there are two main ways of strengthening ex post checks/monitoring.

Two Ways of Ex-post Monitoring

The first is to have complete and transparent documentation of all the contracts awarded and the related documents, a “keep the receipt” mentality and practice, and making these records publicly available as soon as possible. Several countries have been moving in this direction as a response to the crisis, often with the help of NGOs like the Open Contracting Partnership (The Economist 2020). Examples include Ukraine, that require the submission of a report for each contract within a day of its conclusion, which is then made publicly available on an internet platform; and as of 2016 a third of government contracts in Colombia were published on an e-procurement platform where they can then be scrutinized by the public. In the US, the user-friendly website USAspending.govprovide data on federal contracts, with advanced search functions including tags specific to Covid-19 contracting.

The organization Open Contracting Partnerships provide a list of suggestions for any government that is looking to increase transparency in procurement; it includes the timely publication of contracts, licenses, concessions, permits, grants, as well as related pre-studies and bid documents. A full list of best practices, which can be implemented at a low cost, can be found on their website (Open Contracting Partnerships 2020).

The second is to protect and incentivize whistleblowers. Adequate protection of whistleblowers is a first step, but protection is always partial and imperfect, and may therefore be insufficient to induce those close to frauds to come forward, given the terrible consequences they typically face (see e.g. Rothschild and Miethe 1999, Nyreröd and Spagnolo 2020c).

In the U.S., the False Claims Act (FCA), first enacted by President Lincoln to curb fraud on military supplies during the civil war, and strengthened in 1986, has gone one step further by providing whistleblowers with substantial monetary rewards when they report on procurement fraud. Building on the success of the FCA, the US has introduced similar programs in several areas, most prominently with respect to tax evasion (in 2006) and securities fraud (in 2011).

Providing meaningful monetary incentives to whistleblowers who report on particularly egregious frauds and corruption can have a substantial deterrent effect on potential fraudsters as several studies show (see e.g.  Wilde 2017, Johannesen and Stolper 2017, Wiedman and Zhu 2018, Amir et al. 2018, Leder-Lewis 2020; see Nyreröd and Spagnolo 2020a for a review of the earlier literature). Simple cost-benefit analysis shows that a well-designed and implemented whistleblower incentives scheme can be a highly cost-effective continuous monitoring tool for enforcement agencies and public prosecutors (see e.g. Nyreröd and Spagnolo 2020b).

As for the EU, it is conspicuously lagging behind. Even prior to the Covid-19 crisis there was a need for increased monitoring evidenced by a 2019 European Court of Auditors (ECA) report entitled “Fighting fraud in EU spending: action needed.” A central emphasis of this report is that the Commission lacks insight into the scale, nature, causes, and level of fraud, as well as the level of undetected fraud. In 2018 the EU adopted a Directive that would harmonize and strengthen whistleblower protection in the EU. While the new EU Directive on whistleblowing is a step in the right direction, it failed to provide a framework for whistleblower rewards.

This may have been a mistake, as standard detection methods, including whistleblower protections, have often proven inadequate. The recent Wirecard scandal is a testament to the failure of standard fraud detection methods. In June of 2020, the stock price of Wirecard dropped from €100 to sub €2 in less than nine days after it was revealed to be an Enron-level accounting fraud. The firm has also allegedly laundered money for mobsters and was involved in a range of shady practices. Since 2008, fraud accusations have been leveled several times against the firm and Wirecard´s response was to label their critics “market manipulators”. The German financial supervisors, instead of investigating Wirecard, went after those who correctly claimed that the firm was a fraud, including reporters at the Financial Times. This fraud went undetected for at least 12 years, costing investors millions and undermining trust in financial markets. Moreover, those correctly accusing Wirecard of fraud allege they were subject to harassment campaigns, including phishing attacks by hackers and intimidating surveillance outside their homes and offices [16]. This is perhaps not surprising given that Germany is a country with some of the worst protections for whistleblower [17].

The shortcomings of traditional methods of fraud detection may turn out to be especially costly and ineffective during the Covid-19 pandemic.

Conclusions

With increased public spending being a cornerstone of the response to this crisis, adequate monitoring of abuse of public funds will become more urgent. Some EU institution, such as the European Public Prosecutor’s Office, or the European Anti-Fraud Office, could be suitable for a whistleblower reward program, as investigators are likely stuck looking for needles in haystacks, or lack the necessary information to bring/recommend actions to recover funds. Irrespective of the lost opportunity of the Directive, evidence shows it is time to introduce serious (high stakes) whistleblower rewards programs in Europe, unless of course Europeans are not able to manage them, or are more interested in hiding rather than airing their dirty laundry.

