EIC accelerator and firm scaling-up evidence :
Date: 2025-06-27
Institutions: European Innovation Council and SMEs Executive Agency
Subjects: data collection, financial instrument, health policy, innovation, investment, report, sample survey, small and medium-sized enterprises

EIC Accelerator and Firm Scaling-
Up Evidence 

Authors:  
Giordano Mion*† 
ESSEC Business School and University of Nottingham 

Aminata Sissoko‡ 
European Commission 

Acknowledgments: 
We are grateful to the colleagues of the European Commission and European Innovation Council and SMEs 
Executive Agency (EISMEA) for helpful comments and support. We are particularly thankful to Stéphane 
Ouaki,  Keith  Sequiera,  Andreas  Konstantinidis,  Michele 
Ibba,  Alexandre  Polvora,  Sivasegaram 
Manimaaran,  Lars  Nilsson  and  Antoine  Dechezleprêtre.  The  authors acknowledge financial support from 
EISMEA and the European Commission, DG Research & Innovation. 

*†Department  of  Economics,  ESSEC  Business  School,  France;  School  of  Economics,  University  of 
Nottingham, UK. Also affiliated to the Centre for Economic Performance, the Centre for Economic Policy 
Research,  the  CESifo  research  network,  the  National  Institute  of  Economic  and  Social  Research  and 
THEMA. 

‡European Commission, DG R&I, Belgium. Also affiliated to the ICHEC Brussels Management School. 

EUROPEAN COMMISSION 

Directorate-General for Research & Innovation 
Directorate A — European Research Area & Innovation 
Unit A.3 — European Innovation Council 

Contact:  Aminata Sissoko 

E-mail:   Aminata.SISSOKO@ec.europa.eu 

European Commission 
B-1049 Brussels 

 
 
 
 
 
 
 
EUROPEAN COMMISSION 

Accelerator and Firm Scaling-Up 
Evidence 

Directorate-General for Research & Innovation 

    
Accelerator and Firm Scaling-Up Evidence 

Manuscript completed in May 2025 

This document has been prepared for the European Commission however it reflects the views only of the 
authors, and the European Commission is not liable for any consequence stemming from the reuse of this 
publication. 

Luxembourg: Publications Office of the European Union, 2025 

© European Union, 2025 

The reuse policy of European Commission documents is implemented by Commission Decision 
2011/833/EU of 12 December 2011 on the reuse of Commission documents (OJ L 330, 14.12.2011, 
p. 39). Unless otherwise noted, the reuse of this document is authorised under a Creative Commons 
Attribution 4.0 International (CC BY 4.0) licence (https://creativecommons.org/licenses/by/4.0/). This 
means that reuse is allowed provided appropriate credit is given and any changes are indicated. 

PDF 

ISBN 978-92-9412-189-9  doi:10.2826/0993987 EA-01-25-107-EN-N 

 
 
 
Accelerator and Firm Scaling-Up Evidence 

Contents 

1.  Executive Summary ................................................................................. 1 

2. 

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

3.  Data............................................................................................................ 6 

4.  Econometric framework........................................................................... 9 

5.  Descriptive Statistics ............................................................................. 11 

6.  Results .................................................................................................... 16 

6.1.  EIC-Pilot-SMEI scheme........................................................................ 16 

6.2.  A focus on the Blended Finance stage of EIC Pilot scheme ................ 28 

6.3.  EIC HE Accelerator scheme ................................................................. 35 

7.  Robustness and extensions of the results for the Blended Finance stage 
of the EIC Pilot scheme.......................................................................... 42 

7.1.  Using an alternative control group ........................................................ 42 

7.2.  Heterogeneity of the impact on the Blended Finance 2020 cohort of the 
EIC Pilot ............................................................................................... 48 

8.  Conclusions ............................................................................................ 50 

9.  References .............................................................................................. 51 

10.  Appendixes ............................................................................................. 53 

10.1. Appendix A: EIC Accelerator centred around deep tech, high risk and high 

growth potential firms ........................................................................... 53 

10.2. Appendix B: Details on the quantification of Revenue Total Factor 

Productivity ........................................................................................... 54 

10.3. Appendix C: Additional tables and figures ............................................ 56 

1 

 
 
Accelerator and Firm Scaling-Up Evidence 

1. Executive Summary

Abstract:  In  this  report,  we  have  conducted  an  econometric  analysis  of  the
impacts of the EIC Accelerator, with a particular focus on the Blended Finance
stage  of  the  EIC  Accelerator  Pilot,  by  means  of  a  difference-in-difference
framework  combined  with  propensity  score  matching  and  using  firms  who
obtained a Seal of Excellence as control group. Our analysis highlights a number
of significant impacts of the EIC Accelerator on firm performance measures like
sales,  capital stock, average  wage,  wage  bill  and  value  of  deals.  The analysis
also highlights the absence of heterogeneity of the effects along the productivity
dimension, as well as the lack of a differential impact for projects related to Health.

We use information on firms participating to the Accelerator scheme of the European 
Innovation Council (EIC), as well as to the SME Instrument (phase 2) scheme, to 
conduct an econometric evaluation of the impacts of those funding programmes on 
a range of firm outcomes: sales, capital stock, wages expenditure, average wage, 
Total Factor Productivity (TFP), as well as the number and value of investment deals. 
More  specifically,  we  use  a  difference-in-difference  approach  combined  with 
propensity score  matching techniques to  pin down  causal  effects.  In  our  baseline 
results, we use as control group for successful firms the set of unsuccessful firms 
eligible to receive the so-called Seal of Excellence. 

Our results can be summarised as follows. First, when maximising data availability 
by combining the SME Instrument (phase 2) and the EIC Accelerator Pilot, we find 
significant positive impacts on firm sales, capital stock, wages expenditure, average 
wage, as  well  as  on  the  value  of  investment  deals.  No  evidence  is  found  on  the 
number of deals and TFP. We show that impacts vary by year and cohort and notice 
that they are considerably larger for the cohorts of 2019 and 2020, which is when 
the Blended Finance stage of the EIC Accelerator Pilot (integrating the investment 
option) kicks in. 

When analysing separately the Blended Finance stage of the EIC Accelerator Pilot 
we find, despite the shorter data window available, that there is still a positive and 
significant impact on firm sales, capital stock, wages expenditure, average wage, as 
well as a delayed effect (3 years after the award) on the value of deals (around 2.3 
million euros). Again, no evidence is found on TFP suggesting that perhaps TFP is 
not an appropriate measure to capture the “technology dynamism” of startups. The 
lack of evidence could also suggest that early-stage firms, in particular in deep tech, 
pay a distinctive attention to the development of new products and firm scaling-up 
and  consequently  they  are  less  engaged simultaneous  in  process  innovation.  In 
terms  of  the  robustness  of  our  findings  on  the  Blended  Finance  stage  of  the 
EIC  Accelerator  Pilot  and 
the  presence  of  heterogeneous  effects,  our 
investigations 

1 

Accelerator and Firm Scaling-Up Evidence 

suggest that comparing successful firms with Seal of Excellence firms is better than 
comparing successful firms with firms in the same country and industry in terms of 
causality  interpretation.  Furthermore,  we  do  not  find  strong  evidence  that  Health-
related projects display higher or lower impacts or that impacts are higher or lower 
for more productive firms. 

Finally,  when  considering  the  EIC  HE  Accelerator  in  its  full-fledged  form  we  find 
significant impacts on sales as well as on the value of deals (around 800 thousand 
euros). No strong evidence is found on wage bill, capital stock, average wage and 
TFP.

2 

 
Accelerator and Firm Scaling-Up Evidence 

2. Introduction

The European Innovation Council (EIC) is the EU flagship programme established to identify and 
support game changing innovations throughout the lifecycle from early-stage research to proof of 
concept,  and  the  financing  of  the  "near-to-the-market"  innovation  activities  and  scaling-up  of 
startups and other promising SMEs. The EIC was launched as pilot in 2018, integrating existing 
schemes  such  as  the  Future  &  Emerging  Technology  (FET)  scheme,  under  the  Horizon  2020 
programme (H2020) established for the period 2014 – 2020. The fully-fledged EIC was launched 
in  April  2021  under  the  Horizon  Europe  programme (HE)  with  a  budget  of  EUR  10.1  billion.  A 
unique feature of the EIC is the supply of funding for individual companies (mainly startups and 
SMEs) through both grants and investments under the EIC Accelerator scheme. The investments 
take the form of direct equity or quasi-equity investments, managed by the EIC Fund, and the first 
companies for such investments were selected in October 2019 during the pilot phase. 

Although  the  implementation  of  the  Accelerator  investment  component  is  recent,  an  in-depth 
investigation of the profile of the companies selected, the origins of a potential causal effect and 
the role the cross-border investors can play will enable to draw further data-driven policy advice 
that  could  nurture  the  Accelerator  design  all  along  the  framework  period  of  HE  (2021-  2027). 
Furthermore, this kind of early study could contribute to structure the evolution of the Accelerator 
under the upcoming Framework Programme (FP10). 

In this report we use information on firms participating to the EIC Accelerator scheme, as well as 
to the SME Instrument (phase 2) scheme, to conduct an econometric evaluation of the impacts of 
those funding programmes on a range of firm outcomes: sales, capital stock, wages expenditure, 
average wage, Total Factor Productivity (TFP), as well as number and value of investment deals. 
More  specifically,  we  use  a  difference-in-difference  approach  combined  with  propensity  score 
matching techniques to pin down causal effects. In our baseline results, we use as control group 
for successful firms the set of firms who applied for funding and arrived at the last stage of the 
selection process (i.e. the jury interview) while not getting the money in the end. Such firms are 
eligible  to  receive  the  so-called  Seal  of  Excellence  and  are  more  likely  to  be  comparable  to 
successful firms in terms of both pre-treatment observable and unobservable variables. We also 
provide some robustness based on another control group (firms in the same industry and country 
of the successful firms) while providing evidence that the former control group choice is superior 
in terms of causality interpretation. 

We divide our analysis in four parts. In the first part, we study the overall impact (also broken down 
by year and cohort) of the following funding schemes: i) the SME Instrument (phase 2) and the 
first  stage  of  the  EIC  Accelerator  Pilot,  both  restricted  to  grants,  under  H2020;  ii)  the  Blended 
Finance stage of the EIC Accelerator Pilot in which since October 2019 firms can request both a 
grant and an equity or quasi-equity investment (2019 - 2020). While bearing the disadvantage of 
mixing different schemes, this first group of investigations has the advantage of focusing on a long 
period  of  time  characterised  by  a  large  pool  of  participating  firms  for  which  abundant  data  is 
available. In the second part of our analysis, we instead focus on the Blended Finance stage of 
the EIC Accelerator Pilot and provide a causal assessment specific to this scheme. Despite the 
shorter data window available, and the smaller number of participants involved, our analysis points 
to a number of significant impacts of this funding scheme. 

3 

Accelerator and Firm Scaling-Up Evidence 

In the third part of our report, we move to the EIC HE Accelerator in its full-fledged form under HE 
(established for the period 2021 – 2027). Data constraints are particularly binding for this scheme 
but our analysis does suggest that some impacts are already visible in the data. Finally, in the 
fourth  part  of  our analysis we  come back to  the  Blended Finance  stage  of  the  EIC  Accelerator 
Pilot to assess the robustness of certain results while providing some additional findings regarding 
the heterogeneity of the impacts. 

Our results can be summarised as follows. First, when maximising data availability by combining 
the SME Instrument (phase 2) and the EIC Accelerator Pilot, we find significant positive impacts 
on  firm  sales,  capital  stock,  wages  expenditure,  average  wage,  as  well  as  on  the  value  of 
investment deals. No evidence is found on the number of deals and TFP. We show that impacts 
vary by year and cohort and notice that they are considerably larger for the cohorts of 2019 and 
2020,  which  is  when  the  Blended  Finance  stage  of  the  EIC  Accelerator  Pilot  kicks  in.  When 
analysing separately the Blended Finance stage of the EIC Accelerator Pilot we find, despite the 
shorter  data  window  available,  that  there  is  still  a  positive  and  significant  impact  on  firm  sales, 
capital stock, wages expenditure, average wage, as well as a delayed effect (3 years after the 
award)  on  the  value  of  deals  (around  2.3  million  euros).  Again,  no  evidence  is  found  on  TFP 
suggesting that perhaps TFP is not an appropriate measure to capture the “technology dynamism” 
of startups. The lack of evidence could also suggest that early-stage firms, in particular in deep 
tech,  pay  a  distinctive  attention  to  the  development  of  new  products  and  firm  scaling-up  and 
consequently  they  are  less  engaged  in  simultaneous  process  innovation.  In  terms  of 
the  robustness  of  our  findings  on  the  Blended  Finance  stage  of  the  EIC  Accelerator  Pilot 
and  the  presence  of  heterogeneous  effects,  our  investigations  suggest  that  comparing 
successful  firms  with  Seal  of  Excellence  firms  is  better  than  comparing  successful  firms  with 
firms  in  the  same  country  and  industry  in  terms  of  causality  interpretation.  For  example,  in 
the  former  case  our  analysis  does  not  reveal  any  significant  pre-trends  while  in  the  latter 
case  significant  pre-trends are about. Furthermore, we do not find strong evidence that Health-
related projects display higher or  lower  impacts  or  that  impacts  are  higher  or  lower  for  more 
productive  firms.  Finally,  when  considering the EIC HE Accelerator in its full-fledged form we 
find significant impacts on sales as well as on the value of deals (around 800 thousand euros). 
No  strong  evidence  is  found  on  wage  bill,  capital  stock,  average  wage  and  TFP.  In  this 
respect,  we  believe  that  the  small  number  of  firms  involved  so  far  in  this  recent  funding 
scheme,  and  the  limited  time  window  available,  are  likely  to  play  a  big  role  and  we  look 
forward to studies revisiting this issue in the future when more data will be available. 

Our  paper  contributes  to  the  literature  on  programme  evaluation  of  public  funding  schemes 
supporting  innovation  including,  among  other,  Mulkay  and  Mairesse ( 2013), Rao  ( 2016) 
Dechezleprêtre et al. (2023), European Commission - Mitra, A. and Niakaros, K. of Directorate- 
General for Research & Innovation (2023) and Del Sorbo et al. (2024). The closest paper to ours 
is Santoleri et al. (2024) in which they use a regression discontinuity design to evaluate the causal 
impacts of the SME instrument scheme. We extend their analysis to both the Blended Finance 
stage of the EIC Accelerator Pilot scheme and the EIC HE Accelerator scheme while developing 
an  alternative  (to  regression  discontinuity)  causality  assessment  framework.  Our  paper  is also 
related to the literature on staggered difference-in-difference empirical frameworks with respect to 
which the literature has recently made significant progress (Callaway and Sant’Anna, 2021; Sun 
and Abraham, 2021). In particular, we follow the general linear model introduced by Wooldridge 
(2021). 

The rest of the paper is organised as follows. Section 3 provides details about the data sources 
and variables used, while Section 4 details our econometric framework. Section 5 contains some 

4 

Accelerator and Firm Scaling-Up Evidence 

intriguing descriptive statistics while Section 6 delivers our empirical results. Section 7 focuses on 
robustness and extensions of the results for the Blended Finance stage of the EIC Pilot scheme 
while  Section  8  concludes  and  provides  our  recommendations  and  suggestions  for  further 
research. Finally, Appendix A explains the EIC Accelerator background while Appendix B contains 
details  about  our TFP  estimations  and  Appendix  C  provides  a  number  of  additional  tables and 
figures. 

5 

Accelerator and Firm Scaling-Up Evidence 

3. Data

In  our  analysis  we  make  use  of  several  datasets.  First  of  all,  we  study  three  kinds  of  funding 
schemes:  i)  the  SME  Instrument  (phase  2)  and  the  first  stage  of  the  EIC  Accelerator  Pilot, 
both restricted to grants, under H2020 (2014 - 2019); ii) the Blended Finance stage of the EIC 
Accelerator Pilot in which since October 2019 firms can request both a grant and an equity (2019 
- 2020); and iii) the EIC HE Accelerator in its full-fledged form under HE (established for the period
2021 – 2027). In what follows, we collapse the schemes i) and ii) of H2020 together and label them
as the EIC-Pilot-SMEI scheme.  We will separately analyse for instance the impact for the EIC-
Pilot-SMEI scheme and the EIC HE Accelerator scheme by cohort year to study the patterns of
the firms selected by the schemes from 2014 onwards.  To this end, we use data regarding firms
that applied to the schemes along with information about successful funding. To be more precise,
the data we use contains information about the identity of the applicant(s) (firm national ID number,
as well as name and address of the company, that we use to match to BvD identifiers) and the role
in the project, the application call, an identifier for the proposal, the  subject  area  of  the  project,
the  amount  of  funding  requested,  the  eligibility  of  the  project (with a score for EIC-Pilot-SMEI
scheme),  whether  the  project  has  ultimately  been  financed  and  by  how  much,  as  well  as  the
duration of the project and whether (particularly for the Blended Finance stage of the EIC Pilot and
the EIC HE Accelerator) only a grant, or only equity or both were requested and/or approved.

