EU start-up calculator : impact of COVID-19 on aggregate employment : scenario analysis for Bulgaria, Croatia, Czechia, Luxemburg, Netherlands, Poland, Romania, Slovakia and Slovenia.
Date: 2020-12-11
Institutions: Joint Research Centre
Subjects: Bulgaria, Croatia, Czechia, EU employment policy, European undertaking, Luxembourg, Netherlands, Poland, Romania, Slovakia, Slovenia, economic consequence, report, start-up, testing

EU start-up calculator: impact of 
COVID-19 on aggregate 
employment  

Scenario analysis for Bulgaria, Croatia, Czechia, 
Luxemburg, Netherlands, Poland, Romania, Slovakia 
and Slovenia 

Benedetti Fasil, C. 

Sedláček, P. 

Sterk, V.  

2020 

EUR 30515 EN 
XX

This  publication  is  a  Technical  report  by  the  Joint  Research  Centre  (JRC),  the  European  Commission’s  science 
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Contact information  
Name: Cristiana Benedetti Fasil 
Address: European Commission, Joint Research Centre, Brussels 
Email: cristiana.benedetti-fasil@ec.europa.eu 

EU Science Hub 
https://ec.europa.eu/jrc 

JRC123086 

EUR 30515 EN 

PDF 

ISBN 978-92-76-27387-5 

ISSN 1831-9424 

doi:10.2760/232741 

Luxembourg: Publications Office of the European Union, 2020 

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How to cite this report: Benedetti Fasil, C., P. Sedláček and V. Sterk, EU start-up calculator: impact of COVID-
for  Bulgaria,  Croatia,  Czechia,  Luxembourg, 
19  on  aggregate  employment.  Scenario  analysis 
Netherlands,  Poland,  Romania,  Slovakia  and  Slovenia,  EUR  30515  EN,  Publications  Office  of  the  European 
Union, Luxembourg, 2020, ISBN 978-92-76-27387-5, doi:10.2760/232741, JRC123086.

Contents 

Foreword ............................................................................................................. 3	

Acknowledgements ............................................................................................... 4	

Abstract .............................................................................................................. 5	

1	 Introduction ..................................................................................................... 6	

2	 What is the start-up calculator? ........................................................................ 11 

3 Data and methodology .....................................................................................12 

4 Country analysis ..............................................................................................13	

4.1	 EU start-up calculator: Bulagria .................................................................. 13	

4.1.1	Summary ......................................................................................... 13 

4.1.2	Business dynamism in Bulgaria ........................................................... 13 

4.1.3	Scenario analysis .............................................................................. 14 

4.2	 EU start-up calculator: Croatia ................................................................... 18	

4.2.1	Summary ......................................................................................... 18 

4.2.2	Business dynamism in Croatia ............................................................ 19 

4.2.3	Scenario analysis .............................................................................. 19 

4.3	 EU start-up calculator: Czechia ................................................................... 23	

4.3.1	Summary ......................................................................................... 23 

4.3.2	Business dynamism in Czechia ............................................................ 24 

4.3.3	Scenario analysis .............................................................................. 24 

4.4	 EU start-up calculator: Luxemburg .............................................................. 28	

4.4.1	Summary ......................................................................................... 28 

4.4.2	Business dynamism in Luxemburg ....................................................... 28 

4.4.3	Scenario analysis .............................................................................. 29 

4.5	 EU start-up calculator: Netherlands ............................................................. 33	

4.5.1	Summary ......................................................................................... 33 

4.5.2	Business dynamism in Netherlands ...................................................... 33 

4.5.3	Scenario analysis .............................................................................. 34 

4.6	 EU start-up calculator: Poland .................................................................... 38	

4.6.1	Summary ......................................................................................... 38	

4.6.2 Business dynamism in Poland ............................................................. 38 

4.6.3	Scenario analysis .............................................................................. 39 

4.7	 EU start-up calculator: Romania ................................................................. 43	

4.7.1	Summary ......................................................................................... 43	

4.7.2 Business dynamism in Romania .......................................................... 43 

4.7.3	Scenario analysis .............................................................................. 45 

4.8	 EU start-up calculator: Slovakia .................................................................. 48	

i 

4.8.1	Summary ......................................................................................... 48	

4.8.2 Business dynamism in Slovakia .......................................................... 49 

4.8.3	Scenario analysis .............................................................................. 50 

4.9	EU start-up calculator: Slovenia ...................................................................... 54	

4.9.1	Summary ......................................................................................... 54	

4.9.2 Business dynamism in Slovenia .......................................................... 54 

4.9.3	Scenario analysis .............................................................................. 55 

5	 Conclusions ................................................................................................... 60 

References ........................................................................................................ 62	

Appendix ........................................................................................................... 63	

Appendix I. Figures ......................................................................................... 63	

Appendix II. Interpolation, extrapolation and creation of scenarios ........................ 65	

Appendix III. Adjusting for general equilibrium effects ......................................... 67	

ii 

Foreword 

This paper is the third and last of a series of publications that use a newly developed EU 
start-up  calculator.  This  is  a  simulator  that  allows  to  assess  the  disruptive  impact  of 
COVID-19  on  start-up  activity  and  ultimately  aggregate  employment  in  the  European 
Union  Member  States.  The  paper  explains  the  tool  and  performs  scenario  analysis  for 
Bulgaria,  Croatia,  Czechia,  Luxemburg,  the  Netherlands,  Poland,  Romania,  Slovakia  and 
Slovenia.  After  a  general  introduction  and  presentation  of  the  tool,  the  presentation  of 
the  scenario  analysis  of  each  country  is  self-contained.  This  allows  the  reader  to  focus 
directly on the countries of interest. 

The first report, Benedetti Fasil, Sedláček and Sterk (2020), presents a similar structure 
and  discusses  the  scenario  analysis  for  Austria,  Belgium,  Germany,  Hungary,  Italy  and 
Spain,  while  the  second  report,  Benedetti  Fasil,  Sedláček  and  Sterk  (2020b)  analysis 
Denmark, Estonia, Finland, France, Latvia, Lithuania, Portugal and Sweden. The analysis 
cannot  be  carried  out  for  Cyprus,  Greece,  Ireland  and  Malta  due  to  the  lack  of  the 
necessary EuroStat data.   

The  start-up  calculator  has  been  firstly  developed  by  Sedláček  and  Sterk  (2020)  and 
applied  to  the  US  economy.  It  has  subsequently  been  adapted  to  fit  the  employment 
profile of young firms in individual EU Member States. 

3 

Acknowledgements 

We  are  grateful  to  colleagues  in  the  JRC,  especially  James  Gavigan,  Zoltan  Csefalvay, 
Clemens  Domnick  and  Miguel  Sanchez-Martinez,  and  in  DG  ECFIN,  particularly  Jorge 
Duran  Laguna  and  Theo  Aphecetche  that  contributed  with  useful  feedback  on  the  work. 
We  would  also  like  to  thank  the  Chief  Economist  teams  of  DG  RTD,  DG  COMP  and  DG 
GROW  for  their  comments  on  earlier  stages  of  this  work.  Finally,  the  Bulgarian  National 
Statistical  Institute,  the  Danish  Statistical  Institute,  Statistics  Estonia,  Statistics  Finland, 
Statistics Lithuania, the Federal Statistical Office of Germany, the Slovak Statistical Office 
and  the  Business  Register  of  Slovenia  provided  us  with  recent  and  provisional  data  on 
firm creation. 

Authors 

Cristiana  Benedetti  Fasil,  European  Commission,  Joint  Research  Centre,  Brussels, 
Belgium 

Petr Sedláček, University of Oxford, UK, CFM-LSE & CEPR 

Vincent Sterk, University College London, UK, CFM-UCL & CEPR 

4 

Abstract 

Early  data  show  that  the  COVID-19  pandemic  has  affected  particularly  strongly  start-up 
business  activity.  This  may  have  dramatic  and  lasting  effects  on  aggregate  employment 
which  persist  as  the  cohort  of  new  firms  age.  To  assess  such  an  impact,  we  developed 
the  EU  start-up  calculator.  A  first  application  targeted  to  Austria,  Belgium,  Germany, 
Hungary, Italy and Spain is discussed in Benedetti Fasil, Sedláček and Sterk (2020a) and 
a  second  focusing  Denmark,  Estonia,  Finland,  France,  Latvia,  Lithuania,  Portugal  and 
Sweden  is  presented  in  Benedetti  Fasil,  Sedláček  and  Sterk  (2020b).  The  EU  start-up 
calculator  is  an  empirical  tool  that  allows  to  conduct  scenario  analysis  to  compute  the 
impact  that  the  disruption  of  start-up  activity  has  on  aggregate  employment  on  EU 
Member  States  and  their  economic  sectors.  In  this  paper,  we  simulate  the  effects  of  a 
strong (i.e. of magnitude equivalent to the Great Recession of 2008 and 2009) but short-
lived  (i.e.  lasting  one-year)  crisis  in  Bulgaria,  Croatia,  Czechia,  Luxemburg,  the 
Netherlands,  Poland,  Romania,  Slovakia  and  Slovenia.  This  shock  generates  important 
and  persistent  job  losses  in  all  the  countries  ranging  between  0.25  (Luxemburg)  and 
6.9% (Slovakia) of negative deviation from the employment trend in 2020 and results in 
a  computed  potential  cumulative  loss  of  jobs  for  the  period  2020-2030  ranging  from 
5,600  (Luxemburg)  to  2179,000  (Poland).  The  potential  negative  impact  is  particularly 
high in Bulgaria, Croatia, Poland, Romania and Slovakia as well as in the service sector, 
which  are  characterized  by  a  high  firm  turnover  and  a  reliance  on  start-ups  and  young 
firms for job creation. We also find that in most countries the deterioration of the survival 
rate  of  young  firms  plays  an  important  role  in  driving  employment,  seconded  by  the 
number of new entrants. As a consequence, policies aimed at supporting young firms and 
incentivizing the creation of new ones may significantly mitigate the medium-term effect 
of the pandemic. In fact, when we simulate bounce-back scenarios where the number of 
firms  entering  the  economy  rapidly  increases  in  2021,  in  every  country  the  outlook  is 
significantly improved, the recovery is faster and the aggregate job loss is lower. 

Key words: COVID-19, start-ups, employment 

5 

1  Introduction 

The EU start-up calculator is an empirical tool that allows researchers and policy analysts 
to compute an estimate of the medium-run impact, i.e. up to 2030, that COVID-19 may 
have on aggregate employment due to the disruption of start-ups and young firms. The 
calculator allows to create different scenarios with the possibilities to vary three margins: 
(i) the  number  of  start-ups,  (ii)  the  survival  rate  of  young  firms  and  (iii)  the  growth
potential  of  start-ups  (i.e.  the  post-entry  growth  of  firms  in  terms  of  employment).  The
start-up  calculator  uses  publicly  available  data  from  Eurostat  allowing  analysis  of  the
whole economy, the industrial and service sectors in each Member State.

The focus is on the impact of COVID-19 on the employment-generating potential of start-
ups and young firms. For the purpose of this paper, start-ups are firms age zero or new 
entrants,  while  young  firms  are  less  than  six  years  old  from  when  they  registered  as  a 
new  business.  This  category  of  firms  is  particularly  important  for  a  dynamic  and 
productive economy. Start-ups and young firms are job creators and account for a large 
share  of  employment  in  the  EU  Member  States  (Figure  1).  At  the  EU  27  level,  35%  of 
firms are less than 5 years old and account for 12% of total European employment, with 
start-ups accounting for 2.5%. Figure 1 shows that in Bulgaria, Croatia, Czechia, Poland 
and Slovakia young firms are particularly relevant actors in terms of their contribution to 
aggregate  employment  when  compared  to  the  EU  average.  In  fact,  among  these 
countries  the  share  of  employment  of  young  firms  ranges  between  10.71%  in  Czechia 
and 19.41% in Croatia. Even more striking is the disproportional contribution of start-ups 
and young firms to employment growth. In the EU 27, young firms accounted for 36% of 
employment growth in the period 2013-2017. This pattern is qualitatively present also in 
the  other  European  countries  analysed  which  show  a  contribution  of  young  firms  to 
employment growth ranging between  2.62% in Luxemburg to 53.42% in Poland. Figure 
A1  in  Appendix  I  reports  the  same  descriptive  statistics  for  a  larger  set  of  EU  Member 
States.  

Start-ups and young firms

60% 
50% 
40% 
30% 
20% 
10% 
0% 

Share	of	young	firms

employment	share	of	start-ups

start-up	contribution	to	employment	growth

employment	share	of	young	firms

young	firm	contribution	to	employment	growth

Figure 1. Importance of start-ups and young firms for aggregate employment 

Source: JRC, Eurostat, Business Demography dataset, 2020. 

Note(1):  The  time  series  for  the  EU27,  Bulgaria,  Croatia,  Poland  and  Slovakia  is  between  2012  and  2017,  for 
Czechia, Luxemburg, Netherlands, Romania and Slovenia between 2008 and 2017. 

6 

Importantly,  start-ups  are  also  pivotal  for  the  creation  of  new  jobs.  Figure  2  shows  net 
job  creation  (i.e.  creation  minus  destruction)  by  firm  age,  in  the  same  set  of  countries. 
On  average,  the  employment  share  of  start-ups  accounts  for  1.8  in  Czechia  and 
Luxemburg to 4.5% in Poland of aggregate employment. Thus, a lack of start-up activity 
can  lead  to  an  important  loss  of  aggregate  employment.  Figure  2  also  shows  that  for 
firms  of  age  1  the  contribution  to  net-job  creation  is  substantially  lower  and  even 
negative  for  Croatia  and  Poland.  Beyond  age  1,  net  job  creation  is  negative  in  all  the 
countries.  That  is,  these  age  groups  on  average  destroy  more  jobs  (either  via  exit  or 
scaling  back)  than  they  create.  Again,  this  highlights  the  importance  of  start-ups  in 
sustaining  aggregate  employment.  Finally,  the  figure  suggests  that  there  is  substantial 
variation  across  countries  in  the  importance  of  start-ups  for  job  creation,  although 
qualitatively  the  patterns  are  similar.  The  interested  reader  can  refer  to  Figure  A2  in 
Appendix I to assess the net job creation in a larger set of EU Member States. 

At  the  same  time,  start-ups  (i.e.  firms  age  0  in  Figure  2)  and  young  firms  find 
themselves in a fragile stage of their firm life-cycle being more susceptible to disruption 
of  supply  chains,  a  drop  in  demand  for  their  products  or  services,  limited  access  to 
funding and more stringent regulations. For this reason, the COVID-19 pandemic and its 
consequences  in  terms  of  containment  measures  adopted,  changes  in  consumer 
preferences,  increased  uncertainty  and  related  economic  crisis  is  deemed  to  impact 
particularly  strongly  on  start-ups  and  young  firms.  This  is  confirmed  by  recent  data 
(available  for  sub-set  of  countries)  on  the  number  of  new  business  registrations  in  the 
first  semester  of  2020  which  show  a  sharp  decline  with  respect  to  the  same  months  of 
the  previous  year  across  several  EU  countries.  Figure  3  shows  how  the  number  of  new 
companies  created  declines  in  the  first  and  second  quarter  of  2020  with  respect  to  the 
first  and  second  quarter  of  2019,  i.e.  -20.67%  in  Q1  and  -39.55%  in  Q2  in  Romania, 
3.71% in Q2 in the Netherlands, -35.46% in Q1 and -38.95% in Q2 in Romania, -3.73% 
in Q1 and -24.66% in Q2 in Slovakia, -8.55% in Q1 and -35.86 in (Figure A3 in Appendix 
I considers the annual variations for a larger set of EU Member States). These data hide 
large monthly drops in the number of new start-ups registered during the lockdowns and 
the consequent freeze of administrative activities. For instance, at the height of the first 
wave  in  Bulgaria  the  number  of  start-ups  dropped  by  more  than  65%,  in  Romania  by 
81%,  in  Slovakia  by  45%  and  in  Slovenia  by  about  78%,  in  all  cases,  in  April  2020 
compared  to  April  2019.1  The  resulting  decline  in  job  creation  potential,  due  to  the 
missing  generation  of  start-ups,  can  have  a  dramatic  and  lasting  effect  on  aggregate 
employment  that  persists  as  the  cohorts  of  new  firms  mature  (see  Gourio  et  al.  (2016) 
and Sedláček (2020)). 

1 See Section 4 for further data on each country analysed in this paper. 

7 

 
                                   
5,0% 

4,0% 

3,0% 

2,0% 

1,0% 

0,0% 

-1,0% 

l

t
n
e
m
y
o
p
m
e
e
t
a
g
e
r
g
g
a
f
o
%

Net job creatiom by firm age

0

1

2

3

4

5

firm	age

EU	27	(2015-2016)

Bulgaria	(2013-2017)

Croatia	(2015-2017)

Czechia	(2010-2017)

Luxemburg	(2011-2017)

Netherlands	(2012-2017)

Poland	(2015-2017)

Romania	(2009-2017)

Slovakia	(2014-2017)

Slovenia	(2009-2017)

Figure 2. Importance of start-ups for job creation 

Source: JRC, Eurostat, Business Demography dataset, 2020. 

Acknowledging  that  challenging  times  may  spark  radical  innovations  (e.g.  teleworking, 
contact-tracing applications) and the creation of new successful enterprises, the COVID-
19 crisis is likely to affect negatively not only the start-up rate but also the survival rate 
of young firms and the growth potential of start-ups for which we do not have available 
data yet. In general, data show that start-ups and young firms have a much higher exit 
rate than older firms. Figure 4 shows the evolution of the average survival rate of start-
ups during the first five years of activities. In particular, in the EU 27 about 20% of start-
ups  exit  the  market  during  their  first  year  of  activity,  43%  within  three  years  and  only 
about half survive for five years.2 Survival rates also vary across countries. For instance, 
in  Bulgaria  and  Slovakia  about  20%  of  start-ups  exit  during  their  first  year  while  more 
than  half  survives  longer  than  five  years.  Instead  in  the  Netherlands  less  than  7%  of 
start-ups survive their first year of operation, while then half survive the first five years. 
Figure  A4  in  Appendix  I  shows  the  survival  rates  for  a  larger  set  of  EU  Member  States. 
Furthermore,  the  literature  suggests  that  the  exit  rates  of  young  firms  increase  during 
downturns  (e.g.  Haltiwanger,  Jarmin,  and  Miranda  (2013)).  Moreover,  Sedláček  and 
Sterk  (2017)  show  that  the  growth  potential  of  firms  is  linked  to  the  business  cycle. 
During  business  cycles  job  creation  by  start-ups  and  aggregate  employment  growth  co-
move  and  drop  during  recessions  with  a  correlation  between  entrants  employment  and 
aggregate  employment  growth  (GDP  growth)  of  0.36  (0.45).  Hence,  firms  born  during 
recessions are in general smaller and tend to stay smaller during their life-cycle. This is 
indicative  that  companies  like  Uber  or  Airbnb,  born  during  previous  crisis,  represent  an 
exception rather than a rule. 

2 Also business survival rates for Europe provided by statista.com show a similar pattern: in 2017 almost one in 
five start-ups exited the market within the first year of operation, about 58% of new companies survived 
for  three  years  and  only  44%  survived  for  five  years.  Some  sectors,  such  as  the  IT  sector  seems  to  be 
particularly characterized by high exit rates. CBInsights.com reports that about 70% of tech start-ups fail 
within 20 months after obtaining financing, and about 97% of consumer hardware start-ups eventually fail.   

8 

 
 
 
 
 
 
 
 
 
                                   
	
	
	
New companies registered in the first and second quarter of 
2020 with respect to 2019

e
g
n
a
h
c

l

a
u
n
n
A

20% 

10% 

0% 

-10% 

-20% 

-30% 

-40% 

-50% 

Bulgaria

Netherlands

Romania

Slovakia

Slovenia

Q1

Q2

Figure 3. Annual change in the number of new companies registered in the first and second 
quarter of 2020 with respect to the first and second quarter of 2019 (percentage) 

Source: JRC, data from the Bulgarian National Statistical Institute (BNSI), Netherlands StatLine (CBS), the 
Romanian National Trade Statistical Office (ONRC), the Slovak statistical office (www.statistics.sk) and the 
business register of Slovenia (AJPES). 

Note(1):  The  data,  which  come  from  national  statistical  offices  or  enterprise  registers,  are  not  harmonized 
across countries. As a consequence, Figure 3 does not allow for quantitative cross-country comparisons per se. 
Nevertheless, it is useful to visualize the different impact that COVID-19 has had across countries in terms of 
start-up creation. Finally, we were unable to obtain data for Croatia, Czechia, Luxemburg and Poland. 