References

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.

Problems and Progress in the Historiography of the USSR: Robert W. Davies and his Pioneering Research

Posted on July 2, 2020 | Policy Brief

Stack of books

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.

Figure 1

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).

Figure 2

Figure 3

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.

Figure 4

Figure 5

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.

Figure 6

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.

Governance in the Times of Corona: Preliminary Policy Lessons from Scandinavia

Posted on June 25, 2020 | Policy Brief

Areal image of empty restaurant tables with only one table occupied by two people representing governance and Covid-19

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

Posted on June 18, 2020 | Policy Papers

An image with ambulance car at night representing COVID-19 Health Risks and Healthcare Resources

Drawing on the most fundamental conclusions from the early research on the Covid-19 pandemic, in this policy paper we examine the regional prevalence of a number of risk factors related to severe consequences of Covid-19. Using the examples of Germany and Poland, two neighbouring countries which have generally dealt relatively well with the outbreak in recent months, we show that there is significant regional variation both in the distribution of health status and healthcare resources. Highly differentiated demographic and epidemiological risks related to the pandemic between as well as within Germany and Poland call for a decentralised evaluation of risks and point out the need to consider an application of regionally focused policy reactions such as lockdowns and social distancing regulations. The cross-country regional perspective adds a valuable angle to the analysis of challenges raised by the Covid-19 pandemic and should urgently be considered regarding any possible consequences of future outbreaks of the virus.

Introduction

In the first five months of 2020 the Covid-19 crisis has grown from a local epidemic outbreak in the Chinese city of Wuhan to a global pandemic, which by the end of May, according to official statistics, took the lives of over 370 thousand people and has been detected in nearly all countries around the world. In the initial phase of the pandemic, the healthcare systems of many countries were pushed to the brink of collapse, and in the severely hit regions even the need of “prioritizing” patients with a high chance of survival became reality. In most European countries the total number of identified cases has continued to grow throughout the month of May, but the rate of growth generally decreased, and in some countries, such as Austria or Slovenia, only a handful of cases were identified in the last two weeks of May. As a result, countries eased the social and economic lockdown, and in many parts of Europe life is beginning to portray a certain restricted semblance of pre-Covid-19 normality. At least in this part of the world, it seems that the first wave of the pandemic is behind us: the “hammer” is over, the “dance” has begun. Thus now that the spread of the virus is slowing down and we are in a phase of smaller local outbreaks, it is time to take a step back and use the information available to draw lessons before the arrival of a potential second wave, which according to many epidemiologists is likely to happen later this year.

Drawing on the most fundamental conclusions from the early research on the Covid-19 pandemic and taking a cross-country perspective, in this policy paper we examine the prevalence of a number of risk factors related to severe consequences of Covid-19 from a regional perspective. In our analysis we focus on Germany and Poland — two neighbouring countries which differ in the demographic structure of their populations as well as with respect to their healthcare infrastructure. Epidemiological research suggests that the risk of serious health complications as well as the risk of dying as a result of Covid-19 grows rapidly with age and is much higher among people with pre-existing health conditions such as cardiovascular conditions, diabetes, hypertension, chronic pulmonary disease and malignancy (Emami et al. 2020). Thus, the prevalence of these risk factors might serve as an indicator for the need of (in-hospital) health care in times of larger outbreaks. We then extend the analysis by a discussion of regional statistics on systemic features of healthcare resources reflecting the potential for addressing the pandemic. One can generally say that both in Germany and Poland the first wave of the pandemic, while placing additional heavy strain on healthcare in some regions, has not led to the collapse of healthcare provision. Yet, regions with lower level of service are at greater risk of healthcare rationing, thus further raising the likelihood of severe consequences to the local populations in the future.

We begin this policy paper with a discussion of the key demographic and epidemiological risk factors related to severe health consequences of Covid-19 (Section 1), which is followed by a presentation of the regional distribution of Covid-19 cases in Germany and Poland, as reflected in official statistics at the end of May 2020 (Section 2). We then discuss regional differences in the proportion of people aged 65+ and in the rates of the relevant comorbidities by showing regional statistics on the main causes of death (Section 3). This is complemented in Section 4 by a discussion of the regional distribution of healthcare resources as indicated by the number of hospital beds and the number of doctors. All aspects of our analysis are presented at the level of “powiat” for Poland and “Kreise” for Germany, referred to below as “counties”. There are 380 counties in Poland (including township with county status) and 401 counties in Germany, which in the international Nomenclature of Territorial Units for Statistics (NUTS) correspond to the former NUTS level 4 (former LAU 1) and NUTS level 3 respectively.