The data for the EIC-Pilot-SMEI scheme cover the submission period June 2014 to October 2020 
within  which  the  firms  can apply  for  a  grant and/or  an  equity  as  of  October  2019  following  the 
establishment  of  the  EIC  Fund.  The data  for  the  EIC  HE  Accelerator  scheme,  maintaining the 
operations of the EIC Fund, covers the time frame June 2021 to March 2024. In what follows, we 
exclude from the analysis of the EIC-Pilot-SMEI scheme, the year 2014 given the small number 
of  firms  involved.  As  for  the  EIC  HE  Accelerator,  we  use  all  the  available  information  in  the 
descriptive analysis, while focusing on the years 2021 and 2022 for the econometric analysis. 

The  second  dataset  we  employ  is  the  BvD  Orbis  historical  database.  The  dataset  provides 
historical information on balance sheet and P&L account items, activity, location and ownership of 
firms (each one being associated with a BvD identifier) around the world. The underlined data is 
collected  from  public  sources  by  the  Bureau  van  Djik  and  is  subsequently  harmonised  and 
organised  into  the  Orbis  historical  database.  For  the  purpose  of  this  project,  we  collected 
information for the following balance sheet and P&L account items (all values being recorded in 
current USD that we converted into EUR): date and consolidation code of the balance sheet and 
P&L  account  declaration,  number  of  months  covered  by  the  declaration,  original  currency, 
exchange rate  applied,  fixed  assets,  intangible  fixed  assets,  tangible  fixed  assets,  total  assets, 
non-current  liabilities,  long  term  debt,  current  liabilities,  other  current  liabilities,  number  of 
employees,  operating  revenue,  costs  of  goods  sold,  gross  profit,  other  operating  expenses, 
operating PL, financial revenue, financial expenses, financial PL, PL before tax, taxation, PL after 
tax, export revenue, material costs, costs of employees, interest paid, added value, and research 
& development expenses. 

6 

Accelerator and Firm Scaling-Up Evidence 

Table  1:  List  of  countries  that  participated  to 
the EIC-Pilot-SMEI scheme and/or the EIC HE 
Accelerator  scheme  (period  June  2014  - 
March 2024) 

Country name 

Country code 

Albania 

Armenia 

Austria 

  Bosnia and Herzegovina 

Belgium 

Bulgaria 
Switzerland 
Cyprus 
Czech Republic 
Germany 
Denmark 
Estonia 
Spain 
Finland 
France 
United Kingdom 
Georgia 
Gibraltar 
Greece 
Croatia 
Hungary 
Ireland 
Israel 
Iceland 
Italy 
Lithuania 
Luxembourg 
Latvia 
Moldova, Republic of 
Montenegro 

Republic of North Macedonia 

Malta 
Netherlands 
Norway 
Poland 
Portugal 
Romania 
Serbia 
Sweden 
Slovenia 
Slovakia 
Tunisia 
Turkey 
Ukraine 

AL 

AM 

AT 
BA 
BE 

BG 
CH 
CY 
CZ 
DE 
DK 
EE 
ES 
FI 
FR 
GB 
GE 
GI 
GR 
HR 
HU 
IE 
IL 
IS 
IT 
LT 
LU 
LV 
MD 
 ME 
MK 
MT 
NL 
NO 
PL 
PT 
RO 
RS 
SE 
SI 
SK 
TN 
TR 
UA 

The data spans over the period 2007-2022 and we applied the procedure described in Mion and 

7 

Accelerator and Firm Scaling-Up Evidence 

Tong (2021) to obtain a unique balance sheet and P&L account declaration for every firm- year in 
the  data. (1)  We  also  retrieve  from  Orbis  historical  information  over  the  NACE  rev  2  primary 
industry  activity  code  of  the  firm,  as  well  as  the  identity  (BvD  identifier)  of  the  Global  Ultimate 
Owner Corporate (GUO50c) to analyse whether the firms are controlled by non-domestic entities. 
The latter information is time-varying and we have extracted data over the whole available period: 
2007-2020.  Finally, all of the above data has been collected for the universe of available firms in 
the 44 countries (listed in Table 1) that participated to the EIC- Pilot-SMEI scheme and/or the EIC 
HE Accelerator scheme while values have been converted from dollars to euros. 

The third dataset we deploy is the BvD M&A database. M&A (formerly Zephyr) is a comprehensive 
database  of  deal  information  (M&A,  IPO,  private  equity  and  venture  capital  deals),  with  pan-
European  transactions  dating  back  to  1997  and  US  deals  from  2001.  For the purpose of this 
study, we extract information on completed and confirmed value of the private equity and venture 
capital deals, also called investment rounds, (in thousand USD that we converted into thousand 
EUR) and number by firm-year over the period January 2010 to June 2024.  The firm in the data 
is the one being targeted by the financial operation and we extract data for all of the 44 countries 
listed in Table 1.  A firm in the data is identified by its BvD identifier, which ultimately allows us to 
merge this data with both the Orbis historical database and the database on applicants to the EIC-
Pilot-SME and/or the EIC HE Accelerator calls. Last but not least, in order to obtain clean results 
over the causal impact on the number and value of deals, we have amended deals corresponding 
to equity financing by the EIC Fund under the EIC-Pilot and EIC HE Accelerator schemes. (2) 

(1)  Sometimes firms file more than one balance sheet and P&L account declaration in a given year. This is often due 

to fulfilling different consolidation requirements. 

(2)  More specifically, we exploit information on the name of the financing entity and identify deals corresponding to 
the name “EUROPEAN INNOVATION COUNCIL”. We then drop the deal entirely if the value of the deal is less 
or equal to the financing by the EIC Fund. We instead keep the deal if the value of the deal is greater than the 
financing by the EIC Fund, while imputing the difference as the amended deal value. This is because the recorded 
EIC deals in the BvD M&A database often involve the investors other than the EIC Fund that participate to the 
closed  investment  rounds.  Sometimes  deals  involving  the  EIC  Fund  are  recorded  one  or  two  years  after  the 
award year and we take this into account in our cleaning procedure. 

8 

 
 
Accelerator and Firm Scaling-Up Evidence 

4.  Econometric framework 

In what follows, we describe the econometric model we will be estimating.  The baseline approach 
consists  in  combining  a  matching  estimator  with  a  (staggered)  Difference-in-Difference  (DiD) 
approach.  More specifically, in order to get a more compelling control group, we start by selecting 
(among the unsuccessful applicants) firms who applied for funding and arrived  at  the  last  stage 
of  the  selection  process  (i.e.  the  SME  Instrument  or  EIC  jury  interview) while  not  getting  the 
money  in  the  end.  Such  firms  are  eligible  to  receive  the  so-called Seal of Excellence and are 
more likely to be comparable to successful firms in terms of both pre-treatment observable and 
unobservable variables.  We often simply refer to those firms as the unsuccessful applicants.  We 
then, to each successful firm having applied in year 
 match an unsuccessful firm who also applied 
in year 

 and, while unsuccessful, received the Seal of Excellence. 

𝑡𝑡,

𝑡𝑡

The matching between successful and unsuccessful firms is done separately by year  through a 
propensity score estimation in which the matching variables used are the log level of  sales and 
capital in 
, the evaluation score of the proposal (for the EIC-Pilot-SME scheme) as 
well as dummies for the country and the NACE rev2 2-digit sector of the firm. (3) 

 till 

𝑡𝑡  −  1

𝑡𝑡  −  3

Once selected the treated and control groups, we estimate a staggered DiD model following the 
recent insights of Callaway and Sant’Anna (2021) and Sun and Abraham (2021).  In particular, we 
follow the general linear model introduced by Wooldridge (2021) and only use never treated firms 
  the  first  year 
in the control group.  Let us denote a firm by 
of  the  data  (2012  for  the  EIC-Pilot-SMEI  scheme;  2017  for  the  EIC HE  Accelerator  scheme) 
  the  last  year  of  the  data  (2022  for  most  of  the  analysis of both the EIC-Pilot-SMEI 
and  with 
scheme and the EIC HE Accelerator scheme). (4)  Each treated firm 
 is associated with the year 
) is  a  set  that  indicates 
) and 
𝑖𝑖
.  In  the  case of the EIC-Pilot-SMEI scheme 
𝐺𝐺(𝑖𝑖
. In what follows, 
.  We  thus  have  the  following  estimation 

in  which  year  g  the  treatment  started  for  any  firm 
𝑔𝑔(𝑖𝑖) ∈ 𝐺𝐺(𝑖𝑖)
we  simply  use 
𝑔𝑔(𝑖𝑖) ∈ [2015,2020]
equation: 

 in which she receives treatment (treatment group 

, while for the EIC HE Accelerator scheme 

𝑔𝑔(𝑖𝑖) ∈ [2021,2022]

  as well as  with 

 and time with 

  instead  of 

  instead  of 

 and 

𝑡𝑡0

𝑡𝑡,

𝑔𝑔

𝑇𝑇

𝑖𝑖

𝑖𝑖

𝑔𝑔

𝑔𝑔(𝑖𝑖)

𝐺𝐺

𝐺𝐺(𝑖𝑖)

𝑔𝑔−1

𝑇𝑇

𝑝𝑝𝑝𝑝𝑝𝑝
𝑦𝑦𝑖𝑖,𝑡𝑡 = α + � � θ𝑔𝑔,𝑡𝑡
+ � � θ𝑔𝑔,𝑡𝑡
 is an outcome of interest (revenue, capital stock, value of deals, etc.)  of firm
𝑡𝑡=𝑔𝑔
𝑔𝑔∈𝐺𝐺
 is a dummy variable that takes the value of 
𝑦𝑦𝑖𝑖,𝑡𝑡

𝐷𝐷𝑖𝑖,𝑔𝑔,𝑡𝑡
g
 is in the treatment group 

+ μ𝑖𝑖 + μ𝑡𝑡 + ϵ𝑖𝑖,𝑡𝑡, (1)

𝐷𝐷𝑖𝑖,𝑔𝑔,𝑡𝑡

𝑡𝑡=𝑡𝑡0

𝑝𝑝𝑝𝑝𝑝𝑝𝑡𝑡

𝑔𝑔∈𝐺𝐺

where 
, 
  and 
𝐷𝐷𝑖𝑖,𝑔𝑔,𝑡𝑡
𝑡𝑡
treatment  group 
0
𝑡𝑡
between the treated group 

  otherwise,  coefficients 

  experiences  at  time 

𝑝𝑝𝑝𝑝𝑝𝑝𝑡𝑡

𝜃𝜃𝑔𝑔,𝑡𝑡

 and the control group, 

 if firm 

 and time 
  on period 
 𝑖𝑖
  represent  the  average  treatment  effect  (ATE)  that  the 
𝑔𝑔
  measure  pre-trends  differences 
𝑝𝑝𝑝𝑝𝑝𝑝
 are firm and time fixed effects, and 
𝜃𝜃𝑔𝑔,𝑡𝑡
µ𝑡𝑡

1
  coefficients 
 and 

µ𝑖𝑖

𝑡𝑡,

𝑖𝑖

𝑔𝑔

𝑔𝑔

(3)  We only consider observations corresponding to unconsolidated accounts and also trim observations in the top 
and  bottom  0.5%  of  the  distribution  of  log  sales.  The  matching  is  implemented  using  the  Stata  command 
psmatch2 and it is a nearest neighbour matching with a caliper of 0.1, no replacement and with the imposition of 
a common support. 

(4)  When turning to the number and value of deals as outcome variables, we are able to further consider the year 

2023. 

9 

 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

finally 
follows, we will be setting the year before the treatment 
of average treatment effects. 

 is  an  error  term  that  is  assumed  to  be  uncorrelated  with  the  regressors.  In what 
 as the baseline for the computation 

𝜖𝜖𝑖𝑖,𝑡𝑡

(𝑔𝑔 − 1)

Details on the estimation procedure for TFP-R are provided in Appendix B while the coefficients 
of the production function (estimated separately for each 1-digit NACE industry across all the 44 
countries in the analysis over the period 2012-2022) are reported in Figure B-1 in Appendix B. 

10 

 
Accelerator and Firm Scaling-Up Evidence 

5.  Descriptive Statistics 

In this section we provide a number of summary statistics about the applications to the EIC- Pilot-
SMEI scheme and/or the EIC HE Accelerator scheme. First of all, Tables C-1 and C-2 in Appendix 
C provides a breakdown of applications by country for the EIC-Pilot-SMEI scheme and the EIC 
HE Accelerator scheme, respectively. From Table C-1, it emerges that Italy is the country with the 
largest amount of applications (5,003), while Spain leads in terms of both successful applications 
(253)  and  applications  receiving  the  Seal  of  Excellence  (2,254).  The  situation  is  somewhat 
different in the case of the EIC Accelerator with France leading in all categories: 755 applications, 
101 successful applications and 143 applications leading to a Seal of Excellence. 

In order to gain insights into these numbers, we have conducted a simple regression analysis where 
we attempt to explain applications in terms of the size (population) of the country (
), its GDP 
), as well as 
per capita (
distance to Belgium (

), its research and development expenditure as a % of GDP (

) and contiguity to Belgium (

). (5) 

𝑃𝑃𝑃𝑃𝑃𝑃𝑐𝑐

𝐺𝐺𝐷𝐷𝑃𝑃𝑃𝑃𝐺𝐺𝑐𝑐

𝑅𝑅&𝐷𝐷𝑐𝑐

This is implemented with a simple Poisson multiplicative model instead of a log linear model: (6)  

𝐶𝐶𝑃𝑃𝐶𝐶𝑡𝑡𝑖𝑖𝑔𝑔𝑐𝑐

𝐷𝐷𝑖𝑖𝐷𝐷𝑡𝑡𝑐𝑐

where 
country 

β3
 ∗  𝑅𝑅&𝐷𝐷𝑐𝑐
is the number of applications/successful applications/Seal of Excellence applications of 
 and parameters 

β1
𝐴𝐴𝑐𝑐 = 𝐶𝐶𝑃𝑃𝐶𝐶𝐷𝐷𝑡𝑡  ∗  𝑃𝑃𝑃𝑃𝑃𝑃𝑐𝑐
 to 

β2
  ∗  𝐺𝐺𝐷𝐷𝑃𝑃𝑃𝑃𝐺𝐺𝑐𝑐

β5
  ∗  𝐶𝐶𝑃𝑃𝐶𝐶𝑡𝑡𝑖𝑖𝑔𝑔𝑐𝑐

β4
 ∗  𝐷𝐷𝑖𝑖𝐷𝐷𝑡𝑡𝑐𝑐

 are elasticities. 

  ∗  ϵ𝑐𝑐, (2)

𝐴𝐴𝐺𝐺 
𝐺𝐺

𝛽𝛽1

𝛽𝛽5

Estimations  of  equation  (2)  for  the  EIC-Pilot-SMEI  scheme  are  provided  in  Table  2  while  the 
corresponding estimations for the EIC Accelerator are reported in Table 3. (7)  There are a few 
common  insights  stemming  from  Tables  2  and  3.  First,  in  both  cases  the  key  determinants of 
applications/successful  applications/Seal  of  Excellence  applications  are  the  size  of  the  country 
(with  an  elasticity  of  about  0.8  for  the  EIC-Pilot-SMEI  scheme  and  0.6  for  the  EIC  HE 
Accelerator scheme) and the GDP per capita of the country (with an elasticity in between 1.2 and 
1.6 for both the EIC-Pilot-SMEI scheme and the EIC HE Accelerator scheme).  In the case of the 
size of the country, coefficients are below one and so applications/successful applications/Seal of 
Excellence applications increase less than proportionally with the population  of the country.  By 
that 
contrast,  considering  GDP  per  capita,  coefficients  are  above  one  meaning 
applications/successful  applications/Seal  of  Excellence  applications 
than 
increase  more 
proportionally with the GDP per capita of the country.  Interestingly, GDP per capita coefficients 
are  larger  (for  both  the  EIC-Pilot-SMEI  scheme  and  the  EIC  HE  Accelerator  scheme)  when 
considering  successful  applications/Seal  of  Excellence  applications  suggesting  that  the  best 
applications are even more disproportionately from higher GDP per capita countries. Furthermore, 
neither research and development expenditure as a % of GDP (R&Dc), nor the gravity variables 

(5)  Population, GDP per capita (in current USD) and research and development expenditure as a % of GDP 
refer to the year 2018 and are borrowed from the World Development Indicators of the World Bank.  Distance and 
contiguity with Belgium come from the CEPII gravity dataset. 

(6)  The Poisson multiplicative model in equation (2) could be made linear by taking the log of both sides, as it 
is typically done in international trade gravity regressions. However, the current leading practice is to estimate the 
Poisson level model for reasons that are explained in Silva and Tenreyro (2006). 

(7)  The number of observations in the two tables is 42 rather than 44 because R&D information is not 

available for Albania and Gibraltar. 

11 

 
 
 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

distance to Belgium (Distc) and contiguity to Belgium (Contigc) seem to affect many applications. 
Finally, in all cases the Pseudo R2  is quite high indicating that the model (2) is quite powerful in 
capturing variation in the data. 

In order to gain further insights into the EIC-Pilot-SMEI and the EIC HE Accelerator processes, 
we  provide  in  Figures  1  (for  the  EIC-Pilot-SMEI  scheme)  and  2  (for  the  EIC  HE  Accelerator 
scheme) a comparison of the number of successful applications (y-axis) and the predicted number 
of successful applications (x-axis) from equation (2) along with a 45-degree line. 

