Tag: COVID19

Economic Perspectives on Domestic Violence | Insights from the FROGEE Webinar | Part 2

Posted on November 23, 2020 | Policy Brief

A view of a window with broken glass representing representing perspectives of domestic violence

This policy brief is the second in a series of two briefs summarizing the research presented at the online workshop “Economic Perspectives on Domestic Violence”, organized as part of the Forum for Research on Gender Economics (FROGEE). The current brief offers an overview of the presentations that specifically studied the implications of the Covid-19 crisis for domestic violence. The remaining research  presented at the workshop is addressed in the first policy brief of this series.

Introduction

As governments around the globe are continuing to enforce contagion management strategies to limit the spread of COVID-19, many experts are voicing their concerns about a different kind of pandemic.  Alarming reports have surfaced from a wide range of countries suggesting significant increases in domestic violence (DV), including one of its most prevalent forms – intimate partner violence (IPV).

In Europe, the number of IPV emergency calls has increased by 60%, according to the UN’s regional director of Europe (WHO, May 07, 2020). In the Hubei province of China, a police department reported three times as many DV cases in February 2020 compared to the same month in 2019 (Axios, March 2020). In El Salvador, 95% of local and government DV support services closed due to the pandemic, while reports show that the demand for such services among women increased by 70% (IRC, 2020). Reduced social interaction and mobility, high rates of unemployment, and restricted access to support services are just some indirect consequences of the pandemic that are likely to exacerbate DV.

At the same time, data from other countries have suggested the opposite trends. In the Italian region of Lombardy, the number of women requesting support services decreased, although the region was one of the most severely hit by the pandemic (Giussy et.al., 2020). While DV hotlines in the US anticipated increases in calls for support, some regions experienced a 50% decline (The Guardian, April 2020). Many have stressed that these trends have a much darker side – underreporting. Measures aimed at limiting the spread of COVID-19, as well as the fear of getting infected, force victims to stay at home in direct contact with their abusive partner, limiting their ability to report on the violence, and restricting access to support services such as women’s shelters.

As much as pandemic-related trends in DV have heightened the concerns about the well-being of victims and increased the need for sufficient and adequate policies, the unique settings created by the pandemic have offered new opportunities for researchers to better understand the underlying causes of DV.

This policy brief is the second in a series of two briefs summarizing the papers 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).

Domestic Violence and COVID-19

While studying different research settings, all the papers summarized in this brief examine the relationship between COVID-19 and DV. Most of them are focused on the effects of lockdown measures and highlight the need of combining measurements of DV in order to get an encompassing picture of the phenomenon.

Damian Clarke presented evidence on the DV-implications of quarantine in Chile. To rule out the possibility that an increase in DV was caused by other factors brought about by the pandemic, Clarke and co-authors take advantage of Chile’s rolling quarantines (i.e., regional quarantines implemented at different points in time) and compare municipalities that imposed lockdowns with those that did not.  At the start of the pandemic in March, the nation-wide number of calls to domestic violence hotlines increased by 250%, and by 350% for municipalities that imposed quarantines. Police reporting on DV decreased by 11% nation-wide, and by around 27% in quarantined areas. The sharp increase in distress calls may have several explanations. It could be due to an increase in instances of DV and/or increased anxiety, or reduced tolerance. Moreover, the decline in DV reporting to the police may be explained by limited access to DV support services during quarantine, or to the fact that the victim’s opportunity to report is constrained by the abuser’s presence at home.  The authors are exploring these channels in current work, including the implementation of a nationally representative survey, aiming to identify key determinants of observed patterns, as well as how they may evolve with the removal of quarantines.

Melissa Spencer offered an analysis of the pandemic’s impact on domestic abuse in Los Angeles, US. Spencer and co-authors investigate the immediate effect of the pandemic by using data on DV incidents and arrests, DV calls for service, and hotline calls. During the initial lockdown in March, they find significant effects on both crimes and calls, but in opposite direction: calls for service and hotline calls increased while DV crime and arrests for those crimes declined. During the re-opening period at the end of May, both DV crimes and arrests, calls for service and hotline calls decreased.

Ria Ivandic presented findings from a study on the pandemic´s effect on DV in the Greater London area. Using data on DV calls for service and DV crime/incidents the study shows that, for service calls, there was a 35% increase in third-party reporting in densely populated areas, whereas in low-density areas there was only a 15% increase. This effect was particularly strong in areas of high deprivation and suggests substantial under-reporting in households where abuse cannot be reported by an outsider. As for DV crimes, the study finds an average increase of 4.5% during the lockdown and a significant shift in abuse composition: current partner abuse crimes increased by 8.5%, DV by family members rose by 16.4%, while ex-partner crimes decreased by about 9.4%.

Much like England, the US, or Chile, most countries around the world adopted some kind of lockdown policy to mitigate the spread of COVID-19, but how would the pandemic affect DV in the absence of lockdown, if at all? Maria Perrotta Berlin presented her findings on the case of Sweden, a country that has had a significantly softer policy response to the pandemic. By utilizing data on DV-crime and mobility, the preliminary results show that the pandemic reduced individuals’ mobility, even in the absence of a formal lockdown. Further, Berlin finds that an increased presence in residential areas is associated with a significant increase in non-battery crimes committed by an intimate partner, whereas a reduction in mobility in retail and recreation areas is associated with an increase in other crimes.  A more detailed summary of this research is presented in a recent FREE policy brief.

The workshop has offered insights into a problem that has been in urgent need of effective policies for a long time, and that has attracted renewed attention during the pandemic. Not surprisingly, it has created a large interest among the participants. FROGEE and SITE would like to thank the speakers for their contributions to the workshop and SIDA for their generous funding.

References

  • Allen-Ebrahimian, Bethany. “China’s Coronavirus Quarantines Raise Domestic Violence Fears.” Axios, 7 Mar. 2020, www.axios.com/china-domestic-violence-coronavirus-quarantine-7b00c3ba-35bc-4d16-afdd-b76ecfb28882.html.
  • Giussy, Barbara, et al. “Covid-19, lockdown, and intimate partner violence: some data from an Italian service and suggestions for future approaches.” Journal of Women’s Health (2020).
  • Graham-Harrison, Emma, et al. “Lockdowns around the World Bring Rise in Domestic Violence.” The Guardian, Guardian News and Media, 28 Mar. 2020, www.theguardian.com/society/2020/mar/28/lockdowns-world-rise-domestic-violence.
  • International Rescue Service, 2020. The Essentials for Responding to Violence Against Women and Girls During and  After COVID-19.
  • World Health Organization, Europe, 2020. WHO Warns Of Surge Of Domestic Violence As COVID-19 Cases Decrease In Europe.

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.

Food Security in Times of Pandemic in Georgia

Posted on November 16, 2020 | Policy Brief

An image of the wheat field with with grain harvester representing food security

The lockdowns and trade restrictions related to the COVID-19 pandemic resulted in shortages of some major food commodities on international and local markets. In this policy brief, we discuss and analyze Georgia’s response to the crisis in terms of food security and agricultural policy. Furthermore, we provide recommendations to ensure fewer disruptions in food supply chains and low volatility in food prices.

Background

COVID-19 has posed significant risks to the food security of many countries including Georgia. Lockdowns and pandemic-related trade restrictions across the world have resulted in shortages of some major food commodities on international and local markets (e.g. sunflower oil shortage in Russia). As of October 16, 2020, according to a World Bank report, 62 jurisdictions have executed a total of 62 export controls in food commodities since the beginning of 2020 (Table 1).

Table 1. Total number of new export controls and import reforms in the food sector globally since January 2020, by month.

Source: World Bank Group, Global Alert Team, 2020

Most of the interventions have involved import reforms with the largest number of new regulations imposed in March-April.  On August 18, 2020, the Eurasian Economic Commission announced an EAEU import tariff quota on certain agricultural goods, valid for 2021. Turkey has also conducted a price stabilization policy by announcing purchasing prices for apricots, paddy, and dried raisin. On August 5, 2020, the government of Turkey introduced additional customs duties on certain agricultural products including chocolate, pasta, and some food preparations. It also eliminated import duties on wheat and barley in October.

Given that Georgia is a net importer of food, and in light of the trade restrictions imposed by its major trade partners, food security moved up on Georgia’s agricultural policy agenda. In order to weaken the adverse impact of the pandemic, keep food prices stable, and reduce input prices for farmers, the state designed the following set of measures:

  • 10M Georgian lari (GEL) from the Ministry of Environmental Protection and Agriculture (MEPA) budget were allocated to subsidize imports of 9 food products: pasta, buckwheat, vegetable oil, sugar, wheat, wheat flour, milk powder, and beans (Legislative Herald of Georgia, 2020). The program subsidized importers’ additional costs resulting from exchange rate fluctuations and was implemented between March 15-May 15;
  • Additional 16M GEL were allocated for purchasing sugar (5,000 tons), vegetable oil (1,500 thousand liters), and pasta (500 tons) stocks from private companies;
  • An anti-crisis plan, “Caring for Farmers and Agriculture”, was presented by the state on March 12. The plan entailed two forms of aid: direct assistance to farmers and sectoral support. Some of the support measures included the distribution of so-called “agricultural cards”– subsidies for cattle-breeding and land cultivation services for smallholder farmers (registered farms with plots in the range of 0.25-10 ha); provision of cheap diesel fuel for farmers; nullification of costs of land reclamation services; provision of agricultural loans and insurance; grants for machinery, equipment, and cooperatives.