Average survival rates of start-ups

100% 
90% 
80% 
70% 
60% 
50% 
40% 
30% 
20% 
10% 
0% 

1-year

2-year

3-year

4-year

5-year

Figure 4. Y-o-y survival rate of start-ups 

Source: JRC, Eurostat, Business Demography dataset, 2020. 

Note(1): The Eurostat database provides the y-o-y survival rates for start-ups (age zero), after one, two, three, 
four and five years of activities. The plotted percentages are the averages of the period from 2013 to 2017 for 
the  EU  27  and  Croatia,  from  2008  to  2018  for  Bulgaria,  Czechia,  Luxemburg,  Netherlands,  Poland,  Romania, 
Slovakia and Slovenia. 

9 

 
 
 
 
 
 
 
 
 
	
At this point of the COVID-19 pandemic it is not clear whether the current economic crisis 
will  be  short  lived  or  sustained  over  a  much  longer  period  and  if  the  implications  for 
start-ups  will  be  consistent  with  what  has  been  documented  during  previous  economic 
downturns.  For this reason, our baseline scenario assumes a strong but brief contraction 
with  start-up  activities  affected  negatively  by  the  crisis,  i.e.  the  number  of  start-ups, 
their  growth  potential  and  the  survival  rate  of  young  firms  drop  for  only  one  year  to  a 
level corresponding to that experienced during the Great Recession of 2008 and 2009 (or 
drop  to  the  minima  of  the  underling  Eurostat  time  series).  This  baseline  scenario 
constitutes  a  plausible  scenario  and  we  postpone  to  further  research  the  analysis  of 
alternatives  conjectures.  According  to  the  EU  start-up  calculator,  this  scenario  would 
create  substantial  and  persistent  aggregate  employment  losses,  especially  in  Bulgaria, 
Croatia, Poland, Romania and Slovakia. A full recovery to realign employment to its pre-
crisis  trend  may  take  about  a  decade,  even  if  start-up  activity  recovers  to  its  pre-crisis 
level  in  one  year.3  The  outlook  is  considerably  improved  in  every  country  if  specific 
policies would be introduced in 2021 targeted at increasing the number of firms entering 
the  economy.  Likewise,  policies  aimed  at  increasing  the  survival  rate  of  young  firms 
would  also  be  quite  effective  in  mitigating  the  negative  impact  of  the  crisis.4  These 
results  suggest  that  policy  makers  have  ample  space  of  manoeuvre  for  policies 
specifically  targeting  an  easily  identifiable  category  of  firms,  i.e.  start-ups  and  young 
firms. Finally, in all the countries analyzed, the service sector may be affected more than 
the industry and manufacturing sectors, as young firms are important job creators in this 
sector. This also in the case in which the three margins are shocked symmetrically across 
sectors as the calculator embeds the employment structure of each sector considered.   

In what follows, Section 2 introduces the EU start-up calculator. Section 3 discusses the 
data  used  and  the  methodology  adopted  to  create  the  simulator.  Section  4  applies  the 
calculator  to  simulate  how  the  destruction  of  start-up  activities  affects  aggregate 
employment  in  Bulgaria,  Croatia,  Czechia,  Luxemburg,  the  Netherlands,  Poland, 
Romania, Slovakia and Slovenia. Business dynamism and scenario analysis are discussed 
separately for each country. A cross-country comparison is deferred to when the tool will 
be available for a larger set of Member States. Section 5 concludes. 

3  The  persistence  of  our  results  is  aligned  to  similar  simulations  based  on  the  OECD  DynEmp3  Database 
performed by Calvino et al. (2020). They evaluate the impact on aggregate employment over 3 to 14 years 
of a 20% decline in the number of entering firms in a year as average across 15 countries.  

4  For  instance,  Germany  is  initiating  a  start-up  program  aiming  at  supporting  and  expanding  venture  capital 
financing, France has created a 4 billion euros fund to support young firms liquidity, Italy has created the 
programme “Smart&Start Italia” which allocates 100 million euros to refinance innovative start-ups, as well 
as  200  million  euros  to  support  venture  capital  financing.  This  adds  to  further  programs  aiding  start-ups 
and  young  firms  with  training  and  couching  and  the  34  billion  euros  more  generally  assigned  to  support 
liquidity needs of SMEs. 

10 

 
 
                                   
2  What is the start-up calculator? 

The  EU  start-up  calculator  is  an  empirical  tool  which  allows  researchers  and  policy 
analysts  to  compute  the  medium-run  impact,  up  to  2030,  that  COVID-19  has  on 
aggregate  employment  due  to  the  destruction  of  the  activities  of  start-ups  and  young 
firms.  In  the  context  of  this  study,  start-ups  are  defined  as  firms  of  age  0,  i.e.  newly 
registered firms, and young firms are those up to 5 years old. The calculator was firstly 
developed by Sedláček and Sterk (2020) for the US economy and then adapted to fit the 
employment  profile  of  young  firms  in  the  EU  Member  States  using  publicly  available 
Eurostat data (Benedetti Fasil et al (2020a) and Benedetti Fasil et al (2020b).5  

The  EU  start-up  calculator  will  be  made  publically  available  as  a  web-based  tool  by  the 
end  of  2020.  A  user  will  be  able  to  easily  simulate  the  time  path  for  aggregate 
employment  for  a  given  scenario  of  start-up  activity.  Different  scenarios  can  be  created 
varying the following three parameters or “margins”6 (in economics parlance) related to 
entry, exit and growth of young firms which tend to worsen during a recession: 

i) 

ii) 

iii) 

the  number  of  start-ups,  i.e.  shift  the  number  of  start-ups  (for  incoming 
cohorts).  
the survival rate of young firms: i.e. shift the profile of firm survival rates by 
age (for all firms up to age fifteen). 
the  growth  potential  of  start-ups:  i.e.  shift  the  profile  of  average  size  by  age 
(for incoming cohorts). 

A  decline  in  the  number  of  start-ups  directly  translates  into  a  decline  in  the  number  of 
new  jobs  created  and  hence  employment.  Furthermore,  this  lost  generation  of  firms 
creates  a  persistent  dent  in  aggregate  employment  as  subsequent  years  will  be 
characterized  by  a  lower  number  of  firms  (see  for  instance  Gourio,  Messer,  and  Siemer 
(2016) and Sedláček (2020)). The survival rate of young firms is directly linked to their 
exit  rate.  In  general,  start-ups  and  young  firms  are  more  fragile  than  established 
incumbents.  This  fragility  is  exacerbated  during  recessions  -  see  Haltiwanger,  Jarmin, 
and  Miranda  (2013).  An  economic  consequence  of  this  pandemic  is  expected  to  be  a 
higher  exit  rate  of  young  firms,  a  lower  firm  survival  rate  and,  thus,  more  job 
destruction.  Finally,  the  growth  potential  of  start-ups  entering  the  economy  in  2020  is 
expected to decline. Firms that are born during a recession start smaller and tend to stay 
smaller even when the economy has recovered as shown by Sedláček and Sterk (2017). 
Changing the growth potential margin will result in shifting downwards the entire growth 
of the employment profile of the cohort of companies entering the market in 2020. 

When  creating  a  scenario  changing  the  three  margins,  the  calculator  computes  the 
implied  time-dependent  changes  in  aggregate  employment  for  the  given  country  and 
sector from 2020 to 2030 and the cumulative job loss. The flexibility of the tool permits 
the  user  to  analyse  the  overall  employment  effects  or  outcomes  by  country  and  sector 
resulting from different degrees of severity of the economic impact that COVID-19 has on 
start-ups and young firms. The scenarios can reflect V-, U-, W- or L- shaped recessions 
with  the  analysis  being  comparable  across  economic  sectors  and  countries.  The  user 
should  constrain  the  flexibility  of  the  tool  with  knowledge  of  the  possible  impacts  of 
COVID-19 on the three margins are likely to be in order to analyse plausible scenarios.   

5The 

start-up 

calculator 

for 

US 

is 

available 

at 

the 

following 

website 

http://users.ox.ac.uk/~econ0506/Main/StartupCalculator.html 

6  where  a  marginal  change  is  a  relaxing  or  tightening  of  constraints  or  the  response  which  this  relaxation  or 

tightening produces 

11 

 
 
 
 
 
                                   
3  Data and methodology 

The EU start-up calculator is tailored to individual Member States. It allows scenarios to 
be  analysed  for  the  whole  business  economy,  the  industrial  sector,  the  manufacturing 
sector  and  the  service  sector.7  It  uses  data  from  Eurostat  Business  Demography 
Statistics  on  the  number  of  firms,  persons  employed,  average  size  and  survival  rate  of 
cohorts of firms for the age bins 0, 1, 2, 3, 4, 5, and all covering the years from 2008 to 
2017 for Czechia, Luxemburg, the Netherlands, Romania and Slovenia and from 2012 to 
2017  for  Bulgaria,  Croatia,  Poland  and  Slovakia.  It  considers  data  of  only  employer 
businesses, that is businesses that have at least one employee.  

The  data  for  2018  and  2019  are  extrapolated  for  each  age  group  assuming  that  the 
survival rates, the firm sizes and the number of start-ups linearly converge to their 2008-
2017 average.8 Similarly, the age profile of firm size and survival rate between age 6 and 
15 for the years 2008 to 2017 are interpolated to obtain values per age bin that reflect 
trends  consistent  with  the  aggregate  average  values  per  age  bin  and  year.  The  age 
profile  of  the  number  of  firms  older  than  5  years  is  then  simply  obtained  using  the 
information on the interpolated yearly survival rate per age bin 6 to 15. Firms older than 
15 are unaffected by the three margins and their time-path does not quantitatively affect 
the analysis. This implies that the simulations should be considered as lower boundaries 
given that they do not take into account the impact of the crisis on firms older than 15. 
Finally, the different scenarios are simulated from 2020. The calculator allows analysis of 
bounce-back  scenarios  to  capture  the  case  in  which  the  three  margin  would  recover 
strongly,  i.e.  above  the  time  series  average,  from  2021,  for  instance  due  to  a  policy 
intervention. See Appendix II for technicalities behind the interpolation extrapolation and 
creation of scenarios.  

The  start-up  calculator  allows  also  to  adjust  the  simulations  taking  into  consideration 
general equilibrium effects, i.e. potential employment reallocation towards surviving and 
older  firms,  which  are  driven  by  a  lower  wage  rate  caused  by  the  decline  in  start-up 
employment.  To  this  end  the  calculator  is  embedded  in  a  standard  heterogeneous  firm 
model,  which  allows  to  connect  the  calculator  with  the  literature  on  firm  dynamics.9  In 
models with firm heterogeneity, firms are distributed over different levels of productivity, 
unproductive firm exit the economy and are replaced by new entrants, while households 
consume  and  supply  labour  to  firms.  The  interested  reader  can  find  in  Appendix  II  the 
derivations  formalizing  the  embedding  of  the  calculator  in  a  general  equilibrium  model 
with  firm  dynamics.  When  the  COVID-19  pandemic  hits  the  activity  of  start-ups  and 
young  firms,  the  general  equilibrium  reallocation  mechanism  dampens  the  effect.  The 
disruption of start-up and young firm activities results in lower employment and a lower 
wage  rate.  Firms  that  remain  in  the  market  hire  more  labour  partially  absorbing  the 
negative  shock  on  employment.  The  magnitude  of  the  equilibrium  dampening  effect 
depend  on  the  labour  supply  and  demand  elasticities.  In  the  EU  start-up  calculator,  the 
elasticities are consistent with the literature and with the values adopted by the European 
Commission  QUEST  and  RHOMOLO  models.  In  particular,  the  labour  supply  elasticity  is 
set  at  0.25  and  the  labour  demand  elasticity  at  -0.1.  These  elasticities  result  in  a 
dampening effect of 29% (see Appendix III).10 

7 The sectoral aggregation analysed reflects the classification available in Eurostat. That is, business economy 
except the activities of holding companies, the industrial sector except construction, and the service sector 
of the business economy except activities of holding companies.  

8  In  the  case  of  Bulgaria,  Croatia,  Poland  and  Slovakia,  Eurostat  data  for  the  survival  rate  start  in  2013.  The 
short time series does not allow us to have 5 years to construct the 1-5 survival rate. As a consequence, 
the minimum, maximum and average value of the survival rate are the same number. 

9  As  a  reference  to  the  canonical  model  of  firm  heterogeneity  see  Hopenhayn  (1993)  and  Hopenhayn  and 

Rogerson (1995). 

10 The elasticities chosen result in a conservative dampening effect. When setting the elasticities at values more 
commonly used in the macroeconomic literature, such that for the labour supply elasticity and –0.246 for 
the labour demand elasticity (Lichter et al. (2015)), the dampening effect reduces to 20%. In light of the 
large  range  of  elasticities  discussed  in  the  literature  the  calculator  features  easily  changeable  elasticities 
enabling for robustness checks.  

12 

 
 
 
                                   
4  Country analysis 

4.1  EU start-up calculator: Bulgaria 

4.1.1  Summary 

•  The COVID-19 crisis is likely to heavily affect young firms, leading to a decline in 

the start-up rate, higher exit of young firms, and lower growth of start-ups. 

•  According  to  the  EU  start-up  calculator,  these  developments  are  likely  to  create 
substantial aggregate employment losses, of up to -2.6% in 2020. The cumulative 
employment loss for the period 2020-2030 could be up to 190,000. 

•  Effects  are  very  persistent:  full  recovery  may  take  more  than  a  decade,  even  if 

start-up activity recovers to its pre-crisis level in one year.  

•  More  than  60%  of  the  aggregate  employment  loss  is  accounted  for  by  the 
reduction  of  the  survival  rate  and  about  40%  by  the  reduction  in  the  number  of 
start-ups  and  their  growth  potential.  Policies  targeted  to  support  young  firm 
survival  and  promote  new  firms’  entry  and  scale  up  would  seem  to  be  the  most 
effective.  The  outlook  is  improved  if  in  2021  there  is  a  rapid  increase  in  the 
number of firms entering the economy. i.e. the cumulative aggregate employment 
loss is reduced to 156,000. 

•  The  Bulgarian  service  sector  may  be  affected  relatively  strongly,  as  young  firms 
are  particularly  important  job  creators  in  this  sector.  In  all  three  sectors  more 
than 60% of the job loss is due to a decline in the survival rate of young firms.  

4.1.2  Business dynamism in Bulgaria 

Before  presenting  the  scenario  analysis,  we  consider  a  number  of  statistics  on  the 
dynamism of Bulgarian firms, see Table I.  

Table I. Descriptive statistics on the economy and sector dynamics of young firms - Bulgaria 

EU 27 
All 

All 

Industry 

Manufacturing 

Services 

start-up rate 

survival rate 

9.2% 

12.2% 

8.6% 

8.4% 

12.8% 

92% 

88.2% 

92% 

92.2% 

87.7% 

share of young firms 

36% 

44.6% 

34.6% 

34% 

46.2% 

employment share of start-ups 

2.5% 

3.3% 

1.6% 

14.3% 

4.2% 

employment share of young firms 

12% 

21.4% 

13.2% 

1.8% 

26% 

Source: JRC, Eurostat, 2020. 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. The time series is from 2012 to 2017. 

The statistics show that, when it comes to entry and exit of firms, the Bulgarian economy 
has  a  higher  start-up  rate  than  the  EU  average.  An  important  share  of  job  creation  is 
attributed  to  start-ups  and  young  firms,  almost  doubling  the  EU  average.  Over  the 
sample,  about  12%  of  firms  exit  and  enter  within  a  given  year.  Start-ups  (firms  of  age 
zero)  account  for  more  than  3%  of  aggregate  employment  whereas  firms  up  to  age  5 

13 

 
 
	
 
 
together account for more than 21%.11  When looking at the sectoral disaggregation, the 
service sector contributes to most of the business dynamic of the overall economy with a 
start-up rate of almost 13% and an employment share of start-ups above 4%. 

Moreover,  recent  data  provided  by  the  Bulgarian  National  Statistical  Institute  shows  an 
increasing  contraction  in  the  number  of  new  businesses  created  since  the  beginning  of 
the  year  with  respect  to  the  same  period  in  2019.  In  particular,  the  contraction,  which 
shows  already  in  February,  it  has  sharply  strengthened  since  the  start  of  the  COVID-19 
pandemic  and  the  ensuing  hibernation  of  most  of  the  economic  activities.  In  particular, 
47.3%  less  businesses  were  created  in  March,  (when  the  COVID-19  pandemic  started), 
65.7%  less  in  April,  39.7%  less  in  May,  in  each  case  compared  to  the  corresponding 
month of 2019. The decline in the registration of new firms improved during the summer 
months but it still shows a considerable contraction with respect the same period in 2019 
(see  Figure  5).  These  statistics  show  an  important  and  persistent  disruption  of  start-up 
activity  due  to  the  COVID-19  pandemic.  This  can  have  important  repercussions  for 
aggregate employment, especially if the number of start-ups does not recover quickly as 
Figure 5 seems to suggest.  

New companies registered

e
g
n
a
h
c

l

a
u
n
n
A

10% 

0% 

-10% 

-20% 

-30% 

-40% 

-50% 

-60% 

-70% 

2020	01

2020	02

2020	03

2020	04

2020	05

2020	06

2020	07

2020	08

2020	09

Figure 5.	Annual change in the number of new companies registered in 2020 with respect to the 
same period in 2019 (percentage) - Bulgaria 

Source: JRC, data from the Bulgarian National Statistical Institute (BNSI), 2020. 

4.1.3  Scenario analysis 

We  consider  a  deterioration  of  the  three  margins  described  above.  Specifically,  we 
assume  that  the  number  of  start-ups  and  the  growth  potential  fall  from  the  sample 
averages to the sample minima (which were reached in the years following the financial 
crisis of 2008), while the survival rate drops considerably12, see Table II.13 Moreover, we 

11 Table I also indicates that industry and manufacturing are very similar to each other as the latter is a large 

component of the former.  

12 In the case of Bulgaria, Eurostat data for the survival rate start in 2013. The short time series does not allow 
us  to  have  5  years  to  construct  the  1-5  survival  rate.  As  a  consequence,  the  minimum,  maximum  and 
average value of the survival rate are the same number. 

13  Note  that  in  Table  II  Industry,  Manufacturing  and  Serving  sector  are  set  to  their  sample  minima  which 
happens  to  be  the  same  for  the  number  of  start-ups.  However,  the  COVID-19  crisis  seems  to  have 
impacted  particularly  strongly  the  service  sector  imposing  strict  lockdown  measures  to  sectors  such  as 
transport, tourism, and hospitality. Alternative scenarios that take into account this asymmetry can easily 
be computed as soon as sectoral data become available.  

14 

 
 
 
 
 
                                   
	
assume that this decline lasts for one year only. From 2021 onwards, the three margins 
are assumed to be back at their sample averages. The scenario is the one of a strong but 
short-lived crisis expecting that once the second wave is contained, the economic activity 
of  start-ups  and  young  firms  will  resume.  This  expectation  seems  to  be  consistent  with 
the recent economic trend.  

Table II. Scenario assumptions - Bulgaria 

All 

Industry  Manufacturing 

Services 

# Start-ups 

-8% 

-8% 

Growth potential 

-8% 

-4% 

-8% 

-4% 

-8% 

-10% 

Survival rate 

-10% 

-10% 

-10% 

-10% 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. 

Figure 6. Scenario analysis for employment and the three margins of start-up activity - Bulgaria 

Source: EU start-up calculator, Bulgaria, 2020. 

Note(1):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample  minima  and  of  one  year  duration  (for  the  survival  rate  it  is  arbitrarily  assumed  to  be  of  -10%).  The 
Business Demography data of Eurostat for Bulgaria are available from 2012 to 2017. The short time series does 
not allow to compute the 1-5 survival rate. As a consequence, the minimum, maximum and average value of 
the survival rate collapse to the same number (yellow line in the plot related to the shock to the survival rate). 
Firms older than 15 are unaffected by the three margins and their time-path does not quantitatively affect the 
analysis. The blue line represents the impact on aggregate employment taking into account general equilibrium 
effects.  