Table 2:  EIC-Pilot-SMEI scheme.  Poisson model analysis of the 
determinants of applications (period June 2014 - October 2020) 

Variable 

Number of applications 

Popc 

GDPpcc 

R&Dc 

Distc 

Contigc 

Constant 

0.7709*** 
(0.0965) 
1.3073*** 
(0.2365) 
-0.0089 
(0.1253) 
0.4594** 
(0.1909) 
-0.2554 
(0.2221) 
-22.345221*** 
(4.2907) 

Number of successful 
applications 
0.7776*** 
(0.1358) 
1.7053*** 
(0.2597) 
-0.0709 
(0.1471) 
0.5096** 
(0.2541) 
-0.1152 
(0.2694) 
-30.2034*** 
(5.7773) 

Number of Seal of 
Excellence  applications 
0.8003*** 
(0.1241) 
1.5255*** 
(0.2849) 
0.0115 
(0.1452) 
0.4997** 
(0.2416) 
-0.2327 
(0.2631) 
-26.5367*** 
(5.5931) 

Pseudo  R2 
Observations 

0.8381 
42 

0.7715 
42 

0.8193 
42 

Notes: Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. 

Table 3:  EIC HE Accelerator scheme.  Poisson model analysis of the 
determinants of applications (period June 2021 - March 2024) 

Variable 

Number of applications 

Popc 

GDPpcc 

R&Dc 

Distc 

Contigc 

Constant 

Pseudo  R2 
Observations 

0.5922*** 
(0.0824) 
1.2661*** 
(0.1414) 
-0.2227 
(0.1656) 
0.0677 
(0.1491) 
-0.0249 
(0.4111) 
-17.8298*** 
(2.7929) 

0.6930 
42 

Number of successful 
applications 
0.6077*** 
(0.1230) 
1.3553*** 
(0.2065) 
-0.2461 
(0.2269) 
-0.0576 
(0.1986) 
0.3370 
(0.4702) 
-20.7269*** 
(3.7593) 

Number of Seal of 
Excellence  applications 
0.5906*** 
(0.1056) 
1.5699*** 
(0.1813) 
-0.1393 
(0.1802) 
0.1459 
(0.1861) 
0.2123 
(0.4229) 
-23.5823*** 
(3.7457) 

0.5849 
42 

0.6412 
42 

Notes: Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. 

Countries above/below the 45-degree line have a number of successful applications higher/lower 
than  the  one  predicted  by  the  model,  i.e.,  on  the  basis  of  the  size  and  GDP per capita of the 
country.  From  Figure  1,  Spain  appears  as  the  single  country  who  is  strongly  outperforming  in 
terms  of  successful  applications  for  the  EIC-Pilot-SMEI  scheme.  Considering  the  EIC  HE 

12 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Accelerator, Figure 2  suggests that France is strongly outperforming while Germany  is  strongly 
under-performing.  Other  strongly  outperforming  countries  from  Figure  2  are  the  Netherlands, 
Spain, Sweden and Finland.  Other strongly under-performing countries from Figure 2 are instead 
Austria, Luxembourg, Switzerland and Italy. 

Figure 1: EIC-Pilot-SMEI scheme. Comparison of the number of successful applications (y-axis) 
and the  predicted number  of  successful  applications  (x-axis)  from  equation (2) that  is mainly 
driven by country population and GDP per capita 

Figure 2: EIC HE Accelerator scheme.  Comparison of the number of successful applications (y-
axis)  and  the  predicted  number  of  successful  applications  (x-axis)  from  equation  (2)  that  is 
mainly driven by country population and GDP per capita 

Moving from the country to the industry dimension, Table C-3 in Appendix C provides a breakdown 
of EIC-Pilot-SMEI scheme applications by NACE rev2  2-digit industries. The top 5  industries in 
terms of applications are ‘Computer programming, consultancy and related activities’ (code 62), 
‘Scientific research and development’ (code 72), ‘Architectural and engineering activities; technical 
testing and analysis’ (code 71), ‘Manufacture of computer, electronic and optical products’ (code 
26) and ‘Manufacture of machinery and equipment’ (code 28). The same 5  industries also lead in 
terms of successful applications and applications leading to a Seal of Excellence. In order to get 

13 

 
 
 
 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

some ideas on how concentrated are applications with respect to employment, we use information 
on employment by NACE rev2 2-digit industry of EU and EFTA countries in 2021 and compute the 
ratio  between  applications/successful  applications/Seal  of  Excellence  applications  and  industry 
employment. (8)  In  terms  of  the  ratio  between  industry  applications  and  employment,  the  top  3 
places  are  occupied  by  3  of  the  5  industries  identified  above;  namely  ‘Scientific  research  and 
development’, ‘Computer programming, consultancy and related activities’, and ‘Manufacture of 
computer, electronic and optical products’. The 4th and 5th place are instead taken by ‘Information 
service activities’ (code 63) and ‘Remediation activities and other waste management services’ 
(code  39).  Considering  instead  the  ratio  between  industry  successful  applications  and 
employment, the same 3 industries named above occupy the first 3 places while the 4th and 5th 
place are taken by ‘Other manufacturing’ (code 32) and ‘Architectural and engineering activities; 
technical testing and analysis’. 

Switching to the EIC HE Accelerator scheme, Table C-4 in Appendix C provides the companion 
breakdown  of  applications  by  NACE  rev2  2-digit  industries.  The  top  5  industries in terms of 
applications are very similar to the EIC-Pilot-SMEI scheme: ‘Scientific research  and development’ 
(code 72), ‘Computer programming, consultancy and related activities’ (code 62), ‘Architectural 
and  engineering activities;  technical  testing  and  analysis’  (code  71),  ‘Manufacture  of  computer, 
electronic and optical products’ (code 26) and finally the new  entry ‘Other professional,  scientific 
and technical activities’ (code 74).  The first usual four  also occupy the top 4 spots in terms of 
number of successful applications and applications leading to a Seal of Excellence while the 5th 
place  is  taken  by  ‘Other  manufacturing’  (code  32)  for  successful  applications  and  ‘Other 
professional,  scientific  and  technical  activities’  (code  74)  for  applications  leading  to  a  Seal  of 
Excellence. 

Again, in order to get some ideas of how concentrated are applications with respect to industry 
employment, we use information on employment by NACE rev2 2-digit industry of EU and EFTA 
countries  in  2021  and  compute  the  ratio  between  applications/successful  applications/Seal  of 
Excellence  applications  and  industry  employment. (9)  In  terms  of  the  ratio  between  industry 
applications and employment the top 3 places are the same as for the EIC-Pilot-SMEI scheme: 
‘Scientific  research  and  development’,  ‘Computer  programming,  consultancy  and  related 
activities’, and ‘Manufacture of computer, electronic and optical products’.  The 4th and 5th places 
are instead taken by ‘Other manufacturing’ and ‘Architectural and engineering activities; technical 
testing and analysis’.  Considering instead the ratio between industry successful applications and 
employment, the same 3 industries named above occupy the 1st, 2nd and 4th places, while 3rd place 
is  taken by  ‘Other manufacturing’  and  the  5th  place  by ‘Architectural and  engineering activities; 
technical testing and analysis’. 

Overall, the industry analysis points to a substantial coherence between the profile of firms in the 
EIC-Pilot-SME and EIC HE Accelerator schemes while highlighting a stable group of industries 
taking the lead in terms of applications, successful application and Seal of Excellence applications. 

In order to complete the description of the application data, we report in Appendix C information 
on the breakdown of applications, successful applications and Seal of Excellence applications by 
thematic area/evaluation panel.  Each application/proposal reaching the last stage of the selection 
is  assigned  to  a  thematic  area/evaluation  panel  depending  on  the  nature  and  specifics  of  the 
proposed  project.  Table  C-5  in  Appendix  C  provides  the  information  for  the  EIC-Pilot-SME 
scheme  for  which  the  thematic  area  of  the  proposed  project  is  available. As  for  the  EIC  HE 

(8)  Employment is not available for industries 01, 02, 03, 07, 13, 69, 79, 84 and 94. 
(9)  Employment is not available for industries 01, 02, 03, 07, 13, 69, 79, 84 and 94. 

14 

 
 
Accelerator and Firm Scaling-Up Evidence 

Accelerator,  Table  C-6  in  Appendix  C  provides  the  information  based on the evaluation panel. 
Unfortunately, a common classification is not available for the two schemes but Tables C-5 and 
C-6 show a good amount of general overlap between the thematic areas/evaluation panels.  For 
example, Health-related projects dominate in terms of number  of applications (overall, successful 
and Seal of Excellence) in both the EIC-Pilot-SME and EIC HE Accelerator schemes.  Moreover, 
while ICT-related projects get the 2nd place of the applications in both schemes, the ’Engineering 
and Technology’ ones come before them in the ranking of the successful and Seal of Excellence 
applications. 

15 

 
 
Accelerator and Firm Scaling-Up Evidence 

6.  Results 

6.1.  EIC-Pilot-SMEI scheme 

In what follows we describe the results of our DiD analysis for the EIC-Pilot-SMEI scheme while 
focusing  on  firm  sales,  wage  bill,  capital  stock,  TFP  and  deals  (both  number  and  value) as 
outcome variables.  First of all, concerning the initial matching procedure, Figure C-1 in Appendix 
C provides, for each year of the match, the plot of the propensity score values for both matched 
successful  and  unsuccessful  firms.  As  one  can  notice,  the  quality  of  the  match  in  terms  of 
propensity score similarity is quite good in all years. (10)  At the same time, Figures C-2 to C-7 in 
Appendix  C  show  balancing  tests  stats  for  each  year-specific  matching  and  overall  provide  a 
reassuring message about the quality of the match in terms of observables between successful 
and unsuccessful firms. 

Our matching procedure leads us to focus on 549  matched successful firms and 549  matched 
unsuccessful firms. As already suggested above, the fact that we have chosen firms who received 
a  Seal of Excellence  for  the  control  group  should  allay  concerns  over  the  similarity of the two 
groups in terms of both observables (on which we have further improved by means of the matching 
procedure) and unobservables.  In terms of observables, Table 4  provides some  summary stats 
about  matched  successful  and  unsuccessful  firms.  In  particular,  Table  4  displays  the  mean, 
median, standard deviation, 5th percentile (P5), 95th percentile (P95) and number of non-missing 
observations for a range of firm-level variables (sales, wage bill, capital stock, materials cost, and 
a dummy indicating whether the firm belongs to a non-domestic group (11)) referring to the year in 
which the match has taken place. Three things are worth noting at this stage. 

First,  the  comparison  of  matched  successful  and  unsuccessful  firms  in  terms  of  sales,  capital 
stock,  etc.  is  very  encouraging  suggesting  that  the  two  sets  of  firms are  indeed  similar  also in 
terms of variables that have not been used in the propensity score matching procedure. Second, 
there are 1,510 successful firms (12)  in the EIC-Pilot-SMEI scheme —see Table C-1—  and we 
end up considering only 549 firms (13)  in the regression analysis.  This is mainly due to missing 
balance  sheet  information  for  firms  in  the  EIC-Pilot-SMEI  scheme  which  are  then  ‘lost’  when 
matching to Orbis.  The propensity score matching procedure is also responsible for some  data 
loss, particularly because we impose a matching by year while also using industry and country 
dummies.  Third,  the  fact  that  a  firm  has  been  matched  on  pre-treatment  observables  is  no 
guarantee that information is available throughout the sample period. For example, from Table 4 
it emerges how a few observations are missing in the year of the matching; particularly for materials 
cost. This problem of course spans also to other years. 

Successful firms whose propensity score is outside the common support are not used in the subsequent 

(10) 
DiD analysis. 
(11) 
A non-domestic group is defined as a group of companies of which the Global Ultimate Owner Corporate is 
not located in the same country as the EIC-SMEI applicant.  The GUO50c variable of Orbis historical information is 
taken to establish the country of the Global Ultimate Owner Corporate. 
(12) 
(13) 

1,237 successful firms are from the SME Instrument and 273 pertain to the EIC Pilot. 
478 matched successful firms were selected by the SME Instrument and 71 were selected by the EIC Pilot. 

16 

 
 
Accelerator and Firm Scaling-Up Evidence 

Table  4:  EIC-Pilot-SMEI  scheme.  Comparison  of  matched 
successful  and unsuccessful firms 

Variable 

Matched Successful Firms 

Sales 
Wage Bill 
Capital Stock 
Materials Cost 
Part Non-dom. Group 
Variable 

Sales 
Wage Bill 
Capital Stock 
Materials Cost 
Part Non-dom. Group 

Mean 
2,444,593 
853,016 
582,655 
1,350,176 
0.0419 

Mean 
2,613,197 
958,382 
665,721 
1,411,337 
0.0474 

P5 

P95 

St. Dev. 

Median 
643,260  5,243,576  19,957  11,300,000 
2,936,048 
433,000  1,278,246  29,735 
2,723,596 
56,170 
2,121,323 
6,366,212 
238,867  3,491,685 
– 

0 
1,448 
– 

0.2005 

– 

Matched Unsuccessful Firms 

P5 
Median 
St. Dev. 
445,000  6,004,371 
6,480 
350,852  1,575,094  30,817 
2,845,570 
52,463 
169,289  3,492,371 

– 

0.2126 

0 
699 
– 

P95 
13,000,000 
3,713,657 
2,330,437 
8,312,572 
– 

N. observ. 
529 
483 
511 
362 
549 

N. observ. 
537 
463 
510 
353 
549 

Notes: Monetary values are in current euros. 

Notwithstanding  these  limitations,  we  use  panel  data  information  on  matched  successful  and 
unsuccessful firms to estimate the DiD model in equation (1) using the Stata command jwdid while 
clustering standard errors at the firm-level. 

Sales  Using  log  sales  as the outcome  variable,  we have estimated  the  full  model  with  details 
provided in Figure C-8 in Appendix C. The model features 8,099 observations, has an R2  of about 
0.80 and ATT coefficients, along with pre-trend coefficients, are reported for each cohort and year. 
As  suggested  by  Wooldridge  (2021),  the  model  (1)  allows  for  a  lot  flexibility  but  needs  to  be 
simplified in its exposition to best interpret results. For example, based on the estimates of the full 
model in equation (1), the implied ATT effect across all cohorts and years is 32.50% higher sales 
post treatment which, with a t-ratio of 4.35, is also highly significant.  Given that the average sales 
of  matched  successful  firms  in  the  awarding year  is  about  2.5  million  euros  (see Table  4),  the 
implied impact of the EIC-Pilot-SMEI scheme corresponds to around 810,000 euros per year. 

There  is,  however,  heterogeneity  across  cohorts.  Figure  3  provides  ATEs  across  the  different 
treatment cohorts (2015 to 2020). The ATE of the 2015 cohort is not significantly different from zero 
while the ATEs of the 2016 to 2018 cohorts are around 25% and significant. Interestingly, the ATEs 
of the 2019 and 2020 cohorts are much stronger and around 50% or more, which is in line with 
the fact that additional funding through equity was introduced in this period so boosting the impact 
on  the  successful  firms.  Finally,  F igure  4  provides  an  event  assessment  of  ATEs  and  pre-
trends with a window of 4  years before and after the treatment. 

As the reader  might  notice,  there  are  some  pre-trends  visible  in  the  data  but  only  for  the  year 
t-4. (14)  Furthermore, F igure 4 suggests that the impact of the EIC-Pilot-SMEI scheme in terms 
of firm sales is already present in the year of the treatment (year 0) and peaks in year 1 while 
continuing being significant up to year 3.  Overall, this suggests that the impact of the EIC-Pilot- 
SMEI scheme on successful firms’ sales is positive and strong while extending well beyond the 
awarding year. 

(14)  When considering the largest possible window (from time -8 to time 7), one cannot reject the null hypothesis 

that all pre-trends coefficients are equal to zero at the 5% significance level. 

17 

 
 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure 3: EIC-Pilot-SMEI scheme. Cohort analysis of the average treatment effect on firm log 
sales 

Figure 4: EIC-Pilot-SMEI scheme. Event analysis of the average treatment effect on firm log 
sales 

Wage bill and capital stock  Using log wage bill and log capital stock as the outcome variables, 
we have again estimated the full model with details provided in Figures C-9 and C-10 in Appendix 
C. Based on the estimates of the full model, the implied ATT effect across all cohorts and years is 
39.16% higher wage bill (44.66% higher capital stock) post treatment which, with a t-ratio of 5.51 
(5.07),  is  also  highly  significant.  Given  that  the  average  wage  bill  (capital  stock)  of  matched 
successful firms in the awarding year is about 0.85  (0.6) million euros (see Table  4), the implied 
impact of the EIC-Pilot-SMEI scheme corresponds to around 330,000 (270,000) higher wage bill 
(capital stock) per year. 

18 

 
 
 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Interestingly, the % impact on capital is considerably higher than the one on sales and the wage 
bill.  Furthermore,  as in the case of sales,  there is heterogeneity across cohorts, as depicted  in 
Figures 5  and 6  with, yet again, the last two cohorts of 2019  and 2020  displaying a higher ATT; 
something  that  can  be  (as  said  above)  rationalised  by  the  higher  financing  means  provided  in 
those years. Finally, Figures 7 and 8 provide an event assessment of ATEs and pre-trends, with a 
window  of  4  years  before  and  after  the  treatment,  for  log  wage  bill  and  log  capital  stock, 
respectively. 

