High-growth, innovative enterprises in Europe : counting them across countries and sectors.
Date: 2017-08-17
Institutions: Joint Research Centre
Subjects: company growth, economic activity, entrepreneurship, innovation, job creation, research report, size of business

High-growth, innovative 
enterprises in Europe 

Counting them across 
countries and sectors 

Vértesy, D., Del Sorbo, M., Damioli, G. 

2017 

EUR 28606 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 scientific output expressed does not imply a policy position of the European Commission. Neither 
the European Commission nor any person acting on behalf of the Commission is responsible for the use that 
might be made of this publication. 

Contact information 
Name: Daniel Vertesy 
Address: Via E. Fermi 2749 
Email: daniel.vertesy@ec.europa.eu 
Tel.: +39 0332 783556 

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

JRC106419 

EUR 28606 EN 

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ISSN 1831-9424 

doi:10.2760/328958 

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How to cite this report: Vértesy, D., Del Sorbo, M., Damioli, G.,  High-growth, innovative enterprises in 
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Contents 

Acknowledgements ................................................................................................ 1 

Abstract ............................................................................................................... 2 

1 

Introduction .................................................................................................... 3 

2  Theoretical considerations ................................................................................. 4 

2.1  Defining and measuring high-growth ............................................................ 4 

2.2  Defining and measuring innovativeness at the firm level ................................. 6 

3  Methodology: the growth and innovation matrix .................................................. 8 

3.1  Preparing the dataset .................................................................................. 9 

3.1.1  Employment growth ............................................................................. 10 

3.1.2  Turnover change .................................................................................. 12 

3.1.3  The growth of innovators and non-innovators .......................................... 14 

3.2  Variables defining high-growth firms for the matrix ...................................... 16 

3.3  Variables defining innovation for the matrix ................................................. 19 

4  Results ......................................................................................................... 20 

4.1  High-growth firms and innovative firms ....................................................... 21 

4.2  High-growth and innovative firms ............................................................... 23 

4.3  High-growth and innovative performance of countries and sectors .................. 27 

4.3.1  Association between high-growth and innovation variables ........................ 27 

4.3.2  Towards aggregate scores of high-growth and innovation .......................... 29 

4.3.3  Cross-country and cross-sectoral evidence .............................................. 30 

5  Conclusions ................................................................................................... 34 

References ......................................................................................................... 36 

Appendix .................................................................. Error! Bookmark not defined. 

i 

Acknowledgements 

The authors would like to thank the valuable feedback and suggestion of colleagues from 
the  JRC’s  unit  I1  Modelling,  Indicators  and  Impact  Evaluation,  in  particular  to  Marcos 
Alvarez  and  Michaela  Saisana,  the  participants  of  the  16th  Congress  of  the  International 
Schumpeter  Society  in  Montreal,  as  well  as  Richard  Deiss  and  Diana  Ognyanova  (DG 
RTD).  Special  thanks  are  due  to  Pawel  Stano  for  statistical  support,  and  Genevieve 
Villette  for  her  support  with  accessing  CIS  microdata  at  the  Eurostat  Safe  Centre.  The 
preparation of the study benefitted from funding through the INNOVA_Measure 2 (H2020 
690804) project. 

Authors 

Dániel Vértesy, Maria Del Sorbo and Giacomo Damioli – JRC, I1 / CC-COIN 

1 

Abstract 

High-growth,  innovative  enterprises  are  a  key  source  of  business  dynamics,  but  little  is 
known  about  their  actual  share  in  the  enterprise  population.  This  is  due  to  an  inherent 
uncertainty in how to define the threshold that distinguishes high-growth firms from non-
high-growth firms – illustrated by the lack of agreement between  the definitions applied 
by  Eurostat  and  the  OECD.  This  explorative  study  aims  to  help  measure  the  share  of 
high-growth, innovative enterprises in the European enterprise population, test how the 
choice  of  definition  affects  their  share.  We  introduce  a  methodology  to  address  the 
uncertainty in the definition, and compute national and sectoral average scores for high-
growth and innovation in order to assess their distribution across countries and sectors of 
economic activity. We test the impact of a number of alternative definitions on a  pooled 
sample  of  92,960  European  firms  observed  by  the  2012  wave  of  the  Community 
Innovation Survey (CIS). Our finding suggests that the share of high-growth, innovative 
enterprises in Europe may range between 0.1 to 10%, depending on the definitions, and 
the  outcomes  are  most  sensitive  to  the  growth  measure  (employment-  or  turnover-
based) and threshold (absolute or relative), as well as the degree of novelty expected of 
the innovations introduced by firms. With the help of aggregate measures, we observe a 
trade-off  between  high-growth  and  innovation  performance  at  the  country-level,  which 
disappears  at  the  overall  European  sectoral  level.  This  observation  highlights  the 
importance  of  structural  differences  across  EU  Member  States  in  terms  of  firms’ 
innovation profile, size and associated high-growth performance. 

Keywords:  high-growth,  innovative  enterprises,  indicators,  uncertainty,  innovation, 
business dynamics, entrepreneurship, firm growth 

2 

 
1  Introduction 

High-growth,  innovative  enterprises  are  seen  as  particularly  important  elements  of  the 
business economy, which account for a disproportionate share in new job creation. While 
an  increasing  number  of  studies  are  analysing  high-growth,  innovative  enterprises 
(HGIEs),  very  little is  known  about  their  share  in  the  European  firm  population.1  This is 
not surprising, because it is very difficult to measure what is difficult to define, and there 
is  a  lack  of  convergence  to  a  clear  definition  that  distinguishes  high  growth  from  low 
growth,  innovative  firms.  The  use  of  different  definitions  of  growth  limits  the 
generalizability of findings on high-growth (see Daunfeldt et al., 2014; Hölzl and Janger, 
2014).  Despite  the  fact  that  most  studies  on  the  topic  acknowledge  definitions  as  a 
source  of  sensitivity,  there  is  little  empirical  evidence  on  what  proportion  of  firms  is 
affected by changing certain thresholds of growth or innovativeness.  

A main issue to address is the uncertainty in the application of thresholds. For a firm to 
qualify  as  a  high-growth  one,  should  it  double  its  size,  or  perform  at  least  10  or  20% 
growth over a given period? For how long should a firm demonstrate strong growth to be 
considered as high growth? What makes a firm innovative? Can a firm that introduced a 
product  it  had  not  produced  or  sold  before  be  considered  as  innovative,  or  is  it  a 
necessary condition for innovativeness that this product is new to the market? We argue 
that answers to these questions are far from obvious, and need to be carefully addressed 
especially  when  HGIEs  are  policy  targets.  Obviously,  a  higher  growth  threshold  flags  a 
significantly  smaller  set  of  companies  as  HGIEs,  but  it  is  unclear  what  the  actual 
difference is. 

While there is no single, official definition of “high-growth, innovative firms”, the scale of 
their  presence  is  considered  to  be  an  important  measure  of  business  dynamics  in  a 
country.  The  2016  editions  of  the  European  Innovation  Scoreboard  (EIS)  and  the 
Innovation  Output  Indicator  (IOI)  of  the  European  Commission  both  have  benchmarked 
countries  in  terms  of  “employment  dynamism  of  high-growth  enterprises  in  innovative 
sectors”. The main consideration for such an indicator is that high-growth firms generate 
a  disproportionate  amount  of  new  jobs  as  well  as  other  measures  of  economic  growth 
(see  i.e.  Schreyer,  2000;  Daunfeldt  et  al,  2014),  and  their  concentration  in  the  most 
innovative  sectors  drives  structural  change  and  fosters  competitiveness.  The  indicators 
used in the EIS and IOI are derived from sectoral-level calculations. However, in order to 
measure  business  dynamics  associated  with  HGIEs  in  a  more  precise  way,  one  would 
ideally  need  to  measure  both  growth  as  well  as  innovation  for  the  same  firm.  The 
availability  of  such  firm-level  micro  data  for  multiple  countries  would  significantly 
improve our understanding of the HGIEs and support policy making. 

The main purpose of this explorative  study is to help better measure the share 
of  high-growth,  innovative  enterprises  in  the  European  enterprise  population, 
test  how  the  choice  of  definition  affect  their  share.  Following  a review  of  relevant 
literature  on  the  definition  and  measurement  of  high-growth  and  innovation,  we 
introduce a methodology to assess the scale of their co-occurrence across countries and 
sectors of economic activity. We test the impact of a number of alternative definitions on 
a  sample  of  92,960  firms  observed  by  the  2012  wave  of  the  Community  Innovation 
Survey (CIS). 

The novelty of this study is three-fold. First, it estimates the share of HGIEs in Europe for 
the first time using firm-level data from 20 European countries. Second, that rather than 
providing  a  single  estimate,  the  study  introduces  a  high-growth  and  innovation  matrix, 
which  addresses  the  uncertainties  in  the  definition  of  HGIEs  and  offers  a  direct 
comparison  of  alternative  definitions.  Third,  the  study  provides  evidence  on  negative 
correlation  between  high-growth  and  innovation  performance  of  firms  observed  at  the 
country-level, which is not found at the sectoral level for the pooled European sample.  

1 In this study, we use the term firm and enterprise interchangeably. 

3 

 
                                           
2  Theoretical considerations 

Employment  creation  and  the  induction  of  structural  change  are  among  the  top  key 
priorities  of  EU  policy  makers  in  the  aftermath  of  the  global  financial  crisis  in  de  facto 
stagnating advanced economies. In this context, HGIEs play a central role, and especially 
a  small  group  of  them  is  able  to  generate  a  large  share  of  new  employment  as  well  as 
positive  externalities  through  demand  and  demonstration  effects.  At  a  time  when 
Europe’s knowledge- and technology-intensity gap vis-à-vis countries such as the US  or 
South  Korea  widens,  high-growth  innovative  firms  have  a  central  role  to  play  to  ensure 
productivity  growth  and  sustained  competitiveness  through  structural  change  towards  a 
more knowledge-intensive European economy.  

It  is  therefore  not  surprising  that  high-growth,  innovative  firms  have  captured  a 
synchronized interest at the policy and academic levels (Audretsch, 2012; Capasso et al., 
2015;  Coad  et  al.,  2014b;  European  Commission,  2015,  2013;  Henrekson  and 
Johansson,  2010a;  OECD,  2012).  Nevertheless,  empirical  evidence  on  the  nature  and 
drivers  of  high  growth  innovative  firms  is  quite  scanty,  and  often  focus  on  single 
countries or certain sectors of the economy. Given the data demand, only a few of such 
studies can take a more in-depth view on the innovation process. There are a few single-
country studies investigating the barriers to innovation and growth, and only very few of 
them  offer  cross-country  comparisons  (Hessels  and  Parker,  2013;  Hölzl  and  Janger, 
2013). Thus, evidence on innovative high growth at a multi-country, multi-sector scale is 
certainly  needed  for  a  better  understanding  of  the  phenomena  and  to  support  policy 
making in Europe.  

There  is  controversial  evidence  showing  that  small  firms  generate  more  jobs  than  large 
ones in US (Birch, 1979; Birch and Medoff, 1994); that there is no association between 
firm  size  and  job  creation  (Davis  et  al.,  1996);  especially  when  controlling  for  age 
(Haltiwanger et al., 2013). Nevertheless, several scholars find that most small firms have 
a  low  or  zero  growth  rate  and  that  a  few  high-growth  firms  are  key  for  increasing  jobs 
(Acs  et  al.,  2008;  Acs  and  Mueller,  2008;  Birch  and  Medoff,  1994;  Brüderl  and 
Preisendörfer,  2000;  Davidsson  and  Henrekson,  2000;  Fredrick  Delmar  et  al.,  2003; 
Halabisky et al., 2006; Littunen and Tohmo, 2003).  

A synthesis of the most recent literature points to a list of seven stylized facts to consider 
when studying high-growth firms (Coad et al., 2014b; Moreno and Coad, 2015): 

1.  Growth rates distributions are heavy-tailed 
2.  Small number of high-growth firms create a large share of new jobs  
3.  High-growth firms tend to be young but are not necessarily small 
4.  High-growth firms are not more common in high-tech industries  
5.  High growth is not to be persistent over time  
6.  Difficult to predict which firms are going to grow  
7.  The use of different growth indicators selects a different set of firms. 

This report focuses on the 7th stylized fact listed above. 

2.1  Defining and measuring high-growth  

The  term  “high-growth  enterprise”  is  used  in  official  statistics,  but  a  lack  of  global 
agreement  on  their  definition  is  a  potential  source  of  confusion.  Eurostat  defines  high-
growth enterprises as those with at least 10 employees in the beginning of their growth 
and  having  average  annualised  growth  in  number  of  employees  greater  than  10%  per 
annum,  over  a  three-year  period.2 The  OECD  applies  a  stricter  definition  with  a  20% 
threshold  (and  considers  enterprises  with  the  average  annualised  growth  mentioned 
above  between  10  and  20%  as  medium  growth),  but  measures  growth  both  by  the 

2  Commission  implementing  regulation  (EU)  No  439/2014  [http://eur-lex.europa.eu/legal-
content/EN/TXT/PDF/?uri=OJ:JOL_2014_128_R_0013&from=EN] 

4 

                                           
limits 

number  of  employees  as  well  as  by  turnover.3    The  purpose  of  the  size  threshold  of  10 
employees  is  to  reduce  statistical  noise  (i.e.,  to  avoid  classifying  a  small  enterprise 
growing  from  1  to  2  employees  over  three  years).  Official  statistics  are  produced 
accordingly  at  the  level  of  sectors  or  the  business  economy.  This  leads  to  three  main 
international 
issues.  Firstly,  the  use  of  two  rather  different  definitions 
comparability, i.e. the performance of the US with that of the EU.  Second, as a result of 
the absolute growth thresholds, the three-year observation window and the publication of 
aggregate  statistics,  a  changing  pool  of  firms  are  captured  in  each  year’s  statistics, 
making  inter-temporal  comparisons  difficult  to  interpret.  For  instance,  a  company  that 
achieved a 40% growth rate in the first year, but 0% in three subsequent years qualifies 
as  a  high-growth  enterprise  according  to  the  Eurostat  definition  over  the  3  years,  but 
would  not  qualify  if  the  observation  period  starts  in  the  2nd  and  ends  at  the  4th  year. 
Hence, it is part of the pool of firms for which aggregate sectoral or country-wide data is 
produced in the third year, but is outside the pool of firms in the same sector or country 
in  the  fourth  year.  Third,  aggregate  figures  in  business  demography  statistics  may  be 
useful  to  characterize  sectors  or  entire  economies  on  the  occurrence  of  high-growth 
firms.  However,  aggregate  figures  offer  limited  information  on  high-growth  and 
innovative firms, since innovation cannot be measured at the level of firms for the same 
firms.  In  sum,  these  limitations  of  official  statistics  imply  that  exploring  the  occurrence 
and  characteristics  of  high-growth,  innovative  firms  requires  other,  firm-level  data 
sources.  

In  the  burgeoning  literature  on  HGIEs,  there  is  a  lack  of  convergence  to  a  single 
definition  of  what  distinguishes  high  growth  from  low  growth,  innovative  from  non-
innovative  firms.  It  is  therefore  not  surprising  that  a  common  conclusion  of  the  various 
studies is that definition matters for the outcomes of interest. While it would be tempting 
to select, based on the above conclusions, a definition for HGIEs that best fits the model 
and  gives  the  most  intuitive  results,  the  policy  relevance  of  any  such  study  would  be 
severely  limited  or  outright  biased,  as  models  would  be  run  on  a  qualitatively  different 
set of firms depending on the identification method (Daunfeldt et al., 2014).  

As economic outcomes  are highly sensitive to the definition of firm growth (Coad et al., 
2014a), it is important to address the issue of defining firm growth, and identifying high-
growth  firms.  Following  the  four  points  proposed  by  Delmar  (1997)  and  Delmar  et  al 
(2003)  as  well  as  Coad  et  al  (2014a),  we  can  conclude  that  there  is  need  for 
methodological  prudence  when  it  comes  to  measuring  firm  growth  the  following 
parameters of any potential definition: 

1. 
2. 
3. 
4. 
5. 

the indicator of growth; 
the calculation of the growth measure; 
the period analysed; 
the process of growth 
the selection of the growth threshold 

Regarding the indicator of growth, sales (or turnover) and number of employees are the 
most commonly used in the literature. Authors have measured firm growth using multiple 
indicators, indicators on performance or market shares (in some cases, even subjective, 
perception-based  measures),  or  assets.  Different  indicators  may  be  more  pertinent  to 
capture different phases in the development of a firm – and also, different dynamics. For 
instance,  sales  growth  typically  precedes  employment  growth  in  a  firm,  but  not 
necessarily. In fact, the dynamic sequence has been  shown to be the  reverse in certain 
cases where a firm decided to outsource certain activities (Delmar, 2006).  

