Mobilising European structural and investment funds and Horizon 2020 in support of innovation in less developed regions : S2E technical report.
Date: 2018-11-21
Institutions: Joint Research Centre
Subjects: Structural Funds, innovation, international cooperation, less-favoured region, organisation of research, research and development, research policy, research report

Mobilising European Structural 
and Investment Funds and 
Horizon 2020 in support of 
innovation in less developed 
regions 

S2E Technical Report 

Pontikakis, D 

Doussineau, M 

Harrap, N 

Boden, M 

2018  

EUR 29298 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. 

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E-41092 Seville (Spain) 
Email: @ec.europa.eu 
Tel.: +34 954 48 8318 

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

JRC112442 

EUR 29298 EN 

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How to cite this report: Pontikakis, D., Doussineau, M., Harrap, N. and Boden, M., Mobilising European 
Structural and Investment Funds and Horizon 2020 in support of innovation in less developed regions, EUR 
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Mobilising European Structural and Investment 
Funds and Horizon 2020 in support of innovation 
in less developed regions  

Dimitrios Pontikakis, Mathieu Doussineau, Nicholas Harrap, Mark Boden 

European Commission, Joint Research Centre, Seville (Spain) 

S2E Technical Report – November 2018 

This publication has been produced within the context of the Stairway to Excellence (S2E) project. 
The project is implemented by JRC Seville in collaboration with the Directorate General for Regional 
and Urban Policy under a mandate from the European Parliament.  

3 

Abstract 

How can EU policies support the development of innovation capabilities in less developed regions? 
This  note  examines  the  mobilisation  of  the  EU’s  two  major  innovation  support  instruments:  the 
European  Structural  and  Investment  Funds  (ESIF)  and  Horizon  2020  (H2020).  Using  data  from 
Eurostat  and  European  Commission  administrative  data  on  ESIF  and  H2020  funding,  we  observe  a 
number of salient patterns. While newer member states benefit from higher research and innovation 
allocations from ESIF, participation in H2020 remains a formidable challenge. Across Europe we find 
that  H2020  participation  is  closely  associated  with  a  number  of  proxies  of  the  development  of 
national and regional innovation systems. With few exceptions (most notably Slovenia and the Czech 
Republic) newer member states are characterised by lower overall R&D intensity, their research and 
innovation  systems  are  less  internationalised  and  most  R&D  is  performed  by  public  research 
institutions  rather  than  businesses.  Based  on  a  review  of  literature  on  the  determinants  of 
participation  in  the  H2020  (and  its  predecessor  Framework  Programmes),  the  history  of  today's 
advanced innovation systems and a consideration of the objectives of, modes of intervention of and 
possible  complementarities  between  ESIF  and  H2020  we  single out  international  collaboration  and 
business  innovation  capabilities  as  important  instrumental  objectives  for  development-minded 
policy. 

4 

 
 
 
 
 
 
 
 
Executive Summary 

How can EU policies support the development of innovation capabilities in less developed regions? 
This  note  examines  the  mobilisation  of  the  EU’s  two  major  innovation  support  instruments:  the 
European Structural and Investment Funds (ESIF) and Horizon 2020 (H2020). ESIF – with its focus on 
productivity and income convergence and attendant support to broad-based innovation investments 
-  is  more  relevant  to  regions  with  low  innovation  capabilities  whereas  H2020  –  with  its  focus  on 
precompetitive research of global significance – becomes progressively more relevant to regions with 
higher  innovation  capabilities.  In  principle,  both  ESIF  and  H2020  are  open  to  all  regions.  However, 
take-up  of  ESIF  for  innovation  and  participation  in  H2020  are  very  unequally  distributed  across 
regions  with  similar  capability  deficits.  Unequal  distribution  is  not  merely  confined  to  partly 
inevitable  territorial  disparities  within  countries  but,  importantly,  is  also  evident  across  national 
borders.  Regions  in  Central  and  Eastern  Europe  -  largely  new  member  states  -  and  also  Southern 
Europe appear to be particularly affected. 

Participation  in  H2020  is  often  singled  out  as  a  particularly  pressing  problem  in  less  developed 
regions. In addition to much-needed resources from its sizeable budget, H2020 provides unparalleled 
opportunities to participants from less developed regions to engage with international scientific and 
technological  networks  and  address  research  questions  of  global  economic  significance.  To 
understand  possible  reasons  for  low  participation,  we  review  literature  on  the  determinants  of 
success  in  H2020  and  predecessor  Framework  Programmes  for  Research  and  Technological 
Development  (FP).    We  find  that  prior  participation  in  the  FP,  networks  and  collaboration, 
organisational characteristics, and importantly, national system and funding structures all combine to 
construct the scientific excellence that the FP/H2020 is known to select for. Additionally, FP/H2020 
participation may reflect self-selection, whereby either very weak participants, or very strong ones, 
who  can  tap  on  alternative  funding  sources,  choose  not  to  participate.  H2020  presupposes  a 
minimum  level  of  research  and  innovation  capability  necessary  to  engage  with  global  research 
questions  of  economic  significance.  However,  less  developed  regions  are  typically  characterised  by 
weak business innovation capabilities and by public research institutions and tertiary and vocational 
education that struggle to keep up with the demands of international science or globalised industry.  

We  recall  the  policy objectives  of  ESIF  and  H2020  and  examine  their  potential  impacts  in  terms of 
regional  innovation  capability  accumulation.  Taking  cues  from  the  economic  history  of  advanced 
innovation systems, we highlight the central role of business innovation capabilities in the  long-run 
development  of  thriving  innovation  systems.  ESIF  can  help  prop  up  capabilities  in  less  developed 
regions with weak innovation systems until such time as they can participate more fully in H2020. In 
support  of  this  goal  ESIF  innovation  investments  can  be  focused  on  two  specific  instrumental 
objectives: to support the internationalisation of public research on the one hand, and to support the 
development of business innovation capabilities on the other. 

As  discussed  elsewhere  (e.g.  see  European  Commission,  2014)  mobilising  ESIF  for  innovation  is 
largely  down  to  administrative  capacities  to  orient,  coordinate,  programme  and  implement 
innovation  projects.  RIS3  provides  a  new  framework  for  this  kind  of  mobilisation  to  happen. 
However,  even  with  RIS3  and  the  mobilisation  of  sizeable  ESIF  resources  for 
innovation, 
accumulation of  the necessary  innovation  capability within  both the  business  sector and the wider 
innovation system takes time and is not guaranteed. The risk of insufficiently tailored policy mixes is 
well recognised but not always addressed. This may materialise, for instance, with too little support 

5 

 
innovation, 

including  non-R&D 

innovation  activities 

in  design,  marketing, 
for  broad-based 
engineering,  training,  information  technology  and management  that  are  important  for  new-to-the-
firm  and  new-to-the-industry  innovation,  and  which,  on  the  long  run,  can  be  a  stepstone  to  and 
complementary  to  R&D.  Poor  tailoring  may  manifest  in  insufficient  attention  to  the  types  (e.g. 
organisational  and  marketing  versus  technological  innovation)  and  modes  of  innovation  (e.g.  in-
house  broad-based  innovation  activities  versus  collaborative  research)  that  are  most  relevant  to 
businesses in the region.  

We explore  some  potential  complementarities  between  ESIF  and  H2020  at  the level  of  policies.  At 
least  conceptually,  the  ways  in  which  the  two  instruments  impact  on  capability  accumulation  can 
overlap  and  reinforce  one  another.  In  developing  systems  that  are of  primary interest  to  cohesion 
policy,  the  two  instruments  complement  each  other  in  a  stepwise  manner,  with  ESIF  investments 
helping build the scientific excellence required to further benefit from H2020. However, a range of 
obstacles can stand in the way of harnessing complementarities. At the level of policies (as distinct 
from  projects),  these  can  include  the  absence  of  long-term  planning  horizons,  the  absence  of  the 
necessary  governance  infrastructure  and  reforms  required  and  the  lack  of  suitable  policies  for 
capability development that are in keeping with stage of the innovation system’s development.  

A key issue appears to be that administrative rules and procedures for ESIF and H2020 are not always 
compatible. Rules may also require adaptation to the  economic and institutional realities of newer 
member states. Other important barriers include the absence of national sources of funding to pick 
up those parts of the innovation value chain not supported by EU funds and, crucially, the absence of 
a  critical  mass  of  R&D-performing  businesses,  which  appear  to  be  important  in  steering  public 
research  and  skills  provision  and  are  in  any  case  necessary  if  H2020  participation  is  to  have  local 
economic impact. Along with opportunities to harness complementarities, possible and anecdotally 
reported undesirable outcomes deserving further study are identified such as long-term dependence 
on funding from European sources and substitution between ESIF and H2020.  

We then examine some salient structural features of EU innovation systems, drawing from statistical 
information on R&D expenditures (most notably the shares of public and business innovation within 
the national aggregate) and administrative information regarding participation in ESIF and H2020 at 
the national and regional level. With few exceptions (most notably Slovenia and the Czech Republic) 
newer member states are characterised by lower overall R&D intensity. Most R&D is performed by 
public  research  institutions  rather  than  businesses,  and  ESIF  research  and  innovation  allocations 
account  for  a  larger  share  of  GDP  (around  0.4%  for  Poland,  Slovakia,  Lithuania  and  Latvia  and  as 
much  as 0.6%  for Estonia).  In  contrast,  with  some  exceptions  (most  notably  Slovenia  and  Estonia), 
the countries where H2020 funding accounts for a greater share of GDP, tend to be older member 
states,  with  higher  overall  R&D  intensities,  and  in  which  businesses  perform  most  R&D.  Newer 
member states appear to be less well integrated into international scientific networks, as suggested 
by  the  overall  lower  shares  of  international  co-publications.  At  the  national  level,  and  among  the 
variables  considered,  international  co-publications,  followed  by  business  R&D  intensity  are  the 
variables most closely associated with H2020 participation.   

The regional picture is more nuanced. Among the 20 member states for which our data permit us to 
examine  sub-national  variance  (usually  at  the  NUTS2  level)  we  observe  greater  within-country 
differences in regional allocation of funding (defined as the percentile difference between the region 

6 

 
with  the  national  minimum  and  the  region  with  the  national  maximum)  for  H2020  than  for  ESIF. 
Greece,  followed  by  Spain,  Belgium  and  Italy  are  the  countries  with  the  greatest  intra-national 
regional  differences  in  H2020.  Poland,  Portugal  and  Hungary  are  the  countries  with  the  greatest 
intra-national regional differences in ESIF funds. Finally, capital regions tend to attract more H2020 
funding, which underscores the importance of co-location for innovation activity of this kind.  

We  finally  identify  some  salient  characteristics  of  regions  that  stand  to  benefit  from  greater 
participation  in  H2020.  We  examine  200  EU  regions  for  which  data  are  available  in  terms  of  their 
participation in ESIF, H2020 and their R&D intensity, the latter being an important indicator of overall 
innovation capability. As a result of this analysis we divide all EU regions into three groups: lagging, 
transition and better-performing regions. “Lagging regions” are regions with low R&D intensity (less 
than 1 per cent of GDP) and a ratio of H2020 to ESIF funding below 1. They tend to be concentrated 
in newer member states and also in Southern Europe. “Intermediary regions” comprises of regions 
with a notable R&D intensity (at least 1 per cent of GDP) and a ratio of H2020 to ESIF funding below 
1.  Intermediary  regions  could  be  of  particular  interest  to  policy,  as  their  participation  in  H2020  is 
below that expected from their overall R&D intensity, and may be where the largest policy gains can 
be had at short order. “Better-performing regions” exhibit both a notable share of R&D and a high 
(greater than 1) ratio of H2020 to ESIF funding. Lagging and Transition regions will inevitably have to 
ensure that ESIF funding is channelled in support of innovation capability accumulation. Support for 
both  the  internationalisation  of  public  research  and  the  development  of  business  innovation 
capabilities  seem  relevant  to  most  regions  in  these  groups  and  offer  fruitful  directions  for  future 
European  policy.  However,  the  precise  targets  for  policy  attention  are  likely  to  differ  between 
countries and regions and will require context-specific analysis. 