Results of Government Interventions

As of October 9, 2020, state support schemes had the following results:

  • Up to 165,000 farmers had been granted agricultural cards. The size of the subsidy exceeded 28.9M GEL;
  • Under the agro-diesel program (which subsidized fuel prices for agro-producers) 122,000 beneficiaries received discount cards on 32,000 tons of agro-diesel;
  • More than 17,000 policies had been issued and 18,000 hectares (around 2% of agricultural land) had been insured under the agro-insurance program. The value of the insured crop exceeded 160M GEL;
  • Across different regions of Georgia, 255 applications for modernization of the dairy sector were approved. In total, 12.4M GEL were spent on this program;
  • 2,215 agro-loans had been issued with a 6-month interest rate covered by the state. The total amount of loans exceeded 40M GEL, including the co-financing of interest rates, which exceeded 3.3M GEL.

While many farmers have benefited from state support programs, these programs were not directly focused on the main consequences of the pandemic. The major threats posed by the pandemic – disruptions in food supply chains leading to decreased sales of agricultural products and price volatility – were not sufficiently addressed by the state support programs. According to the Georgian Farmers’ Association (GFA), 55% of surveyed farmers and agricultural business representatives encountered complications with product realization due to pandemic-related restrictions. Most farmers depend on the HoReCa (hotels, restaurants, and cafés) and hospitality sector, and their products are largely procured for accommodation and food facilities. 60% of those surveyed claimed that they were simply unable to sell their products due to the closure of hotels, restaurants, and cafés.

Food Price Dynamics

During March-May 2020 – the first months of the pandemic – food prices in Georgia showed upward trends on both a month-on-month and year-on-year basis (Figure 1).

Figure 1. Month-on-month and year-on-year changes in food prices

Source: GeoStat, 2020

The main explanation is likely the depreciation of the GEL against the US dollar: during March-May 2020, the GEL depreciated against the USD by 15.8% from 2.71 to 3.14 compared to March-May 2019 (National Bank of Georgia, 2020). As Georgia is a net importer of food commodities, the depreciation of the GEL put upward pressure on food prices. To limit the GEL depreciation and its impact on food prices, the Government of Georgia subsidized additional costs of importers of major food commodities arising from exchange rate fluctuations. The price restraint mechanism involved negotiating with food importers to not increase prices of their commodities and setting the exchange rate of the GEL against the USD at 3, while the Government of Georgia subsidized the corresponding difference between the actual and fixed exchange rates. Despite minimizing the effects of GEL depreciation, food prices in Georgia experienced a significant increase during the observed period: disruptions in supply chains associated with the COVID-19 pandemic led to food shortages that further increased food prices.

In April, annual food price inflation marked its highest level at 16.1% during March-August 2020.  Since then, annual food price inflation has been decreasing as farming activities resumed after COVID-19-related restrictions were relaxed and seasonal (locally produced) agricultural products appeared on the market. Accordingly, food prices started to decrease on a monthly basis.

However, with very few exceptions, prices for major food commodities that were subsidized by the state during March-May increased for both month-over-month and year-on-year comparison (Table 2). On a monthly basis, the biggest price changes were observed for sugar; while on annual basis prices for buckwheat increased the most.

Table 2. Year-on-year changes in prices of major food commodities, March-September 2020

Source: GeoStat, 2020

While food prices could have increased even more in the absence of subsidies, it appears that the state measures did not fully reach their objectives and could not fully overshadow the adverse impact of the pandemic and GEL depreciation.

Recommendations

The pandemic has shown the need for increasing the level of food security in Georgia. Given the multidimensional nature of food security, a longer-term policy should consider not only an increase in domestic production of key food commodities but also a diversification of import markets to ensure low volatility in food supply and prices. As an immediate response to the pandemic, it is recommended to:

  • further subsidize farm inputs in order to reduce the current costs of production;
  • support farmers in selling their produce;
  • develop state programs that strengthen local producers;
  • focus on diversification of import markets for food commodities which constitute a high share of households’ consumption basket.

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.

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.

COVID19 | FREE Network Project

Posted on June 26, 2020 | Policy Brief

An image with COVID19 virus visualisation

The Covid-19 pandemic is affecting all the inhabited continents of this planet and leaves none of us untouched. It has already killed thousands of people across the globe, closed down cities, borders and businesses and most countries are still just in the initial phase of this crisis. Although there is 24/7 reporting on the pandemic, much of the focus in international media has been on the most affected countries and richer countries in Eastern Asia, the EU and the US. Much less attention has been given to countries around the Baltics, in Eastern Europe and the Caucasus.

However, these countries are home to more than 200 million people and to the institutes that form the Forum for Research on Eastern Europe and Emerging Economies, i.e. the FREE network. We have therefore started to collect data on this region from official sources with the ambition to offer a regularly updated, comprehensive and easily comparable overview of the health impact of the Covid-19 pandemics, as well as the policies and practices countries in the region adopt to deal with it.

The countries in the network and the region we include are Belarus, Georgia, Latvia, Poland, Russia, Sweden, and Ukraine. For comparison, we also include Italy as a point of comparison since it is a country that has been particularly badly affected and we have several people in our faculties that know Italian and follow these developments closely. In addition to FREE Network countries in our reporting, we partially cover Armenia, Estonia, Lithuania, Moldova and Germany due to close links with economists and researchers specialised in these countries, therefore extending our covered region. 

The quality of the health data will by necessity vary between countries and this also affects the comparability of numbers. For example, the ability and willingness to test the population for the virus differs significantly between countries and will obviously affect the number of infections that is reported to the European Centre for Disease Prevention and Control (ECDC), the main source of data on health outcomes in our tables and graphs. Other data that we report, such as border or school closures, are easier to compare, but there may still be differences in how these policies are implemented on the national level. However, we still believe that it is useful to compile this data for our region in one place as a starting point for discussions on how the virus is spreading and governments respond to the crisis.

Since the FREE Network focuses on economic issues, we put particular emphasis on high-frequency indicators in this area and on measures governments have taken to deal with the economic consequences of the pandemic. In the initial phase of this collaborative project, the focus will be on providing a descriptive picture of the state of the situation using the best data we can find, but over time, this will be complemented by more in-depth policy analysis of the measures and implications for the economies in the region.

Country Reports

The main data is presented in a summary page that facilitates comparisons between countries, and this is complemented with more detailed country reports.

Belarus country report
Georgia country report
Italy country report
Latvia country report
Poland country report
Sweden country report 


Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes. 

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

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
  • McMichael, T., Currie, D., Clark, S., Pogosjans, S., Kay, M., Schwartz, N., Lewis, J., Baer, A., Kawakami, V., Lukoff,  M., Ferro, J., Brostrom-Smith, C., Rea,  T., Sayre,  M., Riedo, F., Russell, D., Hiatt, B., Montgomery, P., Rao, A., Chow, E., Tobolowsky, F., Hughes, M., Bardossy, A., Oakley, L., Jacobs, J., Stone, N., Reddy, S., Jernigan, J., Honein, M., Clark, T., Duchin J. (2020) Epidemiology of Covid-19 in a long-term care facility in king county, Washington. N Engl J Med.  https://www.ncbi.nlm.nih.gov/pubmed/32220208
  • Moore, K. A., Lipsitch, M., Barry, J. M., Osterholm, M. T. (2020) COVID-19: The CIDRAP viewpoint. Part 1: The future of the COVID-10 pandemic: Lessons learned from pandemic influenza. https://www.cidrap.umn.edu/sites/default/files/public/downloads/cidrap-covid19-viewpoint-part1_0.pdf
  • Myck M., Oczkowska M., Trzciński K. (2020) Safety of older people during the Covid-19 pandemic: Co-residence of people aged 65+ in poland compared to other European countries. FREE Policy Paper.  https://freepolicybriefs.org/2020/05/18/safety-older-people-covid-19/
  • Oke, J., Heneghan, C. (2020) Global Covid-19 case fatality rates. https://www.cebm.net/covid-19/global-covid-19-case-fatality-rates/
  • Pasquariello, P., Stranges, S. (2020) Excess mortality from COVID-19: Lessons learned from the italian experience. Preprints. https://www.preprints.org/manuscript/202004.0065/v1
  • Remuzzi, A. & Remuzzi, G. (2020) COVID-19 and Italy: what next? Lancet, 395, 1225-1228. https://www.thelancet.com/article/S0140-6736(20)30627-9/fulltext
  • Roser, M., Ritchie, H., Ortiz-Ospina, E., Hasell, J. (2020) Mortality risk of COVID-19. https://ourworldindata.org/mortality-risk-covid#case-fatality-rate-of-covid-19-by-age
  • Verelst, F, Kuylen, E & Beutels, P (2020) Indications for healthcare surge capacity in European countries facing an exponential increase in coronavirus disease (COVID-19) cases, March 2020. Euro Surveill, 25. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7140594/pdf/eurosurv-25-13-3.pdf
  • Roser, M., Ritchie, H., Ortiz-Ospina, E., Hasell, J. (2020) Mortality risk of COVID-19. https://ourworldindata.org/mortality-risk-covid#case-fatality-rate-of-covid-19-by-age
  • WHO (2020a) “Coronavirus disease 2019 (COVID-19)”. Situation Report – 46.
  • WHO (2020b) Report of the WHO-China joint mission on coronavirus disease 2019 (COVID-19). https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Whistleblowers During the Covid-19 Pandemic

Posted on June 15, 2020 | Policy Brief

Image with people standing in a building representing whistleblowers During the Covid-19 Pandemic

Numerous stories have emerged about whistleblowers being silenced and retaliated against during the Covid-19 pandemic. In this policy brief we consider some cases of retaliation against whistleblowers and cases illustrating the significance of the information they bring forward. Two facts about Covid-19 and whistleblowers become salient. First, it is hard to externally monitor behavior within care homes due to the risk of contagion (auditor-patient/patient-auditor). Second, it is hard to infer from outcomes (e.g. number of deaths) that management misbehaved, due to the high uncertainty and the many possible factors involved in the spread of Covid-19. Adequate whistleblower protections and confidential reporting channels are therefore essential to ensure transparency, compliance with safety rules, and more generally public accountability in the management of this crisis.