Second,  the  choice  of  using  an  absolute  or  relative  measure  of  growth  produces 
significant differences, especially when considering the firm size.  Smaller firms are more 
easily  appearing  as  HGEs  if  growth  is  defined  using  a  relative  rate  rather  than  an 
absolute  measure.  Hybrid  growth  indicators  make  use  of  both  absolute  and  relative 

3 See the Eurostat − OECD Manual on Business Demography Statistics, 2007. 

5 

                                           
employment growth, such as the Birch index (defined as (Et – Et-k)*Et/Et-k, where Et notes 
employment  at  time  t) that is less  biased  towards  small  firms  and lowers  the  impact  of 
firm size on the growth indicator (Hölzl, 2009; Schreyer, 2000).  

Third, the length of the period for which the growth measure is computed is intrinsically 
linked to the research problem addressed. While the choice of a longer period flattens the 
statistical  noise  (Henrekson  and  Johansson,  2010b),  it  may  hide  high  growth  spurts 
experienced  over  a  shorter  period  (Daunfeldt  et  al.,  2014;  Hölzl,  2014).  At  the  same 
time,  the  selection  of  the  observation  period  is  also  conditioned  by  the  availability  of 
time-series data. 

Fourth,  there  is  a  variation  in  the  processes  by  which  firm  growth  occurs.  Typically, 
acquired  (or  external)  growth  –  growth  resulting  from  acquisitions  or  mergers  –  is 
distinguished from organic (or internal) growth. McKelvie and Wiklund (2010) argue that 
one  should  also  take  into  consideration  that  over  time,  a  firm  may  choose  between  the 
two processes of growth resulting in hybrid modes. 

A final issue is the identification of a growth threshold, which aims at distinguishing high-
growth  and  non-high-growth  firms  (including  the  rest  of  the  population,  or  only  those 
growing).  Coad  et  al  (2014a)  distinguish  two  methods  to  identify  HGEs.  First,  identify 
HGEs as the share of firms in a population that see the highest growth during a particular 
period  (the  top  N  %  of  the  distribution  –  for  instance,  the  1%  or  5%  of  firms  with  the 
highest growth rate). The other method is to define HGEs as firms growing at or above a 
particular  pace  or  threshold.  The  advantage  of  the  former  method  is  that  it  is  non-
parametric, based on an observed distribution, however, the disadvantage is  the lack of 
comparability across time or across  countries. Furthermore, it is very likely that smaller 
firms  will  be  overrepresented  among  the  share  of  firms  with  the  highest  growth 
performance.  This  could  be  overcome  by  grouping  the  firms  into  size  classes  before 
selecting  the  top  N%  from  each  class.  A  certain  degree  of  arbitrariness  nevertheless 
remains regarding the  cut-off threshold (i.e. what justifies the selection of the top 1, 5, 
10  or  20%  of  firms?),  which  is  why  it  is  important  to  have  more  empirical  findings 
available across time, countries and sectors. As for the second method – define HGEs as 
those  with  a  growth  rate  above  a  fixed  absolute  threshold  –  is  that  while  the  growth 
distribution of firms may change across time and space, a fixed threshold offers clearer 
comparisons.  However,  this  is  its  major  shortcoming  (alongside  the  arbitrariness  of 
establishing  thresholds  on  the  continuous  scale  of  growth):  restrictively  defined 
thresholds  may  select  very  few  observations  in  certain  cases,  which  may  reduce  the 
reliability of obtained statistics.  

2.2  Defining and measuring innovativeness at the firm level 

Defining  what  makes  firms  innovative  is  no  less  challenging  than  defining  what  makes 
them high-growth. We address the main consideration in this sub-section with an interest 
in finding an inclusive definition of innovation for high-growth firms. In this study, we are 
less interested in why firms innovate, rather, how they do it and how to measure it. 

Innovation covers a wide set of activities that involve bringing new ideas to the market, 
and  may  refer  to  products,  processes  or  other  activities  firms  perform.  Based  on  the 
work of Schumpeter, the 3rd edition of the OECD-Eurostat Oslo Manual  (2005) proposes 
the following four types of innovation: 

1.  Product innovation: A good or service that is new or significantly improved. This 
includes  significant  improvements  in  technical  specifications,  components  and 
materials,  software  in  the  product,  user  friendliness  or  other  functional 
characteristics;  

6 

 
2.  Process  innovation:  A  new  or  significantly  improved  production  or  delivery 
method.  This  includes  significant  changes  in  techniques,  equipment  and/or 
software; 

3.  Marketing  innovation:  A  new  marketing  method  involving  significant  changes  in 

product design or packaging, product placement, product promotion or pricing; 

4.  Organisational  innovation:  A  new  organisational  method  in  business  practices, 

workplace organisation or external relations.  

Following  the  Oslo  Manual,  the  minimum  requirement  for  an  innovation  is  that  the 
product,  process,  marketing  method  or  organisational  method  must  be  new  or 
significantly  improved  to  the  firm.  This  includes  products,  processes  and  methods  that 
firms are the first to develop and also those that have been adopted from other firms or 
organisations.  OECD  and  Eurostat  distinguish  “innovation  active”  from  “non-innovative” 
enterprises.  An  enterprise,  in  this  definition  is  innovation  active  if  it  successfully 
introduced any kind of innovation in the past three years, or have ongoing or abandoned 
activities.4  

Scholars intending to measure innovation usually rely on hard data (such as research and 
development (R&D) spending; R&D intensity; patents; product announcements, etc.), or 
survey data. Both types involve a set of limitations: R&D is a measure of input, but not 
output,  though  R&D  intensity  (R&D  expenditure/  sales)  is  a  combined  input  and  output 
index;  patents  measure  inventions  and  thus  may  be  seen  as  both,  input  and  output 
according  to  how  they  feed  into  the  innovation  process;  they  are  not  necessarily 
comparable  to  measure  the  inventiveness  in  all  the  industries,  such  as  in  the  services 
sectors,  or  for  small  firms.  Survey  data,  such  as  CIS  may  present  limitations, 
nevertheless it allows comparisons across industries and countries (Coad and Rao, 2008; 
Gault, 2013).   

The  scope  of  possible  definitions  is  closely  linked  to  the  nature  of  data.  Innovation 
surveys,  particularly  the  CIS,  combine  quantitative  and  qualitative  data  on  firms’ 
innovation activities including the types of innovation (e.g. product, process, marketing, 
organization innovation, etc.), their degree of novelty, as well as the importance of new 
of  significantly  improved  products  to  a  firm’s  turnover  (Cucculelli  and  Ermini,  2012; 
Mairesse  and  Mohnen,  2010).  CIS  survey  results  have  triggered  a  rich  economic 
literature over the past two decades. The many papers that used CIS data have opted for 
a  variety  of  ways  to  define  innovative  firms.  Pellegrino  and  Savona  (2013)  considered 
firms to be ‘innovative’ if they have introduced or developed a new product or process or 
had been in the process of doing so during the surveyed period’. Others built composite 
innovation  indicators  from  quantitative  and/or  qualitative  data  in  the  CIS  in  order  to 
measure the innovation intensity (Coad and Rao, 2008; Mohnen and Dagenais, 2000) or 
to distinguish R&D innovators from non-R&D innovators (Hervas-Oliver et al., 2008; Hölzl 
and Janger, 2013).  

4  See  i.e.  Eurostat  Reference  metadata  to  the  Results  of  the  community  innovation  survey  2012 
(CIS2012) (inn_cis8) [http://ec.europa.eu/eurostat/cache/metadata/en/inn_cis8_esms.htm] 

7 

 
                                           
3  Methodology: the growth and innovation matrix 

Rather than making any a priori selection of a HGIE definition, in our study we aim to be 
as  open  and  comprehensive  as  possible  by  developing  a  methodology  accommodating 
multiple  definitions  for  high-growth  as  well  as  innovativeness.  This  follows  from  the 
conclusion that the definition of firm growth has a significant impact on outcomes (Coad 
et al, 2014).  

The  uncertainty in  establishing  growth  thresholds is  highly  visible in  the  parallel  system 
of  definitions  used  by  Eurostat  and  the  OECD.  The  OECD-Eurostat  Entrepreneurship 
Indicators  Programme  (EIP)  definition  uses  the  20%  definitions  both  in  terms  of  sales 
and  employment,  while  Eurostat  elsewhere  uses  a  10%  employment  growth  threshold 
(both  consider  annual  average  growth  over  a  3-year  time  frame,  for  firms  above  10 
employees).  In  the  context  of  innovativeness,  as  seem  above,  there  is  at  least  in  the 
academic  literature  uncertainty  as  to  what  constitutes  innovativeness.  Neglecting  the 
existence of valid arguments in support of a broad range of alternative classification (or, 
in other words, the “fuzzines” of definitions) would easily lead to mismeasurement of the 
scale  of  HGIEs.  The  HGIE  matrix  we  propose  acknowledges  the  viability  of  different 
definitions  of  both  ‘high-growth’  (applying  different  thresholds)  and  degrees  of 
innovativeness  (applying  different  definitions  of  innovation)  and  considers  all  of  these 
simultaneously.  

Based on the literature and information available in the CIS 2012 dataset, we propose a 
set  of  alternative  (potentially  overlapping,  not  mutually  exclusive)  definitions  for  high-
growth  (hg1  to  hgI)  and  for  innovativeness  (inn1  to  innJ).  If  we  consider  all  of  these 
definitions  valid,  their  combination  will  be  valid  as  well.  The  combination  of  the  HG  and 
Inn definition results in a HGI definition matrix. 

Figure 1 The high-growth and innovation (HGI) definition matrix 

innJ 
inn1  … 
𝐻𝐺𝐼1,1 ⋯ 𝐻𝐺𝐼1,𝐽
⋱
𝐻𝐺𝐼𝐼,1 ⋯ 𝐻𝐺𝐼𝐼,𝐽

[

⋮

⋮

] 

hg1 
… 
hgI 

For  each  firm  in  the  CIS  dataset  (k=1  to  K),  we  assess  whether  it  meets  or  not  the 
different  high-growth  and  innovation  criterion,  and  attribute  a  score  of  1  if  so,  and  0 
otherwise. We test 30 definitions of high-growth (I=30) and 50 definitions for innovation 
(J=50) which will be further elaborated in sections 3.2 and 3.3 respectively.5 By summing 
these  values  for  each  firm  (that  is,  the  number  of  times  it  meets  the  combined  high-
growth and innovative criteria), we  obtain a  HGIk(i,j) score for the  k-th firm. This score 
can  range  from  0  to  IxJ.  Firms  with  a  score  of  0  –  we  expect  that  this  will  characterize 
the  majority  of  firms  –  fail  to  meet  any  of  the  combined  high-growth  and  innovative 
criterion. A score equal to IxJ means that a firm meets all potential high-growth criteria 
and  can  be  safely  assumed  to  be  a  high-growth,  innovative  firm.  The  higher  the  value 
the  more  frequently  the  enterprise  is  labelled  as  high-growth  and  innovative,  implying 
that  more  robust  conclusions  can  be  drawn  in  subsequent  firm-level  studies  on  the 
various  factors  behind  HGI.  Firms  with  low  scores  are  particularly  sensitive  to  the  HGI 
definition. 

Summing up the HGIi,j scores (i.e., for the  entire economy or for a given sector)  shows 
the  total  number  of  firms  that  meet  a  given  definition  combination.  This  allows  to 
compare how restrictive or broad various definition combinations are, and understand the 
impact of changing certain thresholds. 

5  For  instance  -  anticipating  the  specific  definitions  introduced  later  in  this  report  –,  the  Eurostat 
10% employment growth definition combined with the introduction of any type of innovation refers 
to cell (i, j)=(3, 6). 

8 

 
 
                                           
The  main  advantages  of  this  approach  are  its  ability  to  accommodate  the  overlapping 
definitions  of  high-growth  and  innovativeness,  in  a  non-arbitrary  way,  as  well  as  its 
relatively  low  computation  demands.  Furthermore,  although  one  could  say  that  a  few 
combinations  of  high-growth  &  innovativeness  may  be  more  frequently  used  in  the 
literature  than  others,  our  method  considers  each  combination  of  equal  importance.6 
While the scores may be influenced by the set of high-growth and innovation definitions 
used,  the  methodology  is  sufficiently  flexible  to  accommodate  any  newly  proposed 
definitions.  

3.1  Preparing the dataset 

We decided to use the firm level microdata from the most recent, 2012 wave of the CIS 
for  our  analysis  for  two  main  reasons.  First,  because  it  contains  information  on  growth 
(employment  and  turnover  growth)  and  innovation  performance  (innovation  types 
introduced,  novelty  of  innovations,  etc.)  of  manufacturing  and  service  sector  firms. 
Although  only  accessible  at  the  Eurostat  Safe  Centre  in  Luxembourg,  the  harmonized 
dataset  offers  a  cross-European  comparison.  The  main  shortcoming  of  this  CIS  data  is 
that the observation of firm growth is limited to a 2-year window, thus it is not possible 
to analyse longer growth trajectories. 

The  CIS  2012  data  used  for  the  high-growth,  innovativeness  matrix  was  prepared 
according  to  the  following  steps.  First,  firms  with  missing  employment  or  turnover  data 
for  any  of  the  two  years  were  removed  in  order  to  be  able  to  measure  growth.  As  a 
result, 4,722 firms, including all Finnish firms (for which no values were reported for the 
variables  of  2010)  were  excluded  from  the  initial  sample  of  148,153.  In  a  second  step, 
we  removed  firms  undergoing  non-organic  growth  (mergers  or  acquisitions),  affecting a 
further 8,468 companies. We next removed micro firms (applying an upper threshold of 
10 employees and 1 million Euros turnover in any of the two years observed), in order to 
avoid  observing  high  growth  fluctuation  due  to  the  very  small  scale.  This  step  affected 
41,149  firms.7    In  a  final  step,  we  trimmed  what  we  considered  outlier  growth 
performance  in  terms  of  employment  as  well  turnover  change,  that  is,  the  top  0.5 
percentile.8  We  considered  it  necessary  to  purge  spurious  variation  in  the  growth 
variables  of  interest.  This  affected  a  further  854  firms.  After  the  cleaning  process,  our 
final  sample  consisted  of  92,960  observations  from  19  EU  Member  States  as  well  as 
Norway.  These  represent  about  450,000  European  firms,  when  applying  the  sampling 
and  –  where  available  –  the  non-response  weights,  see  Table  1.  About  half  of  the 
observations are from Spain, France and Italy (see unweighted sample, left side of Table 
1);  while  about  47%  of  sampled  companies  are  located  from  Italy  and  Germany,  and  a 
further 32% in Spain and France (see weighted sample, right side of Table 1).  

About  50%  (or  76%)  of  the  firms  are  small  firms,  33%  (or  20%)  medium-sized,  and 
13% (4%) large (applying weighted measure).9 

6 A future extension could also attribute weights to the various proposed options. 
7 We noted that in case we used a more restrictive 2 million euro turnover threshold, our sample 
would have been reduced by an additional 21,030 firms. 
8  The  thresholds  applied  were  1.8  and  3.33  for  the  employment  and  turnover  change  ratios, 
respectively. This is in accordance with the literature to ensure that clerical or measurement errors 
do  not  influence  results.  Since  the  study  focuses  on  the  top  of  the  distribution,  we  implement  a 
cautious approach and only trip the top 0.5 percentile. 
9 We defined size classes by employment levels reported for 2010  – 10-50 employees: small, 50-
250: medium, and above 250: large. 