Figure  6  shows  the  scenario  assumptions  on  the  three  margins,  as  well  as  the 
implications  for  aggregate  employment  produced  by  the  calculator.  For  2020,  the 

15 

 
 
 
 
 
calculator  shows  that  the  three  margins  together  reduce  aggregate  employment  by  just 
over  2.6%.  The  recovery  is  slow:  by  2026,  aggregate  employment  is  still  more  than 
0.3%  below  the  level  it  would  have  attained  without  the  disruption  of  start-up  activity. 
The  employment  loss,  cumulated  up  to  2030  is  190,000.  Accounting  for  equilibrium 
adjustments, aggregate effects are dampened by 29%, leaving a cumulative employment 
loss of about 136,000 (blue line in Figure 6). The decline in the survival rate accounts for 
about more than 60% of the effect. The number of start-ups and their growth potential 
account  for  about  20%  each.  These  findings  provide  an  important  input  for  the  policy 
discussion.  The  three  different  margins  can  be  influenced  by  targeted  policies.  Potential 
employment benefits of policies targeted towards firm survival and to promote firm entry 
and  scaling  up  seems  high.  However,  the  cost  of  different  policy  options  needs  to  be 
taken into account.  

Moreover,  we  consider  how  sectors  may  be  affected  differently.  Table  I  shows  that, 
compared  to  manufacturing  and  overall  industry,  firms  in  the  service  sector  are 
somewhat  more  dynamic.  This  sector  has  higher  start-up  and  exit  rates,  and  a  much 
higher employment share of start-ups and other young firms.  

Figure  7  shows  the  results  of  the  calculator  for  the  three  industries.  The  service  sector 
shows a larger employment loss, even though the decline in the three margins assumed 
is not much stronger (Table II). This result is driven by the fact that the service sector is 
more dynamic in terms of entry and exit and therefore reliant on young firms to provide 
employment. 

Aggregate employment

d
n
e
r
t

m
o
r
f
n
o
i
t
a
i
v
e
D

0,5% 

0,0% 

-0,5% 

-1,0% 

-1,5% 

-2,0% 

-2,5% 

-3,0% 

-3,5% 

2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

All

Industry

Manufacturing

Service

Figure 7. Sectors employment (not equilibrium adjusted) - Bulgaria 

Source: JRC, EU start-up calculator, Bulgaria, 2020. 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. 

Note(2):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample minima in the respective sectors and of one year duration (for the survival rate it is arbitrarily assumed 
to be of -10%). The Business Demography data of Eurostat for Bulgaria are available from 2012 to 2017. The 
short time series does not allow to compute the 1-5 survival rate. As a consequence, the minimum, maximum 
and average value of the survival rate collapse to the same number (yellow line in the plot related to the shock 
to  the  survival  rate).  Firms  older  than  15  are  unaffected  by  the  three  margins  and  their  time-path  does  not 
quantitatively affect the analysis. The employment path does not take into account general equilibrium effects.  

16 

 
 
 
  
 
	
	
Table III. Share of aggregate employment loss accounted for by the margins - Bulgaria 

All 

Industry 

Manufacturing 

Services 

Cumulative employment loss 

190 k 

41 k 

# Start-ups 

18% 

20% 

Growth potential 

18% 

9% 

Survival rate 

64% 

71% 

37 k 

18% 

10% 

72% 

138 k 

17% 

21% 

62% 

Source: JRC, EU start-up calculator, Bulgaria, 2020. 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. 

Hence,  a  disruption  to  start-ups  and  other  young  firms  affects  the  service  sector 
relatively  strongly  causing  a  loss  of  aggregate  employment  of  more  than  138,000  jobs 
(Table  III).  In  all  the  three  sectors  more  than  60%  of  the  job  loss  is  attributed  to  the 
decline in the survival rate of young firms, in the industry and manufacturing sector this 
margin  explains  more  than  70%.  The  decline  in  the  number  of  start-ups  accounts  for 
about 20% across the three sectors. In the industry and service sector the decline of the 
growth  potential  explains  a  marginal  part  of  the  overall  employment  loss  as  a 
consequence  of  a  small  magnitude  of  the  shock  affecting  this  margin.  These 
considerations  are  indicative  that  policy  targeted  to  support  firms  to  tackle  survival 
challenges and support entry could have a significant effect in mitigating the impact that 
the COVID-19 pandemic will have on aggregate employment across sectors. 

Subdued  firm  entry  in  2020  due  to  the  COVID-19  crisis  may  be  the  result  of  a 
postponement of start-up decisions with more firm entry in 2021 especially if supported 
by  specific  policies  targeted  to  start-ups  that  could  reduce  the  entry  barriers,  promote 
innovation and relax financial constraints. The optimistic outlook for a bounce-back in the 
number  of  start-ups  is  simulated  in  Figure  8  where,  after  the  initial  negative  shock,  the 
number  of  start-ups  is  increased  in  2021  to  the  maximum  level  registered  in  the  time 
series.  After  an  initial  sharp  decline  in  aggregate  employment,  the  increased  number  of 
start-ups entering in 2021 causes an improvement of the employment trend and a lower 
cumulative  job  loss  totalling  to  about  156,000.  Thanks  to  the  better  outlook  aggregate 
employment  reaches  its  pre  COVID-19  level  by  2026.  This  is  due  to  the  fact  that  the 
number  of  start-ups  account  for  an  important  share  of  the  impact  on  aggregate 
employment, i.e. about 20%, of the total effect on employment (see Table III). 

17 

 
 
 
 
 
Figure 8. Bounce-back scenario in the number of firms, results for aggregate employment - 
Bulgaria 

Source: EU start-up calculator, Bulgaria, 2020. 

Note(1):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample minima and of the duration of one year (for the survival rate it is arbitrarily assumed to be of -10%). 
The Business Demography data of Eurostat for Bulgaria are available from 2012 to 2017. The short time series 
does not allow to compute the 1-5 survival rate. As a consequence, the minimum, maximum and average value 
of  the  survival  rate  collapse  to  the  same  number  (yellow  line  in  the  plot  related  to  the  shock  to  the  survival 
rate) The bounce-back in the number of start-ups is assumed to take place in 2021, to be equal to the sample 
maximum  and  of  one  year  duration.  Firms  older  than  15  are  unaffected  by  the  three  margins  and  their  time-
path does not quantitatively affect the analysis. The blue line represents the impact on aggregate employment 
taking into account general equilibrium effects.  

4.2  EU start-up calculator: Croatia 

4.2.1  Summary 

•  The COVID-19 crisis is likely to heavily affect young firms, leading to a decline in 
the start-up rate, a higher exit of young firms, and lower growth of start-ups. 
•  According  to  the  EU  Start-up  Calculator,  these  developments  are  likely  to  create 
an important aggregate employment loss, of up to 5.6% in 2020. The cumulative 
employment loss for the period 2020-2030 could be up to 237,000. 

•  Effects  are  persistent:  full  recovery  may  take  a  decade,  even  if  start-up  activity 

recovers to its pre-crisis level in one year.  

•  The  decline  in  the  survival  rate  number  accounts  for  more  than  60%  of  the 
employment  loss,  while  the  decline  in  the  growth  potential  and  in  the  number  of 
start-ups  for  about  19%.  Policies  targeted  to  reduce  exit  of  young  firms  would 
seem to be the most effective in Croatia. The outlook is significantly improved if in 
2021  there  is  a  rapid  increase  in  the  number  of  firms  entering  the  economy,  i.e. 
the cumulative aggregate employment loss is reduced to 188,000. 

18 

 
 
 
 
•  The Croatian service sector may be affected relatively strongly, as young firms are 
particularly  important  job  creators  in  this  sector.  In  all  three  sectors  more  than 
50% of the job loss is due to a decline in the survival rate of young firms.  

4.2.2  Business dynamism in Croatia 

Before  presenting  the  scenario  analysis,  we  consider  a  number  of  statistics  on  the 
dynamism  of  Croatian  firms,  see  Table  IV.  The  statistics  show  that,  when  it  comes  to 
firms’ entry and exit, Croatia is dynamic and relies on start-ups for job creation. Over the 
sample,  11.5%  of  firms  exit  within  a  given  year,  whereas  the  start-up  rate  is  about  12 
%. Both values are higher than the EU average. Start-ups (firms of age zero) account for 
about  3.6%  of  aggregate  employment  whereas  firms  up  to  age  5  together  account  for 
almost  16%,  which  is  higher  than  the  EU  average  of  12%.  The  importance  of  start-ups 
and young firms is evident in the service sector where young firms account for more than 
43%  of  the  total  number  of  active  firms  and  for  more  than  18%  of  total  employment, 
whereas in the industry and manufacturing sectors young firms account for about 2% of 
employment.14  As  a  consequence,  we  expect  that  effect  of  the  disruption  of  start-up 
activity due to COVID-19 will be particularly high in the service sector.  

Table IV. Descriptive statistics on the economy and sector dynamics of young firms - Croatia 

EU 27 
All 

All 

Industry  Manufacturing 

Services 

start-up rate 

survival rate 

9.2% 

12.1% 

9.5% 

9.4% 

12.7% 

92% 

88.5% 

90% 

89.8% 

88.4% 

share of young firms 

36% 

39.3% 

35.9% 

35.9% 

43.3% 

employment share of start-ups 

2.5% 

3.6% 

2% 

2.2% 

4.2% 

employment share of young firms 

12% 

15.9% 

10.6% 

11.8% 

18.1% 

Source: JRC, Eurostat, 2020. 

Note(1): The sector aggregate reflects the classifications available in Eurostat –i.e. all business economy except 
the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of  the 
business economy except activities of holding companies. The time series is from 2012 to 2017. 

4.2.3  Scenario analysis 

We  consider  a  deterioration  of  the  three  margins  described  above,  in  which  we  assume 
that  the  number  of  start-ups,  the  survival  rate,  and  the  growth  potential  fall  from  the 
sample  averages  to  the  sample  minima  (which  were  reached  in  the  years  following  the 
financial  crisis  of  2008),  while  the  survival  rate  drops  considerably15,  see  Table  V.16 
Moreover, we assume that this decline lasts for one year only. From 2021 onwards, the 
three margins are assumed to be back at their sample averages. The scenario is the one 

14 Table IV also indicates that industry and manufacturing are very similar to each other as the latter is a large 

component of the former.  

15 In the case of Croatia, Eurostat data for the survival rate start in 2013. The short time series does not allow 
us  to  have  5  years  to  construct  the  1-5  survival  rate.  As  a  consequence,  the  minimum,  maximum  and 
average value of the survival rate are the same number. 

16  Note  that  in  Table  V  Industry,  Manufacturing  and  Serving  sector  are  set  to  their  sample  minima  for  the 
number of start-up and growth potential and set to -20% for the survival rate across sectors. However, the 
COVID-19  crisis  seems  to  have  impacted  particularly  strongly  the  service  sector  imposing  strict  lockdown 
measures  to  sectors  such  as  transport,  tourism,  and  hospitality.  Alternative  scenarios  that  take  into 
account this asymmetry can easily be computed as soon as sectoral data become available.  

19 

 
 
 
 
                                   
of a strong but short-lived crisis expecting that once the COVID-19 crisis is tackled, the 
economic activity of start-ups and young firms will resume. 

Table V. Scenario assumptions - Croatia 

All 

Industry  Manufacturing 

Services 

# Start-ups 

-20% 

-30% 

-30% 

-25% 

Growth potential 

-20% 

-35% 

-35% 

-20% 

Survival rate 

-20% 

-20% 

-20% 

-20% 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. 

Figure  9  shows  the  scenario  assumptions  on  the  three  margins,  as  well  as  the 
implications  for  aggregate  employment  produced  by  the  calculator.  For  2020,  the 
calculator shows that the three margins together reduce aggregate employment by more 
than  5.6%.  The  recovery  is  slow:  by  2026,  aggregate  employment  is  about  1%  lower 
than  the  level  it  would  have  attained  without  the  disruption  of  start-up  activity.  The 
employment  loss,  cumulated  up  to  2030  is  237,000.  Accounting  for  equilibrium 
adjustments, aggregate effects are dampened by 29%, leaving a cumulative employment 
loss of about 169,000.  

Despite symmetric shocks across the margins (i.e. all the three margins decline by 20% 
in  2020),  the  decline  in  the  survival  rate  accounts  for  about  62%  of  the  effect.  The 
decline  in  the  number  of  start-ups  and  in  the  growth  potential  account  for  about  19% 
each.  These  findings  provide  an  important  input  for  the  policy  discussion.  The  three 
different  margins  can  be  influenced  by  targeted  policies.  Potential  employment  benefits 
of policies targeted towards reducing firms exit suggest to be highest. However, the cost 
of different policy options must be considered. 

Moreover,  we  consider  how  sectors  may  be  affected  differently.  Table  IV  shows  that, 
compared  to  manufacturing  and  overall  industry,  firms  in  the  service  sector  are  more 
dynamic:  This  sector  has  much  higher  start-up  and  exit  rates,  and  a  much  higher 
employment share of start-ups and other young firms (Table IV).  

Figure 10 shows the results of the calculator for the three industries. The service sector 
shows a larger employment loss than the one occurred in the manufacturing and industry 
sector even if the shocks to this sector as somewhat smaller in magnitude. This result is 
driven  by  the  fact  that  the  service  sector  has  a  high  business  dynamism  and  therefore 
reliant  on  start-ups  and  young  firms  to  provide  employment.  A  disruption  to  start-ups 
and other young firms affects the service sector causes a loss of aggregate employment 
of more than 165,000 jobs (Table VI). In all three sectors the decline in the survival rate 
accounts  for  more  than  50%  of  the  effect.  This  is  indicative  that  policy  targeted  to 
support  firm  survival  could  have  a  significant  effect  in  mitigating  the  impact  that  the 
COVID-19 pandemic will have on aggregate employment, especially in the service sector. 

20 

 
 
 
 
 
 
 
 
 
Table VI. Share of aggregate employment loss accounted for by the margins - Croatia 

All 

Industry  Manufacturing 

Services 

Cumulative employment loss 

237 k 

59 k 

# Start-ups 

19% 

23% 

Growth potential 

19% 

27% 

Survival rate 

62% 

50% 

55 k 

23% 

27% 

50% 

165 k 

24% 

19% 

57% 

Source: JRC, EU start-up calculator, Croatia, 2020. 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. 

Figure 9. Scenario analysis for employment and the three margins of start-up activity - Croatia 

Source: EU start-up calculator, Croatia, 2020. 

Note(1):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample minima and of the duration of one year (for the survival rate it is arbitrarily assumed to be of -20%). 
The Business Demography data of Eurostat for Croatia are available from 2012 to 2017. The short time series 
does not allow to compute the 1-5 survival rate. As a consequence, the minimum, maximum and average value 
of  the  survival  rate  collapse  to  the  same  number  (yellow  line  in  the  plot  related  to  the  shock  to  the  survival 
rate).  Firms  older  than  15  are  unaffected  by  the  three  margins  and  their  time-path  does  not  quantitatively 
affect the analysis. The blue line represents the impact on aggregate employment taking into account general 
equilibrium effects.  

21 

 
 
 
 
 
 
 
Aggregate employment

d
n
e
r
t

m
o
r
f
n
o
i
t
a
i
v
e
d

1,00%

0,00%

-1,00% 

-2,00% 

-3,00% 

-4,00% 

-5,00% 

-6,00% 

-7,00% 

2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

All

Industry

Manufacturing

Service

Figure 10. Sectors employment (not equilibrium adjusted) - Croatia 

Source: JRC, EU start-up calculator, Croatia, 2020. 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. 

Note(2):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample minima in the respective sectors and of one year duration (for the survival rate it is arbitrarily assumed 
to  be  of  -20%).  The  Business  Demography  data  of  Eurostat  for  Croatia  are  available  from  2012  to  2017.  The 
short time series does not allow to compute the 1-5 survival rate. As a consequence, the minimum, maximum 
and average value of the survival rate collapse to the same number (yellow line in the plot related to the shock 
to  the  survival  rate).  Firms  older  than  15  are  unaffected  by  the  three  margins  and  their  time-path  does  not 
quantitatively affect the analysis. The employment path does not take into account general equilibrium effects.  

Subdued  firm  entry  in  2020  due  to  the  COVID-19  crisis  may  be  the  result  of  a 
postponement  of  start-up  decisions  with  more  firm  entry  in  2021,  which  could  be 
stimulated  by  policies  supporting  entrants  and  the  creation  of  new  business  ideas  that 
could lead to radical innovations. The optimistic outlook for a bounce-back in the number 
of start-ups is simulated in Figure 11 where, after the initial negative shock, the number 
of start-ups is increased in 2021 to a level corresponding to the 2020 drop registered in 
the  time  series.  After  an  initial  sharp  decline  in  aggregate  employment,  the  increased 
number  of  start-ups  entering  in  2021  causes  an  improvement  of  the  employment  trend 
and  a  lower  cumulative  job  loss  totalling  to  about  188,000.  Moreover,  already  in  2021 
the  aggregate  employment  loss  is  reduced  to  only  4%.  Tre  pre  COVID-19  level  of 
employment is reached by 2026.  

22 

 
 
 
   
 
	
	
Figure 11. Bounce-back scenario in the number of firms, results for aggregate employment - 
Croatia 

Source: EU start-up calculator, Croatia, 2020. 

Note(1):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample minima and of the duration of one year (for the survival rate it is arbitrarily assumed to be of -20%). 
The bounce-back in the number of start-ups is assumed to take place in 2021, to be equal to 20% and last one 
year.    Firms  older  than  15  are  unaffected  by  the  three  margins  and  their  time-path  does  not  quantitatively 
affect the analysis. The blue line represents the impact on aggregate employment taking into account general 
equilibrium effects.  

4.3  EU start-up calculator: Czechia 

4.3.1  Summary 

•  The COVID-19 crisis is likely to negatively affect young firms, leading to a decline 
in the start-up rate, a higher exit of young firms, and lower growth of start-ups. 
•  According  to  the  EU  start-up  calculator,  these  developments  are  likely  to  create 
aggregate  employment  losses,  of  up  to  1.8%  in  2020.  The  cumulative 
employment loss for the period 2020-2030 could be up to 241,000. 

•  Effects  are  somewhat  persistent:  full  recovery  may  take  about  a  decade,  even  if 

start-up activity recovers to its pre-crisis level in one year.  

•  About  43%  of  the  aggregate  employment  loss  is  accounted  for  by  a  reduction  of 
the  survival  rate,  about  35%  by  the  reduction  in  the  number  of  start-ups  and 
about  22%  by  the  reduction  in  the  growth  potential.  A  policy  mix  targeted  to 
reduce exit of young firms, incentivise entry and scale-ups would seem to be the 
most  effective.  The  outlook  is  significantly  improved  if  in  2021  there  is  a  rapid 
increase  in  the  number  of  firms  entering  the  economy.  i.e.  the  cumulative 
aggregate employment loss is reduced to 195,000. 

23 

 
 
 
 
•  The  Czech  service  sector  is  affected  more  strongly  than  the  industry  and 
manufacturing  sectors,  as  young  firms  are  important  job  creators  in  this  sector. 
This also in the case of symmetric shocks across sectors. 

4.3.2  Business dynamism in Czechia 

Before  presenting  the  scenario  analysis,  we  consider  a  number  of  statistics  on  the 
dynamism of Czech firms, see Table VII. The statistics show that the Czech economy has 
a slightly higher firm entry and exit rates compared to the average EU one, especially in 
the service sector which seems to be characterized by a large share of young firms. Over 
the  sample  about  11%  of  firms  exit  within  a  given  year,  whereas  the  start-up  rate  is 
9.5%. These values are above the EU average of 9.2% and 8% for entry and exit rate, 
respectively.  However,  the  employment  share  of  Czech  start-ups  and  young  firms  is 
lower  than  the  EU  average.  Start-ups  (firms  of  age  zero)  account  for  about  1.7%  of 
aggregate employment whereas firms up to age 5 together account for 11.5% while EU 
average is 12%.17  

Table VII. Descriptive statistics on the economy and sector dynamics of young firms - Czechia 

EU 27 
All 

All 

Industry 

Manufacturing 

Services 

start-up rate 

survival rate 

9.2% 

9.5% 

7.2% 

7.1% 

10.2% 

92% 

89.8% 

93% 

92.8% 

88.7% 

share of young firms 

36% 

38.4% 

29.9% 

29.8% 

employment share of start-ups 

2.5% 

  1.7% 

0.8% 

employment share of young firms 

12% 

11.5% 

5.9% 

Source: JRC, Eurostat, 2020. 

0.7% 

5.4% 

41% 

2.3% 

15.4% 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. The time series is from 2008 to 2017. 