Whistleblowers are Silenced and Suffer Retaliation

The Covid-19 crisis has created pressure on governments, hospitals and secondary health institutions – in particular elderly care homes – to control the narrative on the spread of the virus and their response to it. As a result, we have already seen several whistleblowers being silenced and retaliated against.

The most (in)famous case is probably that of Li Wenliang, the Chinese doctor at Wuhan Central Hospital who warned his colleagues about a new SARS-like virus to on the 30th of December 2019. Four days later he was summoned to the Public Security Bureau where he was ordered to sign a letter in which he was accused of “making false comments” and “severely disturbed the social order”. Another seven persons were also arrested on suspicion of “spreading rumors”.

China is perhaps not the first country that comes to mind when considering adequate whistleblower protections, but the problem is a broad one.

In the US, several doctors and nurses have been fired and disciplined for expressing worries about their work conditions, also in relation to a lack of personal protective equipment (PPE). Nor is the issue of retaliation against whistleblowers localized to healthcare. The Vice President of Amazon’s cloud computing arm, Tim Brady, quit his job “in dismay at Amazon firing whistleblowers who were making noise about warehouse employees frightened of Covid-19”. Nine US senators also sent Amazon a request to explain its policy for firing workers after several employees who had expressed concerns over working conditions were laid off.

In Russia three doctors, two of which had protested their working conditions during the crisis suspiciously fell out of hospital windows, allegedly due to excessive work pressure. Two of the doctors died, and one doctor was threatened with criminal charges for spreading “fake news” about Covid-19. We do not know whether these cases were accidents, suicides, or retaliation for speaking up, but an investigation is currently ongoing.

The problem has been particularly severe in residential elderly care homes, where in many countries there have been extreme rates of contagion and deaths.

In Italy, complaints from caring personnel about lack of PPE and safety procedures in terms of restrictions in visits of relatives from ‘red zones’ and transfer of personnel and patients across departments, emerged already in the end of February. At the private elderly care home Trivulzio in Milan, caretakers claim that their early complaints were ignored by management, who allegedly also harassed those wearing face masks on the ground that they would scare guests. An investigation is currently ongoing, but if the allegations turn out to be true, numerous deaths in Lombardy could potentially be attributed to this negligence and could have been avoided.

In the UK, a country that had much more time than others to prepare for the arrival of the virus, a recent report by the whistleblower hotline Compassion in Care registers a dramatic increase in calls to their hotline: over 170 since the Covid-19 outbreak, while they normally receive no more than 30 cases per month. These whistleblowers at residential care homes, care agencies, and nursing homes continue to detail a widespread lack of protective equipment, and retaliation for raising these concerns. Five lost their jobs and are considering taking legal action.

In some countries there is instead a noticeable absence of whistleblowers at nursing homes and the like. Germany is one example. While this can be due to the country’s fast and apparently adequate response to Covid-19, the country also has an infamous history of mistreating whistleblowers, and the country´s protections are some of the weakest in the EU, which may have deterred potential whistleblower from reporting. A well-known example related exactly to nursing homes is the case of Heinisch vs. Germany, where a nurse was fired for reporting improper working conditions in 2005, and then lost her case for reinstatement at all levels of German labor courts, even though it was recognized that her claims were correct. She had to turn to the European Court of Human Rights to be vindicated, only after six years of legal hassle, though.

These are just some examples of the systemic issue of silencing and retaliation that is now emerging. Watchdog organizations are warning about a widespread and extensive mistreatment of whistleblowers worldwide during this pandemic. The Government Accountability Project details several cases of maltreatment of whistleblowers, describing the situation created by the Covid-19 crisis as “the largest attack on whistleblowers in the world”.

Other cases of whistleblowing, absent retaliation, further illustrate the crucial value of the information they bring forward. In Sweden for example, a country that should have been particularly careful given its softer approach to contain the virus, whistleblowers still reported a lack of PPE and poor safety routines in elderly care institutions. At one home, employees detailed how they went from caring for Covid-19 patients to caring for non-Covid patients while wearing inadequate safety protections. At that same home, it is estimated that more than 35 persons died from Covid-19: over a third of all residents.

Fighting Misinformation and Uncovering Wrongdoing

Protecting whistleblowers is also crucial to fight misinformation and fraud related to Covid-19. For example, a whistleblower recently alleged that the founder of JetBlue, who previously had argued against lockdowns, helped fund an influential yet controversial study which found that the infection rate in Santa Clara County, California, was 85 times higher than believed – which would have driven down the local fatality rate to flu levels at 0.12% – 0.2%. The whistleblower complaint also contained emails suggesting the authors of the study disregarded warnings raised by two other Stanford professors who attempted to verify the accuracy of the antibody tests used in the study.

Worries about abuse and fraud related to stimulus packages linked to Covid-19 have also been mounting. And indeed, the US Securities and Exchange Commission has seen a 35% increase in whistleblower claims received between mid-March and mid-May compared to the previous year.

There is already strong public support for whistleblower protections with respect to important matters like healthcare and elderly care (Butler et al., 2019), and as we have argued elsewhere (Nyreröd and Spagnolo, 2020a, 2020b), whistleblowers are currently not adequately protected or incentivized in the EU: they do not speak up to the degree desirable from a law enforcement/public interest point of view. The negative consequences of speaking up are often significant: blacklisting from the industry, harassment, and social and economic uncertainty are frequently associated with whistleblowing. This is not different with Covid-19 whistleblowers.

What Can Be Done

The state of whistleblower protection in Europe has been rather poor and uneven (Wolfe et al., 2014). In 2013, Transparency International rated a disappointing four countries in Europe as having “advanced” legal protection for whistleblowers. In recent years, several countries have enacted legislation to remedy the issue. France enacted Sapin II in 2017, which prohibits retaliation against whistleblowers; Sweden improved its protection in 2016 (Proposition 2015/16:128); and since November 2017, whistleblower protection in Italy, which was previously limited to the public sector, has been extended to the private sector.

It is only now, however, with the new EU Directive on Whistleblowing that we will see even protection levels for whistleblowers throughout the EU. Among other things the Directive would require firms with more than 50 employees to establish confidential internal whistleblower channels. The deadline for transposing the directive (implementing it into national law) is December 17, 2021.

EU member states should try to transpose the directive as soon as possible, as whistleblower protections are not only needed at nursing homes, but also at firms who may choose to put employees at excessive risk of infection when faced with high cost of compliance with safety measures. This is important, because monitoring compliance with safety measures externally will likely be difficult and costly, while the new directive contains several articles that would improve the informational flow within organizations but also externally to supervisory agencies.

To conclude, the Covid-19 crisis has created pressure to silence whistleblowers to control reputational risks for governments and private firms. If whistleblowers are successfully silenced, we risk ending up with an incomplete picture of the spread of the virus, a lack of public accountability, unnecessary deaths, and several good faith whistleblowers being retaliated against without adequate protection. Hastening the implementation of the new Whistleblower Directive is one way to ensure some level of protection for whistleblowers throughout the EU.

References

Nyreröd, T; and G Spagnolo, 2020a. “Myths and Numbers on Whistleblower Reward”, Regulation and Governance, forthcoming.

Nyreröd, T; and G Spagnolo, 2020b. “Financial Incentives for Whistleblowers: A Short Survey”, forthcoming in Cambridge Handbook of Compliance. Sokol, D., van Rooij, B. (Eds). Cambridge University Press.

Butler, J; D Serra; G Spagnolo, 2019. ”Motivating Whistleblowers”, Management Science, 66(2), 605-621.

Wolfe, S; M Worth; S Dreyfu; A Brown, 2014. “Whistleblower Protection Laws in G20 Countries, Priorities for Action.” Blueprint for Free Speech, The University of Melbourne, Griffith University, Transparency International Australia.

