Pandenomics 2.0 How countries faced the second wave of pandemic and the second dip of the recession
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JANUARY 2021
WARSAW
Pandenomics 2.0
ISBN 978-83-66698-06-2
How countries faced
the second wave of pandemic
and the second dip of the recessionCitation:
Błoński, Ł., Dębkowska, K., Kubisiak, A., Leśniewicz, F., Szymańska, A., Śliwowski, P., Święcicki, I.,
Zybertowicz, K. (2021), Pandenomics 2.0. How countries faced the second wave of pandemic and the second
dip of the recession, Polish Economic Institute, Warsaw.
Warsaw, January 2021
Authors: Łukasz Błoński, Katarzyna Dębkowska, Andrzej Kubisiak, Filip Leśniewicz, Anna Szymańska,
Paweł Śliwowski, Ignacy Święcicki, Katarzyna Zybertowicz
Editing: Annabelle Chapman
Graphic design: Anna Olczak
Graphic collaboration: Liliana Gałązka, Tomasz Gałązka, Sebastian Grzybowski
Text and graphic composition: Sławomir Jarząbek
Polish Economic Institute
Al. Jerozolimskie 87
02-001 Warsaw, Poland
© Copyright by Polish Economic Institute
ISBN 978-83-66698-06-23
Table of contents
Key findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Key numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2. Pandemic dynamics between spring and autumn 2020
in the EU and UK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.1. The severity of the health crisis and government restrictions . . . . . . . . . . . . 8
2.2. The pandemic’s impact on economic forecasts . . . . . . . . . . . . . . . . . . . . . . . . 11
3. Government responses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.1. Fiscal policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.2. Monetary policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.3. Job protection schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.4. Impact on public debt and the evolution of fiscal policy . . . . . . . . . . . . . . . . 21
4. Non-economic policy tools used during the pandemic . . . . . . . . . . 23
4.1. Mass-testing in Slovakia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.2. Challenges for developing countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.3. Official statistics and the use of data during the COVID-19 pandemic . . . 24
4.4. The institutional response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.5. Sweden – an alternative model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.6. The Asian Tigers’ success stories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
5. Pandemic scenarios for 2021 and possible government
responses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Scenario 1: Optimistic (pandemic under control in the first half of 2021) . . . . . 28
Scenario 2: Realistic (pandemic under control in the second half of 2021) . . . 30
Scenario 3: Pessimistic (pandemic under control not earlier than 2022) . . . . . 31
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Appendix 1. Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
Economic Forecast Index (EFI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364
Key findings
The COVID-19 pandemic is an asymmetri- Secondly, we aimed to demonstrate both
cal shock. The scale of the health crisis, restric- the scale of the health crisis and its economic
tions and economic impact on national econo- implications. By combining data on COVID mor-
mies vary between European countries. In this tality with European Commission economic
paper, we aim to summarise the situation during forecasts, we created an Economic Forecasts
the first and second wave of the pandemic in the Index (EFI). Lithuania, Luxembourg and the
EU and propose a new Economic Forecasts In- Netherlands occupy the top three positions,
dex that encapsulates these differences. which means that their economic outlook is the
We first analysed clusters of EU member best, while Greece, Croatia and Spain close
states according to the severity of the pandem- the ranking. The index includes the forecasts
ic and the severity of the restrictions during the for GDP growth, unemployment rate, govern-
spring and autumn of 2020 and identified four ment deficit and gross public debt.
groups: → Combining clusters and positions in the
→ Hard-hit, hard-locked: Belgium, France, EFI, we map the relative situation in all the
Ireland, Italy, Portugal, Spain and the EU countries in terms of the pandemic ef-
United Kingdom. In this group, both the fect and economic perspectives. The first
number of cases and deaths, as well as group consists of countries with a high EFI
the severity of restrictions were among the and a higher-than-average mortality: Bel-
highest in Europe in the first half of 2020. gium, the Czech Republic, Hungary, Luxem-
During the second wave, the number of bourg, the Netherlands, Poland, Romania,
cases and deaths within this category was Slovenia, Sweden and UK.
moderate, but the restrictions remained → The second group comprises countries
relatively strict compared to other Euro- with relatively positive forecasts and mor-
pean countries. tality below the EU average: Austria, Den-
→ From bad to worse: Austria, Bulgaria, mark, Ireland, Malta, Germany, Finland,
Croatia, Cyprus, the Czech Republic, Latvia, Lithuania, Slovakia and Estonia.
Germany, Greece, Hungary, Lithuania, → The third group, with less favourable
Malta, the Netherlands, Poland, Roma- forecasts and low mortality, consists of
nia, Slovakia and Slovenia. In these coun- Greece, Cyprus and Portugal.
tries, the first wave of the pandemic led to → The fourth, hard-hit group, with both high
the lowest number of cases and deaths, mortality and negative forecasts, com-
but the second wave was much more se- prises Bulgaria, Croatia, France, Italy and
vere. The restrictions were moderate dur- Spain.
ing both waves. In addition to the quantitative analysis, we
→ Lucky losers: Denmark, Estonia, Finland show the scale of fiscal and monetary policies
and Latvia. Both the severity of the pan- implemented so far. The fiscal instruments
demic and the restrictions were relatively designed to mitigate the crisis amounted to
mild during both waves of the pandemic. $11.7 trillion in discretionary fiscal support,
→ Outliers: Sweden and Luxembourg. the equivalent of nearly 12% of global GDP,Key findings
5
significantly higher than the amount approved out smoothly and the pandemic is under con-
in response to the 2008-2009 global financial trol before the summer of 2021. In the realis-
crisis. As a result, the International Monetary tic scenario, vaccination takes several months
Fund projects that the global level of public and there is a third wave of the pandemic in the
debt will reach 98.7% in 2020, up from 83% in spring of 2021, but the situation is under control
2019. The monetary policies go hand in hand during the autumn. In the pessimistic scenario,
with fiscal ones. The assumed scale of corpo- there are significant problems with vaccination,
rate bond purchases varies greatly, from 0.2% which results not only in a serious third wave
of GDP in Sweden to nearly 2% of GDP in the during the spring, but also a fourth wave, which
United States. Countries with more developed has a major impact on the economy in late 2021.