4.3.3  Scenario analysis 

We  consider  a  deterioration  of  the  three  margins  described  above.  Specifically,  we 
assume that the number of start-ups, and the growth potential and the survival rate fall 
from  the  sample  averages  to  the  sample  minima  (which  were  reached  in  the  years 
following  the  financial  crisis  of  2008),  see  Table  VIII.18  Moreover,  we  assume  that  this 
decline lasts for one year only. From 2021 onwards, the three margins are assumed to be 
back at their sample averages. Hence, the scenario is the one of a strong but short-lived 
crisis  expecting  that  once  the  pandemic  is  under  control,  the  economic  activity  of  start-
ups and young firms will resume. 

17 Table VII also indicates that industry and manufacturing are very similar to each other as the latter is a large 

component of the former.  

18  Note  that  in  Table  VIII  Industry,  Manufacturing  and  Serving  sector  are  set  to  their  sample  minima  which 
happens to be the same for the growth potential. The COVID-19 crisis seems to have impacted particularly 
strongly  the  service  sector  imposing  strict  lockdown  measures  to  sectors  such  as  transport,  tourism,  and 
hospitality.  This  is  partly  reflected  by  the  sample  minima  for  the  number  of  start-ups  and  survival  rate.  
Alternative  scenarios  that  take  into  account  this  asymmetry  can  easily  be  computed  as  soon  as  sectoral 
data become available.  

24 

 
 
 
 
 
                                   
Table VIII. Scenario assumptions -Czechia 

All 

Industry  Manufacturing 

Services 

# Start-ups 

-40% 

-30% 

-30% 

-40% 

Growth potential 

-25% 

-25% 

-25% 

-25% 

Survival rate 

-15% 

-10% 

-8% 

-15% 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. 

Figure 12. Scenario analysis for employment and the three margins of start-up activity - Czechia 

Source: EU start-up calculator, Czechia, 2020. 

Note(1):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample minima and of one year duration. The Business Demography data of Eurostat for Czechia are available 
from  2008  to  2017.  Firms  older  than  15  are  unaffected  by  the  three  margins  and  their  time-path  does  not 
quantitatively  affect  the  analysis.  The  blue  line  represents  the  impact  on  aggregate  employment  taking  into 
account general equilibrium effects.  

Figure  12  shows  the  scenario  assumptions  on  the  three  margins,  as  well  as  the 
implications  for  aggregate  employment  produced  by  the  calculator.  For  2020,  the 
calculator shows that the three margins together reduce aggregate employment by about 
1.8%. The recovery is slow: only by 2030 does aggregate employment reach the level it 
would  have  attained  without  the  disruption  of  start-up  activity.  The  employment  loss, 
cumulated  up  to  2030  is  240,790.  Accounting  for  equilibrium  adjustments,  aggregate 

25 

 
 
 
 
 
 
 
 
effects are dampened by 29%, leaving a cumulative employment loss of about 171,990. 
The  decline  in  the  survival  rate  accounts  for  more  than  40%  of  the  effect,  although  the 
magnitude  of  the  shock  to  this  margin  is  the  lowest.  The  number  of  start-ups  accounts 
for 35% and the growth potential for roughly 22%. These findings provide an important 
input for the policy discussion. The three different margins can be influenced by targeted 
policies. Potential employment benefits of policies targeted towards firm survival suggest 
to be highest. At the same time policies to reduce entry barriers and increase young firm 
growth  potential  could  support  a  faster  recovery.  However,  the  cost  of  different  policy 
options needs to be taken into account.  

Moreover,  we  consider  how  sectors  may  be  affected  differently.  Table  VII  shows  that, 
compared to manufacturing and overall industry, firms in the service sector are relatively 
dynamic:  This  sector  has  much  higher  start-up  and  exit  rates,  and  a  much  higher 
employment share of start-ups and other young firms. 

Figure  13  shows  the  results  of  the  calculator  for  the  three  sectors,  namely  industry, 
manufacturing  and  service  sector.  The  service  sector  shows  a  much  larger  employment 
loss, partly due to larger shocks and mostly driven by the fact that the service sector has 
a higher turnover rate and is more reliant on young firms to provide employment. Hence, 
a  disruption  to  start-ups  and  other  young  firms  affects  the  service  sector  stronger  than 
the  other  sectors  causing  a  loss  of  aggregate  employment  of  more  than  352,000  jobs 
(Table  IX).  In  all  the  three  sectors  the  decline  in  the  number  of  start-ups  account  for 
more  than  30%  while  the  survival  rate  accounts  between  35%  (manufacturing  sector) 
and 43% (service sector) of the employment loss. This is indicative that policies targeted 
to incentivize firm entry, scale-up and reduce firm exit could all have a significant effect 
in  mitigating  the  impact  that  the  COVID-19  pandemic  will  have  on  aggregate 
employment.  

Aggregate employment

d
n
e
r
t

m
o
r
f
n
o
i
t
a
i
v
e
d

0,0% 
-0,5% 
-1,0% 
-1,5% 
-2,0% 
-2,5% 
-3,0% 
-3,5% 
-4,0% 
-4,5% 
-5,0% 

2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

All

Industry

Manufacturing

Service

Figure 13. Sectors employment (not equilibrium adjusted) - Czechia 

Source: EU start-up calculator, Czechia, 2020. 

Note(1): The sector aggregate reflects the classifications available in Eurostat – i.e. business economy except 
the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of  the 
business economy except activities of holding companies. 

Note(2):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample minima in the respective sectors and of one year duration. The Business Demography data of Eurostat 
for Czechia are available from 2008 to 2017. Firms older than 15 are unaffected by the three margins and their 
time-path does not quantitatively affect the analysis. The employment path does not take into account general 
equilibrium effects.  

26 

 
 
  
	
	
Table IX. Share of aggregate employment loss accounted for by the margins - Czechia 

All 

Industry  Manufacturing 

Services 

Cumulative employment loss 

241 k 

92 k 

# Start-ups 

35% 

33% 

Growth potential 

22% 

27% 

Survival rate 

43% 

40% 

32 k 

35% 

30% 

35% 

352 k 

35% 

22% 

43% 

Source: JRC, EU start-up calculator, Czechia, 2020. 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. 

Figure 14. Bounce-back scenario in the number of firms, results for aggregate employment - 
Czechia 

Source: EU start-up calculator, Czechia, 2020. 

Note(1):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample minima and of one year duration. The Business Demography data of Eurostat for Czechia are available 
from 2008 to 2017. The bounce-back in the number of start-ups is assumed to take place in 2021, to be equal 
in magnitude to the sample maxima. Firms older than 15 are unaffected by the three margins and their time-
path does not quantitatively affect the analysis. The blue line represents the impact on aggregate employment 
taking into account general equilibrium effects.  

27 

 
 
 
 
 
 
 
Subdued  firm  entry  in  2020  due  to  the  COVID-19  crisis  may  be  the  result  of  delayed 
entry in 2021 and it should be supported by policies promoting and facilitating firm entry. 
The  optimistic  outlook  for  a  bounce-back  in  the  number  of  start-ups  is  simulated  in 
Figure 14 where, after the initial negative shock, the number of start-ups is increased in 
2021  to  reach  the  sample  maximum.  After  an  initial  sharp  decline  in  aggregate 
employment,  the  increased  number  of  start-ups  entering  in  2021  causes  a  speedy 
recovery  of  aggregate  employment  and  a  lower  cumulative  job  loss  totalling  to  about 
195,000.  

4.4  EU start-up calculator: Luxemburg 

4.4.1  Summary 

•  The COVID-19 crisis is likely to heavily affect young firms, leading to a decline in 

the start-up rate and lower growth of start-ups. 

•  According  to  the  EU  start-up  calculator,  these  developments  are  likely  to  create 
aggregate employment losses, of up to 0.25% of employment trend in 2020. The 
cumulative  employment  loss  for  the  period  2020-2030  could  be  up  to  5,600. 
Hence,  for  Luxemburg  the  effects  of  COVID-19  on  employment  are  relatively 
modest. 

•  Effects  are  somewhat  persistent:  full  recovery  may  take  about  a  decade,  even  if 

start-up activity recovers to its pre-crisis level in one year.  

•  The decline in the number of start-ups accounts for 62%, the growth potential for 
about  23%  and  the  survival  rate  for  about  15%  of  the  aggregate  employment 
loss. Policies targeted to support firm scalability and incentivise entry would seem 
to be the most effective in Luxemburg. The outlook is significantly improved if in 
2021  there  is  a  rapid  increase  in  the  number  of  firms  entering  the  economy,  i.e. 
the cumulative aggregate employment loss is reduced to 2,100 jobs and by 2026 
the  employment  would  surpass  the  level  that  it  would  have  attained  without  the 
COVID-19 crisis. 

•  The  service  sector  may  be  affected  strongly,  as  young  firms  are  important  job 
creators in this sector. This also in the case of mild shocks to start-up activity. 

4.4.2  Business dynamism in Luxemburg 

As  before,  we  consider  a  number  of  statistics  on  the  dynamism  of  firms  in  Luxemburg, 
see  Table  X.  The  statistics  show  that  in  terms  of  firms’  entry  and  exit  the  economy  is 
similar  to  the  EU  average.  Over  the  sample  about  8%  of  firms  exit  within  a  given  year, 
whereas the start-up rate reaches almost 10%. Start-ups (firms of age zero) account for 
1.8%  of  aggregate  employment  whereas  firms  up  to  age  5  together  account  for  about 
11.8%.19  Nonetheless,  young  firms  represent  41.4%  of  the  population  of  active  firms, 
which is higher than the EU average of about 36%.  

19 Table XI also indicates that industry and manufacturing are very similar to each other as the latter is a large 

component of the former.  

28 

 
	
 
 
 
 
 
                                   
Table X. Descriptive statistics on the economy and sector dynamics of young firms - Luxemburg 

EU 27 
All 

All 

Industry  Manufacturing 

Services 

start-up rate 

survival rate 

9.2% 

9.7% 

4.5% 

4.4% 

10.1% 

92% 

92.2% 

94.4% 

94.6% 

92% 

share of young firms 

36% 

41.4% 

22.7% 

28.2% 

42.8% 

employment share of start-ups 

2.5% 

1.8% 

0.3% 

0.3% 

employment share of young firms 

12% 

11.8% 

2.6% 

1.9% 

Source: JRC, Eurostat, 2020. 

2% 

13% 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. The time series is from 2008 to 2017. 

4.4.3  Scenario analysis 

We  consider  a  deterioration  of  the  three  margins  described  above.  Specifically,  we 
assume that the number of start-ups, the growth potential and the survival rate of young 
firms all fall from the sample averages to the sample minima (which were reached in the 
years  following  the  financial  crisis  of  2008),  see  Table  XI.20  Moreover,  we  assume  that 
this decline lasts for one year only. From 2021 onwards, the three margins are assumed 
to  be  back  at  their  sample  averages.  As  for  the  previous  countries,  our  benchmark 
scenario assumes a one-off and short-lived negative effect of the Corona-crisis on start-
up activities, assuming that once the pandemic is under control, the economic activity of 
start-ups and young firms will resume. 

Table XI. Scenario assumptions - Luxemburg 

All 

Industry  Manufacturing 

Services 

# Start-ups 

-8% 

-25% 

-20% 

Growth potential 

-3% 

-20% 

-45% 

-8% 

-4% 

Survival rate 

-0.6% 

-4% 

-6% 

-0.9% 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. 

20 Note that in Table XI Industry, Manufacturing are hit by large shocks with respect to the service sector. This 
is  due  to  the  low  number  of  observations  in  the  industry  and  manufacturing  sectors  which  increase 
volatility.  With  this  caveat  in  mind,  the  COVID-19  crisis  seems  to  have  impacted  particularly  strongly  the 
service  sector  imposing  strict  lockdown  measures  to  sectors  such  as  transport,  tourism,  and  hospitality. 
Alternative  scenarios  that  take  into  account  this  asymmetry  can  easily  be  computed  as  soon  as  sectoral 
data become available.  

29 

 
 
 
 
 
 
 
                                   
Figure  15  shows  the  scenario  assumptions  on  the  three  margins,  as  well  as  the 
implications  for  aggregate  employment  produced  by  the  calculator.21  For  2020,  the 
calculator shows that the three margins together reduce aggregate employment by only 
0.25%. Nevertheless, full recovery of aggregate employment to its pre-COVID level may 
take  up  to  a  decade.  The  employment  loss,  cumulated  up  to  2030  is  5,600.  Accounting 
for  equilibrium  adjustments,  aggregate  effects  are  dampened  by  29%,  leaving  a 
cumulative  employment  loss  of  about  4,000.  The  decline  in  the  number  of  start-ups 
account  for  62%  of  the  negative  employment  effect,  while  the  decline  in  the  growth 
potential accounts for about 23%. Instead, the survival rate has only a marginal impact 
of  15%:  the  low  magnitude  of  the  shock  to  this  margin  reveals  that  exit  rate  does  not 
fluctuate  much  in  Luxemburg.  As  a  consequence,  it  has  only  a  minor  impact  on 
aggregate  employment.  These  findings  provide  an  important  input  for  the  policy 
discussion.  The  different  margins  can  be  influenced  by  targeted  policies.  Potential 
employment benefits of policies targeted towards firm scaling-up, entry of high-potential 
start-ups,  and  ease  entry  suggest  to  be  highest.  At  the  same  time  policies  to  reduce 
entry  barriers  could  support  a  faster  recovery  (see  also  Figure  17  which  simulates  a 
bounce-back  scenario).  However,  the  cost  of  different  policy  options  needs  to  be  taken 
into account. 

Figure 15. Scenario analysis for employment and the three margins of start-up activity - 
Luxemburg 

Source: EU start-up calculator, Luxemburg, 2020. 

Note(1):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample  minima  and  of  one  year  duration.  Firms  older  than  15  are  unaffected  by  the  three  margins  and  their 
time-path  does  not  quantitatively  affect  the  analysis.  The  blue  line  represents  the  impact  on  aggregate 
employment taking into account general equilibrium effects.  

21 Note that the shock on the survival rate amount to 0.6% is equivalent to set the survival rate to its sample 
minima during the period 2008-2017. Hence, firm exit seems to be somewhat constant in Luxemburg and 
not so sensitive to crisis.  

30 

 
 
 
 
                                   
Moreover,  we  consider  how  sectors  may  be  affected  differently.  Table  X  shows  that, 
compared  to  manufacturing  and  overall  industry,  firms  in  the  service  sector  are  more 
dynamic.  This  sector  has  a  much  higher  start-up  rate,  and  a  much  higher  employment 
share of start-ups and other young firms. 

Figure  16  shows  the  results  of  the  calculator  for  the  three  industries.  Upon  impact,  all 
three  sectors  react  to  the  shock  with  the  manufacturing  sector  displaying  the  larger 
negative  impact.  Recalling  that  the  shocks  applied  to  the  industry  and  especially  the 
manufacturing  sectors  are  much  larger  than  the  ones  applied  to  the  service  sector,  it  is 
interesting to note how strongly the employment in the service sector reacts. This result 
is  driven  by  the  fact  that  the  service  sector  is  more  dynamic  and  therefore  reliant  on 
young  firms  to  provide  employment.  Hence,  a  disruption  to  start-ups  and  other  young 
firms  affects  the  service  sector  relatively  strongly  causing  a  loss  of  aggregate 
employment  of  more  than  5,000  jobs  (Table  XII).  The  decline  in  all  the  three  margins 
seems  to  play  an  important  role  in  explaining  the  employment  loss  (Table  XII).  The 
number  of  start-ups  accounts  from  23%  (manufacturing)  to  53%  (service  sector),  the 
growth  potential  accounts  from  27%  (service  sector)  to  50%  (manufacturing)  and  the 
survival  rate  from  20%  (service  sector)  to  27%  (manufacturing).  This  is  indicative  that 
policy  targeted  to  incentivize  firm  entry,  scalability  and  survival  could  have  a  significant 
effect  in  mitigating  the  impact  that  the  COVID-19  pandemic  will  have  on  aggregate 
employment in Luxemburg.  

Aggregate employment

d
n
e
r
t

m
o
r
f
n
o
i
t
a
i
v
e
d

0,0% 

-0,1% 

-0,1% 

-0,2% 

-0,2% 

-0,3% 

-0,3% 

-0,4% 

-0,4% 

-0,5% 

2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

All

Industry

Manufacturing

Service

Figure 16. Sectors employment (not equilibrium adjusted) - Luxemburg 

Source: JRC, EU start-up calculator, Luxemburg, 2020. 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. 

Note(2):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample  minima  in  the  respective  sectors  and  of  one  year  duration.  Firms  older  than  15  are  unaffected  by  the 
three  margins  and  their  time-path  does  not  quantitatively  affect  the  analysis.  The  employment  path  does  not 
take into account general equilibrium effects.  

31 

 
 
 
 
  
 
 
 
	
	
Table  XII.  Share  of  aggregate  employment  loss  accounted  for  by  the  margins  - 
Luxemburg 

All 

Industry  Manufacturing 

Services 

Cumulative employment loss 

5.6 k 

0.7 k 

0.9 k 

5.1 k 

# Start-ups 

62% 

41% 

Growth potential 

23% 

33% 

Survival rate 

15% 

26% 

23% 

50% 

27% 

53% 

27% 

20% 

Source: JRC, EU start-up calculator, Luxemburg, 2020. 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. 

Figure 17. Bounce-back scenario in the number of firms, results for aggregate employment - 
Luxemburg 

Source: EU start-up calculator, Luxemburg, 2020. 

Note(1):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample minima and of one year duration. The bounce-back in the number of start-ups is assumed to take place 
in  2021,  to  be  equal  to  the  sample  maximum  and  last  one  year.    Firms  older  than  15  are  unaffected  by  the 
three  margins  and  their  time-path  does  not  quantitatively  affect  the  analysis.  The  blue  line  represents  the 
impact on aggregate employment taking into account general equilibrium effects.  

Subdued  firm  entry  in  2020  due  to  the  COVID-19  crisis  may  be  the  result  of  a 
postponement of start-up decisions with more firm entry in 2021, especially if stimulated 
by  policies  targeted  to  support  the  creation  of  new  firms  and  innovative  ideas.  The 

32 

 
 
 
 
 
optimistic outlook for a bounce-back in the number of start-ups is simulated in Figure 17 
where,  after  the  initial  negative  shock,  the  number  of  start-ups  is  increased  in  2021  to 
the  maximum  level  registered  in  the  time  series.  After  an  initial  decline  in  aggregate 
employment,  the  increased  number  of  start-ups  entering  in  2021  causes  a  speedy 
recovery  of  aggregate  employment  and  a  lower  cumulative  job  loss  during  the  period 
2020-2030  totalling  to  about  2,100.  Thanks  to  this  enhanced  firm  entry  aggregate 
employment surpasses its pre COVID-19 trend by 2026.    

4.5  EU start-up calculator: Netherlands 

4.5.1  Summary 

•  The COVID-19 crisis is likely to heavily affect young firms, leading to a decline in 
the start-up rate, a higher exit of young firms, and lower growth of start-ups. 
•  According  to  the  EU  Start-up  Calculator,  these  developments  are  likely  to  create 
aggregate  employment  losses,  of  up  to  almost  1.4%  in  2021.  The  cumulative 
employment loss for the period 2020-2030 could be up to 462,000. 

•  Effects  are  persistent  showing  a  U-shaped  path  for  aggregate  employment:  full 

recovery may take a decade. 

•  The decline in the number of start-ups accounts for about 43% of the employment 
loss and the decline of the growth potential of start-ups for about 52%. A holistic 
approach  to  policies  targeted  to  incentivise  entry  and  productivity  of  new  firms 
would  seem  to  be  the  most  effective  in  the  Netherlands.  The  outlook  is 
significantly  improved  if  in  2021  there  is  a  rapid  increase  in  the  number  of  firms 
entering the economy, i.e. the cumulative aggregate employment loss is reduced 
to 203,000 and aggregate employment surpasses its pre-COVID level by 2026. 
•  The Dutch service sector may be affected particularly strongly, as young firms are 

particularly important job creators in this sector. 

4.5.2  Business dynamism in the Netherlands 

Before  presenting  the  scenario  analysis,  we  consider  a  number  of  statistics  on  the 
dynamism  of  firms  in  the  Netherlands,  see  Table  XIII.  The  statistics  show  that,  when  it 
comes  to  firms’  entry  and  exit,  the  Dutch  economy  is  somewhat  in  line  with  the  EU 
average,  and  relies  importantly  on  start-ups  for  job  creation.  Over  the  sample,  about 
10% of firms enter and exit within a given year. Start-ups (firms of age zero) account for 
about  2.5%  of  aggregate  employment  (as  the  EU  average).  Firms  up  to  age  5  together 
account  for  more  than  20%  of  aggregate  employment  (while  the  EU  average  settles  at 
12%).  