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Addressing the Covid-19 Pandemic: Policy Responses Across Eastern Europe

Posted on June 1, 2020 | Policy Brief

20200601 Addressing the Covid-19 Pandemic FREE. Network Image 01

The world holds its breath as Covid-19 continues to spread and challenge local health care systems as well as local economies. The focus of international media has mostly been on China and then Western Europe and the US. However, countries around the Baltic Sea, Eastern Europe and the Caucasus differ from the West with respect to their socio-economic development, trade integration, and political systems. The webinar “Addressing the Covid-19 Pandemic in Eastern Europe: Policy Responses Across Eastern Europe” hosted by the the Forum for Research on Eastern Europe and Emerging Economies (FREE) Network on May 28, 2020 aimed to fill this gap in the current discourse and give voice to experts from Latvia, Russia, Georgia, Belarus, Poland, Ukraine as well as Sweden, in order to contextualize their countries’ policy choices and experiences in the crisis. Policy recommendations can only be of preliminary nature at this point of time. Yet, it becomes clear that even though transition countries have fared relatively well during the health crisis, they will not be spared from the ensuing economic crisis and will require policy tools which are adapted to the local context.

Introduction

Less than six months after the outbreak of the Covid-19 crisis in China, the pandemic has spread across the globe. The epicenter has moved from Asia to Europe and the US, and in late May 2020 some voices are warning that it is now shifting towards Latin America. While the world´s eyes have been on Milan and Paris, little has been said about how the new EU member states and countries to the East of the European Union cope with the pandemic. Some observers have claimed the emergence of a new “iron curtain” in the corona crisis; Eastern Europe, the Baltic States and the Caucasus having been relatively unscathed compared to the West. Persisting differences in trade and travel patterns, demographic and socio-economic differences, as well as differences in trust levels could account for such an observation.

Yet, the most recent statistics suggest that this may be a premature interpretation and the overall picture is much more heterogeneous. Infections in Russia seem to be rising quickly, Georgia by contrast has turned out to be one of the top students.

Figure 1: Total confirmed Covid-19 cases vs. deaths per million.

Source: Our World in Data, 2020. • CC BYa.
Note: Data includes the most recent numbers as of May 25, 2020. Both measures are expressed per million people of the country’s population. The confirmed counts are lower than the totals. The main reason for this is limited testing.

On May 28, 2020, the Forum for Research on Eastern Europe and Emerging Economies (FREE) Network hosted a webinar with its member institutes: BEROC in Belarus, BICEPS in Latvia, CEFIR@NES in Russia, CenEA in Poland, ISET-PI in Georgia, KSE in Ukraine, and SITE in Sweden to discuss how their countries have fared in the corona crisis so far. The webinar provided an opportunity to share experiences and to add some interpretations and insights to the crude statistics, which often become unintelligible in the current overflow of information.

Figure 2: FREE Network Countries.

Source: SITE 2020.

The webinar started with Torbjörn Becker, director of SITE, introducing recent developments in terms of health statistics in the region and the research being done within the framework of the FREE Network.

SITE on Sweden

Jesper Roine, Professor at the Stockholm School of Economics and SITE, then presented the case of Sweden, the country which – with regards to death rates – has surpassed all other FREE Network countries by far. The Swedish case has been very controversially discussed in international media throughout the pandemic. Yet, the common claim that in Sweden everything was “business as usual” is not true, according to Roine. Compared to its direct neighboring countries Finland, Denmark and Norway, Sweden has chosen a relatively lenient approach to Covid-19, but high schools and universities have moved to distance learning since March and working from home is highly encouraged. Mobility reports show that Swedes have reduced their movement a lot, but less so than their Scandinavian neighbors. Roine confirmed that the Swedish health policy has been dominated by the public health agency, Folkhälsomyndigheten. Even though this is the default option in Swedish law, Roine stressed that this does not mean that the government’s hands are tied.

He presented two preliminary conclusions regarding the impact of the Swedish strategy: first, Sweden’s mitigation strategy has worked relatively well; the public health system is seriously strained but not overwhelmed. Yet, Roine said that the “lack of testing [remained] a mystery”, even for advocates of the current mitigation strategy. Second, in Roine’s opinion the attempt to protect the elderly has failed. The virus has spread to numerous nursing homes and excess death rates indicate that mortality has increased sharply for citizens above 65 years of age, much less for other age groups. Geographically, Stockholm has been the center of the epidemic. Other parts of the country have been affected to a much lesser degree.

BICEPS on Latvia

Sergejs Gubins, Research Fellow at the Baltic International Centre for Economic Policy Studies (BICEPS) presented the Latvian experience of the corona crisis. A small country of about 2 million inhabitants, Latvia currently presents the second lowest Covid-19 mortality rate within the EU. Gubins related this to the Latvian government’s quick and determined policy reaction. After the first cases were reported in early March, schools and universities were closed, public gatherings forbidden, international travel halted, and a two-meter social distance rule imposed. Given the success of this strategy, Latvia has started to loosen its restrictions. A “Baltic Schengen area” was announced very recently and travel among the Baltic states is now possible again. The economic support package announced by the government amounts to 45 percent of GDP and includes a large equity investment in the airline airBaltic as well as important investments in infrastructure. According to Gubins, the current policy discussion focuses on the accessibility and size of help funds, widely deemed insufficient. Furthermore, the economic outlook of the country in terms of unemployment rates and GDP growth is bleak despite its success in containing the virus.

CEFIR on Russia

According to Natalia Volchkova, Director of the Centre for Economic and Financial Research (CEFIR) at the New Economic School in Moscow, Russia has pursued a “standard European strategy” in its fight against Covid-19. Two new hospitals exclusively for Covid-19 patients were created in Moscow, the current epicenter of the pandemic, and nearby. Most money spent on health care went to these new facilities, less was transferred as bonuses to medical workers. Russia has emphasized testing: around 10 million tests were performed; close to 400,000 cases of Covid-19 were confirmed. On May 27, free antibody testing was started in Moscow and is to be extended to other parts of the country. State-financed testing will serve to measure the potential degree of immunization of the population. While cases have started to decline in Moscow, other regions of Russia lag behind and are still expected to peak.

Volchkova stressed the role of the Russian shadow economy, which has been severely hit by the crisis. The size of the informal sector makes it difficult for the Kremlin to pass efficient support packages for the economy. Another policy problem lies in the weakness of the social security net, particularly unemployment benefits are hard to obtain. Therefore, most policy measures have focused on companies. Family allowances are the government’s second heavily used tool, which to Volchkova’s mind is an efficient policy choice. She concluded that the current help measures may already amount to 3 percent of GDP.

ISET-PI on Georgia

As of May 28, 2020, Georgia had only reported 12 corona deaths. According to Yaroslava V. Babych, Lead Economist at ISET Policy Institute in Tbilisi, the key explanation for Georgia’s relative success in the corona crisis is that, as in Latvia, testing started very early. She explained that even before Georgia’s neighbor Iran confirmed an outbreak of Covid-19, passengers’ temperatures were taken at the border crossing. The government in Tbilisi then soon imposed harsh quarantine measures, local quarantines in regional hotspots, a shutdown of public transport, an evening curfew and very high fines. Compliance with the measures was very high. Orthodox Easter celebrations were allowed to take place under strict hygiene measures and did not result in a spike in infection rates.

The country, largely reliant on tourism and agriculture, is now focusing on the economic consequences of the crisis. According to Babych, Georgia holds the ambition to become the first European country to open up to international tourism again from July 1, 2020. The government is also determined to avoid another meltdown of the important construction sector, as happened in 2008 – 2009. However, similar to the Russian case, Babych identified two factors which crucially weaken the Georgian economy: the lack of automatic stabilizers in the form of unemployment benefits and the large informal sector. Policymakers have therefore resorted to monthly cash payments to those who stopped paying income tax around March and fixing prices for specific food products. While the effectiveness of these measures still has to be evaluated, the policy discourse in Georgia has moved on to the socio-economic consequences of the crisis.

BEROC on Belarus

Lev Lvovskiy, Senior Research Fellow at the Belarusian Economic Research and Outreach Center (BEROC), provided an overview of the Belarusian policy measures. According to Lvovskiy, Belarus has a high number of nurses and doctors and a relatively efficient “Soviet style of fighting pandemics”. There have been hardly any restrictions to public gatherings and events, both the Orthodox and the Catholic Easter festivities were maintained, as were soccer games and the national Victory parade. Initially, the official policy was to trace and isolate cases, but this did not prove to be very efficient, supposedly due to poor enforcement. Lvovskiy said that testing is rare which is why statistics on the spread of the virus and its effects remain of questionable quality.

While Belarus disposed of a solid health care system, it was not well prepared economically, which explains why the government has not been very proactive in Lvovskiy’s opinion. The Belarusian industrial production decreased by 7 percent in April 2020 compared to the same month the year before; unemployment has started to increase, yet, there are no significant unemployment benefits. Increasing the height of unemployment pay is the key policy issue under discussion in Minsk but in the absence of international loans, the government´s hands are tied. The issue is urgent: the most recent BEROC survey suggests that 46% of individuals living in urban areas have already seen their income decrease. Lvovskiy’s preliminary conclusion is that the Belarusian policy response to the Covid-19 crisis was not as bad as expected by many international observers: the health crisis has mostly been contained. But like in the Georgian case, the socio-economic implications of the crisis are becoming more pressing now.