9 

 
                                           
Table 1 Number of firms in the unweighted and weighted sample by size class 

Unweighted sample 

Weighted sample 

Total 

Country 

Small  Medium 

Large 

260 
460 
31 
764 
1,222 
56 

% 
4% 
5% 
1% 
4% 
5% 
1% 
24% 
15% 
2% 
3% 
14% 
1% 
1% 
1% 
4% 
4% 
5% 
4% 
1% 
2% 
30,857  12,428  92,960  100% 

BE 
BG 
CY 
CZ 
DE 
EE 
ES 
FR 
HR 
HU 
IT 
LT 
LU 
LV 
NO 
PT 
RO 
SE 
SI 
SK 
Total 
% 
33% 
Source: authors’ calculations using CIS2012 microdata 

N 
3,692 
4,220 
802 
3,304 
4,307 
939 
2,923  22,083 
1,797  14,105 
1,705 
3,147 
1,299  13,163 
1,060 
667 
769 
3,439 
3,962 
4,359 
3,896 
1,327 
2,014 

2,361 
2,096 
603 
1,252 
1,753 
444 
11,289 
9,659 
558 
1,140 
8,919 
288 
300 
317 
2,116 
1,914 
987 
2,188 
658 
833 
49,675 
53% 

1,071 
1,664 
168 
1,288 
1,332 
439 
7,871 
2,649 
905 
1,513 
2,945 
603 
292 
344 
1,105 
1,623 
2,411 
1,251 
535 
848 

169 
75 
108 
218 
425 
961 
457 
134 
333 

242 
494 

100% 

13% 

Small  Medium 

Large 

8,642 
2,096 
761 
7,207 
70,648 
1,062 
66,588 
44,124 
2,318 
4,144 
96,381 
1,577 
845 
941 
5,864 
5,366 
5,356 
11,291 
1,752 
3,245 
340,206 
76% 

2,391 
1,664 
168 
3,620 
25,050 
543 
16,164 
9,978 
1,191 
2,205 
13,411 
1,188 
341 
597 
1,374 
2,537 
3,658 
2,374 
692 
1,784 
90,931 
20% 

434 
460 
31 
879 
5,297 
64 
3,153 
2,316 
266 
505 
1,975 
176 
81 
106 
220 
457 
1,047 
518 
142 
373 
18,499 
4% 

Total 

N 
11,467 
4,220 
960 
11,706 
100,995 
1,669 
85,904 
56,417 
3,775 
6,854 
111,766 
2,941 
1,267 
1,645 
7,457 
8,360 
10,061 
14,183 
2,587 
5,402 

% 
3% 
1% 
0% 
3% 
22% 
0% 
19% 
13% 
1% 
2% 
25% 
1% 
0% 
0% 
2% 
2% 
2% 
3% 
1% 
1% 
449,636  100% 

100% 

3.1.1  Employment growth  

Figure 2 shows the employment growth broken down by size classes, and the left part of 
Table  2  shows  how  country  growth  rates  correlate  across  different  size  classes. 
Unsurprisingly,  due  to  the  high  share  of  small  firms  (with  10-50  employees)  in  the 
sample, the overall average rates correlates very strongly with the growth rate observed 
for  small  firms  (correlation  r  =  0.96),  in  other  words,  in  countries  where  the  average 
growth is low (i.e. Italy or Portugal), we also find low growth among small firms. Average 
employment  growth  over  the  2-year  period  from  2010  to  2012,  ranges  among  small 
firms from 16% in Latvia through 13% in Lithuania and Romania to 2% in Italy. Medium-
sized firms grow slower than small ones in all countries, and faster than large ones in all 
but  two  countries.  The  two  exceptions  are  Slovakian  and  Cypriot  large  firms  that 
outperform  medium-sized  ones,  with  positive  growth  in  the  case  of  Slovakia  and  more 
modest  decline  in  the  case  of  Cyprus.  The  percentage  point  differences  in  growth  rates 
between  small  and  medium-sized  companies  vary  largely  between  countries,  from  1 
percentage  point  observed  in  the  case  of  German  firms  and  12  percentage  points 
observed  in  the  case  of  Cypriot  firms.  Large  firms,  unsurprisingly,  show  the  weakest 
relative  growth  performance,  with  no  growth  on  average  for  the  20  country  weighted 
average  (Eur20).  As  we  go  up  in  size  classes,  we  observe  a  particularly  strong  drop  in 
the rate of growth in the case of Latvian (15-percentage point drop) as well as Norwegian 
and  Romanian  firms  (13-percentage  point  drop).  At  the  other  extreme,  German  large 
firms  grow  by  only  2  percentage  points  slower  than  small  firms,  Slovakian  and 
Luxembourgish  large  firms  grow  by  4  percentage  points  slower  than  small  ones.  There 
are still considerable differences across countries, with the strongest performance among 
Lithuanian,  Luxembourgish  and  German  large  firms  (4%  growth)  and  strongest  decline 
among Cypriot (-5%), Italian (-4%), French, Croatian and Portuguese large firms (-3%). 
We note that firms show at least a 3% growth (at or above the cross-European average) 
in all three size classes in three countries: Lithuania, Luxembourg and Germany. 

10 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
Figure 2 Average employment growth in the weighted sample by country & size class, 2012/2010 

Source: authors’ calculations using CIS2012 microdata  

Table 2 Correlation of country growth rates in the weighted sample across indicators & size classes 

Employment growth, 2012/2010 
Medium 

Large 

Average 

   Turnover growth, 2012/2010 
Large 

Medium 

Small 

Average 

Indicator 

Employment 
growth, 
2012/2010 

Turnover 
growth, 
2012/2010 

Size class  Small 
Small 
Medium 
Large 
Average 
Small 
Medium 
Large 
Average 

1 
0.727 
0.557 
0.968 
0.824 
0.920 
0.775 
0.862 

1 
0.671 
0.789 
0.420 
0.764 
0.633 
0.501 

1 
0.531 
0.429 
0.620 
0.714 
0.469 

1 
0.792 
0.949 
0.782 
0.850 

1 
0.851 
0.811 
   0.989 

1 
0.906 
0.910 

1 
0.850 

1 

Notes: Pearson correlation coefficients; N=21 

Looking at growth distribution in greater details, we  observe for the  weighted  European 
sample of 20 countries a 0% median growth. However, there is a considerable variation 
across  countries.  As  shown  in  panel  a)  of  Figure  3,  the  median  growth  is  higher  for 
Latvian  (7.4%),  Norwegian  (6.2%),  Estonian  (4%),  Lithuanian  and  Luxembourgish 
(3.7%),  Bulgarian  (3.4%)  and  German  (3.2%)  firms.  Spanish  firms  in  our  weighted 
sample  are  different  from  those  in  other  countries,  due  to  the  negative  median  growth 
(2.4%  decline).  Countries  show  an  even  higher  variation  when  it  comes  to  relative  top 
performance, if measured as the top 10% of the distribution (the black dots in panel  a) 
of  Figure  3  showing  the  90th  percentile,  also  the  ordering  principle  of  countries  in  the 
chart).  The  top  10%  fastest  growing  firms  of  Germany  achieved  at  least  20%  growth, 
hardly outperformed by firms from other larger EU Member States such as Spain, France, 
or  Italy.  They  fall  below  the  25%  growth  observed  for  the  20-country  sample,  which  is 
driven  mostly  by  the  strong  performance  of  Romania  (50%),  Latvia  (47%),  Norway 
(46%), Bulgaria and Lithuania (42%) and Sweden (36%).  There is also a high variation 
across  countries  in  terms  of  the  growth  observed  for  the  top  5%  of  firms,  which  is 
notably higher than the variation in the decline of the bottom 5%. Such figures, however, 

11 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
   
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
call  for  caution,  given  the  low  number  of  observations  behind  the  respective  values 
particularly in the case of the Baltic States or Cyprus. Interestingly, the variation in terms 
of  firm  growth  is  the  lowest  in  Germany  among  the  countries  observed,  so  a  relatively 
modest  high-growth  performance  is  coupled  with  a  strong  overall  performance  –  about 
75% of all German firms in our weighted sample show positive growth. 

It  is  also  remarkable  that  the  absolute  threshold  used  by  Eurostat,  the  10%  annual 
average  growth  average  growth  –  which  translates  to  a  21%  growth  for  the  two-year 
period 2010 to 2012 we were forced to consider given the CIS data constraints  (dashed 
orange line in panel a) of Figure 3) –, distinguishes a very different share of companies 
across countries. While it captures the top 10% of the Spanish firms, it selects somewhat 
less  in  the  case  of  German  firms,  but  as  much  as  a  quarter  or  more  of  Latvian, 
Romanian,  Norwegian  or  Lithuanian  firms.  The  high-growth  threshold  of  20%  annual 
average  growth  applied  by  the  OECD  EIP  translates  to  44%  overall  growth  in  our  case 
(dotted orange line in panel a) of Figure 3). This threshold proves to be very restrictive, 
as it captures less than 5% of the companies in the case of the largest EU Member States 
in  the  sample  –  Germany,  Spain,  France  and  Italy  –,  as  well  as  Belgium,  Portugal  and 
Slovenia.  

We  also  investigated  how  good  a  “predictor”  of  high-growth  performance  can  be  the 
more easily accessible average growth performance by country. As shown in panel b) of 
Figure 3, there is a strong positive association between employment change at the 90th 
percentile  and  average  employment  change  (r2=0.69).  However,  we  note  that  in  our 
case, Romania, Bulgaria, Cyprus, Slovakia and Spain exceed the expected rank in terms 
of high-growth, while Germany and France perform weaker than expected. 

Figure 3 The distribution of employment change by country and a comparison of high-growth (90th 
percentile) with average growth in the weighted sample (2012/2010) 

a)  Distribution of employment change by country 

b)  High- vs. Average employment growth 

Source:  authors’ calculations  using  CIS2012 microdata.  Notes  for panel  a):  Shaded  area  of box  plots  capture 
50% of the growth distribution, while 90% is captured within the whiskers. Black dot shows the 90th percentile, 
which  is  the  ordering  principle  for  countries  in  the  chart.  Dashed orange  line  shows  the  10%  annual  average 
growth threshold (21% overall); the orange dotted line shows the 20% (44% overall) growth threshold. Eur20 
refers to the overall distribution for the 20 countries in the sample. 

3.1.2  Turnover change 

The average turnover (or sales) growth of companies exceeds their employment growth 
in all countries and all size classes with the exception of Portuguese small firms (and the 
overall  average),  as  shown  in  Figure  4.  In  contrast  with  employment  change,  average 

12 

 
 
 
 
turnover  change  is  nearly  always  positive,  with  the  exception  of  Portuguese  and  large 
Cypriot  firms.  Average  turnover  growth  is  about  2.7  times  higher  than  average 
employment  growth,  for  the  overall  sample,  and  exceeds  20%  for  the  2  years  between 
2010  and  2012  in  six  countries,  including  Lithuania  (32%),  Estonia  (29%),  Norway 
(28%) as well as Latvia, Sweden and Bulgaria.  

As in the case of employment change, due to their prevalence, small firms are the main 
drivers of our observed overall average turnover change, although there is a significantly 
higher  correlation  across  the  various  size  classes  in  the  case  of  turnover  change  (right 
part  of  Table  2).  Small  firms  in  seven  out  of  the  twenty  countries  show  a  growth 
performance of at least 20%, and in a further eight countries above 10%.  Medium-sized 
firms  show  a  rather  strong  performance,  with  three  countries  out  of  the  twenty 
exceeding 20% growth in the class (Latvia, Lithuania, Norway) and a further 10 countries 
exceeding  10%  growth.  Large  companies  in  all  the  three  Baltic  states  grow  faster  than 
20%, and in a further 8 countries faster than 8%. Across all size classes, we observe the 
weakest performance in Portugal, Italy and Croatia.  

Figure 4 Turnover change (orange) and employment change (blue) in the weighted sample by 
country and size classes (2012/2010) 

Source: authors’ calculations using CIS2012 microdata  

Company  performance  in  terms  of  turnover  change  shows  an  even  more  skewed 
distribution  compared  to  what  we  observed  above  for  employment  change  (Figure  5). 
The  median  turnover  growth  is  4%  for  the  entire  weighted  sample,  which  varies  by 
country ranging from a 5% (Spain and Portugal) to 3% (Cyprus) decline to growth up to 
23%  and  22%  (for  Estonia  and  Lithuania,  respectively).  This  has  a  number  of 
implications on the relative and absolute thresholds distinguishing performance groups of 
firms. The absolute thresholds of 10% and 20% annual average growth (21% and 44% 
overall,  see  dashed  and  dotted  orange  lines  respectively  in  panel  a)  of  Figure  5) 
captures a significantly larger share of firms than in the case of employment growth. The 
10% annual average growth threshold used by Eurostat captures as much as about half 
of  the  Estonian,  Lithuanian  and  Norwegian  firms.  The  20%  annual  average  growth 
threshold used by the OECD EIP in the case of turnover captures at least 5% of the firms 
in  all  countries,  and,  apart  from  Spain,  Portugal,  Belgium,  Germany,  Italy  and  Croatia, 

13 

 
capture the top 10% of firms. Notably, the top 10% of firms in 11 of the countries in our 
sample grew faster than 50% over the two-year period. 

In  the  case  of  turnover  change,  average  growth  is  a  better  predictor  of  high  growth  if 
high-growth is measured in terms of minimum growth of the top 10% of firms (panel  b) 
of  Figure  5)).  Notable  outliers  are  Cyprus  that  exceeds,  as  well  as  Belgium,  Germany 
and France that fall behind expected high-growth performance. 

While the growth rates  obtained for each country  are different when measured in terms 
of employment or turnover, we observe a strong, positive correlation at the country level 
between  the  median,  90th  and  even  the  95th  percentiles  (r=0.79,  0.80  and  0.71, 
respectively). This calls for caution when setting the same absolute growth thresholds for 
employment as well as turnover. 

The  observed  difference  between  firms’  growth  performance  in  terms  of  turnover  and 
employment  change  is  in  line  with  past  literature  and  highlights  the  need  to  dedicate 
special attention to the two measures separately. 

Figure 5 The distribution of turnover change by country and a comparison of high-growth (90th 
percentile) with average growth in the weighted sample 

a)  Distribution of turnover change by country 

b)  High- vs. Average turnover growth 

Source:  authors’ calculations  using  CIS2012 microdata.  Notes  for panel  a):  Shaded  area  of box  plots  capture 
50% of the growth distribution, while 90% is captured within the whiskers. Black dot shows the 90th percentile, 
which  is  the  ordering  principle  for  countries  in  the  chart.  Dashed orange  line  shows  the  10%  annual  average 
growth threshold (21% overall); the orange dotted line shows the 20% (44% overall) growth threshold. Eur20 
refers to the overall distribution for the 20 countries in the sample. 

3.1.3  The growth of innovators and non-innovators 

The CIS2012 dataset makes it possible to study growth and innovation at the same time, 
in  a  cross-sectional  view.  Table  3  presents  country  average  growth  rates  measured  by 
employment  and  turnover  for  the  two  main  types  of  innovators  (product  and  process), 
and  highlights  the  difference  between  the  average  growth  observed  for  innovators  and 
non-innovators. For the 20 country weighted sample, we find that innovators grow faster 
than  non-innovators.  The  difference  is  about  3.1  percentage  points  in  the  case  of  both 
product and process innovators when growth is measured in terms of employment; and 
5.7  and  5.4  percentage  points  for  product  and  process  innovators  respectively,  when 
growth is measured in terms of turnover.  

There are a few apparent peculiarities at the level of countries in Table 3. First, that in 
many  countries,  process  innovators  appear  to  grow  on  average  faster  in  terms  of 
employment  than  product  innovators  (including  Member  States  such  as  Germany  or 
Sweden).  Such  comparisons  can  be  misleading  because  there  is  a  considerable  overlap 

14 

 
 
 
between product and process innovators (i.e., process innovators are often also product 
innovators).10  Second,  while  on  average  innovators  grow  faster  than  non-innovators,  a 
few countries show a reverse picture. Notable examples in both measures of change are 
Lithuanian  and  Norwegian  firms  with  no  product  innovation,  or  Latvian  firms  with  no 
process  innovations,  or,  in  terms  of  turnover  change,  Luxembourgish  firms  with  no 
process  innovation.  While  it  is  difficult  to  explain  this  trend,  it  is  important  to  highlight 
that in all these cases, the average growth performance of the innovator firms was at or 
above the overall European sample average.  

Table 3 Average employment and turnover change 2012/2010 by country and product- and 
process innovators (percent; weighted sample) 

Country  All firms 

Average Employment Change (%) 
Product Innovators 
No  Diff. 
Yes 
0.7 
4.2 
4.9 
2.2 
7.2 
9.4 
2.0 
2.2 
4.2 
2.3 
5.8 
8.1 
1.7 
4.7 
6.4 
3.2 
7.5 
10.7 
2.9 
-2.6 
0.3 
1.2 
3.7 
5.0 
2.0 
2.6 
4.6 
2.8 
5.3 
8.1 
3.3 
0.5 
3.8 
-4.9 
5.2  10.1 
8.5 
12.6 
4.0 
1.5 
13.4  11.9 
-3.0 
7.5  10.4 
3.1 
0.4 
3.5 
1.9 
8.9 
10.8 
3.4 
6.2 
9.6 
0.2 
3.1 
3.3 
3.7 
0.5 
4.2 
3.1 
2.1 
5.2 

BE 
BG 
CY 
CZ 
DE 
EE 
ES 
FR 
HR 
HU 
IT 
LT 
LU 
LV 
NO 
PT 
RO 
SE 
SI 
SK 
Eur20 
Source: authors’ calculations using CIS2012 microdata. 