The importance of start-ups and young firms is particularly evident in the service sector 
rather than in the industry and manufacturing sector. In fact, in the service sector young 
firms account for about 40% of the total number of active firms and for more than 23% 
of  total  employment  (Table  XIII).22  Hence,  it  has  to  be  expected  that  this  sector  will  be 
most  strongly  hit  by  a  disruption  of  start-up  activities  also  when  shocks  across  sectors 
are symmetric. 

22  Table  XIII  also  indicates  that  industry  and  manufacturing  are  very  similar  to  each  other  as  the  latter  is  a 

large component of the former.  

33 

 
 
 
 
 
                                   
Table XIII. Descriptive statistics on the economy and sector dynamics of young firms - 
Netherlands 

EU 27 
All 

All 

Industry  Manufacturing 

Services 

start-up rate 

survival rate 

9.2% 

9.8% 

6.5% 

6.2% 

10.3% 

92% 

90.5% 

93.2% 

93.4% 

90.4% 

share of young firms 

36% 

38.2% 

28.7% 

28% 

39.5% 

employment share of start-ups 

2.5% 

2.5% 

1.4% 

1.2% 

2.7% 

employment share of young firms 

12% 

21.9% 

13.6% 

12% 

23.5% 

Source: JRC, Eurostat, 2020. 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. The time series is from 2008 to 2017. 

Nevertheless, the COVID-19 pandemic seems to have hit only marginally the creation of 
new  companies  in  the  Netherland.  StatLine,  the  database  of  Statistics  Netherlands, 
shows that in the first quarter of 2020 the number of new firms registered increased by 
12.9% and only in the second quarter it shows a slight contraction of 3.7%, in each case 
compared  to  the  first  and  second  quarter  of  2019.  Furthermore,  data  from  the  Dutch 
Chamber of Commerce shows that the number of newly formed companies has declined 
by 1.82% between January and August 2020 with respect the same period in 2019. This 
highlights  a  persistence  in  the  negative  trend  of  new  firm  entry  during  the  summer 
months.23  

4.5.3  Scenario analysis 

As for the previous countries analysed, we consider a deterioration of the three margins 
described above. Specifically, we assume that the number of start-ups, the survival rate, 
and the growth potential all fall from the sample averages to the sample minima (which 
were  reached  in  the  years  following  the  financial  crisis  of  2008),  see  Table  XIV.24 
Moreover, we assume that this decline lasts for one year only. From 2021 onwards, the 
three margins are assumed to be back at their sample averages. The scenario is one of a 
strong but short-lived crisis expecting that once the COVID-19 pandemic will be tackled, 
the economic activity of start-ups and young firms will resume. 

23  StatLine  data  are  available  at  https://opendata.cbs.nl/statline/#/CBS/nl/dataset/83148NED/table?dl=4041A 
from 

while 
https://www.kvk.nl/download/KVK_Bedrijvendynamiek_januari_tot_en_met_augustus_2020_tcm109-
492408.pdf . 

the  Dutch  Chamber 

of  Commerce 

extracted 

from 

data 

can 

the 

be 

24  Note  that  in  Table  XIV  industry,  manufacturing  and  service  sector  are  set  to  their  sample  minima  which 
results  in  larger  shocks  to  manufacturing  and  industry.  However,  the  COVID-19  crisis  seems  to  have 
impacted  more  the  service  sector  with  reduced  activities  in  sectors  such  as  transport,  tourism,  and 
hospitality. Alternative scenarios that take into account this asymmetry can easily be computed as soon as 
sectoral data become available.  

34 

 
 
 
 
 
 
 
 
 
                                   
Table XIV. Scenario assumptions - Netherlands 

All 

Industry  Manufacturing 

Services 

# Start-ups 

-15% 

-14% 

-14% 

-13% 

Growth potential 

-18% 

-30% 

-30% 

-25% 

Survival rate 

-0.5% 

-0.5% 

-0.7% 

-0.5% 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. 

Figure 18. Scenario analysis for employment and the three margins of start-up activity - 
Netherlands 

Source: EU start-up calculator, Netherlands, 2020. 

Note(1):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample  minima  and  of  one  year  duration.  Firms  older  than  15  are  unaffected  by  the  three  margins  and  their 
time-path  does  not  quantitatively  affect  the  analysis.  The  blue  line  represents  the  impact  on  aggregate 
employment taking into account general equilibrium effects.  

Figure  18  shows  the  scenario  assumptions  on  the  three  margins,  as  well  as  the 
implications for aggregate employment produced by the calculator. The shocks results in 
a  U-shaped  path  for  aggregate  employment  in  the  period  2020-2030.  For  2020,  the 
calculator  shows  that  the  three  margins  together  reduce  aggregate  employment  by  1% 
and  by  1.4%  between  2021  and  2025.  The  recovery  is  slow:  only  by  2030,  aggregate 
employment is reaching the level it would have attained without the disruption of start-
up  activity.  The  employment  loss,  cumulated  up  to  2030  is  462,000.  Accounting  for 

35 

 
 
 
 
 
 
equilibrium adjustments, aggregate effects are dampened by 29%, leaving a cumulative 
employment loss of about 330,000. The decline in the number of start-ups accounts for 
about  43%  of  the  effect  and  the  growth  potential  for  about  35%.  This  leaves  only  a 
marginal  role  played  by  the  survival  rate,  also  due  to  a  shock  applied  that  is  small  in 
magnitude.  Nevertheless,  these  findings  provide  an  important  input  for  the  policy 
discussion.  Potential  employment  benefits  of  policies  targeted  towards  firm  scaling  up 
and entry suggest to be highest. However, the cost of different policy options needs to be 
taken into account.  

Moreover,  we  consider  how  sectors  may  be  affected  differently.  Table  XIII  shows  that, 
compared  to  manufacturing  and  overall  industry,  firms  in  the  service  sector  are  more 
dynamic.  This  sector  has  much  higher  start-up  rate,  and  a  much  higher  employment 
share of start-ups and other young firms. 

Aggregate employment

d
n
e
r
t

m
o
r
f
n
o
i
t
a
i
v
e
d

0,2% 
0,0% 
-0,2% 
-0,4% 
-0,6% 
-0,8% 
-1,0% 
-1,2% 
-1,4% 
-1,6% 
-1,8% 
-2,0% 

2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

All

Industry

Manufacturing

Service

Figure 19. Sectors employment (not equilibrium adjusted) -Netherlands 

Source: JRC, EU start-up calculator, Netherlands, 2020. 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. 

Note(2):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample  minima  in  the  respective  sectors  and  of  one  year  duration.  Firms  older  than  15  are  unaffected  by  the 
three  margins  and  their  time-path  does  not  quantitatively  affect  the  analysis.  The  employment  path  does  not 
take into account general equilibrium effects.  

Figure 19 shows the results of the calculator for the three industries. The service sector 
shows  a  much  larger  reaction  to  the  negative  shock  to  the  three  margin.  Employment 
declines  up  to  1.8%  in  2021  and  the  cumulative  employment  loss  in  the  period  2020-
2030  is  of  more  than  452,000  jobs.  This  result  is  driven  by  the  fact  that  the  service 
sector is more dynamic (in terms of entry and exit rates) and therefore reliant on young 
firms  to  provide  employment.  Hence,  a  disruption  to  start-ups  and  other  young  firms 
affects the service sector relatively strongly. Finally, in all the three sectors the decline in 
the  number  of  start-ups  accounts  for  about  30%  of  the  effect  and  the  growth  potential 
for about 65%, leaving only a marginal role to the survival rate (Table XV).  

36 

 
 
  
 
 
 
	
	
Table XV. Share of aggregate employment loss accounted for by the margins Netherlands 

All 

Industry  Manufacturing 

Services 

Cumulative employment loss 

462 k 

35 k 

# Start-ups 

43% 

31% 

Growth potential 

52% 

65% 

Survival rate 

5% 

4% 

31 k 

30% 

65% 

5% 

452 k 

33% 

63% 

4% 

Source: JRC, EU start-up calculator, Netherlands, 2020. 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. 

Figure 20. Bounce-back scenario in the number of firms, results for aggregate employment - 
Netherlands 

Source: EU start-up calculator, Netherlands, 2020. 

Note(1):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample minima and of one year duration. The bounce-back in the number of start-ups is assumed to take place 
in  2021,  to  be  equal  to  the  sample  maximum  and  last  one  year.    Firms  older  than  15  are  unaffected  by  the 
three  margins  and  their  time-path  does  not  quantitatively  affect  the  analysis.  The  blue  line  represents  the 
impact on aggregate employment taking into account general equilibrium effects.  

37 

 
 
 
 
 
 
 
 
Subdued  firm  entry  in  2020  due  to  the  COVID-19  crisis  may  be  the  result  of  delayed 
entry,  pushing  up  the  start-up  rate  in  2021.  Following  the  discussion  above,  policies 
aimed  at  facilitating  the  entry  of  new  firms  in  the  market  can  be  quite  effective.  The 
optimistic outlook for a bounce-back in the number of start-ups is simulated in Figure 20 
where, after the initial negative shock, the number of start-ups is increased in 2021 to its 
sample  maximum.  After  an  initial  decline  in  aggregate  employment,  the  increased 
number  of  start-ups  entering  in  2021  causes  a  significant  improvement  in  the 
employment trend and a lower cumulative job loss totalling to about 203,000. Thanks to 
the better outlook aggregate employment surpasses its pre COVID-19 level by 2026.    

4.6  EU start-up calculator: Poland 

4.6.1  Summary 

•  The COVID-19 crisis is likely to heavily affect young firms, leading to a decline in 

the start-up rate, higher exit of young firms, and lower growth of start-ups. 

•  According  to  the  EU  start-up  calculator,  these  developments  are  likely  to  create 
substantial aggregate employment losses, of up to 5.6% of employment trend in 
2020.  The  cumulative  employment  loss  for  the  period  2020-2030  could  be  up  to 
2179,000. 

•  Effects  are  very  persistent:  full  recovery  may  take  more  than  a  decade,  even  if 

start-up activity recovers to its pre-crisis level in one year.  

•  More  than  half  of  the  aggregate  employment  loss  is  accounted  for  by  decline  in 
the survival rate, and 28% by the reduction in the number of start-ups. Only 20% 
is attributed to the decline in the growth potential of start-ups. Policies targeted to 
support  young  firm  survival  and  promote  new  firms’  entry  would  seem  to  be  the 
most effective. The outlook is improved if in 2021 there is a rapid increase in the 
number of firms entering the economy. i.e. the cumulative aggregate employment 
loss is reduced to 1334,000. 

•  The Polish service sector may be affected particularly strongly, as young firms are 
particularly  important  job  creators  in  this  sector.  In  this  sector  most  of  the 
employment  loss  is  due  to  the  decline  in  the  number  of  start-ups  and  in  the 
survival rate.   

4.6.2  Business dynamism in Poland 

Before  presenting  the  scenario  analysis,  we  consider  a  number  of  statistics  on  the 
dynamism of Polish firms, see Table XVI.  

The  statistics  show  that,  when  it  comes  to  entry  and  exit  of  firms,  the  Polish  economy 
has almost a double start-up rate with respect to the EU average. An important share of 
job  creation  is  attributed  to  start-ups  and  young  firms,  which  is  higher  than  the  EU 
average.  Over  the  sample,  about  15%  of  firms  exit  within  a  given  year,  whereas  the 
start-up  rate  is  about  17%.  Start-ups  (firms  of  age  zero)  account  for  about  4%  of 
aggregate employment whereas firms up to age 5 together account for more than 16%.25  
When looking at the sectoral disaggregation, the service sector contributes to most of the 
business  dynamic  of  the  overall  economy  with  a  start-up  rate  of  above  17%  and  an 
employment share of start-ups of 19.5%. 

25 Table XVI also indicates that industry and manufacturing are very similar to each other as the latter is a large 

component of the former.  

38 

 
 
 
 
                                   
Table XVI. Descriptive statistics on the economy and sector dynamics of young firms - Poland 

EU 27 
All 

All 

Industry 

Manufacturing 

Services 

start-up rate 

survival rate 

9.2% 

16.7% 

11.7% 

11.6% 

17.2% 

92% 

84.9% 

89% 

89% 

84.6% 

share of young firms 

36% 

43.5% 

32.8% 

32.3% 

employment share of start-ups 

2.5% 

4.1% 

1.9% 

2% 

47% 

5% 

employment share of young firms 

12% 

16.3% 

9.2% 

9.4% 

19.5% 

Source: JRC, Eurostat, 2020. 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. The time series is from 2012 to 2017. 

4.6.3  Scenario analysis 

We  consider  a  deterioration  of  the  three  margins  described  above.  Specifically,  we 
assume  that  the  number  of  start-ups,  and  the  growth  potential  fall  from  the  sample 
averages to the sample minima (with shock that happen to be symmetric across sectors), 
while  the  survival  rate  is  assumed  to  arbitrarily  decline  by  20%,  see  Table  XVII.26 
Moreover, we assume that this decline lasts for one year only. From 2021 onwards, the 
three margins are assumed to be back at their sample averages. The scenario is the one 
of a strong but short-lived crisis expecting that once the pandemic is under control, the 
economic activity of start-ups and young firms will resume. This expectation seems to be 
consistent with the recent economic trend.  

Table XVII. Scenario assumptions - Poland 

All 

Industry  Manufacturing 

Services 

# Start-ups 

-30% 

-30% 

-30% 

-30% 

Growth potential 

-22% 

-22% 

-22% 

-22% 

Survival rate 

-20% 

-20% 

-20% 

-20% 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. 

26  Note  that  in  Table  XVII  Industry,  Manufacturing  and  Serving  sector  are  set  to  their  sample  minima  which 
shocks happen to be the same for the number of start-ups and growth potential. However, the COVID-19 
crisis  seems  to  have  impacted  particularly  strongly  the  service  sector  imposing  strict  lockdown  and 
restrictive measures to sectors such as transport, tourism, and hospitality. Alternative scenarios that take 
into account this asymmetry can easily be computed as soon as sectoral data become available.  

39 

 
	
 
 
 
 
 
 
                                   
Figure 21. Scenario analysis for employment and the three margins of start-up activity - Poland 

Source: EU start-up calculator, Poland, 2020. 

Note(1):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample  minima  and  of  one  year  duration  (for  the  survival  rate  it  is  arbitrarily  assumed  to  be  of  -20%).  The 
Business Demography data of Eurostat for Poland are available from 2012 to 2017. The short time series does 
not allow to compute the 1-5 survival rate. As a consequence, the minimum, maximum and average value of 
the survival rate collapse to the same number (yellow line in the plot related to the shock to the survival rate). 
Firms older than 15 are unaffected by the three margins and their time-path does not quantitatively affect the 
analysis. The blue line represents the impact on aggregate employment taking into account general equilibrium 
effects.  

Figure  21  shows  the  scenario  assumptions  on  the  three  margins,  as  well  as  the 
implications  for  aggregate  employment  produced  by  the  calculator.  For  2020,  the 
calculator  shows  that  the  three  margins  together  reduce  aggregate  employment  by 
5.6%. The recovery is slow: by 2026, aggregate employment is still more than 1% below 
the  level  it  would  have  attained  without  the  disruption  of  start-up  activity.  The 
employment  loss,  cumulated  up  to  2030  is  2179,000.  Accounting  for  equilibrium 
adjustments, aggregate effects are dampened by 29%, leaving a cumulative employment 
loss  of  about  1556,000  (blue  line  in  Figure  21).  The  decline  in  the  number  of  start-ups 
accounts  for  28%,  while  the  decline  in  the  survival  rate  for  more  than  half  of  the  total 
effect.  These  findings  provide  an  important  input  for  the  policy  discussion.  The  three 
different  margins  can  be  influenced  by  targeted  policies.  Potential  employment  benefits 
of  policies  targeted  towards  firm  survival  and  to  promote  firm  entry  suggest  to  be 
highest. However, the cost of different policy options needs to be taken into account.  

Moreover,  we  consider  how  sectors  may  be  affected  differently.  Table  XVI  shows  that, 
compared  to  manufacturing  and  overall  industry,  firms  in  the  service  sector  are  more 
dynamic. This sector has higher start-up and exit rates, and a much higher employment 
share of start-ups and other young firms.  

Figure 22 shows the results of the calculator for the three industries. The service sector 
shows  a  larger  employment  loss,  even  though  the  shocks  to  the  three  margins  are 
assumed  to  be  symmetric  across  sectors  (Table  XVII).  This  result  is  driven  by  the  fact 

40 

 
 
 
that  the  service  sector  is  more  dynamic  in  terms  of  entry  and  exit  and  therefore  reliant 
on young firms to provide employment. 

Aggregate employment

0,00%

-1,00% 

-2,00% 

-3,00% 

-4,00% 

-5,00% 

-6,00% 

-7,00% 

-8,00% 

2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

All

Industry

Manufacturing

Service

Figure 22. Sectors employment (not equilibrium adjusted) - Poland 

Source: JRC, EU start-up calculator, Poland, 2020. 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. 

Note(2):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample minima in the respective sectors and of one year duration. (for the survival rate it is arbitrarily assumed 
to  be  of  -20%).  The  Business  Demography  data  of  Eurostat  for  Poland  are  available  from  2012  to  2017.  The 
short time series does not allow to compute the 1-5 survival rate. As a consequence, the minimum, maximum 
and average value of the survival rate collapse to the same number (yellow line in the plot related to the shock 
to  the  survival  rate).  Firms  older  than  15  are  unaffected  by  the  three  margins  and  their  time-path  does  not 
quantitatively affect the analysis. The employment path does not take into account general equilibrium effects. 

Table XVIII. Share of aggregate employment loss accounted for by the margins - Poland 

All 

Industry 

Manufacturing 

Services 

Cumulative employment loss 

2179 k 

475 k 

429 k 

1441 k 

# Start-ups 

28% 

26% 

Growth potential 

20% 

20% 

Survival rate 

52% 

54% 

26% 

20% 

54% 

27% 

20% 

53% 

Source: JRC, EU start-up calculator, Poland, 2020. 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. 

41 

 
 
 
  
 
 
 
 
Hence,  a  disruption  to  start-ups  and  other  young  firms  affects  the  service  sector 
relatively strongly causing a loss of aggregate employment of more than 1441,000 jobs 
(Table  XVIII).  The  decline  in  the  number  of  start-ups  explains  around  27%  of  the 
employment  effect,  while  the  survival  rate  more  than  half,  leaving  only  one  fifth  of  the 
effect explained by the growth potential. This pattern is present also in the industry and 
manufacturing  sector  as  a  consequence  of  a  symmetric  shocks  across  sectors.  These 
considerations  are  indicative  that  policy  targeted  to  support  firms  to  tackle  survival 
challenges and support entry could have a significant effect in mitigating the impact that 
the COVID-19 pandemic will have on aggregate employment. 

Figure 23. Bounce-back scenario in the number of firms, results for aggregate employment - 
Poland 

Source: EU start-up calculator, Poland, 2020. 

Note(1):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample  minima  and  of  one  year  duration  (for  the  survival  rate  it  is  arbitrarily  assumed  to  be  of  -20%).  The 
Business Demography data of Eurostat for Poland are available from 2012 to 2017. The short time series does 
not allow to compute the 1-5 survival rate. As a consequence, the minimum, maximum and average value of 
the survival rate collapse to the same number (yellow line in the plot related to the shock to the survival rate). 
The  bounce-back  in  the  number  of  start-ups  is  assumed  to  take  place  in  2021,  to  be  equal  to  the  maximum 
value registered in the time series. Firms older than 15 are unaffected by the three margins and their time-path 
does  not  quantitatively  affect  the  analysis.  The  blue  line  represents  the  impact  on  aggregate  employment 
taking into account general equilibrium effects. 