Similar to the case of sales, the ATT for both the wage bill and the capital stock is present already 
in the year of the award and increases up to two years later while decreasing afterwards. The ATT 
on the wage bill is not significant any more 4 years after treatment, while the ATT on the capital 
stock keeps its significance also in year 4.  Again, there are some minor pre-trends visible in the 
data  (mainly  for  the  wage  bill)  but  we  cannot  reject  the  null  hypothesis  that  all  pre-trends 
coefficients are equal to zero at the 5% significance level. 

19 

 
 
Accelerator and Firm Scaling-Up Evidence 

Figure 5: EIC-Pilot-SMEI scheme. Cohort analysis of the average treatment effect on firm log 
wage bill 

Figure 6:  EIC-Pilot-SMEI scheme.  Cohort analysis of the average treatment effect on firm log 
capital stock 

20 

 
 
 
 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure 7: EIC-Pilot-SMEI scheme. Event analysis of the average treatment effect on firm log 
wage bill 

Figure 8:  EIC-Pilot-SMEI scheme.  Event analysis of the average treatment effect on firm log 
capital  stock 

21 

 
 
 
 
 
 
 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Average firm wage  Turning to the log of the average firm wage (the latter obtained as the total 
wage bill divided by the number of employees) as the outcome variable, figure C-11 in Appendix 
C provides detailed estimation results for the full model.  In this respect, the average firm wage is 
available for a bit less observations that in the previous cases, because the number of employees 
is less systematically reported in the data.  Bearing this in mind, the implied ATT effect across all 
cohorts and years is 10.39% higher average firm wage post treatment which, with a t-ratio of 2.27 
is significant at the 5% level. 

The  significant  impact  on  the  average  firm  wage  extends  to  both  the  cohort  and  event 
assessments  of  the  ATTs  as  indicated  by  Figures  9  and  10.  In  particular,  the  cohort  analysis 
in Figure 9 highlights how the average ATT is driven by the positive and significant effects of the 
cohorts of 2019 and 2020 including firms selected to receive an equity funding through the EIC 
Fund. In terms of the event analysis, Figure 10 indicates that the impact is already present at the 
time of the award and remains significant up to two years post-treatment.  At the same time, one 
cannot reject the null hypothesis that all pre-trends coefficients are equal zero at the 5% level. 

Figure 9:  EIC-Pilot-SMEI scheme.  Cohort analysis of the average treatment effect on average 
firm wage 

22 

 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure 10: EIC-Pilot-SMEI scheme. Event analysis of the average treatment effect on average 
firm wage 

TFP-R  Moving to log total factor productivity as the outcome variable, Figure C-12 in Appendix C 
provides detailed estimation results for the full model. Importantly, TFP-R is available for much less 
observations that in the previous cases (roughly half of the observations). Bearing this in mind, the 
implied ATT effect across all cohorts and years is 1.22% higher TFP post treatment which, with a 
t-ratio of 0.39 is not significant. 

The lack of a significant impact on productivity extends to both the cohort and event assessments 
of the ATTs as indicated by Figures 11 and 12. 

23 

 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure 11: EIC-Pilot-SMEI scheme. Cohort analysis of the average treatment effect on firm 
productivity 

Figure 12: EIC-Pilot-SMEI scheme. Event analysis of the average treatment effect on firm 
productivity 

24 

 
 
 
 
 
 
 
 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Number and value of deals  As a final step in the assessment of the EIC-Pilot-SMEI scheme, 
we  consider  here  both  the  number  and  value  of  deals,  with  information  on  deals  coming from 
the BvD M&A database (the value of deals is measured in thousand EUR). In this instance, data 
is available also for the year 2023 and we exploit such additional year in the analysis. Furthermore, 
given the presence of many zeroes in the data (the vast majority of firms are not involved in any 
deal) we use a Poisson model version of equation (1), which is still implemented using the stata 
command jwdid, while clustering standard errors at the firm-level. We also drop firm fixed effects 
from the analysis and replace them with group fixed effects. 

Estimations of the full model for the number and value of deals are reported in Figures C-13 and 
C-14 in Appendix C. The implied ATT effect across all cohorts and years for the number of deals 
is basically zero (-0.003 deals), and with a t-ratio of -0.27 which makes it largely not significant. 
Small and not significant ATTs (as well as pre-trends) for the number of deals  also characterise 
the cohort and event analyses as indicated by Figures 13 and 15.  One noticeable exception is the 
ATT effect for the 2016 cohort.  

Turning to the value of deals, the implied ATT effect across all cohorts and years is 390 thousand 
euros higher deal value post treatment which, with a t-ratio of 1.62, it is just short  of significance 
at the 10% level.  At the same time, pre-trends are not significant.  The cohort and event analyses 
of the ATTs are reported in Figures 14  and 16 and portray a complex picture.  In particular, the 
cohort analysis points to significant ATT effects for the 2016 and 2020 cohorts  only.  Furthermore, 
the  event  analysis  indicates  that  an  impact  on  the  value  of  deals of about 830 thousand euros 
significantly arises only 4 years after treatment, suggesting that it takes a good amount of time for 
firms to turn the award into crowding in additional external funding. 

25 

 
Accelerator and Firm Scaling-Up Evidence 

Figure 13:  EIC-Pilot-SMEI scheme.  Cohort analysis of the average treatment effect on the 
number of deals. Poisson model. The unit on the y-axis is the number of deals 

Figure  14:  EIC-Pilot-SMEI  scheme.  Cohort analysis  of  the  average  treatment  effect  on  the 
value  of deals.  Poisson model.  The unit on the y-axis is the value of deals in thousand EUR 

26 

 
 
 
 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure 15:  EIC-Pilot-SMEI scheme.  Event analysis of the average treatment effect on the 
number of  deals. Poisson model. The unit on the y-axis is the number of deals 

Figure 16: EIC-Pilot-SMEI scheme. Event analysis of the average treatment effect on the value 
of deals. Poisson model.  The unit on the y-axis is the value of deals in thousand EUR 

27 

 
 
 
 
 
 
 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

6.2.  A focus on the Blended Finance stage of EIC Pilot scheme 

In  what  follows  we  focus  on  the  EIC  pilot  scheme  to  assess  whether  there  is  enough  data  to 
confirm  and/or  refine  the  combined  EIC-Pilot-SMEI  scheme  results  provided  in  the  previous 
section. In particular, we have so far seen that the 2019 and 2020 cohorts of the EIC-Pilot-SMEI 
scheme often stand out of the previous cohorts in terms of the size and significance of the  ATT 
effects.  The  2019  cohort  includes  both  the  companies  selected  only  for  grants  before  the 
introduction of Blended Finance in October 2019 and the ones selected to receive an equity. There 
are not enough observations on the latter companies to obtain a clean propensity score matching 
for them and so we focus here on the 2020 cohort of the EIC pilot. 

First of all, concerning the initial matching procedure, Figure C-15 in Appendix C provides the plot 
of the propensity score values for both matched successful and unsuccessful firms. As one can 
notice,  the  quality  of  the  match  in  terms  of  propensity  score  similarity  is  quite good. (15)  At  the 
same time, Figure C-16 in Appendix C show balancing tests stats and overall provide a reassuring 
message  about  the  quality  of  the  match  in  terms  of  observables  between  successful  and 
unsuccessful firms. 

Table 5:  EIC Pilot scheme - 2020 cohort.  Comparison of matched 
successful and unsuccessful firms 

Variable 

Matched Successful Firms 

Sales 
Wage Bill 
Capital Stock 
Materials Cost 
Part Non-dom. Group 
Variable 

Sales 
Wage Bill 
Capital Stock 
Materials Cost 
Part Non-dom. Group 

P5 

Mean 

Median 

St. Dev. 
1,575,027  491,034  3,129,915  10,000  8,458,397 
28,175  2,480,974 
998,203 
797,521 
459,000 
1,828,675 
4,582 
741,143 
313,487 
56,307 
645,239 
4,363,502 
3,978 
155,533  1,291,826 
0.0423 

0.2026 

P95 

Matched Unsuccessful Firms 

Mean 

St. Dev. 
Median 
1,053,648  307,196  1,848,620 
683,103 
293,322 
593,603 
0.0282 

P95 
4,351,955 
264,230  1,238,073  14,904  2,854,762 
1,921,937 
42,812 
3,090,095 
66,164 
– 
– 

560,454 
1,380,892 
0.1666 

0 
1,410 
– 

P5 
6,137 

N. observ. 
71 
61 
66 
45 
71 

N. observ. 
71 
58 
57 
45 
71 

Notes: Monetary values are in current euros. 

Our  matching  procedure  leads  us  to  focus  on  71  matched  successful  firms  and  71  matched 
unsuccessful firms. In terms of observables, Table 5 provides some summary stats about matched 
successful  and  unsuccessful  firms.  In  particular,  Table 5  displays  the  mean, median,  standard 
deviation, 5th percentile (P5), 95th percentile (P95) and number of non-missing observations for a 
range of firm-level variables (sales, wage bill, capital stock, materials cost, and a dummy indicating 
whether  the  firm  belongs  to  a  non-domestic  group)  referring  to  the  year  (2020)  in  which  the 
matching has taken place. 

Notwithstanding the limitations of the low number of firms, as well as the missing data problems 
discussed  in  the  previous  section,  we  use  panel  data  information  on  matched  successful  and 

(15)  Successful firms whose propensity score is outside the common support are not used in the subsequent DiD 

analysis. 

28 

 
 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

unsuccessful firms to estimate the DiD model in equation (1) using the Stata command jwdid while 
clustering standard errors at the firm-level.  Detailed results on the estimation of the full model for 
the different outcome variables are reported in Figures C-17 to C-23 in Appendix C. 

Sales  Based on the estimates of the full model, the implied ATT effect across all years is 54.40% 
higher sales post treatment which, with a t-ratio of 2.18, is significant at the 5% level. This nicely 
replicate what we find (around 50% ATT) for the 2020 cohort in the previous section focused on 
the  so-called  combined  EIC-Pilot-SMEI  scheme.  Figure  17  provides  an  event  assessment  of 
ATEs  and  pre-trends.  First  of  all,  one  cannot  reject  the  null  hypothesis  that  all  pre-trends 
coefficients are equal to zero at the 5% significance level.  Second,  Figure 17 suggests that the 
effect is strongest one year after treatment while being present but not significant at the time of 
the award.  This is somewhat different from what we find in the previous section, but it might well 
be  the  consequence  of  the  lower  number  of  firms  we  have  here  and  the  fact  that,  firms  being 
treated in 2020, we have at most information 2 years after treatment. 

Figure 17: EIC Pilot scheme - 2020 cohort.  Event analysis of the average treatment effect on 
firm log sales 

Wage bill and capital stock  Based on the estimates of the full model, the implied ATT effect 
across all years is 29.70% higher wage bill (66.75% higher capital stock) post treatment which, 
with a t-ratio of 1.78 (2.43), is significant at the 10% (5%) level. Again, this roughly replicates what 
we find for the 2020 cohort in the previous section and in particular the stronger effect on the capital 
stock. Figures 18 and 19 provide an event assessment of ATEs and pre-trends. First of all, one 
cannot reject the null hypothesis that all pre-trends coefficients are equal to zero at the (1%) 5% 
significance level for the wage bill (capital stock). 

29 

 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure 18: EIC Pilot scheme - 2020 cohort.  Event analysis of the average treatment effect on 
firm log wage bill 

Figure 19: EIC Pilot scheme - 2020 cohort. Event analysis of the average treatment effect on 
firm log capital stock 

Second, Figure 18 suggests, like in the case of sales, that the effect on the wage bill is strongest 
one year after treatment while being present but not significant at the time of the award.  Again, 
this  might  well  be  the  consequence  of  the  lower  number  of  firms  we  have here and the fact 
that,  firms  being  treated  in  2020,  we  have  at  most  information  2  years  after  treatment.  Third, 
Figure 19 indicates that the effect on the capital stock is strongest one year after treatment while 
being present and significant already at the time of the award.  This suggests that the EIC Pilot 
scheme has particularly strong (in terms of both magnitude and significance) effects on the capital 
stock of the successful firms.

30 

 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Average firm wage  Moving to log average firm wage as the outcome variable we find, as in the 
previous section, that there is a significant overall ATT effect. From the estimation of the full model 
(see Figure C-20), the implied ATT effect across all years is 28.81% higher average firm wage 
post treatment which, with a t-ratio of 3.32, is significant at 1% level. The impact is higher than the 
roughly  10%  we  found  for  the  combined  EIC-Pilot-SMEI  scheme,  in  which  the  analysis  was 
already pointing to stronger effects related to the EIC Pilot cohorts of 2019 and 2020 including 
firms selected to receive an equity funding through the EIC Fund. 

Figure 20 provides an event assessment of ATEs and pre-trends.  First of all, one cannot reject 
the null hypothesis that all pre-trends coefficients are equal to zero at the 1% significance level. 
Second, the impact is already present in the year of the award and peaks at almost 40% one year 
post-treatment, while being still significant in year 2. 

Figure 20:  EIC Pilot scheme - 2020 cohort.  Event analysis of the average treatment effect on 
log firm average wage 

TFP-R  Turning to log total factor productivity as the outcome variable we find, as in the previous 
section, that there is no significant overall ATT effect. Figure 21 confirms this finding also from an 
event point of view.  One interpretation of the finding might be that early-stage firms, in particular 
in  deep  tech,  pay  a  distinctive  attention  to  develop  new  products  and  firm  infrastructure  for 
instance and then they are less focused at the same time to attempt to produce more efficiently. 

31 

 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure  21:  EIC  Pilot  scheme  -  2020  cohort.  Event  analysis  of  the  average  treatment  effect 
on  firm productivity 

Number and value of deals  We consider here both the number and value of deals as outcome 
variables, with information on deals coming from the BvD M&A database (the value of deals  is 
measured in thousand EUR). As already pointed out, data is available also for the year 2023 and 
we exploit such additional year in the analysis. (16) 

(16)  As indicated in the previous section, given the presence of many zeroes in the data (the vast majority of firms 
are not involved in any deal) we use a Poisson model version of equation (1), which is still implemented using 
the stata command jwdid, while clustering standard errors at the firm-level. We also drop firm fixed effects from 
the analysis and replace them with group fixed effects. 

32 

 
 
 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure 22: EIC Pilot scheme - 2020 cohort. Event analysis of the average treatment effect on 
the number of deals. Poisson model. The unit on the y-axis is the number of deals 

Figure 23:  EIC Pilot scheme - 2020 cohort.  Event analysis of the average treatment effect on 
the value of deals.  Poisson model.  The unit on the y-axis is the value of deals in thousand 
EUR 

33 

 
 
 
 
 
 
 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

The  implied  ATT effect  across  all years  for  the  number  of deals  is  (as  in  the previous  section) 
basically zero (-0.037 deals), and with a t-ratio of -0.43 which makes it largely not significant. Small 
and not significant ATTs (as well as pre-trends) for the number of deals also characterise the event 
analysis as indicated by Figure 22.  Turning to the value of deals, the implied ATT effect across all 
years is 758 thousand euros higher deal value post treatment which, with a t-ratio of 1.02, is not 
significant.  The event analysis of the ATTs is reported in Figure 23 and, while obtaining that all 
pre-trends are not significantly different from zero at the 5% level, suggests that a significant effect 
arises about 3 years after treatment while being particularly sizeable (around 2.3 million EUR). 
Overall, this confirms that the impact of the EIC Pilot on external investment crowding-in is through 
higher values of deals while being somewhat delayed with respect to the year of the award. 

34 

 
Accelerator and Firm Scaling-Up Evidence 

6.3.  EIC HE Accelerator scheme 

In  this section,  we move  to the assessment of  the  EIC  HE  Accelerator  scheme  in  terms of  the 
outcome variables used above. The EIC HE Accelerator scheme information we have allows us to 
study the funding years 2021 and 2022. Given that the BvD Orbis historical database covers up to 
2022 (2023 when considering the number and value of deals coming from the BvD M&A database) 
our results have to be considered as very preliminary and will need to be extended over time when 
data availability will allow. 

First of all, concerning the initial matching procedure, Figure C-24 in Appendix C provides the plot 
of the propensity score values for both matched successful and unsuccessful firms. As one can 
notice,  the  quality  of  the match  in  terms  of  propensity  score  similarity  is  quite  good. (17)  At the 
same time, Figures C-25 and C-26 in Appendix C show balancing tests stats and overall provide 
a  reassuring  message  about  the  quality  of  the  match  for  each  year  in  terms  of  observables 
between successful and unsuccessful firms. 

Table 6:  EIC HE Accelerator scheme - 2021 and 2022 cohorts. 
Comparison of matched successful and unsuccessful firms 

Variable 

Matched  Successful  Firms 

Sales 
Wage Bill 
Capital  Stock 
Materials  Cost 
Part Non-dom. Group 
Variable 

Sales 
Wage Bill 
Capital  Stock 
Materials  Cost 
Part Non-dom. Group 

P5 

Mean 

Median 

St. Dev. 
759,667  359,007  1,088,400  11,000  3,187,696 
58,938  2,099,324 
661,336 
654,848  452,175 
366,845 
38,281 
168,413 
992,367 
1,275,124 
729,274 
84,562 
347,742 
– 
0.3831
–
0.1757 

0 
1,142 
– 

P95 

Matched  Unsuccessful  Firms 

Mean 

Median 

St. Dev. 
766,655  416,840  1,198,280 
839,684 
617,527  341,679 
1,248,813 
28,626 
355,044 
3,492,371 
45,037 
216,143 
0.3711
–
0.1622 

P95 
P5 
1,523 
2,925,175 
25,620  2,737,001 
1,874,353 
969,381 
– 

0 
95 
– 

N. observ.
74
69
72
42
74

N. observ.
73
65
71
50
74

Notes: Monetary values are in current euros. 