Keywords: Synergies, Horizon 2020, ESIF, Innovation,  

Acknowledgements 

We  acknowledge  the  suggestions  and  overall  direction  provided  by  Karel  Haegeman  (JRC). 
Furthermore, the work benefited from comments and clarifications from colleagues in DG Regional 
and Urban Policy and EUROSTAT. An earlier version of this work was presented at the 2018 SMARTER 
Conference on Smart Specialisation and Territorial Development, Seville September 2018. 

7 

 
 
 
 
 
 
 
 
   
 
Contents 

Abstract .............................................................................................................................................................4 

Executive Summary ...........................................................................................................................................5 

1 

Introduction .......................................................................................................................................................9 

2  What determines H2020 funding mobilisation? A review of the literature ....................................................11 

Prior participation in the Framework Programme ..........................................................................................11 

Pre-existing research networks and collaboration structures .........................................................................12 

National or regional R&I system characteristics..............................................................................................12 

Organisational characteristics .........................................................................................................................13 

3  ESIF and H2020: Policy objectives and potential complementarities from a development perspective ........15 

Policy complementarities for development ....................................................................................................15 

Obstacles to complementarities ......................................................................................................................16 

Business innovation capabilities as an instrumental development objective .................................................17 

4  Mobilisation of ESIF and H2020 across the EU ................................................................................................18 

Research and innovation funding by source ...................................................................................................18 

The role of business R&D ................................................................................................................................20 

Regional variations ..........................................................................................................................................22 

Structure of research and innovation system: international engagement .....................................................23 

5  H2020 mobilisation relative to ESIF: regional patterns and groupings ...........................................................26 

Methodological notes ......................................................................................................................................26 

Resulting groups of regions .............................................................................................................................28 

6  Concluding remarks: creating virtuous cycles of European funding, research excellence and economically 
useful innovation ..................................................................................................................................................35 

7  References .......................................................................................................................................................37 

Annex List of categories of intervention taken into account to define R&I in ESIF .........................................39 

8 

 
 
 
 
 
 
1 

Introduction 

Innovation  policy  is  central  to  tackling  the  multifaceted  challenges  facing  Europe’s  regions,  among 
others: sluggish economic growth, globalisation, demographic changes and climate change.  Regions 
with incomes  per  capita below the  EU  average  or with sluggish economic growth in particular, are 
finding  it  difficult  to  benefit  from  either  national  and  EU  innovation  policy  frameworks  and 
instruments.  Territorially  differentiated, 
inclusive,  and  appropriately  sequenced  responses 
supporting broad-based and progressively more ambitious innovation seem necessary. Research and 
Innovation  Smart  Specialisation  Strategies  (RIS3)  provide  a  framework  that  facilitates  long-term 
planning and resource mobilisation. 

The  creation of  synergies between  the  two main  sources  of  EU  funding  dedicated  to  research  and 
innovation - the European Structural and Investment Funds (ESIF) and Horizon 2020 (H2020)  - is an 
important challenge in the implementation of RIS3. Harnessing synergies is a challenge in more ways 
than one. Until now the bulk of the discussion has been about synergies at the level of projects – that 
is,  combining  different  instruments  to  ensure  that  scale  and  scope  economies  are  achieved 
(European  Commission,  2014)1.  However,  synergies  at  level  of  policies  -  and  of  appropriate 
adaptations to the national and regional policy mix in particular - have not been investigated. As we 
discuss  in  this  report,  synergies  at  the  level  of  policies  imply  territorial  tailoring  of  instruments, 
sequencing between them, and their meaningful embedding into long-term RIS3 processes. 

This  report  provides  an  overview  of  mobilisation  of  ESIF  and  H2020  at  the  regional  level  and 
examines  some  of  its  aggregate  patterns.  Specifically,  we  examine  mobilisation  experience  against 
the following questions: 

  What are the possible complementarities, at the level of policies rather than projects, 

between ESIF and H2020? 

  What are the main structural features of EU innovation systems at the national and regional 

levels and how do they shape the mobilisation of ESIF and H2020 funds? 

  What are the roles of EU, national and regional policies in bringing about meaningful change 

in the innovation systems of regions with different capability deficits?   

The overarching objective is to articulate possible directions2 for policies that strengthen innovation 
capabilities  and  outcomes  in  the  less  developed  regions  of  Europe.  To  this  end  we  investigate  in 
particular  how  ESIF  and  H2020  can  be  used  in  a  mutually  reinforcing  fashion  and  identify 
instrumental policy objectives. The report is structured as follows.  

As H2020 participation is identified as a particularly pressing problem in less developed regions, we 
begin  with  a  brief  review  of  the  determinants  of  participation  in  the  FP,  showing  that  prior 

1  This  discussion  elsewhere  focuses  on  complementarities  (or  synergies)  in  the  narrow  sense  of  combining 
funding from various instruments to maximise the overall amount devoted to a particular project, theme or 
priority  (or  support  different  parts  of).  However,  unexploited  scale  economies  are  not  always  a  problem, 
and  may  not  be  the  key  bottleneck  preventing  capability  accumulation  and  development.  For  this  reason 
we  consider  here  complementarities  at  the  level  of  the  policy  mix,  which  is  combining  specialised 
instruments and their various support mechanisms to achieve long-term, aggregate policy objectives, such 
as  to  build  business  innovation  capabilities,  to  foster  scientific  excellence  and  internationalisation, 
university-industry  interaction  etc.  This  perspective  brings  issues  related  to  sequencing  and  to  focusing 
resources to binding constraints for aggregate development to the fore. 

2 This overview would also help inform two EC initiatives co-implemented by DG REGIO and JRC, namely:  S2E 

and RIS3 Support to Lagging Regions, to better define their future activities. 

9 

 
                                           
participation in the  FP,  networks  and collaboration, organisational characteristics, and importantly, 
national system and funding structures all combine to construct the scientific-excellence that the FP 
is known to select for. Additionally, FP participation may reflect self-selection, whereby either very 
weak  participants, or  very  strong  ones,  who  can  tap  on  alternative  funding  sources,  choose  not to 
participate. We provide an overview of the rationales for synergies between ESIF and H2020 at  the 
level  of  policies  and  consider  how  ESIF  can  support  the  development  of  scientific  excellence,  and, 
importantly, innovation capabilities more generally, particularly in the business sector. We argue that 
business innovation capabilities are of central importance in the long-term development of regional 
innovation systems and deserve closer attention. 

Drawing  from  administrative  data  on  pre-allocated  and  place-based  ESIF  and  spatially-blind  and 
competitive H2020 funding, we show the extent of mobilisation across EU member states and their 
within-country variation. We observe that funding mobilisation in each instrument is broadly shaped 
by structural features of national research and innovation systems, such as the presence of capital or 
metropolitan  regions,  international  cooperation  and  the  share  of  business  R&D  funding  and 
performance. 

Finally,  we  show  that  the  relative  mobilisation  of  ESIF  and  H2020  across  200  European  regions  is 
closely associated with their R&D intensity, which we take as a proxy of the overall development of 
their innovation system. We then classify each region and provide some initial thoughts on the kinds 
of policies what would be most appropriate in each type. 

10 

 
2  What determines H2020 funding mobilisation? A review of the 

literature 

Take-up  of  ESIF  for  innovation  and  participation  in  H2020  are  distributed  very  unequally  across 
regions  with  similar  capability  deficits.  Unequal  distribution  is  not  merely  confined  to  partly 
inevitable  territorial  disparities  within  countries  but,  importantly,  is  also  evident  across  national 
borders.  Regions  in  Central  and  Eastern  Europe  -  largely  new  member  states  -  and  also  Southern 
Europe appear to be particularly affected. Whereas, mobilising ESIF for innovation is largely down to 
administrative  capacities  to  orient,  coordinate,  programme  and  implement  innovation  projects 
(European  Commission,  2014;  Gianelle  et  al.,  2016;  Boden  et  al.,  2016),  success  in  the  FP  is  a 
multifaceted challenge. 

Participation  in  H2020  is  often  singled  out  as  a  particularly  pressing  problem  in  less  developed 
regions. Given the disparities in scientific and technological capabilities between EU member states, 
the  take  up  of  Framework  Programme  funding  in  the  EU  is  skewed  towards  member  states  that 
joined  the  EU  before  2004  (Annerberg  et  al,  2010;  European  Commission,  2017).  This  is  especially 
problematic  from  a  development  perspective,  as  in  addition  to  much-needed  resources  from  its 
sizeable  budget,  H2020  provides  unparalleled  opportunities  to  participants  from  less  developed 
regions  to  engage  with  international  scientific  and  technological  networks  and  address  research 
questions of global significance.  

A  number  of  studies  have  shown  success  in  the  FP  is  overwhelmingly  determined  by  participant 
attributes that can be good proxies of scientific excellence – such as the number of publications and 
the  average  number  of  citations  (Geuna,  1998;  Henriques  et  al.,  2009).  However,  scientific 
excellence, both in its construction where it previously did not exist and in its visible manifestations 
such as high-impact publications, is both an economic and a social process. Learning and improved 
human capital are natural outcomes of sustained international collaboration. But familiarity among 
research teams who have previously worked together, manifesting in lower coordination costs, may 
also result in biases. The award of research funding is not always a natural process by which the best 
researchers cumulate  advantages based on the quality of their ideas  (Viner et al., 2004). Rather, it 
can  be  a  cumulative-causation  process,  whereby  initial  success  becomes  a  self-perpetuating 
phenomenon, rather than one invigorated by competition.  

Prior participation in the Framework Programme 

The so-called "Matthew effect"  in science  refers to the  phenomenon of  research groups that have 
been  successful  in  obtaining  external  funding  having  a  greater  chance  of  producing  publishable 
research that in turn improves the chance of getting further funds in the future (Geuna, 1996). More 
specifically,  it  has  been  argued  that  overall  research  output  has  an  effect  on  participation  in  FP 
(Ukrainski,  2014).  Furthermore,  an  important  factor  in  relation  to  the  successful  application  for 
external funding is whether there have been collaborative relationships prior to the award of funding 
(Defazio et al., 2009). Along with the low level of FP participation it can be demonstrated that less 
developed  regions  have  a  low  level  of  overall  international  co-publication  intensity  (Harrap  and 
Doussineau, 2017). This implies good prior performance is an advantage in obtaining funding as such 
an institution is seen as less risky than one that has never been funded (Geuna, 1996).  

11 

 
Such success due to prior participation means that the universities and research institutes with high 
reputations become more connected and central to a network through repeated participation in EU 
Framework Programmes (FPs) (Protogerou et al., 2010). Such selection mechanisms mean that early 
entrants  have  advantages  in  terms  of  repeated  participations  (Geuna,  1998;  Heller-Schuh  et  al., 
2011), as they achieve a more central position in the FP networks that confer further advantages in 
terms of FP participation. It follows that the lack of prior participation puts institutions from the EU13 
at a disadvantage. The role of prior participation is also evident with regards to business participation 
in the FP. A recent study for the European Commission (Open Evidence and Ernst and Young, 2016) 
found that lack of necessary skills and networks is a major barrier to business participation in the FP, 
particularly in new member states. 

Pre-existing research networks and collaboration structures 

Participation  in  FPs  is  concentrated  in  a  highly  clustered  small  group  of  institutions  with  high 
reputations (Lepori et al., 2015). While such small world networks are efficient for the transmission 
of knowledge, it can be difficult for new entrants to join and are therefore less effective for fostering 
integration  (Heller-Schuh  et  al.,  2011).  A  study  of  the  area  of  information  society  technologies 
(Protogerou  et  al.,  2010)  suggests  that  the  instruments  of  the  FPs  consolidate  the  position  of 
incumbent core network actors while not allowing for promising peripheral organisations to assume 
more central roles.  