Subdued  firm  entry  in  2020  due  to  the  COVID-19  crisis  may  be  the  result  of  a 
postponement of start-up decisions with more firm entry in 2021 especially if supported 
by  specific  policies  targeted  to  start-ups  that  could  reduce  entry  barriers,  promote 
innovation and relax financial constraints. The optimistic outlook for a bounce-back in the 
number of start-ups is simulated in Figure 23 where, after the initial negative shock, the 
number  of  start-ups  is  increased  in  2021  to  the  maximum  level  registered  in  the  time 
series.  After  an  initial  sharp  decline  in  aggregate  employment,  the  increased  number  of 
start-ups entering in 2021 causes an improvement of the employment trend and a lower 

42 

 
 
 
 
 
 
cumulative job loss totalling to about 1334,000. Thanks to the better outlook aggregate 
employment  reaches  its  pre  COVID-19  level  by  2026.  This  is  due  to  the  fact  that  the 
number  of  start-ups  account  for  a  large  share  of  the  impact  on  aggregate  employment, 
i.e. about 30%, of the total effect on employment (see Table XVIII) and a large positive 
shock simulated. 

4.7  EU start-up calculator: Romania 

4.7.1  Summary 

•  The COVID-19 crisis is likely to heavily affect young firms, leading to a decline in 
the start-up rate, a higher exit of young firms, and lower growth of start-ups. 
•  According  to  the  EU  start-up  calculator,  these  developments  are  likely  to  create 
substantial aggregate employment losses, of up to 3.4% in 2020. The cumulative 
employment loss for the period 2020-2030 could be up to 632,000. 

•  Effects  are  very  persistent:  full  recovery  may  take  more  than  a  decade,  even  if 
start-up  activity  recovers  to  its  pre-crisis  level  in  one  year.  By  2026,  aggregate 
employment is still more than 0.5% below the level it would have attained without 
the disruption of start-up activity 

•  The decline in the number of start-ups accounts for about 39% of the employment 
loss, the decline in the survival rate for about 43% and the decline in the growth 
potential of start-ups for about 18%. Policies targeted to favour firm survival and 
incentivise entry would seem to be the most effective in Romania. The outlook is 
significantly  improved  if  in  2021  there  is  a  rapid  increase  in  the  number  of  firms 
entering the economy, i.e. the cumulative aggregate employment loss is reduced 
to 331,000. 

•  The Romanian service sector may be affected particularly strongly, as young firms 

are particularly important job creators in this sector. 

4.7.2  Business dynamism in Romania 

Before  presenting  the  scenario  analysis,  we  consider  a  number  of  statistics  on  the 
dynamism of Romanian firms, see Table XIX. The statistics show that, when it comes to 
firm  entry  and  exit,  the  Romanian  economy  is  in  line  with  the  EU  27  average,  relying 
importantly  on  start-ups  for  job  creation.  Over  the  sample,  about  12%  of  firms  exit 
within  a  given  year,  whereas  the  start-up  rate  is  above  10%.  Start-ups  (firms  of  age 
zero)  account  for  about  3%  of  aggregate  employment  whereas  firms  up  to  age  5 
together  account  for  more  than  17%  implying  a  higher  contribution  from  young  firm  as 
job creators in Romania relative to the EU average of 12%. 27 

27 Table XIX also indicates that industry and manufacturing are very similar to each other as the latter is a large 

component of the former.  

43 

 
 
 
 
 
 
 
 
 
 
                                   
Table XIX. Descriptive statistics on the economy and sector dynamics of young firms - Romania 

EU 27 
All 

All 

Industry  Manufacturing 

Services 

start-up rate 

survival rate 

9.2%  10.2% 

8.6% 

8.4% 

10% 

92% 

92.1% 

93% 

93.1% 

92.1% 

share of young firms 

36% 

37.5% 

32.5% 

31.5% 

36.9% 

employment share of start-ups 

2.5% 

3% 

1.8% 

1.7% 

3.5% 

employment share of young firms 

12% 

17.6% 

12.7% 

11.4% 

20% 

Source: JRC, Eurostat, 2020. 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. The time series is from 2008 to 2017. 

Moreover,  recent  data  from  the  Romanian  National  Trade  Statistical  Office  shows  an 
increasing  contraction  in  the  number  of  new  businesses  created  since  the  beginning  of 
the  year  with  respect  to  the  same  period  in  2019.  While  the  negative  trend  is  present 
since the beginning of the year, it has sharply deteriorated since the start of the COVID-
19  pandemic  and  the  ensuing  hibernation  of  most  of  the  economic  activities.  In 
particular,  54%  less  businesses  were  created  in  March,  (when  the  COVID-19  pandemic 
started),  81%  less  in  April  and  34%  less  in  May,  in  each  case  compared  to  the 
corresponding  month  of  2019.  Then,  the  number  of  new  firms  seems  to  have  slightly 
bounced  back  in  summer  (see  Figure  24).  These  statistics  show  an  important  and 
persistent  disruption  of  start-up  activity  due  to  the  COVID-19  pandemic.  This  can  have 
important repercussions for aggregate employment, especially if the number of start-ups 
does not recover quickly as Figure 24 seems to suggest.  

New companies registered

40% 

20% 

0% 

-20% 

-40% 

-60% 

-80% 

-100% 

2020	01 2020	02 2020	03 2020	04 2020	05 2020	06 2020	07 2020	08

Figure 24.	Annual change in the number of new companies registered in 2020 with respect to the 
same period in 2019 (percentage) - Romania 

Source: JRC, data from the Romanian National Trade Statistical Office (ONRC), 2020. 

44 

 
	
 
 
 
 
 
4.7.3  Scenario analysis 

We  consider  a  deterioration  of  the  three  margins  described  above.  Specifically,  we 
assume that the number of start-ups, the survival rate, and the growth potential all fall 
from  the  sample  averages  to  the  sample  minima  (which  were  reached  in  the  years 
following  the  financial  crisis  of  2008),  see  Table  XX.28  Moreover,  we  assume  that  this 
decline lasts for one year only. From 2021 onwards, the three margins are assumed to be 
back at their sample averages. The scenario is the one of a strong but short-lived crisis 
expecting that once the containment measures are lifted, the economic activity of start-
ups and young firms will resume. 

Table XX. Scenario assumptions - Romania 

All 

Industry  Manufacturing 

Services 

# Start-ups 

-33% 

-30% 

-30% 

-33% 

Growth potential 

-15% 

-33% 

-33% 

-15% 

Survival rate 

-12% 

-12% 

-12% 

-11% 

Note(1): The sector aggregate reflects the classifications available in Eurostat, i.e.  all business economy except 
the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of  the 
business economy except activities of holding companies.  

Figure  25  shows  the  scenario  assumptions  on  the  three  margins,  as  well  as  the 
implications  for  aggregate  employment  produced  by  the  calculator.  For  2020,  the 
calculator shows that the three margins together reduce aggregate employment by over 
3.4%.  The  recovery  is  very  slow:  by  2026,  aggregate  employment  is  still  more  than 
0.5%  below  the  level  it  would  have  attained  without  the  disruption  of  start-up  activity. 
The  employment  loss,  cumulated  up  to  2030  is  632,000.  Accounting  for  equilibrium 
adjustments, aggregate effects are dampened by 29%, leaving a cumulative employment 
loss of about 452,000. The decline in the number of start-ups accounts for about 39% of 
the  effect  and  the  survival  rate  for  about  43%.  The  decline  in  the  growth  potential 
accounts for the remaining 18%. These findings provide an important input for the policy 
discussion.  The  three  different  margins  can  be  influenced  by  targeted  policies.  Potential 
employment  benefits  of  policies  targeted  towards  promote  firm  survival  and  ease  firm 
entry  suggest  to  be  highest.  However,  the  cost  of  different  policy  options  needs  to  be 
taken into account.  

28  Note  that  in  Table  XX  Industry,  Manufacturing  and  Serving  sector  are  set  to  their  sample  minima  which 
happens to be the similar for the number of start-ups and the survival rate. However, the COVID-19 crisis 
seems  to  have  impacted  particularly  strongly  the  service  sector  imposing  strict  lockdown  measures  to 
sectors  such  as  transport,  tourism,  and  hospitality.  Alternative  scenarios  that  take  into  account  this 
asymmetry can easily be computed as soon as sectoral data become available.  

45 

 
 
 
 
 
                                   
Figure 25. Scenario analysis for employment and the three margins of start-up activity - Romania 

Source: EU start-up calculator, Romania, 2020.  

Note(1):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample  minima  and  of  one  year  duration.  Firms  older  than  15  are  unaffected  by  the  three  margins  and  their 
time-path  does  not  quantitatively  affect  the  analysis.  The  blue  line  represents  the  impact  on  aggregate 
employment taking into account general equilibrium effects.  

Moreover,  we  consider  how  sectors  may  be  affected  differently.  Table  XIX  shows  that, 
compared to manufacturing and overall industry, firms in the service sector are relatively 
more  dynamic.  This  sector  has  higher  start-up  and  exit  rates,  and  a  much  higher 
employment share of start-ups and other young firms.   

Figure 26 shows the results of the calculator for the three industries. The service sector 
shows  a  much  larger  employment  loss.  This,  despite  the  fact  that  the  decline  in  the 
survival rate and growth potential assumed is actually smaller than the one assumed for 
the  manufacturing  and  industry  sector.  This  result  is  driven  by  the  fact  that  the  service 
sector  is  more  dynamic  and  therefore  reliant  on  young  firms  to  provide  employment. 
Hence, a disruption to start-ups and other young firms affects the service sector strongly 
causing  a  loss  of  aggregate  employment  of  more  than  340,000  jobs  (Table  XXI).  The 
decline in the number of start-ups and in the survival rate account for about 41% of the 
employment  loss  each,  while  the  growth  potential  for  about  18%.  In  the  industry  ad 
manufacturing  sectors,  the  decline  of  the  three  margins  account  for  more  than  30% 
each. This is indicative that different policy mixes should be targeted at different sectors. 
Policies  aimed  at  encouraging  firm  entry  and  survival  seem  particular  effective  in  the 
service  sector,  while  a  policy  mixed  targeted  to  incentivize  firm  entry,  scalability  and 
reduce  exit  in  the  industry  and  manufacturing  sector  could  have  a  significant  effect  in 
mitigating the impact that the COVID-19 pandemic will have on aggregate employment.  

46 

 
 
 
Aggregate employment

d
n
e
r
t

m
o
r
f
n
o
i
t
a
i
v
e
d

0,0% 

-0,5% 

-1,0% 

-1,5% 

-2,0% 

-2,5% 

-3,0% 

-3,5% 

-4,0% 

2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

All

Industry

Manufacturing

Service

Figure 26. Sectors employment (not equilibrium adjusted) - Romania 
 Source: JRC, EU start-up calculator, Romania, 2020. 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  -  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. 

Note(2):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample  minima  in  the  respective  sectors  and  of  one  year  duration.  Firms  older  than  15  are  unaffected  by  the 
three  margins  and  their  time-path  does  not  quantitatively  affect  the  analysis.  The  employment  path  does  not 
take into account general equilibrium effects.  

Table  XXI.  Share  of  aggregate  employment  loss  accounted  for  by  the  three  margins  - 
Romania 

All 

Industry  Manufacturing 

Services 

Cumulative employment loss 

452 k 

183 k 

148 k 

340 k 

# Start-ups 

39% 

30% 

Growth potential 

18% 

32% 

Survival rate 

43% 

38% 

30% 

32% 

38% 

41% 

18% 

41% 

Source: JRC, EU start-up calculator, Romania, 2020. 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. 

Subdued  firm  entry  in  2020  due  to  the  COVID-19  crisis  may  be  the  result  of  a 
postponement  of  start-up  decisions  with  more  firm  entry  in  2021,  especially  when 
targeted  policies  to  promote  firm  entry  are  put  in  place.  The  optimistic  outlook  for  a 
bounce-back in the number of start-ups is simulated in Figure 26 where, after the initial 
negative  shock,  the  number  of  start-ups  is  increased  in  2021  to  the  maximum  level 
registered in the time series. After an initial sharp decline in aggregate employment, the 
increased number of start-ups entering in 2021 causes a significant improvement of the 
employment trend and a lower cumulative job loss totalling to about 331,000. Thanks to 
the better outlook aggregate employment out-reaches its pre COVID-19 level in 2026.    

47 

 
 
 
 
 
	
	
Figure 26. Bounce-back scenario in the number of firms, results for aggregate employment - 
Romania 

Source: EU start-up calculator, Romania, 2020. 

Note(1):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample minima and of one year duration. The bounce-back in the number of start-ups is assumed to take place 
in  2021,  to  be  equal  to  the  sample  maximum  and  last  one  year.    Firms  older  than  15  are  unaffected  by  the 
three  margins  and  their  time-path  does  not  quantitatively  affect  the  analysis.  The  blue  line  represents  the 
impact on aggregate employment taking into account general equilibrium effects.  

4.8  EU start-up calculator: Slovakia 

4.8.1  Summary 

.  

•  The COVID-19 crisis is likely to heavily affect young firms, leading to a decline in 

the start-up rate, higher exit of young firms, and lower growth of start-ups.  

•  According  to  the  EU  start-up  calculator,  these  developments  are  likely  to  create 
substantial aggregate employment losses, of up to 6.9% of employment trend in 
2020.  The  cumulative  employment  loss  for  the  period  2020-2030  could  be  up  to 
341,000. 

•  Effects are very persistent: full recovery may take more a decade, even if start-up 

activity recovers to its pre-crisis level in one year.  

•  About half of the aggregate employment loss is accounted for by the reduction of 
the  survival  rate  and  about  27%  by  the  reduction  in  the  number  of  start-ups. 
Policies  targeted  to  support  young  firm  survival  and  promote  new  firms’  entry 
would seem to be the most effective. The outlook is improved if in 2021 there is a 
rapid  increase  in  the  number  of  firms  entering  the  economy.  i.e.  the  cumulative 
aggregate employment loss is reduced to 231,000. 

48 

 
 
 
 
•  The  Slovak  service  sector  may  be  affected  particularly  strongly,  as  young  firms 
are particularly important job creators in this sector. Across the three sectors, the 
survival rate accounts for about half of employment loss. 

4.8.2  Business dynamism in Slovakia 

Before  presenting  the  scenario  analysis,  we  consider  a  number  of  statistics  on  the 
dynamism of Slovak firms, see Table XXII.  

Table XXII. Descriptive statistics on the economy and sector dynamics of young firms - Slovakia 

start-up rate 

survival rate 

EU 27 
All 

9.2% 

92% 

All 

Industry 

Manufacturing 

Services 

11.8% 

10.7% 

10.9% 

11.6% 

85.2% 

89.2% 

89.2% 

84.9% 

share of young firms 

36% 

40.4% 

33.5% 

39.9% 

50.2% 

employment share of start-ups 

2.5% 

3.2% 

1.2% 

employment share of young firms 

12% 

16.7% 

7.3% 

Source: JRC, Eurostat, 2020. 

1.3% 

7.3% 

4.2% 

21.8% 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. The time series is from 2012 to 2017. 

The  statistics  show  that,  when  it  comes  to  entry  and  exit  of  firms,  the  Slovak  economy 
has  a  higher  start-up  rate  and  exit  rate  than  the  EU  average.  A  considerably  important 
share of job creation is attributed to start-ups and young firms. Over the sample, about 
15% of firms exit within a given year, whereas the start-up rate is about 12%. Start-ups 
(firms  of  age  zero)  account  for  more  than  3%  of  aggregate  employment  whereas  firms 
up  to  age  5  together  account  for  almost  17%.  When  looking  at  the  sectoral 
disaggregation,  the  service  sector  contributes  to  most  of  the  business  dynamic  of  the 
overall  economy  with  a  share  of  young  firms  above  50%  and  an  employment  share  of 
young  firms  of  almost  22%,  while  industry  and  manufacturing  have  an  employment 
share of young firms just above 7%.29 

Similarly  to  other  European  countries,  also  the  business  dynamism  of  the  Slovak 
economy  has  been  affected  by  the  crisis.  The  contraction  in  the  creation  of  new 
companies appear evident starting from March 2020. Figure 27 shows how the number of 
new  companies  created  starts  to  decline  in  March  by  19.7%,  to  then  drop  in  April  by 
45.2%  and  in  May  by  25.9%,  in  each  case  compared  to  the  corresponding  month  of 
2019.  Hence,  Slovakia  has  suffered  from  a  significant  disruption  of  start-up  activity 
during the first peak of the COVID-19 pandemic. However, after an improvement of the 
situation in the months of June and especially July, August registered again a decline in 
the  number  of  new  business  registered.  Moreover,  in  Slovakia,  as  in  many  other 
European countries the pandemic seems to dramatically accelerate again. If this will once 
more  slow  down  start-up  activity  the  repercussion  for  aggregate  employment  may  be 
persistent and important. 

29  Table  XXII  also  indicates  that  industry  and  manufacturing  are  very  similar  to  each  other  as  the  latter  is  a 

large component of the former.  

49 

 
	
 
 
 
                                   
New companies registered

20% 

10% 

0% 

-10% 

-20% 

-30% 

-40% 

-50% 

2020	01

2020	02

2020	03

2020	04

2020	05

2020	06

2020	07

2020	08

Figure 27. Annual change in the number of new companies registered in 2020 with respect to the 
same period in 2019 (percentage) - Slovakia 

Source: JRC, provisional data on the total of new economic subjects registered in the statistical register of 
organizations during the period January 2019 and October 2020. The data are provided by the Slovak statistical 
office (www.statistics.sk).  

4.8.3  Scenario analysis 

We  consider  a  deterioration  of  the  three  margins  described  above  consistent  with  the 
scenarios assumed in the previous analysis. Specifically, we assume that the number of 
start-ups and the growth potential fall from the sample averages to the sample minima, 
while the survival rate drops considerably and seemingly to what assumed for other East 
European Countries30, see Table XXIII.31 Moreover, we assume that this decline lasts for 
one  year  only.  From  2021  onwards,  the  three  margins  are  assumed  to  be  back  at  their 
sample averages. The scenario is the one of a strong but short-lived crisis.  

Table XXIII. Scenario assumptions - Slovakia 

All 

Industry  Manufacturing 

Services 

# Start-ups 

-35% 

-35% 

-35% 

-35% 

Growth potential 

-25% 

-20% 

-20% 

-25% 

Survival rate 

-20% 

-20% 

-20% 

-20% 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. 

30 In the case of Slovakia, Eurostat data for the survival rate start in 2013. The short time series does not allow 
us  to  have  5  years  to  construct  the  1-5  survival  rate.  As  a  consequence,  the  minimum,  maximum  and 
average value of the survival rate are the same number. 

31  Note  that  in  Table  XXIII  Industry,  Manufacturing  and  Serving  sector  are  set  to  their  sample  minima  which 
happens  to  be  the  same  for  the  number  of  start-ups.  However,  the  COVID-19  crisis  seems  to  have 
impacted  particularly  strongly  the  service  sector  imposing  strict  lockdown  measures  to  sectors  such  as 
transport, tourism, and hospitality. Alternative scenarios that take into account this asymmetry can easily 
be computed as soon as sectoral data become available.  

50 

 
 
 
 
 
 
                                   
Figure 28. Scenario analysis for employment and the three margins of start-up activity - Slovakia 

Source: EU start-up calculator, Slovakia, 2020. 

Note(1):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample  minima  and  of  one  year  duration  (for  the  survival  rate  it  is  arbitrarily  assumed  to  be  of  -20%).  The 
Business Demography data of Eurostat for Slovakia are available from 2012 to 2017. The short time series does 
not allow to compute the 1-5 survival rate. As a consequence, the minimum, maximum and average value of 
the survival rate collapse to the same number (yellow line in the plot related to the shock to the survival rate). 
Firms older than 15 are unaffected by the three margins and their time-path does not quantitatively affect the 
analysis. The blue line represents the impact on aggregate employment taking into account general equilibrium 
effects.  

Figure  28  shows  the  scenario  assumptions  on  the  three  margins,  as  well  as  the 
implications  for  aggregate  employment  produced  by  the  calculator.  For  2020,  the 
calculator  shows  that  the  three  margins  together  reduce  aggregate  employment  by 
6.9%.  The  recovery  is  slow:  by  2026,  aggregate  employment  is  still  more  than  1.2% 
below  the  level  it  would  have  attained  without  the  disruption  of  start-up  activity.  The 
employment  loss,  cumulated  up  to  2030  is  341,000.  Accounting  for  equilibrium 
adjustments, aggregate effects are dampened by 29%, leaving a cumulative employment 
loss  of  about  244,000  (blue  line  in  Figure  28).  The  decline  in  the  survival  rate  accounts 
for more than half of the effect, while the decline in the number of start-ups accounts for 
about  27%.  These  findings  provide  an  important  input  for  the  policy  discussion.  The 
three  different  margins  can  be  influenced  by  targeted  policies.  Potential  employment 
benefits  of  policies  targeted  towards  firm  survival  and  to  promote  firm  entry  suggest  to 
be highest. However, the cost of different policy options needs to be taken into account.  