Our  matching  procedure  leads  us  to  focus  on  74  matched  successful  firms  and  74  matched 
unsuccessful  firms.  In  terms  of  observables,  Table  6  provides  some  summary  stats  about 
matched successful and unsuccessful firms.  Interestingly,  by comparing Table 6  with Tables 
4  and  5 reveals that matched firms of the EIC HE Accelerator scheme, reporting among other 
the log level of sales and capital in 
 (as aforementioned), are on average smaller 
and more likely to  be  part  of  a  non-domestic  group  than  matched  firms  in  the  EIC-Pilot-SMEI 
𝑡𝑡  − 3
scheme  or  the  EIC  Pilot  scheme.  Those  firms  also  look  younger  and  less  established,  as 
suggested by the  fact  that  they do  not  yet  have  a  value  of  sales  capable  of  covering  the  cost 
of materials and labour they use; 

𝑡𝑡  − 1

 till 

(17) The number  of  successful  and  (especially)  unsuccessful  firms in  the  EIC  HE  Accelerator  scheme is  too  low
to allow for industry and country dummies in the propensity score estimation.  We thus refrain from using such
controls and employ the other matching variables described above (lags up to 
 of sales and capital stock)
to implement the procedure. As usual, successful firms whose propensity score is outside the common support
are not used in the subsequent DiD analysis.

𝑡𝑡 − 3

35 

Accelerator and Firm Scaling-Up Evidence 

something that also apply to the comparison of matched firms from the 2020 cohort of the EIC 
Pilot  scheme  with  matched  firms  from  the  EIC-Pilot-SMEI  scheme.  Overall,  this  suggests  that 
funding  by  the  EIC  schemes  that  embed  the  equity  component  over  time progressively  shifted 
away  from  larger  and  more  established  firms  financed  by  the  SME  Instrument  to  smaller  and 
younger  firms.  This  finding  appears  to  ensue  from  the  design of  the  EIC  Accelerator,  as  the 
successor  of  the  SME  Instrument,  that  targets  the  funding  of  the  innovation  and  scale-up 
activities of startups and other high growth potential SMEs conducting projects on cutting-edge 
deep tech technologies for instance in advanced materials, artificial intelligence, energy, health 
biotech and semiconductors.  

Notwithstanding the limitations of the low number of firms and the limited time horizon available, 
as  well  as  the  missing  data  problems  discussed  in  the  previous  sections,  we  use  panel  data 
information on matched successful and unsuccessful firms to estimate the DiD model in equation 
(1)  using  the  Stata  command  jwdid  while  clustering  standard  errors  at  the  firm-level.  Detailed 
results  on  the  estimation  of  the  full  model  for  the  different  outcome  variables  are  reported  in 
Figures C-27 to C-33 in Appendix C. 

Sales  Based  on  the  estimates  of  the  full  model,  the  implied  ATT  effect  across  all  years  and 
cohorts is 19.29% higher sales post treatment which, with a t-ratio of 1.21, is not significant. Figure 
24 provides an event assessment of ATEs and pre-trends.  First of all, one cannot reject the null 
hypothesis that all pre-trends coefficients are equal to zero at the 5% significance level. Second, 
Figure  24  suggests  that  a  significant  effect  arises  one  year  after  treatment  (with  the  50% 
magnitude seen for the EIC Pilot) while being present but not significant at the time of  the award. 
This  is  overall  consistent  with  the  results  obtained  for  the  EIC  Pilot  scheme  and  it  is  entirely 
possible  that  the  lack  of  significance  of  the  overall  ATT  effect  is  due  to  the  low  number  of 
observations and the short time frame allowed by the data. 

36 

 
Accelerator and Firm Scaling-Up Evidence 

Figure 24: EIC HE Accelerator scheme - 2021 and 2022 cohorts. Event analysis of the average 
treatment effect on firm log sales 

Figure 25: EIC HE Accelerator scheme - 2021 and 2022 cohorts. Event analysis of the average 
treatment effect on firm log wage bill 

37 

 
 
 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure 26: EIC HE Accelerator scheme - 2021 and 2022 cohorts. Event analysis of the average 
treatment effect on firm log capital stock 

Wage bill and capital stock  Based on the estimates of the full model, the implied ATT effect 
across all years and cohorts is 5.32% higher wage bill (7.05% higher capital stock) post treatment 
which, with a t-ratio of 0.40 (0.39), is not significant.  Again, caution is needed to compare those 
results with the ones we obtain for the EIC-Pilot-SMEI scheme or the 2020 cohort of the EIC Pilot 
scheme because of both the low number of observations and the limited time horizon allowed by 
the data. Figures 25 and 26 provide an event assessment of ATEs and pre-trends. First of all, one 
cannot  reject  the  null  hypothesis  that  all  pre-trends  coefficients  are  equal  to  zero  at  the  5% 
significance level in both cases. 

Second, Figures 25 and 26 do not provide evidence of significant effects even though, especially 
for the wage bill, coefficient values are qualitatively similar to those obtained for  the EIC Pilot. 

Average firm wage  Based on the estimates of the full model (see Figure C-30), the implied  ATT 
effect across all years and cohorts is 0.41% higher log average firm wage post treatment which, 
with a t-ratio of 0.04, is not significant. Figure 27 provides an event assessment of ATEs and pre-
trends. First of all, one cannot reject the null hypothesis that all pre-trends coefficients are equal 
to zero at the 1% significance level.  Second, Figure 27 does not provide evidence of significant 
effects. 

TFP-R  Moving to log total factor productivity as the outcome variable we find, as in the previous 
sections,  that  there  is  no  significant  overall  ATT  effect.    Based  on  the  estimates of  the  full 
model,  the implied ATT effect across all years and cohorts is 9.76% higher TFP post treatment 
which, with a t-ratio of 0.58, is not significant.

38 

 
 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure 27: EIC HE Accelerator scheme - 2021 and 2022 cohorts. Event analysis of the average 
treatment effect on log firm average wage 

Figure 28 provides an event assessment of ATEs and pre-trends.  First of all, one cannot reject 
the null hypothesis that all pre-trends coefficients are equal to zero at the 5% significance level. 
Second,  while  significance  of  the  ATT  effect  is  not  reached  at  time 
,  the  value 
(32.27%)  and  t-ratio  (1.22)  of  the  ATT effect  one  year  post  award  is  suggestive  of  a  possible 
delayed increase in TFP as a consequence of the EIC HE Accelerator scheme. 

  or  time 

1

0

39 

 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure 28: EIC HE Accelerator scheme - 2021 and 2022 cohorts. Event analysis of the average 
treatment effect on firm productivity 

Number and value of deals  We consider here both the number and value of deals as outcome 
variables, with information on deals coming from the BvD M&A database (the value of deals  is 
reported in thousand EUR). (18)  

Figure 29:  EIC HE Accelerator scheme - 2021 and 2022 cohorts.  Event analysis of the average 
treatment effect on the number of deals.  Poisson model.  The unit on the y-axis is the number of 
deals 

(18)  As  already  pointed out,  data  is  available also for  the  year  2023  and we  exploit  such  additional year  in  the 

analysis. 

40 

 
 
 
 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure 30: EIC HE Accelerator scheme - 2021 and 2022 cohorts. Event analysis of the average 
treatment  effect  on  the  value  of  deals.  Poisson model.  The unit  on  the  y-axis  is  the  value of 
deals in thousand EUR 

The implied ATT effect across all years and cohorts for the number of deals is 0.038 additional 
deals post-treatment which, with a t-ratio of 0.77, is not significant and so are all of the pre-trends. 
However, the event analysis provided in Figure 29 does indicate a significant (5% level) ATT effect 
at time zero (about 0.12 additional deals).  Turning to the value of deals, the implied ATT effect 
across all cohorts and years is 22 thousand euros higher deal value post treatment which, with a 
t-ratio of 0.04, is not significant and so are all of the pre-trends. The event analysis of the ATTs is 
reported in Figure 30 and suggests that a significant effect (5% level) of about 800 thousand euros 
arises at the time of the award.  Overall, this suggests that the impact of the EIC HE Accelerator 
scheme on the value of deals might materialise before than in the case of the 2020 cohort of the 
EIC-Pilot-SMEI scheme and the EIC Pilot scheme. 

41 

 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

7.  Robustness and extensions of the results for the Blended 

Finance stage of the EIC Pilot scheme 

In what follows,  we focus again on the Blended Finance stage of the EIC Pilot scheme to  assess 
the  robustness  of  certain  results  while  providing  some  additional  findings  regarding  the 
heterogeneity of the impacts.  In this respect, we believe it is too early to perform these analyses 
for  the  EIC  HE  Accelerator  scheme,  while  the  SME  Instrument  scheme  has  already  been  the 
object of a number of investigations and it is currently less policy relevant. 

7.1.  Using an alternative control group 

So far, we have been using the group of firms receiving the Seal of Excellence as control group 
for  the  successful  firms.  In  what  follows,  we  explore  the  robustness  of  the  findings  using a 
different control group.  More specifically, we use firms in the same 2-digit industry and country of 
the successful firms that did not apply for funding. As above, we use a propensity score matching 
to select, among the non-participating firms, those that are more similar to the successful firms. 
The matching is again done separately by year (2019 and 2020). 

First of all, concerning the initial matching procedure, Figure C-34 in Appendix C provides the plot 
of the propensity score values for both matched successful and unsuccessful firms. As one can 
notice,  the  quality  of  the  match  in  terms  of  propensity  score  similarity  is  quite good. (19)  At  the 
same time, Figures C-35 and C-36 in Appendix C show balancing tests stats and overall provide 
a reassuring message about the quality of the match in terms of observables between successful 
and control firms. 

Our matching procedure leads us to focus on 201  matched successful firms and 201  matched 
control firms.  We use panel data information on matched successful and control firms to estimate 
the DiD model in equation (1) using the Stata command jwdid while clustering standard errors at 
the  firm-level.  Detailed  results  on  the  estimation  of  the  full  model  for  the  different  outcome 
variables are reported in Figures C-37 to C-43 in Appendix C. 

Sales  Based on the estimates of the full model, the implied ATT effect across all cohorts and 
years is 47.60% higher sales post treatment which, with a t-ratio of 2.18, is significant at the 5% 
level. This is similar to the effect we found in Section 6.2 when using unsuccessful firms as control 
group (54.40%).  Figure 31 provides an event assessment of ATEs and pre-trends.  As one can 
clearly  see  from  the  figure,  pre-trends  coefficients  are  strongly  significant  suggesting  that  the 
control  group  represented  by  the  most  similar  firms  in  the  same  industry  and  country  of  the 
successful firms is not as good as the control group represented by firms who received the Seal 
of Excellence. 

(19)  Successful firms whose propensity score is outside the common support are not used in the subsequent DiD 

analysis. 

42 

 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure 31:  EIC Pilot scheme - 2019 and 2020 cohorts of the Blended Finance stage; using a 
different control group. Event analysis of the average treatment effect on firm log sales 

Wage bill and capital stock  Based on the estimates of the full model, the implied ATT effect 
across all years is 69.78% higher wage bill (74.90% higher capital stock) post treatment which, 
with a t-ratio of 5.57 (4.68), is significant at the 1% (1%) level.  These ATT effects are higher and 
more  significant  than  those  we  found  in  Section  6.2.  Figures  32  and  33  provide  an  event 
assessment of ATEs and pre-trends. As in the case of sales, pre-trends are clearly present and 
significant limiting the causality interpretation of the findings obtained with the alternative control 
group. 

43 

 
 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure 32:  EIC Pilot scheme - 2019 and 2020 cohorts of the Blended Finance stage; using a 
different control group. Event analysis of the average treatment effect on firm log wage bill 

Figure 33:  EIC Pilot scheme - 2019  and 2020  cohorts of the Blended Finance stage;  using a 
different control group. Event analysis of the average treatment effect on firm log capital stock 

44 

 
 
 
 
 
 
 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Average  firm  wage  Based  on  the  estimates  of  the  full  model,  the  implied  ATT  effect  across 
all years is 14.62% higher log firm average wage post treatment which, with a t-ratio of 1.89,  is 
significant at the 10% level.  This ATT effect is a bit lower and less significant than the one we 
found (roughly 29%) in Section 6.2.  Figure 34 provides an event assessment of ATEs and pre-
trends. In this instance, pre-trends do not look particularly strong and the null hypothesis that all 
related coefficients are zero cannot be rejected at the 5% level. 

Figure 34:  EIC Pilot scheme - 2019 and 2020 cohorts of the Blended Finance stage; using a 
different control group. Event analysis of the average treatment effect on log firm average wage 

TFP-R  Turning to log total factor productivity as the outcome variable we find, as in basically all 
of our analyses, that there is no significant overall ATT effect. Figure 35 confirms this finding also 
from an event point of view. 

45 

 
 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure 35:  EIC Pilot scheme - 2019 and 2020 cohorts of the Blended Finance stage; using a 
different control group. Event analysis of the average treatment effect on firm productivity 

Number and value of deals  We consider here both the number and value of deals as outcome 
variables, with information on deals coming from the BvD M&A database (the value of deals  is 
measured in thousand EUR). As already pointed out, data is available also for the year 2023 and 
we exploit such additional year in the analysis. 

Figure  36:  EIC  Pilot  scheme  -  2019  and  2020  cohorts  of  the  Blended  Finance  stage;  using  a 
different control group.  Event analysis of the average treatment effect on the number of deals. 
Poisson model. The unit on the y-axis is the number of deals. 

46 

 
 
 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure 37:  EIC Pilot scheme - 2019 and 2020 cohorts of the Blended Finance stage; using a 
different  control  group.  Event  analysis  of  the  average  treatment effect  on  the  value  of  deals. 
Poisson model. The unit on the y-axis is the value of deals in thousand EUR 

The implied ATT effect across all years for the number of deals is (as in Section 6.2) basically 
zero (-0.033  deals), and with a t-ratio of -0.54  which makes it largely not significant.  Small and 
not significant ATTs (as well as pre-trends) for the number of deals also characterise the event 
analysis (with the exception of a negative ATT in year 1) as indicated by Figure 36. Turning to 
the value of deals, the implied ATT effect across all years is 762 thousand euros higher deal 
value post treatment which, with a t-ratio of 4.39, is significant at the 1% level. This is very similar 
in  magnitude  to  what  we obtained  in Section  6.2,  with  the  difference  being  that  this  time the 
overall ATT effect is significant.  Unfortunately, we cannot reject the null hypothesis that all pre-
trends coefficients are zero at the 5% level so limiting the causality interpretation of the findings.

47 

 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

7.2.  Heterogeneity of the impact on the Blended Finance 2020 

cohort of the EIC Pilot 

In what follows we come back to the main approach of comparing successful and unsuccessful 
firms, as in Section 6.2, and we explore the heterogeneity of the impact of the EIC Pilot scheme 
with respect to two characteristics. The first one is firm productivity (TFP-R), and in particular we 
will be assessing whether ATTs are weaker or stronger for firms with higher/lower TFP-R. In order 
to  avoid  any  issues  of  simultaneity,  we  will  consider  the  productivity  of  firms  in the year prior 
to the year 
in which they receive the award or the Seal of Excellence.  The second  dimension  of 
heterogeneity  we  will  be  exploring  is  related  to  the  thematic  area  of  the  project.  More 
specifically, we will be looking at whether projects in the thematic area of “Health/e-Health” are 
characterised by stronger or weaker ATTs. 

𝑡𝑡 

In  order  to  accomplish  both  tasks,  we  will  build  upon  a  simpler  difference-in-difference model 
than the one used so far, and in particular we will consider: 

𝑝𝑝𝑝𝑝𝑝𝑝𝑡𝑡

𝑖𝑖𝑖𝑖𝑡𝑡𝑝𝑝𝑝𝑝

and 
𝑖𝑖

 and time 

𝑦𝑦𝑖𝑖,𝑡𝑡 = α + θ

where 
firm 

𝐷𝐷𝑖𝑖,𝑡𝑡𝑋𝑋𝑖𝑖 + μ𝑖𝑖 + μ𝑡𝑡 + ϵ𝑖𝑖,𝑡𝑡,                                      (3)
𝐷𝐷𝑖𝑖,𝑡𝑡 + θ
i,t is a dummy variable that takes the value of 
 if firm 
 is a measure of firm heterogeneity (the TFP-R of firm 

 is (as usual) an outcome of interest (revenue, capital stock, value of deals, etc.)  of 
 is treated in period 
𝑦𝑦𝑖𝑖,𝑡𝑡
 or 
 evaluated in 
 otherwise, 
𝑖𝑖
𝐷𝐷
a dummy taking value of 
𝑋𝑋𝑖𝑖
𝑡𝑡 
 is an error term that is assumed to be 
and 
µ𝑖𝑖
𝑖𝑖
uncorrelated  with  the  regressors.  The  average  treatment  on  the  treated  (ATT)  effect  from 
equation  (3)  is 
and  thus  allows  for  heterogeneity  of  the  impact  across  the 
distribution of 

 is in the thematic area “Health/e-Health”), 
𝑡𝑡 − 1

0
 are firm and time fixed effects,  and finally 

 if the project of firm 

𝜖𝜖𝑖𝑖,𝑡𝑡

𝑖𝑖𝑖𝑖𝑡𝑡𝑝𝑝𝑝𝑝

µ𝑡𝑡

𝑝𝑝𝑝𝑝𝑝𝑝𝑡𝑡

𝑡𝑡,

1

1

𝑖𝑖

. 
𝜃𝜃

+ θ

 𝑋𝑋𝑖𝑖 

𝑋𝑋𝑖𝑖

Sales  Figure  C-44  in  Appendix  C  reports  the  estimated  coefficients  of  the  model  specified  in 
equation  (3),  but  without  the  interaction  term  involving  firm  heterogeneity Xi,  i.e.,  a  simple  DiD 
model that can be compared with what we found with a more complex model in Section 6.2, and 
serve as a baseline for the exploration of heterogeneous impacts. Figure C-44 indicates an ATT 
effect on sales of 71.55% post treatment (significant at the 1% level) that compares with the 54.40% 
found  in  Section  6.2.  Figures  C-45  and  C-46  in  Appendix  C  provide  estimates  of  the  model 
specified  in  equation  (3)  with  respect  to  the  heterogeneity  in  the  thematic  area  and  the 
heterogeneity  in  productivity,  respectively.  Results  suggest  that  neither  type  of  heterogeneity 
significantly affects the impacts on firm sales. 