financial markets and longer QE institutional ex- No matter which scenario materialises, it is clear
perience use this instrument more broadly and that the policy responses are worlds apart from
frequently. the reactions during the global financial crisis
We conclude with three pandemic sce- and that measures considered radical in recent
narios for 2021, each with a set of economic decades, such as increased capital taxation or
challenges and possible policy responses. In a universal basic income, are now entering the
the optimistic scenario, vaccines are rolled mainstream discussion.6
Key numbers
4
Poland’s place in the Economic
Forecast Index ranking during
the COVID-19 crisis
number of EU-28 countries with good
11 economic forecasts before and during
the COVID-19 economic crisis,
according to Economic Forecast Index
11.5% average budget deficit in OECD
countries this year
number of jobs supported by job
50 million retention schemes during the peak of
the first wave (April-May 2020) in OECD
countries
20.2 pp increase in gross debt in advanced
economies
government deficit in Canada,
19.9% of GDP the largest projected deficit in any
country, according to IMF economic
forecasts7
1. Introduction
W
e are now eight months into survived in relatively good condition and may be
‘pandenomics’, a distinct state the first to return to their pre-crisis growth path.
of the economy full of uncer- We synthesise these insights in a single index,
tainty, a looming recession and selective lock- the Economic Forecast Index. Yet no matter
downs. After the first wave in the spring, when how positive or negative the current forecasts,
decisions had to be made rapidly, without prior the level of uncertainty is still extremely high.
research, the reaction to the second wave in The economy and people’s lives are dependent
the autumn could have been more evidence- on medical developments at an unprecedented
based. In this report, we look at the first results rate – the vaccine – and the efficiency of pub-
in terms of government deficits and debt, as well lic health systems. Taking this into account, we
as monetary policy. We also dig deeper to pre- present three possible scenarios for the near
sent selected best practices from around the future and economic policy recommendations.
world. One of the most striking aspects of the From earmarking funds for continued fiscal stim-
current crisis is the unequal way in which it af- ulus to the reform of tax systems and a universal
fect various countries, economic sectors and basic income, we match policy tools to possi-
social groups. The impact on countries can ble outcomes in terms of vaccinations and the
be seen in the changing economic forecasts. economic situation. The pandemic might not be
Some countries that were in a relatively good over soon, but we should increase our resilience
situation before the crisis were hard-hit during to tackle what the current uncertainty will bring
the spring and will suffer well into 2021. Others in 2021.8
2. Pandemic dynamics between
spring and autumn 2020
in the EU and UK
2.1. The severity of the health crisis and government
restrictions
When we compare the situation in coun- hit during the first and second wave of the pan-
tries during the first and second wave of the demic? To answer this question, we conducted
COVID-19 pandemic, we see a certain trend. hierarchical cluster analysis, in which we used
The first wave, in spring 2020, led to fewer in- the following variables:
fections, but prompted many governments to The scale of the health crisis:
apply more severe economic restrictions than → COVID-19 cases per 100,000 people –
during the second wave, when many more separately for the first and second wave
people were infected. (source: European Centre for Disease Pre-
However, comparing the pandemic situ- vention and Control (ECDC) – www1),
ation in different countries is difficult. Firstly, → COVID-19 deaths per 100,000 people –
the epidemiological situation is very dynamic. separately for the first and second wave
Secondly, countries’ legal systems have their (source: European Centre for Disease Pre-
own specificity. In addition, there are cultural vention and Control (ECDC).
contexts, different behaviour and customs. In The scale of the restrictions:
countries where GDP is highly dependent on → Mean of the Government Response Strin-
tourism, regulations such as flight cancellations, gency Index – separately for the first and
hotel closures and restrictions on the number of second wave (www2),
people in various places have had serious eco- → The number of days (sum) in which the
nomic effects. The poorly predictable dynamics Government Response Stringency Index
of the pandemic’s spread and the specificity of (max value 100) was higher than 75 – sepa-
individual countries make it difficult to clearly rately for the first and second wave.
diagnose that a lockdown leads to more social We applied a simple rule of thumb and split
and economic losses in one country, but less in the data into time series corresponding to the
another country. We therefore need to be very first and second half of the year. The data con-
careful when drawing conclusions and formulat- cerning the first wave covers all the weeks up
ing hypotheses. to the beginning of July (05.07.2020), while the
Nevertheless, we attempted to answer data on the second wave covers the period from
the following question: were countries similarly 06.07.2020 to 13.12.2020.2. Pandemic dynamics between spring and autumn 2020 in the EU and UK
9
Taking into account all these variables at a similar situation when it comes to morbidity,
the same time, the EU-28 countries1 were divid- mortality and restrictions.
ed into four groups made up of countries with
↘ Chart 1. COVID-19 cases in the EU and UK during the first and second wave of the pandemic
(per 100,000 people)
7000
6000
COVID-19 cases during second wave
5000
4000
3000
2000
1000
0
0 100 200 300 400 500 600 700 800 900
COVID -19 cases during first wave
Hard-hit, hard-locked From bad to worse Lucky losers Outliers
Note: The size of the bubble represents the number of COVID-19 related deaths (per 100,000 people, up until 13.12.2020).
Source: prepared by PEI based on ECDC weekly data.
Group 1 – ‘hard-hit, hard-locked’ is made with the most stringent restrictions during both
up of 7 countries: Belgium, France, Ireland, waves of the pandemic. It can therefore be said
Italy, Portugal, Spain and the UK, where there that these countries were the most affected by
were the most cases of morbidity and mortality the first wave and approached the second wave
during the first wave of the pandemic. During the with great caution, introducing strong restric-
second wave, morbidity and mortality was aver- tions, which meant that the morbidity and mor-
age compared to the other groups of countries.2 tality rate during the second wave was no longer
In terms of restrictions, these are the countries the highest in the three groups of countries.
1
The 28 countries are the EU member states and the UK. The latter was included because it was a member of the
EU in autumn 2019, when the economic forecasts from before the pandemic used in this study were formulated.
2
Belgium and Ireland are outliers in this regard (Belgium with high morbidity, Ireland on the opposite side of the
scale).10 2. Pandemic dynamics between spring and autumn 2020 in the EU and UK
↘ Chart 2. Mean values of Stringency Index in European countries during the first and second wave
of the pandemic (index value between 0 and 100)
75
70
65
Mean value of Stringency Index during second wave
60
55
50
45
40
35
30
25
20
30 35 40 45 50 55 60
Mean value of Stringency Index during first wave
Hard-hit, hard-locked From bad to worse Lucky losers Outliers
Note: The size of the bubble represents the number of days in 2020 in which the Stringency Index in a given country
was higher than 75, up until 13.12.2020).