Process Innovators 
Yes  No  Diff. 
2.4 
3.7 
6.1 
0.7 
7.4 
8.1 
2.0 
2.0 
4.0 
4.8 
5.1 
9.9 
2.5 
4.7 
7.2 
4.9 
6.7 
11.6 
2.9 
-2.8 
0.1 
1.3 
3.7 
5.0 
3.5 
2.2 
5.7 
3.6 
5.2 
8.9 
3.9 
0.3 
4.2 
1.2 
9.1 
10.3 
3.3 
8.8 
5.5 
-8.1 
5.4  13.6 
0.5 
9.9 
10.4 
4.1 
-0.3 
3.9 
0.2 
9.0 
9.2 
5.9 
5.9 
11.9 
-0.1 
3.2 
3.0 
1.7 
0.8 
2.5 
3.1 
2.2 
5.3 

4.4 
7.5 
2.6 
6.5 
5.3 
8.2 
-2.4 
4.0 
3.0 
5.7 
1.4 
9.3 
6.6 
12.2 
10.0 
1.4 
9.0 
7.2 
3.1 
1.0 
2.9 

Average Turnover Change (%) 
Product Innovators 
No  Diff. 
Yes 
2.3 
13.0  10.7 
7.5 
25.9  18.4 
-8.0 
10.0  18.0 
5.7 
7.2 
12.9 
3.9 
13.5 
9.6 
-0.7 
27.9  28.7 
5.6 
-2.6 
3.0 
4.2 
15.1  10.9 
4.7 
8.4 
3.7 
0.1 
11.6  11.4 
6.0 
3.4 
9.4 
-8.8 
24.2  33.0 
1.5 
15.7  14.2 
-2.0 
23.5  25.5 
-2.7 
25.4  28.2 
3.9 
-2.3 
1.6 
7.3 
22.1  14.7 
1.2 
22.5  21.3 
1.7 
9.4 
11.1 
5.7 
9.0 
14.7 
5.7 
6.5 
12.2 

Process Innovators 
Yes  No  Diff. 
2.9 
13.5  10.5 
5.7 
24.3  18.6 
4.9 
19.5  14.7 
7.6 
6.7 
14.3 
4.7 
14.4 
9.7 
-0.3 
28.3  28.6 
5.8 
-2.9 
2.9 
4.9 
15.7  10.8 
8.8 
11.4 
2.7 
4.1 
15.1  11.0 
6.0 
3.4 
9.4 
2.4 
33.6  31.2 
-7.1 
9.9  17.0 
-3.1 
22.6  25.7 
-4.7 
23.5  28.2 
7.4 
-4.1 
3.4 
8.4 
23.0  14.5 
4.6 
25.3  20.7 
0.8 
9.7 
10.4 
6.8 
8.9 
15.7 
5.4 
6.6 
12.0 

All firms 

11.4 
19.5 
16.2 
8.9 
11.0 
28.5 
-2.2 
11.9 
4.5 
11.5 
5.0 
31.6 
14.6 
25.2 
27.7 
-1.0 
15.1 
21.7 
9.9 
9.8 
7.8 

10 Process innovation is, in  general, understood as having a labor-saving effect, since often a key 
reason for companies to implement process innovation is to reduce costs by i.e. automation. At the 
same time, product innovations have the tendency to create  new business opportunities or opens 
new markets, resulting in a positive employment effect (see i.e. Pianta and Vivarelli, 2003). These 
firm-level effects, however,  may be  mitigated at  the country level if new products cannibalize old 
ones, or the effect may occur with a certain lag. 

15 

 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                           
3.2  Variables defining high-growth firms for the matrix 

With the aim to be comprehensive, we  constructed 30 measures to identify high-growth 
firms  taking  into  account,  and  further  broadening  the  scope  of  previous  empirical  work 
and  the  considerations  suggested  by  Delmar  (1997)  and  Delmar  et  al  (2003)  and 
Daunfeldt  et  al  (2014).  As  summarized  in  Table  4,  we  consider  both  the  number  of 
employees and turnover as indicators of growth, relative growth as well as a measure of 
growth  less  biased  by  size  (the  Birch  index).  Given  the  constraints  of  our  dataset,  we 
focus  on  growth  over  the  period  2010  to  2012.  This  is  restrictive  in  two  aspects.  First, 
accelerated growth may be sporadic events in the evolution of a firm, so in effect we can 
only  focus  on  growth  spurts  that  may  be  exceptions.  Second,  we  are  forced  to  depart 
from  the  3-year  observation  window  used  in  the  Eurostat  and  OECD  definitions  and 
consider only a 2-year period.11 We exclude mergers and acquisitions and study organic 
growth only, to avoid spurious values. We further consider, for the identification of high-
growth  firms,  both  absolute  (following  the  philosophy  of  the  Eurostat  and  OECD 
approaches,  but  introducing  a  broader  set  of  alternatives),  and  relative  thresholds  (a 
more  data-driven  method)  and  consider  potential  growth  differences  across  industries 
and size classes (considering the findings of Coad et al., 2014a). Admittedly, many of our 
definitions may be  overlapping, may be too  restrictive or too broad.  Considering such a 
large set of alternative definitions is in line with the explorative nature of our study, and 
our primary aim is to be able to draw more nuanced conclusions that may be informative 
for future studies of high-growth firms. 

Table 4 Alternatives considered for the definition of high-growth firms 

Element of definition  Alternatives considered 
Indicator of growth 
Measure of growth 
Growth period 
Growth process 
Identification 

number of employees; value of turnover 
Relative; Birch index 
2010-2012 
Organic only (excl. mergers and acquisitions) 
Distribution-based: top P%, where 𝑃 ∈ (1, 5, 10, 15); as well as absolute 
threshold-based: growth ≥ N%  𝑁 ∈ (10, 15, 21, 44, 100)  
all firms vs. growing firms (<=0 growth excluded), by size class;  
by industry 

Additional qualification 

The  30  definitions  we  tested  are  presented  in  the  Variable  and  Description  columns  of 
Table  5.  Ten  of  these  definitions  (hg1-hg10)  are  based  on  relative  sales  and 
employment  growth  applying  a  fixed  threshold  (i.e.,  10-100%  growth).  Another  four 
definitions  (hg11-hg14)  are  based  on  the  Birch  index,  which  aims  to  be  less  biased 
towards  small  firms  (see  discussion  in  section  2.1).  We  defined  the  Birch  index  both  in 
terms of employment and sales (although many use it only in employment context, see 
i.e.  Hölzl,  2009).  A  further  sixteen  definitions  (hg15-hg30)  are  based  on  taking  the  top 
N%  of  the  distribution  of  firms  by  3  size  classes,  in  terms  of  employment  and  sales 
growth as well as the Birch Index. We compute the various measures as follows:  

 
 
 

total employment growth is computed using the formula: EMPL2012/EMPL2010-1; 
sales growth is computed as TURN2012/TURN2010-1; 
the Birch index (defined in terms of employment as well as sales) is computed as: 
(EMPL2012-EMPL2010)*EMPL2012/EMPL2010 or (TURN2012-TURN2010)*TURN2012/TURN2010  
respectively. 

  When we consider the top N% of the distribution, we control for firm size, where 
we  distinguish  small,  medium  and  large  firms  using  50  and  250  employees  as 
thresholds.     

11 Accordingly, we re-compute relevant growth rates in the following way: annual average growth 
of 10% refers to 21%, 20% refers to 44% growth over the 2-year period. 

16 

 
                                           
Using  a  similar  methodology  as  described  in  the  preceding  section,  alongside  scores  for 
the  HGI  matrix,  we  also  compute  a  high-growth  vector.  For  each  firm,  we  compute  a 
total high-growth score [hgtot], which is a sum of the various hgi scores (in effect, equal 
to the number of times the firm meets the given criteria). 

17 

Table 5 Descriptive statistics of the variables defining high-growth firms in the weighted sample 

Variable  Description 

Min  Max  unweighted sample 
sd 
N 

mean 

skew.  kurt. 

   weighted sample 
   N 

mean 

sd 

skew.  kurt. 

1 
1 
1 
1 
1 
1 
1 
1 
1 
1 

0 
0 
0 
0 
0 
0 
0 
0 
0 
0 

Relative sales and employment growth, fixed thresholds 
total employment growth > 10% 
hg1 
total employment growth > 15% 
hg2 
total employment growth > 21% 
hg3 
total employment growth > 44% 
hg4 
total employment growth > 100% 
hg5 
sales growth >= 10% 
hg6 
sales growth >= 15% 
hg7 
sales growth >= 21% 
hg8 
sales growth >= 44% 
hg9 
hg10 
sales growth >= 100% 
Using the Birch Index (absolute x relative growth) 
Birch Index (empl) > 10% 
hg11 
Birch Index (empl) > 100% 
hg12 
Birch Index (sales) > 10% 
hg13 
hg14 
Birch Index (sales) > 100% 
Top of the distribution (Top N% in terms of employment and sales growth and Birch Index; by size class) 
hg15 
hg16 
hg17 
hg18 
hg19 
hg20 
hg21 
hg22 
hg23 
hg24 
hg25 
hg26 
hg27 
hg28 
hg29 
hg30 
hgtot 

Among top 5% relative empl. growth (by size class, growing firms) 
Among top 10% relative empl. growth (by size class, growing firms) 
Among top 15% relative empl. growth (by size class, growing firms) 
Among top 25% relative empl. growth (by size class, growing firms) 
Among top 5% relative sales growth (by size class, growing firms) 
Among top 10% relative sales growth (by size class, growing firms) 
Among top 15% relative sales growth (by size class, growing firms) 
Among top 25% relative sales growth (by size class, growing firms) 
Among top 5% in terms of Birch Index (empl), by size class, growing firms) 
Among top 10% in terms of Birch Index (empl), by size class, growing firms) 
Among top 15% in terms of Birch Index (empl), by size class, growing firms) 
Among top 25% in terms of Birch Index (empl), by size class, growing firms) 
Among top 5% in terms of Birch Index (sales), by size class, growing firms) 
Among top 10% in terms of Birch Index (sales), by size class, growing firms) 
Among top 15% in terms of Birch Index (sales), by size class, growing firms) 
Among top 25% in terms of Birch Index (sales), by size class, growing firms) 
Total HG scores by firm 

1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
30 

0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 

0 
0 
0 
0 

1 
1 
1 
1 

Source: authors’ calculations using CIS2012 microdata. 

18 

92,960 
92,960 
92,960 
92,960 
92,960 
92,960 
92,960 
92,960 
92,960 
92,960 

92,960 
92,960 
92,960 
92,960 

92,960 
92,960 
92,960 
92,960 
92,960 
92,960 
92,960 
92,960 
92,960 
92,960 
92,960 
92,960 
92,960 
92,960 
92,960 
92,960 
92,960 

0.27 
0.20 
0.14 
0.05 
0.01 
0.41 
0.33 
0.26 
0.11 
0.03 

0.46 
0.45 
0.59 
0.59 

0.02 
0.05 
0.07 
0.12 
0.03 
0.06 
0.09 
0.15 
0.02 
0.05 
0.07 
0.12 
0.03 
0.06 
0.09 
0.15 
5.06 

0.44 
0.40 
0.35 
0.22 
0.10 
0.49 
0.47 
0.44 
0.32 
0.16 

0.50 
0.50 
0.49 
0.49 

0.15 
0.21 
0.25 
0.32 
0.17 
0.23 
0.28 
0.35 
0.15 
0.21 
0.25 
0.32 
0.17 
0.23 
0.28 
0.35 

1.0 
1.5 
2.1 
4.1 
10.1 
0.4 
0.7 
1.1 
2.4 
5.9 

0.1 
0.2 
-0.3 
-0.3 

6.3 
4.3 
3.4 
2.4 
5.6 
3.8 
2.9 
2.0 
6.3 
4.3 
3.4 
2.4 
5.6 
3.8 
2.9 
2.0 

2.1 
3.2 
5.2 
17.5 
102.6 
1.1 
1.5 
2.2 
6.9 
35.3 

1.0 
1.0 
1.1 
1.1 

41.0 
19.6 
12.4 
6.7 
32.2 
15.1 
9.5 
5.0 
41.0 
19.6 
12.4 
6.7 
32.2 
15.1 
9.5 
5.0 

92,926 
92,926 
92,926 
92,926 
92,926 
92,926 
92,926 
92,926 
92,926 
92,926 

92,926 
92,926 
92,926 
92,926 

92,926 
92,926 
92,926 
92,926 
92,926 
92,926 
92,926 
92,926 
92,926 
92,926 
92,926 
92,926 
92,926 
92,926 
92,926 
92,926 
92,926 

0.26 
0.19 
0.12 
0.04 
0.01 
0.39 
0.31 
0.23 
0.10 
0.02 

0.46 
0.45 
0.58 
0.58 

0.01 
0.03 
0.05 
0.09 
0.02 
0.04 
0.07 
0.12 
0.01 
0.03 
0.04 
0.08 
0.02 
0.04 
0.07 
0.12 
4.57 

0.44 
0.39 
0.33 
0.20 
0.08 
0.49 
0.46 
0.42 
0.29 
0.14 

0.50 
0.50 
0.49 
0.49 

0.12 
0.17 
0.22 
0.29 
0.14 
0.20 
0.25 
0.33 
0.11 
0.16 
0.21 
0.27 
0.14 
0.20 
0.25 
0.32 

1.1 
1.6 
2.3 
4.7 
12.4 
0.5 
0.8 
1.3 
2.7 
6.9 

0.2 
0.2 
-0.3 
-0.3 

8.0 
5.4 
4.1 
2.9 
6.8 
4.5 
3.5 
2.3 
8.8 
5.8 
4.4 
3.0 
6.8 
4.5 
3.4 
2.3 

2.2 
3.6 
6.4 
23.0 
154.5 
1.2 
1.7 
2.6 
8.6 
48.7 

1.0 
1.0 
1.1 
1.1 

65.3 
30.1 
17.8 
9.3 
47.8 
21.4 
13.3 
6.5 
78.4 
34.1 
20.6 
10.3 
47.6 
21.6 
12.7 
6.5 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3.3  Variables defining innovation for the matrix 

The matrix captures various types of innovation and their degree of novelty (i.e., whether it is new 
to  the  firm,  market  or  to  the  world).  Furthermore,  it  offers  “hard  data”  on  what  firms  spend  on 
innovation  –  alongside  R&D,  firms  also  report  other  innovation  expenditure.  This  is  particularly 
important  for  the  service  sector,  as  R&D  expenditure  is  typically  concentrated  to  manufacturing 
industries.  

In the same vein as in the case of our variables defining growth thresholds, our various measures of 
innovativeness  considers  multiple  innovation  profiles  for  firms.  Our  key  consideration  for  defining 
variables were to start with a broad definition which flags a firm innovative if it introduced any kind 
of technological (product or process) or non-technological (organizational or marketing) innovation, 
which is considered new to the firm. These can be further restricted by selecting innovators by: 

 

type of innovations – successful implementation of product, process or a combination of the 
four types; 

  degree  of  novelty  –  to  take  into  account  whether  a  new  technological  innovation  (for  which 
data  exists)  is  new  to  the  firm,  to  the  market,  or  to  the  world.  We  further  test  how  the 
information provided on the share of sales associated with certain degrees of novelty further 
sharpens  the  definition.  In  sum,  we  propose  a  set  of  indicators  ranging  from  diffusion  of 
innovation to radical innovations. 
the  innovation  process  (whether  the  firm  performed  in-house  R&D,  and  if  so,  whether  it  is 
among the top R&D spenders in certain aspects (controlling for differences across industries); 

 

The  50  indicators  described  in  Table  6  take  the  value  of  1  for  each  firm  which  meets  the  given 
criteria.  For  the  expenditure  variables,  we  apply  both  absolute  thresholds  (i.e.,  R&D  intensity  at 
least 10%) as well as relative ones (i.e., within the top n % in terms of R&D expenditure or overall 
innovation expenditure). We also make use of information on how important innovative products are 
in the total sales of a given company. 

As we use the CIS data, we cannot include other often used output measures, such as those relating 
to intellectual property (i.e. patents).  

We  include  among  the  variables  of  innovation  also  variables  based  on  R&D  or  innovation 
expenditure,  or  other  measures  such  as  knowledge-intensity.  While  we  do  not  consider  this  as  a 
“core”  measure  of  innovativeness,  the  main  purpose  is  to  offer  a  contextual  understanding  of 
innovative  performance.  The  CIS  data  is  rather  exceptional  in  providing  information  about 
innovation outcomes, R&D spending is more widely available from firm-level financial data. 