Wage bill, capital stock and average firm wage  Figure C-47 (C-50) in Appendix C indicates an 
ATT effect on the wage bill (capital stock) of 56.44% (37.95%) post treatment, which compares 
with  the  29.70%  (66.75%)  found  in Section 6.2.  Figures  C-48  and  C-49  for  the  wage  bill,  and 
figures C-51 and C-52 for the capital stock, confirm the finding we just saw on sales that the two 
types  of  heterogeneity  we  consider  do  not  significantly  impact  the  effect  on  the  EIC  Pilot.  A 
somewhat similar message applies to the analysis of the ATT effect on log average firm wage, as 
indicated in Figures C-53 to C-55.  Yet, in such an instance we do find a negative and significant 
interaction coefficient with firm TFP in Figure C-55. 

48 

 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Number and value of deals  We consider here both the number and value of deals as outcome 
variables, with information on deals coming from the BvD M&A database (the value of deals  is 
measured in thousand EUR). As already pointed out, data is available also for the year 2023 and 
we  exploit  such  additional  year  in  the  analysis.  As  usual,  we  use  a  Poisson  model  here as 
opposed to a log-linear model. Figures C-56 to C-61 in Appendix C provide our estimation results 
and confirm the overall absence of heterogeneous effects. 

49 

 
Accelerator and Firm Scaling-Up Evidence 

8.  Conclusions 

In this report we have conducted an econometric analysis of the impacts of the EIC Accelerator, 
with a particular focus on the Blended Finance stage of the EIC Accelerator Pilot,  by means of 
a  difference-in-difference  framework  combined  with  propensity  score matching  and  using firms 
who  obtained  a  Seal  of  Excellence  as  control  group.  Our  analysis  highlights  a  number  of 
significant impacts of the EIC Accelerator on firm performance measures like sales, capital stock, 
average  wage,  wage  bill  and  value  of  deals.  The  analysis  also  highlights  the  absence  of 
heterogeneity of the effects along the productivity dimension, as well as the lack of a differential 
impact for projects related to Health. 

In terms of insight from the analysis, the first one is that there does not seem to be a clear way to 
improve  what  is  already  done  in  terms  of  the  selection  process  of  the  EIC  Accelerator.  For 
example, if impacts were heterogeneous along the TFP dimension, one suggestion could have 
been to target more high productive firms, which is not the case here.  In this respect, our analysis 
suggests that perhaps TFP is not an appropriate measure to capture the “technology dynamism” 
of startups. One possibility to take this further would be to consider other measures based, for 
example, on the portfolio of innovations and patents of firms. The second insight from our analysis 
is that the EIC Accelerator is having stronger impacts on  firm outcomes than its predecessors, 
like the SME Instrument scheme, which is line with the ambitions and the finances put in place for 
the EIC Accelerator. 

In terms of recommendations on the EIC, we believe a few key points could be taken into account 
to make future studies easier and more compelling. First, the collection of the constant new data 
flow available at the European Investment Bank (acting as EIC Fund investment adviser) and the 
European  Innovation  Council  and  SMEs  Executive  Agency  (EISMEA)  as  well  as  the  data 
organisation  process  for  the  firms  participating  to 
the  EIC  could  be  further  improved  and 
harmonised,  which  would  turn  into  an  easier  use of  the  EIC  bountiful  data  for  firm  analysis 
and  recurrent  monitoring  to  hasten  the  scaling-up  pace  and  magnitude.  Second,  a  more 
systematic collection of national firm identifiers (such as the national register number and the VAT 
number)  and  firm  website  would  make  the  matching  with  balance  sheet  and  equity  data  of 
external  service  providers  more  reliable  while  allowing  tracing  back  more  firms.  Third,  a  more 
routine  use  of  the  information  on  the  Global  Ultimate  Owner  Corporate  and  the  parent 
company/companies  of  firms  in  the  EISMEA  systems  combined  with  historical  database  of 
external  service  providers,  could  facilitate  the  process  of  eligibility  checking  and  administration 
authorisation  of  the  firms  participating  to  the  EIC  schemes.  Moreover,  it  could  simplify  the 
monitoring  of  the  location  changes  of  the  Global  Ultimate  Owner  Corporate  and  parent 
company/companies over time.  In particular, in case some extra resources in EISMEA are freed 
up,  it might be investigated whether the data on  the  parent  company/companies  can  be  easily 
extracted for instance from the PDM tool used by  REA  for  the  SME  status  validation  and  the 
Control  Ownership  Assessment.  Fourth,  one important feature of the descriptive statistics is that 
the GDP per capita and country population are the main drivers of the (successful) applications. 
In turn, some analysis on the country distribution of the EIC Accelerator might consider as far as 
possible the GDP per capita at  first (as it outlines a higher elasticity) followed by the population 
(or another measure of the country size) to normalise the observations.  Finally, we look forward 
to subsequent studies focusing on the impacts of the EIC HE Accelerator in its full-fledged form 
when more data  will become available.

50 

 
Accelerator and Firm Scaling-Up Evidence 

9.  References 

Ackerberg, D. A., Caves, K., and Frazer, G. (2015). Identification properties of recent production 

function estimators.  Econometrica, 83(6):2411–2451. 

Callaway, B. and Sant’Anna, P. H. (2021). Difference-in-differences with multiple time periods.  

Journal of Econometrics, 225(2):200–230. 

De Loecker, J., Goldberg, P. K., Khandelwal, A. K., and Pavcnik, N. (2016).  Prices, markups, 

and trade reform.  Econometrica, 84(2):445–510. 

Dechezleprêtre,  A.,  Einiö,  E.,  Martin,  R.,  Nguyen,  K.-T., and  Van  Reenen,  J. (2023).  Do  tax 
incentives  increase  firm  innovation?  An  RD  design  for  R&D,  patents,  and  spillovers. 
American Economic Journal: Economic Policy, 15(4):486–521. 

Del Sorbo, M., Faber, C., Grazzi, M., Matteucci, F., and Ruß, M. (2024). Assessing changes in 
EU  innovation  policy  programs:  from  SME  Instrument  to  EIC  Accelerator  for  start-up 
funding.  Università Cattolica del Sacro Cuore, Dipartimento di Politica Economica Working 
Paper No. 2024-37. 

European  Commission  -  Mitra,  A.  and  Niakaros,  K.  of  Directorate-General  for  Research 
&  Innovation  (2023).  The  Horizon  effect  –  A  counterfactual  analysis of EU research 
Union, 
innovation 
https://data.europa.eu/doi/10.2777/584781. 

Publications 

European 

grants. 

Office 

the 

of 

European  Commission:  European  Innovation  Council  and  SMEs  Executive  Agency  (2025). 
Scaling  Deep  Tech  in  Europe  –  European  Innovation  Council  –  Impact  Report  2025. 
Publications Office of the European Union, https://data.europa.eu/doi/10.2826/1391424. 

Forlani, E., Martin, R., Mion, G., and Muûls, M. (2023). Unraveling firms: Demand, productivity 

and markups heterogeneity. The Economic Journal, 133(654):2251–2302. 

Mion, G. and Tong, M. (2021).  The Impact of Offshore Profit Shifting on the Measurement of 
GDP: The Case of the UK.  Economic Statistics Centre of Excellence (ESCoE) Discussion 
Paper No. 2021-05. 

Mulkay, B. and Mairesse, J. (2013).  The R&D tax credit in France:  assessment and ex-ante 

evaluation of the 2008 reform.  Oxford Economic Papers, 65(3):746–766. 

Rao, N. (2016).  Do tax credits stimulate R&D spending?  The effect of the R&D tax credit in its 
first decade.  Journal of Public Economics, 140:1–12. 

Santoleri, P., Mina, A., Di Minin, A., and Martelli, I. (2024).  The causal effects of R&D grants: 
from  a  regression  discontinuity.  Review  of  Economics  and  Statistics, 

Evidence 
106(6):1495– 1510. 

Silva, J. S. and Tenreyro, S. (2006). The log of gravity. The Review of Economics and statistics, 

pages 641–658. 

Sun,  L.  and  Abraham,  S.  (2021).  Estimating  dynamic treatment  effects  in  event  studies with 

heterogeneous treatment effects.  Journal of econometrics, 225(2):175–199. 

51 

 
Accelerator and Firm Scaling-Up Evidence 

Wooldridge, J. M. (2009). On estimating firm-level production functions using proxy variables to 

control for unobservables.  Economics Letters, 104(3):112–114. 

Wooldridge,  J.  M.  (2021).  Two-way  fixed  effects,  the  two-way  mundlak  regression,  and 

difference-in-differences estimators.  Available at SSRN 3906345. 

52 

 
Accelerator and Firm Scaling-Up Evidence 

10.  Appendixes 

10.1. Appendix A: EIC Accelerator centred around deep tech, high 

risk and high growth potential firms 

The EIC is an initiative of the European Commission.  Launched in 2018 under the Horizon 2020 
programme (2014 - 2020), it supports projects at all development stages on deep tech innovation 
anchored in breakthrough science, technology and engineering. 

The Accelerator, one of the three EIC schemes, is dedicated to narrow the funding gap of high-
risk individual startups and other high growth potential SMEs. The scheme is specifically designed 
to uphold the firm scaling-up by financing the “near-to-the-market” innovation activities of cutting-
edge  projects  and  activate  the  implementation  in  practice  of  the  production  industrialisation. 
Started  as  the  successor  of  the  SME  Instrument  scheme  within  which  the  entitled  “phase  2” 
allocates a grant of EUR 2.5m on average to SMEs for innovation activities, the pilot of the EIC 
Accelerator  (known  as  EIC  Pilot)  refines  therefore  the  scope  of  activities  and  the  profile  of  the 
firms selected. 

A grant,  amounting generally to EUR 2.5m,  was the only funding option in the first stage  of the 
EIC Pilot.  The equity and quasi-equity investment option is introduced in the second stage that 
begins in October 2019 with the selection of the firms eligible for the provision of an investment 
financed by the European Commission for the first time.  From then on, the firms can apply to the 
EIC Pilot to obtain an equity or quasi-equity investment (managed by the EIC Fund) which can be 
combined  with  a  grant.  This  financing  approach  of  the  Accelerator,  called  Blended  Finance, 
leaning on a mix of a non-dilutive grant and a dilutive investment,  continues under the Horizon 
Europe  programme  (2021  –  2027)  with  the  full-fledged  EIC  (European  Commission:  European 
Innovation Council and SMEs Executive Agency, 2025). 

53 

 
Accelerator and Firm Scaling-Up Evidence 

10.2. Appendix B: Details on the quantification of Revenue Total 

Factor Productivity 

In order to estimate revenue TFP, we use a production function approach. We take (from Orbis 
historical data) operating revenue as a measure of output/revenue, materials cost as a measure 
of intermediates expenditure, tangible fixed assets as a measure of the capital stock, and the wage 
bill as a measure of the labour input.  We employ information over the time period 2012-2022 for 
all the 44 countries listed in Table 1 in order to estimate revenue TFP. 

First, we apply some trimming to the data.  More specifically, we discard observations where the 
value of intermediates is higher than the value of sales and further apply a bottom and top trimming 
of  0.5%  (by  industry)  based  on  the  ratios  of:  i)  intermediates  to  sales;  ii)  capital  to  labour;  iii) 
revenue to labour. Second, we use a second-order polynomial in intermediates, capital and labour 
to smooth revenue and purge it from measurement error as suggested in  De Loecker et al. (2016) 
and Forlani et al. (2023) among others. 

Denoting firms by 
Cobb-Douglas: 

 and time by 

,  t he production function we estimate is the following 

 inputs 

𝑖𝑖

𝑡𝑡 

3

α𝐿𝐿
 is Revenue Total Factor Productivity (TFP-R) of firm 
𝑅𝑅𝑖𝑖𝑡𝑡 = 𝐿𝐿𝑖𝑖𝑡𝑡
  and 
𝑖𝑖

where 
is  labour, 
𝐴𝐴𝑖𝑖𝑡𝑡
elasticities. Considering the log production function, we thus have: 
𝑖𝑖𝑡𝑡
𝛼𝛼𝐾𝐾
𝐾𝐾

α𝐾𝐾
𝐾𝐾𝑖𝑖𝑡𝑡
  is  capital  and 

  is  intermediates, 

α𝑀𝑀
𝑀𝑀𝑖𝑖𝑡𝑡

𝑖𝑖𝑡𝑡
𝑀𝑀

𝛼𝛼𝑀𝑀

𝛼𝛼𝐿𝐿

, 

 is revenue, 
 at time 
𝐴𝐴𝑖𝑖𝑡𝑡,                                                                  (𝐵𝐵 − 1) 

, 

  are  the  related  output 
𝑖𝑖𝑡𝑡

𝑖𝑖𝑡𝑡
𝑅𝑅

𝑡𝑡

𝐿𝐿 

where  small  case  letters  indicate  logs  (for  example 
literature, we assume that the TFP-R process is driven by an autoregressive component: 

𝑞𝑞𝑖𝑖𝑡𝑡 = α𝐿𝐿𝑙𝑙𝑖𝑖𝑡𝑡 + α𝑀𝑀𝑚𝑚𝑖𝑖𝑡𝑡 + α𝐾𝐾𝑘𝑘𝑖𝑖𝑡𝑡 + 𝑎𝑎𝑖𝑖𝑡𝑡,                                       (𝐵𝐵 − 2)

).  In  line  with  the  productivity 

  = l

𝑖𝑖𝑡𝑡

𝑘𝑘

𝑖𝑖𝑡𝑡
𝑃𝑃𝑔𝑔 𝐾𝐾

denotes productivity shocks that represent innovations with respect to the information 

𝑎𝑎𝑖𝑖𝑡𝑡 = ϕ𝑎𝑎 𝑎𝑎𝑖𝑖𝑡𝑡−1 + ν𝑎𝑎𝑖𝑖𝑡𝑡,                                                             (𝐵𝐵 − 3)

 and are i.i.d across firms and time. 

where 
set of the firm in 
𝜈𝜈𝑎𝑎𝑖𝑖𝑡𝑡

𝑡𝑡  −  1
In line with the literature, we assume capital 
 to be predetermined in the short  run, i.e., the 
 and cannot immediately adjust to current period 
current capital level has been chosen in 
𝑖𝑖𝑡𝑡
. (20)  We further assume, as standard in the literature, that intermediates 
𝑘𝑘
shocks 
 are a 
𝑡𝑡 − 1
variable input free of adjustment costs. This means that intermediates can be optimally chosen 
based on, among others, the particular realization of νait.  Concerning labour, we assume it 
in 
 but 
to be a semi-flexible input meaning that it can, to some extent, adjust to current shocks in 

𝜈𝜈𝑎𝑎𝑖𝑖𝑡𝑡

𝑚𝑚

𝑖𝑖𝑡𝑡

𝑡𝑡 

𝑡𝑡

(20)  Intuitively,  the  restriction  behind  this  assumption  is  that  it  takes  a  full  period  for  new  capital  to  be  ordered, 
. However, this 
. For example, investment decisions 
𝑣𝑣𝑎𝑎𝑖𝑖𝑡𝑡
. In 

 is uncorrelated with the current productivity level 
𝑘𝑘𝑖𝑖𝑡𝑡

 are likely to be determined by both the level of capital in 

 is uncorrelated with current period shocks 

delivered, and installed. Note this means that 
does not mean that 
in 
this light, 
𝑡𝑡  −  1

 should be correlated with 

. See Ackerberg et al. (2015) for more details. 

 and the level of productivity in 
𝑎𝑎𝑖𝑖𝑡𝑡

 and so with 

𝑡𝑡  −  1

𝑡𝑡  −  1

𝑘𝑘𝑖𝑖𝑡𝑡

𝑘𝑘𝑖𝑖𝑡𝑡

𝑎𝑎𝑖𝑖𝑡𝑡−1

𝑎𝑎𝑖𝑖𝑡𝑡

54 

 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

not to the optimal cost-minimizing level determined only by wages and marginal productivity. (21) 

At  time t,  firms  have  already  chosen  capital and  labour and  so  these  inputs  are  considered  as 
given  in  their  decision  process  along  with  the  cost  of  intermediates 
.  At  the  same  time, 
productivity 
 use the above information and 
constraints to choose intermediates in order to minimize production costs and choose quantity or 
𝑡𝑡
price (depending upon the features of competition) in order to maximize profits. 

 becomes known at time 

. We assume firms in 

𝑊𝑊

𝑎𝑎

𝑀𝑀𝑖𝑖𝑡𝑡

𝑡𝑡

𝑖𝑖𝑡𝑡

In  terms  of  estimating  the  parameters  of  the  production  function  (B-2),  we  use  the  intuition 
 in equation (B-2) using (B-3); ii) 
developed in Wooldridge (2009), i.e., we:  i) substitute for 
;  iii)  in  the  final  augmented  production 
substitute  for 
  using  a  polynomial  in 
 and 
function equation we do not instrument capital 
𝑖𝑖𝑡𝑡−1
 with time lags. (22)  We estimate the parameters of the production function using data for all 
𝑖𝑖𝑡𝑡
the  44  countries  in  our  analysis.  In  particular,  we  use  data  on  the  time  span  2012-2022  and 
𝑖𝑖𝑡𝑡
𝑚𝑚
perform estimations separately for each 1-digit NACE industry, while adding as controls a battery 
of time and country dummies. Standard errors are clustered at the firm-level. 

 but instrument labour and intermediates 

  and 

𝑚𝑚

𝑖𝑖𝑡𝑡−1

𝑖𝑖𝑡𝑡−1

𝑎𝑎

𝑎𝑎

𝑘𝑘

𝑘𝑘

𝑙𝑙

𝑖𝑖𝑡𝑡

𝑖𝑖𝑡𝑡

Figure B-1 provides estimates of the coefficients of log materials, log wage bill and log capital for 
each of the 10 aggregate industries we consider, along with the number of observations, R² and 
test statistics for both under-identification and weak identification. More specifically, both the KP 
rk LM test for under-identification and the KP rk Wald F test for  weak identification provide, across 
the  10  industries,  strong support  for  the  validity  of  the  instruments.  Estimations  are  carried  out 
using the Stata command ivreg2. 

