Monitoring SMEs’ performance in Europe : methodological assessment of the SBA Scoreboard 2022.
Date: 2023-07-10
Institutions: Joint Research Centre
Subjects: EU Member State, State aid, database, digitisation, economic indicator, entrepreneurship, environmental policy, financial transparency, financing, globalisation, innovation, performance measurement, policymaking, professional qualifications, public procurement, research report, single market, small and medium-sized enterprises

JRC TECHNICAL REPORT 

Monitoring SMEs’ performance in Europe 

Methodological assessment of the SBA Scoreboard 2022 

Katsinis, A. 

Lagüera González, J. 

Di Bella, L.  

Pedraza, P. de  

2023 

ISSN 1831-9424

EUR 31588 EN

 
 
 
 
 
 
 
 
 
 
This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It 
aims  to  provide  evidence-based  scientific  support  to  the  European  policymaking  process.  The  contents  of  this  publication  do  not 
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authorities, or concerning the delimitation of its frontiers or boundaries. 

Contact information  
Name: Anastasis Katsinis 
Address: Joint Research Centre 
Directorate S – Innovation in Science and Policymaking 
Unit S.3 - Science for Modelling, Monitoring and Evaluation. 
TP 361 – Via E. Fermi 2749 – I-21027 – Ispra (Va) - ITALY 
Email: anastasis.katsinis@ec.europa.eu 
Tel.: +39 0332 78 6053 
EU Science Hub 
https://joint-research-centre.ec.europa.eu 

JRC134313 

EUR 31588 EN 

PDF  

ISBN 978-92-68-05428-4 

ISSN 1831-2494 

doi: 10.2760/822066  KJ-NA-31-588-EN-N   

Luxembourg: Publications Office of the European Union, 2023 

© European Union 2023  

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How to cite this report: Katsinis, A., Lagüera-González, J., Di Bella, L. and De Pedraza Garcia, P., Monitoring SMEs’ performance in Europe, 
Publications Office of the European Union, Luxembourg, 2023, doi:10.2760/822066, , JRC134313. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents  

Contents ........................................................................................................................................................................................................................................................................ i 

Abstract ....................................................................................................................................................................................................................................................................... 1 

Acknowledgements .......................................................................................................................................................................................................................................... 2 

Executive summary .......................................................................................................................................................................................................................................... 3 

Introduction..................................................................................................................................................................................................................................................... 4 

  Literature Review and Framework rationale .................................................................................................................................................................. 5 

2.1  Literature review .......................................................................................................................................................................................................................... 5 

2.2  Framework Rationale ............................................................................................................................................................................................................ 10 

  SME Fact Sheets ..................................................................................................................................................................................................................................... 11 

3.1  Framework and data sources ....................................................................................................................................................................................... 11 

3.2  Refinement of the SBA framework ......................................................................................................................................................................... 14 

  SME Fact Sheets – Methodology ............................................................................................................................................................................................. 17 

  Conceptual and statistical coherence ................................................................................................................................................................................. 24 

  Conclusions .................................................................................................................................................................................................................................................. 41 

References ............................................................................................................................................................................................................................................................. 42 

List of tables ........................................................................................................................................................................................................................................................ 45 

i 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Abstract  

The European Commission’s Directorate-General for Internal Market, Industry, Entrepreneurship and SMEs (DG 
GROW), with the scientific support by the European Commission's Joint Research Centre (JRC), assesses the 
Performance  of  Small  and  Medium  Enterprises  in  Europe.  We  study  performance  in  ten  dimensions:  (1) 
Entrepreneurship, (2) ‘Second chance’, (3), ‘Think small first’ and ‘Responsive administration’, (4) State aid and 
public  procurement,  (5)  Access  to  finance,  (6)  Single  market,  (7)  Skills  and  innovation,  (8)  Environment,  (9) 
Internationalisation and (10) Digitalisation.  

This  JRC  technical  report  describes  the  underlying  rationale  for  the  quantitative  measurement  of  these 
principles.  It  discusses  the  methodological  approach  to  calculate  how  countries  perform  in  the  outlined 
principles.  This  takes  into  account  the  choice  of  the  indicators,  the  data  quality  controls  (including  missing 
data and outliers), normalisation and weightings, and the statistical coherence and robustness checks of the 
SBA 2022 edition. 

1 

 
 
 
Acknowledgements 

The  authors  are  grateful  to  the  COIN  team  and  in  particular  to  Michaela  Saisana,  Daniel  Vertesy,  Marcos 
Alvarez,  Giulio  Caperna  and  Markus  Hell  for  sharing  their  insights  on  constructing  and  assessing  indicator 
frameworks and for their support in interpreting the statistical analysis of the current report. 

2 

 
Executive summary 

Small and medium-sized enterprises (SMEs) are essential for the prosperity of the European Union (EU). There 
is a growing interest in improving the overall approach to the specific business environment of SMEs. Europe 
has placed the needs of SMEs at the heart of its strategies and pursues ambitious policy initiatives to help 
SMEs  to  innovate  and  create  jobs,  benefit  from  digitalisation,  and  become  global  leaders  in  sustainable 
practices. This is the case for the new SME strategy1, complementing the Small Business Act for Europe (SBA) 
(EC, 2008).  

A scoreboard of indicators is a quantitative tool that aims to measure a latent complex phenomenon. Usually, 
this requires simplification of a real-life concept and several statistical decisions. The SME scoreboard 2022 
uses quantitative indicators covering ten SME-relevant dimensions and presents them in a scoreboard of ten 
dimensions. This report explains the conceptual and methodological process implemented to calculate the SBA 
2022 scoreboard. It is structured as follows. 

Section 2 provides a literature review on indicators and composite indices measuring aspects relevant to the 
SBA  principles.  It  is  a  slightly  revised  version  of  Section  2  of  previous  reports  (Stano  and  Ghisetti,  2016; 
Ghisetti and Pedraza 2017; Pedraza and Katsinis, 2019, 2020, 2021).  

Section  3  presents  the  ten-dimensions  of  the  framework,  corresponding  to  the  key  principles  of  the  Small 
Business  Act  and  the  SME  Strategy.  It  explains  the  rationale  behind  each  principle  and  the  underlying 
indicators selected by DG GROW after consultation with national experts. In the SBA2022 framework, a total 
of  95  indicators  were  selected  from  about  20  different  sources,  such  as  Flash  Eurobarometer  on 
Entrepreneurship, World Bank Doing Business, European Payment Index, European Central Bank database on 
interest rates, DG GROW internal market scoreboard and others.  

Section  4  discusses  the  methodological  approach  used  to  calculate  the  SBA  principles.  It  deals  with  data 
quality issues (missing data, potential outliers), choice of normalisation, weighting and aggregation formula. 
Raw data were first checked for reporting errors, and then outliers were treated. Missing data were estimated 
using a hybrid approach developed jointly by the JRC and DG GROW combining a bootstrap time-series, cross-
sectional, expectation-maximisation algorithms and a number of heuristic rules based on trend identification. 
The SBA principles are the results of simple averages of the normalised (with min-max) indicators per country. 

Section 5 studies the statistical coherence of the SBA 2022 framework by analysing the covariance structure 
within and across the principles. The analysis suggests that, at least from the statistical point of view, the SBA 
principles  are  strongly  multidimensional  and  the  underlying  indicators  capture  very  diverse  aspects.  On  the 
one hand such  diversity can be considered as advantageous, but on the other hand it is a strong argument 
against building up a composite indicator in which all the SBA principles are aggregated into an overall index.  

Section 6 assesses the robustness of the methodological assumptions. It examines to what extent the results 
depend on the methodological decisions made during the calculations of the SBA country fact sheets that are 
country  specific  summaries  elaborated  upon  the  results  of  the  calculation  of  the  scoreboard.  We  conclude 
that  97.0%  of  countries’  positioning  with  respect  to  the  EU  average  are  not  sensitive  to  methodological 
assumptions. 

Section 7 concludes. 

1 https://single-market-economy.ec.europa.eu/smes/sme-strategy_en  

3 

 
                                                        
 
Introduction  

Small and medium-sized enterprises (SMEs) are essential for the prosperity in the European Union (EU). This is 
why the European Commission (henceforth "the Commission") is pursuing ambitious policy initiatives - such as 
the  new  SME  strategy,  complementing  the  Small  Business  Act  for  Europe  (SBA),  the  Start-up  and  Scale-up 
Initiative and other existing SME policy framework and support programmes - to help SMEs, including start-
ups  and  scale-ups,  to  innovate  and  create  jobs,  benefit  from  digitalisation,  become  global  leaders  in 
sustainable practices as well as grow and operate freely in the Single Market and beyond. 

Since  2008,  the  European  Commission’s  Directorate-General  for  Internal  Market,  Industry,  Entrepreneurship 
and SMEs  (DG  GROW) has  produced the SME Performance Review2.  Among  other  measures, it includes  the 
SME country fact sheets whose main purpose is to  describe the performance of the SMEs across the EU27 
MSs.  It  uses  quantitative  indicators  covering  the  broad  range  of  ten  SME-relevant  dimensions:  (1) 
Entrepreneurship, (2) ‘Second chance’, (3) ‘Think small first’ and ‘Responsive administration’, (4) State aid and 
public  procurement,  (5)  Access  to  finance,  (6)  Single  market,  (7)  Skills  and  innovation,  (8)  Environment,  (9) 
Internationalisation and (10) Digitalisation. Dimensions are based on the SBA and the SME Strategy. The SBA 
invited the Commission and Member States to adopt the SBA principles to guide SMEs policy-making, and it 
aimed  to  improve  competitiveness  and  promote  entrepreneurship.  Dimensions  are  presented  together  as  a 
scoreboard. 

Since 2011, the SME Fact sheets have been produced by DG GROW with support by the Joint Research Centre 
(JRC)  that  has  elaborated  this  methodological  report.  Information  about  country  fact  sheets  from  previous 
exercises  can  be  found  in  Stano  and  Ghisetti  (2016),  Ghisetti  and  Pedraza  (2017),  Pedraza  and  Katsinis 
(2018), Pedraza and Katsinis (2019), Pedraza and Katsinis (2020), Pedraza and Katsinis (2021). 

2 This report refers to the SMEs Scoreboard 2022. It is the update of the 2016, 2017, 2018, 2019, 2020 and 2021 reports. Analysis is 
carried out on an updated framework following the same rationale and methodological choices (Stano and Ghisetti, 2016, Ghisetti and 
Pedraza 2017, Pedraza and Katsinis 2018, Pedraza and Katsinis 2019, Pedraza and Katsinis 2020, Pedraza and Katsinis 2021). In terms 
of  dimensions,  the  scoreboard  follows  the  SBA  conceptual  framework  (EC  2008)  with  the  addition  of  the  digital  dimension  that  was 
included in 2019. The indicators included in the scoreboard may slightly change in each exercise. The SMEs Scoreboard 2022 does not 
include  any  new  dimension  nor  indicator  with  respect to  2021.  The  SMEs  Performance  review  is a  wider  project  that  aims  to  monitor 
link  https://ec.europa.eu/growth/smes/sme-
several  SMEs  related  topics.  More 
strategy/performance-review_en  

information  can  be  found 

in  the 

4 

 
 
                                                        
 
  
  Literature Review and Framework rationale 

2.1  Literature review 

A scoreboard of indicators is a quantitative tool that aims to measure a latent complex phenomenon. Usually, 
this  requires  simplification  of  a  real-life  concept  through  some  sort  of  agglomerative  statistical  model  in 
which indicators are aggregated together at pre-defined stages. In some cases, the aggregation proceeds all 
the  way  through,  until  a  single  number  (a  composite  index)  is  obtained.  In  other  cases,  the  aggregation  is 
stopped  at  a  certain  intermediate  level  resulting  in  a  scoreboard  of  indicators.  The  final  product,  either  a 
single  summary  measure  or  several  measures,  facilitates  the  understanding  of  a  complex  issue  for  policy 
decision-makers and the public.  

Simplifications often come at the expense of information loss which has to be taken into account in decision 
making  (Paruolo,  Saisana,  &  Saltelli,  2013).  Another  concern  comes  from  subjective  choices  that  include 
issues  such  as:  framework  specification,  normalisation  procedure,  weights  assignment  and  aggregation 
method (Saisana and Philippas, 2012).  

Nevertheless, despite all the criticism, the popularity of composite indices and scoreboards for policy use has 
been  steadily  increasing  over  the  years.  Bandura  (2011)  lists  over  four  hundred  country-level  indices  that 
address a variety of topics such as economic progress, environmental sustainability, or quality of education. 
Rotberg et al. (2013) identified more than hundred country-level indices and databases related to governance 
and  its  components.  United  Nations  (Yang,  2014)  also  reviews  in  detail  more  than  one  hundred  composite 
like  happiness-adjusted  and 
measures  of  human  well-being  and  progress,  which  cover  subjects 
environmentally-adjusted  income,  child  development,  and  information  and  communications  technology 
development.  

The OECD, together with European Commission, the European Bank for Reconstruction and Development, and 
the European Training Foundation, developed the “SME Policy Index”. It assesses the policy dimensions related 
to  each  SBA  principle  based  on  governmental  self-assessments  and  local  consultants’  evaluations.  It  is  a 
benchmarking tool to guide policies towards SMEs. It allows comparisons in the evolution of country policies 
over time and gives country-specific recommendations (OECD et al., 2015).  

Few  examples  of  the  multiple  indicators  that  capture  Entrepreneurship  and  the  leverages  that  drive  its 
growth and success in different and specific contexts are the following.  

The European Commission, DG CNECT and DG JRC, have developed the Entrepreneurship and Scale-up Indices 
(ESIS)  (Van  Roy  and  Nepelski,  2016)  to  enhance  ICT  innovation  and  entrepreneurship  in  Europe.  Using  two 
composite indicators for European countries they assess framework conditions that are related to the growth 
of entrepreneurial activities, such as entrepreneurial culture, access to human capital, support to initiatives for 
knowledge  creation  and  networking,  market  conditions,  availability  of  sufficient  and  appropriate  finance, 
prevailing  business  regulations,  the  quality  of  the  supporting  infrastructure.  More  recently,  the  European 
Commission  has  analysed  the  geographical  (by  EU  Member  State  and  subnational  level)  and  sectoral 
distributions of High Growth Enterprises (EC, 2020). 

The Standard Chartered Hong Kong SME Leading Business Index is a quarterly composite indicator on SMEs. It 
uses  data  from  more  than  800  Hong  Kong  based  operating  firms  to  elaborate  a  COIN  upon  the  following 
dimensions: “Staff Number”, “Investments”, “Sales Amount”, “Profit Margin” and “Global Economic Growth”. It is 
sponsored by Standard Chartered Hong Kong and uses data from the Hong Kong Productivity Council.  

Table 1 summarizes several indicators related to the SBA principles. Some of them refer to EU and some to 
non-European countries.   

5 

 
 
 
 
 
Table 1: Review of indices relevant in the Small Business Act context 

SBA Principle  

Indicator/Index 

Developers 

Global Entrepreneurship Index  

Female Entrepreneurship Index  

GEDI 

GEDI 

1. Entrepreneurship 

OECD-Eurostat Entrepreneurship Indicators 
Programme  

OECD 

Global Entrepreneurship Monitor 

Global Entrepreneurship 
Research Association 

2. ‘Second chance’ 

World Bank “Doing Business” 

World Bank 

3. ‘Think small first’ 

World Bank “Doing Business” 

World Bank 

4. Responsible 
Administration  

World Bank “Doing Business” 

World Bank 

World Bank “Doing Business” 

World Bank 

5. Public Procurement 

SMEs access to and demand aggregation in 
public procurement 

DG MARKT, PwC, ICF GHK and 
Ecorys 

State aid Scoreboard 

Single Market Scoreboard 

DG COMP 

DG GROW 

European Payment Index 

Intrum Justitia 

World Bank “Doing Business” 

World Bank  

Global Entrepreneurship Monitor 

Global Entrepreneurship 
Research Association 

6. Access to Finance 

Access to Finance of Enterprises 

European Central Bank 

Financing SMEs and Entrepreneurs 2016 
Scoreboard 

OECD 

7. Single Market 

Global Entrepreneurship Monitor 

Global Entrepreneurship 
Research Association 

Digital Economy and Society Index (DESI) 

European Commission 

8. Skills and 
Innovation 

European Innovation Scoreboard 

European Commission  

6 

 
  
Innovation Output Indicator 

European Commission  

Global Innovation Index 

9. Environment 

Eco-Innovation Scoreboard 

Cornell University, INSEAD and 
WIPO 

Eco-Innovation Observatory for 
European Commission, DG 
Environment 

Green Growth Indicators 

OECD 

World Bank “Doing Business” 

World Bank 

UNCTAD Internationalisation Statistics 

UNCTAD 

eGovernment Benchmark 

European Commission 

Digital Readiness Index 

Cisco 

10. 
Internationalisation  

11. Digitalisation 

Transversal indicators 

SME Policy Index 

OECD, European Commission, 
European Bank for 
Reconstruction and 
Development, European 
Training Foundation 

the Entrepreneurship and Scale-up Indices 

European Commission 

Standard Chartered Hong Kong SME 
Leading Business Index 

Hong Kong Productivity Council, 
Standard Chartered Hong Kong. 