As we demonstrate, there are significant differences both across and within the two countries with respect to the relevant demographic and epidemiological risk factors. At the same time there is high heterogeneity across Germany and Poland in the resources of the respective healthcare systems. We show that the cross-country regional perspective adds an additional valuable angle to the analysis of challenges raised by the Covid-19 pandemic. Epidemiologists have modelled various scenarios of future Covid-19 waves including recurring small outbreaks, a new “monster wave” or even a persistent crisis (Moore et al. 2020). Whatever the shape of future outbreaks, the pandemic is expected to persist until “herd immunity” is reached, be it through successful vaccination or through developing immunity in response to illness. Thus, regions potentially facing more serious consequences of the pandemic need to be brought to the attention of central governments as they prepare to address the challenge of future outbreaks of the Covid-19.

1. Macro-Level Determinants of the Health-Related Consequences of Covid-19

At the initial stage of the pandemic, the WHO estimated the fatality rate of the Covid-19 disease at 3-4% (WHO 2020a). As the public health crisis developed, this general conclusion has been challenged given a high number of asymptomatic infections, low testing capacities in most countries and relatively low test accuracy for antibodies as well as PCR testing (Ghandi et al. 2020, Kandel et al. 2020, Manski & Molinari 2020). The available statistics should thus be treated more as “fatality-case” ratios, i.e. the ratios of deaths resulting from Covid-19 to the number of individuals tested positive. According to the most recent studies, this ratio differs substantially between countries, from as low as 0.04% in Qatar and 0.08% in Singapore to over 15% in Belgium or France (Oke & Heneghan 2020). Such high variation is unlikely to reflect “real” differences in the way the virus affects people in different countries, but is more likely to be a consequence of specific factors as the testing strategies, the demographic structure of the population, the characteristics of the part of the population affected (e.g. young holiday makers vs. patients of care institutions), as well as the ability of the healthcare system to deal with a sudden surge in the number of hospitalisations.

There is mounting evidence that the probability of developing severe symptoms of the infection, of hospitalisation and finally of dying, increases significantly with age. According to some early estimates the fatality-case rates grow from 1.8-3.6% among people aged 60-69, through 4.8-12.8% among those aged 70-79, up to 13-20.2% among those 80+ (Roser et al. 2020). Higher hospitalization and fatality rates are also strongly correlated with underlying health conditions, in particular with cardiac disorders, chronic lung diseases, diabetes and cancer (ECDC 2020). This further puts older individuals, among whom these health conditions are most prevalent, at much greater risk as compared to the younger population.

While the risk of severe consequences of Covid-19 substantially increases at older ages, several competing mechanisms are at play with regard to the role of the demographic structure for a potential spread of the virus. On the one hand, since levels of economic activity are generally lower among older people, their compliance with self-isolation rules is likely to be less sensitive to the intensity of economic activity at regional or country level. On the other hand, however, as social life now returns to a higher level of interaction, different forms of living arrangements of older individuals place certain groups at a particular risk. The first months of the pandemic in Europe have revealed high vulnerability of people living in long-term care facilities, many of which became Covid-19 clusters with high rates of mortality among their residents (Comas-Herrera et al. 2020; Gardner et al. 2020; McMichael et al. 2020). On the other hand, in countries characterised by low rates of institutionalization, older individuals are more likely to co-reside in households with children and younger adults (Myck et al. 2020), i.e. groups which in conditions of lifted lockdown restrictions will be exposed to the risk of infection. Studies at the early stages of the epidemic showed that intra-household transmission of the virus may be responsible for the majority of clusters (WHO 2020b). This implies that while the strategies to protect the most vulnerable groups may differ depending on the specific living arrangements, regions with a higher proportion of older people face an increased risk of severe health consequences of Covid-19 outbreaks.

Similar arguments apply to the regions where incidence of the relevant comorbidities is particularly high. Systemic constraints related to healthcare played an important role at the height of the recent Covid-19 crisis in countries such as Italy or Spain where the number of patients in need of in-hospital treatment exceeded the capacities of the healthcare systems (Pasquariello & Stranges 2020, Remuzzi & Remuzzi 2020, Verelst et al. 2020). We thus argue that regions with populations facing highest risks related to the Covid-19 pandemic ought to be particularly vigilant to the spread of the disease and ensure that their healthcare infrastructure can respond adequately to future outbreaks.