CenEA on Poland

Michal Myck, Director of the Centre for Economic Analysis (CenEA) in Szczecin, explained that Poland also successfully avoided a spike in infection rates thanks to a quick policy response. Poland was one of the first countries to impose international travel restrictions and very harsh social distancing measures, yet, infection rates remain higher than in other FREE Network countries. Since the second half of April, most measures have been lifted and the spread of the virus seems under control and concentrated in the region of Silesia.

All limitations were implemented without invoking a state of emergency. Myck suggested that the government may have made this choice because the presidential elections would have been automatically postponed otherwise, an outcome the government wanted to avoid. The elections were eventually postponed, but doubts persist with regards to the constitutional validity of the way this decision was taken. Myck stressed the persisting political uncertainty. Economic policy in Poland has focused on protecting jobs and providing liquidity to enterprises. State loans have been primarily directed to SMEs and will be partly written off, conditional on continued activity and employment. In Myck’s opinion, the economic outcome for Poland will depend on whether investments from and exports to Western Europe quickly resume or not.

KSE on Ukraine

Tymofiy Mylovanov, President of the Kyiv School of Economics and former Minister of Economic Development, Trade and Agriculture, stressed that in the first few weeks of the pandemic, Ukraine enforced harsher policy measures than its neighbors. The lock down was almost complete, with only grocery stores and pharmacies allowed to open. Compliance was high during the first few weeks but then started to decline.

The government allocated 3 percent of GDP to a Covid-19 support fund, there has been a lot of deregulation on the labor market, but the central bank’s key interest rate remains at 8 percent. Pressure for a looser monetary policy increases according to Mylovanov, as GDP has fallen by 1.2 percent and unemployment is expected to reach up to 10 percent by the end of the year.

Mylovanov’s thoughts about Ukraine’s economic prospects are mixed: average salaries continue to grow during the crisis which may be explained by the fact that low-skilled employees get laid off first, suggesting a potentially long-lasting change of the composition of the workforce. At the same time, the political situation is volatile with local elections coming up in October 2020 and public pressure mounting. As Poland, Ukraine did not declare a state of emergency. While Mylovanov thinks that the policy response could have been better, he is optimistic that Ukraine was better prepared to Covid-19 than to previous crises and will not have to resort to international loans.

Preliminary Conclusions

It is too early to draw any definite conclusions, but undoubtedly, a lot can be learned from the very diverse experiences of the corona crisis in the region. The former Soviet countries have a different historical and political legacy than Western European countries and accordingly, have found different ways of handling the crisis. Some have been more successful than their Western neighbors. But even those countries which have not faced a large health crisis have been severely hit economically and are likely to suffer economic hardship in the future.

The lack of a strong tradition of unemployment benefits and automatic stabilizers renders countries like Georgia, Belarus and Russia particularly vulnerable to the economic crisis which will inevitably follow the Covid-19 outbreak. In some countries, the corona shock may also accelerate or trigger political changes. In the view of this, the FREE Network will organize a series of follow-up webinars and briefs on more specific corona-related topics, with the aim of contextualizing statistics and providing a wider picture of the socio-economic consequences and policy implications of the crisis.

Please find a full recording of the webinar below. Updates on further events will be posted on the FREE website and on social media channels (Facebook, Twitter).

List of Speakers

  • Jesper Roine, Professor at the Stockholm Institute of Transition Economics (SITE / Sweden)
  • Sergejs Gubins, Research Fellow at the Baltic International Centre for Economic Policy Studies (BICEPS / Latvia)
  • Natalia Volchkova, Director of the Centre for Economic and Financial Research at New Economic School (CEFIR@NES / Russia)
  • Yaroslava V. Babych, Lead Economist at ISET Policy Institute (ISET / Georgia)
  • Tymofiy Mylovanov, President at the Kyiv School of Economics (KSE / Ukraine)
  • Lev Lvovskiy, Senior Research Fellow at the Belarusian Economic Research and Outreach Center (BEROC / Belarus)
  • Michal Myck, Director of the Centre for Economic Analysis (CenEA / Poland)
  • Torbjörn Becker, Director of the Stockholm Institute of Transition Economics (SITE)

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

The Social Impacts of Covid-19 – Case for a Universal Support Scheme?

Posted on May 28, 2020 | Policy Papers

20200414 Household Exposure to Financial Risks Image FREE Network Policy Paper Image 02

Beyond its impact on the healthcare system, the Covid-19 pandemic has already reached labor markets throughout every economy via economic shocks. As of 1 April 2020, ILO estimates indicate a substantial rise in global unemployment, leading to a 6.7% decline in working hours in the second quarter of 2020, which is equivalent to 195 million full-time workers.[1] In this policy note we will draw the reader’s attention to the potential scale of the impact on the labor market and the respective social consequences in Georgia. We will identify a wide variety of groups affected by the Covid-19 crisis, with a special emphasis on the labor market, and provide our judgement on the possible extent of the repercussions. The current crisis affects almost every segment of the population, including members of the following large social groups:

  • Labor market participants face high risk of job loss. Fewer employment opportunities and broad scale layoffs force a large section of self-employed and salaried workers into challenging circumstances.
  • Recipients of Targeted Social Assistance (TSA) are at great risk of slipping deeper into poverty. While members of this group mostly rely on social assistance layouts, the supplementary income that they receive, often from informal sources, could be cut. In addition, the increased prices on food and other essential goods could be particularly detrimental to this group of people.
  • Senior citizens are extremely exposed to the danger of the virus and struggle with greater health risks.

Our analysis starts with an overview of the Georgian labor market and the short-term impacts of Covid-19 on workforce displacement throughout the various sectors. The impact is not gender neutral, as it affects men and women differently depending on the sector. Therefore, we will further provide the decomposition of the impacts on the labor market and propose gender-responsive solutions to the pandemic. To mitigate adverse effects across various vulnerable groups, we will review the existing theoretical and practical evidence on targeted and universal support schemes. An overview of international social support programs is moreover provided in this note. We will further analyze the relative merits and drawbacks of our pre-defined policy options based on a multi-criteria assessment in the context of the Covid-19 crisis and thereafter provide recommendations for policy implementation.

Covid-19 – Impact Across Sectors and an Overview of the Labor Market

Unemployment in Georgia is expected to experience a large-scale increase in the short-term, leading to massive social problems. Workers have been told to remain at home because of the broad virus containment measures taken during the outbreak. Those with the opportunity to work from home are relatively well-off, unlike the large variety of vulnerable groups affected by the lockdown. Low levels of economic activity impact almost all industries, and the most vulnerable sectors include accommodation and food services, most wholesale and retail trade and entertainment and recreation. These difficulties place hundreds of thousands at risk, either by downward adjustments to income or working hours, or by completely losing their jobs.

In order to evaluate Covid-19’s potential short-run effect on employment across various economic sectors, we have qualitatively assessed the strength of the impact at the sub-sectoral level,[2] taking into account the following: (1) list and scale of economic activities prohibited during the ‘lockdown’; (2) restrictions imposed on transportation; (3) drop in consumer demand; (4) fall in intermediate input use.

In Table 1 we present our assessment of the Covid-19 impact across sectors, coupled with the corresponding labor market statistics.[3]

Table 1: Covid-19 impact on possible workforce displacement across sectors.

Source: Authors’ sectoral assessments and calculations based on Geostat Labor Force Survey (LFS 2018).

The key findings from the labor market assessment include:

  • Close to 30 percent of hired workers face a high risk of job displacement, mostly driven by an expected fall in economic activity in the trade, construction, manufacturing, and accommodation and food services sectors;
  • The least impacted industries are projected to be education, public administration and defense, utilities, and health;
  • The majority of self-employed are active in the agricultural sector, which faces a moderate impact for several reasons: the closedown of open food markets, restrictions on transportation, and a partial decline in demand (mostly from the food service sector). Although agriculture is not projected to be severely affected, a substantial number of the self-employed (mostly subsistence farmers) in this sector, considering their significantly lower than average baseline earnings, may require special policy emphasis within this group.

Finally, it should be emphasized that the severity of impacts across sectors will further depend on the longevity of the lockdown measures and the sequence in which they may be lifted for different economic activities.

In addition to the assessments in Table 1, Annex 1 presents a correlation between our estimates weighted by sub-sectors and the ILO’s assessment of the current global impact of the crisis on economic output across the sectors. It should be further noted that, in most cases, the scale of impacts coincide, and the remaining differences are due to: (1) our approach being based on more detailed sub-sectoral data; (2) the ILO looks at the global impact, whereas we focus solely on Georgia.

Short-term Workforce Displacement Risks in Vulnerable Sectors

To alleviate social problems stemming from the labor market shock during the strict, short-term quarantine measures, the clear need for safety net programs has raised the important questions of how they should be designed and who the recipients of support should be.

The discussion of social program designs requires a thorough analysis of the potential target groups. As mentioned in the previous section, after drastic quarantine and lockdown measures, many people in Georgia are at risk of finding themselves without jobs or with decreased salaries and earnings, which, in turn, is a main cause of social problems, like the inability to provide food and other necessities. The highly affected groups, as outlined in Table 1, can be clustered across the following sectors of economy:

  • The accommodation and food service sector is currently the most directly and highly affected sector. Hotels and restaurants are completely closed for an uncertain period, except for the food delivery business. However, even this is constrained to certain periods of the day, since according to the state’s emergency rules after 21:00 all movement, including delivery, is forbidden.