The  Global Entrepreneurship and  Development Institute (GEDI) calculates The Global Entrepreneurship Index 
(Acs et al., 2017) that summarizes attitudes, resources, and infrastructure (entrepreneurship ‘ecosystem’) on a 
yearly basis for 132 countries (including EU27, UK China, India, Australia and the US). It is a single composite 
indicator, centred on 14 pillars. The aim of the index is to rank countries and provide a picture on how each 
country  performs  in  both  the  domestic  and  the  international  context.  The  same  institute  constructed  and 
launched  the  Female  Entrepreneurship  Index  in  2013.  It  measures  conditions  for  female  entrepreneurship 
development (GEDI, 2015). In its last edition, it covers 77 countries. It contains three main sub-indices on the 
quality  of:  the  entrepreneurial  environment;  the  entrepreneurial  eco-system;  and  women’s  entrepreneurial 
aspirations. They  use  data from the Global Entrepreneurship Monitor, the International Labour Organization, 
the World Economic Forum, the World Bank, UNESCO, and the United Nations Development Program. 

The  OECD-Eurostat  Entrepreneurship  Indicators  Programme  (EIP),  launched  in  2006,  develops  indicators  on 
entrepreneurial  performance  and  entrepreneurial  determinants.  It  aims  to  be  internationally-comparable 
across 37 countries and guides policy making. The OECD (2016) report includes new data extracted from an 
online SME survey prepared by Facebook in co-operation with the OECD and the World Bank. EIP stresses the 
multi-faceted nature of entrepreneurship and does not summarize it into a single composite indicator. The EIP 
uses data from National Statistical Offices, OECD Timely Indicators of Entrepreneurship, OECD Main Economic 
Indicators  Database,  OECD  Structural  and  Demographic  Business  Statistics,  OECD  Trade  by  Enterprise 
Characteristics, Labour Force Surveys and Census Population data, OECD Entrepreneurship Finance Database 
and OECD Patent Database. 

The Global Entrepreneurship Monitor (GEM) (Kelley et al., 2016; Bosma and Kelley, 2018) provides comparable 
country measures of entrepreneurial activity for more than 100 countries (including EU-27, UK, China, India, 
Australia and the US). It focuses on entrepreneurial behaviour and attitudes, and the national context. It was 
launched in 1999 as a joint project between Babson College (USA) and London Business School (UK) under the 

7 

 
  
  
  
  
  
  
  
 
Global  Entrepreneurship  Research  Association’s  supervision.  It  provides  complementary  information  on  the 
Adult  Population  Survey  (APS),  covering  the  entrepreneurial  attitude  of  more  than  2000  adults  in  every 
country,  and  on  the  National  Expert  Survey  (NES),  administered  to  selected  experts  to  assess  the 
Entrepreneurial Framework Conditions. It does not summarize indicators into a composite indicator. The SBA 
uses data from The Global Entrepreneurship Monitor (GEM) in several indicators (e.g., ‘Second chance’, ‘Think 
small first’ and Responsible Administration, Access to Finance, Single Market and Skills and Innovation) (Kelley 
et al., 2016; Bosma and Kelley, 2018). 

The World Bank “Doing Business” is a flagship publication by the World Bank (World Bank, 2017, 2018) that 
captures  regulations  enhancing  business  activity  and  those  constraining  it.  It  provides  a  country-based 
measurement  of  the  ease  of  starting  a  business  in  an  economy  from  multiple  perspectives:  starting  a 
business, dealing with  construction permits,  getting electricity, registering property, getting credit, protecting 
minority  investors,  paying  taxes,  trading  across  borders,  enforcing  contracts  and  resolving  insolvency.  It 
records  procedures  officially  required  or  commonly  done  in  practice  to  start  up  and  formally  operate  an 
industrial  or  commercial  business,  the  time  and  cost  required  to  complete  these  procedures,  and  the  tax 
payments.  It  also  records  the  paid-in  minimum  capital  that  companies  must  deposit  before  registration  as 
well  as  the  costs  to  enforce  contracts.  It  gives  a  ranking  of  economies  on  the  ease  of  starting  a  business 
depending  on  their  distance  to  frontier  scores  and  covers  190  countries  over  time.  The  ‘Second  chance’ 
principle is largely assessed by the World Bank “Doing Business” that also captures the ‘Think small first’ 
and the 'Responsible administration' principles.   

The World Bank “Doing Business” (World Bank, 2018) measures Public Procurement and, for a pilot of 78 
countries,  it  assesses  the  government  indicators  that  have  been  developed  by  the  Benchmarking  Public 
Procurement  project.  They  measure  transaction  costs  of  public  procurement  contracts,  the  accessibility  and 
transparency of particular aspects of the procurement process and constraints that private companies face, 
and the presence of specific legal provisions or policies to promote fair access for SMEs to government. DG 
MARKT’s3  study  on  "SMEs  access  to  and  demand  aggregation  in  public  procurement"  reports,  for  European 
Countries,  the  share  of  SMEs  in  the  contracts  awarded  by  Member  States  (PWC  et  al.,  2014).  DG  COMP 
develops the State aid scoreboard4, comparing European countries with respect to the total aid earmarked for 
SMEs.  DG  GROW  has developed the Single  Market  Scoreboard to evaluate the presence  of  SMEs in  several 
classifications, like won awards, bids etc. Finally, the European Payment Index, developed by Intrum Justitia 
(Intrum  Justitia,  2016),  signals  the  number  of  days  of  delay  before  payments  are  made  by  the  public 
authorities, in 25 European countries. 

Intrum Justitia also provides single indicators on the Access to Finance principle, namely the days it takes 
for a company to get paid and the relative amount of receivables that has to be written as a consequence of 
the lack of  payment.  The World Bank  “Doing  Business” (World  Bank,  2017)  also provides  indicators for  this 
principle.  The  strength  of  legal  rights  index,  measures  the  degree  to  which  collateral  and  bankruptcy  laws 
protect  the  rights  of  borrowers  and  lenders  and  facilitate  lending.  The  Depth  of  credit  information  index, 
measures rules and practices affecting the coverage, scope and accessibility of credit information. The Global 
Entrepreneurship  Monitor  (Kelley  et  al.,  2016;  Bosma  and  Kelley,  2018)  provides  indication  on  the  Equity 
funding  available  for  new  and  growing  firms,  the  professional  Business  Angels  funding  available  to  those 
firms, as well as the crowdfunding possibilities. The European Central Bank Survey on the “Access to Finance 
of Enterprises” (SAFE) (ECB, 2019) gives an indication on the rejected applications, on public financial support 
and on the willingness of banks to provide loans. The perceived external financing gap indicator is a composite 
indicator  for  the  euro  area  and  specifically  for  SMEs.  The  OECD  produces  the  Financing  SMEs  and 
Entrepreneurs  2016  Scoreboard  (OECD,  2019)  and  monitors  access  to  finance  framework  conditions  and 
constrains for 46 countries. 

The internal market scoreboard aims to give an overview of the practical management of the Single Market 
in Europe. It provides a picture on the dismantlement of barriers to the European Single Market.  The Global 
Entrepreneurship  Monitor  (Kelley  et  al.,  2016;  Bosma  and  Kelley,  2018)  provides  multiple  indicators  on  the 
accessibility  to  the  market  for  new  firms,  including  the  effectiveness  of  anti-trust  legislation.  The  Digital 

3 This Directorate-General was dissolved and its components integrated into other DGs on November 1st 2014. A large section of 
the DG was refurbished and integrated into the new Directorate-General for Financial Services. Another fairly large section of DG 
Internal Market was integrated into DG Industry and Enterprise. Other sections were transferred to DG Justice and DG CONNECT. 
4 The scoreboard is available here http://ec.europa.eu/competition/state_aid/scoreboard/index_en.html 

8 

 
                                                        
 
Economy and Society Index (DESI)5 is a composite index that summarises MSs’ digital performance and tracks 
the evolution of EU  digital  competitiveness.  It is centred  on the  dimensions of Connectivity, Human Capital, 
Use of Internet, Integration of the Digital Society and Digital Public Services.  

There is a variety of indicators and composite indicators focusing on Skills and Innovation, both in Europe 
and outside.   

The European Innovation Scoreboard (former Innovation Union Scoreboard) (EC, 2019), compares EU Member 
States  and  other  European  countries  based  on  strengths  and  weaknesses  of  their  national  innovation 
systems. Its Summary Innovation index is organized in three sub-indices: enablers, firm activities and outputs. 
They  are composed  by eight,  nine  and  eight indicators  respectively. The scoreboard  presents a  regionalised 
extension, the Regional Innovation Scoreboard that covers 214 Regions across 22 EU countries and Norway.  

The Innovation Output Indicator (Vertesy and Deiss, 2016), developed by the European Commission (DG RTD) 
benchmarks national innovation policies. It is a composite indicator centred on technological innovation, skills 
in  knowledge-intensive  activities,  the  competitiveness  of  knowledge-intensive  goods  and  services,  and  the 
innovativeness  of  fast-growing  enterprises.  It  covers  EU  Member  States,  Japan,  Switzerland,  United  States, 
Iceland, Norway and Turkey.  

The  Global  Innovation  Index  (GII)  (Cornell  University  et  al.,  2016),  is  the  result  of  a  collaboration  between 
Cornell University, INSEAD, and the World Intellectual Property Organization (WIPO). It provides four indexes 
for 128 countries: a) The Innovation Input sub-index, b) the Innovation Output sub-index, c) the overall Global 
Innovation Index (a simple average of a) and b)), and the Innovation Efficiency Ratio (the ratio of b) over a)). 
Both  score  values and  the ranking  of  each country allow benchmarking and international comparisons. The 
European  Commission’s  Competence  Centre  on  Composite  Indicators  and  Scoreboards  (COIN)  at  the  Joint 
Research Centre (JRC) (Saisana et al., 2016) provides an independent statistical assessment of the GII every 
year, since 2011. The JRC audit focuses on the statistical soundness of the multi-level structure of the index, 
as well as on the impact of key modelling assumptions on the GII results.  This JRC audit helps to guarantee 
the  transparency  and  reliability  of  the  GII  for  both  policy-makers  and  other  stakeholders,  thus  facilitating 
more accurate priority setting and policy formulation in the innovation field. 

The  Eco-Innovation Scoreboard (ECO-IS),  developed  by  the  Eco-Innovation Observatory  for DG Environment, 
ranks European  Member  States coherently  with  the SBA  Environment  principle.  It  provides an overview  of 
the Eco-Innovative performance of European Member States under different dimensions of eco-innovation in 
five  areas:  eco-innovation  inputs,  eco-innovation  activities,  eco-innovation  outputs,  resource  efficiency 
outcomes  and  socio-economic  outcomes6.  Furthermore,  indicators  provided  by  Eurostat  (Environmental 
protection  expenditure  by  size  class)  and  OECD  (green  growth  indicators)  contribute  to  the  environmental 
principle. 

The World Bank “Doing Business” indicators (World Bank, 2018) capture the Internationalisation principle by 
assessing importing and exporting activities. UNCTAD (UNCTAD, 2016) presents Internationalisations statistics 
for the largest non-financial multinational firms in the world although its focus is not applicable to Small and 
Medium Enterprises. 

Finally, the Digitalisation principle is primarily composed by Eurostat indicators which focus on e-commerce, 
cloud  computing,  computer  skills  etc,  eGovernment  benchmark  (Tinholt  et  al.,  2017)  and  Cisco’s  Digital 
Readiness  Index7.  Another  example  of  a  number  of  indexes  aiming  to  capture  different  aspects  of 
digitalisation is the Network Readiness Index (NRI)8. The NRI ranks a total of 134 economies based on their 
performance across 60 variables that are organized in four dimensions: Technology, People, Governance and 
Impact.  The  NRI  covers  issues  ranging  from  future  technologies  such  as  Artificial  Intelligence  (AI)  and  the 
Internet  of  Things  (IoT)  to  the  role  of  the  digital  economy  in  reaching  the  Sustainable  Development  Goals 
(SDGs). The European Commission’s Competence Centre on Composite Indicators and Scoreboards (COIN) at 
the Joint Research Centre (JRC) has provided an independent statistical assessment of the 2020 edition of the 
NRI. 

5 DESI index is available here https://ec.europa.eu/digital-single-market/en/desi 
6 The scoreboard is available at the following link http://ec.europa.eu/environment/ecoap/scoreboard_en  
7 Cisco’s Digital Readiness Index is available at the following link https://www.cisco.com/c/m/en_us/about/corporate-social-
responsibility/research-resources/digital-readiness-index.html#/  
8 More information about NRI can be found at: https://networkreadinessindex.org/  

9 

 
                                                        
 
2.2  Framework Rationale 

The  eleven  SBA  principles  are  grouped  into  ten  statistical  dimensions:  (1)  Entrepreneurship,  (2)  ‘Second 
chance’,  (3)  ‘Think  small  first’  and  ‘Responsive  administration’,  (4)  State  aid  and  Public  procurement,  (5) 
Access  to  finance,  (6)  Single  market,  (7)  Skills  and  Innovation,  (8)  Environment,  (9)  Internationalisation  and 
(10) Digitalisation. Table 2 lists the SBA principles and includes the rationale behind each one according the 
Commission Communication (EC 2008).  

Table 2: SBA principles and rationale   

SBA Principle 

Rationale (from COM(2008) 394 final) 

1. Entrepreneurship 

2. ‘Second chance’ 

To  create  an  environment  in  which  entrepreneurs  and  family  businesses  can 
thrive and entrepreneurship is rewarded  

To ensure that honest entrepreneurs who have faced bankruptcy quickly get a 
second chance  

3. ‘Think small first’ and 
‘Responsive administration’ 

To design rules according to the ‘Think small first’ principle 
To make public administrations responsive to SMEs’ needs 

4. State aid and public 
procurement 

To  adapt  public  policy  tools  to  SME  needs:  facilitate  SMEs’  participation  in 
public procurement and better use State Aid possibilities for SMEs  

5. Access to finance 

6. Single market 

To  facilitate  SMEs’  access  to  finance  and  develop  a  legal  and  business 
environment supportive to timely payments in commercial transactions 

To  help  SMEs  to  benefit  more  from  the  opportunities  offered  by  the  Single 
Market  

7. Skills and innovation 

To promote the upgrading of skills in SMEs and all forms of innovation 

8. Environment 

To enable SMEs to turn environmental challenges into opportunities  

9. Internationalisation 

To encourage and support SMEs to benefit from the growth of markets 

10. Digitalisation 

.  

To  assist  SMEs  exploit  all  benefits  of  the  digital  era  and  its  state  of  the  art 
achievements  

10 

 
 
  SME Fact Sheets 

The  SME  country  fact  sheets  have  been  prepared  by  DG  GROW  annually  since  2008.  The  indicators’ 
framework behind the fact sheets is refined each year in order to align the quantitative information with the 
newest scientific discoveries and policy trends in the field of SMEs’ performance in Europe. Thus, although the 
SBA principles remain fixed over time, the quantitative framework may vary from year to year, with changes 
including  aspects  such  as  removing/incorporating  indicators,  merging  dimensions,  revising  data  collection 
methodologies for indicators, and inclusion of new dimensions.   

3.1  Framework and data sources  

The 2022 edition calculates SBA profiles for the 27 EU Member States plus 16 non-Member States9. Except 
the UK, Israel and the USA, all other non-Member States are also part of the Competitiveness of Enterprises 
and Small and Medium-sized Enterprises (COSME) Programme. Out of the 13 COSME countries, four countries 
were included for the first time in 201910.  

Each principle is composed by several individual indicators, with a total of 94 indicators that were selected by 
DG GROW in consultation with national experts. Indicators came from around 20 sources of information that 
include, among others:  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The Flash Eurobarometer “Businesses’ attitudes towards corruption in the EU”,  

The  Flash  Eurobarometer  on  Entrepreneurship,  former  DG  MARKT’s  study  on  “SMEs  access  to  and  demand  aggregation  in 
public procurement”,  

The Global Entrepreneurship Monitor (GEM),  

The World Bank Doing Business,  

The Global Competitiveness Report,  

DG GROW’s study on “start-up procedures for the SME”,  

The Eurobarometer survey on SMEs and the environment,  

The Eurostat Community survey on ICT usage and e-Commerce in enterprises,  

Eurostat structural business statistics,  

Survey on the Access to Finance of Enterprises (SAFE),  

National Expert Survey (NES) of the Global Entrepreneurship Monitor (GEM),  

The State aid scoreboard,  

The European Payment Index, 

The European Central Bank database on interest rates,  

The Eurostat report on Venture Capital,  

The Comext database on international trade,  

The Internal market scoreboard,  

The Eurostat Community Innovation Survey, CVT survey,  

The OECD trade facilitation indicators, 

The Single Market Scoreboard launched by DG GROW,  

The eGovernment Benchmark.  