2. The Regional Spread of Covid-19 infections in Germany and Poland

The first official case of the disease in Germany was confirmed on 27 January, while the first infection in Poland dates to 4 March. Since then 183 thousand Covid-19 infections have been identified in Germany and 23 thousand in Poland by the end of May 2020. The corresponding fatality-case ratio at that point stood at the average country levels of 4.69% and 4.47% respectively. The difference in the overall number of cases relates both to the greater spread of the virus and the more extensive testing conducted in Germany as well as to a simple difference in the size of population (83 vs. 38 million inhabitants). Importantly, when we take a regional perspective on the pandemic, as we can see in Figure 1, the distribution of the infection rate is far from homogenous. In Germany, the level of infection rates is much higher in some of the southern and western regions (Bavaria, Baden-Württemberg and North Rhine Westphalia), while in Poland the region of Silesia is a clear local “hot-spot” of the pandemic.

Figure 1. COVID-19 infections per 100 thousand inhabitants by county
(as of 31 May 2020)

Source: own compilation based on data from Robert Koch Institute (RKI) and Federal Agency for Cartography and Geodesy (BKG) in case of Germany and data collected individually by Michał Rogalski (https://www.micalrg.pl/) from Voivodeship Offices, Voivodeship and Powiat Epidemiological-Sanitary Stations, media and materials sent on request and Head Office of Geodesy and Cartography (GUGiK) in case of Poland.
Note: Class 3, 4 and 5 cover 20% of the observations, the other classes 10% each.

In Germany, the first outbreaks were attributed to business travel and skiing tourism and the spread within certain communities went on via close contacts during large gatherings such as those at the time of carnival festivities and at church services, and also as a result of specific economic activities (e.g. delivery services or workers in slaughterhouses). Numerous cases have also been reported in institutionalised accommodation such as nursing and refugee homes. As Figure 1 shows, the counties with the highest rates of infections were located in Bavaria. By the end of May one of the Bavarian counties (Tirschenreuth) had an infection rate far higher than any other county – 1,568 infections per 100,000 inhabitants, when this rate was 891 and 890 in the next highest scoring counties of Straubing and Wunsiedel. At the same time the counties of Uckermark and Prignitz (in the region of Brandenburg), Friesland and Wilhelmshaven (Niedersachsen), Ostholstein (Schleswig-Holstein) and Rostock (Mecklenburg-Vorpommern) recorded infections rates of below 35 per 100,000 inhabitants.

The origins of the first reported cases in Poland were also directly related to international travel – to Germany and Italy. Further local outbreaks were reported in hospitals and social welfare homes. The virus often spread between such institutions due to a transmission via medical and care personnel working in several institutions in parallel. Initially, only Warsaw and neighbouring counties stood out with regard to the infection rate, which could be due to higher mobility and population density in the first case, and local outbreaks in social welfare homes in the latter. However, about two months after the beginning of the pandemic, a major surge in new cases was recorded in the region of Silesia where the bulk of infections concentrated among mine workers. Often asymptomatic, infections were identified as a result of extensive screening of miners and their families. By the end of May, about one third of Poland’s total infections were found in Silesia alone. Together with the cases reported in the Mazovian region (with Warsaw as capital), these two regions represented about half of the total number of infections in Poland. The highest infection rate in Poland exceeding 500 infections per 100,000 inhabitants was observed in the counties of Silesia (Bytom, Jastrzębie-Zdrój and powiat lubliniecki), Mazovia (powiat białobrzeski) and Greater Poland voivodship (powiat kępiński), while a handful of counties located throughout Poland (powiaty: bartoszycki, bieszczadzki, drawski, gołdapski, kolski, lidzbarski, międzyrzecki, sejneński, żuromiński) have not recorded any infections.

Figure 2 provides another angle on the aftermath of the epidemic in both countries – regional case fatality rates, calculated as a ratio of deaths to recorded infections and presented at a higher level of aggregation – the level of Bundesländer in Germany and Voivodship in Poland (due to the lack of comparable data on county level in Poland). Even though, as mentioned above, the country average death rates are very similar, the within-country regional differences are striking. As compared to Poland, the regional death ratios in Germany do not deviate much from the country average (4.7), with the lowest rate in the region of Mecklenburg-Vorpommern (2.6) and the highest one in the region of Saarland (6.0). On the other hand, the differences between Polish regions are substantial, with no deaths per 120 infections in the lubuskie region and the fatality rate exceeding 9.0 in the podkarpackie region. At this early stage of the pandemic such differences might reflect a number of factors and may not be systematically related to specific risks. However, as we show below, the most clearly identified risk factors are far from evenly distributed both between and within the two countries, which in cases of broader outbreaks is likely to lead to significant systematic differentiation of risks at the regional level.