Source: prepared by PEI based on ECDC weekly data.
Group 2 – ‘from bad to worse’ is made up during the first wave, with average restrictions
of 15 countries: Austria, Bulgaria, Croatia, Cy- compared to other groups of countries. How-
prus, the Czech Republic, Germany, Greece, ever, these countries suffered much more dur-
Hungary, Lithuania, Malta, the Netherlands, ing the second wave of the pandemic, with the
Poland, Romania, Slovakia and Slovenia, highest levels of morbidity and mortality, yet
which had the lowest morbidity and mortality relatively moderate government restrictions. It2. Pandemic dynamics between spring and autumn 2020 in the EU and UK
11
can therefore be said that these countries were and Denmark had higher mean values, but fewer
mildly affected by the first wave but hit hard by days with the highest level of restrictions.
the second. At the same time, the restrictions Group 4 - Outstanding duo – Sweden and
introduced were not very severe, which allowed Luxembourg. These countries share two traits:
the economy to function fairly effectively. they had the highest number of cases during
Group 3 – ‘Lucky losers’ is made up of the first wave and were among the member
4 countries: Denmark, Estonia, Finland and states with the lowest mean Oxford Stringency
Latvia. Interestingly, these are all Baltic States. Index. Their situation diverged somewhat dur-
They had the fewest registered cases and rela- ing the second wave: Sweden managed to re-
tively low mortality during both the first and sec- duce the number of cases, while Luxembourg
ond wave of the pandemic. They took different remained the state with the highest number of
approaches to restrictions, as measured by the registered infections (while keeping deaths per
Oxford Stringency Index. Estonia is the member 100,000 people slightly above the EU average).
state with the lowest mean of Index during both In addition, the mean Stringency Index during
waves, but a relatively high number of days with second wave increased significantly more in
high restrictions (index over 75). Finland, Latvia Sweden.
2.2. The pandemic’s impact on economic forecasts
The analysis was conducted based on based on: economic development forecasts for
European Commission forecasts for 2020 con- 2020 issued before the pandemic and during the
cerning the economic situation in 28 European pandemic, in autumn 2020 (Table 1).
countries published in autumn 2019 (before the The ranking shows us the group of coun-
pandemic) and autumn 2020 (during the second tries that we can characterise based on the
wave of the pandemic). The forecasts were used forecast economic situation before and dur-
to create an Economic Forecast Index (EFI).3 ing the pandemic:
When creating the EFI, GDP forecasts, the A. Good forecast before and during the pan-
unemployment rate, the deficit and public debt, demic – Lithuania, Poland, Ireland, Hunga-
along with imports and exports, were taken into ry, Romania, Malta, Czech Republic, Esto-
account. The analysis deliberately did not con- nia, Slovakia, the UK, Slovenia.
sider forecast inflation, a variable that could B. Good forecast before the pandemic
have interfered with EFI levels. For many coun- and bad during the pandemic – Bulgaria,
tries, deflation – which is not a positive phenom- Croatia.
enon – is currently being forecast. C. Bad forecast before the pandemic and
Therefore, if we included inflation in the good during the pandemic – the Nether-
set of variables and made it a destimulant, the lands, Luxembourg, Germany, Latvia, Den-
country with the highest deflation would receive mark, Finland, Austria, Belgium.
the highest score for this variable. D. Bad forecast before and during the pan-
The six variables identified above were demic – France, Cyprus, Italy, Portugal,
used to create rankings of European countries Spain, Greece.
3
For a detailed description of how the EFI was calculated, see the Methodology appendix.12 2. Pandemic dynamics between spring and autumn 2020 in the EU and UK
↘ Table 1. Ranking of countries according to economic forecasts for 2020 issued
before (autumn 2019) and during the pandemic (autumn 2020)
Before the pandemic During the pandemic
Country Economic Ranking Economic Ranking
Forecast Economic Forecast Economic
Index Forecasts Index Forecast
Lithuania 66.40 8 76.69 1
Luxembourg 52.14 15 75.76 2
The Netherlands 50.32 19 73.72 3
Poland 78.44 2 73.65 4
Ireland 70.11 4 72.93 5
Hungary 69.91 5 71.40 6
Romania 87.98 1 70.16 7
Malta 61.70 10 68.90 8
Germany 48.53 21 67.87 9
Czech Republic 56.89 12 67.23 10
Latvia 55.76 14 67.08 11
Sweden 42.82 24 65.61 12
Estonia 56.59 13 65.60 13
Denmark 51.94 16 65.60 14
Finland 50.52 17 65.39 15
Austria 49.22 20 65.05 16
Belgium 43.81 23 64.62 17
Slovakia 67.42 6 64.02 18
United Kingdom 56.97 11 61.43 19
Slovenia 73.56 3 60.71 20
Bulgaria 67.42 7 59.55 21
France 46.72 22 51.26 22
Cyprus 35.46 28 50.14 23
Italy 35.62 26 43.84 24
Portugal 50.50 18 39.69 25
Croatia 64.27 9 31.98 26
Spain 40.36 25 30.68 27
Greece 35.56 27 27.28 28
Note: Green – very good forecast; Light green – good forecast; Pink – bad forecast; Red – very bad forecast.
Source: prepared by PEI based on European Commission forecasts.2. Pandemic dynamics between spring and autumn 2020 in the EU and UK
13
Chart 3 shows European countries ranked addition, the countries were colour-coded to
according to the index of economic forecasts reflect the group that they were included in dur-
for 2020 published during the pandemic. In ing cluster analysis.
↘ Chart 3. Countries based on macroeconomic forecasts for 2020 (colour-coded to show countries
with a similar pandemic situation)
80
70
60
50
40
30
20
10
0
Lithuania
Luxembourg
Netherlands
Poland
Ireland
Hungary
Romania
Malta
Germany
Czechia
Latvia
Sweden
Estonia
Denmark
Finland
Austria
Belgium
Slovakia
United Kingdom
Slovenia
Bulgaria
France
Cyprus
Italy
Portugal
Croatia
Spain
Note: Group 1 Hard-hit, hard-locked; Group 2 From bad to worse; Group 3 Lucky losers; Outliers. Greece
Source: prepared by PEI.
