Economic impacts of the recovery and resilience facility : new insights at sectoral level and the case of Germany.
Date: 2025-05-21
Institutions: Directorate-General for Economic and Financial Affairs
Subjects: EU financial instrument, Germany, artificial intelligence, economic consequence, economic recovery, public investment, structural adjustment

6 &

ISSN 2443-8022 (online)

Economic Impacts 
of the Recovery and 
Resilience Facility: New 
Insights at Sectoral Level 
and the Case of Germany 

Anne Michels, Daria Ciriaci, José Manuel 
Rueda-Cantuche, Luis Pedauga, Valeria 
Ferreira, Christina Kattami, Daniel 
Schulz and Marta Pilati

DISCUSSION PAPER 221 | MAY 2025

EUROPEAN ECONOMY

Economic and 
Financial Affairs

European  Economy  Discussion  Papers  are  written  by  the  staff  of  the  European  Commission’s 
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How to cite: European Commission: Directorate-General for Economic and Financial Affairs, Michels, A., Ciriaci, 
D., Rueda-Cantuche, J. M., Pedauga, L., Ferreira, V., Kattami, C., Schulz, D., Pilati, M., Economic Impacts of the 
Recovery and Resilience Facility: New Insights at Sectoral Level and the Case of Germany, Publications Office 
of the European Union, 2025, https://data.europa.eu/doi/10.2765/4285022. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
European Commission  

Directorate-General for Economic and Financial Affairs 

Economic Impacts of the Recovery and Resilience Facility: 
New Insights at Sectoral Level and the Case of Germany 

Anne Michels, Daria Ciriaci, José Manuel Rueda-Cantuche, Luis Pedauga, Valeria Ferreira, Christina 
Kattami, Daniel Schulz and Marta Pilati 

Abstract  

The  Recovery  and  Resilience  Facility  (RRF) is  the  cornerstone  of  NextGenerationEU,  the  EU’s  post-covid 
Recovery  Plan.  With  a  budget  of  EUR  650  billion,  amounting  to  around  4%  of  the  EU  GDP,  the  RRF 
finances over 2,800 reform and investment measures aross the EU from 2020 to 2026. To date, most 
studies on the RRF have focused on the broad macro-economic picture or on specific policy domains and 
country experiences. This paper introduces a novel approach to enhance the understanding of the RRF. 
First, by leveraging artificial intelligence, a new sectoral database was created, which classifies all RRF 
reform  and  investment  measures  according  to  economic  sectors  and  enables  more  detailed  and 
systematic analysis of macro-economic, sectoral and policy impacts. The paper presents an overview of 
this classification, across the EU and for each Member State. The sectors benefitting most from the RRF 
include those related to the green transition, such as energy efficiency, renewable energy production and 
sustainable  transport;  those  supporting  the  manufacturing  sector  and  broader  business  environment, 
including digitisation, public administration and justice reforms; and those related to societal resilience, 
such  as  healthcare,  skills  and  education.  We  also  observe  significant  synergies  between  reform  and 
investment  measures  in  those  areas.  Second,  by  applying  the  well-established  FIDELIO  model  to  this 
new  sectoral  dataset,  it  is  now  possible  to  assess  the  direct  and  spillover  impacts  of  RRF-funded 
investments  and  to  break  down  these  results  across  64  economic  sectors,  for  the  EU  and  for  each 
Member  State.  Looking  at  Germany  by  way  of  illustration,  the  analysis  shows  that,  assuming  full 
implementation of the 27 plans, the German economy stands to benefit from a medium-term stimulus 
of about EUR 66 billion (1.6% of Germany’s GDP), more than twice the size of Germany’s Recovery and 
Resilience  Plan.  This  reflects  both  the  direct  impact  of  the  RRF  and  significant  positive  spillovers,  
driven  by  the  strong  integration  within  the  EU  Single  Market.  This  paper  identifies  the  sectors  that 
benefit most from these effects and highlights how these novel data and techniques can support further 
research and evaluations. 

JEL Classification: C82, E61, E62, F15, F17, F41, F42, F62. 

Keywords: Recovery and Resilience Facility, public investment, structural reforms, economic impact, 
general equilibium model, FIDELIO, artificial intelligence. 

Contact:    Anne  Michels,  anne.michels@ec.europa.eu,  Daria  Ciriaci,  daria.ciriaci@ec.europa.eu,  José 
Manuel  Rueda-Cantuche,  josem.rcantuche@ec.europa.eu,  Luis  Pedauga,    luis.pedauga@ec.europa.eu, 
Valeria  Ferreira,  valeria.ferreira@ec.europa.eu,  Christina  Kattami,  christina.kattami@ec.europa.eu,  Daniel 
Schulz,  daniel.schulz@ec.europa.eu  and  Marta  Pilati,  marta.pilati@ec.europa.eu.  European  Commission, 
Directorate-General for Economic and Financial Affairs and Joint Research Centre. 