The choice of consortium partners for a FP proposal could have important consequences for project 
participation.  To  minimise  cultural  issues  and  geographic  constraints,  there  can  be  a  tendency  to 
maintain  well-known  individual  and  team-level  links  (Okubo  and  Zitt,  2004).  Makkonen  and  Mitze 
(2016)  have  shown  that  while  there  was  an  increase  in  co-publication  intensities  between old  and 
new member states, following the latter's accession to the EU, the increase between new  member 
states was more significant.  

Co-publication analysis also indicates that the new member states collaborate strongly together and 
this  is  replicated  in  FP  participation,  although  some  countries  become  less  peripheral  in  their  FP 
participation  (Harrap  and  Doussineau,  2017).  This  indicates  that  the  prior  performance  of  a 
consortium  influences  the  FP  success  of  new  members.  This  is  also  influenced  by  prior  links  in  a 
researcher's wider international collaboration networks.  

FP networks show a stable core with a large number of projects with the same partners. Around this 
core there are some highly frequent participants as well as others that participate more rarely. These 
latter  organisations  will  rarely  join  the  core  (Breschi  and  Malerba,  2009).  It  is  further  argued  that 
research  priorities  and  network organisation  are  defined  by  the  core  and  that they  demonstrate  a 
tendency towards resistance  to change. A counter argument is that  the core  co-regional pairs that 
co-publish frequently do not receive a disproportionate amount of funding from FP as it is distributed 
equally based on past scientific performance (Hoekman et al., 2013). This issue of performance and 
reputation  is  an  important  factor  as  it  is  a  horizontal  issue  pertinent  to  the  national  system  and 
characteristics of the organisation.   

National or regional R&I system characteristics  

One  characteristic  that  appears  particularly  important  is  the  degree  of  internationalisation  in  the 
national R&I system. A high degree of international openness, as reflected in international mobility, 

12 

 
participation  in  transnational  research  teams  and  cross-border  co-authorship,  can  have  positive 
effects on the capacity of researchers to apply to international programmes such as the FP (Dinges 
and Lepori, 2006; Langfeldt et al., 2012).  

The relationship between internationalisation and the performance of the science system runs both 
ways.  A  recent  study  of  the  mobility  of  scientists  shows  that  the  attraction  of  a  country  is  not 
systematically based on obtaining greater funding than their origin: High quality in the performance 
of a national science system is important and research excellence initiatives have an influence (Cuntz, 
2016). Furthermore, open science systems with more researchers coming in and going out produce 
higher impact research (Wagner and Jonkers, 2017). These are, collectively, strong indications of the 
interactive relationship between an internationally open system, research quality and attractiveness 
to researchers.  

While  R&D  intensity  can  indicate  the  regional  presence  of  research-active  organisations  that  can 
potentially participate in the FP, it is not just overall R&D intensity that matters but its composition 
too. As also shown in Section 4 of this report, countries with a higher share of R&D performed and 
funded by businesses typically perform better in the FP, in view of the instrument's focus on the pre-
competitive  stage  of  research.  For  instance,  a  study  of  the  extent  of  university-industry  R&D 
collaboration in the FP by Azagra-Caro et al. (2013) found that much of the observed variance across 
European regions could be explained by the incidence of business R&D in the region. It is likely that it 
is not business R&D performance in the region per se that is important for FP participation, but what 
it signifies: sizeable business R&D investments are only possible when a constellation of problems in 
the innovation system have been effectively tackled, including the appropriate framework conditions 
for  investment,  the  supply  of  adequate  relevant  skills  and  successful  reform  of  public  research  so 
that it is synergetic and responsive to the demands of industry.  

Organisational characteristics 

International  reputation  has  a  strong  influence  on  the  extent  of  FP  participation  (Henriques,  et  al, 
2009; Lepori et al., 2015; Defazio et al., 2009). Prestigious institutions become better connected and 
more central in the network leading to repeat participation in the FP (Protogerou et al., 2010). The 
expectation  that  reputation  improves  the chances of  a  successful  bid  encourages  applicants  to  act 
strategically  in  their  selection  of  partners.  This  behaviour  further  fans  the  cumulative  causation 
relationship  between  first-entry,  high  scientific  research  quality,  high  reputation  and  potential  for 
repeat participation. The corollary is that newer participants have a higher chance of being left out. 
The  lack  of  international  reputation  is  an  important  barrier  for  new  entrants  to  overcome.  This  is 
typically  the  case  in  new  EU  member  states,  where  there  are  smaller  numbers  of  world  class 
institutions with the required reputation (Annerberg, et al., 2010).  

The size of the institution can also be a factor as large institutions may be better able to support and 
manage complex international collaboration projects (Geuna, 1996; Lepori et al., 2015). A common 
issue, anecdotally reported at S2E National Events,3 was the lack of expertise and experience in the 
proposal writing stage and the implementation of transnational collaborative projects (Özbolat and 
Harrap,  forthcoming).  Larger  institutions,  particularly  those  in  older  EU  member  states,  may  have 
greater resources to establish support mechanisms. 

3 Details on all 13 National Events including the Joint Statements issued by S2E and national authorities after 

the event can be found at: http://s3platform.jrc.ec.europa.eu/national-events  

13 

 
                                           
A recent study by Enger and Castellacci (2016) considers the decision to actually submit a proposal; 
the  stage  during  which  some  potential  participants  may  opt  not  to  participate  (so-called  self-
selection).  Using  a  dataset  comprising  the  entire  population  of  research  organisations  in  Norway, 
Enger and Castellacci (2016) find that, in addition to the importance of prior participation, access to 
national  funding  schemes  that  are  complementary  to  the  FP  is  an  important  determinant.    The 
challenge  in  the  design  of  national  funding  schemes  is  to  ensure  that  they  complement  and  not 
substitute the  FP,  as  unconditional availability of more dependable national funds may  also mean 
there is less need to apply to competitive and therefore uncertain funding streams such as H2020.  

14 

 
3  ESIF and H2020: Policy objectives and potential complementarities 

from a development perspective 

Policy complementarities for development 

Enhancing  the  global  competiveness  of  European  industry  on  the  one  hand  and  supporting  the 
convergence  of  productivity  and  income  levels  between  European  regions  on  the  other,  are  the 
innovation  policies  and  EU  regional 
overarching  economic  objectives  of  EU  research  and 
development policies respectively (Guzzetti, 1995; Tondl, 1995; Chorafakis and Pontikakis, 2010). In 
keeping with these distinct  objectives,  historically the  two sets of policies  have followed their own 
development paths. This is evident in their main instruments:  

 

 

the Framework Programme, known in its current iteration as the EUR 80 billion-plus 
“Horizon 2020” programme (H2020), which has spearheaded support for precompetitive 
research of global significance (chiefly in the form of centrally-administered and 
competitively-awarded funding for mainly collaborative research projects); and  
the European Structural and Investment Funds (ESIF), which have supported productivity 
and income convergence across EU regions and countries by way of (targeted support for 
EU-funded but nationally-administered) investments.  

The recognition that the two objectives of competitiveness and cohesion can be mutually reinforcing 
is motivating an active search for overlaps and complementarities between the two sets of policies 
and their instruments. In principle, ESIF can be complementary to H2020 in several ways. In weaker 
innovation systems that are of primary interest to cohesion policy, the two instruments complement 
each other in a stepwise manner.   First, ESIF can support the initiation of innovation activities and 
accumulation  of  innovation  capabilities,  both  in  public  research  and  education  and  in  businesses. 
Then,  as  innovation  capabilities  accumulate  in  firms,  and  public  research  capacities  are  upgraded, 
weaker  innovation  systems  progressively  become  better  able  to  engage  with  H2020,  access  its 
networks  and  conduct  research  of  global  significance  (see  simplified  diagram  in  Figure  1).  In  this 
stylised  sequence,  ESIF  is  much  like  the  scaffolding  meant  to  prop  up  a  structure  during  its 
construction;  and,  although  this  is  not  always  appreciated,  just  like  the  scaffolding,  its  gradual 
withdrawal is part of the plan. 

Figure  1:  The  role  of  ESIF  and  H2020  in  regionally,  nationally  and  globally  significant 
innovation capability  

ESIF 

innovation 
investments 

Innovation capacities in 
businesses and public 
research  

participation and 
funds mobilisation 

Innovation capacities in businesses 
and public research  

Time    

t0 

H2020 

t1  

collaborative 
research & 
internationalisation 

t2 

Regionally- and 
nationally-
important research 
and innovation 

Globally-important 
research and 
innovation 

Productivity improvements / Economic growth and convergence 

tn… 

15 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Complementarities also exist because each of the two instruments can support the policy objectives 
of the other. H2020 obviously supports cohesion by linking research teams across European borders 
and facilitating learning. However, its main role in capability accumulation arguably comes by way of 
its ability to “raise the bar”. By supporting research questions of global significance, H2020 tends to 
raise  the  bar  of  quality  and  impact  at  that  level  (Henriques  et  al.,  2009).  Even  before  doing  so 
directly, it can chart the way for the necessary reforms and steering of capabilities development that 
lead  to  convergence  not  just  of  capacities  but  also  of  outcomes.  ESIF,  courtesy  of  supporting  less 
developed  systems,  is  liable  to  support  a  wide  range  of  sectors,  technologies,  firms  and  products. 
Other  things  being  equal,  over  the  long-run  ESIF  may  contribute  to  the  development  of  a  more 
populous and, importantly, diverse  pool of globally innovative European companies. These  are  just 
the obvious complementarities at the level of countries and regions which is where most important 
innovation  policy  decisions  take  place.  At  the  level of  projects  (or  collections of  projects tackling  a 
particular challenge), the gamut of complementarities and potential for improvement is likely greater 
still4. 

Obstacles to complementarities 

However,  a  number  of  obstacles  can  stand  in  the  way  of  realising  such  complementarities.  The 
inability  to  plan  strategically  has  been  a chief  obstacle.  The  absence  of  a  framework  for  long-term 
planning in regional (and some cases national) innovation policy has prevented the strategic use of 
funds and of the necessary governance and institutional reforms demanded at various stages of an 
innovation system’s development. The policy idea of Smart Specialisation Strategies (RIS3) has come 
as a response to the need for long-term planning. RIS3 is now helping extend planning horizons and 
unite  previously  disparate  funds  and  instruments,  although  much  progress  remains  to  be  made, 
especially in less developed regions and in countries with little experience in innovation policy. RIS3 
provides an opportunity to harness these complementarities by embedding ESIF and H2020 into the 
region's  long-term  strategy.  But  this  opportunity  is  not  always  recognised  and  the  potential  links 
often remain unexploited in a region's strategy and attendant policy interventions. Failing to harness 
these complementarities within the RIS3 process raises the risk of the use of ESIF for innovation in 
ways that do not support meaningful capability accumulation, for instance, by failing to upgrade and 
internationalise public research institutions or by failing to engage with a broad-base of the region’s 
business sector or failing to attenuate territorial imbalances5.  

A key issue appears to be whether there are elements in the design of ESIF and H2020 that prevent 
such complementarities, including mismatches in administrative rules and procedures (see European 
Commission,  2014)  and  possible  lack  of  adaptation  to  the  economic  and  institutional  realities  of 
newer member states. Other important barriers include the absence of national sources of funding 
to develop complementary lines of research and to pick up those parts of the innovation value chain 
not  supported  by  EU  funds  and,  crucially,  the  absence  of  a  critical  mass  of  R&D-performing 
businesses, necessary if H2020 participation is to have an economic impact in the region. 

4 See the S2E Joint Statements (http://s3platform.jrc.ec.europa.eu/national-events) and Examples of Synergies 

(http://s3platform.jrc.ec.europa.eu/synergies-examples) for possible project complementarities 

5 Another possible, but not necessarily likely, outcome of the inability to plan strategically is the use of ESIF to 
postpone  (if  not  prevent)  the  necessary  changes  by  providing  a  dependable  source  of  financing  that 
sustains  outmoded  modes  of  governance  and  institutions  (and  attendant  orientations  and  functions  of 
innovations systems). For example, readily available ESIF funding may be acting as a substitute for H2020 
funding,  which  in  developing  regions  may  remove  the  incentive  to  reform  and  the  opportunities  to  learn 
and improve. 