The top four countries in the ranking of – dominate the bottom of the ranking. During
economic forecasts were three countries from the second wave, these starts had the strictest
Group 2 and one from Group 3. These are there- restrictions, which lowered their position in the
fore countries with low morbidity and mortality ranking of economic forecasts.
during the first wave of the pandemic. During the In addition, the economic forecast index was
second wave, morbidity and mortality in these compared with the level of mortality – the total
countries was high. In these countries, both dur- number of deaths per million inhabitants (Chart 4).
ing the first and second wave, the restrictions These allows us to identify groups of countries that:
were fairly moderate, which may have translated A. Have high economic forecasts index and
into the freer functioning of the economy, and a higher than average mortality: Belgium,
better economic results and forecasts there. the Czech Republic, Hungary, Luxembourg,
Countries from Group 1 – those hit the the Netherlands, Poland, Romania, Slove-
hardest during the first wave of the pandemic nia, Sweden and United Kingdom.14 2. Pandemic dynamics between spring and autumn 2020 in the EU and UK
B. Have high economic forecasts index and Bulgaria and Slovakia (a drop of 14 and 12 plac-
mortality lower than average in the EU: es respectively). The first three countries are in
Austria, Denmark, Ireland, Malta, Germany, Group 2, so they were severely affected by the
Finland, Latvia, Lithuania, Slovakia, Estonia. second wave of the pandemic. In contrast, the
C. Have low economic forecasts and low mor- large drop (from 6th to 18th place) of Slovakia
tality: Greece, Cyprus and Portugal. (Group 3), which has been mildly affected by the
D. Have low economic forecasts and high pandemic, might seem surprising. Meanwhile,
mortality: Bulgaria, Croatia, France, Italy a series of countries advanced in the ranking.
and Spain. The Netherlands and Luxembourg advanced
The analysis shows that the pandemic and the most (16 places), followed by Germany and
the restrictions will probably have major but var- Sweden (12 places). Luxembourg aside, these
ied economic consequences in individual coun- countries are in Group 3, so their relatively mild
tries, which is resembled in forecasts changes. experience of the pandemic may explain their
The pandemic’s negative impact could be espe- improved position in the ranking. However, Lux-
cially visible in the case of Slovenia and Croatia embourg (Group 2) also advanced clearly, even
(a drop of 17 places in the ranking of forecasts though it was strongly affected by the second
from autumn 2020, compared to the ranking of wave. Poland is among the countries that largely
forecasts from before the pandemic), as well as maintained their economic position.
↘ Chart 4. Countries according to the Economic Forecast Index and the level of mortality
1800
COVID-19 deaths per million inhabitants
Belgium
1600
(from pandemic start to 13/12/2020)
1400
1200
Italy
Spain
United Kingdom
1000
Czechia
France Bulgaria
800 Sweden Hungary
Croatia Slovenia Luxembourg
Romania
600 Poland
Netherlands
Portugal
Austria Ireland
400
Greece Malta
Lithuania
Slovakia Germany
Total
200 Latvia
Denmark
Cyprus
-
Estonia
0 Finland
0 10 20 30 40 50 60 70 80 90 100
Economic Forecast Index
Source: prepared by PEI.15
3. Government responses
3.1. Fiscal policy
The negative consequences of COVID-19 approved in response to the 2008-2009 global
are unprecedented and the cost of tackling financial crisis.
them is enormous: this year’s average budg-
et deficit in OECD countries will reach 11.5% a) First wave of the pandemic
of nominal GDP and only decrease to 8.4% in (March – June 2020)
2021. With little further scope to cut interest After the global outbreak of the pandem-
rates, states have mainly relied on fiscal le- ic, most states decided to resort to automatic
vers to mitigate the crisis. The IMF estimates stabilisers and new fiscal impulses. Their pur-
(www3) that countries announced USD 11.7 tril- pose was to provide citizens with adequate fi-
lion in discretionary fiscal support, nearly 12% nancial means and companies with essential
of global GDP and much more than the amount liquidity.
↘ Table 2. Fiscal support implemented during the first wave of the pandemic
COVID-19 fiscal packages:
Country
spending + lost revenue (% of 2019 GDP)
Czech Republic 4.9%
France 6%
Germany 8.9% + additional help from Länder (4.3%)
Italy 6.7%
Japan 11.3%
Poland 5.2% + financial liquidity programme (4.5%)
United Kingdom 9%
United States 11.8%
Source: prepared by the PEI, based on IMF data.16 3. Government responses
↘ Box 1. Pandenomics fiscal toolkit (instruments commonly used during the first wave
of the pandemic):
→ access to a short-term work (“Kurzarbeit”) subsidy to preserve jobs and workers’ incomes;
→ expanded income support for families, childcare benefits for low-income parents and easier
access to basic income support for the self-employed;
→ expanded unemployment benefits and duration of unemployment insurance;
→ grants to small business owners and the self-employed;
→ liquidity support through the postponement of social security and tax payments for compa-
nies and accelerated refund of tax credits;
→ a temporary VAT reduction, particularly for the most-hit sectors;
→ expanded credit guarantees for exporters and export-financing banks.
After the initial period of widespread and rapid GDP growth. If this pace of recov-
lockdowns in the second quarter of 2020, ery is maintained, most countries could re-
which led to a drop in economic activity and turn to their pre-pandemic GDP volumes as
a quarterly downward spike in GDP, the posi- early as 2021. However, in the autumn, many
tive results of these interventions could be states were confronted with the second wave
observed. Data from the third quarter of 2020 of COVID-19, which could significantly affect
shows a strong rebound in economic activity their economies.
↘ Chart 5. Quarterly GDP of selected countries (Volume Index, 100 = 2015)
130
125
120
115
110
105
100
95
90
85
80
Q1/19 Q2/19 Q3/19 Q4/19 Q1/20 Q2/20 Q3/20
France Germany Italy Korea Poland UK USA
Source: prepared by PEI, based on IMF data.3. Government responses
17
b) Second wave of the pandemic and November (www4). Nevertheless, econom-
(October 2020 – ?) ic activity generally remained higher than in the
Many countries reimposed lockdowns in second quarter of 2020 (www5), which indicates
October 2020. However, having learnt lessons that value chains and manufacturing sectors
from the spring, they have managed to protect were not as affected by the lockdowns. Compa-
most economic activity. According to EU con- nies and factories managed to introduce strict
sumer and business confidence indicators (ESI), sanitary rules, which enabled them to continue
sentiment deteriorated significantly in October production.