9 From the participating 16 non-EU countries, the twelve which existed in every previous version were: UK, Albania, FYROM, Iceland, Israel, 
Liechtenstein, Moldova, Montenegro, Norway, Serbia, Turkey and the United States of America (USA). These non-EU countries (with 
the exception of the USA,  which were  included only for  comparison purposes) are in the data base for historical reasons as they 
were included in the old Competitiveness and Innovation Framework Programme (CIP). In the new COSME programme there are only 
seven non-member states (Albania, FYROM, Iceland, Moldova, Montenegro, Serbia, Turkey) 

10 Additional non-EU countries which have signed the COSME agreement and have been included since 2019 are: Armenia, Bosnia and 

Herzegovina, Ukraine and Kosovo. 

11 

 
 
                                                        
 
Table  3  lists  the  95  indicators  underlying  the  2022  SBA  profiles.  An  excel  file  containing  the  data  and  a 
dictionary with detailed information about every indicator (source, frequencies, methodological changes, etc.) 
is available.  

Twelve 
indicators  compose  the  first  principle  on  Entrepreneurship.  They  measure  early  stage 
entrepreneurial  activity  of  men  and  women,  ownership  rate  of  established  businesses,  improvement-driven 
activity, entrepreneurial intention, degree to which school education develops entrepreneurial spirit, share of 
people  who  consider  starting  a  business  as  a  desirable  career,  share  of  people  who  consider  successful 
entrepreneurs  as  those  receiving  high  status,  the  share  of  high  growth  enterprises,  the  expectation  rate  of 
high job creation and media attention for entrepreneurship.  

Four  indicators  compose  the  second  principle,  Second  chance:    time  and  cost  to  close  a  business,  fear  of 
failure rate and the strength of insolvency framework index.  

The  third  and  fourth  principles  merge  into  one  single  statistical  dimension,  Think  small  first  and 
Responsive  administration.  It  is  built  by  thirteen  indicators  that  describe  burden  of  fast-changing 
legislation  and  complexity  of  administrative  procedures,  burden  of  government  regulations,  government’s 
effectiveness in supporting new and growing firms, measure time, cost and number of procedures to start a 
business, paid in minimum capital, time and cost required to transfer property, number of tax payments per 
year, time required to comply with major taxes and cost to enforce contracts.  

The fourth statistical dimension corresponds to the State aid and Public procurement principle that draws 
on seven indicators: SMEs' share in the total value of public contracts awarded, share of businesses  having 
taken part in a public tender of public procurement procedure, delay in payments from public authorities, the 
percentage  of  awards  for  which  the  winner  was  an  SME,  the  proportion  of  bids  coming  from  SMEs, 
eProcurement and finally the percentage of calls for tenders which were split into lots.  

The fifth statistical dimension corresponds to the Access to Finance principle that is built on ten indicators 
that measure venture capital investments, strength of legal rights, total duration to get paid, bad debt losses, 
rejected  loan  applications/offers,  Annual  average  of  interest  rate  for  small  loans,  access  to  public  financial 
support  including  guarantees,  willingness  of  banks  to  provide  a  loan,  equity  funding  available  for  new  and 
growing firm and professional Business Angels / private lenders’  funding available for new and growing firms. 

Table 3: SBA Framework 11 

1. Entrepreneurship (12 indicators) 
1.1 Total early-stage Entrepreneurial Activity (% adults who have 
started a business or are taking the steps to start one) 
1.2 Total early-stage Entrepreneurial Activity for Female Working Age 
Population (% women who have started a business or are taking the 
steps to start one) 
1.3 Established Business Ownership (%) 
1.4 Improvement-driven opportunity entrepreneurial activity (% of 
entrepreneurs) 
1.5 Entrepreneurial intention (% adults who intend to start a business 
within 3 years) 
1.6 Entrepreneurship as Desirable Career Choice (%) 
1.7 High-status to successful entrepreneurship (%) 
1.8 Media attention for entrepreneurship (%) 
1.9 Entrepreneurship Education (the extent to which training in 
creating or managing SMEs is incorporated within the education and 
training system at basic school) 
1.10 Entrepreneurship Education (the extent to which training in 
creating or managing SMEs is incorporated within the education and 
training system at post-secondary levels) 
1.11 Share of high growth enterprises (%) 
1.12 High Job Creation Expectation Rate (%) 
2. ‘Second chance’ (4 indicators) 
2.1 Time to resolve insolvency (in years) (-) 
2.2 Cost to resolve insolvency (cost to recover debt as % of debtor's 
estate) (-)  

6. Single market (9 indicators) 

6.1 Number outstanding single market directives (directives not 
notified or not transposed into national legislation) (-) 
6.2 Average transposition delay- overdue directives (months) (-)  
6.3 Number of pending infringement proceedings (-)  
6.4 Public contracts secured abroad (by total value of contracts) 
6.5 Intra-EU imports of goods by SMEs in industry (% of SMEs) 
6.6 SMEs with intra-EU exports (%) 
6.7 Selling Online Cross-border to other EU countries (% of 
SMEs) 
6.8 New and growing firms can easily enter new markets 
(1=worst, 5=best) 
6.9 New and growing firms can enter markets without being 
unfairly blocked by established firms (1=worst, 5=best) 

7. Skills and innovation (14 indicators) 
7.1 SMEs innovating in-house (%) 
7.2 Innovative SMEs collaborating with others (%) 
7.3 SMEs introducing product innovations (%) 

11 All sources of the individual indicators are listed here: https://ec.europa.eu/docsroom/documents/50477 

12 

 
 
 
 
                                                        
 
2.3 Fear of Failure (% of pop. who indicate that fear of failure would 
prevent them from setting up a business) (-)  
2.4 Strength of insolvency framework index (0-16) 

7.4 SMEs introducing business process innovations (%) 
7.5 Sales of new-to-market and new-to-firm innovations (% 
turnover) 
7.6 SMEs selling online (% of SMEs) 
7.7 SMEs purchasing online (% of SMEs) 
7.8 Enterprises providing training to their employees (%) 
7.9 Turnover from e-commerce 
7.10 Percentage of enterprises employing persons with ICT 
specialist skills (%) 
7.11 Digital skills and e-leadership Enterprise provided training 
to their personnel to develop/upgrade their ICT skills 
7.12 RandD Transfer 
7.13 Innovation rate - TEA impact (national) 
7.14 Innovation rate - TEA impact (international) 

3. ‘Think small first’ and ‘Responsive administration’ (13 
indicators) 

8. Environment (9 indicators) 

3.1 Time to start a business (in calendar days) (-) 
3.2 Cost to start a business (in Euro) (-) 
3.3 Paid-in minimum capital (% of income per capita) (-) 
3.4 Time required to register property (in calendar days) (-) 
3.5 Cost required to register property (% of prop. value) (-) 
3.6 Number of tax payments per year (-) 
3.7 Time required to comply with major taxes (hours/y) (-) 
3.8 Cost to enforce contracts (% of claim) (-) 
3.9 Fast-changing legislation and policies are a problem when doing 
business (% of businesses who agree with the statement) (-) 
3.10 The complexity of administrative procedures is a problem when 
doing business (% of businesses who agree with the statement) (-) 
3.11 Starting a business (number of procedures) (-) 
3.12 Burden of government regulations (1=worst, 7=best) 
3.13 The people working for government agencies are competent and 
effective in supporting new and growing firm (1=worst, 5=best) 

8.1 SMEs that have introduced resource-efficiency measures 
(%)  
8.2 SMEs that have benefitted from public support measures for 
resource-efficiency actions (%) 
8.3 SMEs that offer green products or services (%) 
8.4 SMEs with more than 50% turnover generated by green 
products or services (%) 
8.5 SMEs that have benefitted from public support measures for 
production of green products (%) 
8.6 SME investment in equipment and plant for pollution control 
(as % of manufacturing GDP) 
8.7 SME investment equipment and plant linked to cleaner 
technology (as % of manufacturing GDP) 
8.8 Environmental technologies as % of all technologies  
8.9 CO2 productivity (GDP per unit of energy-related CO2 
emissions) 

4. Public procurement (7 indicators) 

9. Internationalisation (9 indicators) 

4.1 SME's share in total value of public contracts awarded (%) 
4.2 Share of businesses having taken part in a public tender of public 
procurement procedure (%) 
4.3 Average delay in payments from public authorities (days) (-) 
4.4 Enterprises submitting a proposal in a public electronic tender 
system (eProcurement) 
4.5 Percentage of awards for which the winner was an SME (%) 
4.6 Proportion of bids coming from SMEs (%) 
4.7 Percentage of calls for tenders which were split into lots (%) 

5. Access to finance (10 indicators) 
5.1 Venture capital investments (% of GDP) 
5.2 Strength of legal rights (0=worst, 12=best) 
5.3 Total duration to get paid (number of days) (-) 
5.4 Bad debt loss (% of total turnover) (-) 
5.5 Annual average of interest rate for small loans (-) 
5.6 Rejected loan applications and loan offers (% of loan applications 
by SMEs) (-) 
5.7 Access to public financial support including guarantees (% of 
respondents who indicated a deterioration) (-) 
5.8 Willingness of banks to provide a loan (% of respondents who 
indicated a deterioration) (-) 
5.9 Equity funding available for new and growing firms (1=worst, 
5=best) 
5.10 Professional Business Angels funding available for new and 
growing firms (1=worst, 5=best) 

9.1 SMEs having done electronic sales to the rest of the world 
(%)  
9.2 Information availability (0 - 2) 
9.3 Involvement of trade community (0 - 2) 
9.4 Advance rulings (0 - 2) 
9.5 Formalities – automation (0 - 2) 
9.6 Formalities – procedures (0 - 2) 
9.7 Border Agency Co-operation (internal) (0 - 2) 
9.8 SMEs with extra-EU imports of goods (%) 
9.9 SMEs with extra-EU exports of goods (%) 

10. Digitalisation (8 indicators) 

10.1 Enterprises sending e-invoices (%)  
10.2 Enterprises having website-homepage (%) 
10.3 Enterprises that buy cloud computing services (%) 
10.4 Enterprises using their own websites or apps for sale (%) 
10.5 Individuals who have used a programming language (%) 
10.6 Enterprises having access internet <10Mbps speed (%) (-) 
10.7 Online availability of info for business mobility (%) 
10.8 Start-up environment (0 - 1) 

The sixth statistical dimension corresponds to the  Single Market principle that is captured by nine indicators, 
measuring  single  market  directives  not  transposed  or  notified,  transposition  delay  for  overdue  directives, 
number  of  pending  infringement  procedures,  public  contract  secured  abroad,  SMEs  with  intra-EU 
imports/exports, share of SMEs selling online to other EU countries and the easiness of entering new markets 
for new and growing firms without being blocked by established enterprises.   

13 

 
 
 
 
 
The seventh statistical dimension corresponds to the   Skills and Innovation  principle and is composed by 
fourteen  indicators  that  measure  share  of  SMEs  innovating  in-house,  innovative  SMEs  collaborating  with 
others,  SMEs  introducing  product  or  business  process  innovations,  sales  of  new-to-market  and  new-to-firm 
innovations, SMEs  selling/purchasing online, enterprises  providing training to their employees, turnover from 
e-commerce, digital skills, SMEs providing training focused on the development, upgrade of ICT skills, RandD 
transfer and finally TEA at least national and international scope for market, for new product or new process. 
Last two indicators (national and international TEA) are two indicators included for the first time in the 2020 
exercise that replaced the former innovation rate. 

The  eighth  statistical  dimension  corresponds  to  the  Environment  principle  that  builds  on  nine  indicators, 
namely  SMEs  that  have  introduced  resource-efficiency  measures,  SMEs  that  have  benefitted  from  public 
support measures for resource-efficiency actions, SMEs that offer green products or services, SMEs with more 
than 50% turnover generated by green products or services, SMEs that have benefitted from public support 
measures for production of green products, SME investment in equipment and plant for pollution control and 
for  cleaner  technology,  Environmental  technologies  as  percentage  of  all  technologies  and  finally  CO2 
productivity. Last four indicators are introduced for the first time in the 2020 exercise.  

The  ninth statistical dimension corresponds to the Internationalisation principle and  is  composed  by  nine 
indicators. It describes the SMEs’ internationalisation based on extra-EU online exporters (SMEs having done 
electronic sales to the rest of the world), information availability, degree of involvement of trade community, 
advance  rulings,  automation-formalities  procedures,  border  Agency  Co-operation  and  SMEs  with  extra-EU 
imports/exports of goods. 

Finally,  the  tenth  statistical  dimension  corresponds  to  the  Digitalisation  principle.  It  is  composed  by  eight 
indicators  to  measure  enterprises  that  send  e-invoices,  have  website-homepage,  buy  cloud-computing 
services, use their own websites or apps for sale, have decent internet access, the online availability of info 
for  business  mobility,  the  start-up  environment,  and  the  percentage  of  individuals  who  have  used  a 
programming  language.  This  dimension  and  its  eight  indicators  are  included  for  the  first  time  in  the  2020 
exercise (Pedraza and Katsinis 2020). 

It  is  important  to  notice  that  during  previous  exercises,  data  collection  of  the  above  indicators  took  place 
mainly  during  the  months  of  June  and  July.  The  SBA  2022  has  undergone  a  change  in  the  data  collection 
period  that  was  split  in  two  distinct  time  periods:  The  first  cut-off  date  was  set  mid-July  and  the  second 
update was scheduled for January 2023. 

3.2  Refinement of the SBA framework  

The SBA exercise is updated yearly to account for new developments in the methodology and refinements in 
the  existing  data  sources.  The  2017  edition  of  the  fact  sheets  underwent  a  major  refinement  of  the 
framework. Eight indicators were dropped due to a discontinued methodology in the data collection, and eight 
new  indicators  were  introduced.  More  information  on  this  refinement  can  be  found  in  Ghisetti  and  Pedraza 
(2017). The 2018 edition maintained the general 2017 framework. Five new indicators were introduced in the 
2019  exercise  (codes  1.13,  4.5,  4.6,  4.7  and  7.13  as  displayed  in  table  3).  Five  new  indicators  were 
incorporated in the 2020 exercise in already existing dimensions: 7.14, 8.6, 8.7, 8.8, 8.9. The dimension about 
digitalisation  which  includes  8  indicators  (10.1  to  10.8)  was  introduced  in  the  2020  exercise  as  well.  More 
information on these revisions is provided in Pedraza and Katsinis (2020). In the 2021 exercise, the general 
framework is the same as in 2020, only one minor change took place: The context of indicators 7.3 and 7.4 
has been modified by the source. For 7.3, the indicator now is “SMEs introducing product innovations” (instead 
of  “SMEs  introducing  product  or  process  innovations  as  %  of  SMEs”).  The  former  indicator  7.4  (“SMEs 
introducing  marketing  or  organizational  innovations  (%  of  SMEs)”)  is  discontinued  and  replaced  by  “SMEs 
introducing  business  process  innovations”.  The  framework  used  in  the  2022  exercise  is  the  same  as  in  the 
previous year. 

Data are collected from 2007 to 2022. The SME fact sheets uses indicators of the last year available. Table 4 
summarises  the  percentages  of  country/year  indicators  by  last  year  available  in  each  dimension.  Some 
indicators  are  discontinued,  some  others  are  not  updated  every  year  and,  in  some  cases,  there  is  a  “time 
delay”. As a result, about 4% of the indicators are available for the year 2022, 31% for 2021, 29% for the 
period 2019-2020, 6% for the period 2017-2018 and only 13% from years before 2017. For example, 41% 
of the observations in dimension ‘1. Entrepreneurship’ are from 2021. 