16 

 
                                           
Business innovation capabilities as an instrumental development objective 

In a broader sense, identifying exactly how ESIF and H2020 can complement each other, requires an 
understanding  of  what  kind  of  innovation  capabilities  are  central  to  productivity  and  income 
improvements and how they accumulate over time. This understanding can be greatly enhanced by 
studying the developmental paths of what are today advanced innovation systems. The most striking 
feature  of  advanced  innovation  systems  is  the  central  role  of  the  business  sector  (Bell,  2009).  In 
thriving  innovation  systems,  most  R&D  is  both  financed  and  performed  by  businesses.  Public 
research  systems  are,  of  course,  central  to  advancing  the  frontiers  of  knowledge  and  as  such  are 
necessary  pillars  of  the  innovation  system,  but  most  economically  useful  innovation  takes  place 
within  firms.  The  path  to  both  economic  convergence  and  competitiveness  therefore  inevitably 
crosses from the accumulation of business innovation capabilities.  

This  is  why  focusing  on  the  smaller  subset  of  policies  designed  to  strengthen  business  innovation 
capabilities may be a way to enhance the impact of and complementarities between ESIF and H2020. 
Historically,  in  what  are  now  developed  innovation  systems,  business  innovation  capabilities  are 
accumulated  over  long  periods  of  time  as  a  function  of  the  systematic  carrying  out  of  innovation 
activities  internal  to  firms.  These  capabilities  can  be  initiated  (or  "bootstrapped"  e.g.  with  the 
insertion of a first engineer or a first researcher into a company)  where they do not exist, stimulated 
(with  innovation  vouchers,  prizes  etc.)  and  supported  in  many  ways,  including  by  way  of  targeted 
public  investments  in  human  resources,  by  supporting  innovation  activities  within  firms  that  are 
complementary  to  R&D  (see  practical  policy  directions  in  Arnold  and  Thuriaux,  1997;  Bell,  2009; 
Cirera et al., 2017) and by way of collaborative research and innovation projects with universities and 
public research organisations (Hanna et al., 1995, p. 80; Arnold and Thuriaux, 1997; Bell, 2009; OECD, 
2014).  It  would  seem  therefore  that,  in addition  to national  innovation  policies,  EU  investments  in 
innovation capacities as supported by ESIF, and in collaborative R&D projects as supported by H2020 
can play an important role in the accumulation of business innovation capabilities.  

Empirical  literature  suggests  that  differences  in  business  innovation  capabilities  lie  behind  the 
variable  outcomes  of  most  measurable  dimensions  of  economically  useful  innovation:  the  rate  of 
product and process innovation and its economic impact (Frenz and Ietto-Gillier, 2009; Varga et al. 
2014),  the  extent  and  quality of  university-industry  interaction  (Azagra-Caro et  al.,  2013)  and  even 
the ability of innovation systems to diversify into new sectors (Smith et al., 2005; Bell, 2009). In view 
of this evidence, in the analysis that follows we treat business innovation capabilities not just as an 
instrumental  objective  of  both  competitiveness  and  cohesion  policies  but  also  as  a  defining 
characteristic of regional and national innovation systems in the EU and major differentiator of their 
experiences and needs with respect to both ESIF and H2020. 

17 

 
 
 
 
4  Mobilisation of ESIF and H2020 across the EU  

Several aspects related to success in FP/H2020 have been discussed in the previous sections. Many of 
these  success  factors  have  so  far  been  primarily  described  at  the  level  of  the research  performing 
organisation,  but  there  are  also  aspects  affecting  participation that  relate  to  aggregate  features of 
the innovation system, at both the national and regional levels.   

In this section we attempt to make sense of structural features of European innovation systems and 
the ways in which they shape FP and ESIF mobilisation. Some of the key features examined include: 
overall  R&D  investments; the  weight  of  business  in  the  funding  and  performance  of  R&D;  and  the 
internationalisation  of  research,  with  its  implications  for  other  aspects  such  as  access  to  networks 
and consortia building. 

Research and innovation funding by source 
Two sets of data were extracted from the JRC R&I regional viewer6: the total of H2020 allocations up 
to  July  2017  by  NUTS2  region  and  planned  ESIF  investments  in  RTDI  relevant  categories  of 
intervention7. Yearly averages were calculated for each region and were then aggregated to obtain 
the yearly average funding8 at the national level. 

Table  1  provides  an  overview  of  R&D  expenditures  among  EU  member  states,  ranked  by  R&D 
intensity  [i.e.  Gross  domestic  expenditure  on  R&D  (GERD)  as  a  percentage  of  GDP].  With  few 
exceptions  (most  notably  Slovenia  and  the  Czech  Republic,  and  to  a  lesser  extent  Estonia  and 
Hungary),  new member states are concentrated towards the bottom of this ranking. The  picture  is 
not too dissimilar for the Business Enterprise Sector (BES) investments as a proportion of GDP.  

6 http://s3platform.jrc.ec.europa.eu/synergies-tool 
7 002 - Research and innovation processes in large enterprises; 056 – Investment in infrastructure, capacities 
and equipment in SMEs; 057 – Investment in infrastructure, capacities and equipment in large enterprises; 
058  –  Research  and  innovation  infrastructures  (public);  059  –  Research  and  innovation  infrastructures 
(private, including science parks); 060 -  research and innovation activities in public research centres; 061 
– Research and innovation activities in private research centres; 062 – Technology transfer and university-
enterprise  cooperation;  063  –  Cluster  support  and  business  networks;  064  –  Research  and  innovation 
processes in SMEs (including vouchers); 065 – Research and innovation processes, technology transfer in 
low carbon economy; 066 – Advanced support services for SMEs and groups of SMEs; 067 – SME business 
development, support to entrepreneurship and incubation; 073 – Support to social enterprises (SMEs); 101 
– Cross financing under the ERDF: support to ESF type actions. 

8 The economic indicators for research and innovation investments were extracted from the EUROSTAT website 
(January  2018).  The  EUROSTAT  indicators  for  research  and  development  (R&D)  include  statistics  on 
expenditure, personnel and government budget allocations for R&D. Data used related to performing sector 
and  the  source  of  the  funding.  Data  for  the  year  2015  were  used  except  where  all  the  data  were  not 
available, then the next most complete year was used. This is indicated in the tables and charts. By way of 
a caveat, it should be noted that the H2020 funds are actually allocated whereas the ESIF data used are 
only for planned investments. However, data from the 2013 Annual Implementation Report for the period 
2007-2013 show that absorption was generally over 90% (except for developing human potential). While 
there  were  variations  between  countries  the  rate  is  generally  high  and  should  increase  as  the  funds  are 
allocated post 2013. This provides some reassurance that planned amounts can be a reasonable estimation 
of investments for the 2014-2020 programming period. 

18 

 
                                           
Table 1: RTDI investments as a percentage of GDP (2015) 

*GDP (2014)  
** GDP (2013) 
***it is not known whether public funding for RTDI as extracted from EUROSTAT would include structural funds or not. It 
depends  whether  the  authority  reporting  the  statistics  has  the  ability  to  track  the  funds.  However,  there  is  no  crossover 
between BES, ESIF and H2020 intensities 

As  far  as  Gross  Expenditure  on  R&D  (GERD)  by  the  government  and  higher  education  sectors  is 
concerned,  particularly  low  levels  (below  0.35%  of  GDP)  are  evident  in  six  new  member  states 
(Croatia,  Cyprus,  Malta,  Latvia,  Romania,  and  Bulgaria).  ESIF  research  and  innovation  related 
investments,  however,  account  for  a  higher  share  of  GDP  in  new  member  states.  This  is  hardly 
surprising  for  a  cohesion  instrument  meant  to  facilitate  development.  However  the  figures  also 
highlight possible mobilisation challenges for ESIF – up to the time covered by our data – particularly 
for Romania.  

By contrast, H2020 tends to account for a higher share of GDP in older member states, which on the 
whole tend to spend more on R&D in general, and more of it is performed by businesses. High R&D 

19 

CountryGERD %  of GDP(1)BES %  of GDP(2)Public GERD % of GDP***(3)ESIF % of GDP(4)H2020 % of GDP(5)Spread between H2020 and ESIF intensity(5)-(4)  SE**3.3101.8700.9670.0180.0620.044AT3.0501.5201.0120.0120.0630.050DK2.9601.7600.8690.0100.0700.060DE2.9201.9100.8140.0220.0420.019FI2.9001.5900.8450.0340.0810.047BE2.4701.4400.6040.0150.0850.070FR*2.2301.2400.7940.0140.0370.023SI2.2001.5200.4440.2930.120-0.173NL2.0000.9700.6670.0110.0880.077CZ1.9300.6700.6350.2570.030-0.228UK1.6700.8200.4860.0140.0450.031EE1.4900.6100.6930.6540.118-0.537HU1.3600.6800.4720.3240.045-0.279IT1.3400.6700.5230.0420.038-0.004LU1.2700.6000.6270.0030.0390.035PT1.2400.5300.6050.3180.069-0.249ES1.2200.5600.5520.0730.065-0.008IE1.2000.5800.3280.0100.0510.042SK1.1800.2900.4140.3620.027-0.335LT1.0400.3000.3830.3810.024-0.357PL1.0000.3900.4420.4530.016-0.437EL0.9700.3000.5370.1110.093-0.019BG0.9600.3400.1960.2080.033-0.174HR0.8400.3900.3230.2770.028-0.250MT0.7700.3500.2620.1210.052-0.069LV0.6300.1300.2180.3930.048-0.344RO0.4900.1800.2120.1020.017-0.085CY0.4800.1000.2710.0620.1450.083 
 
intensities,  to  a  large  extent  supported  by  businesses  are  the  hallmark  of  dynamic,  developed 
innovation systems, thriving on virtuous cycles of innovation investments, synergies between public 
and private R&D  activities, higher  revenues  and productivity improvements due to innovation, and 
further innovation investments. 

As mentioned above, Slovenia, Estonia and the Czech Republic are exceptions among new member 
states.  The  three  countries  are  among  the  highest  R&I  investors,  a  relatively  high  share  of  their 
national  research  effort  is  performed  by  businesses  and  are  also  among  the  biggest  ESIF 
beneficiaries.  In  particular,  Estonia  stands  out  as  it  mobilises  a  significant  amount  of  ESIF  in  R&I 
(around one billion for the 2014-2020 period) but is also a good performer in Horizon 2020 based on 
H2020 funding as a percentage of GDP.  

The role of business R&D  

It  can  be  argued  that  business  funding  is  an  appropriate  summary  indicator  of  a  productive 
innovation  system.  When  business  funding  flows  into  public  research  it  is  because  there  is  an 
expectation it will lead to economically useful innovation – which on the whole corresponds to public 
research  systems  which  are  both  well-funded  through  other  sources  and  rigorously  steered  and 
governed (OECD, 2005: pp. 16-20; also see OECD, 2011 for recent trends in the governance of public 
research  in  advanced  innovation  systems).  Like  other  indicators  of  business  investment,  business 
funding  of  R&D  tends  to  be  higher  in  countries  with  legal  institutions  that  protect  the  rights  of 
investors  and  with  effective  public  policy  and  governance  (Guellec  and  van  Pottelsberghe,  2000; 
Brown  et  al.,  2017).  On  the  whole,  countries  where  businesses  are  willing  to  fund  the  bulk  of  the 
national  R&D  effort  (irrespective  of  whether  performed  in-house,  or  by  external  R&D  services 
suppliers  such  as  universities  and  PROs)  are  those  who  have  managed  to  instigate  virtuous  cycles 
between  resources  for  innovation,  profitable  innovation  and  more  resources  for  innovation.  As  a 
result they manage to mobilise more resources to innovation overall.  