19 

 
Table 6 Descriptive statistics of variables defining innovativeness of firms in the weighted sample  

Var. 

Description 

Min Max 

unweighted sample 
N  mean  sd  skew. kurt. 

weighted sample 
N  mean  sd  skew. kurt. 

product or process innovation is new to the market 
Prod. Inn. is new to the market and first in the country  
Radical: New to market prod/proc. Inn is a World or Eur. 1st  

product innovation (good or service) 
process innovation (any) 
product or process innovation (any) 
organizational or marketing innovation (any) 
organizational or marketing innovation only (not prod/proc) 
any kind of inn. (prod/proc/organizational or marketing) 

0  1  92,917 0.27  0.44  1.1  2.1    92,917 0.24 0.43  1.2  2.5 
0  1  92,896 0.27  0.44  1.0  2.1    92,896 0.23 0.42  1.3  2.7 
0  1  92,924 0.37  0.48  0.5  1.3    92,924 0.33 0.47  0.7  1.5 
0  1  91,879 0.41  0.49  0.4  1.1    91,879 0.39 0.49  0.4  1.2 
0  1  92,644 0.15  0.36  2.0  4.9    92,644 0.16 0.37  1.8  4.4 
0  1  92,643 0.52  0.50  -0.1  1.0    92,643 0.49 0.50  0.0  1.0 

By main types 
inn1 
inn2 
inn3 
inn4 
inn5 
inn6 
By novelty 
0  1  92,960 0.18 0.38  1.7  3.9    92,926 0.14 0.35  2.1  5.3 
inn7 
0  1  92,960 0.09 0.28  2.9  9.5    92,926 0.07 0.26  3.3  11.7 
inn8 
0  1  92,960 0.05 0.22  4.0  16.8   92,926 0.05 0.22  4.1  18.0 
inn9 
0  1  92,960 0.05 0.22  4.2  18.6   92,926 0.05 0.21  4.4  20.2 
inn10  =inn9, and the company is an exporter  
0  1  92,960 0.19 0.39  1.6  3.5    92,926 0.17 0.38  1.7  3.9 
inn11  New to firm product or process innovation 
Innovation new to firm/market represent at least 90% of sales  0  1  92,960 0.02 0.15  6.2  39.2   92,926 0.01 0.11  8.5  73.5 
inn12 
Innovation new to firm/market represent at least 75% of sales  0  1  92,960 0.03 0.18  5.2  27.6   92,926 0.02 0.15  6.5  42.7 
inn13 
Innovation new to firm/market represent at least 50% of sales  0  1  92,960 0.05 0.23  3.9  16.5   92,926 0.04 0.20  4.5  21.5 
inn14 
Innovation new to firm/market represent at least 25% of sales  0  1  92,960 0.09 0.29  2.8  8.6    92,926 0.08 0.27  3.0  10.3 
inn15 
0  1  92,960 0.01 0.10  9.4  88.7   92,926 0.01 0.08 12.9 166.7 
inn16  New to market Innovation represent at least 75% of sales 
0  1  92,960 0.02 0.14  6.6  44.9   92,926 0.01 0.12  8.3  70.5 
inn17  New to market Innovation represent at least 50% of sales 
0  1  92,960 0.04 0.20  4.6  22.3   92,926 0.03 0.17  5.5  31.1 
inn18  New to market Innovation represent at least 25% of sales 
inn19  At least 5% of turnover from world-first product innovations 
0  1  92,960 0.01 0.09 10.8 118.4   92,926 0.01 0.12  8.4  72.0 
inn20  At least 10% of turnover from world-first product innovations  0  1  92,960 0.01 0.07 13.7 188.1   92,926 0.01 0.09 11.0 121.9 
inn21  At least 25% of turnover from world-first product innovations  0  1  92,960 0.00 0.05 21.9 479.7   92,926 0.00 0.06 18.0 326.4 
By the innovation process: R&D performance, innovation expenditures    
0  1  92,781 0.23 0.42  1.3  2.6    92,781 0.17 0.38  1.8  4.1 
inn22  Perform in-house R&D 
0  1  92,799 0.21 0.40  1.5  3.1    92,799 0.15 0.36  1.9  4.7 
inn23  Perform in-house R&D & product or process innovator 
0  1  83,302 0.15 0.35  2.0  5.0    83,302 0.07 0.26  3.3  11.7 
inn24  Continuously in-house R&D performer with perm. R&D staff 
0  1  90,611 0.10 0.30  2.7  8.1    90,611 0.05 0.22  4.1  17.6 
inn25  Among top 10% absolute R&D spender (all firms, 0's incl.) 
0  1  90,611 0.02 0.15  6.6  43.9   90,611 0.01 0.09 11.4 130.0 
inn26  Among top 10% absolute R&D spender (all firms, 0's excl.) 
0  1  90,611 0.41 0.49  0.4  1.1    90,611 0.45 0.50  0.2  1.0 
inn27  Among top 10% absolute R&D spender (by nace, 0's incl.) 
0  1  90,611 0.02 0.16  6.1  38.1   90,611 0.01 0.10  9.4  89.8 
inn28  Among top 10% absolute R&D spender (by nace, excl. 0's) 
0  1  90,611 0.22 0.41  1.4  2.9    90,611 0.10 0.30  2.7  8.4 
inn29  Among top 10% absolute R&D spender (by country, 0's incl.) 
0  1  90,611 0.02 0.15  6.5  43.7   90,611 0.01 0.08 12.8 165.4 
inn30  Among top 10% absolute R&D spender (by country, excl. 0's) 
0  1  90,611 0.10 0.30  2.7  8.1    90,611 0.07 0.25  3.5  13.4 
inn31  Among top 10% in terms of R&D intensity (all firms, 0's incl.) 
0  1  90,611 0.02 0.15  6.6  43.9   90,611 0.01 0.11  8.6  75.0 
inn32  Among top 10% in terms of R&D intensity (all firms, 0's excl.) 
0  1  90,611 0.41 0.49  0.4  1.1    90,611 0.46 0.50  0.2  1.0 
inn33  Among top 10% in terms of R&D intensity (by nace, 0's incl.) 
0  1  90,611 0.03 0.16  6.1  37.7   90,611 0.02 0.13  7.6  59.5 
inn34  Among top 10% in terms of R&D intensity (by nace, excl. 0's) 
0  1  90,611 0.22 0.41  1.4  2.9    90,611 0.11 0.32  2.4  6.9 
inn35  Among top 10% in terms of R&D int. (by country, 0's incl.) 
0  1  90,611 0.02 0.15  6.5  43.7   90,611 0.02 0.12  7.9  62.8 
inn36  Among top 10% in terms of R&D int. (by country, excl. 0's) 
0  1  91,790 0.10 0.30  2.7  8.1    91,790 0.08 0.28  3.0  10.0 
inn37  Among top 10% overall inn spending int. (all firms, 0's incl.) 
0  1  91,790 0.03 0.18  5.1  26.7   91,790 0.03 0.16  5.8  35.1 
inn38  Among top 10% overall inn spending int. (all firms, 0's excl.) 
inn39  Among top 10% overall inn spending int. (by nace 2-d, 0's incl)  0  1  91,790 0.12 0.33  2.3  6.3    91,790 0.13 0.34  2.2  5.7 
inn40  Among top 10% overall inn spending int. (by nace 2-d, 0's excl)  0  1  91,790 0.04 0.19  4.8  24.1   91,790 0.03 0.18  5.2  28.0 
inn41  Among top 10% overall inn. spending int. (by country, 0's incl)  0  1  91,790 0.10 0.30  2.7  8.1    91,790 0.08 0.27  3.1  10.8 
inn42  Among top 10% overall inn. spending int. (by country, 0's excl)  0  1  91,790 0.03 0.18  5.1  26.6   91,790 0.03 0.17  5.4  30.4 
0  1  92,960 0.02 0.13  7.3  54.5   92,926 0.01 0.10  9.6  94.1 
inn43  R&D intensity (RD/turnover) is 15% or more (YIC definition) 
0  1  92,605 0.11 0.32  2.4  7.0    92,605 0.13 0.33  2.2  6.0 
inn44  R&D or machinery purchaser, without in-house R&D 
0  1  92,669 0.10 0.31  2.6  7.7    92,669 0.11 0.32  2.4  6.9 
inn45  R&D or machinery purchaser, not performer, prod/proc inn’r 
inn46  Non-R&D innovator 
0  1  92,798 0.16 0.37  1.8  4.4    92,798 0.18 0.38  1.7  3.9 
By knowledge-intensity 
inn47  Knowledge-intensive product or process innovator 
inn48  Knowledge-intensive any kind of innovator 
inn49  =inn48, with new to market/firm innovation >= 75% of sales 
inn50  =inn48, with new to market innovation >= 75% of sales 
Inntot  Total inn scores by firm 

0  1  92,960 0.10 0.29  2.7  8.5    92,926 0.06 0.25  3.5  13.6 
0  1  92,960 0.13 0.34  2.2  5.9    92,926 0.09 0.29  2.8  8.9 
0  1  92,960 0.01 0.10  9.7  94.3   92,926 0.01 0.07 13.5 182.0 
0  1  92,960 0.00 0.06 16.4 270.6   92,926 0.00 0.04 24.0 578.5 
0  43  92,960 6.00 6.35  1.5  5.2    92,926 5.18 5.64  1.6  6.1 

Source: authors’ calculations using CIS2012 microdata. 

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4  Results 

This  section  presents  results  on  firms  within  the  population  of  92,960  CIS  microdata  firms 
(representing  almost  450,000  firms)  that  meet  the  high-growth  and  innovative  criteria  as  an 
outcome of the HGI Matrix. We noticed that about 40% of firms in the  pooled CIS 2012 sample do 
not  meet  any  of  the  30  definitions  of  high-growth,  and  slightly  less  than  50%  of  the  firms  do  not 
meet any of the 50 definitions for different degrees of innovativeness. From the patterns shown by 
the rest of companies that may be considered either as high-growth, or innovative, we can draw a 
number  of  conclusions  on how firms perform in terms  of the various  measures  of high-growth and 
innovation making use of the descriptive statistics on the individual indicators presented in  Table 5 
and  Table  6.  We  next  analyse  the  European  share  of  companies  identified  as  high-growth  and 
innovative using the definition combinations reported in a matrix format in Figure Error! No text of 
specified  style  in  document..  As  the  many  dimensions  make  it  difficult  to  interpret  the  results  for 
policy purposes, in a subsequent step, we study the association between the variables with the aim 
to  reduce  dimensionality  by  eventually  aggregating  a  selected  set  of  variables.  Using  these 
measures,  we  focus  our  analysis  on  the  performance  of  countries  as  well  as  1-  and  2-digit  NACE 
sectors in terms of high-growth and innovativeness. 

4.1  High-growth firms and innovative firms 

From  among  the  30  potential  variables  we  tested  for  identifying  high  growth,  we  see  a  large 
variance  in  terms  of  the  number  of  firms  that  meet  a  certain  definition.  As  shown  by  the  mean 
scores reported in Table 5 – and in a graphical way, in Figure 6 – these can range from 0.6% (in 
the case of hg5) to 57.8% (hg13 or hg14). 

The color-coding in Figure 6 help identify patterns in the various set of high-growth definitions. The 
Birch  indices  applying  fixed  thresholds  of  10-100%  growth  (yellow  bars,  variables  hg11-13)  flags 
about  half  of  the  firms  (44.7-57.8%)  as  “high-growth”,  which  proves  to  be  an  excessively  broad 
definition.  There  remains  to  be  a  considerable  variation  among  the  share  of  high-growth  firms 
selected by the other three types of definitions, mostly due to those applying the absolute threshold 
(orange bars). While these definitions encompass the Eurostat and OECD definitions that range from 
4%  to  23.3%  of  firms,  if  the  threshold  for  employment  growth  over  the  2  years  is  set  at  100% 
(hg5), only 0.6% of firms can be considered as high-growth ones, whereas a sales growth threshold 
of 10% (hg6) of the 2 years flags 38.7% of firms as high-growth.  

The  relative  definitions  (top  N%  of  the  distribution,  gray  bars)  were  selected  by  restricting  the 
measure to growing firms only, but take the top of the distribution by size class, resulting in a share 
ranking between 1.5% (hg15) and 12% (hg22). This range would double if all, not only the growing 
firms were included in the definitions. 

The  set  of  definitions  shows  that  there  are  considerable  differences  between  turnover  and 
employment-based definitions in the share of firms flagged as high-growth ones, but more overlap 
between  the  definitions  applying  an  absolute  fixed  growth  threshold  and  those  applying  a  relative 
one. The association between the various definitions will be further discussed in section 4.3.1.  

21 

 
Figure 6 The share of European companies in the weighted sample meeting a certain high-growth definition 

Source: Authors’ calculations based on CIS2012 20-country microdata (Table 5) 

Figure 7 offers a graphic overview of the average firm performance in the 50 innovation variables 
presented  in  Table  6.  The  color  coding  distinguishes  the  main  set  of  variables  which  distinguish 
innovation by type, degree of novelty, top R&D performance and innovation expenditure, as well as 
knowledge-intensity. In a rather clear pattern, the variables capturing the main types of innovation 
(product,  process,  organizational  or  marketing  –  blue  bars)  select  the  largest  share  of  firms  in  the 
weighted sample of 20 European countries. In fact, 51.9% of the sample firms qualify as innovative 
if  the  criterion  is  having  successfully  introduced  any  type  of  innovation  (inn6).12  This  baseline 
definition,  unsurprisingly,  lies  at  the  upper  extreme  of  the  distribution,  all  other  definitions  used 
selected  a  significantly  smaller  share  of  firms.  Technological  product  or  process  innovators  (inn3) 
represent  only  36.9%  of  firms  in  the  sample.  Adding  as  a  further  qualification  the  novelty 
requirement that technological innovations should be new at least to the firm (inn11) nearly halves 
the set of flagged innovators to 19.2%. This is not much different from the share of firms with new-
to-market product or process innovation (inn7), 17.5%. The orange bars in Figure 7 show that by 
adjusting the expected degree of novelty for an innovation, such as a requirement that at least 5% 
of a firm’s turnover should come from technological innovations that are new to the world (inn19), 
the share of – admittedly, highly – innovative firms drops below 1% of the sample. 

12 This definition is somewhat more restrictive compared to the Eurostat definition of “innovative enterprises”, 
which also considers firms with ongoing or abandoned innovative activities. 

22 

 
 
                                           
Figure 7 The share of European companies in the weighted sample meeting an innovativeness definition  

Source: authors’ calculation based on CIS2012 microdata (Table 6). 

Innovation  is  usually  associated  with  R&D  activities,  but  there  is  a  considerable  share  of  non-R&D 
innovators:  16.2%  of  sample  firms  introduced  an  innovation  but  did  not  perform  in-house  R&D 
(inn46).  23.4%  of  sample  firms  performed  in-house  R&D  (regardless  of  successful  innovation 
outcomes;  inn22).  As  the  gray  bars  of  Figure  7  show,  we  find  rather  different  performances  in 
terms  of  R&D  expenditure  and  intensity.  As  R&D  tends  to  concentrate  to  high-tech,  manufacturing 
industries, it is not surprising that when we select the top 10% R&D spenders by industry (inn33), 
about  41%  of  firms  are  selected,  rather  than  the  10%,  as  expected  if  we  select  all  firms  (inn31). 
While  R&D  spending  characterizes  manufacturing  industries,  a  high  share  of  tertiary  graduates 
(“knowledge intensity”) characterizes service sectors. Nevertheless, we find a more restricted set of 
firms  that  qualify  as  knowledge-intensive  innovators  (of  any  type):  only  about  12.9%  (inn48).  An 
even smaller, 9.6% are knowledge-intensive technological innovators. 

As highlighted earlier, the point of including among the variables R&D or innovation expenditure, or 
other  measures  such  as  knowledge-intensity,  was  to  offer  a  more  contextual  understanding  of 
innovative  performance.  The  share  reported  above  suggest  that  these  variables  show  little 
similarities  with  the  “core”  variables  based  on  innovation  type  –  but  we  report  more  details  on 
associations between the variables in section 4.3 below. 

4.2  High-growth and innovative firms  

In this step, we analyse the differences in the share of firms that meet the various combinations of 
the high-growth and innovation definitions discussed  above separately. In other words, if we found 

23 

 
 
that  51.9%  of  firms  are  innovative  if  the  definition  allows  any  type  of  innovation,  how  does  this 
share drop if we select  only the high growth firms? As  described in section 3 above,  since there is 
uncertainty with regards to the definition of both dimensions, we are confronted with a matrix where 
each cell represents a different combination of high-growth and innovativeness.  