Most people hired within accommodation businesses face temporary job loss. This group includes hotel administration staff, housekeeping staff, people working in hotel restaurants, etc. Similarly affected are employees in restaurants and cafés, faced with cutbacks in salaries, if not complete job loss.

Another significant group within this sector are the self-employed. Owners of small family hotels and restaurants, typically dependent on tourism expenditure, now find themselves without any cashflow.

  • A significant portion of the wholesale and retail trade sector also faces major shutdowns. To begin with, employees of trade centers and individual stores are now out of work for an indefinite period. These include consultants in clothing stores, hardware stores, household appliance stores, etc. A very limited number of shops that continue to work via online sales have retained several employees on decreased salaries.

The reality is also harsh for the self-employed in retail trade. Open marketplaces, including construction materials shops and farmers’ markets have been shut, and such people are left without a vital income source. It should also be noted that most of these workers are members of a lower social strata and are less likely to have enough, if any, savings for the quarantine period.

  • As for the relatively small, but equally affected, arts, entertainment and recreation sector, art galleries, museums, night clubs, theatres, movies, and sports and spa facilities, have all been closed down due to their ‘non-vital’ function. Salaried as well as self-employed workers in these sectors found themselves without employment soon after the state emergency was announced.
  • Additional highly affected groups are those hired and self-employed in the transportation sub-sectors. The closing of public transportation has left hired bus, metro, and minibus drivers entirely without work.

Other than hired employees, self-employed drivers for intercity transportation are now left without work since intercity commuting is now forbidden under the state of emergency. Comparatively less affected are self-employed taxi drivers, who are still allowed to work, however only between 06:00-21:00. The fact that many drivers previously worked night shifts, combined with declined daytime demand, results in significant cutbacks in daily earnings for taxi drivers.

  • Another significantly affected group are those workers employed in households. These include housemaids, nannies, private tutors, handymen, etc. Since everyone is being cautious and following social distancing instructions, many households have dismissed their hired help for an indeterminate period, and even those still employed have a hard time getting to work due to the suspension of public transport, and are therefore left without vital daily income.
  • The agriculture, forestry, and fishing sector, the largest in terms of employment, remains less affected relatively, though it is facing restrictions since restaurants and cafés require fewer agricultural products than before. Moreover, as farmers’ markets have closed, their access to marketplaces has become significantly constrained. Farmers are now supplying only supermarket chains and restaurants with delivery services, a significant economic decrease compared to the normal environment. It should also be noted that self-employed small farmers are in the majority in the sector. Such workers are likely without strong links to supermarket chains or restaurants, and therefore, they will be more noticeably affected by the economic impact.

An important specificity of self-employed and domestic workers is that many are also informally employed, thus their identification by official sources (i.e. in tax returns or small business registers) is extremely problematic. Thus, the existence of a large variety of potentially affected groups, as well the inability to correctly estimate the severity of impacts across groups, highlights the need for a temporary social protection mechanism that will cover all affected parties, particularly since the people included in the groups above are not typically the main recipients of social assistance programs.

Decomposition of Labor Market Impacts by Gender

In this section, we present the gender decomposition of labor market impacts, and conclude that unemployment-driven assistance may benefit men considerably more than women.

Chart 1 summarizes the distribution of self-employed and salaried men and women across low, medium, and highly affected industries, based on the sub-sectoral assessments previously described and using gender-disaggregated employment data.

Figure 1: COVID-19 impact on possible workforce displacement, by gender

Source: Authors’ sectoral assessments and calculations based on Geostat’s Labor Force Survey (LFS, 2018).

It is evident that the proportion of employed men is significantly higher in the most vulnerable sub-sectors. Such a picture is highlighted by the high male-employment ratios in construction, transportation, and parts of manufacturing, as well as the high female-employment in the minimally affected education and healthcare industries[4].

To summarize, during the current crisis men are more susceptible to job displacement, and if a social assistance policy is solely based on labor market outcomes, they will yield higher benefits. Such social support mechanisms will deepen existing gender inequalities[5] in the country as women face disproportionate and increasing burden of care work (in situation of lockdown).

Social Assistance Policy Objectives in a Crisis

Considering the diversity of groups influenced by the lockdown, any assistance program should have several main policy objectives:

  1. Maximizing the reach of a policy to those in need and minimizing their risk of impoverishment – a large part of the population is affected by the lockdown, thus there is a substantial risk of increasing poverty directly from job loss and indirectly via job losses within families. Social assistance should, in a best-case scenario, reach the maximum number of disadvantaged people, while avoiding providing assistance to the affluent.
  2. Minimizing fiscal pressure – social assistance can create substantial pressure on the budget, especially in the current situation as revenues have decreased due to the lockdown. Furthermore, people who do not require support should not receive assistance, thus, decreasing unjustified pressure on the budget.
  3. Progressivity and gender responsiveness – an assistance program should provide proportionally larger support to those in greater need and aim to balance support by gender.

To mitigate the negative social impact of the economic lockdown, the government will have to provide significant and effective social assistance. And this is where all governments face a key dilemma, as they decide between providing targeted versus universal assistance.

An Overview of Targeted vs. Unconditional Universal Assistance

Targeted assistance is based on the methodology to define target groups, this could be under a points-based system (similar to current targeted social assistance available in Georgia) or a certain criterion defining affected groups. Under any targeting approach, two major challenges exist: (i) missing certain affected people (exclusion error), where defining an ideal criterion is impossible; and (ii) supporting those who do not require any assistance (inclusion error). Hanna and Olken (2018)[6] show that targeted programs have the potential to maximize welfare, however, they require a substantial amount of data and effort to minimize errors in the inclusion and exclusion of recipients. They further illustrate that, under normal circumstances, to reach 80% of poor people, the inclusion error will be around 22-31%. Deciding on a targeting methodology can also be costly and time consuming. Klasen and Lange (2016)[7] highlight that there is little difference between simple targets, such as demography or geography, and more complex asset-based measures, and both make poor proxies as they do not capture poverty effects in great enough detail.

In contrast, a universal support scheme can also be considered; defined as an unconditional transfer to every member of society. From the administrative perspective it is substantially easier to organize and administer, as it will not require the formation of targeting methodology or identification of target groups. Compared to targeted assistance, universal support will simply not have exclusion errors. However, the universality of the scheme would be associated with large inclusion errors. Nevertheless, considering the current situation in Georgia, with a large variety of affected groups, the inclusion error need not be as high as in normal circumstances. As previously noted, due to the lockdown, the number of vulnerable groups will have increased substantially.

Unlike targeted support schemes, there is limited practical evidence behind the implementation of universal programs (Banerjee et al., 2019).[8] However, some of the impacts can be identified from existing pilot case studies, impact assessments of existing targeting schemes, and an analysis of theoretical knowledge. The key here is that the expected impacts depend substantially on the duration and type of the support scheme (i.e. direct cash transfers, provision of vouchers or coupons, tax credit).

For our purposes we assume that the duration of the support scheme will be relatively short-term (related to the length of the lockdown). Furthermore, there is nearly no practical evidence on the impact of the long-lasting universal support schemes (Banerjee et al. 2019). Theoretically, long-lasting universal support can have a negative impact on labor force participation. Moreover, Banerjee et al. (2017)[9] finds no evidence that unconditional transfers discourage work. Considering the characteristics of the crisis, labor market participation is already limited because of the lockdown.

In addition, direct unconditional cash transfers could serve the progressivity purpose well, as households in greater need will receive a larger portion of their income, compared to those who require less assistance. Progressivity will depend on whether the recipient of a cash transfer is a household or an individual. Providing a cash transfer to households might have a disproportionate impact on larger households, requiring them to sustain themselves with less money per capita. Another important point to consider is whether money should be provided to everyone or only to the working age population (those above 15 years of age).

Coupons and Vouchers vs. Direct Cash Transfer

The type of support scheme can have a substantial influence on its impacts from the welfare and macroeconomic perspectives. One form of support scheme is the provision of vouchers or coupons to help households with utility payments or to purchase essential goods. Utility vouchers will disproportionally support more well-off households that use more appliances. The universality of such vouchers is also questionable, as some households are not connected to the utility networks (for instance the natural gas network), and thus will not benefit at all from vouchers. Considering the situation, the positive impact of vouchers is that during such a lockdown utility companies will not face liquidity problems that may otherwise arise from increased delinquency rates.

On the other hand, cash transfers allow recipients to rationalize between the consumption of different types of goods. As opposed to the provision of coupons and vouchers, transfers could further increase welfare by allowing individuals to self-rationalize (Ghatak & Maniquet, 2019).[10]

A Review of Social Support Programs Internationally

In this section, we discuss various governments’ (Table 2) social protection measures during the Covid-19 crisis. The actions taken cover the different functions of social protection, such as unemployment benefits; special social assistance or direct cash transfers; wage subsidies; deferrals of tax payments; pensions and pension fund adjustments; sickness and childcare benefits; etc.