For  these  reasons,  the  share  of  GERD  funded  by  business  can  be  a  revealing  indicator  of  the 
development of a national innovation system. Figure 2 presents the share of different  institutional 
funders of R&I (businesses, government, private non-profit and funding from abroad) in the national 
total  (GERD),  with  member  states  ranked  according  to  the  share  of  business  funding.  It  is 
immediately striking that countries in which businesses finance the bulk of the national R&D effort 
are  those  with  the  most  dynamic  innovation  systems  (as  attested,  for  example,  by  the  latest 
European  Innovation  Scoreboard:  EIS,  2018),  and  broadly  coincide  with  those  that  achieve  the 
highest overall R&D intensities too.  

20 

 
 
 
Figure 2: Sources of RTDI funds in EU – 2015 (*2014, **2013) 

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

I
S

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*
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* F
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BES

Public

Private non-profit

ESIF

H2020

Other Abroad

Where  does  EU  funding  fit  into  this  picture?  To  answer  this  question  additional  estimates  are 
necessary. Eurostat R&D statistics report funding sourced by the various institutional funders within 
the  country  and  from  abroad,  including  a  dedicated  category  for  EU  funding  "Abroad  -  European 
Commission" (ABR_EC).  We have further distinguished the broad Eurostat category "Funding from 
Abroad  –  European  Commission"  into  H2020  and  ESIF  funding,  by  multiplying  the  level  of  funding 
reported  by  Eurostat  by  the  share  of  H2020  and  ESIF  in  the  sum  total  of  our  administrative-data 
derived indicators. This comes with the caveat that while the distinction between business and public 
funds  is  clear  and  certain,  we  cannot  be  sure  that  the  same  is  true  of  the  distinction  between 
national and EU funds, the latter of which may on occasion have been (mis)classified as coming from 
national sources9. This suggests that ESIF in particular and likely H2020 may be underrepresented10. 
Presenting  ESIF  and  H2020  in  comparison  to  other  sources  of  R&D  funding  has  the  advantage  of 
appreciating their relative importance for individual countries, within a framework that is consistent 
with formal R&D statistics. 

9  According  to  the  Frascati  Manual  2015  publicly-financed  GERD  should  contain  only  national  resources  (and 
therefore  exclude  ESIF  and  H2020  funding).  According  to  EUROSTAT  (in  a  bilateral  email  exchange  on 
15/03/2018), if however, the reporting unit is not capable of tracing the source of funds correctly EU funds 
may be (mis)classified under the national or regional authority. As far as national accounts are concerned, 
according  to  Eurostat  (in  a  bilateral  email  exchange  on  7/11/2018)  ESIF  funds  would  be  recorded  as  a 
transfer from the "Rest of the World". 

10  An  ex  post  verification  of  the  estimated  levels  of  H2020  funding  using  this  method  compared  to  nearest 
available  year  estimates  of  levels  of  H2020  funding  available  from  alternative  sources  shows  that  this 
method  and  the  alternatives are  within  the  same  order of  magnitude  (average percentile  differences  less 
than 50%) and exhibit a high cross-country correlation (Pearson's R=0.9) 

21 

 
 
 
                                           
With respect to EU funding, Figure 2 shows that low shares of business funding correspond to high 
dependence on ESIF. Countries with weaker innovation systems (in terms of both R&D intensity and 
share of business  sector in total R&D)  tend to be  the ones  in which the  largest proportion of  R&D 
funding comes from the EU (including H2020 and ESIF) – new member states in particular but also 
Greece. Only SI goes against this pattern among new member states, and is indeed the country with 
the highest share of BERD in the EU. 

Regional variations 

The  national  structure  of  funding  provides  an  important  reflection  of  the  national  research  and 
innovation  system.  However,  there  can  be  significant  variation  between  the  different  regions  in  a 
country. These are politically important, especially given the impulse given to the regional dimension 
of innovation policy by RIS3.  

Figures  3  and  4  present  H2020  and  ESIF  intensities  for  each  country  respectively,  showing  the 
national  average  (as  the  dot)  as  well  as  the  maximum  and  minimum  regional  intensities 
(corresponding  to  either  extremity  of  the  vertical  lines).  There  are  eight  smaller  countries11  where 
the NUTS2 accounts for the  whole country or have been treated as one territory  due to their size. 
This constraint confines our examination of sub-national variance to 20 EU member states only.  We 
use mostly the NUTS2 and in some cases NUTS1 level of territorial aggregation (see Methodological 
Notes in section 5 for a fuller explanation). 

Figure 3: H2020 funding intensity showing national average and regional max/min 

H2020 funding as % of GDP 

0.25 

0.20 

0.15 

0.10 

0.05 

0.00 

Y
C

I
S

E
E

L
E

L
N

E
B

I
F

K
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T
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S
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A

T
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K
U

U
H

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D

U
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T
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*
R
F

G
B

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R
H

K
S

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R

L
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*
*
E
S

Max regional % of GDP 

Min regional % of GDP 

National H2020 % of GDP 

Greece, followed by Spain, Belgium and Italy are the countries with the greatest variations between 
regions in H2020. In the case of ESIF, the largest variations between regions are in Poland, Portugal 
and  Hungary.  We  observe  greater  differences  in  regional  allocation  of  funding  (defined  as  the 

11 Croatia, Cyprus, Estonia, Latvia, Lithuania, Luxembourg, Malta, Slovenia 

22 

 
 
 
                                           
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
difference  between  the  region  with  the  national  minimum  and  the  region  with  the  national 
maximum) for H2020 than for ESIF.  

Figure 4: ESIF STI funding intensity showing national average and regional max/min 

ESIF STI funding as % GDP 

1.00 

0.90 

0.80 

0.70 

0.60 

0.50 

0.40 

0.30 

0.20 

0.10 

0.00 

E
E

L
P

V
L

T
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S

R
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B

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*
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*
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K
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E
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U
L

Max regional % of GDP 

Min regional % of GDP 

National ESIF % of GDP 

It is also possible to distinguish between capital regions, metropolitan regions and other regions. The 
NUTS3  level  is  used  to  identify  capital  and  metropolitan  regions,  the  latter  being  either  a  NUTS3 
region on its own right or a combination of NUTS 3 regions which represent all agglomerations of at 
least 250 000 inhabitants12. Of the 20 countries with regional variance, 14 of those with maximum 
H2020  intensity  contain  capital  regions,  three  have  metropolitan  regions  and three  do  not  contain 
any metropolitan region (EL, IT and SE). For the minimum intensity regions 11 are metropolitan and 
nine are other regions. Capital regions tend to attract more H2020 funding, which underscores the 
importance of co-location for innovation activity of this kind. It also suggests that the magnitude of 
the  development  challenge  for  lagging  regional  innovation  systems  is  proportional  to  existing 
economic  disparities  within  countries,  which  should  serve  to  temper  expectations  about  what  is 
feasible within politically-relevant timeframes. 

Structure of research and innovation system: international engagement 

The review of determinants for H2020 participation in Section 2 highlighted the role of access to and 
integration  into  international  research  networks.  International  engagement  stands  to  improve  the 
innovative  performance  of  a  country  or  region.  Furthermore,  trans-regional  and  international 
cooperation  is  also  an  important  dimension  of  smart  specialisation.  New  member  states  have  a 
strong dependence on foreign direct investment (FDI) and global value chains and yet their Research 
and  Innovation  Strategies  for  Smart  Specialisation  (RIS3)  are  not  internationalised  (Radosevic  and 
Stancova,  2015).  However,  while  networks  and  knowledge  flows  are  considered  important  for  the 
it  should  be 
innovation  performance  of  countries  and  regions  (Runiewicz-Wardyn,  2013) 

12 http://ec.europa.eu/eurostat/web/metropolitan-regions/background 

23 

 
 
 
 
                                           
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
remembered  that  just  connecting  internationally  is  not  enough:  knowledge  spillovers  may  not 
happen  in  the  absence  of  a  minimum  necessary  level  of  innovation  capabilities  in  the  region 
(Miguélez and Moreno, 2015). 

Figure  5  plots  the  proportion  of  internationally  co-authored  papers13  against  the  number  of 
researchers  and  engineers  in  each  EU  member  states  (using  EUROSTAT  data).  The  horizontal  axis 
shows the proportion of international co-publications over the country's total publication output in 
the  time  period.  Figure  5  shows  that  EU  member  states  tend  to  follow  the  well-known  pattern  of 
smaller countries being engaged in international collaboration more than larger countries (Schubert 
and Braun, 1990; Leclerc  and Gagné,  1994). However, it is noticeable  that  while  both old and new 
member  states  follow  this  pattern,  new  member  states  (in  red)  as  a  group  are  less  internationally 
engaged.  The  lower  proportions  of  international  co-authorship  are  dominated  by  new  member 
states,  suggesting  that  they  are  not  as  extensively  linked  in  international  research  as  other  EU 
countries.  Lack  of  international  connectedness  is  itself  both  a  cause  and  an  effect  of  difficulties  in 
participating in H202014, but probably goes well beyond it. 

Figure 5: International  co-authorship against number  of researchers and engineers in a 
country15 

Source: Harrap and Doussineau (2017) 

A concerted policy to open up of research and innovation systems in new member states and favour 
internationalisation, should not just support participation in joint research projects, but also include 

13  This  data  was  extracted  from  Scopus.  The  time  frame  for  extracted  publications  was  2007-2013.    The 
analysis was restricted to articles, conference papers, book chapters and books. International collaboration 
is inferred when the authors' affiliation addresses are in two or more different countries. The co-authorship 
can be attributed to a country (or region) through whole counting where every country with a contributing 
author is counted (Leydesdorff and Wagner, 2008). In fractional counting the country is assigned a fraction 
of the paper. In our case, as our interest is primarily with the presence or absence of an international link 
(rather than the volume of authorship), the whole counting approach is appropriate. 

14  It  is  indicative  that  in  pairwise  correlations  between  H2020  funding  intensity,  and  a  handful  of  national 
innovation indicators (such as overall R&D intensity, public R&D intensity, business R&D intensity, and co-
publications)  the  share  of  international  co-publications,  followed  by business R&D  intensity,  were  the  two 
variables with the highest correlation coefficient (0.44 and 0.21 respectively). 

15 Extracted from Scopus and restricted to articles, conference papers, book chapters and books. 

24 

 
 
                                           
initiatives catering to the entire chain of knowledge generation and circulation, including training and 
human resource mobility initiatives, the harnessing of diaspora networks, and greater participation in 
international funding consortia.   

25 

 
5  H2020 mobilisation relative to ESIF: regional patterns and groupings 

In this section we attempt to characterise EU regions according to their levels of R&D intensity, on 
the one hand and the relative weight of H2020 versus ESIF funding on the other. This mapping can 
highlight some common trends, allowing us to group regions, relate these to possible needs and may 
thus allow policy makers at both the EU and the national levels to better tailor support for innovation 
in those territories. More tailored support to regional authorities would facilitate the identification of 
good practices and their possible transfer between similar regions. 

Regional  innovation  systems  vary  enormously  and  are  themselves  parts  of  broader  national  and 
sectoral  innovation  systems  with  distinct  trajectories  and  needs  for  support.  On  the  basis  of  prior 
literature that underscores the role of scientific excellence, internationalisation, business innovation 
activity and also complementarities between national funding sources and H2020 participation (see 
section 2), we anticipate that the better a research system is performing on these counts, the more 
funding it can obtain from H2020. Focusing on the specific objective of enhancing the participation of 
regions with weak innovation capabilities in H2020, our attention can be profitably focused on those 
regions where relatively small changes in research and innovation policies can have large impacts. At 
either extremity, there are of course systems with weak innovation capabilities in both the public and 
the  business  sector  and  systems  with  generally  strong  innovation  capabilities,  underpinned  by 
scientific excellence. Of  particular  interest  here  are the  various  types  of other systems  in  between 
these two extremes. These include systems with relatively strong public research but only moderate 
or weak  business  innovation capabilities (e.g. EL, PT,  and CY)  and systems which have  managed to 
mobilise considerable resources for research and innovation but have not yet managed to translate 
these into improvements in scientific excellence or economically useful innovation. The latter group 
includes a handful of countries, mostly in Central and Eastern Europe which have relatively recently 
secured large business investments in innovation, primarily as a by-product of FDI, and stand to gain 
the most from further participation in H2020.  