The resulting matrix with the share of enterprises in the 20-country European CIS2012 dataset that 
may, in a certain aspect, be flagged as high-growth and innovative, are shown in Figure 4. As the 
range  of  the  definitions  considered  for  the  two  dimensions  offered  a  rather  broad  variance,  it  is  of 
little surprise that the share of HGIEs range from 0.1 to 31% of sample firms. The matrix serves as 
a  reference  for  understanding  the  differences  in  scale  across  the  various  definitions.  We  limit  our 
discussion to a few selected definitions.  

For  instance,  we  notice  that  about  22%  of  European  firms  grow  at  least  at  10%  (5%  annual 
average)  in  terms  of  turnover  and  have  introduced  a  technological  or  non-technological  innovation 
(hg6 & inn6). This share falls to 16% in the case of technological product or process innovators (hg6 
&  inn3),  and  further  to  12%  if  only  product  innovators  are  considered  to  be  “innovative”  (hg6  & 
inn1).  Moving  vertically,  rather  than  horizontally  in  the  matrix  and  fixing  the  innovation  variable 
shows that increasing the annual turnover growth threshold to 21% (10% annual) reduces the share 
of HGIEs to 14% (hg8 & inn6), and further increasing the sales growth threshold to 100% reduces 
HGIEs to 1% (hg10 & inn6).  

About  7%  of  European  firms  are  HGIEs  following  the  definition  in  accordance  with  the  Eurostat 
definition  of  high-growth  and  any  type  of  innovation  for  innovativeness  (hg3  &  inn6).  The  HGIE 
share  can  double  to  14%  in  case  a  lower  growth  threshold  is  applied  (10%  for  the  2  years,  5% 
annual average; hg1 & inn6). The share can drop from 7 to 3% in case the OECD EIP’s 20% (44% 
over the two years) employment threshold is applied (hg4 & inn6), and further to less than 0.5% if 
a  100%  threshold  is  applied  for  employment  growth  over  the  2  years  (hg5  &  inn6).  Applying,  in 
contrast, the OECD EIP’s threshold using turnover change as the growth measurement and keeping 
the introduction of any type innovations as a condition for innovativeness results in flagging 6% of 
firms  as  HGIEs  (hg9  &  inn6).  Recalling  that  the  difference  between  hg3  and  hg9  in  terms  of  the 
firms  covered  is  about  2.5%,  the  observed  1%  difference  between  the  hg3&inn6  and  hg9&inn6 
suggests  that  there  are  more  innovative  firms  among  the  high-growth  firms  selected  by  the  OECD 
EIP’s 20% turnover-based hg9 definition. 

Restricting innovativeness by the degree of novelty, in general, reduces the share of HGIEs to about 
two-thirds  if  we  consider  product  or  process  innovator  firms  (irrespective  of  the  growth  definition) 
that  report  that  their  innovation  is  new  to  the  firm.  Only  about  22%  of  the  product  and  process 
innovators report that this innovation is new to the world.   

HGIEs appear to have  a portfolio of, rather than one single, innovative products.  We find that less 
than 0.5% of firms in the sample report that at least 25% of their sales originates from new to the 
market innovations, and less than 0.5% of firms in the sample report that such a product represents 
5% of turnover. 

In terms of R&D activity, we find that HGIEs perform above average in R&D spending. Nearly half of 
high-growth firms with any kind of innovation perform in-house R&D (inn22); about a quarter of the 
rest purchase R&D from outside the firm. At the same time, non-R&D innovators (inn46) represent 
about  0.1  to  10%  of  high-growth  firms,  depending  on  the  high-growth  definition.  Introducing  R&D 
intensity  thresholds,  such  as  a  minimum  of  15%  related  to  turnover13  (inn43)  proves  to  be  rather 
strict:  we  notice  that  at  most  1%  of  the  companies  are  flagged  as  HGIEs,  depending  on  the  high-
growth  definition  –  for  instance,  the  Eurostat  and  OECD  high-growth  thresholds  (applying  any 
growth measure) renders less than 0.5% of the companies HGIEs. 

It is fair to conclude that the share of HGIEs in Europe are highly sensitive to the definitions applied. 
Much  of  the  observed  sensitivity  is  due  to  the  uncertainty  in  the  growth  threshold  used  for  the 
absolute measures, rather than for the relative (top of growth distribution) measures. As shown by 
Table  7,  the  differences  between  the  turnover  and  employment-based  measures  tends  to  be 
significantly  higher  for  the  absolute  measures  than  for  the  relative  ones  (5.2  vs.  1.1%  for  the  set 

13  This  is  one  of  the  elements  of  the  “young,  innovative  company  (YIC)”  definition  applied  in  various  funding 
instruments across Europe. 

24 

                                           
selected measures, or 1.4% in the case of the Birch indices), and the difference only decreases if we 
apply a high growth threshold (100%).  

Uncertainty in the measurement  of innovation is a further source of  sensitivity. The main selection 
criteria are the inclusion of certain types of innovation (such as technological or non-technological), 
or the implementation of a degree of novelty threshold reduces the share of HGIEs regardless of the 
high-growth measurement.  

Table 7 Difference in the share of firms identified as HGIEs due to employment- vs. turnover-based measures 
of high-growth if any type of innovation is considered 

Absolute  Growth threshold 
(relevant variables) 
Employment 
Turnover 
Difference 

Relative 

Top … % of growth distribution 
(relevant variables) 
Employment 
Turnover 
Difference 
Top … % of growth distribution 
(relevant variables) 

  Birch index (employment) 
  Birch index (turnover) 
  Difference 

10% 
(hg1, hg6) 
14.3% 
21.8% 
7.5% 

5% 
(hg15, hg19) 
1.2% 
1.6% 
0.4% 
5% 
(hg23, hg27) 
1.2% 
1.8% 
0.6% 

15% 
(hg2, hg7) 
10.6% 
17.7% 
7.2% 

10% 
(hg16, hg20) 
2.3% 
3.2% 
0.8% 
10% 
(hg24, hg28) 
2.5% 
3.5% 
1.1% 

21% 
(hg3, hg8) 
7.4% 
14.0% 
6.6% 

15% 
(hg17, hg21) 
3.6% 
4.8% 
1.2% 
15% 
(hg25, hg29) 
3.8% 
5.3% 
1.5% 

44% 
(hg4, hg9) 
2.6% 
6.1% 
3.5% 

25% 
(hg18, hg22) 
6.2% 
8.0% 
1.8% 
25% 
(hg26, hg30) 
6.3% 
8.7% 
2.4% 

100% 
(hg5, hg10) 
0.5% 
1.4% 
1.0% 

Average 

7.1% 
12.2% 
5.2% 

Average 

3.3% 
4.4% 
1.1% 
Average 

3.4% 
4.8% 
1.4% 

Note: the shares were obtained using any type of innovation (inn6) in combination with the high-growth measures reported 

in the table. 

25 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure Error! No text of specified style in document. Heat-map of the share of high-growth and innovative enterprises in Europe 

Source: authors’ calculations based on CIS2012 microdata; N=92,926. The graph presents the share of HGIEs according to the different definition combinations described above 
in the weighted, pooled European data.

26 

inn1inn2inn3inn4inn5inn6inn7inn8inn9inn10inn11inn12inn13inn14inn15inn16inn17inn18inn19inn20inn21inn22inn23inn24inn25inn26inn27inn28inn29inn30inn31inn32inn33inn34inn35inn36inn37inn38inn39inn40inn41inn42inn43inn44inn45inn46inn47inn48inn49inn50Prod.Proc.Pd/PcOrg/MktAnynew2mkcntry1stworld1stnew2frmRDi15%+No inRDNonR&Dhg17%8%10%11%4%14%5%3%2%2%5%1%1%2%3%0%1%1%0%0%0%7%6%4%3%1%10%1%7%1%3%1%11%1%7%1%3%1%4%1%3%1%1%3%3%4%3%4%0%0%hg25%6%7%8%3%11%4%2%1%1%4%1%1%1%2%0%1%1%0%0%0%5%4%3%2%0%8%0%5%0%2%1%8%1%6%1%2%1%3%1%2%1%1%2%2%3%2%3%0%0%hg34%4%5%6%2%7%3%1%1%1%3%0%1%1%2%0%0%1%0%0%0%3%3%2%1%0%5%0%4%0%2%0%6%0%4%0%2%1%2%1%2%1%0%2%2%2%2%2%0%0%hg41%1%2%2%1%3%1%0%0%0%1%0%0%0%1%0%0%0%0%0%0%1%1%1%0%0%2%0%2%0%1%0%2%0%2%0%1%0%1%0%1%0%0%1%1%1%1%1%0%0%hg50%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%c.c.0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%c.hg612%12%16%17%6%22%8%4%3%2%8%1%1%3%5%1%1%2%0%0%0%11%9%6%5%1%15%1%10%1%4%1%15%1%10%1%5%2%5%2%5%2%1%5%5%7%4%5%0%0%hg79%10%13%14%5%18%6%3%2%2%7%1%1%2%4%0%1%2%0%0%0%9%8%5%4%1%12%1%8%1%4%1%12%1%8%1%4%1%4%1%4%1%1%4%4%5%3%5%0%0%hg87%8%10%11%4%14%5%3%2%2%5%1%1%2%3%0%1%1%0%0%0%7%6%4%3%1%9%1%7%1%3%1%9%1%7%1%3%1%3%1%3%1%1%3%3%4%3%4%0%0%hg93%3%4%5%2%6%2%1%1%1%2%0%0%1%1%0%0%1%0%0%0%3%2%2%1%0%4%0%3%0%1%0%4%0%3%0%1%0%1%0%1%0%0%1%1%2%1%2%0%0%hg101%1%1%1%0%1%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%1%1%0%0%0%1%0%1%0%0%0%1%0%1%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%hg1113%14%18%20%7%25%9%5%3%3%10%1%2%3%5%1%1%2%0%0%0%12%11%7%5%1%18%1%11%1%5%1%18%1%11%1%5%2%6%2%5%2%1%6%5%8%5%7%1%0%hg1213%13%18%19%7%25%9%5%3%3%9%1%2%3%5%1%1%2%0%0%0%12%11%6%5%1%18%1%11%1%5%1%17%1%11%1%5%2%6%2%5%2%1%6%5%8%5%7%1%0%hg1317%17%23%24%9%31%11%6%4%3%12%1%2%3%6%1%1%3%1%0%0%15%13%8%6%1%22%2%13%1%6%1%22%1%13%1%6%2%7%2%6%2%1%7%7%10%6%8%1%0%hg1417%17%23%24%9%31%11%6%4%3%12%1%2%3%6%1%1%3%1%0%0%15%13%8%6%1%22%2%13%1%6%1%22%1%13%1%6%2%7%2%6%2%1%7%7%10%6%8%1%0%hg151%1%1%1%0%1%0%0%0%0%0%0%0%0%0%0%0%0%0%0%c.0%0%0%0%0%1%0%1%0%0%0%1%0%1%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%hg161%1%2%2%1%2%1%0%0%0%1%0%0%0%0%0%0%0%0%0%0%1%1%1%0%0%2%0%2%0%0%0%2%0%2%0%0%0%1%0%1%0%0%1%1%1%0%1%0%0%hg172%2%2%3%1%4%1%1%0%0%1%0%0%0%1%0%0%0%0%0%0%2%1%1%1%0%3%0%2%0%1%0%3%0%2%0%1%0%1%0%1%0%0%1%1%1%1%1%0%0%hg183%3%4%5%2%6%2%1%1%1%2%0%0%1%1%0%0%1%0%0%0%3%2%2%1%0%5%0%3%0%1%0%5%0%4%0%1%1%2%1%1%1%0%1%1%2%1%2%0%0%hg191%1%1%1%0%2%1%0%0%0%1%0%0%0%0%0%0%0%0%0%0%1%1%0%0%0%1%0%1%0%0%0%1%0%1%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%hg202%2%2%3%1%3%1%1%0%0%1%0%0%0%1%0%0%0%0%0%0%1%1%1%1%0%2%0%2%0%1%0%2%0%2%0%1%0%1%0%1%0%0%1%1%1%1%1%0%0%hg213%3%3%4%1%5%2%1%1%1%2%0%0%1%1%0%0%1%0%0%0%2%2%1%1%0%3%0%3%0%1%0%3%0%3%0%1%0%1%0%1%0%0%1%1%1%1%1%0%0%hg224%4%6%6%2%8%3%2%1%1%3%0%1%1%2%0%0%1%0%0%0%4%3%2%2%0%5%0%4%0%2%0%5%0%4%0%2%1%2%1%2%1%0%2%2%2%2%2%0%0%hg231%1%1%1%0%1%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%1%0%0%0%0%1%0%1%0%0%0%1%0%1%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%hg241%1%2%2%1%2%1%0%0%0%1%0%0%0%1%0%0%0%0%0%0%1%1%1%1%0%2%0%2%0%1%0%2%0%2%0%1%0%1%0%1%0%0%1%1%1%1%1%0%0%hg252%2%3%3%1%4%1%1%0%0%1%0%0%0%1%0%0%0%0%0%0%2%2%1%1%0%3%0%2%0%1%0%3%0%2%0%1%0%1%0%1%0%0%1%1%1%1%1%0%0%hg263%3%5%5%2%6%2%1%1%1%2%0%0%1%1%0%0%1%0%0%0%3%3%2%1%0%5%0%4%0%1%0%5%0%4%0%1%1%2%1%1%1%0%1%1%2%1%2%0%0%hg271%1%1%1%0%2%1%0%0%0%1%0%0%0%0%0%0%0%0%0%0%1%1%0%1%0%1%0%1%0%0%0%1%0%1%0%0%0%0%0%0%0%0%0%0%0%0%1%0%0%hg282%2%3%3%1%4%1%1%1%0%1%0%0%0%1%0%0%0%0%0%0%2%2%1%1%0%3%0%2%0%1%0%2%0%1%0%1%0%1%0%0%0%0%1%1%1%1%1%0%0%hg293%3%4%4%1%5%2%1%1%1%2%0%0%1%1%0%0%1%0%0%0%3%2%1%2%1%4%1%2%0%1%0%3%0%2%0%1%0%1%0%1%0%0%1%1%1%1%2%0%0%hg305%5%7%7%2%9%3%2%1%1%3%0%1%1%2%0%0%1%0%0%0%5%4%2%2%1%6%1%4%1%2%0%5%0%3%0%1%0%2%1%1%0%0%2%2%2%2%2%0%0%Top Birch SalesTop SalesKnowledge  Intensive…Top R&D spenders…Top R&D intensity…Top overall inn spending intensityin-house R&D…Top Birch Empl.EMPLSALESBirch Empl.New to world %…New2mkt % salesBirch SalesTop EmplHGiINNjnew2frm/mkt % sales 
 
4.3  High-growth  and  innovative  performance  of  countries  and 

sectors 

While  the  30  and  50  alternative  definitions  for  high-growth  and  innovative  enterprises 
was useful to better understand differences in magnitude across the various definitions, 
the  excessive  complexity  is  less  helpful  when  the  aim  is  comparing  the  high-growth, 
innovative performance of  countries and sectors. Many  of the definitions are potentially 
overlapping. For these reasons, we explore ways of reducing the dimensions. Our aim is 
to propose one or a few aggregate measures for high-growth and innovativeness at the 
company level. In  order to do so, we first assess the  association pattern  of the various 
hgi  and  innj  measures,  before  discussing  the  key  issues  and  results  of  a  proposed 
method of aggregation. 

4.3.1  Association between high-growth and innovation variables 

To  simplify  the  discussion  of  our  findings,  we  consider  as  our  baseline  the  “Eurostat 
definition”-based  measure  –  average  10%  annualized  employment  growth  (21%  over 
the  2-year  period),  hg3.  This  indicator  is  positively  associated  with  the  other  29 
measures  (see  Table  A1  in  the  Appendix)14.  It  is  associated  relatively  more  strongly 
with the  set  of  definitions  using  an  employment-based  indicator  of  growth  –  those  that 
apply  both  the  fixed  (hg1-hg5)  and  the  relative  thresholds  (hg15-18),  as  well  as  those 
defined  according  to  the  top  of  the  distribution  following  the  employment-based  Birch 
index measure (hg23-26). It is worth highlighting the two strongest observed association 
in order to see what other relative and absolute measures are statistically the closest to 
the “Eurostat definition”. The set of firms in our sample selected by the 10% annualized 
average  employment  growth  definition  (hg3)  shows  the  greatest  similarity  to  a  relative 
definition that selects firms among the top 25% in terms of employment growth (hg18). 
The  hg3  definition  is  also  highly  similar  in  statistical  terms  to  hg1  and  hg2,  definitions 
that  apply  –  the  more  restrictive  –  absolute  thresholds  of  10%  and  15%  employment 
growth, respectively, over the two-year period.  