↘ Box 2. Companies’ liquidity
To improve corporate sector liquidity, policy responses were implemented through the fiscal in-
struments mentioned in Box 1. Monetary policy measures were introduced, too: easing financial
conditions and facilitating access to credit, prudential measures that enhance banks’ lending ca-
pacity, corporate lending programmes, and bank and market funding facilities. Firms have also
been able to address liquidity concerns by tapping bank credit and issuing corporate bonds.
According to IMF estimates, these instruments – if implemented as designed – could lower liquidity
risks by the end of 2020. They could help reduce the pandemic-induced liquidity gap by four-fifths.
They could also help save jobs (around 15% of employment in Europe) and avoid output destruction
(almost a quarter of value added). As indicated by the Fund, guaranteed loans, job-retention pro-
grammes and debt moratoria are policy instruments that mostly help lower the liquidity gap, which
reflects their size and broad coverage (www6).
These estimates must be supplemented by two remarks. Firstly, companies’ results, liquidity and
solvency vary considerably. As often mentioned, COVID-19 could lead to the creation of K-shaped
economy, with the rapid development of frontier and laggard companies. Furthermore, the big-
gest losses could be concentrated among SMEs, which often have limited access to support, but
operate in sectors most hit by the crisis.
Governments have reintroduced a num- compensation (Germany) or the extension and
ber of support instruments used during the scaling-up of previously-used instruments
first wave of the pandemic. After the first spring (France, the UK, Poland) were introduced, but
wave of broad, widespread and almost unlim- their size has so far not exceeded 1% of GDP
ited support, the aid has been more targeted (www7). Furthermore, monetary instruments
this time and limited to the sectors most hit by that started being operational at the beginning
the lockdowns: tourism, leisure, gastronomy of pandemic are still in use.
and entertainment. Policies such as revenue
3.2. Monetary policy
In response to the global financial crisis, as quantitative easing, QE) to stabilise their eco-
states used non-standard monetary tools (such nomic situation. The COVID-19 pandemic has18 3. Government responses
prompted another crisis and advanced econo- particular, declarations along the lines of “what-
mies entered it with historically low interest ever it takes” to support companies resulted in
rates, limiting the expansionary impact of mon- readiness for buyouts on an almost unlimited
etary policy. Nonetheless, central banks have scale. However, the demand for this instrument
supported fiscal spending with even further is still moderate, significantly lower than for
balance sheet increases and liquidity meas- government bonds; after elevated demand in
ures. They have not only extended their QE pro- the second quarter, it has decreased in recent
grammes, including buying corporate bonds; months. However, this trend may be reversed
they have also introduced fairly new mecha- by a possible economic collapse caused by the
nisms, usually to support struggling companies. size of the second wave of the pandemic, which
Overall, they managed to reduce the uncertainty could last longer than the first.
of financial markets early in the crisis and main- 2) Providing commercial banks with low-in-
tain accommodative financing conditions. terest (usually at the deposit rate level) capital
The monetary policy instruments most fre- to finance SMEs. This solution has been imple-
quently used during the pandemic can be divid- mented by Australia, Japan, the US, the Euro-
ed into three groups: zone and Hungary, among others. This capital is
1) Quantitative Easing (QE), including buying to be used to maintain banks’ financial liquidity
corporate bonds – in the classic version of QE, and stimulate lending to SMEs. Due to the instru-
central banks buy government bonds on the ments’ repayable nature, its scale can be almost
secondary market to increase monetary sup- unlimited and continuously adjusted to compa-
ply, lower long-term interest rates and expand nies’ needs.
economic activity. In recent years, it has been 3) Alternative solutions: Japan, which has been
extended with the purchase of corporate bonds carrying out an extremely expansive monetary
for companies to avoid liquidity problems and policy for years, is the leader here. The instru-
help them survive economic downturns. This in- ments used by the Bank of Japan include con-
strument is primarily addressed to the largest trolling the yield curve; that is, purchasing
companies that operate in the country where enough bonds to achieve the assumed level of
bonds are bought. In most cases, bonds with in- profitability (a similar solution was recently im-
vestment ratings are being purchased (although plemented by Australia), purchasing ETF and
in both the US and the Eurozone, so-called fallen REIT-type investment funds (with a total value
angels – the bonds of companies that lost their of 2.2% of GDP) and negative interest rates (also
investment rating as a result of the pandemic in use in the Eurozone and Switzerland).
– were also allowed to be traded), with various The application and impact of the new
maturities. monetary instruments calibrated during the
The scale of corporate bonds purchases pandemic will extend far beyond 2020. Low or
varies greatly, from 0.2% of GDP in Sweden to even negative interest rates, QE programmes
nearly 2% of GDP in the US. Countries with more and cheap access to credit will be broadly used
developed financial markets and longer QE in- during the post-COVID recovery period, too,
stitutional experience use this instrument more contributing to the creation of an investment-
broadly and frequently. In larger countries in friendly environment.3. Government responses
19
↘ Table 3. Monetary tools used during the COVID-19 crisis
QE (including Providing banks with
State/ Currency
buying corporate low-interest capital Other
Instrument interventions
bonds) in 2020 (for loans)
Australia 1.4% of GDP, interest controlling yield
(Reserve Bank rate: at the discount curve
of Australia) rate level (0.1%)
Czech Republic interventions
(CNB) against
currency
appreciation
Euro area (ECB) PEPP: 15.4% Size adjusted to negative interest
of 2019 GDP, economic needs, rates
inc. 1.2% for interest rate: 0.5pp
corporate bonds below the discount
rate level
Great Britain 40.3% of GDP, Size adjusted to
(Bank of inc. 0.9% for economic needs,
England) corporate bonds interest rate: at the
discount rate level
(0.1%)
Hungary (MNB) 1% of GDP for 2.2% of GDP, interest
corporate bonds rate: 0%
Japan (Bank of 3% of GDP 1.35% of GDP, interest controlling yield
Japan) rate: 0% curve, negative
interest rates,
ETF and REIT
purchases
Sweden 4% of GDP,
(Riksbank) inc. 0.2% for
corporate bonds
Switzerland Size adjusted to interventions negative interest
(SNB) economic needs, against rates
interest rate: at the currency
discount rate level appreciation
(-0.75%)
United States 13.3% of GDP, PPPLF: 0.5% of GDP, new forward
(Fed) inc. 1.9% for interest rate: 0.35% guidance:
corporate bonds aiming to achieve
(limit of 30% for inflation moderately
fallen angels) above 2% for some
time
Source: prepared by PEI, based on IMF, OECD and the above-mentioned central banks’ data.20 3. Government responses
3.3. Job protection schemes
Previous economic crises (2008-2009 and Eligible employers received subsidies to subsi-
the subsequent downturn) affected the labour dise part of employees’ salaries and associated
markets of some member states profoundly healthcare, social and pension contributions.