Figure 2. Covid-19 death rates by region (DE: Bundesländer, PL: Voivodeships) (as of 31 May 2020)

Source: own compilation based on data from Robert Koch Institute (RKI) and Federal Agency for Cartography and Geodesy (BKG) in case of Germany and data collected individually by Michał Rogalski (https://www.micalrg.pl/) from Voivodeship Offices, Voivodeship and Powiat Epidemiological-Sanitary Stations, media and materials sent on request and Head Office of Geodesy and Cartography (GUGiK) in case of Poland.
Note: Class 3, 4 and 5 cover 20% of the observations, the other classes 10% each.

3. Demographic and Epidemiological Variation at Regional Level in Germany and Poland

There are significant differences in the age structure of the population with a substantially higher proportion of individuals in older age groups in Germany. While 17.5% of the Polish population is over 65 years old and 2.1% is aged 85+, the corresponding proportions in Germany amount to 21.4% and 2.7%. These average differences, however, conceal significant within country variation in the demographic composition, which – as we argue – is very relevant against the background of the potential consequences of the Covid-19 pandemic.

In Figure 3 we present shares of people aged 65+ in the general population by county in 2018. The counties with highest proportions of older individuals in Germany are concentrated in the east of the country. The variation in the proportion of those aged 65+ ranges between 15.7% in Frankfurt am Main (region Hessen) and Freising (region Bavaria) and 31.5% in Suhl (region Thüringen). The ‘youngest’ of German counties resemble some of the oldest ones in Poland, where we find counties with the proportion of people aged 65+ as low as 11.2% or 12.1% (powiats kartuski and gdański, region Pomerania). Only in 15 counties in Poland (less than 4% of counties), the proportion of those aged 65+ exceeds 21% – which we find in about two thirds of counties in Germany. Similar differences are found regarding the proportion of those aged 85+ (not shown here), with a distinct concentration of the “oldest-old” in the eastern parts in both countries. However, while in Poland less than half of counties have a proportion of the 85+ population higher than 2%, this is the case in all but one county in Germany.

Figure 3. Share of people aged 65+ by county, 2018

Source: own compilation based on data from Eurostat and Federal Agency for Cartography and Geodesy (BKG) in case of Germany and Central Statistical Office (GUS) and Head Office of Geodesy and Cartography (GUGiK) in case of Poland.
Note: Class 3, 4 and 5 cover 20% of the observations, the other classes 10% each.

When we compare the regional variation in the number of Covid-19 infections with the population’s age structure, it seems that the pandemic in both countries has so far affected the ‘younger’ regions. The spread of the virus has been relatively slow both in the eastern part of Germany and in the east of Poland. Thus, there is a negative correlation between the within-country spread of Covid-19 and the proportion of older age groups at the county level. This might be due to a higher level of travel and economic activity in younger regions of the two countries which – at least in the initial phase – limited further spread of the virus to the parts with higher proportions of older individuals.

Apart from older age several pre-existing medical conditions have also been identified as risk factors for severe consequences of Covid-19. Figure 4 displays the ratio of deaths due to a selected group of diseases in the total number of deaths among people aged 65+ to proxy the incidence of these health conditions among the living population. The causes of death are coded according to the diagnostic criteria of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) compiled by the WHO. Deaths caused by external factors such as traffic accidents are excluded from the total of fatalities due to different reporting practise in Poland and Germany. Since no clear deviations in reporting deaths due to internal causes has been found, we assume this data is comparable between the two countries and we use deaths due to internal causes as a measure of total deaths in Figure 4. Causes that are especially relevant against the background of Covid-19 include deaths due to circulatory diseases, neoplasms and respiratory diseases (the level of data aggregation does not allow to single out deaths due to diabetes). In contrast to Figure 3, which showed much higher proportions of older people in Germany than in Poland, when it comes to health risks due to the specified conditions, the country picture is reversed. While the rate of deaths resulting from the selected conditions exceeds 90% of all deaths in the 65+ population in multiple counties across Poland (over 8% of all), it does not surpass 84% anywhere in Germany. Importantly, the regional distribution of death ratios in Germany due to the chosen conditions closely reflects the proportion of the older population and is concentrated in eastern parts of the country, in particular in the southern regions of the former East Germany. Epidemiological risks related to Covid-19 seem to be lower in the more prosperous regions in southern and western Germany, as well as in bigger cities such as Hamburg. In Poland there is no apparent relation between the selected health risks and the demographic structure of the regions. The highest proportion of deaths due to the selected conditions is found in the north-western regions and in the south-east, leaving central Poland with somewhat lower incidence rates of death due to these causes – at similar levels observed in many parts of Germany. Moreover, the within-country variation in the proportion of these deaths is much higher in Poland, where in sztumski county (Pomerania region) as many as 94.5% of deaths among 65+ can be attributed to the selected conditions, while in ełcki county (Warmia-Masuria region) this number was only 66.6%.