The  “Eurostat”  definition  and  the  “employment-based  OECD  EIP”  definitions  (hg3  and 
hg4)  are  associated  positively,  but  moderately,  as  expected  from  the  fact  that  the  two 
definitions capture 12% and 4% of the sample firms, respectively. The two variables are 
associated in a different way to the top-of-distribution-based set of variables defined in 
terms of employment (hg15-hg18). We notice that in general, the OECD EIP definition is 
in general more strongly associated with all the top-of-distribution-based definitions, and 
particularly  strongly  with  those  applying  the  10  and  15%  growth  threshold  (hg16  and 
hg17).  

The  statistical  similarity  between  employment-based  and  turnover-based  measures  of 
growth  are  relatively  lower  –  as  indicated  by  the  degrees  of  association  between  the 
above-discussed hg3 and absolute and relative turnover-based variables (i.e., hg6-hg10; 
hg19-hg22).  Relatively  the  strongest  association  is  observed  between  hg3  and  the 
variable  using  the  same  10%  annualized  absolute  growth  threshold  hg8  as  well  as  the 
hg22, capturing the top 25% of the turnover distribution.  

The  turnover-based  definitions  in  a  sense  mirror  those  of  the  employment,  given  the 
relatively stronger  “inter-group” association between the variables applying an absolute 
threshold hg6-hg9 (hg10, with the most restrictive 100% growth limit is stands out in a 
similar way as hg5 does from among the employment-based measures).  

We  defined  Birch  indices  in  two  ways,  based  on  absolute  thresholds  and  relative  (top 
N%)  thresholds.  We  observe  that  for  the  absolute  thresholds  that  there  is  virtually  no 
difference between the 10% and 100% thresholds in the case of both the employment- 

14  In  our  tests  we  computed  Pearson  correlation  coefficients.  Obtained  coefficients  should  be 
interpreted  with  caution  in  the  case  of  binary  variables,  so  we  refrain  from  reporting  scores,  but 
focus on the magnitude and signs, which may be considered useful given the similarity with results 
obtained using i.e. Pearson’s ‘Phi coefficient’. 

27 

                                           
(hg11-hg12)  and  turnover-based  (hg13-hg14)  measures  of  Birch  index.  At  the  same 
time, the two sets of measures show a rather low association.  

We also defined Birch indices in the relative way, based on top N% of the distribution  – 
both  using  employment  and  turnover  as  measures  of  growth.  These  two  sets  of  Birch 
indices – based on employment (hg23-hg26) and turnover change (hg27-hg30) – show 
little  or  no  association  with  one  another.  Within-group  association  is  relatively  high  in 
both  cases.  The  main  difference  between  the  top  N%  employment  change-based  and 
turnover  change-based  sets  of  variables  is  the  strength  to  which  the  two  variables  are 
associated  with  the  absolute  and  relative  set  of  measures  of  growth  using  simply 
employment  or  turnover  change,  not  the  Birch  index.  While  the  employment-change-
based, relative (top N%) definitions of the Birch index is overall strongly associated with 
the absolute and (hg23-hg26 vs hg1-hg3) relative measures of employment (hg23-hg26 
vs hg15-hg18), the turnover-based Birch index that applies the relative threshold (hg27-
hg30) shows rather low association with the two sets of sales-based variables (hg6-hg10 
and hg19-hg22). A notable exception is hg30, which applies the least restrictive top 25% 
of  Birch  index  growth  distribution,  which  shows  relatively  higher  degree  of  association 
with the rest of the sales variables. 

These  observations  on  the  association  pattern  leads  us  to  affirm  that  the  key  sources 
of  differences  between  the  high-growth  variables  are  whether  defined  based  on 
employment  or  turnover  change,  and  the  cut-off  threshold  applied.  While  we  see  also 
differences  between  the  absolute  and  relative  measures,  these,  however,  have  a  lesser 
impact  and  a  disproportionate  one  in  the  case  of  employment  and  turnover-based 
definitions.  For  instance,  in  the  case  of  the  employment-based  definition  applying  an 
absolute threshold, a general pattern shows a break between the 21% and 44% growth 
rate over the two years, while in case of sales, the main difference, in general, is due to 
moving from 44% to 100% absolute threshold. This difference is not observable in such 
a marked way in the case of the relative measures.  

Among  the  innovation  variables,  our  two  baseline  measures  are  product  innovators 
(inn1)  and  innovators  of  any  type  (inn6).  Product  innovators  are  positively  associated 
with process innovators (inn2), but not as strongly as product innovators with firms that 
introduced  any  technological  (product  or  process)  innovation  (inn3)  or  new-to-firm 
product or process innovations (inn11). Product innovators are also positively associated 
with firms performing in-house R&D (whether or not continuously employing R&D staff) 
(inn22-24).  At  the  same  time,  the  product  innovation  variable  shows  little  association 
with the non-R&D innovators variable (inn46). 

Firms  with  any  type  of  innovation  are  most  strongly  associated  with  organizational  or 
marketing innovators (inn4), followed by product or process innovators (inn3).  

We notice that process innovator firms (inn2) show a moderate, positive association with 
non-R&D innovators (inn46), and positive association with any type of innovators (inn6). 
Organizational  or  marketing  innovators  stand  somewhat  apart  from  the  rest  of  the 
innovation types (with the exception, by definition, of ‘any type’ of innovators). 

The  basic  definitions  of  innovation  types  (inn1-inn6)  show  little  or  no  association  with 
the  set  of  variables  based  on  top  10%  R&D  and  innovative  spending  (inn25-inn42). 
Within this group, product innovators tend to be relatively more associated with top 10% 
R&D  spenders  across  all  firms  (inn1-inn25),  but  the  association  decreases  for  process 
innovators  and  organizational  or  marketing innovators,  and  decreases  even  more  when 
top  10%  R&D  spenders  are  selected  within  industrial  sectors  or  countries  (inn27  and 
inn33).  Apart  from  these  latter  two  variables,  the  third  variable  that  is  persistently 
negatively  or  not  associated  with  the  rest  of  the  variables  is  the  one  measuring  firms 
that  introduced  a  non-technological  innovation,  but  did  not  introduce  a  technological 
innovation (inn5). 

28 

 
Most  of  the  variable  groups  that  by  construction  show  little  difference  are  closely 
associated, for instance, the group of variables by novelty (inn7-inn10 in which inn9 and 
inn10 are essentially identical, whereas inn11, defining the least radically new innovation 
definition  stands  apart);  the  group  of  variables  with  new-to-market  share  of  sales 
(inn12-inn15) or variables by overall innovation spending intensity (inn37-inn42). 

In  sum,  the  two  main  source  of  differences  across  the  innovation  variables  is  the 
inclusion  or  not  of  organizational  or  marketing  innovation,  and  the  introduction  of  a 
novelty threshold. We also included among the innovativeness variables other measures 
of innovation input, such as R&D expenditure, intensity or innovation spending intensity, 
as  well  as  knowledge  intensity  (a  high  share  of  tertiary  graduates  among  employees). 
We  notice  that  these  contextual  variables  show  little  overlap  with  the  main  set  of 
definitions,  in  other  words,  select  a  different  population  of  firms  in  comparison  to  the 
main types of innovators. 

4.3.2  Towards aggregate scores of high-growth and innovation 

While  in  a  the  cases  reported  above,  some  variables  of  high-growth  and  innovation 
overlap,  but  a  larger  set  of  them  do  not,  implying  that  unless  we  retain  multiple 
dimensions,  much  of  the  information  contained  in  the individual  variables  are  lost.  This 
presents us with a choice whether to retain all the information contained in the variables 
or  select  only  the  most  relevant  ones.  We  recall  that  we  introduced  many  of  the 
alternative  definitions  for  high-growth  and  innovation  with  the  explicit  purpose  to 
increase  the  variance  in  the  set  of  alternatives  for  analytical  purposes,  but  the  set  of 
variables discussed above may not be equally relevant for policy purposes. Therefore, in 
this exercise, we chose to propose a concept-driven selection of  one  or two  statistically 
robust  dimensions,  in  line  with  our  goal  of  offering  sectoral  and  country-level  average 
performance in the two measures derived from firm-based performance. 

We  also  tested,  but  abandoned  two  alternative  ways  of  aggregating  variables  of  high-
growth and innovation. The first approach aimed to make use of all variables, assigning 
firms  a  score  of  1  if  it  performs  above  the  median  in  the  various  measures.  The  main 
problem with such an approach was the fact that the median value was 0 for 28 of the 
30  high-growth  variables  and  49  of  the  50  innovation  variables.  Consequently,  this 
method  would  essentially  measure  the  number  of  times  a  firm  meets  the  28  or  49 
criteria, which would be biased by the double counting of strongly associated variables. A 
second alternative was to follow a principal component analysis (PCA) based approach to 
identify multiple dimensions for both high-growth and innovation. The main limitation of 
this approach was the difficulty to find a conceptual (and intuitive) foundation for the 7 
and 13 latent dimensions identified.15  

For selecting the set of relevant variables of high growth, our baseline measures were 
the  two  variables  following  the  “Eurostat  definition”  (hg3)  as  well  as  the  OECD  EIP’s 
employment-based  definitions  (hg4).  Using  the  observed  association  and  statistical 
support by PCA, we noted that five variables based on the absolute threshold in terms of 
employment growth (hg1, hg2, hg3, hg4) as well as in terms of the Birch index (growth 
>=10%)  (hg11)  were  associated  with  a  single  latent  dimension.  This  dimension,  which 
explained  65.9%  of  variance  in  the  firm-level  data,  can  be  interpreted  as  “high 
employment growth measured applying absolute thresholds”. We henceforth refer 
to this index as the “absolute” high-growth pillar 1, or HG-P1. 

Our strategy was to next identify a statistically coherent pillar which essentially identifies 
high-growth using relative, rather than absolute thresholds. Among these variables, we 
found that relative measures of employment growth in the top 25% of the employment 

15  We  also  note  the  limitations  of  PCA  in  case  of  a  high  number  of  dimensions  of  binary  data, 
however,  alternative  methods  such  as  polychoric  PCA  require  excessive  computation  on  the 
number of observations which would have required longer time at the Eurostat Safe Centre. 

29 

 
                                           
growth (hg15-18) and top 5% of sales (hg19) growth distribution, or the top 5, 15, and 
25%  of  growth  distribution  measured  in  terms  of  the  (employment-based)  Birch  index 
(hg23, hg25, hg26) are strongly associated with a single latent dimension. We interpret 
this  dimension  (which  explains  about  63.7%  of  variance  in  the  data)  as  a  “relative 
measure  of  high  growth”.  We  henceforth  refer  to  this  index  as  the  “relative”  high-
growth pillar HG-P2. 

Using the ‘any type of innovation’ (inn6) as the baseline, we also computed an aggregate 
index  for  innovativeness.  The  purpose  of  an  aggregate  index is  to  be  broader  than  one 
single  measure,  as  we  acknowledge  the  inherent  uncertainty  in  the  measurement  of 
innovativeness.  

Making  use  of  the  association  patterns  discussed  above  and  aiming  to  ensure  a 
statistically  coherent  index,  we  selected  an  aggregate  that  capture  innovators  with 
successful product (inn1), process (inn2), any of these two (inn3), or any type (including 
organizational and marketing) (inn6) of innovations. In additions, we further added two 
variables  quantifying  the  degree  of  novelty  of  technological  innovations,  those  that  are 
new  to  the  world  (inn9)  or  new  to  the  firm  (inn11).  These  six  variables  were  all 
associated  with  a  single  latent  dimension,  capturing  61.4%  of  variance  in the  data.  We 
refer  to  this  index  as  the  “successful  innovators”  pillar  (INN-P1).  We  chose  not  to 
aggregate  further  measures  of  innovation  as  these  already  captured  all  main  types  of 
innovation,  as  well  as  variables  referring  to  degrees  of  novelty  of  innovations,  and  we 
considered many of the additional measures related to R&D and knowledge-intensity as 
contextual ones that do not refer to successful innovation outcomes. 

For each of the three pillars, we computed aggregate pillar index scores at the firm level 
by  taking  the  averages  of  the  relevant  component  variables.  We  subsequently 
aggregated the pillar scores by country as well as 1- and 2-digit NACE sectors.  

4.3.3  Cross-country and cross-sectoral evidence 

We  first  assessed  how  countries  performed  in terms  of  high-growth  and innovativeness 
by  aggregating  relevant  firm-level  scores  by  country.  The  obtained  scores  plotted  in 
Figure  8  show  that  both  the  absolute  and  relative  high-growth  pillars  (HG-P1  and  HG-
P2) are anti-correlated with the successful innovators pillar (INN-P1) at the country level 
(see also left part of Table 8).  The  reason why Spain noticeably stands apart from the 
rest of the countries is that we used data for many of the non-core innovation activities, 
typically  service  sectors  that  are  not  available  for  other  countries.  The  negative 
correlation  between  HG-P2  (relative)  and  INN-P1  are  significant  at  5%  level,  while  the 
correlation  between  HG-P1  and  INN-P1  are  not  significant.  However,  should  we  choose 
to  exclude  Spain,  the  negative  correlation  between  HG-P1  and  INN-P1  increases  in 
strength  (-0.54,  at  5%  significance  level)  as  does  the  correlation  between  HG-P2  and 
INN-P1  (-0.71,  at  1%  sign.).  The  negative  correlation  is  also  consistently  observed 
within the three size classes, and is the strongest among large firms. 

What  we  see  is  that  countries  with  firms  that  are  strongest  in  introducing  successful 
innovation  are  relatively  weaker  in  terms  high-growth  (a  prime  example  are  Germany, 
France  and  Italy),  and  vice  versa,  the  less  innovative  firms  grow  fast  in  countries  of 
Eastern  Europe  and  the  Baltics,  such  as  Romania,  Bulgaria,  Latvia  and  Lithuania,  and 
also  in  Norway.  The  two  high-growth  pillars  HG-P1  and  HG-P2  are  strongly  correlated 
(r=0.9), hardly distinguishing, at this level, country performance. 

30 

 
 
 
Figure 8 High-growth vs innovation performance of sample firms at country level 

Source: authors’ calculations based on CIS2012 microdata; [weighted sample; country average of scores in 
N=92,926] 

Table 8 Correlation between the high-growth and innovation aggregate indices at the country and 
sectoral (NACE 1- and 2-digit) level 

Source:  authors’  calculations  based  on  CIS2012  microdata;  N  countries  =  20;  N  sectors  (1-digit)  =  18;  N 
sectors (2-digit) = 83. Stars indicate significance at 1, 5 and 10%. 

The  negative  correlation  between  high-growth  and  innovation  scores  observed  at  the 
country reverses as soon as we look at the indices in a large industrial cross-section 
(NACE 1-digit level, pooled European data – see middle part of Table 8). The change in 
the  correlation  pattern  suggests  that  country  performance  in  the  two  dimensions  is 
largely influenced by countries’ sectoral specialization. Unfortunately, disaggregating the 
obtained index scores to a fine-grained sectoral level by each country is not possible due 
to confidentiality considerations.  

Figure 9 reveals that service sectors typically associated with the “knowledge economy” 
(J - Information and communication; M – Professional, scientific and technical activities; 
and P – Education) are also strong performers in terms of high growth. Accommodation 
and food service activities (I) turn out to be neither innovative, nor fast-growing sectors. 
We  register  a  relatively  lower  high-growth  performance  for  the  rather  innovative 
financial (K) and manufacturing (C) industries, but a strong growth performance for the 
Agricultural,  forestry  and  fishing  sector  (A),  which  is  weak  in  terms  of  innovative 
performance.  In  fact,  agriculture  is  an  outlier  sector  outside  the  core  innovation 
activities,  thus  countries  provide  data  on  a  voluntary  basis,  which  is  why  it  is  less 
representative. Removing sector A from the sample renders all correlations positive and 
significant at the 2% level. 

31 

inn_p1hg_p1hg_p2inn_p11hg_p1-0.3291hg_p2-0.553 **0.904 ***1Country-levelinn_p1hg_p1hg_p2inn_p11hg_p10.406 *1hg_p20.2570.819 ***1NACE 1-digitinn_p1hg_p1hg_p2inn_p11hg_p10.1831hg_p2-0.0120.774 ***1NACE 2-digit 
 
 
 
 
 
 
Figure 9 High-growth vs innovation performance of sample firms by large sectors (NACE 1-digit) 

Source: authors’ calculations based on CIS2012 microdata 

Figure 10 High-growth vs innovation performance of sample firms by sectors (NACE 2-digit) 

Source: authors’ calculations based on CIS2012 microdata. Note: red lines indicate average performance by 
dimension. 