Methodological notes  

Territories taken into account 

In the interest of policy relevance, to the extent that available statistics permit, the territorial level 
assigned corresponds to either the political or the administrative level. In total, the analysis covers 
200  NUTS2  and  NUTS1  territorial  entities,  including  country-level  “regions”  (e.g.  CY,  MT,  LU).  This 
reflects the  diversity of territorial organisation in the EU and the level of regional policy making in 
each member state16.  

Chosen data and indicators for analysis 

For each region, three types of information help us to position EU regions:  

1.  GERD relative to regional GDP. The latest available indicators (2015) are taken from Eurostat. 
2.  The total planned amount of ESIF dedicated to Research and innovation at regional level for 
the  period  2014-2020.  The  planned  investments  in  European  Structural  and  Investment 
Funds (ESIF) concerns only funding clearly earmarked for research and innovation, activities. 

16 Country codes and NUTS level taken into consideration for this analysis: AT=2; BE=1; BG=2; CY=1 (country 
level); CZ=2; DE=1; DK=2;EE=1 (country level); EL=2; ES=2; FI=2; FR=2; HR=1 (country level); HU=2; 
IE=2;  LT=1  (country  level);  LV=1  (country  level);  MT=1  (country  level);  NL=1;  PL=2;  PT=2;  RO=2; 
SE=2; SI=1 (country level); SK=2; UK=1 

26 

 
                                           
Of the funds that make up ESIF17, these are mostly from the European Regional Development 
Fund  (ERDF),  followed  by  the  European  Social  Fund  (ESF)  and  the  Cohesion  Fund  in 
decreasing proportions. The territorial level (NUTS level) used depends on the availability of 
data  from  regional  operational  programmes  (OP)  and  also  the  national  operational 
programmes.  The  share  of  each  region  in  national  OPs  has  been  estimated  by  taking  into 
account the population size of the regions and their development stage. Data depicted here 
are  thus  estimations  of  planned  investments  and  do  not  reflect  final  investment  figures.  A 
broad  definition  of  research  and  innovation  activities  takes  into  account  categories  of 
intervention both within and outside the Thematic Objective 1 specifically devoted to them. 
Infrastructures, support to SMEs and social innovation are also considered covering in total 
15 categories of intervention related to Research and innovation (see list in annex 1). 
3.  The total amount of Horizon 2020 funding captured by beneficiaries in the regions18. 

Chosen thresholds for grouping regions 

Any attempt to group dissimilar entities entails an element of arbitrariness and the present exercise 
is no exception. In terms of regional R&D intensity, a chief consideration for determining a threshold 
is to set it at a level that is indicative of a regional innovation system of intermediate capability. That 
is, an innovation system that has already made some notable progress in mobilising R&D resources, 
including  private  funds,  and  therefore  stands  a  reasonable  chance  of  progressing  to  the  more 
systematic, interactive, and ambitious innovation activity that can facilitate meaningful participation 
in H202019. Lacking any other yardstick, and for simplicity's sake, we set the threshold for the level of 
research investment at 1% of GDP. This threshold is far from the EU average of 2.3% (Eurostat, 2015) 
and even further from the Barcelona objective of 3%20. However it is not entirely arbitrary. With only 
a handful of rather uncharacteristic exceptions, the vast majority of regions (about fifty) with H2020-
intensive EU innovation support, spend at least 1% of their GDP on R&D (Figure 1). Additionally, as 
most  EU  regions  cluster  around  this  value,  it  is  not  irrelevant  or  unrealistically  beyond  reach,  as  a 
target  to  aspire  to.  It  therefore  seems  a  fitting  threshold  for  something  like  a  minimum  of  overall 
R&D activity necessary for the region to stand a reasonable chance of graduating to H2020-intensive 
EU support.  

We also set the reference point for the ratio H2020/ESIF to 1. The level of parity between H2020 and 
ESIF funds has a more straightforward interpretation. If the ratio is inferior to 1, it means the region 
in  question  is  capturing  less  H2020  funding  than  it  is  allocating  ESIF,  which  would  be  expected  in 

17  There  are  five  European  Structural  and  Investment  Funds:  European  regional  development  fund  (ERDF); 
European  social  fund  (ESF);  Cohesion  fund  (CF);  European  agricultural  fund  for  rural  development 
(EAFRD); and European maritime and fisheries fund (EMFF). 

18  The  regionalisation  of  data    may  be  affected  by  a  headquarter  effect  (HQE)  in  some  Member  States 
(particularly in France, Italy and Spain) due to the difference between the (physical) location of the actual 
research  performer  and  the  (legal)  location  of  the  beneficiaries'  headquarter  (this  problem  affects  in 
particular  large  public  research  organisations).  Data  for  France,  Italy  and  Spain  have  been  corrected  to 
control for the actual regional location of Horizon 2020 beneficiaries. The period covered by Horizon 2020 
indicators goes from 2014 onwards to June 2017 

19  A  single  indicator,  such  as  R&D  intensity,  can  of  course  be  only  very  imperfectly  suggestive  of  the  diverse 
and multifaceted research and innovation activity and implied capability. Implicit in its use here is the, not 
entirely  unjustified  assumption  (see,  among  others,  Bell,  2009)  that  it  correlates  with  a  broad  swathe  of 
complementary  innovation  investments,  activities  and  capabilities  that  are  strictly  not  covered  by  the 
narrow definition of R&D.  

20  The  Barcelona  European  Council’s  objective  to  raise  overall  R&D  investment  to  3%  of  GDP.  See 

http://ec.europa.eu/invest-in-research/action/history_en.htm.  

27 

 
 
                                           
regions  of  low  innovation  capability.  If  the  ratio  is  above  1,  it  means  the  region  in  question  is 
capturing  more  in  H2020  than  the  funding  it  allocates  through  ESIF21,  as  would  be  expected  of 
regions of higher innovation capability, particularly capital regions and other regions encompassing 
large cities.  

Resulting groups of regions 

Figure 6 plots data for EU regions along two axes:  

  a horizontal axis with R&D intensity, which we take as a summary measure of the overall 

research and innovation effort of each region (in public organisations and in businesses) and; 

  a vertical axis showing the ratio between the amount of Horizon 2020 funding and the 
amount of ESIF funding on R&I by each region (henceforth the H2020-to-ESIF ratio) 22.  

It is immediately apparent that there is a moderate correlation between R&D intensity and H2020-
intensive EU support. Regions where R&D accounts for a more significant share of economic activity 
tend to be more  successful in attracting H2020 funding, as reflected in the path of the continuous 
regression line. R&D intensity alone explains about 47% of observed differences in the H2020-to-ESIF 
ratio. 

21  On  R&D  intensity  dimension  (vertical  axis),    if  x<1  then  'b'  else  'a'.    On  the  ratio  H2020/ESIF  dimension 

(horizontal axis), if y<1 then 'b' else 'a' 

22  More  specifically,  the  horizontal  axis  corresponds  to  regional  R&D  intensity  from  Eurostat  (2015),  i.e.  
 GERD(regio)/GDP(regio)  and  the  vertical  axis  corresponds  to  the  (log  of)  the  ratio  H2020/ESIF:  (total 
H2020 funding captured at regional annualised)/(total ESIF allocated to R&I annualised). 

28 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                           
Figure 6: Three main areas defining EU regions according to R&I spending and their use 
of EU funding  

Taking into account the abovementioned thresholds, at least three23 policy-relevant groups of EU 
regions with similar EU fund mobilisation experience for a given R&D intensity can be defined.  

  Group 1: the “Lagging R&I regions” - area defined by low R&D intensity with higher ESIF 

dependence and lower capacities to capture H2020 funding.  

  Group 2: the “Intermediary R&I regions” - area defined by a higher R&I intensity 

combined higher ESIF dependence and lower capacities to capture H2020 funding 

  Group 3: the “Better-performing R&I regions” - area with higher R&D intensity combined 

with lower ESIF dependence.  

Figure 6 points to a notional development path, whereby regions gradually progress from group 1, to 
group 2, through to group 3 as a function of the overall development of their innovation systems  – 
loosely proxied here by R&D intensity. Of course actual development paths, and the policies needed 
to  support  them,  are  not  going  to  be  as  straightforward.  Yet  broad  tendencies  such  as  these  are 
indicative of the expected progression and relative weights of the two funding streams as a function 
of  capability  accumulation  and  are  broadly  suggestive  of  the  workings  of  a  methodical  process  of 
cumulative capability accumulation, such as described in Figure 1. 

The  number  of  Lagging  and  Better-performing  R&I  regions  comprising  groups  1  and  3  is 
approximately the same (77 versus 74 regions) but there are other important differences (see Table 2 
below).  In  terms  of  population,  Better-performing  R&I  regions  are  twice  as  large  as  the  Lagging 
regions, suggesting that urban areas may be more represented in Better-performing regions than in 

23 The remaining quadrant (a possible Group 4) only gathers 5 atypical regions with a low R&I intensity but with 
good  performance  in  H2020  compared  the  ESIF  planning.  However,  the  small  number  and  diverse 
development and policy trajectories of these regions, preclude a meaningful grouping. 

29 

 
 
                                           
other areas. The overall amount of economic activity is an important delineator too. In terms of the 
size  of  their  economies,  as  measured  by  GDP,  Better-performing  regions  are  about  3  times  larger 
than Intermediary regions and 6 times  larger than Lagging regions. However, these differences are 
particularly  pronounced  when  considering  R&D  expenditures.  The  combined  GERD  of  Better-
performing  regions  is  4.5  times  larger  than  Intermediary  regions  and  23  times  larger  than  Lagging 
regions. 

Table 2: Main characteristics of the 4 groupings of regions 

Source: Eurostat. Own calculation 

Figure 7 identifies all regions included in the analysis with their NUTS2 codes and Figure 8 places each 
regional grouping on a map. The discussion that follows examines each regional grouping separately. 

Figure  7:  EU  regions  according  to  their  R&D  intensity  and  participation  in  H2020  and 
planned ESIF allocation 

30 

Number of Regions%Population (2015)%GDP (2015) Meur%Est. GERD in Meur%Area 1 'Lagging' regions7738.5%118 548 49323.4%1 639 15811.3%11 1523.8%Area 2 'Intermediary' regions4422.0%100 063 07819.8%2 091 12214.4%31 90910.8%Area 3 'Performing' regions7437.0%272 429 98453.8%10 093 16669.3%245 55383.0%Area x regions52.5%15 120 3493.0%742 9285.1%7 0642.4%Total200100.0%506 161 904100.0%14 566 374100.0%295 679100.0% 
 
 
 
Figure 8: Geographical distribution of the identified groups 

Group 1: Lagging R&I regions: Higher ESIF allocation than H2020 funding and low R&I 
intensity 

This group of regions is the most numerous, comprising 38.5% of  the 200 EU28 regions considered 
but represent 23.4% of population, 11.3% of GDP and only 3.8% of R&D expenditure. These regions 
allocate the highest share of ESIF to R&I. At the same time their performance in H2020 is generally 
low. This group is made of mostly new member states (outside of capital regions): PL, CZ, HU, SK, LV, 
and LT. The only exception is Portugal:  almost all Portuguese Regions are also in this group. A first 
interpretation  could  be:  Group  1  regions  have  research  and  innovation  capabilities  but  are  lagging 

31 

 
 
 
behind  in  terms  of  performance  in  competitive  programmes  such  as  Horizon  2020  due  to  lack  of 
broad innovation capabilities and even possible competition between ESIF funding and H2020 (which 
is  anecdotally  reported  in  S2E  national  events  but  not  yet  demonstrated).  Except  Portugal,  RIS3 
strategies are designed at the national level and TO1 is managed partially (for Poland) or totally at 
the national level (CZ, HU, SK). As this is a group with many regions, further work may be useful in 
identifying further subgroups, to better discriminate regions and better shape policy directions. 