Figure 4. Share of deaths due to neoplasms, circulatory and respiratory diseases among people aged 65+ by county, 2016

Source: own compilation based on data from European Data Portal and Federal Agency for Cartography and Geodesy (BKG) in case of Germany and Central Statistical Office (GUS) and Head Office of Geodesy and Cartography (GUGiK) in case of Poland.
Note: Class 3, 4 and 5 cover 20% of the observations, the other classes 10% each.

4. Healthcare Resources at the Regional Level in Germany and Poland

The initial wave of the Covid-19 pandemic in several most affected countries resulted in a significant overburden of their healthcare capacities with a sudden wave of patients in need of in-hospital intensive care. While in some hospitals in Germany and Poland the first inflow of patients placed a heavy burden on the available resources, both healthcare systems have so far not been overwhelmed to the extent that was experienced in Italy, Spain, or some states of the USA. However, there are significant differences between the healthcare resources available in Germany and Poland and these differences might become apparent if the next waves of the pandemic result in much higher rates of infections. Health expenditure accounted for 11.3% of Germany’s gross domestic product (GDP) in 2017, with an expenditure of 4,459€ per inhabitant. The spending in Poland was much lower and amounted to 6.5% of the GDP and an expenditure of 731€ per inhabitant (Eurostat 2020a). The differences are not as high in the absolute values of traditional healthcare indicators such as the number of hospital beds per 1,000 people (601.5 in Germany and 485.1 in Poland; Eurostat 2020b) or the number of doctors per 100.000 inhabitants (424.9 in Germany and 237.8 in Poland; Eurostat 2020c), but they are still notable.

We show the regional distribution of hospital beds and practising doctors in Figures 5 and 6. As in the case of the demographic structure and epidemiological conditions, there are significant regional differences in the capacity of healthcare as measured by these indicators. In the latter case the data do not allow for a direct cross-country comparison as the data in Germany only covers medical doctors who provide health services to patients with social health insurance in outpatient clinics. In Poland the data is limited to the medical doctors working directly with patients conditional on their primary workplace / main employer in case of multiple assignments (excluded if private practice is reported as such). This means that the data at hand only covers a proportion of all medical doctors – in Germany it captures 37% of all those with an active medical license (according to the German Medical Association) and in Poland 60% of licensed doctors as reported by the Polish Supreme Medical Chamber. As this data is not directly comparable across countries, the proportions in Figure 6 are presented in shades of blue and green for Germany and Poland respectively. However, the key dimension of the data we present is the high within-country variation in the level of medical staff across regions.

In both countries there is an urban-rural divide of the healthcare capacities that is most pronounced in Poland and in the south-western regions of Germany. In Poland this originates partly from the task division at consecutive levels of local administration. Although county authorities are responsible for the broad network of hospitals, the major clinical hospitals are located in the biggest cities. The north-south difference that we observe in Germany is related to the fact that in northern regions many populated cities compose a county together with neighbouring municipalities, while in the southern and central parts they constitute an independent county. This brings out the contrast between cities and the localities around them, which is also noticeable in the case of Poland. For many areas this means that their inhabitants have to travel or be transported relatively long distances when in need for medical treatment, in particular in cases of specialised interventions. In 2016 there were four counties in Germany and as many as 24 counties in Poland with no hospitals.

Figure 5. Number of hospital beds per 1,000 inhabitants by county, 2016

Source: own compilation based on data from Federal Statistical Office and Statistical Offices of the Länder and Federal Agency for Cartography and Geodesy (BKG) in case of Germany and Central Statistical Office (GUS) and Head Office of Geodesy and Cartography (GUGiK) in case of Poland.
Note: Class 3, 4 and 5 cover 20% of the observations, the other classes 10% each.