14 

 
 
15 

 
 
 
Table 4: Percentages of country/year indicators by last available year and dimension 

&
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2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
NaN

i

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s
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n
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.
1

0.0%
40.7%
2.0%
13.2%
6.0%
4.0%
0.8%
2.1%
4.3%
1.7%
3.7%
0.0%
3.7%
2.2%
0.0%
0.0%
15.7%

e
c
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a
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c
d
n
o
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S
.
2

0.0%
10.7%
57.8%
1.8%
0.4%
0.9%
0.0%
0.4%
1.3%
0.4%
14.7%
0.0%
0.9%
0.4%
0.0%
0.0%
10.2%

&

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7.8%
12.4%
54.0%
1.8%
0.0%
0.7%
0.0%
0.3%
0.4%
4.7%
0.0%
4.6%
0.6%
0.3%
0.0%
0.1%
12.4%

Source: SBA framework, sheet “Year”. 

c
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.
4

t
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15.6%
25.0%
9.4%
0.6%
0.0%
8.6%
0.0%
0.0%
0.8%
8.9%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
31.1%

e
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.
5

4.8%
46.8%
15.4%
7.4%
0.2%
1.0%
0.0%
0.9%
1.7%
1.7%
0.0%
0.0%
0.7%
0.3%
0.0%
0.0%
19.1%

t
e
k
r
a
m
e
l
g
n
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S
.
6

0.0%
40.0%
1.3%
13.0%
0.0%
11.7%
0.0%
1.9%
1.7%
1.7%
0.0%
0.9%
1.5%
0.7%
0.0%
0.0%
25.7%

n
o
i
t
a
v
o
n
n

I

&
s
l
l
i
k
S
.
7

7.4%
30.6%
22.2%
4.3%
2.4%
6.1%
0.6%
1.6%
1.9%
0.8%
0.0%
0.0%
1.7%
0.9%
0.0%
0.0%
19.6%

t
n
e
m
n
o
r
i
v
n
E
.
8

0.0%
46.9%
10.4%
9.6%
11.4%
3.2%
0.2%
0.5%
0.0%
2.5%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
15.3%

n
o
i
t
a
z
i
l

a
n
o
i
t
a
n
r
e
t
n

I

.
9

0.0%
5.0%
0.0%
46.1%
0.1%
0.4%
0.0%
36.6%
0.1%
1.2%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
10.4%

n
o
i
t
a
z
i
l

a
t
i
g
i
D

.
0
1

0.0%
49.4%
21.4%
11.4%
1.7%
0.0%
0.3%
0.0%
0.3%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
15.6%

16 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  SME Fact Sheets – Methodology 

The processing of individual indicators to elaborate a scoreboard involves a number of normative choices. In 
this  section,  we  describe  our  methodological  decisions  (Saisana,  2012).  We  followed  the  five  steps  as 
indicated below: 

Step 1. The pre-selected indicators are analysed in the context of reporting errors and 
outliers that could be present in the raw data. The values which could potentially strongly 
bias the results are treated accordingly.

Step 2. Missing data are estimated using a hybrid approach that combines a bootstrap 
time-series cross-sectional expectation-maximisation algorithms with a number of 
heuristic rules based on trend identification developed by DG GROW.

Step 3. The indicators are adjusted so that all the indicators are oriented in the same 
direction: higher value indicating better performance. Then, the indicators are normalised 
by the min-max method to reduce them to a 0 to 1 scale.

Step 4. Equal weights are assigned to all the indicators. An exception to this applies for 
two pairs of highly correlated indicators that have been treated as two seperate 
indicators.

Step 5. The dimensions which describe the SBA principles are calculated as simple 
arithmetic mean taken over the normalised indicators, per country for years 2006-2021.

These steps are described in detail in the following sections. 

4.1 

Step 1: Selection of indicators and data checks  

Candidate indicators were selected by DG GROW for their relevance to each specific SBA principle (based on 
literature review and consultation with national experts) and for their timeliness. To represent a fair picture of 
country differences, indicators were scaled by e.g., the number of SMEs, total turnover, or other units. 

The most complete time series data were considered for each country, with a cut-off at year 2007. Country 
scores  for  a  given principle  were  calculated only  if  data  availability  was at least  50% in that  principle.  For 
instance,  when  considering  the  dimension  ‘Think  small  first’  and  ‘Responsive  administration’,  which  is 
constituted by 13 indicators, country scores for countries having less or equal to 6 (i.e., below 50%) indicators 
for this dimension were not calculated. 

Potentially problematic indicators that could bias the overall results were flagged as those having skewness 
(absolute) greater than 2 and kurtosis greater than 3.5, which are thresholds widely adopted in the context of 
composite indicators to detect potential outliers. Yet, as they are only heuristic rules of thumb, each flagged 
case  needs  to  be  analysed  individually  to  determine  whether  any  outlier  treatment  is  needed.  When 

17 

 
 
 
 
necessary, the outliers were treated by winsorisation12 (OECD, 2008), where the country values skewing the 
indicator’s  distribution  were  assigned  the  next  highest  (lowest)  value,  up  to  the  level  where  the  values  of 
skewness and kurtosis entered within the specified ranges to be treated as not outliers in the distribution.  

4.2 

Step 2: Missing data 

Up until the 2012 release of the SME fact sheets, missing data were not estimated but principles’ scores were 
calculated  on  a  basis  of  available  information  only.  This  was  motivated  by  the  need  to  achieve  full 
transparency and straightforward replicability of the reported results. It was also motivated by the fact that 
calculations are  made using last year available for which no imputations are  needed. However, the notable 
shortcoming  of  this  approach  might  be  the  discouraging  of  countries  from  reporting  low  data  values13.  For 
this reason, since the 2012 edition of the fact sheets the data set has been complemented with a multiple 
steps  imputation  algorithm  that  combines  a  data-driven  bootstrap  time-series  cross-sectional  expectation-
maximisation approach14 with heuristic trend-based imputation rules developed by the experts in the field15. 
Finally, Likert scale data have been re-scaled to a minimum - maximum (1-5) scale after the imputation, as 
well as other similar indicators (e.g., Strength of insolvency framework index (0-16), Licensing complexity (1-
26) etc.).  

4.3 

Step 3: Normalisation 

Given  that  the  indicators  used  to  measure  achievement  in  each  principle  are  expressed  in  different  units16, 
normalisation to a common scale is required. The methods that are most frequently used are standardisation 
(or z-scores) and rescaling. 

Standardisation: 

(cid:3051)(cid:3284)(cid:2879)(cid:3040)(cid:3032)

((cid:3051))

(cid:3046)(cid:3047)(cid:3031)((cid:3051))

This method converts the indicators to a common scale of mean zero and standard deviation of one.  

Re-scaling: 

(cid:3051)(cid:3284)(cid:2879)(cid:3040)(cid:3036)(cid:3041)((cid:3051))
(cid:3040)(cid:3028)(cid:3051)((cid:3051))(cid:2879)(cid:2923)(cid:2919)(cid:2924) ((cid:3051))

This approach is easier to communicate to a wider public, given that it normalizes indicators to an identical 
range  [0,  1],  where  higher  scores  represent  better  achievement.  A  key  advantage  of  this  method  over 
standardisation,  at  least  in  the  context  of  the  SBA  framework,  is  that  re-scaling  widens  the  range  of  an 
indicator, which is an advantage for those indicators with a small range of values. This is useful for the SBA 
profiles  to  allow  differentiation  between  countries  with  similar  levels  of  SMEs  performance.  However,  this 
method  is  not  appropriate  in  the  presence  of  extreme  values  or  outliers,  which  can  distort  the  normalised 
indicator.  To  control  for  this,  in  step  1  above  we  identified  and  treated  extreme  values.  The  minimum  and 
maximum values needed for the re-scaling were determined in the “complete” dataset after the imputations 
in the 2007-2022 period.  

The direction of the indicators’ effect was considered at this stage. For indicators where higher raw values are 

desirable, such as SMEs with intra-EU exports, the formula was 

(cid:3051)(cid:3284)(cid:2879)(cid:2923)(cid:2919)(cid:2924)((cid:3051))
(cid:3040)(cid:3028)(cid:3051)((cid:3051))(cid:2879)(cid:2923)(cid:2919)(cid:2924) ((cid:3051))

.  

12 On the basis of skewness or kurtosis, values that were detected to be outliers are treated to be assigned to the next closest value, up 
to the level where its skewness and kurtosis become acceptable. This winsorisation occurred for the following nine indicators: Time 
to start a business (3.1), Time required to register property (3.4), Venture capital investments (5.1), Public contracts secured abroad 
(6.4),  Sales  of  new-to-market  and  new-to-firm  innovations  as  %  of  turnover  (7.5),  SMEs  that  have  taken  resource-efficiency 
measures (8.1), Environmental technologies as % of all technologies (8.8), Advanced rulings (9.4) and Start-up environment (10.8). 
See SBA framework “DataRwins”.  

13 Note that here ‘no imputation’ is equivalent to replacing missing values with the average of the available data within each principle.  
14 J. Honaker and G. King, 2010; J. Honaker, G. King, and M. Blackwell, 2012; G. King et al., 2001 
15 In most of the cases when a trend was available but not reasonable (e.g. too high to be reasonable or negative for indicators which are 

only positive) it was set to the oldest available data for the past or to the most recent year for the future. 

16 For instance, in dimension “3. Think Small First”, the indicator “Time to start a business” is measured in days, while the “Cost to start a 

business” is in Euro. Normalisation is needed to report them to a common scale that makes the two indicators comparable. 

18 

 
 
 
 
 
                                                        
 
For  indicators  where  lower  raw  values  are  desirable,  such  as  time  to  start  a  business,  the  formula 

was: 

(cid:2923)(cid:2911)(cid:2934)((cid:3051))(cid:2879)(cid:3051)(cid:3284)
(cid:3040)(cid:3028)(cid:3051)((cid:3051))(cid:2879)(cid:2923)(cid:2919)(cid:2924) ((cid:3051))

, which was applied to 25 indicators, flagged with the symbol (-) in Table 3.  

Since  the  current  edition  of  the  framework,  normalisation  is  based  on  EU-27  values  only.  In  other  words, 
minimum and maximum values for normalising the data are those reported by EU-27 countries.  

4.4 

Step 4: Weights 

The SBA profiles, for simplicity and upon suggestion of the country desks, are calculated using equal weights 
for  the  indicators  underlying  each  principle.  There  are  only  two  exceptions  to  this  rule  that  involves  highly 
correlated17 indicators, which were treated by the JRC as a single indicator (by assigning half weight to each 
normalised  score).  We  anticipate  here  that  assigning  equal  weights  to  the  indicators  does  not  necessarily 
guarantee an equal contribution of the indicators to the variance of the country scores on the SBA principles18.  

4.5 

Step 5: Aggregation 

The arithmetic mean is the aggregation function used in this step for computing the dimensional scores of the 
SBA principles. The advantage of this choice lays in the easiness of communication of the resulting SBA fact 
sheets as the arithmetic mean has been traditionally used to compute most of the well-known indices on the 
international scene. 

One of the main counter arguments against the use of the arithmetic mean is that it belongs to a class of 
functions  characterized  by  perfect  substitutability,  i.e.,  poor  performance  in  one  indicator  can  be  fully 
compensated by good performance in another. However, SBA principles are aggregated to a lower level and 
do  not  result  in  a  single  index  (as  suggested  in  the  Section  6),  but  in  a  scoreboard  for  the  identified 
dimensions.  

To summarize, the SBA principles are calculated using a simple mean of the normalised indicators per country 
for  the  last  available  year,  with  the  exception  of  the  two  highly  correlated  pairs  previously  identified  for 
dimensions 1 (Entrepreneurship) and 7 (Skills and Innovation) are previously added together to form a single 
indicator in their respective dimensions. Country scores for each principle are also calculated using the most 
recent data. To allow for better comparison among countries’ performance, the data for the most recent year 
are re-scaled in the 0-1 scale.  

The EU average serves as a reference point for comparing countries’ performances. For each dimension the 
EU  average  is  calculated  as  a  simple  arithmetic  mean  of  all  the  EU-27  Member  States’  scores  for  a  given 
dimension rather than the average of the indicators (normalised) using the most recent data reported for the 
EU (as a country)19. This choice is to assure the consistency between the EU Member States’ scores and the 
EU average score and is motivated by two facts: (a) the EU (as a country) has significantly more missing data 
than  any  of  the  EU  Member  States;  (b)  the  most  recent  data  available  for  the  EU  (as  a  country)  are  often 
misaligned with the most recent data available for individual countries, as these might vary from country to 
country.  

17 Following previous exercises, two highly correlated indicators (i.e., Pearson correlation coefficients greater than ~ 0.90) were treated as 
a single indicator (adding both indicators weighting by 0,5). These were: the pair of “Total early-stage Entrepreneurial Activity (TEA)” 
and “Total early-stage Entrepreneurial Activity for Female Working Age Population”, belonging to the dimension 1. Entrepreneurship. 
In SBA20 exercise, another couple is added: Indicator 7.13 (Innovation rate - TEA impact (national)) and 7.14 (Innovation rate - TEA 
impact (international)). The formerly unique indicator is separated by its source (GEM) 

18  For  details  regarding the  distinction  between equal  weighting  and equal  importance  see:  Paruolo,  P.,  Saltelli,  A.,  Saisana,  M.  (2013) 

Ratings and rankings: Voodoo or Science? Journal of the Royal Statistical Society A, 176 (2):1-26. 

19 In the case of indicators from Eurostat, the geographical aggregates (e.g. EU-27, EU-15, EA-18) are calculated by Eurostat as the sum 
of the national data expressed in a common unit. Where single Member States' figures are lacking, Eurostat may use unpublished 
estimates to impute country data and hence calculate the European aggregates. European aggregates should be seen as estimates 
and can sometimes deviate from what is obtained when summing up the national data. This can be due to dissemination of single 
or  several national data sets outside the  normal  data treatment  cycles. It can also  be due to possible  inconsistencies  in national 
data,  e.g.  the  totals  were  revised  with  a  different  cycle  than  their  breakdowns.  For  more  details  on  Eurostat  methodology  see 
http://ec.europa.eu/eurostat/data/metadata 

19 

 
 
 
 
                                                        
 
4.6 

Compound growth rates  

To complement the analysis of SMEs’ performance, progress rates per country and dimension are calculated. 
Calculations are  done  over the  period  of  2008-2022 and  depending on  data availability  which  implies that 
calculation of growth rates slightly differ among dimensions. To limit the influence of noise in the data, when 
computing the compound annual growth rates five years averages are taken as the basis of the calculations.  

For a given principle, when data for 2022 as the latest year available is greater than 40%, growth rates are 
calculated  with  respect  to  average  2018-2022  and  the  average  2008-2012.  Furthermore,  when  data  for 
2022 as last available year is below 40% and for 2021 as well but the sum of 2022 and 2021 percentages 
as  last available  years is  bigger than 40% of  the country/indicator  observations  in each dimension,  growth 
rates are also calculated with respect to average 2018-2022 and the average 2008-2012. This is the case 
for dimension 4 (State aid and Public procurement). The formula for the growth rates is: 

where y refers to the country score on a given principle.  

(cid:2869)/(cid:2869)(cid:2868)
(𝑦(cid:2870)(cid:2868)(cid:2870)(cid:2870) + 𝑦(cid:2870)(cid:2868)(cid:2870)(cid:2869) + 𝑦(cid:2870)(cid:2868)(cid:2870)(cid:2868) + 𝑦(cid:2870)(cid:2868)(cid:2869)(cid:2877) + 𝑦(cid:2870)(cid:2868)(cid:2869)(cid:2876))/5
(cid:3440)
(cid:3436)
(𝑦(cid:2870)(cid:2868)(cid:2868)(cid:2876) + 𝑦(cid:2870)(cid:2868)(cid:2868)(cid:2877) +  𝑦(cid:2870)(cid:2868)(cid:2869)(cid:2868) + 𝑦(cid:2870)(cid:2868)(cid:2869)(cid:2869) + 𝑦(cid:2870)(cid:2868)(cid:2869)(cid:2870))/5

− 1 

When data for 2022 as last available year is below 40% and data for 2021 as the last available year is more 
than 40% of the country/indicator observations,  growth rates are calculated with respect to average 2017-
2021 and the average 2008-2012. This is the case for dimensions 1 (Entrepreneurship), 5 (Access to finance), 
6 (‘Single market’), 10 (Digitalisation) and 8 (Environment). Moreover, when data for 2022 as last available 
year is below 40% and 2021 and 2020 as well but the sum of 2021 and 2020 percentages as last available 
years  is  bigger  than  40%  of  the  country/indicator  observations  in  each  dimension,  growth  rates  are  also 
calculated with respect to average 2017-2021 and the average 2008-2012. This is the case for dimension 7 
(Skills and Innovation). The formula for the growth rates is: 

(cid:2869)/(cid:2877)
(𝑦(cid:2870)(cid:2868)(cid:2870)(cid:2869) + 𝑦(cid:2870)(cid:2868)(cid:2870)(cid:2868) + 𝑦(cid:2870)(cid:2868)(cid:2869)(cid:2877) + 𝑦(cid:2870)(cid:2868)(cid:2869)(cid:2876) + 𝑦(cid:2870)(cid:2868)(cid:2869)(cid:2875))/5
(cid:3440)
(cid:3436)
(𝑦(cid:2870)(cid:2868)(cid:2868)(cid:2876) + 𝑦(cid:2870)(cid:2868)(cid:2868)(cid:2877) +  𝑦(cid:2870)(cid:2868)(cid:2869)(cid:2868) + 𝑦(cid:2870)(cid:2868)(cid:2869)(cid:2869) + 𝑦(cid:2870)(cid:2868)(cid:2869)(cid:2870))/5

− 1 

When  data  for  2022  or  2021  as  last  available  year  is  below  40%  and  2020  as  the  last  available  year  is 
above 40% of the country/indicator observations in each dimension, growth rates are calculated with respect 
to average 2016-2020 and the average 2008-2012. This is the case for dimensions 2 (Second Chance) and 3 
(‘Think small first’ and ‘Responsive administration’). The formula for the growth rates is: 