Figure  B-1:  Production  function  estimations  for  each  of  the  10  1-digit  NACE  industries  we 
consider 

Notes:  Firm-level clustered standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. 

In sum, 

  should be correlated (like intermediates 

(21) 
 does 
not simply reflect wages and marginal productivity implying that it cannot be used to recover markups. As far as the 
𝑙𝑙𝑖𝑖𝑡𝑡
𝑚𝑚𝑖𝑖𝑡𝑡
 at time 
timing is concerned, we assume 
being chosen in 
. 
but prior to 
𝑙𝑙𝑖𝑖𝑡𝑡
(22)  We use 
, 

but the amount of labour in 

𝑡𝑡
  being chosen in 

  is chosen by firm 

) with shocks 

0  <  𝑏𝑏  <  1

), after 

𝑡𝑡  −  𝑏𝑏

𝑡𝑡  −  1

  and 

𝜈𝜈𝑎𝑎𝑖𝑖𝑡𝑡 

𝑘𝑘𝑖𝑖𝑡𝑡

 (

. 

, 

𝑖𝑖

𝑚𝑚𝑖𝑖𝑡𝑡 

𝑙𝑙𝑖𝑖𝑡𝑡 − 1

𝑙𝑙𝑖𝑖𝑡𝑡 − 2

𝑡𝑡
𝑚𝑚𝑖𝑖𝑡𝑡 − 2

𝑘𝑘𝑖𝑖𝑡𝑡 − 2

55 

 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

10.3.  Appendix C: Additional tables and figures 

Table C-1: EIC-Pilot-SMEI scheme. Breakdown of applications by country (period June 
2014 - October 2020). 

Country code 

Country name 

Number of applications 

Number of successful 
applications 

Number of Seal of 
Excellence applications 

AL 
AM 
AT 
BA 
BE 
BG 
CH 
CY 
CZ 
DE 
DK 
EE 
ES 
FI 
FR 
GB 
GE 
GI 
GR 
HR 
HU 
IE 
IL 
IS 
IT 
LT 
LU 
LV 
MD 
ME 
MK 
MT 
NL 
NO 
PL 
PT 
RO 
RS 
SE 
SI 
SK 
TN 
TR 
UA 

Albania 
Armenia 
Austria 
Bosnia and Herzegovina 
Belgium 
Bulgaria 
Switzerland 
Cyprus 
Czechia 
Germany 
Denmark 
Estonia 
Spain 
Finland 
France 
United Kingdom 
Georgia 
Gibraltar 
Greece 
Croatia 
Hungary 
Ireland 
Israel 
Iceland 
Italy 
Lithuania 
Luxembourg 
Latvia 
Moldova 
Montenegro 
Republic of North Macedonia 
Malta 
Netherlands 
Norway 
Poland 
Portugal 
Romania 
Serbia 
Sweden 
Slovenia 
Slovakia 
Tunisia 
Turkey 
Ukraine 

4 
2 
740 
60 
708 
395 
1,297 
125 
253 
3,047 
1,471 
434 
5,564 
1,751 
3,035 
3,174 
4 
4 
368 
98 
944 
940 
2,857 
275 
5,003 
103 
97 
206 
14 
2 
16 
46 
1,774 
973 
642 
775 
314 
73 
2,016 
453 
366 
13 
249 
116 

0 
0 
36 
0 
28 
2 
50 
0 
4 
125 
84 
17 
253 
75 
125 
103 
0 
0 
8 
2 
19 
66 
87 
16 
111 
3 
1 
3 
0 
0 
0 
1 
80 
59 
22 
24 
1 
1 
76 
14 
3 
0 
8 
3 

0 
0 
305 
1 
259 
43 
579 
29 
58 
1,135 
619 
126 
2,254 
649 
1,068 
1,015 
2 
0 
54 
28 
273 
319 
1,002 
128 
1,442 
33 
24 
48 
1 
0 
2 
8 
692 
376 
185 
281 
24 
13 
790 
116 
75 
2 
51 
16 

Total 

40,801 

1,510 

14,125 

56 

 
 
 
Accelerator and Firm Scaling-Up Evidence 

Table C-2: EIC HE Accelerator scheme. Breakdown of applications by country (period 
June 2021 - March 2024). 

Country code 

Country name 

Number of applications 

Number of successful 
applications 

Number of Seal of 
Excellence applications 

AL 
AM 
AT 
BA 
BE 
BG 
CH 
CY 
CZ 
DE 
DK 
EE 
ES 
FI 
FR 
GB 
GE 
GI 
GR 
HR 
HU 
IE 
IL 
IS 
IT 
LT 
LU 
LV 
MD 
ME 
MK 
MT 
NL 
NO 
PL 
PT 
RO 
RS 
SE 
SI 
SK 
TN 
TR 
UA 

Albania 
Armenia 
Austria 
Bosnia and Herzegovina 
Belgium 
Bulgaria 
Switzerland 
Cyprus 
Czechia 
Germany 
Denmark 
Estonia 
Spain 
Finland 
France 
Republic of North Macedonia 
Georgia 
Gibraltar 
Greece 
Croatia 
Hungary 
Ireland 
Israel 
Iceland 
Italy 
Lithuania 
Luxembourg 
Latvia 
Moldova 
Montenegro 
Republic of North Macedonia 
Malta 
Netherlands 
Norway 
Poland 
Portugal 
Romania 
Serbia 
Sweden 
Slovenia 
Slovakia 
Tunisia 
Turkey 
Ukraine 

0 
1 
23 
4 
163 
60 
76 
31 
38 
120 
245 
85 
559 
320 
755 
475 
1 
1 
41 
14 
77 
254 
98 
36 
339 
49 
47 
37 
0 
3 
4 
16 
538 
267 
237 
128 
31 
9 
490 
48 
34 
6 
67 
0 

Total 

5,827 

0 
0 
1 
0 
23 
6 
0 
1 
2 
13 
24 
9 
47 
32 
101 
32 
0 
0 
2 
2 
1 
26 
8 
3 
23 
2 
2 
1 
0 
0 
0 
0 
67 
16 
7 
15 
2 
0 
37 
1 
2 
0 
0 
0 

508 

0 
0 
6 
0 
27 
5 
1 
0 
5 
37 
63 
16 
91 
55 
143 
64 
0 
0 
2 
1 
9 
48 
22 
6 
45 
5 
9 
1 
0 
0 
0 
0 
107 
65 
19 
12 
3 
1 
102 
4 
4 
0 
3 
0 

981 

57 

 
 
 
Accelerator and Firm Scaling-Up Evidence 

Table C-3: EIC-Pilot-SMEI scheme. Breakdown of applications by NACE rev2 2-digit industries 
(period June 2014 - October 2020). 

Industry code 

Industry description 

Number of applications 

Number of successful 
applications 

Number of Seal of 
Excellence applications 

1 
2 
3 
5 
6 
7 
8 
9 
10 
11 
12 
13 
14 
15 
16 
17 
18 
19 
20 
21 
22 
23 
24 
25 
26 
27 
28 
29 
30 
31 
32 
33 
35 
36 
37 
38 
39 
41 
42 
43 
45 
46 
47 
49 
50 
51 
52 
53 
55 
56 
58 
59 
60 
61 
62 
63 
64 
65 
66 
68 
69 
70 
71 
72 
73 
74 
75 
77 
78 
79 
80 
81 
82 
84 
85 
86 
87 
88 
90 
91 
92 
93 
94 
95 
96 

Agriculture 
Forestry 
Fishing 
Mining of coal and lignite 
Extraction of crude petroleum and natural gas 
Mining of metal ores 
Other mining and quarrying 
Mining support service activities 
Manufacture of food products 
Manufacture of beverages 
Manufacture of tobacco products 
Manufacture of textiles 
Manufacture of wearing apparel 
Manufacture of leather and related products 
Manufacture of wood and of products of wood and cork, except furniture 
Manufacture of paper and paper products 
Printing and reproduction of recorded media 
Manufacture of coke and refined petroleum products 
Manufacture of chemicals and chemical products 
Manufacture of basic pharmaceutical products and pharmaceutical preparations 
Manufacture of rubber and plastic products 
Manufacture of other non-metallic mineral products 
Manufacture of basic metals 
Manufacture of fabricated metal products, except machinery and equipment 
Manufacture of computer, electronic and optical products 
Manufacture of electrical equipment 
Manufacture of machinery and equipment n.e.c. 
Manufacture of motor vehicles, trailers and semi-trailers 
Manufacture of other transport equipment 
Manufacture of furniture 
Other manufacturing 
Repair and installation of machinery and equipment 
Electricity, gas, steam and air conditioning supply 
Water collection, treatment and supply 
Sewerage 
Waste collection, treatment and disposal activities; materials recovery 
Remediation activities and other  waste management services 
Construction of buildings 
Civil engineering 
Specialised construction activities 
Wholesale and retail trade and repair of motor vehicles and motorcycles 
Wholesale trade, except of motor vehicles and motorcycles 
Retail trade, except of motor vehicles and motorcycles 
Land transport and transport via pipelines 
Water transport 
Air transport 
Warehousing and support activities for transportation 
Postal and courier activities 
Accommodation 
Food and beverage service activities 
Publishing activities 
Motion picture, video and television, sound and music 
Programming and broadcasting activities 
Telecommunications 
Computer programming, consultancy and related activities 
Information service activities 
Financial service activities, except insurance and pension funding 
Insurance, reinsurance and pension funding, except compulsory social security 
Activities auxiliary to financial services and insurance activities 
Real estate activities 
Legal and accounting activities 
Activities of head offices; management consultancy activities 
Architectural and engineering activities; technical testing and analysis 
Scientific research and development 
Advertising and market research 
Other professional, scientific and technical activities 
Veterinary  activities 
Rental and leasing activities 
Employment activities 
Travel agency, tour operator and other reservation service and related activities 
Security and investigation activities 
Services to buildings and landscape activities 
Office administrative, office support and other business support activities 
Public administration and defence 
Education 
Human health activities 
Residential care activities 
Social work activities without accommodation 
Creative, arts and entertainment activities 
Libraries, archives, museums and other cultural activities 
Gambling and betting activities 
Sports activities and amusement and recreation activities 
Activities of organisations 
Repair of computers and personal and household goods 
Other personal service activities 

166 
24 
47 
0 
2 
0 
14 
12 
274 
17 
3 
107 
27 
30 
61 
91 
61 
6 
617 
529 
317 
175 
39 
506 
1,849 
675 
1,706 
130 
308 
30 
886 
306 
290 
35 
15 
232 
46 
129 
81 
292 
70 
1,531 
475 
30 
17 
0 
59 
5 
18 
20 
742 
65 
5 
318 
7,958 
791 
333 
4 
91 
108 
63 
855 
2,892 
6,227 
283 
1,154 
16 
149 
110 
58 
121 
41 
499 
44 
155 
429 
7 
10 
25 
13 
0 
22 
13 
8 
147 

4 
0 
6 
0 
0 
0 
1 
1 
7 
0 
0 
6 
0 
0 
2 
6 
0 
0 
30 
15 
15 
8 
3 
20 
88 
30 
82 
6 
19 
0 
49 
14 
10 
1 
0 
8 
1 
5 
2 
14 
3 
51 
11 
0 
0 
0 
1 
0 
0 
0 
30 
2 
1 
12 
213 
13 
9 
0 
3 
6 
0 
21 
117 
318 
5 
39 
0 
4 
0 
2 
5 
2 
22 
1 
3 
16 
0 
0 
0 
1 
0 
0 
0 
1 
3 

53 
9 
9 
0 
1 
0 
6 
6 
87 
4 
2 
45 
4 
6 
9 
29 
23 
0 
249 
220 
99 
71 
19 
184 
829 
279 
732 
32 
112 
13 
353 
122 
102 
12 
5 
82 
20 
44 
19 
84 
15 
510 
146 
8 
10 
15 
0 
0 
1 
4 
218 
12 
3 
106 
2,501 
220 
115 
1 
30 
22 
17 
215 
1,124 
2,548 
71 
413 
8 
44 
12 
12 
43 
11 
150 
3 
44 
120 
0 
1 
2 
3 
0 
5 
2 
2 
32 

58 

 
Accelerator and Firm Scaling-Up Evidence 

Table C-4: EIC HE Accelerator scheme. Breakdown of applications by NACE rev2 2-digit 
industries (period June 2021 - March 2024). 

Industry code 

Industry description 

Number of applications 

Number of successful 
applications 

Number of Seal of 
Excellence applications 

1 
2 
3 
5 
6 
7 
8 
9 
10 
11 
12 
13 
14 
15 
16 
17 
18 
19 
20 
21 
22 
23 
24 
25 
26 
27 
28 
29 
30 
31 
32 
33 
35 
36 
37 
38 
39 
41 
42 
43 
45 
46 
47 
49 
50 
51 
52 
53 
55 
56 
58 
59 
60 
61 
62 
63 
64 
65 
66 
68 
69 
70 
71 
72 
73 
74 
75 
77 
78 
79 
80 
81 
82 
84 
85 
86 
87 
88 
90 
91 
92 
93 
94 
95 
96 

Agriculture 
Forestry 
Fishing 
Mining of coal and lignite 
Extraction of crude petroleum and natural gas 
Mining of metal ores 
Other mining and quarrying 
Mining support service activities 
Manufacture of food products 
Manufacture of beverages 
Manufacture of tobacco products 
Manufacture of textiles 
Manufacture of wearing apparel 
Manufacture of leather and related products 
Manufacture of wood and of products of wood and cork, except furniture 
Manufacture of paper and paper products 
Printing and reproduction of recorded media 
Manufacture of coke and refined petroleum products 
Manufacture of chemicals and chemical products 
Manufacture of basic pharmaceutical products and pharmaceutical preparations 
Manufacture of rubber and plastic products 
Manufacture of other non-metallic mineral products 
Manufacture of basic metals 
Manufacture of fabricated metal products, except machinery and equipment 
Manufacture of computer, electronic and optical products 
Manufacture of electrical equipment 
Manufacture of machinery and equipment n.e.c. 
Manufacture of motor vehicles, trailers and semi-trailers 
Manufacture of other transport equipment 
Manufacture of furniture 
Other manufacturing 
Repair and installation of machinery and equipment 
Electricity, gas, steam and air conditioning supply 
Water collection, treatment and supply 
Sewerage 
Waste collection, treatment and disposal activities; materials recovery 
Remediation activities and other  waste management services 
Construction of buildings 
Civil engineering 
Specialised construction activities 
Wholesale and retail trade and repair of motor vehicles and motorcycles 
Wholesale trade, except of motor vehicles and motorcycles 
Retail trade, except of motor vehicles and motorcycles 
Land transport and transport via pipelines 
Water transport 
Air transport 
Warehousing and support activities for transportation 
Postal and courier activities 
Accommodation 
Food and beverage service activities 
Publishing activities 
Motion picture, video and television, sound and music 
Programming and broadcasting activities 
Telecommunications 
Computer programming, consultancy and related activities 
Information service activities 
Financial service activities, except insurance and pension funding 
Insurance, reinsurance and pension funding, except compulsory social security 
Activities auxiliary to financial services and insurance activities 
Real estate activities 
Legal and accounting activities 
Activities of head offices; management consultancy activities 
Architectural and engineering activities; technical testing and analysis 
Scientific research and development 
Advertising and market research 
Other professional, scientific and technical activities 
Veterinary  activities 
Rental and leasing activities 
Employment activities 
Travel agency, tour operator and other reservation service and related activities 
Security and investigation activities 
Services to buildings and landscape activities 
Office administrative, office support and other business support activities 
Public administration and defence 
Education 
Human health activities 
Residential care activities 
Social work activities without accommodation 
Creative, arts and entertainment activities 
Libraries, archives, museums and other cultural activities 
Gambling and betting activities 
Sports activities and amusement and recreation activities 
Activities of organisations 
Repair of computers and personal and household goods 
Other personal service activities 