Table  3:  Lagging  R&I  regions:    Higher  ESIF  allocation  than  H2020  funding  and  low  R&I 
intensity 

Group 1 

ESIF allocation > H2020 funding 
R&I intensity <1  

77 Regions (38.5%) 
23.4 % of the EU population 
11.3% the EU GDP 
3.8% of EU R&D expenditure 

AT11 
BG31 
BG32 
BG33 
BG34 
BG42 
CZ04 
EL11 
EL12 
EL13 
EL21 
EL22 
EL23 
EL24 
EL25 
EL41 
EL42 
ES11 
ES12 
ES13 
ES23 
ES42 
ES43 
ES53 
ES62 
ES70 
FR21 
FR26 
FR30 
FR53 
FR83 
HR 
HU22 
HU23 
HU31 
ITC2 
ITF1 
ITF2 
ITF4 

Burgenland 
Severozapaden 
Severen Tsentralen 
Severoiztochen 
Yugoiztochen 
Yuzhen Tsentralen 
Severozapad 
Anatoliki Makedonia 
Kentriki Makedonia 
Dytiki Makedonia 
Ipeiros 
Ionia Nisia 
Dytiki Ellada 
Sterea Ellada 
Peloponnisos 
Voreio Aigaio 
Notio Aigaio 
Galicia 
Principado de Asturias 
Cantabria 
La Rioja 
Castilla-La Mancha 
Extremadura 
Islas Baleares 
Region De Murcia 
Canarias 
Champagne-Ardenne 
Bourgogne 
Nord - Pas-De-Calais 
Poitou-Charentes 
Corse 
Hrvatska 
Nyugat-Dunantul 
Del-Dunantul 
Eszak-Magyarorszag 
Valle dAosta 
Abruzzo 
Molise 
Puglia 

ITF5 
ITF6 
ITG1 
ITG2 
ITI2 
ITI3 
LT 
LV 
MT 
PL11 
PL22 
PL31 
PL33 
PL34 
PL41 
PL42 
PL43 
PL51 
PL52 
PL61 
PL62 
PL63 
PT15 
PT18 
PT20 
PT30 
RO11 
RO12 
RO21 
RO22 
RO31 
RO32 
RO41 
RO42 
SE32 
SK02 
SK03 
SK04 

Basilicata 
Calabria 
Sicilia 
Sardegna 
Umbria 
Marche 
Lietuva 
Latvija 
Malta 
Lodzkie 
Slaskie 
Lubelskie 
Swietokrzyskie 
Podlaskie 
Wielkopolskie 
Zachodniopomorskie 
Lubuskie 
Dolnoslaskie 
Opolskie 
Kujawsko-Pomorskie 
Warminsko-Mazurskie 
Pomorskie 
Algarve 
Alentejo 
Acores 
Madeira 
Nord-Vest 
Centru 
Nord-Est 
Sud-Est 
Sud - Muntenia 
Bucuresti – Ilfov 
Sud-Vest Oltenia 
Vest 
Mellersta Norrland 
Zapadne Slovensko 
Stredne Slovensko 
Vychodne Slovensko 

Group 2: 'Intermediary' R&I regions:  Higher ESIF allocation than H2020 funding and higher 
R&I intensity 

This intermediate group can be considered as a 'transition' stage between groups 1 and 3. Assuming 
that regional innovation systems in this group can continue on a sustainable development trajectory, 
progressively more  R&D–intensive, more  interactive  and  more  ambitious  innovation  activity  would 
reduce the dependence on ESIF funding and reinforce capacity of research organisations (public and 
private) to secure Horizon 2020 funding.  

As  these  regions  are  positioned  away  from  the  central  tendency  of  EU  regions  (denoted  by  the 
continuous  regression  line  in  Figures  1  and  2),  it  can  be  argued  that  their  H2020  performance  is 
below what would be expected in view of their R&D intensity. The challenge for these regions is to 

32 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
identify the necessary bottlenecks, both in the orientation/governance of their public research and in 
terms of capability accumulation in the business sector so that they reap the benefits of the already 
notable  local  R&D  activity.  As  this  group  of  regions  is  also  numerous,  creating  further  subgroups 
could be useful. For instance, South Moravia (CZ06) is among the EU regions with the highest R&D 
intensity but with only a low ratio of H2020/ESIF, and is likely to require different policy interventions 
than other members of this group.    

Table  4:  Intermediary  R&DI  regions:  Higher  ESIF  allocation  than  H2020  funding  and 
higher R&I intensity 

Group 2  

ESIF allocation > H2020 funding  
R&I intensity>1  

44 Regions (22%) 
19.8% of the EU population 
14.4% the EU GDP 
10.8% of EU R&D expenditure 

BG41 
CZ02 
CZ03 
CZ05 
CZ06 
CZ07 
CZ08 
DE4 
DE8 
DED 
DEE 
DEF 
DEG 
DK02 
EE 
EL14 
ES41 
ES52 
ES61 
FR22 
FR23 
FR24 

Yugozapaden 
Stredni Cechy 
Jihozapad 
Severovychod 
Jihovychod 
Stredni Morava 
Moravskoslezsko 
Brandenburg 
Mecklenburg-Vorpomn 
Sachsen 
Sachsen-Anhalt 
Schleswig-Holstein 
Thuringen 
Sjaelland 
Eesti 
Thessalia 
Castilla Y Leon 
Comunidad Valenciana 
Andalucia 
Picardie 
Haute-Normandie 
Centre 

FR25 
FR41 
FR43 
FR51 
FR61 
FR63 
FR72 
HU21 
HU32 
HU33 
ITF3 
PL12 
PL21 
PL32 
PT11 
PT16 
SE21 
SE31 
SI 
SK01 
UKL 
UKN 

Basse-Normandie 
Lorraine 
Franche-Comte 
Pays De La Loire 
Aquitaine 
Limousin 
Auvergne 
Kozep-Dunantul 
Eszak-Alfold 
Del-Alfold 
Campania 
Mazowieckie 
Malopolskie 
Podkarpackie 
Norte 
Centro 
Smaland och oarna 
Norra Mellansverige 
Slovenija 
Bratislavsky Kraj 
Wales 
Northern Ireland 

Group 3: Better-performing R&I regions:  Higher H2020 funding than ESIF allocation and 
higher R&I intensity 

Group 3 could be considered as the target to reach for all other EU28 regions. This group gathers the 
regions with the highest R&D intensity and is mostly composed of regions from western EU (EU 15) 
and capital regions from new Member States.  

Agglomeration  in  a  region  and  the  presence  of  large  companies  in  particular,  may  influence 
considerably  the  level  of  R&D  expenditure.  Among  the  regions  with  the  highest  EU  R&D  intensity 
figure  regions  with  important  corporate  presences,  for  instance  by  Airbus  for  the  Midi-Pyrenees 
NUTS2 (FR62) region (now part of the new Occitanie region) in France.  The Styria region (AT22) in 
Austria and Baden-Württemberg region (DE1) in Germany are also among the biggest R&D investors 
in the EU. Subgroups may better discriminate regions in this group too: as our chosen R&D intensity 
threshold is low (1%) there is ample room to discriminate regions between 1% and regions with the 
highest R&D intensity (near 5%).  

33 

 
 
 
 
 
 
 
 
 
 
Table 5: Better-performing R&I regions: Higher H2020 funding than ESIF allocation and 
higher R&I intensity 

Group 3 

H2020 funding capture >  ESIF allocation  
R&I intensity >1  

74 Regions (37%) 
53.8 % of the EU population 
69.3% the EU GDP 
83% of EU R&D expenditure 

AT12 
AT13 
AT21 
AT22 
AT31 
AT32 
AT33 
AT34 
BE1 
BE2 
BE3 
CZ01 
DE1 
DE2 
DE3 
DE5 
DE6 
DE7 
DE9 
DEA 
Westfalen 
DEB 
DEC 
DK01 
DK03 
DK04 
DK05 
EL43 
ES21 
ES22 
ES30 
Madrid 
ES51 
FI19 
FI1B 
FI1C 
FI1D 
FR10 
FR42 

Niederosterreich 
Wien 
Karnten 
Steiermark 
Oberosterreich 
Salzburg 
Tirol 
Vorarlberg 
Region De Bruxelles-  
Vlaams Gewest 
Region Wallonne 
Praha 
Baden-Wurttemberg 
Bayern 
Berlin 
Bremen 
Hamburg 
Hessen 
Niedersachsen 
Nordrhein-

Rheinland-Pfalz 
Saarland 
Hovedstaden 
Syddanmark 
Midtjylland 
Nordjylland 
Kriti 
Pais Vasco 
Navarra 
Comunidad De 

Cataluna 
Lansi-Suomi 
Helsinki-Uusimaa 
Etela-Suomi 
Pohjois- Ja Ita-Suomi 
Ile De France 
Alsace 

FR52 
FR62 
FR71 
FR81 
FR82 
HU10 
IE01 
IE02 
ITC1 
ITC3 
ITC4 
ITH2 
ITH3 
ITH4 
ITH5 
ITI1 
ITI4 
LU 
NL1 
NL2 
NL3 
NL4 
PT17 
SE11 
SE12 
SE22 
SE23 
SE33 
UKC 
UKD 
UKE 
UKF 
UKG 
UKH 
UKJ 
UKK 
UKM 

Bretagne 
Midi-Pyrenees 
Rhone-Alpes 
Languedoc-Roussillon 
Prov-Alpes-Côte Dazur 
Kozep-Magyarorszag 
Border Midland And W 
Southern and Eastern 
Piemonte 
Liguria 
Lombardia 
Trento 
Veneto 
Friuli-Venezia Giulia 
Emilia-Romagna 
Toscana 
Lazio 
Luxembourg 
Noord-Nederland 
Oost-Nederland 
West-Nederland 
Zuid-Nederland 
Lisboa 
Stockholm 
Östra Mellansverige 
Sydsverige 
Vastsverige 
Övre Norrland 
North East (England) 
North West (England) 
Yorksh and The Humber 
East Midlands (England) 
West Midlands (England) 
East Of England 
South East (England) 
South West (England) 
Scotland 

34 

 
 
  
 
 
 
 
6  Concluding remarks: creating virtuous cycles of European funding, 

research excellence and economically useful innovation 

Targeting  ESIF  support  towards  strengthening  scientific  excellence,  by  investing  in,  upgrading  and 
reorienting  regional  and  national  research  systems  so  that  they  are  increasingly  internationalised, 
appears  a  meaningful  policy  direction.  ESIF  support  for  capability  development  of  this  kind  and 
associated governance reforms – e.g. in terms of recruitment, mobility, collaboration and evaluation 
practices - can progressively anchor research teams, institutions, and entire innovation systems into 
science  and  technology  networks  dealing  with  research  questions  of  global  significance.  Insofar  as 
international scientific  collaboration  is  a  precursor  to  H2020  participation,  it  should  also  help  raise 
the  participation  of  these  regions  in  H2020,  instigating  a  virtuous  cycle  of  capability  accumulation 
and scientific excellence.  

The  evidence  presented  in  this  report  highlights  that  there  are  large  gaps  of  both  capabilities  and 
outcomes  between  and  across  EU  member  states  and  their  constituent  regions.  Mobilising  EU 
funding for research and innovation in the countries and regions that stand to gain the most  from 
them  is  a  formidable  challenge,  requiring  a  context-specific  understanding  of  the  bottlenecks  and 
tailored solutions. However, the stylised development path discussed in this report foresees both a 
division of labour and synergies between ESIF and H2020, and holds the following policy lessons for 
all developing regions: 

 Whereas ESIF is geared towards strengthening innovation capabilities of all kinds, H2020
is very much focused on research problems of global economic significance. For new
member states in particular, improving participation in H2020 is more than just about
access to funding – it is about setting the basis for a public research system that operates
at the global level.
Lack of sufficient international orientation and connectedness is likely both a cause and
an effect of the difficulties new member states face in participating in H2020. In this
context, the opening up and internationalisation of research and innovation systems can
be a profitable instrumental objective for innovation policy in general and RIS3 in
particular.