Curiously,  at  a  more  fine-grained  sectoral  level,  the  positive  association  between  high-
growth and innovation disappears (NACE 2-digit level, pooled European data – see right 
part  of  Table  8).  While  part  of  this  is  due  to  the  within-sectoral  heterogeneity  of  firm 
performance in the three observed dimensions, the scatterplots in Figure 10 also reveal 
that a few subsectors of agriculture and mining, as well as outliers in the service sector, 
such as M75 (veterinary activities) add noise to the data. In fact, excluding these sectors 
results  in  a  clearly  positive  correlation  between  HG_P1  and  the  Innovation  pillar. 
However,  still  no  association  is  found  between  the  “relative  measure  of  high  growth” 
HG_P2 and innovation pillar. In comparison with the country-level or large sectoral level, 
the two high-growth pillars HG_P1 and HG_P2 show somewhat different performance of 
2-digit sectors. 

The sectors found to be both high-growth (in both measures) and innovative, according 
to  Figure  10,  are  rather  intuitive,  as  they  include  a  set  of  knowledge-intensive 
manufacturing  (including  computer,  electronics  and  optical  parts  –  26;  electrical 

32 

123567891011121314151617181920212223242526272829303132333536373839414243454647495051525355565859606162636465666869707172737475777879808182858687889091929395960.1.2.3.4HG_P10.2.4.6INN_P1123567891011121314151617181920212223242526272829303132333536373839414243454647495051525355565859606162636465666869707172737475777879808182858687889091929395960.05.1.15.2HG_P20.2.4.6INN_P1 
 
 
 
equipment  –  27;  machinery  and  equipment  –  28;  motor  vehicles  –  29;  and 
pharmaceuticals  -  21)  and  service  activities  (such  as  scientific  R&D  –  72;  computer 
programming  –  62;  or  Telecommunications  –  61).  At  the  same  time,  many  firms  also 
seem  to  have  found  considerable  growth  opportunities in  many less-innovative  sectors. 
These  are  rather  diverse,  encompassing  natural  resource-based  industries  (oil  and  gas 
extraction – 6; or mining of metal ores – 7), as well as waste collection, treatment, etc. 
– 38 or manufacture of leather – 15. Sectors in the lower right corner of the two graphs 
are more innovative than the average, but underperform in terms of the average share 
of  high  growth  firms.  This  either  refers  to  situations  where  technological  opportunities 
and market opportunities are not aligned, or where innovation is needed for business to 
survive, rather than to grow. A good example for this situation is  the chemical industry 
(20), which is characterized by large, innovative, but slow-growing firms.  

Industry  scores  can  be  further  broken  down  by  size  class.  As  shown  by  Figure  11, 
innovation  performance  (INN_P1)  tends  to  increase  by  firm  size.  This  is  exactly  the 
oppose  trend  observed  for  the  (absolute)  high-growth  performance  (HG_P1),  which 
tends to decrease by size class.  This is an important  observation which reveals at least 
part  of  the  complexity  of  the  relationship  between  high-growth  and  innovation.  It  also 
suggests  that  firms  of  different  size  are  likely  to  face  different  obstacles  and 
opportunities  to  innovation  or  the  likelihood  to  achieve  high  growth,  which  warrants 
more in-depth studies as well as carefully targeted policy measures.  

Figure 11 Distribution of sectoral (NACE 1-digit) HG_P1 and INN_P1 indices by size class 

Source: authors’ calculations based on CIS2012 microdata. Note: Shaded area of box plots capture 50% of the 
index scores’ distribution across NACE 1-digit sectors, 90% of distribution ranges within whiskers. 

33 

 
 
 
5  Conclusions 

In  this  study,  we  attempted,  for  the  first  time,  to  measure  the  share  of  high-growth, 
innovative  firms  in  Europe,  by  recognizing  the  uncertainty  in  the  definition  as  to  the 
delineation  of  high  growth  performance  (as  well  as  innovation  performance).  We 
proposed  a  method  to  identify  the  differences  across  multitude  of  potentially  viable 
methods making use of the growth-innovation matrix.  

Disregarding the uncertainty in the definition, and using Eurostat’s high-growth definition 
and the broadest understanding of innovation (the successful introduction of any kind of 
innovations), we may conclude that 7% of European companies are HGIEs. However, we 
found that, as expected, the definitions significantly influence the share of firms flagged 
as HGIEs. The observed share of HGIEs in the pooled European data from the CIS2012 
ranged  between  0.1  and  31%,  and  between  0.1  and  10%  in  case  of  90%  of  the 
proposed  definition  combinations.  While  our  results  confirm  the  sensitivity  of  outcomes 
to  the  choice  of  the  growth  indicator  and  measure  (Delmar  and  Davidsson,  1998),  we 
also  add  two  qualifications.  First,  we  extended  the  sensitivity  to  the  definition  of 
innovativeness,  and  second,  we  highlighted  the  key  sources  of  sensitivity.  Recognizing 
the  difference  in  the  outcomes  whether  high-growth  is  measured  in  terms  of 
employment  or  turnover  growth,  we  found  that  the  choice  of  absolute  versus  relative 
thresholds mattered even more. As for defining innovation, the  key choices were found 
to  be  whether  to  include  non-technological  (organizational  or  marketing)  innovation 
alongside  the  variables  of  technological  innovation,  and  whether  to  impose  a  degree  of 
novelty (or radicalness) threshold. Policy targeting high-growth, innovative firms need to 
exercise  particular  attention  to  these  uncertainties  in  order  to  better  address  the 
intended target population of firms. 

Using  aggregate  indices  of  high-growth  (based  on  the  absolute  employment  growth 
thresholds  and  a  broader,  relative  threshold)  and  innovation,  we  found  evidence  of 
negative  correlation  between  high-growth  and  innovation  at  the  country  level,  which 
disappeared at a pooled, European sectoral level. This calls for further investigation to go 
beyond the observation of such associations, and understand the driving forces.  One  of 
the  potential  sources  is  the  firm  size  structure  in  countries  and  sectors.  We  notice  that 
high-growth  firms  are  overrepresented  among  small  firms,  while  innovative  firms  are 
overrepresented  among  large  firms.  In  order  to  support  economy-wide  employment 
growth,  inter-sectoral  as  well  as  inter-firm  linkages  –  through  which  companies  of 
different  size  can  benefit  from  innovation  as  well  as  growth  opportunities  –  need  to  be 
carefully studied. There is also need for a better understanding of the different kinds of 
barriers  firms  of  different  size  face  with  regards  to  innovation  and  growth.  It  is  also 
important  to  understand  how  successful  firms  managed  to  achieve  high  growth,  what 
strategies did they follow and what obstacles did they face, i.e. in terms of availability of 
finance or regulatory conditions.  

In light of the observed negative correlation at the country level, it is also important to 
recognize that in many cases, policy may be  chasing two targets that are unachievable 
at  the  same  time.  A  potential  source  of  trade-off  between  high-growth  and 
innovativeness,  especially  in  countries  of  Eastern  Europe,  is  the  need  to  upgrade  the 
technological  capabilities  of  often  large  firms.  A  primary  tool  for  this  is  diffusion  of 
innovations  across  countries  and  across  sectors,  rather  than  the  introduction  of  world-
first novelties. 

Many of the limitations of our study were linked to the properties of the data we chose to 
analyse.  Measuring  the  share  of  high-growth,  innovative  enterprises  requires  firm-level 
data  on  both  growth  performance  as  well  as  innovation,  ideally  for  a  representative 
sample  of  the  European  economy.  Business  registers  are  useful  sources  for  measuring 
growth,  but  offer  little  if  any  information  on  innovation  performance.  CIS  data  offers 
sophisticated information on innovation and some information on firm performance, but 
– at least if the aim is to offer results at the European level  – offers  no information on 
whether the growth of a firm is persistent over time, and includes only limited financial 

34 

data.  Introducing  linkages  across  survey  waves  or  with  other  firm  surveys  would 
therefore  be  highly  welcome  for  enriching  the  analysis.  Similarly,  if  microdata  were 
available for research use for countries currently not covered in this analysis would offer 
a  more  complete  picture  of  European  HGIEs  beyond  the  20  countries  we  could  cover. 
Our  analysis  was  carried  out  on  the  CIS2012.  The  findings  may  be  to  a  certain  extent 
overshadowed  by  the  slow  recovery  from  the  financial  crisis  of  2007/08.  It  may  be 
therefore  informative  to  repeat  the  analysis in the  future  on  other  CIS  waves,  not  only 
for  a  test  of  robustness,  but  also  to  be  able  to  use  the  obtained  country-  or  sectoral 
performance scores as indicators plotting the trends in business dynamics. 

35 

 
 
 
 
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38 

 
List of abbreviations and definitions 

CIS 

EIP 

EIS 

EU 

Community Innovation Survey 

Entrepreneurship Indicators Programme 

European Innovation Scoreboard  

European Union 

HGIE  High-growth, innovative enterprises 

IOI 

Innovation Output Indicator 

NACE  Statistical  Classification  of  Economic  Activities  in  the  European  Community 
(nomenclature  statistique  des  activités  économiques  dans  la  Communauté 
européenne) 

OECD  Organisation for Economic Co-operation and Development 

PCA 

Principal Component Analysis 

R&D 

Research and development 

39 

 
 
List of figures 

Figure 1 The high-growth and innovation (HGI) definition matrix ............................... 8 

Figure  2  Average  employment  growth  in  the weighted  sample  by  country  &  size  class, 
2012/2010 ......................................................................................................... 11 

Figure 3 The distribution of employment change by country and a comparison of high-
growth (90th percentile) with average growth in the weighted sample (2012/2010) ..... 12 

Figure  4  Turnover  change  (orange)  and  employment  change  (blue)  in  the  weighted 
sample by country and size classes (2012/2010) .................................................... 13 

Figure  5  The  distribution  of  turnover  change  by  country  and  a  comparison  of  high-
growth (90th percentile) with average growth in the weighted sample ........................ 14 

Figure  6  The  share  of  European  companies  in  the  weighted  sample  meeting  a  certain 
high-growth definition .......................................................................................... 22 

Figure  7  The  share  of  European  companies  in  the  weighted  sample  meeting  an 
innovativeness definition ...................................................................................... 23 

Figure 8 High-growth vs innovation performance of sample firms at country level ...... 31 

Figure 9 High-growth vs innovation performance of sample firms by large sectors (NACE 
1-digit) .............................................................................................................. 32 

Figure 10 High-growth vs innovation performance of sample firms by sectors (NACE 2-
digit) ................................................................................................................. 32 

Figure  11  Distribution  of  sectoral  (NACE  1-digit)  HG_P1  and  INN_P1  indices  by  size 
class .................................................................................................................. 33 

Figure  A1  Distribution  of  growth  across  firms  in  the  overall  sample  and  kernel  density 
plot ................................................................................................................... 42 

40 

 
 
 
 
List of tables 

Table 1 Number of firms in the unweighted and weighted sample by size class .......... 10 

Table 2 Correlation of country growth rates in the weighted sample across indicators & 
size classes ........................................................................................................ 11 

Table 3 Average employment and turnover change 2012/2010 by country and product- 
and process innovators (percent; weighted sample) ................................................ 15 

Table 4 Alternatives considered for the definition of high-growth firms ...................... 16 

Table 5 Descriptive statistics of the variables defining high-growth firms in the weighted 
sample ............................................................................................................... 18 

Table  6  Descriptive  statistics  of  variables  defining  innovativeness  of  firms  in  the 
weighted sample ................................................................................................. 20 

Table  7  Difference  in  the  share  of  firms  identified  as  HGIEs  due  to  employment-  vs. 
turnover-based measures of high-growth if any type of innovation is considered ......... 25 

Table  8  Correlation  between  the  high-growth  and  innovation  aggregate  indices  at  the 
country and sectoral (NACE 1- and 2-digit) level ..................................................... 31 

41 

 
 
Annex 

Figure A1 Distribution of growth across firms in the overall sample and kernel density plot 

Kernel  density  plots  of  the  employment  change 
[‘rteg’] and turnover change [‘rsg’] variables 

Source: authors’ calculations based on CIS2012 microdata 

42 

01234Density-1012Employment change0.511.52Density-10123Turnover change01234-10123xkdensity rtegkdensity rsg 
 
 
 
 
 
 
 
Table A1 Firm-level correlation among the variables defining high-growth (hg1-hg30) 

Source: authors’ calculations based on CIS2012 microdata; N=92,926. Pearson correlation; all values are significant at 1% level. 

43 

hg1hg2hg3hg4hg5hg6hg7hg8hg9hg10hg11hg12hg13hg14hg15hg16hg17hg18hg19hg20hg21hg22hg23hg24hg25hg26hg27hg28hg29hg30hg11hg20.8231hg30.6660.8091hg40.3830.4650.5751hg50.1610.1960.2420.4221hg60.3320.3090.2830.1910.0871hg70.3420.3290.3080.2150.0980.8491hg80.3430.3380.3240.2400.1130.7180.8451hg90.2810.3030.3190.2990.1580.4330.5100.6041hg100.1490.1720.1930.2330.1790.2010.2370.2800.4641hg110.6540.5390.4360.2500.1060.3350.3210.3000.2120.1021hg120.6670.5490.4440.2550.1080.3360.3220.3020.2150.1030.9821hg130.2790.2450.2150.1360.0580.6940.5900.4980.3010.1400.3400.3381hg140.2790.2450.2150.1360.0580.6940.5900.4980.3010.1400.3400.33811hg150.2540.3080.3810.6620.6370.1360.1540.1760.2360.2100.1660.1690.0950.0951hg160.3630.4400.5440.8740.4450.1850.2090.2350.2900.2260.2370.2420.1330.1330.6991hg170.4500.5460.6750.8520.3590.2190.2450.2700.3110.2240.2940.3000.1590.1590.5640.8071hg180.5970.7260.8690.6410.2700.2700.2970.3150.3170.2010.3910.3980.2040.2040.4240.6070.7521hg190.1530.1770.1990.2370.1740.2100.2480.2930.4860.9340.1060.1080.1460.1460.2220.2380.2330.2081hg200.2130.2390.2600.2850.1760.3020.3560.4210.6980.6650.1550.1570.2100.2100.2530.2870.2890.2710.6961hg210.2500.2750.2950.2960.1670.3760.4430.5240.8550.5350.1890.1920.2610.2610.2480.2950.3100.3030.5600.8041hg220.2990.3150.3240.2840.1420.5020.5910.6990.8630.4010.2390.2430.3480.3480.2180.2810.3110.3270.4190.6020.7491hg230.2490.2980.3650.5990.5720.1320.1480.1670.2150.1940.1660.1690.0940.0940.6700.6220.5400.4100.2010.2200.2180.2031hg240.3550.4250.5190.6920.4450.1810.2010.2210.2630.2010.2370.2420.1330.1330.6380.6900.6790.5850.2080.2530.2610.2550.6991hg250.4390.5260.6320.7060.3590.2160.2370.2570.2880.2030.2940.3000.1610.1610.5620.6980.7270.6860.2110.2620.2810.2850.5640.8071hg260.5790.6760.7410.6220.2710.2620.2840.2970.2990.1870.3890.3970.2030.2030.4260.6070.7020.7520.1950.2510.2820.3050.4260.6090.7551hg270.1000.1060.1170.1260.0830.2040.2300.2540.3170.3720.0900.0910.1460.1460.1080.1180.1210.1160.3680.3560.3350.3030.1450.1590.1570.1461hg280.1410.1470.1580.1610.1040.2900.3250.3580.4230.4200.1250.1260.2100.2100.1370.1530.1610.1590.4190.4380.4350.4120.1770.2010.2040.1970.6961hg290.1740.1800.1900.1860.1140.3580.3990.4320.4780.4170.1570.1590.2610.2610.1520.1790.1870.1890.4160.4610.4770.4750.1920.2250.2300.2320.5600.8041hg300.2170.2200.2250.2000.1150.4700.5140.5400.5410.3710.2070.2090.3480.3480.1580.1930.2070.2190.3730.4700.5190.5530.1890.2330.2460.2620.4190.6020.7491Top Birch SalesEMPLSALESTop Birch Empl.Top Birch SalesEMPLSALESBirch Empl.Birch SalesTop EmplTop SalesBirch Empl.Birch SalesTop EmplTop SalesTop Birch Empl.Europe Direct is a service to help you find answers 
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doi:10.2760/328958 

ISBN 978-92-79-68836-2