21 
1 
1 
0 
0 
0 
0 
1 
34 
1 
0 
9 
6 
0 
7 
12 
6 
0 
88 
81 
18 
16 
3 
25 
261 
57 
115 
14 
34 
2 
161 
9 
41 
3 
4 
30 
4 
14 
14 
18 
1 
157 
39 
2 
0 
1 
4 
0 
0 
4 
81 
7 
3 
17 
981 
84 
52 
0 
11 
10 
8 
79 
459 
1445 
5 
163 
1 
20 
0 
5 
9 
3 
44 
1 
14 
85 
0 
2 
0 
0 
0 
1 
0 
1 
11 

2 
0 
0 
0 
0 
0 
0 
0 
3 
0 
0 
0 
1 
0 
0 
3 
1 
0 
13 
4 
2 
3 
0 
5 
29 
8 
8 
0 
4 
0 
20 
1 
1 
0 
0 
1 
0 
0 
1 
0 
0 
9 
3 
0 
0 
1 
0 
0 
0 
0 
7 
0 
0 
0 
75 
4 
3 
0 
1 
0 
0 
4 
55 
168 
0 
15 
0 
2 
0 
0 
2 
0 
2 
0 
0 
6 
0 
0 
0 
0 
0 
0 
0 
0 
0 

4 
1 
0 
0 
0 
0 
0 
0 
5 
0 
0 
0 
2 
0 
2 
2 
0 
0 
15 
22 
5 
1 
1 
1 
48 
15 
28 
2 
6 
0 
33 
3 
6 
2 
0 
5 
0 
1 
3 
4 
0 
21 
2 
0 
0 
0 
0 
0 
0 
0 
16 
0 
0 
3 
114 
7 
13 
0 
1 
2 
1 
11 
87 
318 
0 
39 
1 
3 
0 
0 
1 
0 
7 
0 
3 
10 
0 
0 
0 
0 
0 
0 
0 
0 
2 

59 

 
 
Accelerator and Firm Scaling-Up Evidence 

Table C-5: EIC-Pilot-SMEI scheme. Breakdown of applications by thematic area (period 
June 2014 - October 2020). 

Thematic area 

Number of applications 

Agriculture/Food 
Biotechnology 
Blue Growth 
Construction/Transport 
Consumer products and services 
Cultural and creative economy 
EcoInnovation/Environmental 
Energy 
Engineering/Nano/Manufacturing 
Finance 
Health/e-Health 
Information and Communication Technology (ICT) 
New Business Models/Public sector innovation 
Security 
Space 

3,060 
1,331 
244 
3,181 
1,245 
503 
2,359 
3,743 
5,081 
630 
8,198 
8,154 
1,161 
1,415 
496 

Number of successful 
applications 
135 
64 
18 
155 
20 
4 
102 
163 
235 
7 
266 
222 
42 
42 
35 

Number of Seal of 
Excellence applications 
1,071 
529 
89 
1,094 
275 
161 
911 
1,407 
1,895 
177 
2,885 
2,559 
344 
561 
167 

Total 

40,801 

1,510 

14,125 

Table C-6: EIC HE Accelerator scheme. Breakdown of applications by evaluation panel 
(period June 2021 – March 2024). 

Number of successful 
applications 
16 
29 
8 
8 
30 
0 
42 
90 
8 
182 
62 
2 
1 
19 
11 

Number of Seal of 
Excellence applications 
49 
65 
26 
13 
60 
6 
94 
131 
29 
358 
105 
2 
11 
9 
23 

508 

981 

Evaluation panel 

Number of applications 

Agriculture / Rural Development / Fisheries 
Biotechnology 
Construction, Civil engineering, Infrastructures 
Consumer products and services 
Earth and related environmental sciences 
Education and Culture 
Energy 
Engineering and technology 
Food and beverages 
Health 
Information and Communication Technology (ICT) 
Public sector innovation 
Security 
Space 
Transport & Mobility 

Total 

233 
334 
172 
121 
310 
56 
521 
757 
121 
1,852 
916 
24 
79 
86 
244 

5,827 

60 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-1: EIC-Pilot-SMEI scheme. Plots of propensity score matching by year. 

61 

 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-2: EIC-Pilot-SMEI scheme. Balancing tests of propensity score matching year 
2015. 

62 

 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-3: EIC-Pilot-SMEI scheme. Balancing tests of propensity score matching year 
2016. 

63 

 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-4: EIC-Pilot-SMEI scheme. Balancing tests of propensity score matching year 
2017. 

64 

 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-5: EIC-Pilot-SMEI scheme. Balancing tests of propensity score matching year 
2018. 

65 

 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-6: EIC-Pilot-SMEI scheme. Balancing tests of propensity score matching year 
2019. 

66 

 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-7: EIC-Pilot-SMEI scheme. Balancing tests of propensity score matching year 
2020. 

67 

 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-8: EIC-Pilot-SMEI scheme. DiD full model estimations with log sales as the 
outcome variable. 

68 

 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-9: EIC-Pilot-SMEI scheme. DiD full model estimations with log wage bill as the 
outcome variable. 

69 

 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-10: EIC-Pilot-SMEI scheme. DiD full model estimations with log capital stock as 
the outcome variable. 

70 

 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-11: EIC-Pilot-SMEI scheme. DiD full model estimations with log average firm 
wage as the outcome variable. 

71 

 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-12: EIC-Pilot-SMEI scheme. DiD full model estimations with revenue 
productivity (TFP-R) as the outcome variable. 

72 

 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-13: EIC-Pilot-SMEI scheme. DiD full model estimations with the number of 
deals as the outcome variable. Poisson model. 

73 

 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-14: EIC-Pilot-SMEI scheme. DiD full model estimations with the value of deals 
as the outcome variable. Poisson model. 

74 

 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-15: EIC Pilot scheme - 2020 cohort. Plots of propensity score matching year 
2020. 

75 

 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-16: EIC Pilot scheme - 2020 cohort. Balancing tests of propensity score 
matching year 2020. 

76 

 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-17: EIC Pilot scheme - 2020 cohort. DiD full model estimations with log sales 
as the outcome variable. 

Figure C-18: EIC Pilot scheme - 2020 cohort. DiD full model estimations with log wage 
bill as the outcome variable. 

77 

 
 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-19: EIC Pilot scheme - 2020 cohort. DiD full model estimations with log capital stock as the 
outcome variable. 

Figure C-20: EIC Pilot scheme - 2020 cohort. DiD full model estimations with log average firm wage as 
the outcome variable. 

78 

 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-21: EIC Pilot scheme - 2020 cohort. DiD full model estimations with revenue productivity 
(TFP-R) as the outcome variable. 

Figure C-22: EIC Pilot scheme - 2020 cohort. DiD full model estimations with the number of deals as 
the outcome variable. Poisson model. 

79 

 
 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-23: EIC Pilot scheme - 2020 cohort. DiD full model estimations with the value of deals as the 
outcome variable. Poisson model. 

Figure C-24: EIC HE Accelerator scheme - 2021 and 2022 cohorts. Plots of propensity score matching. 

80 

 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-25: EIC HE Accelerator scheme - 2021 and 2022 cohorts. Balancing tests of propensity score 
matching year 2021. 

81 

 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-26: EIC HE Accelerator scheme - 2021 and 2022 cohorts. Balancing tests of propensity score 
matching year 2022. 

82 

 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-27: EIC HE Accelerator scheme - 2021 and 2022 cohorts. DiD full model estimations with log 
sales as the outcome variable. 

Figure C-28: EIC HE Accelerator scheme - 2021 and 2022 cohorts. DiD full model estimations with log 
wage bill as the outcome variable. 

83 

 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-29: EIC HE Accelerator scheme - 2021 and 2022 cohorts. DiD full model estimations with log 
capital stock as the outcome variable. 

Figure C-30: EIC HE Accelerator scheme - 2021 and 2022 cohorts. DiD full model estimations with log 
firm average wage as the outcome variable. 

84 

 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-31: EIC HE Accelerator scheme - 2021 and 2022 cohorts. DiD full model estimations with 
revenue productivity (TFP-R) as the outcome variable. 

Figure C-32: EIC HE Accelerator scheme - 2021 and 2022 cohorts. DiD full model estimations with the 
number of deals as the outcome variable. Poisson model. 

85 

 
 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-33: EIC HE Accelerator scheme- 2021 and 2022 cohorts. DiD full model estimations with the 
value of deals as the outcome variable. Poisson model. 

86 

 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-34: EIC Pilot scheme - 2019 and 2020 cohorts of the Blended Finance stage; using a different 
control group. Plots of propensity score matching. 

87 

 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-35: EIC Pilot scheme - 2019 and 2020 cohorts of the Blended Finance stage; using a different 
control group. Balancing tests of propensity score matching year 2019. 

88 

 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-36: EIC Pilot scheme - 2019 and 2020 cohorts of the Blended Finance stage; using a different 
control group. Balancing tests of propensity score matching year 2020. 

89 

 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-37: EIC Pilot scheme - 2019 and 2020 cohorts of the Blended Finance stage; using a different 
control group. DiD full model estimations with log sales as the outcome variable. 

Figure C-38: EIC Pilot scheme - 2019 and 2020 cohorts of the Blended Finance stage; using a different 
control group. DiD full model estimations with log wage bill as the outcome variable. 

90 

 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-39: EIC Pilot scheme - 2019 and 2020 cohorts of the Blended Finance stage; using a different 
control group. DiD full model estimations with log capital stock as the outcome variable. 

Figure C-40: EIC Pilot scheme - 2019 and 2020 cohorts of the Blended Finance stage; using a different 
control group. DiD full model estimations with log firm average wage as the outcome variable. 

91 

 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-41: EIC Pilot scheme - 2019 and 2020 cohorts of the Blended Finance stage; using a different 
control group. DiD full model estimations with revenue productivity (TFP-R) as the outcome variable. 

Figure C-42: EIC Pilot scheme - 2019 and 2020 cohorts of the Blended Finance stage; using a different 
control group.  DiD full model estimations  with  the number  of deals as  the  outcome  variable.  Poisson 
model. 

92 

 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-43: EIC Pilot scheme - 2019 and 2020 cohorts of the Blended Finance stage; using a different 
control group. DiD full model estimations with the value of deals as the outcome variable. Poisson model. 

93 

 
 
Accelerator and Firm Scaling-Up Evidence 

Figure C-44: EIC Pilot scheme - 2020 cohort; investigating heterogeneity. Simplified DiD model 
estimations with log sales as the outcome variable. No heterogeneity. 

94 

 
 
Accelerator and Firm Scaling-Up Evidence 

Figure  C-45:  EIC  Pilot  scheme  -  2020  cohort;  investigating  heterogeneity.  Simplified  DiD  model 
estimations with log sales as the outcome variable. Heterogeneity with respect to the thematic area of 
the project. 

95 

 
 
Accelerator and Firm Scaling-Up Evidence 

Figure  C-46:  EIC  Pilot  scheme  -  2020  cohort;  investigating  heterogeneity.  Simplified  DiD  model 
estimations with log sales as the outcome variable. Heterogeneity with respect to the productivity of the 
firm. 

Figure C-47: EIC Pilot scheme - 2020 cohort; investigating heterogeneity. Simplified DiD model 
estimations with log wage bill as the outcome variable. No heterogeneity. 

96 

 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure  C-48:  EIC  Pilot  scheme  -  2020  cohort;  investigating  heterogeneity.  Simplified  DiD  model 
estimations with log wage bill as the outcome variable. Heterogeneity with respect to the thematic area 
of the project. 

97 

 
 
Accelerator and Firm Scaling-Up Evidence 

Figure  C-49:  EIC  Pilot  scheme  -  2020  cohort;  investigating  heterogeneity.  Simplified  DiD  model 
estimations with log wage bill as the outcome variable. Heterogeneity with respect to the productivity of 
the firm. 

Figure C-50: EIC Pilot scheme - 2020 cohort; investigating heterogeneity. Simplified DiD model 
estimations with log capital stock as the outcome variable. No heterogeneity. 

98 

 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure  C-51:  EIC  Pilot  scheme  -  2020  cohort;  investigating  heterogeneity.  Simplified  DiD  model 
estimations with log capital stock as the outcome variable. Heterogeneity with respect to the thematic 
area of the project. 

99 

 
 
Accelerator and Firm Scaling-Up Evidence 

Figure  C-52:  EIC  Pilot  scheme  -  2020  cohort;  investigating  heterogeneity.  Simplified  DiD  model 
estimations with log capital stock as the outcome variable. Heterogeneity with respect to the productivity 
of the firm. 

Figure C-53: EIC Pilot scheme - 2020 cohort; investigating heterogeneity. Simplified DiD model 
estimations with log firm average wage as the outcome variable. No heterogeneity. 

100 

 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure  C-54:  EIC  Pilot  scheme  -  2020  cohort;  investigating  heterogeneity.  Simplified  DiD  model 
estimations  with  log  firm  average  wage  as  the  outcome  variable.  Heterogeneity  with  respect  to  the 
thematic area of the project. 

101 

 
 
Accelerator and Firm Scaling-Up Evidence 

Figure  C-55:  EIC  Pilot  scheme  -  2020  cohort;  investigating  heterogeneity.  Simplified  DiD  model 
estimations  with  log  firm  average  wage  as  the  outcome  variable.  Heterogeneity  with  respect  to  the 
productivity of the firm. 

Figure C-56: EIC Pilot scheme - 2020 cohort; investigating heterogeneity. Simplified DiD model 
estimations with the number of deals as the outcome variable. Poisson model. No heterogeneity. 

102 

 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure  C-57:  EIC  Pilot  scheme  -  2020  cohort;  investigating  heterogeneity.  Simplified  DiD  model 
estimations  with  the  number  of  deals  as  the  outcome  variable.  Poisson  model.  Heterogeneity  with 
respect to the thematic area of the project. 

103 

 
 
Accelerator and Firm Scaling-Up Evidence 

Figure  C-58:  EIC  Pilot  scheme  -  2020  cohort;  investigating  heterogeneity.  Simplified  DiD  model 
estimations  with  the  number  of  deals  as  the  outcome  variable.  Poisson  model.  Heterogeneity  with 
respect to the productivity of the firm. 

Figure C-59: EIC Pilot scheme - 2020 cohort; investigating heterogeneity. Simplified DiD model 
estimations with the value of deals as the outcome variable. Poisson model. No heterogeneity. 

104 

 
 
 
 
 
Accelerator and Firm Scaling-Up Evidence 

Figure  C-60:  EIC  Pilot  scheme  -  2020  cohort;  investigating  heterogeneity.  Simplified  DiD  model 
estimations with the value of deals as the outcome variable. Poisson model. Heterogeneity with respect 
to the thematic area of the project. 

105 

 
Accelerator and Firm Scaling-Up Evidence 

Figure  C-61: EIC Pilot  scheme  -  2020  cohort;  investigating heterogeneity.  Simplified 
DiD model estimations with the value of deals as the outcome variable. Poisson model. 
Heterogeneity with respect to the productivity of the firm. 

106 

 
 
 
 
 
 
 
 
 
 
 
Getting in touch with the EU 

In person 

All over the European Union there are hundreds of Europe Direct centres. 
You can find the address of the centre nearest you online (european-
union.europa.eu/contact-eu/meet-us_en). 

On the phone or in writing 

Europe Direct is a service that answers your questions about the European 
Union. You can contact this service: 

–  by freephone: 00 800 6 7 8 9 10 11 (certain operators may charge for 

these calls), 

–  at the following standard number: +32 22999696, 
–  via the following form: european-union.europa.eu/contact-eu/write-us_en. 

Finding information about the EU 

Online 

Information about the European Union in all the official languages of the EU is 
available on the Europa website (european-union.europa.eu). 

EU publications 

You can view or order EU publications at op.europa.eu/en/publications. 
Multiple copies of free publications can be obtained by contacting Europe 
Direct or your local documentation centre (european-union.europa.eu/contact-
eu/meet-us_en). 

EU law and related documents 

For access to legal information from the EU, including all EU law since 1951 
in all the official language versions, go to EUR-Lex (eur-lex.europa.eu). 

EU open data 

The portal data.europa.eu provides access to open datasets from the EU 
institutions, bodies and agencies. These can be downloaded and reused for 
free, for both commercial and non-commercial purposes. The portal also 
provides access to a wealth of datasets from European countries. 

 
 
 
 
 
 
 
 
 
 
 
 
 
E
A
-
0
1
-
2
5
-
1
0
7
-
E
N
N

-