 Disparity in business innovation capabilities, particularly between new and old member

states, also appears to be a delineator of H2020 mobilisation. Strengthening the
innovation capabilities of businesses can be a complementary instrumental objective for
ESIF that would have many other obvious benefits besides H2020 mobilisation.
 National funding is also important. Success in the mobilisation of EU instruments also
depends on complementary policy interventions at the national level. Regional
innovation ecosystems are themselves part of national, and technological/sectoral
innovation systems that cannot always be supported by EU funds.
The synergies at the level of policies we highlight here – such as the conditionality of
H2020 success on ESIF support for scientific excellence and of support for business
innovation capabilities - are especially important for RIS3, as they imply that finely
tailored territorial strategies employing a sophisticated and appropriate sequenced
policy mix can be profitable.



35 

Further  analytical  work  can  provide  more  specific  directions  for  policy  development.  For  instance, 
matching  the  regional  data  already  examined  with  the  specialisation  areas  available  on  the  JRC's 
Eye@RIS3  database24  or other  economic  indicators  (unemployment  rate,  research  capacity  such  as 
number of researchers etc.), similarities between regions within a group can be further investigated. 
A thematic approach can be envisaged by selecting areas in H2020 and considering only regions that 
have chosen the same area of specialisation in their RIS3. Boundaries can be refined or adapted in 
order  to  investigate  more  precisely  regional  characteristics.  For  example,  Group  3  and  4  can  each 
potentially be divided further  into two sub-groups according to H2020 performance. The incidence 
and  conditions  under  which  (the  anecdotally  reported  but  not  yet  confirmed)  discretionary 
substitution between ESIF and H2020 can occur should be investigated further. 

24 Innovation Priorities in Europe - http://s3platform.jrc.ec.europa.eu/map.  

36 

 
 
 
 
                                           
7  References  

Arnold, E. and Thuriaux, B. (1997), “Developing Firm’s Technological Capabilities”, Technopolis, Brighton. 

Azagra-Caro, J., Pontikakis, D. and Varga, A. (2013), “Delocalization Patterns in University-Industry Interaction: 
Evidence from the Sixth R&D Framework Programme”, European Planning Studies, Vol. 21(10), pp. 1676-1701. 

Annerberg, R., Begg, I., Acheson, H., Borrás, S., Hallén, A., Maimets, T., Mustonen, R., Raffler, H., Swings, J-P. 
and  Ylihonko,  K.  (2010),  ''Interim  Evaluation  of  the  Seventh  Framework  Programme:  Report  of  the  Expert 
Group'', Final Report 12 November 2010. 

Bell, M. (2009), “Innovation Capabilities and Directions for Development”, STEPS Working Paper 33, Brighton: 
STEPS Centre.  

Breschi, S. and Malerba, F. (2009), ''ERA and the role of networks, in European Science and Technology Policy: 
Towards integration of fragmentation?'' Eds Delanghe, H., Muldur, U. and Soete, L., Edward Elgar, Cheltenham 
UK. 

Boden, M., dos Santos, P., Haegeman, K., Marinelli, E., Valero, S. (2016), “Implementing RIS3 in the Region of 
Eastern Macedonia and Thrace: Towards a RIS3 Toolbox”, JRC S3 Policy Brief Series, No. 20/16. 

Brown,  J.  R.,  Martinsson,  G.  and  Petersen,  B.  C.  (2017),  “What  promotes  R&D?  Comparative  evidence  from 
around the world”, Research Policy, 46(2), pp. 447-462. 

Chorafakis,  G.  and  Pontikakis,  D.  (2010)  “Theoretical  underpinnings  and  future  directions  of  European  Union 
research policy: a paradigm shift?”, Prometheus: Critical Studies in Innovation, Vol. 29(2), pp. 13-161. 

Cirera, X., Goni Pacchioni, E. A. and Maloney, W. (2017), “Why poor countries invest too little in R&D”, VOX, 
Center for Economic Policy Research, https://voxeu.org/article/why-poor-countries-invest-too-little-rd.  

Cuntz,  A.  (2016),  ''Do  public  R&D  funds  affect  the  location  choices  of  elite  scientists  in  Europe?'',  Research 
Evaluation, Volume 25, Issue 4, Pages 383–395. 

Defazio,  D.,  Lockett,  A.  and  Wright,  M.  (2009),  ''Funding  incentives,  collaborative  dynamics  and  scientific 
productivity: Evidence from the EU framework program'', Research Policy, 38, 293-305. 

Dinges,  M.  and  Lepori,  B.  (2006),  ''Public  Project  funding  of  research  activities:  national  differences  and 
implications  for  the  creation  of  a  European  research  council,  Platform  fteval  conference  -  New  Frontiers  in 
Evaluation'', Vienna 24th – 25th April 2006. 

Enger,  S.  G.  and  Castellacci,  F.  (2016),  ''Who  gets  Horizon  2020  research  grants?  Propensity  to  apply  and 
probability to succeed in a two-step analysis'', Scientometrics, 109, 1611-1638. 

Gianelle, C., Kyriakou D., Cohen, C., Przeor, M. (eds.) (2016), ''Implementing Smart Specialisation: A Handbook, 
European Commission'', EUR 28053 EN, doi:10.2791/53569. 

European Commission (2014), Enabling synergies between European Structural and Investment Funds, Horizon 
2020 and other research, innovation and competitiveness-related Union programmes, http:// 
ec.europa.eu/regional_policy/sources/docgener/guides/synergy/synergies_en.pdf     

European  Commission  (2017),  Commission  staff  working  document:  In-depth  interim  evaluation  of  Horizon 
2020, SWD (2017) 220 final. 

European 
https://ec.europa.eu/docsroom/documents/30281.  

Commission 

(2018), 

European 

Innovation 

Scoreboard 

2018, 

Frenz,  M.  and  Ietto-Gillies,  G.  (2009),  “The  impact  on  innovation  performance  of  different  sources  of 
knowledge: Evidence from the UK Community Innovation Survey”, Research Policy, 38, pp. 1125-1135. 

Geuna,  A.  (1996),  ''The  participation  of  higher  education  institutions  in  European  Union  Framework 
Programmes'', Science and Public Policy, 23, 5, 287-296. 

Geuna, A. (1998), ''Determinants of university participation in EU-funded R&D cooperative projects'', Research 
Policy, 26, 6, 677-687. 

Guellec, D. and van Pottelsberghe, B. (2000),  “The Impact  of Public Expenditure on Business R&D”, OECD STI 
Working Papers 2000/4. 

37 

 
Guzzetti,  L.  (1995),  ''A  Brief  History  of  European  Union  Research  Policy'',  Luxembourg:  Office  for  Official 
ublications of the European Communities. 

Hanna,  N.,  Guy,  K.,  Arnold,  E.  (1995),”  The  Diffusion  of  Information  Technology:  Experience  of  Industrial 
Countries and Lessons for Developing Countries”, World Bank Discussion Papers, No 281. 

Harrap,  N.  and  Doussineau,  M.  (2017),  ''Collaboration  and  networks:  EU13  participation  in  international 
science'', Stairway to Excellence – JRC Policy Insight, issue 2. 

Henriques, L., Schoen, A., Pontikakis, D. (2009), “Europe's top research universities in FP6: scope and drivers of 
participation”, JRC Technical Note, JRC 53681, http://ipts.jrc.ec.europa.eu/publications/pub.cfm?id=2639.  

Hoekman, J., Scherngell, T., Frenken, K. and Tijssen, R. (2013), ''Acquisition of European research funds and its 
effect on international scientific collaboration'', Journal of Economic Geography, 13, pp 23-52. 

Heller-Schuh, B., Barber, M., Henriques, L., Paier, M., Pontikakis, D., Scherngell, T., Veltri, G. A.  and Weber, M. 
(2011), ''Analysis of Networks in European Framework Programmes (1984-2006)'', JRC Scientific and Technical 
Research Series, ISSN 1018-5593. 

Langfeldt, L., Godø, H., Gornitzka, A., and Kaloudis, A. (2012), ''Integration modes in EU research: Centrifugality 
versus coordination of national research policies'', Science and Public Policy, 39, 88-98. 

Lepori, B., Veglio, V., Heller-Schuh, B., Scherngell, T., Barber, M. (2015), ''Participations to European Framework 
Programmes  of  higher  education  institutions  and  their  association  with  organizational  characteristics'', 
Scientometrics, 105, 2149-2178. 

Makkonen, T., Mitze, T. (2016), Scientific collaboration between ‘old’ and ‘new’ member states: Did joining the 
European Union make a difference?, Scientometrics, 106, 1193-1215. 

Miguélez, E. and Moreno, R. (2015), Knowledge flows and the absorptive capacity of regions, Research Policy, 
44, 833–848. 

OECD (2005), Innovation Policy and Performance: A Cross-Country Comparison, OECD Publishing, Paris. 

OECD (2011), Public Research Institutions: Mapping Sector Trends, OECD Publishing, Paris. 

Okubo, Y. and Zitt, M. (2004), ''Searching for research integration across Europe: a closer look at international 
and inter-regional collaboration in France'', Science and Public Policy 31, 213–226 

Open Evidence and Ernest and Young (2016), "An analysis of the role and impact of industry participation in the 
Framework  Programmes",  Final  Report  under  contract  N°  2012/S  144-240132,  Directorate-General  for 
Research and Innovation, doi:10.2777/257273. 

Özbolat,  N.  and  Harrap, N.  (forthcoming),  ''Lessons  learnt  from  the  activities  of  Stairway  to  Excellence  (S2E): 
Pilot Phase'', JRC Technical Report. 

Protogerou, A. et al (2010), ''Policy-driven collaborative research networks in Europe'', Economics of Innovation 
and New Technology, 19, 4, 349-372. 

Smith, A., Stirling, A. and Berkhout, F. (2005), “The governance of sustainable socio-technical transitions”, 
Research Policy, Vol. 34, pp. 1491-1510. 

Tondl, G. (1995), “Can EU's Cohesion Policy Achieve Convergence?”, Institute for European Affairs, University of 
Economics and Business Administration Vienna, http://epub.wu.ac.at/1536/1/document.pdf.  

Ukrainski,  K.  (2014),  ''Cooperation  patterns  in  science  within  Europe:  the  standpoint  of  small  countries'', 
Scientometrics, 99, 845-863. 

Varga, A., Pontikakis, D. and Chorafakis, G. (2014), “Metropolitan Edison and cosmopolitan Pasteur?  
Agglomeration and interregional research network effects on European R&D productivity”, Journal of Economic 
Geography, Volume 14(2), pp. 229–263. 

Viner,  N.  et  al  (2004),  ''Institutionalised  biases  in  the  award  of  research  grants:  a  preliminary  analysis  of 
revisiting the principle of accumulative advantage'', Research Policy, 33, 443-454. 

Wagner, S.W. and Jonkers, K. (2017), "Open countries have strong science", Nature, vol 550, 32-33. 

38 

 
Annex 

Annex List of categories of intervention taken into account to define R&I in ESIF 

002 Research and innovation processes in large enterprises-  
056 Investment in infrastructure in SMEs-  
057 Investment in infrastructure capacities and equipment in large enterprises- 
058 Research and innovation infrastructures (public)-  
059 Research and innovation infrastructures (private incl. science parks)-  
060 Research and innovation activities in public research centre)- 
061 Research and innovation activities in private research centre- 
062 Technology transfer and university-enterprise cooperation- 
063 Cluster support and business networks- 
064 Research and innovation processes in SMEs (including voucher)- 
065 Research & innovation processes, Techno transfer in low carbon economy- 
066 Advanced support services for SMEs and groups of SMEs-  
067 SME business development support to entrepreneurship and incubation- 
073 Support to social enterprises (SMEs)- 
101 Cross financing under the ERDF: support to ESF type actions 

39 

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