and persistently. Lessons from the past were At the peak of the first wave (April-May 2020),
used during the COVID-19 crisis, when most job-retention schemes supported around
governments decided to protect jobs in a more 50 million jobs across the OECD, ten times
consistent way. Indeed, the scale of state inter- as many as during the global financial crisis
vention in the labour market and work arrange- (OECD 2020). The scale of the response varied,
ments was unprecedented, not only in the EU, but the peak occurred in April, when more than
but also in other OECD countries. First of all, 8 million employments were supported by the
several countries introduced or significantly French (Activité partielle) and British (Coronavi-
expanded work- or wage-protection schemes. rus Job Retention Scheme) programmes.
↘ Chart 6. Number of employments supported by government job-retention schemes
in the five largest European economies
10,000,000
8,000,000
6,000,000
4,000,000
2,000,000
0
Mar. Apr. May Jun. Jul. Aug. Sep. Oct.
Germany France UK Italy Spain
Source: prepared by PEI based on www8, www9, www10, www11, www12.
As a result, the European labour market the quarterly GDP losses in several countries,
has been cushioned from a stronger shock. such as Spain, Italy and France peaked at his-
During the first wave of the pandemic, em- torical levels, employment losses were smaller
ployment declined in a more moderate way than during the financial crisis of 2008-09. Al-
compared to the output loss (www13), while though the initial response in Europe was more3. Government responses
21
effective than that in the US (allowing firms long-term consequences remain uncertain and
to lay off employees and support unem- dependent on how the pandemic develops in
ployed people with benefits), the medium- and the future (www14).
3.4. Impact on public debt and the evolution of fiscal policy
The effects of policy actions by the fis- (to 125.5% of GDP) in advanced economies,
cal authorities are best illustrated by soaring by 10 pp (to 62.2% GDP) in emerging markets
public debt and fiscal positions in 2020 and and middle-income economies, and by 5.5 pp
2021. According to IMF projections (IMF, 2020), (to 48.8%) in low-income developing countries.
global public debt will reach 98.7% in 2020, up On the one hand, these results are the effect of
from 83% in 2019. This overall value, the high- the new fiscal measures (with the biggest im-
est ever recorded, masks a differentiated situa- pact in the first of these country groups). On the
tion in particular countries and groups of states. other hand, they were caused by the fall in GDP
Gross debt increased by 20.2 percentage points (IMF, 2020).
↘ Chart 7. Changes in the level gross debt and overall fiscal balance in relation to GDP in country
groupings for 2020-2024 (autumn 2020 forecast - autumn 2019 forecast, in pp)
25
20
15
10
5
0
-5
-10
-15
2020 2021 2022 2023 2024
Changes in gross debt: Fiscal balance:
Advanced Economies Advanced Economies
Emerging Market and Middle-Income Economies Emerging Market and Middle-Income Economies
Low-Income Developing Countries Low-Income Developing Countries
Source: prepared by PEI based on IMF (2019) and IMF (2020).22 3. Government responses
In previous chapters of this report, we announced in September (www1) is to a large ex-
presented the values of the fiscal measures an- tent focused on digital and green investments,
nounced during the early stage of the pandemic which are expected to stimulate both the recov-
and the lockdowns. We can now assess these ery and accelerate economic growth in the long-
efforts more precisely, looking at forecasts of er run. Similarly, the fiscal package announced in
government gross debt levels and the fiscal bal- July in Germany included a major component of
ance. The largest deficits for 2020 are currently measures facilitating the recovery, which were
projected for Canada (19.9% of GDP), the US not present in the spring packages.
(18.7%) and Brazil (16.8%) (IMF 2020). In the Eu- The change in the approach to fiscal sup-
rozone, the leaders are Spain (12.2%), Belgium port can be attributed to several factors:
(11.2%) and Italy (10.8%). On the other side of → Limited fiscal space in some countries and
the spectrum, Bulgaria, Denmark and Sweden increased risk of dependence on favour-
will end 2020 with deficits below 5% of GDP (Eu- able conditions on financial markets;
ropean Commission, 2020). → More data on the state of the economy and
The spring fiscal packages were unprec- the sectoral impact of the crisis;
edented in scale and reflected the “whatever → Economic evidence of the higher effective-
it takes” approach of decision makers, who im- ness of targeted support relative to broad
plemented support packages very broadly. Sub- support (for social security measures)
sequent actions have been more tailored and (IMF, 2020);
targeted specific sectors. Some countries are al- → Banking on a positive scenario for the de-
ready focusing their fiscal impulses on the recov- velopment of the pandemic, which justifies
ery phase and aiming to stimulate post-pandemic moving forward with public investment and
economic growth. In France, the fiscal package the economic transformation.23
4. Non-economic policy tools used
during the pandemic
4.1. Mass-testing in Slovakia
Slovakia is the first European country quarantined afterwards. Around 57,500 new
where the government decided to implement COVID-19 cases were identified, but the test-
mass testing for the whole population. Oth- ing is continued periodically in the most-af-
er countries, e.g. Austria, announced similar fected regions (the third round was sched-
plans (www15). Everyone between the age uled for 21.11 – 22.11) (Dębiec 2020). The
of 10 and 65 was eligible; those who refused whole process led to a decrease in the daily
to get tested were supposed to quarantine number of new cases (although this may part-
for ten days. Around 1% of people tested ly be due to the restrictions introduced ear-
positive during the first phase (3.6 million lier) and to the faster, but gradual, opening of
tests, 95% of the target group during the selected branches of the economy (theatres,
weekend 31.10 – 01.11) and 0.66% during cinemas, churches) (www16). Austria has al-
the second (2 million tests during the week- ready announced that it intends to introduce
end 6.11 – 7.11). Those who tested positive mass testing by the end of November
4.2. Challenges for developing countries
In many developing countries, govern- et al. (2020) document 12 examples of cuts
ments have relied on mobile banking solu- in transaction fees, 11 of increased transfer
tions as an efficient way to disburse funds limits and one of easing the identification
to those most hit by the pandemic and the requirements for new customers. There are
restrictions. These kinds of measures are also examples from countries such as Togo,
especially important in these countries, where, in ten days, the government managed
where the informal sector of the economy to set up a new nation-wide mobile transfer
is larger or institutional safety nets are less system aimed at reaching to most vulner-
developed. Mobile money is especially pop- able groups (such as informal workers). The
ular in African countries, but it also used in growing popularity of mobile payments may