The rural-urban divide is even more evident in Poland when we look at the number of medical doctors, as doctors are clustered in the biggest cities or counties with clinical hospitals (Figure 6). In 2018, three counties had 20 or less medical doctors per 100,000 inhabitants (powiat łomżyński in Podlaskie region, średzki in Lower Silesia and siedlecki in Mazovia), and in 30% of counties this number was below 100. Almost 10% of counties (all big cities and regional capitals) had at the same time 400 or more doctors per 100,000 inhabitants, two counties in South-East Poland – Lublin (Lubelskie region) and Rzeszów (Podkarpackie region) reported over 770 doctors. Thus, the striking feature of several regions in Poland is that besides a strong medical centre, there is a high number of municipalities around them with very low number of doctors. This is the case for example in Olsztyn in the north-east of Poland (region Warmia-Masuria) or Poznań in the west (Greater Poland region).

Since for Germany we only considered doctors working in outpatient clinics and excluded doctors working solely in hospitals and thus concentrated in major regional cities, the medical workforce seems spread out more equally (Figure 6) compared to the availability of hospital beds (Figure 5). However, in particular since in Germany the data covers a much lower proportion of medical doctors compared to Poland, even in the German counties with lowest statistics, the numbers of doctors are still much higher than in many rural areas throughout Poland.

Figure 6. Number of doctors per 100,000 inhabitants by county, 2018

A) in Germany: doctors working in outpatient clinics B) in Poland: doctors working directly with patients in primary workplace

Source: own compilation based on data from Federal Medical Registry (KBV) and Federal Agency for Cartography and Geodesy (BKG) in case of Germany and Central Statistical Office (GUS) and Head Office of Geodesy and Cartography (GUGiK) in case of Poland.
Note: Class 3, 4 and 5 cover 20% of the observations, the other classes 10% each.

Conclusion

The early evidence suggests that people over the age of 65 and those with pre-existing health conditions such as cardiovascular conditions, diabetes, hypertension, chronic pulmonary disease and cancer are at the highest risk of severe consequences of Covid-19. A well-equipped healthcare system is required to respond appropriately to increases in demand for healthcare in order to safeguard the population against the worst outcomes of the disease in potential future waves of the pandemic. This regards the issue of preventing Covid-19 related fatalities, but it also refers to the continued need to provide other general types of healthcare which are constantly required alongside the cases directly related to the pandemic.

Such a combination of health risks related to demographic, epidemiological and systemic factors results in potentially high regional variation of the scale of consequences of the spread of the Covid-19 pandemic. Using the example of Germany and Poland, two neighbouring countries which have generally dealt relatively well with the outbreak of Covid-19 in recent months, this policy paper shows that there is significant regional variation both in the distribution of health risks and healthcare resources. These regional inequalities should be considered regarding the consequences of future outbreaks of the virus. The regional analysis of the first wave of the pandemic – with data until 31 May 2020 – suggests that in both countries the virus spread mainly in ‘younger’ regions (with low proportions of people aged 65+) with lower incidence of the relevant comorbidities. At the same time the number of cases in the two countries was low enough so that both the German and the Polish healthcare systems, notwithstanding the differences between them, were not overwhelmed by the inflow of Covid-19 patients.

Such a situation is by and large not guaranteed in the case of future waves of the pandemic. The virus is likely to spread beyond the best connected and most mobile regional populations, which has been the case so far in Germany and Poland. With respect to the demographic structure of the population, the places most at risk for severe health consequences due to Covid-19 are the counties of the former East Germany and those in the east of Poland, where we observe an outstandingly large proportion of people aged 65+. Similarly – looking at the incidence of relevant comorbidities, the northern and southern counties clearly stand out in Poland, and in this respect the health of the German 65+ population presents a much lower risk compared to the health status of the Polish counterparts.

How these two critical risk factors translate into health outcomes in future waves of Covid-19 depends on the readiness of the local healthcare system to provide support to patients requiring in-hospital and intensive care. Using regional data on the number of beds and medical doctors we have shown that in both countries there is a significant variation in healthcare resources. This variation is particularly visible in Poland with a substantial urban-rural divide and high concentration of healthcare resources and staff in larger cities. A rapid spread of the disease in future months could be devastating in Polish rural areas with poor medical infrastructure and high proportions of the population at risk.

The differences between and within the countries regarding the healthcare infrastructure lead to two crucial conclusions with regard to the potential consequences of future waves of Covid-19. First of all, it is clear that the German healthcare system – with the better hospital infrastructure and higher number of doctors, is overall better prepared to face a surge in Covid-19 cases. Secondly, there is a much higher proportion of counties in Germany with high level of medical resources and few localities standing out with much lower levels of hospital capacity or doctors compared to those with the highest values. This is not the case in Poland where the majority of counties have very low capacities of both hospital beds and doctors. While such inequalities in medical resources may be of less concern in ‘normal times’ when individuals from areas with poorer infrastructure might find a place in their nearest relevant hospital, in the case of a sudden increase in demand for hospitalisations such local medical centres might rapidly become overwhelmed. Additionally, moving patients to distant hospitals would place significant additional demand on medical transportation. In cases of rapid increases in the numbers of infected people problems are also likely to occur at the level of the basic diagnosis before the patients are classified for hospitalisation.