(cid:2869)/(cid:2876)
(𝑦(cid:2870)(cid:2868)(cid:2870)(cid:2868) + 𝑦(cid:2870)(cid:2868)(cid:2869)(cid:2877) + 𝑦(cid:2870)(cid:2868)(cid:2869)(cid:2876) + 𝑦(cid:2870)(cid:2868)(cid:2869)(cid:2875) + 𝑦(cid:2870)(cid:2868)(cid:2869)(cid:2874))/5
(cid:3440)
(cid:3436)
(𝑦(cid:2870)(cid:2868)(cid:2868)(cid:2876) + 𝑦(cid:2870)(cid:2868)(cid:2868)(cid:2877) +  𝑦(cid:2870)(cid:2868)(cid:2869)(cid:2868) + 𝑦(cid:2870)(cid:2868)(cid:2869)(cid:2869) + 𝑦(cid:2870)(cid:2868)(cid:2869)(cid:2870))/5

− 1 

When data for 2022, 2021 or 2020 as last available year is below 40% and 2019 as the last available year 
is  above  40%  of  the  country/indicator  observations  in  each  dimension,  growth  rates  are  calculated  with 
respect  to  average  2015-2019  and  the  average  2008-2012.  This  is  the  case  for  dimension  9 
(Internationalisation). The formula for the growth rates is: 

(cid:2869)/(cid:2875)
(𝑦(cid:2870)(cid:2868)(cid:2869)(cid:2877) + 𝑦(cid:2870)(cid:2868)(cid:2869)(cid:2876) + 𝑦(cid:2870)(cid:2868)(cid:2869)(cid:2875) + 𝑦(cid:2870)(cid:2868)(cid:2869)(cid:2874) + 𝑦(cid:2870)(cid:2868)(cid:2869)(cid:2873))/5
(cid:3440)
(cid:3436)
(𝑦(cid:2870)(cid:2868)(cid:2868)(cid:2876) + 𝑦(cid:2870)(cid:2868)(cid:2868)(cid:2877) +  𝑦(cid:2870)(cid:2868)(cid:2869)(cid:2868) + 𝑦(cid:2870)(cid:2868)(cid:2869)(cid:2869) + 𝑦(cid:2870)(cid:2868)(cid:2869)(cid:2870))/5

− 1 

Six  out  of  the  ten  principles  have  a  unique  attribute,  as  there  are  methodological  changes  after  2014. 
Concerning the calculation methodology, principles 1, 5, 7 and 10 follow the 2021-2008 calculation formula 
of principles 6 and 8, principles 2 and 4 follow the 2020-2008 calculation formula as principle 3 does. Table 
5 summarizes formulas used to calculate growth rates.  

20 

 
 
 
 
 
 
 
 
Table 5: Summary of construction rules for growth rates  

Color legend 

Description 

Additional note 

2022-2008 (5y 
average) 

2021-2008 (5y 
average) 

2020-2008 (5y 
average) 

2019-2008 (5y 
average) 

methodological 
change after 2014, 
2014-2017 

1.- When data for 2022 as last available year is more than 
40%, growth rates are calculated with respect to average 
2018-2022 and the average 2008-2012. When data for 
2022 as last available year is below 40% and 2021 as well 
but adding 2022 and 2021 percentages as last available 
years is bigger than 40% of the country/indicator 
observations in each dimension, growth rates are also 
calculated with respect to average 2018-2022 and the 
average 2008-2012. 
2.- When data for 2022 as last available year is below 40% 
and data for 2021 as the last available year is more than 
40% of the country/indicator observations, growth rates are 
calculated with respect to average 2017-2021 and the 
average 2008-2012. When data for 2022 as last available 
year is below 40% and 2021 and 2020 as well but adding 
2021 and 2020 percentages as last available years is 
bigger than 40% of the country/indicator observations in 
each dimension, growth rates are also calculated with 
respect to average 2017-2021 and the average 2008-
2012.  
3.-When data for 2022 or 2021 as last available year is 
below 40% and 2020 as the last available year is above 
40% of the country/indicator observations in each 
dimension, growth rates are calculated with respect to 
average 2016-2020 and the average 2008-2012.   

4.-When data for 2022, 2021 or 2020 as last available year 
is below 40% and 2019 as the last available year is above 
40% of the country/indicator observations in each 
dimension, growth rates are calculated with respect to 
average 2015-2019 and the average 2008-2012.  
Methodological changes. Principles 1, 5, 7 and 10 follow 
rule 2 but we include them in this category because there 
are methodological changes after 2014. Principle 2 follows 
rule 3 and principle 4 follows rule 1 but we allocate them 
here for the same reason 

First leg: There are no cases 
for this rule 
Second leg: This is the case for 
dimension 4 

First leg:  This is the case for 
dimensions 1, 5, 6, 8 and 10 
Second leg: This is the case for 
dimension 7 

This is the case for dimensions 
2 and 3 

This is the case for dimension 
9 

This is the case for dimensions 
1, 2, 4, 5, 7 and 10 

Source: SBA Framework, sheet “Section 2A” and “datanorm”. 

Lastly,  the  EU  average  scores  are  computed  as  average  of  the  EU-27  countries’  scores  on  individual 
(disaggregated) indicators, which is a slightly different method than the one used to compute the EU average 
for performance scores where we use averages on aggregated dimensions.  By computing the EU averages on 
disaggregated  indicators  level,  we  also  assure  the  consistency  of  the  EU  growth  rate  with  the  combined 
growth rates of individual EU Member States. 

Whenever,  for  a  given  country,  the  share  of  missing  indicators  in  the  time-frame  2008-2022  exceeded  a 
certain threshold - identified by the experts in DG GROW as 40% - growth rates for the dimension are set to 
zero. This is the reason why growth rates have not been calculated for some countries in a few dimensions. 
The  most  representative  case  is  dimension  9,  with  many  counties  below  the  40%  threshold.  Following  the 
case  of  last  year’s  dataset,  there  is  no  dimension  with  all  countries  below  40%.  This  did  not  take  place  in 
previous  exercises.  For  instance,  in  SBA20  (Pedraza  and  Katsinis,  2020),  dimension  10  did  not  present  any 
country exceeding the threshold of 40%. Table 6 reports coverage per country and dimension, the percentage 
of observations available as a proportion of the maximum number of observations possible (the number of 
indicators per dimension from 2008 to last available year). For example, Denmark’s coverage is low (below 

21 

 
 
 
40%) on two cases: 1st and 8th principle. For both of them, the “progress over time” is not calculated, and the 
value is set to zero. 

Table 6: Coverage of indicators per country in years 2008-2022 

Country 

Austria 

Belgium 

Bulgaria 

Croatia 

Cyprus 

Czech Republic 

Denmark 

Estonia 

Finland 

France 

Germany 

Greece 

Hungary 

Ireland 

Italy 

Latvia 

Lithuania 

Luxembourg 

Malta 

Netherlands 

Poland 

Portugal 

Romania 

Slovakia 

Slovenia 

Spain 

Sweden 

Albania 

Armenia 
Bosnia and 
Herzegovina 

Iceland 

Israel 

Kosovo 

p
i

h
s
r
u
e
n
e
r
p
e
r
t
n
E

.

1

31% 

50% 

30% 

88% 

39% 

14% 

32% 

40% 

63% 

69% 

88% 

87% 

64% 

76% 

81% 

76% 

28% 

54% 

4% 

83% 

70% 

48% 

54% 

70% 

88% 

88% 

71% 

0% 

6% 

49% 

22% 

71% 

6% 

d
n
a

t
s
r
i
f

l
l
a
m
s

k
n

i

h
T

.

3

n
o
i
t
a
r
t
s
i

i

n
m
d
a

e
v
i
s
n
o
p
s
e
R

75% 

75% 

74% 

79% 

71% 

73% 

74% 

74% 

77% 

77% 

79% 

79% 

77% 

78% 

79% 

79% 

75% 

77% 

56% 

78% 

78% 

76% 

75% 

77% 

79% 

79% 

78% 

67% 

68% 

71% 

68% 

73% 

53% 

e
c
n
a
h
c

d
n
o
c
e
S

.

2

73% 

78% 

72% 

88% 

72% 

68% 

75% 

75% 

82% 

83% 

88% 

88% 

82% 

85% 

87% 

85% 

72% 

80% 

52% 

88% 

83% 

78% 

80% 

83% 

88% 

88% 

85% 

65% 

67% 

78% 

70% 

85% 

60% 

e
c
n
a
n

i
f

o
t

s
s
e
c
c
A

.

5

77% 

77% 

69% 

77% 

66% 

70% 

73% 

73% 

83% 

81% 

89% 

89% 

78% 

87% 

87% 

85% 

74% 

61% 

46% 

87% 

82% 

81% 

68% 

82% 

81% 

88% 

83% 

25% 

10% 

47% 

48% 

26% 

17% 

c
i
l
b
u
P

d
n
a

d
i
a

e
t
a
t
S

.

4

t
n
e
m
e
r
u
c
o
r
p

54% 

54% 

50% 

41% 

46% 

54% 

54% 

52% 

54% 

55% 

56% 

53% 

54% 

54% 

55% 

52% 

52% 

38% 

38% 

54% 

56% 

54% 

49% 

54% 

50% 

55% 

54% 

0% 

0% 

9% 

31% 

0% 

0% 

22 

n
o
i
t
a
v
o
n
n
I

d
n
a

s
l
l
i
k
S

.

7

50% 

47% 

50% 

54% 

54% 

49% 

44% 

47% 

46% 

52% 

52% 

52% 

53% 

55% 

56% 

57% 

50% 

48% 

44% 

56% 

50% 

53% 

51% 

56% 

52% 

58% 

54% 

16% 

1% 

23% 

25% 

12% 

0% 

n
o
i
t
a
s
i
l
a
n
o
i
t
a
n
r
e
t
n
I

.

9

36% 

39% 

41% 

34% 

35% 

41% 

41% 

36% 

36% 

41% 

39% 

37% 

41% 

33% 

41% 

41% 

41% 

32% 

30% 

39% 

39% 

41% 

41% 

38% 

36% 

41% 

41% 

18% 

16% 

19% 

5% 

13% 

0% 

n
o
i
t
a
s
i
l
a
t
i
g
i
D
0
1

.

43% 

43% 

43% 

43% 

43% 

43% 

43% 

41% 

43% 

42% 

42% 

41% 

43% 

43% 

41% 

43% 

43% 

42% 

42% 

42% 

43% 

43% 

43% 

43% 

43% 

43% 

41% 

8% 

2% 

16% 

23% 

2% 

2% 

t
n
e
m
n
o
r
i
v
n
E

.

8

52% 

50% 

50% 

52% 

52% 

52% 

38% 

47% 

51% 

48% 

51% 

52% 

52% 

50% 

52% 

35% 

52% 

36% 

36% 

36% 

50% 

52% 

52% 

52% 

52% 

52% 

52% 

32% 

18% 

19% 

35% 

24% 

0% 

t
e
k
r
a
m
e
l
g
n
S

i

.

6

67% 

66% 

67% 

57% 

63% 

62% 

65% 

65% 

74% 

73% 

79% 

77% 

73% 

74% 

79% 

79% 

67% 

70% 

49% 

76% 

76% 

71% 

67% 

73% 

80% 

80% 

77% 

0% 

1% 

13% 

9% 

16% 

1% 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Liechtenstein 

Moldova 

Montenegro 

North Macedonia 

Norway 

Serbia 

Turkey 

Ukraine 

United Kingdom 
United States of 
America 

0% 

0% 

6% 

42% 

64% 

12% 

52% 

0% 

87% 

10% 

65% 

67% 

77% 

83% 

68% 

78% 

65% 

88% 

9% 

66% 

67% 

70% 

72% 

68% 

73% 

67% 

78% 

23% 

0% 

0% 

3% 

47% 

8% 

1% 

0% 

52% 

13% 

18% 

39% 

44% 

54% 

45% 

41% 

18% 

81% 

4% 

0% 

6% 

12% 

27% 

5% 

17% 

0% 

72% 

0% 

0% 

26% 

32% 

56% 

34% 

34% 

16% 

52% 

15% 

28% 

29% 

30% 

52% 

39% 

35% 

19% 

39% 

0% 

18% 

19% 

21% 

23% 

19% 

20% 

18% 

36% 

0% 

2% 

23% 

29% 

43% 

28% 

33% 

2% 

38% 

76% 

88% 

68% 

0% 

26% 

19% 

9% 

36% 

18% 

2% 

Source:  SBA  2022  framework,  sheet  “Coverage”.  Note:  Red  cells  highlight  a  coverage  of  indicators  in  a  given  dimension  below  the 
threshold 40% and pink cells highlight a coverage between 40% and 60%. 

It is important to note that, although growth rates have not been calculated for dimensions with more than 
40% missing data, some growth rates still rely on few imputed values. Especially for countries characterised 
by  relatively  poor  coverage  of  data  (Table  6),  growth  rates  on  SBA  dimensions  should  be  taken  rather 
indicatively. In  such  cases, the  main analysis should be  carried  out at the  disaggregated level of  individual 
indicators. 

23 

 
 
 
  Conceptual and statistical coherence 

This section explores the conceptual and statistical coherence of the SBA 2022 framework. We use the most 
recent  available  data  of  the  43  countries  (i.e.,  the  27  EU  Member  States  plus  16  non-Member  States).  We 
study the coherence of the indicators within their dimensions, the presence of silent indicators that contribute 
very little to their dimension and the statistical coherence of a single composite indicator aggregating all the 
dimensions.   

5.1 

Principal component analysis and cross-correlation analysis 

Principal  component  analysis20  confirms  that  the  SBA  principles  are  indeed  multidimensional  and  the 
underlying indicators capture diverse aspects without overlap of information between them. 

Table  7  shows  the  amount  of  indicators’  variance  explained  by  the  first  principal  component  (else  termed 
latent dimension) and by the SBA principle. The first latent dimension in each principle captures between 26% 
and  50%  of  the  total  variance  in  the  underlying  indicators.  More  variance  is  explained  in  the  more 
homogenous  principles  Digitalisation,  Internationalisation,  Skills  and  Innovation  and  ‘Second  Chance’,  whilst 
less variance is captured by the more heterogeneous principles Environment, Access to finance, ‘Think small 
first’  and  ‘Responsive  administration’  and  Entrepreneurship.  The  SBA  principles  are  calculated  as  simple 
means of the underlying indicators. This choice receives statistical justification, at least in terms of the total 
variance explained,  given that the amount of  variance explained by  the SBA  principle  is for  nine  out of ten 
statistical dimensions principles, very similar (below 4% difference) to the maximum variance that could be 
explained  by  a  linear  function).  In  the  case  of  Environment,  the  difference  is  6%.  Ideally,  the  variance 
explained should be close to 69% and not lower than 50%.   

In any case, the multidimensionality of the ten dimensions discussed here emerging from Table 7, suggests 
that it is important to emphasise the individual indicators of the SBA principles, as the scores of the ten SBA 
dimensions  can  be  considered  as  only  indicative  of  the  amount  of  information  contained  in  the  underlying 
indicators. This supports DG GROW approach to discuss the countries scores on the SBA principles, but to also 
release the bulk of information and provide a discussion in the country fact sheets focusing on the individual 
indicators that populate the SBA framework. 

20 The first principal component is a weighted average of the indicators, whereby the indicators receive statistically driven weights based 
on the covariance matrix. An important property of the first principal component is that it captures the maximum possible variance 
in the underlying indicators that could be explained by any weighted arithmetic average of the underlying indicators.  

24 

 
 
 
 
 
 
                                                        
 
Table 7: Variance explained the SBA principles and the principal components 

SBA principle 

1. Entrepreneurship 

2. ‘Second chance’ 

3. ‘Think small first’ and ‘Responsive administration’ 

4. Public procurement 

5. Access to Finance 

6. Single Market 

7. Skills and Innovation 

8. Environment 

9. Internationalisation 

10. Digitalisation 

Variance explained by 
the first principle 
component 

Variance explained by 
SBA principle 

26% 

               36%  

33% 

34% 

29% 

34% 

40% 

27% 

42% 

50% 

27% 

39% 

33% 

33% 

29% 

31% 

39% 

21% 

46% 

52% 

Source: calculations made using SBA 2022 data “DataRnorm” 

A more detailed analysis of the correlation structure within and across the SBA principles confirms in most of 
the  cases  the  expectation  that  the  indicators  are  more  correlated  to  their  own  principle  than  to  any  other 
principle. All correlations, when significant, show the expected sign. An example of such an assessment for the  
“Digitalisation” principle is reported in Table 8.   