other regions where the penetration of tra- become a lasting feature of many economies
ditional banking is low. Bazarbash, Moeller (“The Economist”, 2020).24 4. Non-economic policy tools used during the pandemic
4.3. Official statistics and the use of data
during the COVID-19 pandemic
The COVID-19 pandemic had a large im- surveys (PEI and PFR in Poland (www17), ONS in
pact on official statistics. The traditional work the UK (McLaren, 2020)) or data on job postings
of national statistical offices based on survey (Chen, 2020). Some of the data was had already
and personal data, with relatively long delivery been used before the pandemic (Biancotti et. al.,
times, was unfit for the new situation. To have an 2020), but its use has become more widespread
up-to-date picture of the economy, policymak- and, often, more refined, as in the case of Ger-
ers had to rely on near real-time data and statis- man daily truck index, which was used as a daily
tical offices had to shift from personal interviews indicator of economic activity (www18). Much of
or gathering data on prices to CATI or CAWI the data used in these efforts were sourced from
types of interviews (Tall, 2020), and the results private companies (Google data on mobility is
were further complicated by lower response a prime example). As some scientists suggest,
rates. To overcome these challenges, govern- there are “information gaps”, rather than “data
ments had to rely more on sources as such as gaps”, and the challenge for official statistics is
real-time data on electrical energy consump- to develop appropriate legal and technical tools
tion (a proxy for changes in economic activity), to tap into the data held by private companies
data on mobility (a proxy for reduced consum- and individual users, rather than expanding
er demand; provided by Google, for example), traditional statistical data gathering methods
electronic payment platforms, frequent online (Biancotti et al., 2020).
4.4. The institutional response
The COVID-19 pandemic is a catalyst for Prevention and Control (ECDC) and the Euro-
new ways of thinking about social, economic pean Medicines Agency (EMA) but also to the
and environmental vulnerabilities and strate- creation of EU Health Emergency Preparedness
gies adopted by governments and organisations. and Response Authority (HERA). The concrete
(www19; www20). The response to the pandem- solutions include the development of a binding
ic was based not only on short-term reactions, EU health crisis/pandemic preparedness and
but also on the creation and evolution of insti- response plan, strengthen the ECDC’s access
tutions. We present a few examples of institu- to health data for research and epidemiological
tional changes related, directly or indirectly, to elements, and a new, high-performing epidemio-
the pandemic. logical surveillance system at the EU level.
The European Health Union The High Commissioner for Planning
One of these is the European Health Un- in France
ion mentioned by Ursula von der Leyen in her In France, in reference to the tradition of
2020 State of the Union address. The proposals planification that triumphed after World War II
in the communiqué published by the European during de Gaulle era, at the beginning of Sep-
Commission (www21) are mainly related to the tember 2020 François Bayrou was appointed
strengthening of European Centre for Disease high commissioner for planning (www22). This4. Non-economic policy tools used during the pandemic
25
might be seen as a coincidence, rather than as epidemiological surveillance became crucial
a direct consequence of the pandemic. How- during the pandemic and will be the new nor-
ever, if we look at the whole discussion about mal, at least to some extent. These changes will
COVID-19, where the pandemic was a catalyst have a huge impact on society and the econo-
of the supply chain crisis, with the importance my. Telemedicine allows patients who live far
of industry for the economy and the new social, from medical care points to be reached faster
economic and environmental challenges, this in- (www24). Remote work can affect not only how
stitution is not just a symbolic change – it is also people work (and, for instance, increase the pre-
a change in thinking about the economy and carity of work), but also the real estate market
the state. When talking about the General Plan- (www25). Digital epidemiological surveillance,
ning Commission, French Prime Minister Jean with new institutions like the European Health
Castex said that the state has lost its capacity Union, can be part of an early warning system
for long-term projection (www23) and that the that will be used more often and more effective-
Commission’s goal will be “to plan an economic ly than before the pandemic.
policy, to identify future sources of growth, to
define a perspective, to set a course”. Le Plan The systemic approach as the main
was, during its golden age, a synonym of the de- result
velopmental state approach to the economy. The European Health Union, the High
Such a shift may suggest that the era of the Commissioner for Planning and digital micro-
night-watchman, state which dominated in the institutions mentioned above are just exam-
recent decades and advocated for the opposite ples of the institutional response to COVID-19.
approach – is probably over. However, the most important change prompted
by the pandemic were not concrete measures
Digital institutions adopted to fight with virus and its effects, but
The most visible institutional changes a change in thinking about risks and how to mit-
– not in the sense of a new administrative or- igate them. Building more responsive and solid
ganisation, but in terms of new ways of func- institutions based on systemic concerns that
tioning – were digital. Medical consultations will respond to complex problems – this is the
by phone or video call, remote work or digital new normal.
4.5. Sweden – an alternative model
The Swedish model for managing the there were limits for dispensing medications,
COVID-19 pandemic, which was distinct from and ones concerning restaurants, bars and
other countries’ approach, was based on pub- other table service points. However, the main
lic health agency experts’ decisions and indi- advice was clear and general: take personal
vidual responsibility. In March 2020, when many responsibility (www27). Anders Tegnell, chief
countries had introduced lockdowns, schools epidemiologist of Sweden, explained: “We are
in Sweden were still open and gatherings of up not just working with communicable diseases,
to 49 people were allowed (www26). This does we are working with public health as a whole”
not mean that there were no restrictions. Vis- (www28). Sweden started to change its strat-
iting care homes for the elderly was banned, egy during the second wave of the pandemic,26 4. Non-economic policy tools used during the pandemic
which hit the country more than in the spring. In (www30). Nevertheless, there was no national
November 2020, the government limited pub- lockdown in Sweden and there is still no obliga-
lic gatherings to up to 8 people (www29). The tion to wear a face mask. However masks are
other measure introduced is a nationwide ban recommended in public transport during rush
on alcohol after 10 p.m. at bars and restaurants hours.