Moreover,  we  consider  how  sectors  may  be  affected  differently.  Table  XXII  shows  that, 
compared  to  manufacturing  and  overall  industry,  firms  in  the  service  sector  are 
somewhat  more  dynamic.  This  sector  has  higher  start-up  and  exit  rates,  and  a  much 
higher  employment  share  of  start-ups  and  other  young  firms  compared  to  the  industry 
and manufacturing sectors.  

Figure 29 shows the results of the calculator for the three industries. The service sector 
shows  a  larger  relative  employment  loss  with  a  decline  in  aggregate  employment  that 
reaches  almost  9%  in  2020.  This  result  is  driven  by  the  fact  that  the  service  sector  is 

51 

 
 
 
more  dynamic  in  terms  of  entry  and  exit  and  young  firms  account  for  a  larger  share  of 
employment compare to the industry and manufacturing sectors.  

Aggregate employment

d
n
e
r
t

m
o
r
f
n
o
i
t
a
i
v
e
d

0,00%

-1,00% 
-2,00% 
-3,00% 
-4,00% 
-5,00% 
-6,00% 
-7,00% 
-8,00% 
-9,00% 
-10,00%

2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

All

Industry

Manufacturing

Service

Figure 29. Sectors employment (not equilibrium adjusted) - Slovakia 

Source: JRC, EU start-up calculator, Slovakia, 2020. 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. 

Note(2):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample minima in the respective sectors and of one year duration (for the survival rate it is arbitrarily assumed 
to be of -20%). The Business Demography data of Eurostat for Slovakia are available from 2012 to 2017. The 
short time series does not allow to compute the 1-5 survival rate. As a consequence, the minimum, maximum 
and average value of the survival rate collapse to the same number (yellow line in the plot related to the shock 
to  the  survival  rate).  Firms  older  than  15  are  unaffected  by  the  three  margins  and  their  time-path  does  not 
quantitatively affect the analysis. The employment path does not take into account general equilibrium effects.  

Table XXIV. Share of aggregate employment loss accounted for by the margins - Slovakia 

All 

Industry 

Manufacturing 

Services 

Cumulative employment loss 

341 k 

62 k 

# Start-ups 

27% 

29% 

Growth potential 

19% 

16% 

Survival rate 

54% 

55% 

64 k 

28% 

16% 

56% 

240 k 

27% 

19% 

54% 

Source: JRC, EU start-up calculator, Slovakia, 2020. 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. 

52 

 
 
 
 
  
 
 
 
	
	
Hence,  a  disruption  to  start-ups  and  other  young  firms  affects  the  service  sector 
relatively strongly, causing a loss of aggregate employment of about 240,000 jobs (Table 
XXIV).  In  all  the  three  sectors  the  margin  with  the  highest  impact  on  aggregate 
employment is the survival rate accounting for about half of the job loss. The decline in 
the  number  of  start-ups  accounts  for  almost  30%  across  sectors.  These  considerations 
are  indicative  that  policy  targeted  to  support  firms  to  tackle  survival  challenges  across 
sectors  and  support  entry  in  the  service  sector  could  have  a  significant  effect  in 
mitigating the impact that the COVID-19 pandemic will have on aggregate employment. 

Figure 30. Bounce-back scenario in the number of firms, results for aggregate employment - 
Slovakia 

Source: EU start-up calculator, Slovakia, 2020. 

Note(1):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample minima and of the duration of one year (for the survival rate it is arbitrarily assumed to be of -20%). 
The Business Demography data of Eurostat for Slovakia are available from 2012 to 2017. The short time series 
does not allow to compute the 1-5 survival rate. As a consequence, the minimum, maximum and average value 
of  the  survival  rate  collapse  to  the  same  number  (yellow  line  in  the  plot  related  to  the  shock  to  the  survival 
rate). The bounce-back in the number of start-ups is assumed to take place in 2021, to be equal to the sample 
maximum  and  of  one  year  duration.  Firms  older  than  15  are  unaffected  by  the  three  margins  and  their  time-
path does not quantitatively affect the analysis. The blue line represents the impact on aggregate employment 
taking into account general equilibrium effects.  

Subdued  firm  entry  in  2020  due  to  the  COVID-19  crisis  may  be  the  result  of  a 
postponement of start-up decisions with more firm entry in 2021 especially if supported 
by  specific  policies  targeted  to  start-ups  that  could  reduce  the  entry  barriers,  promote 
innovation and relax financial constraints. The optimistic outlook for a bounce-back in the 
number of start-ups is simulated in Figure 30 where, after the initial negative shock, the 
number  of  start-ups  is  increased  in  2021  to  the  maximum  level  registered  in  the  time 
series.  After  an  initial  sharp  decline  in  aggregate  employment,  the  increased  number  of 
start-ups entering in 2021 causes an improvement of the employment trend and a lower 

53 

 
 
 
 
 
cumulative  job  loss  totalling  to  about  231,000.  Thanks  to  the  better  outlook  aggregate 
employment reaches its pre COVID-19 level by 2026.  

4.9  EU start-up calculator: Slovenia 

4.9.1  Summary 

•  The COVID-19 crisis is likely to heavily affect young firms, leading to a decline in 

the start-up rate, higher exit of young firms, and lower growth of start-ups. 

•  According  to  the  EU  start-up  calculator,  these  developments  are  likely  to  create 
substantial aggregate employment losses, of up to 1.2% of employment trend in 
2020.  The  cumulative  employment  loss  for  the  period  2020-2030  could  be  up  to 
29,000. 

•  Effects  are  very  persistent:  full  recovery  may  take  a  decade,  even  if  start-up 

activity recovers to its pre-crisis level in one year.  

•  More  than  half  of  the  aggregate  employment  loss  is  accounted  for  by  the 
reduction of the growth potential of start-ups, while the decline in the number of 
start-ups account for 27%. Policies targeted to support scaling up of entrants and 
promote  new  firms’  entry  would  seem  to  be  the  most  effective.  The  outlook  is 
improved if in 2021 there is a rapid increase in the number of firms entering the 
economy. i.e. the cumulative aggregate employment loss is reduced to 21,000. 
•  The  Slovenian  service  sector  may  be  affected  relatively  strongly,  as  young  firms 
are  particularly  important  job  creators  in  this  sector.  In  all  three  sectors  more 
than 40% of the job loss is due to a decline in the number of start-ups.  

4.9.2  Business dynamism in Slovenia 

Before  presenting  the  scenario  analysis,  we  consider  a  number  of  statistics  on  the 
dynamism of Slovenian firms, see Table XXV.  

Table XXV. Descriptive statistics on the economy and sector dynamics of young firms - Slovenia 

EU 27 
All 

All 

Industry 

Manufacturing 

Services 

start-up rate 

survival rate 

9.2% 

10.1% 

7.3% 

7.1% 

10.5% 

92% 

 90.4% 

  93% 

93.2% 

90.7% 

share of young firms 

36% 

38.9% 

28% 

27.3% 

39.6% 

employment share of start-ups 

2.5% 

2.2% 

0.9% 

employment share of young firms 

12% 

12.6% 

5.8% 

Source: JRC, Slovenia, 2020. 

0.9% 

5.8% 

2.6% 

14.3% 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. The time series is from 2008 to 2017. 

The statistics show that, when it comes to entry and exit of firms, the Slovenian economy 
has  a  slightly  higher  start-up  rate  than  the  EU  average.  An  important  share  of  job 
creation is attributed to start-ups and young firms, in line with the EU average. Over the 

54 

 
 
 
	
 
  
sample,  about  10%  of  firms  exit  and  entry  within  a  given  year.  Start-ups  (firms  of  age 
zero)  account  for  about  2.2%  of  aggregate  employment  whereas  firms  up  to  age  5 
together  account  for  more  than  12%.  When  looking  at  the  sectoral  disaggregation,  the 
service  sector  contributes  to  most  of  the  business  dynamic  of  the  overall  economy  with 
an  employment  share  of  start-ups  of  almost  3%  and  an  employment  share  of  young 
firms above 14%.32 

The creation of new companies has been affected by the crisis showing an contraction in 
the number of new firms registering especially during the first wave of the pandemic with 
respect  to  the  same  period  in  2019.  In  particular,  Figure  31  shows  how  the  number  of 
new companies created declines in March by 19%, and drops in April by almost 80% and 
in May by 42%. Fortunately, from June onwards there is a bounce-back in the number of 
new businesses registered during all the summer months of 2020. This may mitigate the 
economic impact of the COVID-10 crisis. Nevertheless, the “unborn” start-ups may cause 
important repercussions for aggregate employment, especially if the number of start-ups 
may be further affected by the second wave of the epidemic and a potential slowdown of 
the economy.  

New companies registered

40% 

20% 

0% 

-20% 

-40% 

-60% 

-80% 

-100% 

2020	01 2020	02 2020	03 2020	04 2020	05 2020	06 2020	07 2020	08 2020	09

Figure 31. Annual change in the number of new companies registered in 2020 with respect to the 
same period in 2019 (percentage) - Slovenia 

Source: JRC, provisional data on the total of new companies registered are provided by the business register of 
Slovenia (AJPES) 

4.9.3  Scenario analysis 

We  consider  a  deterioration  of  the  three  margins  described  above.  Specifically,  we 
assume  that  the  number  of  start-ups,  the  growth  potential  and  the  survival  rate  fall  all 
from  the  sample  averages  to  the  sample  minima  (which  were  reached  in  the  years 
following  the  financial  crisis  of  2008),  see  Table  XXVI.33  Moreover,  we  assume  that  this 
decline lasts for one year only. From 2021 onwards, the three margins are assumed to be 
back at their sample averages. The scenario is the one of a strong but short-lived crisis 

32  Table  XXV  also  indicates  that  industry  and  manufacturing  are  very  similar  to  each  other  as  the  latter  is  a 

large component of the former.  

33  Note  that  in  Table  XXVI  Industry,  Manufacturing  and  Serving  sector  are  set  to  their  sample  minima  which 
happens  to  be  lower  for  industry  and  manufacturing.  However,  the  COVID-19  crisis  seems  to  have 
impacted  particularly  strongly  the  service  sector  imposing  strict  lockdown  measures  to  sectors  such  as 
transport, tourism, and hospitality. Alternative scenarios that take into account this asymmetry can easily 
be computed as soon as sectoral data become available.  

55 

 
 
 
 
                                   
expecting that once the containment measures are lifted, the economic activity of start-
ups and young firms will resume. This expectation seems to be consistent with the recent 
economic trend.  

Table XXVI. Scenario assumptions - Slovenia 

All 

Industry  Manufacturing 

Services 

# Start-ups 

-13% 

-25% 

-25% 

-18% 

Growth potential 

-25% 

-12% 

-10% 

Survival rate 

-4% 

-6% 

-6% 

-4% 

-4% 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. 

Figure 32. Scenario analysis for employment and the three margins of start-up activity - Slovenia 

Source: EU start-up calculator, Slovenia 2020. 

Note(1):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample  minima  and  of  one  year  duration.  Firms  older  than  15  are  unaffected  by  the  three  margins  and  their 
time-path  does  not  quantitatively  affect  the  analysis.  The  blue  line  represents  the  impact  on  aggregate 
employment taking into account general equilibrium effects.  

Figure  32  shows  the  scenario  assumptions  on  the  three  margins,  as  well  as  the 
implications  for  aggregate  employment  produced  by  the  calculator.  For  2020,  the 
calculator  shows  that  the  three  margins  together  reduce  aggregate  employment  by  just 
over 1.2%. However, the recovery is slow: by 2026, aggregate employment is still more 
than  0.3%  below  the  level  it  would  have  attained  without  the  disruption  of  start-up 

56 

 
 
 
 
 
 
 
activity.  The  employment  loss,  cumulated  up  to  2030  is  29,200.  Accounting  for 
equilibrium adjustments, aggregate effects are dampened by 29%, leaving a cumulative 
employment  loss  of  about  20,880  (blue  line  in  Figure  32).  The  decline  in  the  growth 
potential is also due to the large shock assumed for this margin. The number of start-ups 
accounts for about 27% and the survival rate for roughly 21%. These findings provide an 
important  input  for  the  policy  discussion.  The  three  different  margins  can  be  influenced 
by  targeted  policies.  Potential  employment  benefits  of  policies  targeted  towards  easing 
firms’  scalability  and  promoting  firm  entry  suggest  to  be  highest.  However,  the  cost  of 
different policy options needs to be taken into account.  

Moreover,  we  consider  how  sectors  may  be  affected  differently.  Table  XXV  shows  that, 
compared  to  manufacturing  and  overall  industry,  firms  in  the  service  sector  are 
somewhat  more  dynamic.  This  sector  has  higher  start-up  and  exit  rates,  and  a  much 
higher employment share of start-ups and other young firms.  

Figure 33 shows the results of the calculator for the three industries. The service sector 
shows a larger employment loss, even though the decline in the three margins assumed 
is  actually  somewhat  smaller,  in  particular  in  the  shock  to  the  growth  potential  and 
number of start-ups (Table XXVI). This result is driven by the fact that the service sector 
is  more  dynamic  in  terms  of  entry  and  exit  and  therefore  reliant  on  young  firms  to 
provide employment. 

Aggregate employment

d
n
e
r
t

m
o
r
f
n
o
i
t
a
i
v
e
d

0,00%

-0,20% 

-0,40% 

-0,60% 

-0,80% 

-1,00% 

-1,20% 

-1,40% 

2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

All

Industry

Manufacturing

Service

Figure 33. Sectors employment (not equilibrium adjusted) - Slovenia 

Source: JRC, EU start-up calculator, Slovenia, 2020. 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. 

Note(2):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample  minima  in  the  respective  sectors  and  of  one  year  duration.  Firms  older  than  15  are  unaffected  by  the 
three  margins  and  their  time-path  does  not  quantitatively  affect  the  analysis.  The  employment  path  does  not 
take into account general equilibrium effects.  

57 

 
 
 
  
 
 
 
 
	
	
Table XXVII. Share of aggregate employment loss accounted for by the margins - Slovenia 

All 

Industry 

Manufacturing 

Services 

Cumulative employment loss 

29 k 

8 k 

# Start-ups 

27% 

44% 

Growth potential 

52% 

21% 

Survival rate 

21% 

35% 

7 k 

46% 

18% 

36% 

17 k 

53% 

12% 

35% 

Source: JRC, EU start-up calculator, Slovenia, 2020. 

Note(1):  The  sector  aggregate  reflects  the  classifications  available  in  Eurostat  –  i.e.  all  business  economy 
except  the  activities  of  holding  companies,  the  industrial  sector  except  construction,  and  the  service  sector  of 
the business economy except activities of holding companies. 

Hence,  a  disruption  to  start-ups  and  other  young  firms  affects  the  service  sector 
relatively  strongly  causing  a  loss  of  aggregate  employment  of  more  than  17,000  jobs 
(Table XXVII). In all the three sectors more than 40% of the job loss is attributed to the 
decline in the number of start-ups plays a prominent role, seconded by the decline in the 
survival  rate  of  young  firms.  These  considerations  are  indicative  that  policy  targeted  to 
support  firms  to  tackle  survival  challenges  and  support  entry  could  have  a  significant 
effect  in  mitigating  the  impact  that  the  COVID-19  pandemic  will  have  on  aggregate 
employment. 

Subdued  firm  entry  in  2020  due  to  the  COVID-19  crisis  may  be  the  result  of  a 
postponement of start-up decisions with more firm entry in 2021 especially if supported 
by  specific  policies  targeted  to  start-ups  that  could  reduce  the  entry  barriers,  promote 
innovation and relax financial constraints. The optimistic outlook for a bounce-back in the 
number of start-ups is simulated in Figure 34 where, after the initial negative shock, the 
number  of  start-ups  is  increased  in  2021  to  the  maximum  level  registered  in  the  time 
series.  After  an  initial  sharp  decline  in  aggregate  employment,  the  increased  number  of 
start-ups entering in 2021 causes an improvement of the employment trend and a lower 
cumulative  job  loss  totalling  to  about  20,850.  Thanks  to  the  better  outlook  aggregate 
employment reaches its pre COVID-19 level by 2028.  

58 

 
 
 
 
 
Figure 34. Bounce-back scenario in the number of firms, results for aggregate employment - 
Slovenia 

Source: EU start-up calculator, Slovenia, 2020. 

Note(1):  The  simulation  on  aggregate  employment  considers  a  shock  on  the  three  margins  equal  to  their 
sample minima and of the duration of one year. The bounce-back in the number of start-ups is assumed to take 
place  in  2021,  to  be  equal  to  the  sample  maximum  and  of  one  year  duration.  Firms  older  than  15  are 
unaffected  by  the  three  margins  and  their  time-path  does  not  quantitatively  affect  the  analysis.  The  blue  line 
represents the impact on aggregate employment taking into account general equilibrium effects.  

59 

 
 
 
 
 
5  Conclusions 

The  COVID-19  crisis  is  likely  to  heavily  affect  start-ups  and  young  firms,  and  ultimately 
also  aggregate  employment.  To  understand  the  impact,  we  developed  the  EU  start-up 
calculator. This is an empirical tool which allows to assess the medium-term impact that 
the  COVID-19  pandemic  has  on  aggregate  employment  via  the  destruction  of  start-up 
activities varying three margins: (i) the number of start-ups, (ii) the growth potential and 
(iii) the survival rate. Different scenarios have been analysed for the business economy, 
the industry, manufacturing and service sector of Bulgaria, Croatia, Czechia, Luxemburg, 
the Netherlands, Poland, Romania, Slovakia and Slovenia. This work follows closely a first 
report on the “EU start-up  calculator”  which analyses the start-up  dynamics  for  Austria, 
Belgium,  Germany,  Hungary,  Italy  and  Spain  (Benedetti  Fasil,  Sedláček  and  Sterk 
(2020a)) and a second report which analyses Denmark, Estonia, Finland, France, Latvia, 
Lithuania, Portugal and Sweden (Benedetti Fasil, Sedláček and Sterk (2020b)). 

A strong but short lived-crisis may result in important and persistent job losses in all the 
countries that range between 0.25 (Luxemburg) to 6.9% (Slovakia) in 2020 and adds to 
a  cumulative  employment  loss  for  the  period  2020-2030  that  ranges  between  5,600 
(Luxemburg)  to  2179,000  (Poland).  These  losses  seem  to  be  particularly  high  in 
countries  and  sectors  characterized  by  a  high  firm  turnover  and  that  rely  on  start-ups 
and young firms for job creation, e.g. Bulgaria, Croatia, Poland, Romania and Slovakia as 
well as the service sector rather than the industry and manufacturing sector. As for the 
margins  considered,  in  most  countries  the  deterioration  of  the  survival  rate  of  young 
firms seems to play an important role in driving employment, seconded by the number of 
new entrants. The exceptions are the Netherlands and Slovenia, where the decline in the 
growth  potential  accounts  for  more  than  half  of  the  aggregate  employment  loss,  and 
Luxemburg  where  the  lion  share  in  explaining  the  employment  loss  is  played  by  the 
decline in the number of start-ups.  

Notwithstanding  the  significant  economic  disruption  caused  by  the  COVID-19  pandemic, 
the  medium-term  effects  on  aggregate  employment  may  be  significantly  mitigated  by 
policies aimed at supporting young firms and incentivizing the creation of new ones. This 
represents  a  positive  factor  as  this  group  of  firms,  i.e.  start-ups  and  young  firms,  is 
easily  identifiable  by  policy  makers  and  hence  represents  an  easy  target  for  policy 
interventions. With this in mind we explored the potential impact that policies may have 
on aggregate employment. In particular, we simulated bounce-back scenarios where the 
number  of  firms  entering  the  economy  rapidly  increases  in  2021.  In  every  country,  the 
outlook  is  significantly  improved,  the  recovery  is  faster  and  the  aggregate  job  loss  is 
lower. Hence, policy makers may have amble margin of manoeuvre to alleviate the crisis 
with a mix of policies that while being targeted may also be efficient.  