Table 8: Example of SBA 2022 coherence test in the Digitalisation principle  

Enterprises sending e-
invoices 

Enterprises having 
website-homepage 

Enterprises that buy cloud 
computing services 

Enterprises using their 
own websites or apps for 
sale 

Individuals who have used 
a programming  language 

Enterprises having access 
internet <10Mbps speed 

Online availability of info 
for business mobility 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

0.28  0.30  0.32 

-0.33 

0.39  0.32  0.41  0.10  0.18  0.54 

0.41  0.38  0.43 

-0.19 

0.65  0.73  0.77  0.53  0.52  0.84 

0.49  0.27  0.59 

-0.33 

0.67  0.55  0.78  0.49  0.63  0.91 

0.25  0.19  0.40 

0.07 

0.42  0.49  0.61  0.42  0.42  0.71 

0.59  0.33  0.61 

-0.35 

0.54  0.59  0.69  0.67  0.40  0.84 

0.22  0.00  0.38 

0.04 

0.18  0.31  0.28  0.34  0.37  0.52 

0.39  0.23  0.55 

-0.06 

0.67  0.59  0.53  0.54  0.52  0.72 

25 

 
 
 
  
Start-up environment 

0.54  0.14  0.48 

-0.07 

0.37  0.38  0.29  0.24  0.29  0.57 

Source: SBA 2022 framework, sheet “DataRnorm” 
Notes: (1) Pearson correlation coefficients between the indicators included in the ‘Second chance’ principle and the ten SBA principles. (2) 
1st  row  legend:  1.  Entrepreneurship,  2.  ‘Second  chance’,  3.  ‘Think  small  first’  and  ‘Responsive  administration’,  4.  State  aid  and  Public 
procurement, 5. Access to Finance, 6. Single Market, 7. Skills and Innovation, 8. Environment, 9. Internationalisation, 10. Digitalisation. (3) 
The  numbers  highlighted  in  light  blue  are  the  correlation  coefficients  of  the  indicators  with  their  own  SBA  principle,  in  this  example 
Digitalisation. 

Table 9: Examples of SBA 2022 better statistical fits in a different dimension    

Indicators 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

Statistical 
better fit 

1.4 Improvement-driven 
opportunity 
entrepreneurial activity 
(% of entrepreneurs) 
1.10 Entrepreneurship 
Education (post-
secondary levels) 

3.8 Cost to enforce 
contracts (% of claim) 

3.13 The people working 
for government agencies 
are competent and 
effective in supporting 
new and growing firm  
6.2 Average transposition 
delay- overdue directives 
(months)  

7.13 Innovation rate - 
TEA impact (national) 

7.14 Innovation rate - 
TEA impact (international) 

0.44 

0.28 

0.37 

0.06 

0.46 

0.28 

0.51 

0.44 

0.36 

0.57 

10. Digitalisation 

0.41 

0.50 

0.37 

-0.21 

0.38 

0.62 

0.45 

0.40 

0.16 

0.52 

6. Single market 

0.14 

0.12 

0.21 

0.06 

0.21 

0.16 

0.05 

0.33 

0.42 

0.24 

8. Environment 

0.27 

0.41 

0.37 

-0.10 

0.44 

0.69 

0.50 

0.53 

0.39 

0.65 

6. Single market 

0.16 

0.08 

0.16 

0.38 

-0.12 

0.10 

-0.09 

-0.04 

-0.07 

-0.11 

0.57 

0.02 

0.35 

0.06 

-0.21 

0.45 

0.20 

0.03 

-0.11 

0.04 

0.67 

0.29 

0.50 

0.02 

0.13 

0.56 

0.37 

0.18 

0.21 

0.32 

4. State aid and 
Public 
procurement 

1. 
Entrepreneurship 

1. 
Entrepreneurship 

0.35 

0.20 

0.18 

9.1 SMEs having done 
electronic sales to the 
rest of the world (%)  
Source: SBA 2022 framework, sheet “DataRnorm” 
Notes: (1) Pearson correlation coefficients between the indicators included in the ‘Second chance’ principle and the ten SBA principles. (2) 
1st  row  legend:  1.  Entrepreneurship,  2.  ‘Second  chance’,  3.  ‘Think  small  first’  and  ‘Responsive  administration’,  4.  State  aid  and  Public 
procurement, 5. Access to Finance, 6. Single Market, 7. Skills and Innovation, 8. Environment, 9. Internationalisation, 10. Digitalisation. (3) 
The  numbers  highlighted  in  light  blue  are  the  correlation  coefficients  of  the  indicators  with  their  own  SBA  principle,  the  numbers 
highlighted in light green are the best alternative correlation available to another dimension, listed in the last column. 

0.43 

0.21 

0.03 

0.06 

0.32 

0.02 

0.45 

10. Digitalisation 

In  a  few  cases,  statistical  analysis  based  on  the  coherence  test  described  above  would  suggest  that  the 
indicators would better fit a dimension other than the one they  have been assigned to. Table 9  reports the 
cases where a significant statistical improvement could be achieved by moving indicators from their current 
dimension (the first column) to the alternative dimension in the last column. We report the cases where either 
the indicator would achieve the correlation threshold of 0.321 or where the change would increase at least 0.1 
its correlation coefficient22.  

For  instance, indicator 1.10 “Entrepreneurship Education at post-secondary  levels  (higher  education such  as 
vocational,  college,  business  schools,  etc.)”  is  assigned  to  ‘1.  Entrepreneurship’  where  it  has  a  correlation 
coefficient of 0.41. This would largely increase (to 0.62) if moved to dimension ‘6. Single Market’, as indicated 
by  the  light  green  highlight  and  reported  in  the  last  column  in  Table  9.  In  the  2022  exercise,  there  are  8 
indicators  that  display  less  statistical  coherence  with  their  dimension  than  desirable  according  to  the 
aforementioned  criteria.  However,  based  on  conceptual  reasons,  DG  GROW  has  decided  not  to  relocate 

21 The generally accepted threshold to consider an indicator silent, not contributing its dimension, is 0.3.   
22 There are in addition few cases where the indicator could slightly improve if moved to another dimension, but improvements of 0.01, 
0.02, … to 0.09 points in the Pearson correlation coefficient are not reported to draw the attention only on the relevant cases. 

26 

 
 
 
 
                                                        
 
indicators  to  an  alternative  dimension.  An  alternative  solution  that  would  avoid  conceptual  inconsistency 
would be to remove silent indicators from the framework. 

5.2 

Assessment of the implicit weights 

Despite  the  equal  weights  assigned  (by  construction)  to  each  indicator,  their  implicit  weights  are  not 
necessarily  equal.  The implicit weights are a function of the nominal weights, the data correlation structure 
and  the  indicators’  variances.  We  calculate  the  implicit  weights  using  the  squared  Pearson  correlation 
coefficient.  

In principle, for indicators that are supposed to be equally important, their implicit weights should not differ 
too  much.  Table  10  shows  that  some  implicit  weights  do  differ  substantially.  For  example,  7.  ‘Skills  and 
innovation’  is  mostly  explained  by  five  indicators:  “7.11  Digital  skills  and  e-leadership  Enterprise  provided 
training  to  their  personnel  to  develop/upgrade  their  ICT  skills”  weighting  60%,  “7.2  Innovative  SMEs 
collaborating with others” weighting 56%, “7.9 Turnover from e-commerce” weighting 54%, “7.6 SMEs selling 
online”  weighting  51%  and  “7.4  SMEs  introducing  business  process  innovations”  weighting  50%,  while  the 
indicators  “7.5  Sales  of  new-to-market  and  new-to-firm  innovations”  and  “7.10  Percentage  of  enterprises 
employing persons with ICT specialist skills”, account, respectively, for 26% and 30%. This means that those 
two indicators have around half – or even less - of the implicit weight of the previous five. Moreover, indicator 
“7.13 Innovation rate - TEA impact (national)” weights 4%, therefore in practice; it does not contribute to the 
dimension. Dimension ‘7. Skills and innovation’ is shaped mostly by several indicators. If the goal is to reach 
an equal contribution of the indicators to the overall variance of the Entrepreneurship scores, then the weights 
attached to the indicators should be adjusted accordingly.   

When  looking  at  ‘2.  Second  Chance’  in  Table  10,  it  is  also  noted  that  one  (out  of  its  four  indicators)  has 
significantly  bigger  weight  than  the  remaining  three  ones.  Again,  this  dimension’s  implicit  weights  are  far 
from being equal. The same applies to all the dimensions in the framework. This remark is highly relevant if 
the goal is to produce a ranking of the countries based on the SBA principles scores. This is not the case if, in 
the  context of  the  SBA  fact  sheets, emphasis  is  given on  the  underlying  indicators and  the SBA scores  are 
exploited to compare country’s performance with respect to the EU average. 

27 

 
 
 
 
 
Table 10: Implicit weights of the indicators in the ten SBA dimensions  

Implicit 
weights 

#.1 

#.2 

#.3 

#.4 

#.5 

#.6 

#.7 

#.8 

#.9 

#.10 

#.11 

#.12 

#.13 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

0.50 

0.48 

0.16 

0.14 

0.40 

0.52 

0.08* 

0.06 

0.01* 

0.41 

0.08* 

0.44 

0.55 

0.35 

0.45 

0.42 

0.29 

0.05 

0.20 

0.47 

0.31 

0.06* 

0.52 

0.24 

0.42 

0.36 

0.32 

0.57 

0.39 

0.27 

0.44 

0.43 

0.23 

0.19 

0.27 

0.14 

0.20 

0.43 

0.34 

0.16 

0.13 

0.20 

0.49 

0.40 

0.02* 

0.14 

0.02* 

0.35 

0.37 

0.04* 

0.66 

0.67 

0.59 

0.37 

0.14 

0.47 

0.56 

0.42 

0.50 

0.26 

0.51 

0.42 

0.29 

0.54 

0.30 

0.60 

0.40 

0.04* 

0.39 

0.14 

0.43 

0.16 

0.02* 

0.09 

0.32 

0.00* 

0.30 

0.00* 

0.57 

0.31 

0.70 

0.47 

0.65 

0.50 

0.41 

0.57 

0.29 

0.71 

0.83 

0.51 

0.70 

0.27 

0.51 

0.33 

#.14 
Source: SBA framework, sheet “Implicit weights” 
Notes: (1) Numbers represent the squared Pearson correlation coefficients. * Identifies correlations close to 0, below 0.1. These implicit 
weights do not sum up to one because of the interdependence between the indicators. (2) The order of the indicators is the same as in 
Table  3.  (3)  Indicators  that  have  much  lower  contribution  to  the  variance  of  the  relevant  SBA  principle  than  the  equal  weighting 
expectation are marked with an asterisk. (4) 1st row legend: 1. ‘Entrepreneurship’, 2. ‘Second chance’, 3. ‘Think small first’ and ‘Responsive 
administration’, 4. ‘State aid and Public procurement’, 5. ‘Access to Finance’, 6. ‘Single Market’, 7. ‘Skills and Innovation’, 8. ‘Environment’, 
9. ‘Internationalisation’, 10 ‘Digitalisation’. 

0.13 

5.3 

The eleven SBA principles: a scoreboard or a composite? 

In every edition of the SBA fact sheets we explore whether it is feasible to combine all the dimensions into a 
single  composite  measure,  providing  a  summary  measure  of  SBA  performance.  From  a  statistical  point  of 
view, in previous editions we did not recommend to combine the individual principles together because of the 
multi-dimensional character of the underlying data. We obtained the same conclusion in the 2022 edition. 

There are three latent dimensions in the eleven SBA principles (grouped into ten dimensions) that altogether 
capture 69% the total variance. In addition to this, the first principal component (unrotated) describes 27% of 
the  total  variance,  which  means  that  any  aggregate  of  the  ten  SBA  dimensions  would  capture  about  one-
fourth of the total variance in the principles.  

The analysis reveals the “statistical” grouping of the SBA principles giving further insight into the relationships 
between the principles ‘5. Access to finance’, ‘9. Internationalisation’, and ‘10. Digitalisation’, are described by 
the  same  latent  dimension  which  altogether  explains  27%  of  the  total  variance23.  A  second  group  with 
principles  ’1 .Entrepreneurship’, ’3. Think Small First and Responsive Administration’, ’4. State Aid and public 
procurement‘  and  ’6.  Single  market‘  explains  22%  of  the  variance  and  finally,  the  third  group  which  is 
composed by principles ’2. Second Chance’, ’7. Skills and Innovation‘ and ’8. Environment‘ explains 20% of the 
variance.  

This grouping is not stable over time. Instead, it differs from previous exercises.   

23 The values reported refer to the factor loadings after a rotation following Kaiser normalised varimax option.    

28 

 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
                                                        
 
Table 11: Principal Components Analysis results for the 2022 SBA principles 

Principal Component 
1 

Principal Component 
2 

Principal Component 
3 

1. Entrepreneurship 

2. ‘Second chance’ 

3. ‘Think small first’ and 
‘Responsive administration’ 

4. State aid and public 
procurement 

5. Access to finance 

6. Single market 

7. Skills and innovation 

8. Environment 

9. Internationalisation 

10. Digitalisation 

Explained variance (% 
total) 

0.0163 

0.0987 

0.0896 

-0.8316 

0.8257 

0.4022 

0.4871 

0.1455 

0.7285 

0.6062 

27% 

0.8830 

0.0011 

0.8429 

0.0448 

0.1612 

0.5087 

0.3517 

0.1152 

0.1467 

0.5020 

22% 

0.0990 

0.7836 

0.0031 

-0.0387 

0.1036 

0.2459 

0.5733 

0.7867 

0.3588 

0.4240 

20% 

Note:  The  dataset of  43 countries +  EU  with data of the latest available year  was used. The numbers in light blue reflect the highest 
component loading of an SBA principle. The analysis restricts the components to 3 and loadings are reported after a varimax rotation. 

5.4 

 Impact of modelling assumptions on the SBA results 

Every country score across the ten dimensions of the SBA is an outcome of different modelling choices: the 
selected  indicators,  the  estimation  of  missing  data,  the  treatment  of  outliers,  the  normalisation  of  the 
indicators, the weights assigned to them and the aggregation method, among other elements. Some of these 
choices  are  based  on  the  opinion  of  experts  in  the  field  (e.g.,  selection  of  indicators,  assigning  of  equal 
weights), common practice (e.g.,  min-max method to normalise the indicators), driven by statistical analysis 
(e.g.,  averaging  pairs  of  highly  correlated  indicators  prior  to  the  final  aggregation  step)  or  simplicity  (e.g. 
arithmetic mean of the indicators). This section assesses  the uncertainty of the SBA principles attributed to 
some of those normative judgments that cannot be fully justified either by conceptual theoretical reasons or 
by the data properties. We explore the effect of relaxing three assumptions: the min-max normalisation of the 
indicators,  the  equal  weights  attached  to  the  indicators  and  the  aggregation  formula  (simple  mean)24.  We 
dealt with these uncertainties simultaneously in order to assess their joint influence on the results. Regarding 
smoothing of outliers (via winsorisation), we did not test it, because the number of winsorised observations is 
negligible. In the present analysis the data are assumed to be error-free since DG GROW already undertook a 
double-check  control  of  potential  erroneous  outliers  and  these  errors  and  typos  were  corrected  during  this 
phase (see Step 2 in Section 3).   

It  is  important  to  note  that  the  uncertainty  analysis  does  not  inform  on  the  quality  of  the  framework 
underpinning the SBA principles and its statistical consistency, which was the aim of the analysis carried out 
in  Section  4.  Instead,  sensitivity  analysis  provides  insights  on  the  validity  and  stability  of  results  after 
changing the aforementioned assumptions. The multidimensionality of the SBA principles is illustrated in table 
7 (any aggregate measure of the underlying indicators could only capture between 26 and 50% of the total 
variance). Therefore, it is not recommended to base the assessment of countries’ performance on their exact 
rankings  (on  a  given  principle).  Instead,  it  is  better  to  discuss  the  country  performance  in  relation  to  the 
remaining EU countries and whether its performance is “below EU”, “close to EU”, or “above EU”.  

24  The  estimation  of  missing  data  has  no  impact  on  countries’  performance  scores  because  it  is  based  on  real  data  (most  recent 

available).  

29 

 
  
 
 
                                                        
 
Table 12: Uncertainty parameters (winsorisation, normalisation, weights, aggregation)  

 Type of uncertainty 
A.  Uncertainty  in  the  normalisation 
method 
B.  Uncertainty 
function 
C.  Uncertainty 
weights 

in  the  aggregation 

intervals 

the 

for 

arithmetic 
average 
Reference value 
for the weight 

Reference 
Min-max 

Alternative 
z-scores 

1. Entrepreneurship (# 1125) 
2. ‘Second chance’ (# 4) 
3. ‘Think small first’ and ‘Responsive 
administration’ (#13) 
4. State aid and public procurement (#7) 
5. Access to finance (#10) 
6. Single market (# 9) 
7. Skills and innovation (#13) 
8. Environment (#9)  
9. Internationalisation (#9) 
10. Digitalisation (#8) 

0.091 
0.250 
0.077 

0.143 
0.100 
0.111 
0.077 
0.111 
0.111 
0.125 

geometric average 

Distribution assigned for 
uncertainty analysis  
(± 25% reference value) 
U[0.068 ,0.114] 
U[0.187 ,0.313] 
U[0.058 ,0.096] 

U[0.107 ,0.179] 
U[0.075 ,0.125] 
U[0.083 ,0.139] 
U[0.058 ,0.096] 
U[0.083 ,0.139] 
U[0.08,0.139] 
U[0.094,0.156] 

Note: the number of indicators within a principle is given in the parenthesis. Highly correlated indicators are counted as one. This applies 
to one pair of indicators in dimension 1. Entrepreneurship and one pair of indicators in dimension 7. Skills and Innovation.  