In order to promote income security and stimulate aggregate demand, several countries have introduced either universal or quasi-universal direct cash payments (e.g. Australia, Hong Kong, Singapore, Serbia, Greece, the US). In order to further ease liquidity constraints on individuals and enterprises, some countries have announced the deferral of certain tax payments, social security contributions, rent, and utility payments (e.g. Bulgaria, Estonia, Spain, Canada). In addition, several governments are providing grants and wage subsidies to SMEs, start-ups, and other hard-hit businesses to avoid the drop in revenues and safeguard employment. In most cases, these measures were supplemented by extended unemployment benefits.

Table 2: Covid-19 social protection measures, by country

Central, South, and Eastern European Countries Certain Social Protection Measures Taken

                                 

Estonia
  • Suspended payments to the Pillar II pension fund;
  • Support the Unemployment Insurance Fund to cover wage reductions.
Poland
  • Wage subsidies for employees of affected businesses and self-employed persons;
  • Self-employed and employees working on civil contracts will receive a one-time benefit.
Latvia
  • Covering 75% of employees’ wages (in sectors suffering losses as a result of the coronavirus crisis) from the state budget, with a maximum monthly payment per employee set at €700;
  • Exempting covered wages from personal income tax and social contributions.
Serbia
  • A universal cash transfer of 100 EUR to each citizen over 18 years old;
  • Wage subsidies, including a payment of minimum wages for all SME employees and entrepreneurs for three months;
  • Payment of 50% of the net minimum wage for three months for employees in large private sector companies and for employees who are currently not working.
Bulgaria
  • Government-backed payment of 60% of the salaries of employees working in affected sectors who might otherwise be laid off;
  • Deferral of various tax and utility payment deadlines;
  • Offering interest-free loans to workers put on leave;
  • The possibility for the registered unemployed to sign labor contracts with agriculture producers, without losing their unemployment benefits.
Albania
  • Government support of small businesses/self-employed that are forced to close activities due to the Covid-19 pandemic by paying their minimum salaries;
  • Doubling social assistance and unemployment payments;
  • Part of defense spending reallocated toward humanitarian relief for the most vulnerable.
Ukraine
  • Adopting legislation that allows households to deduct the expense of Covid-19 medicine from personal income tax;
  • Introducing a one-off pension increase to low-income pensioners of 1,000 UAH and a regular monthly 500 UAH pension top-up for retirees aged 80 years and over;
  • Canceling payment of the Single Social Contribution for several categories of payer between March-May 2020.
Asia-Pacific  
Hong Kong, China
  • A one-off universal cash transfer of 1,280 HKD (165 USD) for 7 million adult residents;
  • A one-off extra allowance for 1.33 million recipients of the standard Comprehensive Social Security Assistance Payment, Old Age Allowance, Old Age Living Allowance, or Disability Allowance.
Australia
  • A one-off payment of 750 AUD (431.9 USD) for social security, veterans and other income support recipients and eligible persons, assisting around 6.5 million lower income Australians.
New Zealand
  • A permanent increase in social spending to protect vulnerable people (over the next four years);
  • Wage subsidies for affected businesses in all sectors and regions;
  • An income support package for the most vulnerable, including a permanent 25 NZD per week benefit increase and a doubling of the Winter Energy Payment for 2020;
  • Covid-19 leave and self-isolation support.
Singapore
  • A one-off payment as quasi-Universal Basic Income. All Singaporeans aged 21 and above will receive a one-off cash transfer of 300 SGD (205.38 USD), 200 SGD (136.9 USD), or 100 SGD (61.5 USD), depending on their income. Cash-payouts will also be given to families with children and elderly parents;
  • Providing a 100 SGD (61.5 USD) supermarket voucher to lower-income households;
  • Taxi and private-hire car drivers affected by the Covid-19 outbreak (around 40,000 eligible drivers in Singapore) will receive up to 20 SGD (13.7 USD) per vehicle per day for three months;
  • Enhanced Wage Credit Schemes – (co-funding wage increases for Singaporean employees);
  • For low-wage workers, the government will provide a Workfare Special Payment. Singaporeans on Workfare will receive 20 per cent more for work completed in the past year, with a minimum cash pay-out of 100 SGD (61.5 USD).
Western Countries  
United States of America
  • Direct cash payments of $1,200 for those earning up to $75,000 and $500 per child;
  • One-time tax rebates for individuals;
  • Expanding unemployment benefits.
Canada
  • Temporary income support to workers staying at home without access to paid sick leave;
  • Support to individuals and families with low and modest incomes with a special top-up payment under the Goods and Services Tax (GST) credit;
  • An increase in childcare benefits;
  • Support to businesses through income and sales tax deferrals.
Germany
  • Providing $55 billion to help small businesses and the self-employed avoid bankruptcies, with cash payments of up to $16,225;
  • Offering $8.5 billion safety-net programs for the self-employed.
Greece
  • Transfers to vulnerable individuals, including cash stipends;
  • Full coverage of pension and health benefit payments for employees working in hard-hit firms and self-employed people;
  • Extension of unemployment benefits by two months, and paid leave for parents who have children not attending school;
  • Liquidity support for hard-hit businesses through subsidized loans, loan guarantees, interest payment subsidies, and deferred payments of taxes and social security contributions.
Spain
  • A temporary subsidy for household employees affected by Covid-19;
  • A temporary monthly allowance of around 430 EUR for temporary workers whose contract (of at least two months) expires during the state of emergency and who are not entitled to collect unemployment benefits;
  • Tax payment deferrals for small and medium enterprises and the self-employed for six months;
  • An allowance for self-employed workers affected by economic activity suspension.
Norway
  • Larger wage subsidies for temporary lay-offs and more generous unemployment benefits;
  • Expanded sickness, childcare, and unemployment benefits;
  • A compensation scheme for heavily affected but otherwise sustainable businesses;
  • Grants for start-ups.

Source: Policy Responses to Covid-19, IMF policy tracker, April 2020; Social protection responses to the Covid-19 crisis, ILO, March 2020; Countries’ public announcements of Covid-19 economic responses.

Alternative Policy Options

Considering the existing social challenges, policy objectives, and possible alternatives implemented around the world, we propose the following five policy options:

Option 1 – Targeted Assistance

Considering the current situation in Georgia, the state’s capacity to implement a targeted exercise is extremely limited. This is largely due to the lockdown and the complexity of matching the current economic challenges and general characteristics of target groups. One way for the government to target different groups would be to use its administrative resources and revenue service databases to identify affected unemployed people no longer receiving salaries. However, using these resources, it will be hard to identify the majority of self-employed and informal workers who have also lost their income (fully or partially) and are facing hardships; examples of these individuals may include a small business owner working at the Eliava construction materials market, a self-employed tourism sector worker, a domestic worker – a nanny or cleaning lady, etc. Under normal circumstances, such individuals do not require any social assistance, however due to the lockdown they may not have enough cash inflow to sustain their families.

Furthermore, targeted assistance can create perverse incentives for some employees. Depending on the amount of the assistance, employees (that are still allowed to work) whose net salaries are close to the assistance threshold, might be discouraged from work. For example, if targeted assistance is 200 GEL, a grocery store worker with a gross salary of 300 GEL might prefer to leave their job temporarily (as unpaid leave for example).

Furthermore, the government could target following socially vulnerable groups that are easier to identify, such as:

  • Receivers of targeted social assistance, adults – 297,094 individuals;
  • Receivers of targeted social assistance, under 18 – 161,374 individuals;
  • Pensioners – 765,911 individuals.

Providing additional support to these groups will mean indirectly covering some self-employed individuals and informal workers. Many of such socially vulnerable groups work informally or are self-employed. Furthermore, some individuals could potentially have family members that are either informally or self-employed.

To calculate the total number of people subject to the targeted scheme, we consider the above listed individuals and add the group of hired employees that may lose the job or may have to take unpaid leave. Based on our estimates, around 200,000 hired workers may lose their income. Adding this to the number of TSA recipients (458,468) and pensioners not receiving TSA payments (692,431) brings the total number of beneficiaries of a targeted assistance scheme to 1,350,899 individuals. Assuming, 150 GEL in assistance per adult, and 75 GEL for under 18s, this will bring the cost of targeted assistance to approximately 191 mln. GEL per month.

Option 2 – Income Tax Breaks

The second policy option to consider is a variation on a tax break (tax credit, lowering income, or other taxes)[11]. Such an assistance mechanism will not be universal and only benefit the taxpayers. Furthermore, it is not a fact that tax relief will be transferred from employers to employees. Thus, essential social assistance may not be provided to a large proportion of the population. In addition, due to the lockdown, opportunities for investments have shrunk and hence, most tax saving will not influence economic growth. Finally, a decrease in tax rates will create additional pressure on government revenues, already negatively influenced by the lockdown, which may potentially create fiscal problems.

Aside from the costs of tax breaks, one should also bear in mind that this policy option is only intended for income tax payers who managed to retain their jobs. In an optimistic scenario, about 200,000 of hired employees will be left jobless, thus, about 640 thousand people will be aided by tax breaks. If income tax for all these employees would be reimbursed, the cost of tax breaks would amount to approximately GEL 136 mln. (monthly). It should also be mentioned that if companies are not paying income tax to the government, they might fail to reimburse this money to their employees, leaving some people without any assistance.