As shown in this policy paper the variance in the demographic structure of the population as well as in the main causes of death at older ages between Germany and Poland and within each of the two countries is substantial. In many regions these underlying demographic and epidemiological factors overlap with relatively low general capacities of the healthcare system to deal with a sudden surge of hospitalisations (Kandel et al. 2020). Thus, the analysis presented in this policy paper points towards the need for a disaggregated regional level risk-management approach to future waves of the Covid-19 pandemic. Highly differentiated demographic and epidemiological risks related to the pandemic between as well as within Germany and Poland call for a decentralised evaluation of risks and point out the need to consider an application of regionally focused policy reactions such as lockdowns and social distancing regulations. If risks and the ability to respond to them vary significantly at the regional level, policies should consider and account for such variation to prepare for potential next outbreaks later this year or next year.

Acknowledgement

The authors wish to acknowledge the support of the German Science Foundation (DFG, project no: BR 38.6816-1) and the Polish National Science Centre (NCN, project no: 2018/31/G/HS4/01511) in the Beethoven Classic 3 funding scheme. We are grateful to Vera Birgel for research assistance.

References

  • Comas-Herrera, A., Zalakaín, J., Litwin, C., Hsu, A.T., Lane, N., Fernández, J.-L. (2020) Mortality associated with COVID19 outbreaks in care homes: early international evidence. LTCcovid.org, CPEC-LSE. https://ltccovid.org/wp-content/uploads/2020/05/Mortality-associated-with-COVID-21-May-6.pdf
  • ECDC – European Centre for Disease Prevention and Control (2020) Disease background of COVID-19. https://www.ecdc.europa.eu/en/2019-ncov-background-disease
  • Eurostat (2020a): Healthcare expenditure statistics. https://ec.europa.eu/eurostat/statistics-explained/index.php/Healthcare_expenditure_statistics
  • Eurostat (2020b): Healthcare resource statistics – beds. https://ec.europa.eu/eurostat/statistics-explained/index.php/Healthcare_resource_statistics_-_beds
  • Eurostat (2020c): Health care personnel statistics – physicians. https://ec.europa.eu/eurostat/statistics-explained/index.php/Healthcare_personnel_statistics_-_physicians#Healthcare_personnel
  • Emami, A., Javanmardi, F., Pirbonyeh, N., Akbari, A. (2020) Prevalence of underlying diseases in hospitalized patients with COVID-19: a systematic review and meta-analysis. Arch Acad Emerg Med, 8, e35. https://www.ncbi.nlm.nih.gov/pubmed/32232218
  • Gardner, W., States, D., Bagley, N. (2020) The Coronavirus and the Risks to the Elderly in Long-Term Care. J Aging Soc Policy, 1‐6. https://pubmed.ncbi.nlm.nih.gov/32245346/
  • Ghandi, M., Yokoe, D. S., Havlir, D. V. (2020) Asymptomatic transmission – the achilles’ heel of current strategies to control Covid-19. N Engl J Med, 382, 2158-2160. https://www.nejm.org/doi/full/10.1056/NEJMe2009758
  • Kandel, N., Chungong, S., Omaar, A., Xing, J. (2020) Health security capacities in the context of COVID-19 outbreak: an analysis of International Health Regulations annual report data from 182 countries. Lancet, 395, 1047-1053. https://www.sciencedirect.com/science/article/pii/S0140673620305535
  • Manski, C. F., Molinari, F. (2020) Estimating the COVID-19 infection rate: Anatomy of an inference problem. JoE (Online first). https://www.sciencedirect.com/science/article/pii/S0304407620301676
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  • Moore, K. A., Lipsitch, M., Barry, J. M., Osterholm, M. T. (2020) COVID-19: The CIDRAP viewpoint. Part 1: The future of the COVID-10 pandemic: Lessons learned from pandemic influenza. https://www.cidrap.umn.edu/sites/default/files/public/downloads/cidrap-covid19-viewpoint-part1_0.pdf
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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

Posted on June 8, 2020 | Policy Brief

Tall buildings in Moscow city representing central banks and financial markets

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

Posted on June 4, 2020 | Policy Brief

Image with a building and European Union flags in front of the building representing EU Ukraine DCFTA trade deal

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

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.