The uncertainty analysis of the 2022 SBA principles was based on a combination of a Monte Carlo experiment 
and a multi-modelling approach (see Table 12). This type of assessment aims to respond to possible criticism 
that  the  country  scores  associated  with  indices  are  frequently  presented  as  if  they  were  calculated  under 
conditions of certainty, while, by the very definition of the index, this is never the case (Saisana et al., 2005; 
Saisana et al., 2011). The Monte Carlo simulation consisted of 1,000 runs related to the issue of weighting. 
First,  1000  different  weights  of  the  indicators  were  generated  from  uniform  distributions  centred  in  equal 
weights  reference  values  (±  25%  of  the  reference  value).  The  range  for  the  weights’  variation  has  been 
chosen to accommodate two conflicting needs: on the one hand, the need to ensure a wide enough interval 
for meaningful robustness checks; on the other hand, the need to respect the rationale of the SBA principles 
that no indicator dominates an SBA principle. Reference values, ranges and intervals for each dimension are 
summarized in Table 12.  

Weights  were  then  combined  with  the  multi-modelling  approach,  which  involves  relaxing  assumptions  on 
normalisation method and the aggregation formula. Although there are arguments in favour of the min-max 
method for normalizing the indicators versus the z-scores approach, one may still argue that since countries’ 
achievements on a given SBA principle are seen in relation to the EU average, z-scores could have been used. 
In  addition,  decision-theory  practitioners  have  challenged  the  use  of  arithmetic  average  as  an  aggregation 
function because of its fully compensatory nature, in which a comparative high advantage of a few variables 
can  compensate  a  comparative  disadvantage  of  many  variables  (see  also  comments  in  Section  3)  (Munda, 
2008).  As an alternative to  arithmetic average,  we  considered  the  geometric average26, which  belongs to a 
class of  partially  compensatory  aggregations  functions.  Consequently, we tested four models (22)  based  on 
the  combination  of  the  min-max  versus  z-scores  normalisation,  or  arithmetic  versus  geometric  average. 
Combined  with  the  1,000  simulations  to  account  for  the  uncertainty  in  the  estimates  for  the  weights  of 
indicators, we carried out altogether 4,000 simulations for each SBA principle.     

25  Entrepreneurship  dimension  is  originally  based  on  12  indicators;  two  indicators  having  weight  0.5  have  been  merged  into  1  for 
simplicity  of  the  calculation.  Similarly,  the  7th  dimension  (Skills  and  Innovation)  is  based  on  14  indicators,  but  two  of  them  have 
weight equal to 0.5, so they are merged into 1 as well. 

26  In  the  geometric  average,  indicators  are  multiplied  as  opposed  to  summed  in  the  arithmetic  average.  Indicator  weights  appear  as 
exponents  in the multiplication. To  avoid close  to  zero values biasing the geometric average, we re-scaled linearly  the indicators 
scores to a minimum of 0.1. 

30 

 
 
                                                        
 
We  are  not  relaxing  the  winsorisation  assumption.  Winsorisation  allows  smoothening  the  distribution  of 
scores,  prevents  a  situation  where  an  outlier  associated  to  a  single  country  drives  the  scores  of  all  the 
remaining  countries.  It  is  an  invasive  method  that  reduces  the  amount  of  information  available  and  the 
thresholds  for  winsorisation  are  subjective  choices  that  depend  on  the  particular  structure  of  the  data. 
However, as shown in section 4.1, only seven indicators were winsorised. Therefore, we are not comparing the 
results obtained via winsorisation with those obtained with no winsorisation. 

The  uncertainty  analysis  results  are  shown  in  Tables  13  to  21  with  countries  in  alphabetical  order.  SBA 
column  reports  result  obtained  in  the  SBA  2022  for  a  specific  country  and  dimension.  Below  EU  column 
reports percentage of calculations in the Monte Carlo simulation that place the country below the EU average. 
Close to EU column reports  percentages of calculation in the Monte Carlo simulation that place the country 
close  to  EU  average.  Above  EU  column  report  percentage  of  calculation  in  the  Monte  Carlo  simulation  that 
place  the  country  above  EU  average.  Divergence  column  marks  with  an  x  countries  and  dimensions  where 
more than 50% of the simulations display different result than the SBA 2022 framework.  

In  the  following  paragraph,  Slovakia  is  used  as  an  example  of  how  to  interpret  tables  13  to  21.  We  use 
Slovakia for convenience because it covers a sufficient range of cases.  

In  the  2022  SBA  in  ‘Think  small  first’  and  Responsive  administration  dimension,  Slovakia  is  classified  as 
performing  below  EU  average.  This  result  is  confirmed  only  in  26%  of  the  simulated  cases,  while  in  the 
remaining  74%  of  the  simulations,  Slovakia’s  performance  is  close  to  the  EU  average.  This  divergence  is 
signalled in the last column of Table 20, which highlights that the probabilistic assessment would forecast a 
different  outcome  (“close  to  EU”)  for  Think  small  first’  and  Responsive  administration  than  the  SBA  actual 
score. Slovakia is close to the EU average in the following four dimensions: Single Market, Access to Finance, 
Environment,  and  Internationalisation  (100%  of  the  probabilistic  assessment  simulations  place  close  to  EU 
average in the first two, 73% in the third and fourth). Slovakia is below the EU average on four principles. The 
pattern  is  exactly  the  same  for  all  of  them  (Entrepreneurship,  Second  chance,  Skills  and  innovation  and 
Digitalisation) since 100% of the probabilistic assessment simulations place Slovakia below the EU average. 
Regarding the last principle, State aid and public procurement – the performance is above the EU average in 
80% of the respective probabilistic assessment simulations. 

Overall, when comparing a country’s positioning with respect to the EU average, the statistical robustness and 
coherence analysis confirmed that 97.0% of countries’ positioning are statistically reliable, i.e. divergence is 
found in 8 cases out of the pool of 270 possible cases (10 dimensions covered for each EU-27 country). In 
general, results are similar to those obtained in the previous SBA simulations. With respect to SBA 2018, all 
the  following  scoreboards  displayed  similarly  robust  results,  i.e.,  likewise  sensitive  to  methodological 
assumptions.  In  other  words,  results  using  different  methodologies  differed  analogously  in  those  years 
compared to 2018. The uncertainty analysis presented herein can disentangle a country’s performance from 
the  methodological  judgments  made  in  the  development  of  the  SBA  principles  and  reliably  provide 
information on a country’s strengths or weaknesses compared to the EU average. Thus, this type of analysis is 
critically helpful for policy makers and experts to understand existing successes and areas of improvement in 
each  country.  Needleless  to  emphasize  again  that  this  should  be  done  in  conjunction  with  the  detailed 
information on the indicators within each principle, as this is provided in the specific country fact sheets of DG 
GROW.  

31 

 
 
 
 
 
 
 
Table 13: Uncertainty Analysis (Austria – Bulgaria) 

Austria 

Entrepreneurship 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

Skills and innovation 

Environment 

Internationalisation 

Digitalisation 

Belgium 

Entrepreneurship 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

Skills and innovation 

Environment 

Internationalisation 

Digitalisation 

Bulgaria 

Entrepreneurship 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

Skills and innovation 

Environment 

Internationalisation 

Digitalisation 

Divergence 

X 

Divergence 

Divergence 

X 

SBA 

Below EU 

Above EU 

Below 
EU 
100% 

0% 

Close 
to EU 
0% 

76% 

Close to EU 

0% 

   100% 

Below EU 

100% 

Above EU 

Above EU 

Close to EU 

Above EU 

Above EU 

Above EU 

SBA 

Below EU 

Above EU 

Close to EU 

0% 

0% 

0% 

0% 

0% 

0% 
Below 
EU 
100% 

0% 

7% 

Below EU 

100% 

Above EU 

Above EU 

Above EU 

Close to EU 

Close to EU 

Close to EU 

SBA 

Below EU 

0% 

0% 

0% 

0% 

0% 

0% 
Below 
EU 
100% 

0% 

25% 

0% 
   100% 
18% 

18% 

0% 
Close 
to EU 
0% 

18% 

93% 

0% 

25% 

25% 

0% 
   100% 
   100% 
   100% 
Close 
to EU 
0% 

Close to EU 

14% 

86% 

Below EU 

100% 

0% 

Below EU 

45% 

Close to EU 

41% 

Below EU 

Below EU 

Below EU 

Below EU 

Below EU 

100% 

100% 

100% 

100% 

100% 

55% 

59% 

0% 

0% 

0% 

0% 

0% 

Above 
EU 

0% 

24% 

0% 

0% 

75% 
   100% 
0% 

82% 

82% 
   100% 
Above 
EU 

0% 

82% 

0% 

0% 

75% 

75% 
   100% 
0% 

0% 

0% 
Above 
EU 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

32 

 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
Table 14: Uncertainty Analysis (Croatia – Czech Republic) 

Croatia 

Entrepreneurship 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

Skills and innovation 

Environment 

Internationalisation 

Digitalisation 

Cyprus 

Entrepreneurship 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

Skills and innovation 

Environment 

Internationalisation 

Digitalisation 

SBA 

Below 
EU 

Close to EU 

2% 

Below EU 

100% 

Below EU 

100% 

Close 
to EU 
98% 

0% 

0% 

Close to EU 

0% 

   100% 

Close to EU 

0% 

Below EU 

100% 

Close to EU 

0% 

Below EU 

Below EU 

Above EU 

SBA 

Close to EU 

Below EU 

100% 

100% 

0% 
Below 
EU 

36% 

99% 

   100% 
0% 
   100% 
0% 

0% 

24% 
Close 
to EU 
64% 

1% 

Close to EU 

0% 

   100% 

Below EU 

95% 

Below EU 

100% 

Below EU 

99% 

Close to EU 

Close to EU 

Close to EU 

Close to EU 

0% 

6% 

6% 

0% 
Below 
EU 

5% 

0% 

1% 
   100% 
94% 

94% 

90% 
Close 
to EU 

Above 
EU 

Divergence 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

76% 
Above 
EU 

Divergence 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

10% 
Above 
EU 

Divergence 

Czech Republic 

SBA 

Entrepreneurship 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

Skills and innovation 

Environment 

Internationalisation 

Digitalisation 

Close to EU 

0% 

61% 

39% 

Below EU 

100% 

0% 

0% 

Above EU 

Close to EU 

0% 

1% 

Below EU 

100% 

Above EU 

Below EU 

Below EU 

Below EU 

0% 

100% 

100% 

100% 

24% 

99% 

0% 

27% 

0% 

0% 

0% 

76% 

0% 

0% 

73% 

0% 

0% 

0% 

33 

 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
Skills and innovation 

Close to EU 

Table 15: Uncertainty Analysis (Denmark - Finland) 

Denmark 

SBA 

Below 
EU 

Entrepreneurship 

Close to EU 

36% 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

Skills and innovation 

Environment 

Internationalisation 

Digitalisation 

Estonia 

Entrepreneurship 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

Environment 

Internationalisation 

Digitalisation 

Finland 

Entrepreneurship 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

Skills and innovation 

Environment 

Internationalisation 

Digitalisation 

Close 
to EU 
64% 

0% 

Above 
EU 

0% 
   100% 

25% 

75% 

0% 

25% 

25% 

0% 

65% 

65% 

22% 
Close 
to EU 
13% 

25% 

0% 

75% 

75% 
   100% 
10% 

10% 

78% 
Above 
EU 

87% 

75% 

0% 

   100% 

7% 

25% 

0% 
   100% 
0% 

0% 

25% 
Close 
to EU 
1% 

0% 

93% 

75% 
   100% 
0% 

0% 

0% 

75% 
Above 
EU 

74% 
   100% 

Divergence 

Divergence 

Divergence 

Above EU 

Above EU 

0% 

0% 

Below EU 

100% 

Above EU 

Above EU 

Above EU 

Close to EU 

Close to EU 

Above EU 

SBA 

Above EU 

Above EU 

Above EU 

Above EU 

Above EU 

Above EU 

Below EU 

Below EU 

Above EU 

SBA 

Above EU 

Above EU 

0% 

0% 

0% 

25% 

25% 

0% 
Below 
EU 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

100% 

100% 

0% 
Below 
EU 

25% 

0% 

Above EU 

0% 

25% 

75% 

Close to EU 

0% 

   100% 

0% 

Above EU 

Above EU 

Above EU 

Close to EU 

Close to EU 

Above EU 

0% 

0% 

0% 

25% 

25% 

25% 

0% 

16% 

0% 

75% 

75% 

0% 

   100% 
84% 
   100% 
0% 

0% 

75% 

34 

 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
Table 16: Uncertainty Analysis (France - Greece) 

France 

Entrepreneurship 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

Skills and innovation 

Environment 

Internationalisation 

Digitalisation 

Germany 

Entrepreneurship 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

Skills and innovation 

Environment 

Internationalisation 

Digitalisation 

Greece 

Entrepreneurship 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

Skills and innovation 

Environment 

Internationalisation 

Digitalisation 

SBA 

Close to EU 

Close to EU 

Below 
EU 

0% 

0% 

Close 
to EU 
   100% 
   100% 

Close to EU 

25% 

75% 

Below EU 

100% 

Above EU 

Below EU 

Close to EU 

Close to EU 

Close to EU 

Close to EU 

SBA 

Close to EU 

Above EU 

0% 

96% 

24% 

0% 

0% 

0% 
Below 
EU 

0% 

0% 

0% 

25% 

4% 

76% 
   100% 
   100% 
   100% 
Close 
to EU 
   100% 
0% 

Below EU 

96% 

4% 

Close to EU 

0% 

   100% 

Above EU 

Close to EU 

Close to EU 

Close to EU 

Close to EU 

Close to EU 

SBA 

Below EU 

0% 

0% 

0% 

13% 

13% 

25% 
Below 
EU 

81% 

Below EU 

100% 

25% 
   100% 
   100% 
87% 

87% 

75% 
Close 
to EU 
19% 

0% 

Close to EU 

26% 

74% 

Above EU 

0% 

Below EU 

Below EU 

Close to EU 

Close to EU 

Close to EU 

100% 

100% 

25% 

67% 

67% 

Below EU 

100% 

26% 

0% 

0% 

75% 

33% 

33% 

0% 

Above 
EU 

Divergence 

0% 

0% 

0% 

0% 

75% 

0% 

0% 

0% 

0% 

0% 
Above 
EU 

0% 
   100% 

0% 

0% 

75% 

0% 

0% 

0% 

0% 

0% 
Above 
EU 

0% 

0% 

0% 

74% 

0% 

0% 

0% 

0% 

0% 

0% 

Divergence 

Divergence 

X 

X 

35 

 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
Table 17: Uncertainty Analysis (Hungary - Italy) 

Hungary 

Entrepreneurship 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

Skills and innovation 

Environment 

Internationalsation 

Digitalisation 

Ireland 

Entrepreneurship 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

Skills and innovation 

Environment 

Internationalisation 

Digitalisation 

Italy 

Entrepreneurship 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

25% 

75% 

Close to EU 

0% 

   100% 

SBA 

Below EU 

Below 
EU 
100% 

Close to EU 

0% 

Close 
to EU 
0% 
   100% 

Below EU 

68% 

32% 

Close to EU 

0% 

   100% 

Close to EU 

7% 

Close to EU 

25% 

Below EU 

100% 

Close to EU 

Close to EU 

Below EU 

SBA 

Above EU 

Close to EU 

Above EU 

5% 

5% 

100% 
Below 
EU 

0% 

0% 

0% 

Close to EU 

Close to EU 

Above EU 

Close to EU 

Close to EU 

Above EU 

SBA 

Below EU 

Below EU 

0% 

0% 

0% 

25% 

25% 

0% 
Below 
EU 
100% 

100% 

Below EU 

100% 

Below EU 

100% 

Close to EU 

25% 

Below EU 

100% 

93% 

75% 

0% 

95% 

95% 

0% 
Close 
to EU 
0% 

99% 

   100% 
97% 

0% 

73% 

73% 

48% 
Close 
to EU 
0% 

0% 

0% 

0% 

75% 

0% 

86% 

8% 

8% 

46% 

Above 
EU 

Divergence 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 
Above 
EU 
   100% 
1% 