4.6. The Asian Tigers’ success stories
Some Asian countries, commonly re- Institutional factors:
ferred as the “Asian Tigers”, dealt with the → Cooperative strategy: Asian states are
COVID-19 pandemic more successfully than trying to tackle the COVID-19 pandemic
their European and American counterparts. In through tight cooperation with private en-
particular, Taiwan and South Korea are seen as terprises and non-governmental organisa-
COVID-19 champions. Despite their high pop- tions (NGOs), which have complementary
ulation density and the first COVID-19 cases resources and expertise.
in early 2020, the number of new infections in → Taiwan activated a special-purpose insti-
both of them has been particularly low since tution, the Central Epidemic Command
April. As a result, the economic contraction Centre (CECC), to coordinate cooperation
in this part of the world will be significantly across different government ministries,
lower than anywhere else. Several factors agencies and NGOs. It is responsible for
enabled them to fight the COVID-19 pandemic the coordination of big data analytics, test-
successfully: ing, quarantine and contact tracing.
↘ Box 3. Taiwan’s exemplary contact tracing
Passengers of the Diamond Princess cruise liner, many of them already unknowingly infected with
the virus, left the deck for one day to explore northern Taiwan. Using big data and cell phone login
monitoring, CECC identified more than 600,000 people who may have had contact with infected
passengers from the cruiser. All these people received text messages with telling them to monitor
their health and avoid going outside. People with symptoms were immediately tested.
→ Contact tracing in South Korea: it ex- detection and efficient contact tracing
panded its usual Epidemic Intelligence and prevented infections among high-
Service (EIS) workforce by quickly train- risk populations.
ing staff at around 250 local public health → Digital healthcare system (Taiwan and Ko-
centres, hiring 300 private epidemiolo- rea): every citizen has a healthcare record
gists and leveraging staff at 11 NGOs linked to their name, allowing medical
that train and support IES officers. This personnel to access online medical infor-
multilevel approach led to earlier case mation (from both the e-dossier and data4. Non-economic policy tools used during the pandemic
27
sent by citizens). It provides health offi- Compared to other high-income coun-
cials with almost real-time data on hospi- tries, the number of hospital beds per
tal visits and citizens’ health. This enables capita is much higher, at 13.2 beds in Ja-
health officials to send alerts to doctors pan and 11.5 in South Korea per 1.000 citi-
about higher-risk patients based on their zens (compared to 2.6 in the UK and 6.1 in
travel history. Physicians are alerted France) (www33).
about possible cases and aware of the Cultural factors:
risk related to community transmission. → People in east Asian countries are more
Furthermore, patient trajectories were tolerant when it comes to data-sharing,
made public in Korea to enable citizens to and less sensitive about privacy issues and
track their own movements against those individual freedoms. At the same time, they
of suspected cases. are more familiar with technology, which
→ Public space: there is an obligation in Tai- means that the application and scaling up
wan and Korea to fill out a questionnaire of technological solutions has been more
and provide personal data to enter a pub- successful.
lic place (gym, restaurant, etc.). This infor- → East Asian countries have had more experi-
mation, as well as data from mobile logins ence with other infectious diseases (SARS
and credit card transactions, is used by all or swine flu), which leads to greater social
the “Asian Tigers” for targeted testing and discipline and different attitude to the pan-
contact tracing. demic reality. As a result, masks are com-
→ Quarantine hotels and a home-quarantine monly worn during the winter flu season, as
system in Taiwan using geofencing tech- well as in any other situation when a per-
nology based on data collected from mo- son feels unwell.
bile operators. If a citizen leaves the “elec- → Everyday habits and customs: bowing is in
tronic fence” of his home or hotel, the many cases more common than shaking
alarm goes off. Furthermore, if he switches hands or hugging. The region is also well-
off his mobile phone or the battery dies, known for its high standards of personal
a police patrol is being sent. hygiene. The removal of shoes when en-
→ The number of hospital beds in South tering someone’s home could be another
Korea and Japan: their health systems possible explanation for the low infection
are centred on hospital-based care. rate in the above-mentioned countries.28
5. Pandemic scenarios for 2021
and possible government
responses
I
n this chapter, we propose three possible the assumption that the timespan is too large
scenarios for 2021, based on the findings and that support should be redesigned to help
from previous sections. Health, societal entities find a new place on the market, rather
and economic processes will be significantly than maintain the status quo. Employers could
shaped by the roll-out of the vaccine. As of mid- decide to keep their furloughed staff for longer,
December 2020, vaccination had already started rather than terminate contracts. Moreover, if
in UK and US, with European countries planning the vaccines prove to be effective, they could
to start soon after Christmas. Even though mass be used as a tool to support the faster recovery
vaccination, which could be characterised as of some sectors, such as aviation, tourism and
a “staggeringly complex chain of events” of an HORECA, even before the threshold of herd im-
unprecedented scale and magnitude (www34), munity is reached.
is still ahead and there are some uncertainties However, the economy cannot simply re-
regarding it implementation, the vaccine could turn to the pre-COVID era. Some sectors were
have an impact on the economy before it is even hit hard enough for structural damage to emerge.
delivered. We have already observed that the employment
First of all, the start of vaccination boosts rebound could be slow and lag behind the lifting
business confidence and hopes for the end of of restrictions; that is, some workers are unable
the pandemic and the approaching recovery. to return to their previous employers. In addi-
The initial wave of optimism was particularly tion, some household and consumer behaviour
visible on financial markets (the Dow Jones (spending vs. saving, buying choices concerning
surged past 30,000). But it could also spread channels and categories) could outlast the pan-
to other economic actors. Firstly, governments demic. People might become more fearful and
may decide to extend protection and support unwilling to spend at pre-crisis levels. Some busi-
schemes rather than seek alternatives built on ness models could become obsolete.
Scenario 1: Optimistic (pandemic under control
in the first half of 2021)
Pandemic dynamics: Initial positive data phase is prepared and implemented without
on vaccines’ safety and effectiveness is vali- significant disruptions and most people around
dated and all the potential vaccines are au- the world are willing to be vaccinated. As a re-
thorised by the EMA, FDA and other responsible sult, swift vaccination starts in early 2021 and,
agencies in late 2020 or early 2021. The roll-out by mid-2021, most of the vulnerable groupsYou can also read