Along  these  lines,  the  European  Union  has  launched  a  series  of  guidelines  and 
instruments,  such  as  InvestEu,  the  European  Innovation  Council  and,  as  a  result  of  the 
COVID-19  pandemic,  the  Recovery  Resilience  Facility.  One  of  the  goals  of  these 
instruments  is  to  support  the  creation  of  a  vibrant  and  more  resilient  entrepreneurial 
ecosystem  with  particular  attention  to  promote  start-ups  and  young  firms.  The 
interventions include access to liquidity and funding to start-ups and young firms in the 
forms  of  grants,  equity  or  zero-interest  loans  especially  targeted  to  scale-ups,  to 
promising  innovators  and  R&D  investments,  and  to  support  young  enterprises’  needs. 
Also  the  use  of  in-kind  support,  such  as  training  programs,  creation  of  network 
opportunities  with  peer-entrepreneurs,  customers  and  suppliers  to  rebuild  the  value 
chain,  and  the  promotion  of  knowledge  transfer  from  applied  research  to  the 
entrepreneurial ecosystem are considered as priorities. Focus is also put on the creation 
of  new  opportunities  via  the  promotion  of  digitalization  and  of  the  green  transition. 
Finally,  there  is  the  indication  to  reduce  and  simplify  the  red-tape  costs  upon  entry  and 
the general administrative burden that firms face. If policy makers will be able to design 
and  target  these  instruments  to  promote  start-ups  and  young  firms,  then  the  recovery 
from  the  COVID-19  disruption  will  be  considerably  faster  and  the  outlook  much  more 
positive.  We  postpone  to  further  research  the  assessment  on  how  these  interventions 

60 

 
may affect the three margins that govern the scenario creation of the start-up calculator 
and ultimately the impact that they will have on aggregate employment.  

61 

 
References 

Benedetti Fasil, C., P. Sedláček and V. Sterk (2020b), “EU start-up calculator: impact of 
COVID-  19  on  aggregate  employment.  Scenario  analysis  for  Denmark,  Estonia,  Finland, 
France,  Latvia,  Lithuania,  Portugal  and  Sweden”,  EUR  30426  EN,  Publications  Office  of 
the 
978-92-76-24520-9, 
doi:10.2760/093209, JRC122318.  

Luxembourg, 

European 

Union, 

2020, 

ISBN 

Benedetti Fasil, C., P. Sedláček and V. Sterk (2020a), “EU start-up calculator: impact of 
COVID- 19 on aggregate employment. Scenario analysis for Austria, Belgium, Germany, 
Hungary,  Italy  and  Spain”,  EUR  30372  EN,  Publications  Office  of  the  European  Union, 
Luxembourg, 2020, ISBN 978-92-76-21710-7, doi:10.2760/049945, JRC121715.  

Calvino,  F.,  C.  Criscuolo  and  R.  Verlhac  (2020),  “Start-ups  in  the  time  of  COVID-19: 
Facing 
the  opportunities”,  VoxEU.org,  available  at 
https://voxeu.org/article/challenges-and-opportunities-start-ups-time-covid-19 

challenges, 

seizing 

the 

Gourio, F., T. Messner and M. Siemer (2016), “Firm entry and macroeconomic dynamics: 
a State-level analysis”, American Economic Review Papers and Proceedings 116(5): 214–
8. 

Haltiwanger, J., R. Jarmin and J. Miranda (2013), “Who creates jobs? Small versus large 
versus young”, The Review of Economics and Statistics 95(2): 347–61. 

Lichter  A.,  A.  Peichl,  and  S.  Siegloch  (2015):  “The  Own-Wage  Elasticity  of  Labor 
Demand: A Meta-Regression Analysis”, European Economic Review, 80(C), 94-119. 

Hopenhayn, H. A., (1992), “Entry and Exit and Firm Dynamics in Long Run Equilibrium”, 
Econometrica 60(5), 1127-1150. 

Hopenhayn,  H.  A.  and  Rogerson,  R.,  (1993),  “Job  Turnover  and  Policy  Evaluation:  a 
General Equilibrium Analysis”, Journal of Political Economy 101(5), 915-938. 

Pugsley,  B.,  P.  Sedláček,  and  V.  Sterk  (2017),  “The  Nature  of  Firm  Growth”,  CEPR 
Discussion Papers, 12670. 

Sedláček,  P.  (2020),  “Lost  generations  of 

firms  and  aggregate 

labor  market 

dynamics”, Journal of Monetary Economics, 111: 16-31. 

Sedláček,  P.,  and  V.  Sterk  (2020),  “Startups  and  employment  following  the  COVID-19 

pandemic: a calcualtor” (working paper). 

Sedláček,  P.,  and  V.  Sterk  (2017),  “The  growth  potential  of  startups  over  the  business 

cycle”, American Economics Review 107(10): 3182–210. 

62 

Appendix 

Appendix I. Figures 

The  introduction  avails  of  a  series  of  figures  summarising  summary  statistics  for  start-
ups and young firms, their importance for job creation and timely data on the registration 
of  new  companies  in  the  first  two  quarters  of  2020  with  respect  to  the  same  period  in 
2019. This Appendix proposes the same figures for a larger set of Member States which 
have available data.  

Start-ups and young firms

60% 

40% 

20% 

0% 

Share	of	young	firms

employment	share	of	start-ups

start-up	contribution	to	employment	growth

employment	share	of	young	firms

young	firm	contribution	to	employment	growth

Figure A1. Importance of start-ups and young firms for aggregate employment 

Source: JRC, Eurostat, Business Demography dataset, 2020. 

Note(1): The time series for the EU27, Denmark and Finland, Bulgaria, Croatia, Poland and Slovakia is between 
2012  and  2017,  for  Austria,  Belgium,  Czechia,  Estonia,  France,  Latvia,  Luxemburg,  Netherlands,  Poland, 
Hungary,  Italy,  Portugal,  Romania,  Slovenia  and  Spain  between  2008  and  2017,  for  Lithuania  from  2009  to 
2017. The Business Demography dataset for Germany and Sweden does not have enough data to compute the 
start-up and young firm contribution to growth. Hence, this is omitted in the Figure. For the other statistics the 
time series is between 2012 and 2017. 

63 

 
 
 
 
 
 
 
Net job creatiom by firm age

0

1

2

3

4

5

EU	27	(2015-2016)
Bulgaria	(2013-2017)
Denmark	(2013-2017)
France	(2014-2016)
Italy	(2009-2017)
Luxemburg	(2011-2017)
Portugal	(2012-2014)
Slovenia	(2009-2017)

firm	age

Austria	(2009-2017)
Croatia	(2015-2017)
Estonia	(2009-2017)
Germany	(2017)
Latvia	(2008-2017)
Netherlands	(2012-2017)
Romania	(2009-2017)
Spain	(2009-2017)

Belgium	(2014-2017)
Czechia	(2010-2017)
Finland	(2013-2017)
Hungary	(2009-2017)
Lithuania	(2015-2017)
Poland	(2015-2017)
Slovakia	(2014-2017)
Sweden	(2017)

Figure A2. Importance of start-ups for job creation 

Source: JRC, Eurostat, Business Demography dataset, 2020. 

New companies registered in the first and second quarter of 2020 with 
respect to 2019

6,0% 

4,0% 

2,0% 

0,0% 

-2,0% 

l

t
n
e
m
y
o
p
m
e
e
t
a
g
e
r
g
g
a
g
o
%

20% 
10% 
0% 
-10% 
-20% 
-30% 
-40% 
-50% 
-60% 

Q1

Q2

Figure A3. Annual change in the number of new companies registered in the first and second 
quarter of 2020 with respect to the first and second quarter of 2019 (percentage) 

Source: JRC, data from Statistics Belgium (STABEL), the Bulgarian National Statistical Institute (BNSI), the 
Danish Statistical Institute (DST), Statistics Estonia (SE), Statistics Finland (SF), the National Institute of 
Statistics and Economic Studies (Insee), the Federal Statistical Office of Germany providing preliminary data 
(DESTATIS), the Hungarian Central Statistical Office (KSH), the Italian Association of the Chamber of 
Commerce (IC), the Enterprise Register of Latvia (https://www.ur.gov.lv/en/statistics/), Statistics Lithuania 
(https://www.stat.gov.lt), Netherlands StatLine (CBS), the Portuguese National Statistical Institute (INE), the 
Romanian National Trade Statistical Office (ONRC), the Slovak statistical office (www.statistics.sk), the business 
register of Slovenia (AJPES), the Spanish National Statistical Institute (INE) and the Swedish Registration 
Offices of Companies (Bolagsverket). 

Note(1):  The  data,  which  come  from  national  statistical  offices  or  enterprise  registers,  are  not  harmonized 
across countries. As a consequence, Figure 3 does not allow for quantitative cross-country comparisons per se. 
Nevertheless, it is useful to visualize the different impact that COVID-19 has had across countries in terms of 
start-up creation. Finally, we were unable to obtain data for Austria, Croatia, Czechia, Luxemburg and Poland. 

64 

 
 
 
 
	
	
	
Average survival rates of strat-ups

120% 

100% 

80% 

60% 

40% 

20% 

0% 

1-year

2-year

3-year

4-year

5-year

Figure A4. Y-o-y survival rate of start-ups 

Source: JRC, Eurostat, Business Demography dataset, 2020. 

Note(1): The Eurostat database provides the y-o-y survival rates for start-ups after one, two, three, four and 
five years of activities. The plotted percentages are the averages of the period from 2013 to 2017 for the EU 27 
and Croatia, from 2008 to 2017 for Estonia, France, Latvia, from 2008 to 2018 for Austria, Belgium, Bulgaria, 
Czechia,  Finland,  France,  Germany,  Hungary,  Italy,  Lithuania,  Luxemburg,  Netherlands,  Poland,  Portugal, 
Romania, Slovakia, Slovenia, Spain and Sweden and from 2009 to 2017 for Denmark.  

Appendix II. Interpolation, extrapolation and creation of scenarios 

In  Eurostat  for  firms  of  age  a  =  {0,1,2,3,4,5}  in  year  t  ∈	[2008-2017]  is  directly 
observable  the  number  of  firms  na,t,  firm  size  sa,t  and  the  survival  rate  1-xa,t.  However, 
firms older than 5 are grouped together in Eurostat. Hence, it is necessary to interpolate 
information for each of the individual age categories. Two inputs to the calculator are the 
profiles  of  average  size  and  the  survival  rates  by  age  in  the  baseline  scenario  (i.e. 
without shock), for firms up to age 15. For firms up to age 5, we measure directly in the 
data  as  averages  over  the  sample  period.  For  older  firms,  we  assume  a  functional  form 
for  both  profiles  and  fit  these  to  the  available  data.  Specifically,  for  the  exit  rate  we 
assume the following functional form: 

𝑥$ = 𝛽' + 𝛽)

*+, -.
)/*+, -.

01)

. 

This  implies  a  smooth  profile,  gradually  decaying  from  an  initial  point  𝑥$2) = 𝛽' +𝛽)  to  a 
limit point 𝑥$→4 = 𝛽'.	The parameter 𝛽6	controls the speed of decay. 

Regarding the average size profile we assume a simple linear form: 

The functional forms for these two profiles capture well patterns documented using data 

𝑛$ = 𝛾' + 𝛾)𝑎.	

65 

 
 
 
 
 
 
 
 
 
 
	
	
sets for which exit rates can be computed for all age groups (such as the US Longitudinal 
Business Database, see e.g. Pugsley, Sedláček and Sterk (2017). 

To  estimate  the  parameters  of  these  profiles  we  use  a  minimum  distance  estimator, 
targeting the following outcomes which we can observe in the data: (i) the average exit 
rate by age, for firms up to age 5, (ii) average size of firms by age, for firms up to age 5, 
(iii) the  average  exit  rate  among  all  firms,  and  (iv)  average  size  among  all  firms.  Note
that  given  a  profile  for  the  exit  rate  by  age,  one  can  compute  the  firm  age  distribution,
and then the average exit rate by weighting the exit rates by age with the firm shares in
each  age  bin.  Then,  given  the  age  distribution  and  the  average  size  profile  by  age,  one
can compute average size across all firms. The estimation is implemented in MatLab.

In order to extrapolate the necessary data between 2017 and 2019, we assume that firm 
size by age and exit rates by age (up to age 15), and the number of start-ups, all linearly 
converge to their 2008-2017averages: 

𝑥$,6');/< = 𝑥$,6'); +

𝑠$,6');/< = 𝑠$,6'); +

𝑛',6');/< = 𝑛',6'); +

<

6

<

6

<

6

𝑥$ − 𝑥$,6'); ,	

𝑠$ − 𝑠$,6'); ,	

𝑛' − 𝑛',6'); .	

for	𝜏  =  1,  2  and  a  =  1,  2,…,  15,  and  where  𝑥$,  𝑠$,  and  𝑛'  denote  the  2008  to  2017 
averages of age specific exit rates, firm sizes and the number of start-ups, respectively. 
Using  the  above,  we  can  then  recover  the  number  of  firms  for  the  ages  of  1  to  15  as 
𝑛$,@ = 𝑛$1),@1) 1 − 𝑥$,@ ,  for a = 1, 2, …, 15 and t = 2018, 2019.  

In  order  to  compute  aggregate  employment,  it  is  also  necessary  to  assume  a  particular 
time-path  for  employment  of  16+  year  old  firms.  However,  because  16+  year  old  firms 
are unaffected by our scenarios, the particular time-path is quantitatively unimportant for 
the results which are reported in deviations from the assumed trend. For this reason, we 
simply assume that employment in 16+ year old firms stays fixed at its 2017 level. 

Having  the  above  information,  we  are  ready  to  conduct  scenarios  starting  in  2020  and 
running through to 2030. We consider three types of margins: (i) changes in the number 
of  start-ups,  (ii)  changes  in  growth  potential  and  (iii)  changes  in  survival  rates.  To  be 
concrete,  for  a  given  scenario,  let  us  denote  the  initial  percentage  decrease  in  the 
number  of  start-ups,  the  growth  potential  of  start-ups  and  the  survival  rate  of  young 
firms by 𝜁C ∈ (0,1) where j = {n, s, x}, respectively. Let us further denote the duration of 
these  effects  by  𝜏C>  0,  where  j  =  {n,  s,  x},  respectively.  The  given  scenarios  are  then 
given by: 

𝑛',6')G/@ = 𝑛',6')G 1 − 𝜁H ,

for	𝑡 = 1, … , 𝜏H, 

𝑠$,6')G/@/$ = 𝑠$,6')G 1 − 𝜁N ,

for	𝑡 = 1, … , 𝜏N		and	𝑎 = 0, 1, 2, … , 15, 

𝑥$,6')G/@ = 𝑥$,6')G 1 − 𝜁T ,

for	𝑡 = 1, … , 𝜏T		and	𝑎 = 1, 2, … , 15. 

The  calculator  can  also  accommodate  bounce-back  scenarios.  These  are  always  defined 
as  certain  values  above  the  2008-2017  averages  of  the  number  of  start-ups,  average 
sizes and  survival rates of young firms. Recall that all these margins converge  precisely 

66 

to the respective 2008-2017 averages by 2019. 

Specifically,  let  us  denote  the  percentage  increase  (above  the  respective  long-run 
average)  in  the  bounce-back  scenario  related  to  the  number  of  start-ups,  the  growth 
potential of young firms and their survival rates by 𝜒C , where j = {n, s, x}, respectively. 
Furthermore, let us denote the length of the bounce-back period by 𝜏C, where j = {n, s, 
x}, respectively. The given bounce-back scenarios are then given by 

𝑛',6')G/<V/@ = 𝑛',6')G 1 − 𝜒H ,

for	𝑡 = 1, … , 𝜏H, 

𝑠$,6')G/<W/@/$ = 𝑠$,6')G 1 − 𝜒N ,

for	𝑡 = 1, … , 𝜏N		and	𝑎 = 0, 1, 2, … , 15, 

𝑥$,6')G/<X/@ = 𝑥$,6')G 1 − 𝜒T ,

for	𝑡 = 1, … , 𝜏T		and	𝑎 = 1, 2, … , 15. 

Appendix III. Adjusting for general equilibrium effects 

The  calculator  per  se  is  an  accounting  tool  that  abstracts  from  potential  general 
equilibrium  effects.  To  capture  the  partial  reallocation  of  labour  towards  surviving  firm, 
the calculator is embedded in a canonical model with firm heterogeneity. In what follow, 
we briefly describe the model economy and it contribution to the calculator.  

In the model, there is a measure M of heterogeneous firms.34 Let the production 

function of firm i be given by: 

𝑦Z = 𝑧Z𝑛Z

0 

where  𝑦Z  is  the  firm’s  output,  𝑛Z  its  employment  level,  𝑧Z  is  the  firm’s  productivity  level, 
and  𝛼 ∈ (0,1)  is  the  elasticity  of  production  with  respect  to  labour  input.35  The  wage  per 
employee  is  taken  as  given  by  firms,  and  denoted  by  w.  The  firm  chooses  its  level  of 
employment in order to maximize profits, given by 𝜋Z 𝑛 = 𝑧Z𝑛Z

0	 − 𝑤𝑛Z. This implies 

the following familiar solution for labor demand by firm i: 

𝑛Z = (𝑧Z)

)
)10

)
01)

𝑤
𝛼

Aggregating over all firms, aggregate labor demand is given by: 

N = 𝑀

)
01)

𝑤
𝛼

)

)10𝑑𝐹(𝑧)

𝑧

where  𝜒 ≡ 𝑧

d
def𝑑𝐹(𝑧),  with  F  is  the  CDF  of  the  productivity  distribution.  Taking  logs  and

34 Although	the	model	is	dynamic,	it	can	be	described	entirely	in	static	terms,	hence	we	omit	time	subscripts.	

35  We  abstract  from  capital  for  simplicity.  Augmenting  the  model  with  capital  would  not  change  any  of  our 
results. 

67 

differentiating (keeping idiosyncratic productivities constant), we can decompose changes 
in aggregate labour demand as: 

																																																										𝑑 ln 𝑁 = 		 𝑑 ln 𝑀 + 𝑑 ln 𝜒 +

1
𝛼 − 1

𝑙𝑛	 𝑤	

																																																			(1)

The first two terms reflect changes in, respectively, the number of firms and their growth 
potential (productivity), whereas the third term captures equilibrium effects due to wage 
conditions.36  Equation  (1)  can  be  understood  as  an  aggregate  labour  demand  curve, 
which  is  shifted  by  the  number  of  firms  and  their  growth  potential.  To  close  the  model, 
we  need  to  specify  how  labour  supply  is  determined.  We  assume  there  is  a 
representative household  with Greenwood-Hercowitz-Huffmann preferences. Specifically, 
the household’s level of utility is given by: 

U 𝐶, 𝑁 = 𝐶 − 𝜇

)1o

𝑁)/m
1 + 𝑘

where  C  denotes  consumption  and  𝜅, 𝜇, 𝜎 > 0  are  preference  parameters.  The  household 
chooses C and N to maximize utility, subject to a budget constraint given by 𝐶 = 𝑤𝑁 + Π, 
where  Π  are  aggregate  firm  profits.  Utility  maximization  implies  the  following  labour 
supply curve: µ𝑁u = 𝑤. Taking logs and differentiating gives the labour supply schedule: 

																																																																													𝑑 ln 𝑁 = 		

1
𝜅

𝑙𝑛	 𝑤 																																																																																	(2) 

Combining the labour demand and supply schedules, Equations (1) and (2), we can solve 
for the equilibrium level of aggregate employment: 

																																																									𝑑 ln 𝑁 = 		𝜓	 𝑑 ln 𝑀 + 𝑑 ln 𝜒 																																																														(3)	

Equation  (3)  expresses  aggregate  employment  (in  deviation  from  some  baseline  trend) 
as  a  function  of  the  number  of  firms  and  their  growth  potential.  The  latter  two  are 
∈ 0,1   is  the  equilibrium  dampening  effect, 
outputs  from  the  calculator,  while  𝜓 =

)
)1mxVy

)

01)

is the wage elasticity of labour demand and 1/𝑘 is the Frisch elasticity of
where 𝜀H{ =	
labour  supply.  Based  on  these  two  parameters  and  the  output  from  the  calculator,  we 
can  thus  compute  the  equilibrium  change  in  aggregate  employment  from  Equation  (3). 
Using  elasticities  consistent  with  the  literature  and  with  the  values  adopted  by  the 
European Commission QUEST and RHOMOLO models, we set the labour supply elasticity, 
1/𝑘,  at  0.25  and  the  labour  demand  elasticity,  𝜀H{,  at  -0.1.  These  elasticities  result  in  a 
dampening effect of 29%.  

36  Other  sources  of  equilibrium  dampening  could  derive  from  endogenous  entry  and  exit,  which  we  abstract 
from here.

68 

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doi:10.2760/232741 

ISBN 978-92-76-27387-5