Divergence 

Divergence 

0% 

0% 

3% 
   100% 
2% 

2% 

52% 
Above 
EU 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

Skills and innovation 

Close to EU 

Environment 

Internationalisation 

Digitalisation 

Below EU 

Below EU 

Below EU 

14% 

92% 

92% 

54% 

36 

 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
Table 18: Uncertainty Analysis (Latvia - Luxembourg) 

Latvia 

Entrepreneurship 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

Skills and innovation 

Environment 

Internationalisation 

Digitalisation 

Lithuania 

Entrepreneurship 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

Skills and innovation 

Environment 

Internationalisation 

Digitalisation 

Luxembourg 

Entrepreneurship 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

Skills and innovation 

Environment 

Internationalisation 

Digitalisation 

Divergence 

SBA 

Above EU 

Above EU 

Above EU 

Above EU 

Below 
EU 

0% 

0% 

0% 

0% 

Close 
to EU 
27% 

5% 

25% 

Above 
EU 

73% 

95% 

75% 

0% 

   100% 

0% 

   100% 

Divergence 

X 

X 

Divergence 

0% 

0% 

0% 

75% 

75% 

0% 
Above 
EU 

42% 

0% 

75% 

0% 

75% 

2% 

0% 

0% 

0% 
Above 
EU 

75% 

0% 

1% 

0% 

0% 

7% 

0% 

75% 

75% 

75% 

Close to EU 

41% 

Close to EU 

0% 

Below EU 

100% 

60% 
   100% 
0% 

Above EU 

Above EU 

Close to EU 

SBA 

Above EU 

0% 

0% 

1% 
Below 
EU 

0% 

Below EU 

100% 

Above EU 

Above EU 

0% 

0% 

Close to EU 

78% 

Above EU 

Close to EU 

Close to EU 

Close to EU 

Close to EU 

SBA 

Above EU 

0% 

0% 

25% 

25% 

0% 
Below 
EU 

0% 

25% 

25% 

99% 
Close 
to EU 
58% 

0% 

25% 

22% 

25% 

98% 

75% 

75% 
   100% 
Close 
to EU 
25% 

Below EU 

100% 

0% 

Close to EU 

25% 

74% 

Close to EU 

11% 

89% 

Close to EU 

Close to EU 

Close to EU 

Above EU 

Above EU 

Above EU 

0% 

0% 

   100% 
93% 

25% 

25% 

25% 

0% 

75% 

0% 

0% 

25% 

37 

 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
Table 19: Uncertainty Analysis (Malta - Poland) 

SBA 

Below 
EU 

Close 
to EU 

Above 
EU 

Divergence 

Malta 

Entrepreneurship 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

Skills and innovation 

Environment 

Internationalisation 

Digitalisation 

Netherlands 

Entrepreneurship 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

Skills and innovation 

Environment 

Internationalisation 

Digitalisation 

Poland 

Entrepreneurship 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

Skills and innovation 

Environment 

Internationalisation 

Digitalisation 

Divergence 

Divergence 

25% 

75% 

0% 

0% 

75% 

0% 

1% 

0% 

74% 

74% 

58% 
Above 
EU 
   100% 
   100% 

0% 

0% 
   100% 
98% 

93% 

93% 

95% 
Above 
EU 

0% 

0% 

0% 

67% 

0% 

0% 

0% 

0% 

0% 

0% 

Below EU 

100% 

0% 

Close to EU 

25% 

75% 

Above EU 

0% 

Below EU 

100% 

Close to EU 

Close to EU 

Above EU 

Above EU 

Above EU 

SBA 

Above EU 

Above EU 

Above EU 

20% 

26% 

0% 

0% 

0% 
Below 
EU 

0% 

0% 

0% 

Below EU 

98% 

Close to EU 

25% 

Above EU 

Above EU 

Above EU 

Above EU 

Above EU 

SBA 

Below EU 

0% 

0% 

0% 

0% 

0% 
Below 
EU 
100% 

Close to EU 

0% 

25% 

0% 

79% 

74% 

26% 

26% 

42% 
Close 
to EU 
0% 

0% 

2% 

75% 

0% 

2% 

7% 

7% 

5% 
Close 
to EU 
0% 
   100% 

Below EU 

100% 

0% 

Above EU 

0% 

Below EU 

Below EU 

Below EU 

Below EU 

Below EU 

83% 

100% 

100% 

100% 

100% 

33% 

17% 

0% 

0% 

0% 

0% 

Close to EU 

12% 

88% 

38 

 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
Table 20: Uncertainty Analysis (Portugal - Slovakia) 

Portugal 

Entrepreneurship 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

Skills and innovation 

Environment 

Internationalisation 

Digitalisation 

Romania 

Entrepreneurship 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

Skills and innovation 

Environment 

Internationalisation 

Digitalisation 

Slovakia 

Entrepreneurship 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

Skills and innovation 

Environment 

Internationalisation 

Digitalisation 

SBA 

Above EU 

Close to EU 

Below 
EU 

0% 

5% 

Close 
to EU 
25% 

95% 

Close to EU 

0% 

   100% 

Close to EU 

43% 

Close to EU 

Below EU 

Close to EU 

Above EU 

Above EU 

Close to EU 

SBA 

Below EU 

Below EU 

25% 

91% 

0% 

0% 

0% 

0% 
Below 
EU 
100% 

100% 

Below EU 

100% 

57% 

75% 

9% 
   100% 
25% 

25% 
   100% 
Close 
to EU 
0% 

0% 

0% 

Close to EU 

25% 

71% 

Below EU 

Below EU 

Below EU 

Below EU 

Below EU 

Below EU 

SBA 

Below EU 

Below EU 

100% 

100% 

100% 

100% 

100% 

100% 
Below 
EU 
100% 

100% 

0% 

0% 

0% 

0% 

0% 

0% 
Close 
to EU 
0% 

0% 

Below EU 

26% 

74% 

Above 
EU 

75% 

0% 

0% 

0% 

0% 

0% 

0% 

75% 

75% 

0% 
Above 
EU 

0% 

0% 

0% 

4% 

0% 

0% 

0% 

0% 

0% 

0% 
Above 
EU 

0% 

0% 

0% 

Divergence 

Divergence 

Divergence 

X 

Above EU 

Close to EU 

Close to EU 

0% 

0% 

0% 

Below EU 

100% 

Close to EU 

Close to EU 

24% 

24% 

Below EU 

100% 

20% 

80% 

   100% 
   100% 
0% 

73% 

73% 

0% 

0% 

0% 

0% 

3% 

3% 

0% 

39 

 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
Table 21: Uncertainty Analysis (Slovenia - Sweden) 

Slovenia 

Entrepreneurship 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

Skills and innovation 

Environment 

Internationalisation 

Digitalisation 

Spain 

Entrepreneurship 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

Skills and innovation 

Environment 

Internationalisation 

Digitalisation 

Sweden 

Entrepreneurship 

Second chance 
Think small first and 
Responsive administration  
State aid and public 
procurement 
Access to finance 

Single market 

Skills and innovation 

Environment 

Internationalisation 

Digitalisation 

SBA 

Close to EU 

Above EU 

Below 
EU 

0% 

0% 

Close 
to EU 
84% 

0% 

Above 
EU 

16% 
   100% 

Divergence 

Close to EU 

0% 

   100% 

Close to EU 

25% 

Below EU 

100% 

Close to EU 

Close to EU 

Above EU 

Above EU 

Close to EU 

SBA 

0% 

0% 

0% 

0% 

0% 
Below 
EU 

Close to EU 

0% 

Close to EU 

56% 

75% 

0% 

97% 
   100% 
36% 

36% 

98% 
Close 
to EU 
   100% 
44% 

Close to EU 

0% 

   100% 

Below EU 

100% 

0% 

Close to EU 

Below EU 

Close to EU 

Close to EU 

Close to EU 

Close to EU 

SBA 

Above EU 

Close to EU 

Above EU 

0% 

72% 

0% 

17% 

17% 

30% 
Below 
EU 

0% 

0% 

0% 

   100% 
28% 
   100% 
83% 

83% 

70% 
Close 
to EU 
25% 
   100% 

25% 

0% 

0% 

0% 

3% 

0% 

65% 

65% 

2% 
Above 
EU 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 
Above 
EU 

75% 

0% 

75% 

Below EU 

100% 

0% 

0% 

Above EU 

Close to EU 

Above EU 

Above EU 

Above EU 

Above EU 

0% 

0% 

0% 

0% 

0% 

0% 

0% 
   100% 
0% 

3% 

3% 

0% 

   100% 
0% 
   100% 
97% 

97% 
   100% 

Divergence 

X 

Divergence 

Countries and indicators with less than 50% of data availability are reported as n/a. 

SBA  column  report  result  obtained  in  the  SBA  2022  for  that  specific  country  in  that  specific  dimension.  Below  EU  column  report 
percentages  of  calculation  in  the  Monte  Carlo  simulation  that  place  the  country  below  the  EU  average.  Close  to  EU  column  report 
percentages of calculation in the Monte Carlo simulation that place the country close to EU average. Above EU column report percentages 
of calculation in the Monte Carlo simulation that place the country above EU average. Divergence column marks with an x countries and 
dimensions when more than 50% of the simulations display different result than the SBA 2022 framework 

40 

 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
  Conclusions 

The  SME  country  fact  sheets  have  been  produced  each  year,  since  2008,  by  the  European  Commission’s 
Directorate-General  for  Internal  Market,  Industry,  Entrepreneurship  and  SMEs  (DG  GROW),  and  since  2011, 
with  the  support  of  the  European  Commission’s  Joint  Research  Centre  (JRC).  Fact  sheets  capture  SME’s 
performance across the EU-27 Member States. They are a collection of quantitative indicators covering eleven 
conceptual principles derived from the Small Business Act for Europe (SBA): (1) Entrepreneurship, (2) ‘Second 
chance’, (3) ‘Think small first’, (4) Responsive administration, (5) State aid and public procurement, (6) Access 
to  finance,  (7)  Single  market,  (8)  Skills  and  innovation,  (9)  Environment,  (10)  Internationalisation  and  (11) 
Digitalisation.  Due  to  technical  consideration  regarding  the  statistical  coherence  of  the  framework,  the 
aforementioned  principles  have  been  grouped  into  ten  statistical  dimensions  by  merging  the  principles:  (3) 
‘Think  small  first’ and (4) ‘Responsive administration’ into a single statistical dimension. Thus, the SMEs are 
assessed  across  ten  quantitative  dimensions.  Each  dimension  is  composed  of  between  four  to  fourteen 
indicators.  

A total of 95 indicators were selected from about 20 data sources, among others the Flash Eurobarometer on 
Entrepreneurship,  the  World  Bank  Doing  Business,  the  OECD  Product  market  regulations  database,  the 
European Payment Index, and the European Central Bank database on interest rates. The respective indicators 
are  aggregated  into  ten  dimensions.  The  aggregation  stops  at  dimension  level  and  is  presented  in  a 
scoreboard.    

The  JRC's  Science  for  Modelling,  Monitoring  and  Evaluation  unit  calculated  and  analysed  the  2022  SBA 
dimensions based on international standards, following DG-GROW instructions and the in-house methodology 
to  ensure  transparency.  The  analysis  facilitates  policy-makers  and  other  relevant  stakeholders  to  derive 
accurate and in-depth conclusions.  

In  this  report,  we  have  scrutinized  the  framework  by  describing  the  rationale  behind  each  principle,  the 
underlying indicators, and the methodological approach. After the data collection, potential errors and outliers 
in  the  raw  data  were  checked.  Next,  missing  data  were  estimated  using  a  hybrid  approach.  Then,  we 
aggregated  normalised  indicators  following  a  min-max  approach  with  a  simple  arithmetic  average  for  the 
years 2007-2022. Finally, the compound annual growth rates were calculated per principle and country.  

We  annualised  the  covariance  structure  within  and  across  the  statistical  dimensions  to  investigate  the 
statistical coherence of the SBA framework. The analysis confirms the statistical multi-dimensionality of the 
SBA  scoreboard  of  previous  exercises.  Indeed,  the  SBA  principles  are  highly  diverse,  with  the  underlying 
indicators  capturing  broad  aspects  of  the  SMEs  characteristics  with  little  overlap  of  information  between 
them.  Furthermore,  the  analysis  of  principal  components  (PCA)  revealed  that  the  first  component  describes 
only  27%  of  the  total  variance,  which  means  that  any  linear  aggregate  of  the  ten  SBA  dimensions  would 
capture less  than 28%  of the  total  variance in the principles.  From a statistical point of view, this  diversity 
supports a scoreboard approach rather than further aggregations into a single composite index. The analysis 
also  revealed  the  existence  of  a  two-dimensional  “statistical”  grouping  of  the  data,  which  gives  deeper 
insights  into  the  relationships  between  the  SBA  principles.  Regarding  robustness  of  country  classifications 
relative  to  the  EU  average,  and  how  much  they  depend  on  methodological  choices  (the  methodological 
judgments  on  outlier  treatment,  normalisation,  weighting  and  aggregation),  most  of  country  classifications 
relative to the EU average, in the 2022 SME fact sheets are supported by the simulations we have conducted. 

Overall,  the  assessment  of  the  methodology  behind  the  2022  edition  of  the  SME  fact  sheets  is  positive, 
confirming it to  be  a  sound  tool for  measuring the SME-relevant principles  based on  the SBA  and the SME 
Strategy.  The  analyses  carried  out  have  confirmed  that  measuring  SMEs’  achievements  is  a  complex  and 
multi-dimensional  issue  that,  at  least  at  the  current  stage  of  development,  cannot  be  easily  reduced  to  a 
single number (composite index). This report does not provide the country-specific recommendations, nor the 
policy analyses as, in light of the aforementioned complexities, these require deeper investigation of patterns 
across countries and across all principles of SME policy27.  

27 SME Performance Review (europa.eu) 

41 

 
                                                        
 
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Saisana, M., Domínguez-Torreiro, M. and Vertesy, D. (2016) Joint Research Centre Statistical Audit of the 2016 
Global  Innovation  Index,  In  The  Global  Innovation  Index  2016:  Winning  with  Global  Innovation,  (Eds) 
Cornell University, INSEAD, and WIPO, Ithaca, Fontainebleau, and Geneva. 

Saltelli, A., Ratto,  M.,  Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M. and Tarantola, S. (2008) 

Global Sensitivity Analysis: The Primer. Chichester, England: John Wiley and Sons. 

Stano,  Pawel  M.  and  Ghisetti,  Claudia  (2016)  Monitoring  SMEs’  performance  in  Europe,  Methodological 
assessment of the SME Scoreboard 2016. Joint Research Centre, JRC Technical Reports - EUR 28244 EN; 
doi:10.2788/940531 

Vertesy,  D.  and  Deiss,  R. (2016), The Innovation Output Indicator  2016.  Methodology  Update.  JRC Technical 

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Publication, Geneva.  

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World Bank  (2017),  Doing Business 2017, Equal  Opportunities  for  All. International Bank  for Reconstruction 

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Office. OCCASIONAL PAPER ON METHODOLOGY. 

44 

 
 
 
 
 
List of tables  

Table 1: Review of indices relevant in the Small Business Act context ..........................................................................................6 

Table 2: SBA principles and rationale ............................................................................................................................................................... 10 

Table 3: SBA Framework  ......................................................................................................................................................................................... 12 

Table 4: Percentages of country/year indicators by last available year and dimension .................................................. 16 

Table 5: Summary of construction rules for growth rates .................................................................................................................. 21 

Table 6: Coverage of indicators per country in years 2008-2022 ................................................................................................ 22 

Table 7: Variance explained the SBA principles and the principal components ..................................................................... 25 

Table 8: Example of SBA 2022 coherence test in the Digitalization principle ....................................................................... 25 

Table 9: Examples of SBA 2022 better statistical fits in a different dimension .................................................................. 26 

Table 10: Implicit weights of the indicators in the ten SBA dimensions.................................................................................... 28 

Table 11: Principal Components Analysis results for the 2022 SBA principles ..................................................................... 29 

Table 12: Uncertainty parameters (winsorization, normalization, weights, aggregation) ............................................... 30 

Table 13: Uncertainty Analysis (Austria – Bulgaria) ................................................................................................................................ 32 

Table 14: Uncertainty Analysis (Croatia – Czech Republic) ................................................................................................................ 33 

Table 15: Uncertainty Analysis (Denmark - Finland) .............................................................................................................................. 34 

Table 16: Uncertainty Analysis (France - Greece) .................................................................................................................................... 35 

Table 17: Uncertainty Analysis (Hungary - Italy) ...................................................................................................................................... 36 

Table 18: Uncertainty Analysis (Latvia - Luxembourg) ......................................................................................................................... 37 

Table 19: Uncertainty Analysis (Malta - Poland) ....................................................................................................................................... 38 

Table 20: Uncertainty Analysis (Portugal - Slovakia) ............................................................................................................................. 39 

Table 21: Uncertainty Analysis (Slovenia - Sweden) .............................................................................................................................. 40 

45 

 
 
 
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