Option 3 – Unconditional Universal Cash Transfers

The third policy option is unconditional universal cash transfers. In this case, the government would make an unconditional cash transfer to every member of society. From a practical perspective there are two important questions to be answered: (i) should cash transfers be provided to individuals or to households?; and, (ii) should cash transfers only be made to the working age population or to children as well?

To minimize the potential negative consequences stemming from the possible negative gender impacts, individual payments are the preferred system. This may be as men are more often than not considered to be heads of their households, and if assistance is household-based women may not be able to take full advantage of it.

Furthermore, to ensure the progressivity of a universal cash transfer, it should not be limited to the working age population. A common approach would be to give guardians of children a decreased amount of a standard Universal Basic Income (UBI) payment (Ghatak & Maniquet, 2019). The progressivity of such a scheme is an important advantage, as it ensures support to those people who are not participants of the labor market and dependents of employed family members. Thus, the universal system helps mitigate the substantial indirect impacts on poverty resulting from job losses.

A major drawback of the unconditional universal cash transfer is its expense. This is primarily due to the large inclusion error, which accompanies this system by its very definition. However, alternatively, in a targeted program the vast majority of the affected self-employed and domestic workers (in total, close to 50% of all employment) are nearly impossible to identify. Furthermore, due to the lockdown, the potential group under risk of impoverishment is greater than under normal conditions. Consequently, compared to a perfectly targeted system (without any inclusion or exclusion errors) an unconditional universal cash transfer would be only marginally costlier.

However, with imperfect targeting, an unconditional cash transfer would be substantially costlier compared to targeted assistance. Assuming 150 GEL assistance for all working age population (2,968,964 individuals) and 75 GEL for children (754,500 individuals), the total cost of unconditional universal cash transfers would be 502 mln. GEL per month.

Option 4 – An Opt-out/Opt-in Unconditional Universal Transfers

As previously mentioned, the significant cost of a universal support scheme is a notable challenge, particularly because budgetary fiscal pressure is already high due to decreased economic activity and tax revenues. Thus, implementing a potentially costly assistance program will be hard from a public finance perspective. To partially alleviate this problem and decrease the inclusion error of universal cash transfers, the government could implement it in the following ways:

  1. The government could offer unconditional transfers to all individuals whose income is impossible to identify, while providing an opt-out option in case they do not deem the assistance necessary (for example, individuals and their families with savings or those unaffected by non-labor income);
  2. The government may assist employed workers based on their income using the following two principles:
    1. Offer assistance using an opt-out option to everyone whose income is below a certain threshold (for example, 700 GEL gross salary for the month of March);
    2. Offer assistance using an opt-in option to everyone whose income is above the threshold.

Opt-out/opt-in universal cash transfers have the potential for governmental savings. To evaluate the expected cost of this option we assume that half of all employees (i.e. 430,000) with a salary of over 700 GEL gross would opt-in into the system. In this case, the total cost of opt-out/opt-in universal cash transfers would be up to GEL 470 mln. Furthermore, in the better-case scenario, where no employees with a gross salary over 700 GEL would opt-in into the system, the total cost of the cash transfer scheme would be up to GEL 437 mln. Thus, our expected cost of the opt-out/opt-in universal cash transfer will be an average of GEL 454 mln[12].

Option 5 – Conditional Cash Transfers

To decrease the fiscal pressure associated with unconditional universal cash transfers, the government could use relatively simpler methods to minimize inclusion errors in the system. In this case, the government could potentially exclude employees who may not face an urgent need for assistance. Firstly, the government could exclude individuals who received an income of over 40,000 GEL in 2019 from the program. Secondly, those workers with an average monthly income of 1,200 GEL in 2020 could also be left outside the assistance scheme. This will allow the government to limit the inclusion error of the cash transfer system, while keeping similar overall impacts.

We evaluate the expected cost of the conditional cash transfer assuming 30% of the hired workers (258,048) having monthly income above 1,200 GEL. Based on the same population data, as for calculation of the cost of the unconditional cash transfer, the expected cost for conditional cash transfer will be roughly GEL 463 mln.

Multi-Criteria Analysis of Policy Options

To summarize these options, we have created a multi-criteria assessment of the different possibilities for social assistance using our pre-defined policy objectives. We assess each policy option on a 5-point scale, with 1 representing the worst performance, while 5 showing perfect performance. The overall efficiency of the policy option is a simple average of points in each criterion.

Table 3: Multi-Criteria Assessment of different social assistance systems during Covid-19

Assessment Criteria Option 1 – Targeted Assistance Option 2 – Income Tax Break Option 3 – Unconditional Universal Cash Transfer Option 4 – Opt-out/opt-in Unconditional Universal Cash Transfer Option 5 – Conditional Cash Transfer

 
Monthly Cost of the assistance Scheme (mil. GEL) 191 136 502 454 463
1. Minimization of Exclusion Error (minimization of impoverishment risk) 3 1 5 5 4
2. Minimization of Inclusion Error (minimization of fiscal cost) 4 2 2 3 3
3. Ease of implementation 2 5 5 4 4
4. Progressivity 4 1 4 5 5
5. Gender responsiveness 3 2 5 5 5
 

Overall Efficiency

 3.2 2.3 4.2 4.4 4.2

Summary and Recommendations

In this policy note, we have summarized the potential social impacts of Covid-19 and the subsequent lockdown caused by the pandemic. Our assessment of the sub-categories of employment show that there is a large group of mid to highly affected individuals among the employed populace. Around 30% of hired employees will be significantly influenced, while 22% will suffer a medium impact. The impact on the self-employed will also be substantial, roughly 15% of the group will be highly affected, where 84% of self-employed individuals will feel a medium impact from the lockdown. The impacts are also disproportionate from a gender perspective, posing a risk of unemployment-driven assistance benefitting men more so than women.

Having reviewed international responses to the Covid-19 crisis from 17 selected countries, the evidence compiled has helped to form possible designs for a social assistance program. We believe that direct cash transfers to individuals are preferable to providing assistance for the purchase of specific goods or services, as individuals can self-rationalize.

Our multi-criteria assessment shows that an opt-out/opt-in unconditional universal cash transfer is marginally better compared to other universal cash transfer schemes. It has the best performance in minimizing the risk of impoverishment. Furthermore, our analysis shows that under the current conditions, the government’s ability to correctly design a targeted program that is able to reach all affected individuals is limited. This is primarily due to the relatively high percentage of self-employed on the Georgian labor market. Consequently, a targeted program would have a limited impact on minimizing the risk of impoverishment. This is even more true for possible tax breaks. The greatest merit of a targeted program is that it imposes less fiscal pressure and is thus substantially less costly compared to a universal support scheme.

Annex 1 – Comparison of Sectoral Impact Assessments by ILO (globally) and ISET-PI (for Georgia)

Annex 2 – Summary of the assumptions used for calculating costs of different support schemes

  Indicator Amount
Population
A Working Age Population (>15)  2,968,964
B Population Below Working Age (<15)  754,500
C Total Population  3,723,464
Hired Workers  
D Total Hired Workers  860,161
E Hired Workers with salary above GEL 700  430,081
F Share of hired workers with salary above GEL 1,200 30%
G Total number of hired workers who lose labor income 200,000
H TSA Recipients (>18)  297,094
I Pension Recipients 692,431
J TSA Recipients (<18) 161,374
Cash Transfer
K Cash transfer per adult (GEL) 150
L Cash transfer per child (GEL) 75
  • [1] ILO Monitor 2nd edition: COVID-19 and the world of work, April 2020.
  • [2] NACE 2 classification system, 4-digit level
  • [3] Based on the Labor Force Survey, Geostat (2018)
  • [4] One has to note that the working environment for frontline health workers has changed and they are exposed to higher health risk and psychological stress, which regardless of relatively stable labor market positions makes them more vulnerable physically and psychologically.
  • [5] For example, more women live in poverty as demonstrated by the fact that 55% of social assistance recipients are women.
  • [6] Hanna, R. & Olken, B. (2018). Universal basic incomes vs. targeted transfers: anti-poverty programs in developing
  • countries. J. Econ. Perspect. 32(4):201–26.
  • [7] Klasen, S. & Lange, S. (2016). How narrowly should anti-poverty programs be targeted? Simulation evidence from Bolivia and Indonesia. Discuss. Pap. 213, Courant Res. Cent., Göttingen, Ger.
  • [8] Banerjee, AV., Niehaus, P. & Suri T. (2019). Universal basic income in the developing world. Annu. Rev. Econ. 11:961–85.
  • [9] Banerjee, AV., Hanna, R., Kreindler, G. & Olken B. (2017). Debunking the stereotype of the lazy welfare recipient: evidence from cash transfer programs. World Bank Res. Obs. 32:155–84
  • [10] Ghatak M. & Maniquet F. (2019). Some theoretical aspects of a universal basic income proposal. Annu. Rev. Econ.11.
  • [11] For the purposes of this policy option we will concentrate solely on income tax breaks.
  • [12] These scenarios do not consider additional potential saving from individuals with an opt-out option utilizing this opportunity.

Disclaimer

This policy brief was first published as an ISET policy note on April 17, 2020 under the title “The Social Impacts of COVID-19 – Case for a Universal Support Scheme?”.

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.