EUROPEAN ECONOMY                                                                                              Discussion Paper 221                                            

 
 
 
 
 
   
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

Acknowledgements: The authors would like to thank Luc Tholoniat, Mikel Landabaso, Maarten Masselink, 
Carlos Torrecilla-Salinas, Pablo Florencio, Sotirios Giannoulis, Johannes Könemann and Juliette Angel for 
their valuable contributions to the development of the project. The creation of the database, leveraging AI, 
was made possible thanks to the key contribution of Farid Rahmi, Grégoire Boulet, Philippe Van Wynendaele 
and Robert Tarasiuk. We are also grateful to colleagues in the geographical units of DG ECFIN for their 
support in setting up the sectoral database, and in particular Marlies Humpelstetter, Henriette Goldmann, 
Santiago  Benitez-Alahija,  Irene  Gkiouleka,  Frodewin  Groeger,  Noora  Kovalainen,  Victor  Albert  Pedrero 
Bohne  and  Manuele  Zannini.  Finally,  this  paper  benefited  from  valuable  comments  by  Björn  Dohring, 
Philippe Pfeiffer, Wouter Simons, Janos Varga, Guillaume Debarbat, Elizaveta Archanskaia, Balazs Palvolgyi 
and Katharina Muhr. The authors would like to thank Johannes Luebking, Géraldine Mahieu, Reinhard Felke, 
Christina Jordan and David Lopes for their guidance, and Lourdes Acedo Montoya, Roman Arjona, Paolo 
Pasimeni and Erik Canton for their valuable comments on an early draft of the paper.  

ABBREVIATIONS  

AIDS 

CES 

CGE 

Almost Ideal Demand System 

Constant Elasticieties of Substitution 

Computable General Equilibrium 

DSGE 

Dynamic Stochastic General Equilibirum  

EAGLE 

Euro Area and Global Economy 

ECB 

EV 

European Central Bank 

Electric Vehicle 

FIDELIO   

Fully Interregional Dynamic Econometric Long-term Input-Output  

FIGARO   

Full International and Global Accounts for Research in input-Output analysis 

GDP 

IO   

LES   

Gross Domestic Product 

Input-Output 

Linear Expenditure System 

NACE     

Nomenclature statistique des activités économiques dans la Communauté européenne 

NGEU 

NextGenerationEU 

RRF 

RRP  

SMEs 

Recovery and Resilience Facility 

Recovery and Resilience Plan 

Small- and medium-sized enterprises 

2 

 
 
 
 
 
 
 
 
 
 
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

CONTENTS 

1.

2.

Introduction………………………………………………………………………………………………………………………………. ...................... 5 

Analysis of economic impacts of the RRF to date .................................................................................................................. 6

2.1

2.2

Overview of main overall and specific studies…………………………………………………………………………………………………………… 8

A new approach to enrich and connect macro- and micro-analysis………………………………………………………………………  9 

3.

A new database for macro-economic, sectoral and policy analysis....................................................................... 10

3.1

Overview of RRF reforms by key policy domains….…………………………………………………………………………………………………. 10

  3.2 Overview of RRF investments by key economic sectors………….……………………………………………………………………………....12 

4.

Modelling strategy and macro-analysis:  direct and spillover impacts .................................................................. 15

4.1

4.2

The modelling strategy…………………………………………………………………………………………………………………………………………………15

Macro-analysis: Direct and spillover impacts……………………………………………………………………………………………………………15 

5.

6.

The case of Germany ............................................................................................................................................................................. 19 

Conclusion………………………………………………………………………………………………………………………………. ...................... 21 

REFERENCES……………………………………………………………………………………………………………………………………………….…………….22 

Annex I: 

Classification methodology ............................................................................................................................................................ 24

Annex II:  

The FIDELIO model .............................................................................................................................................................................. 27 

Annex III:   Main modelling assumptions ........................................................................................................................................................ 30 

Annex IV:   Fidelio NACE codes .............................................................................................................................................................................. 31 

FIGURES 

Figure   1: 

RRF envelopes by Member State……………………………….………..……………….………………………...…….…………7 

Figure   2: 
Figure   3: 
Figure   4: 

Overall distribution of reforms by key policy domains……………………………….…..………………….………..11 

Country-level distribution of reforms by key policy domains……………………….……………..………...……12 

Overall distribution of investment measures by key sectors………………………………..…….………..…….13 

Figure   5: 

Sectoral distribution of investment measures by country……………………………..……….....………..……..14 

Figure   6: 

Direct and spillover impacts on value added relative to RRF envelopes……………………….….…….…16 

Figure   7: 

Spillovers as a percentage of GDP…………………………………………………………………..………...…………………17 

Figure   8: 

Non-EU spillovers…………………………………………..………………………………………………………….………….…..…..18 

RRF spillovers to Germany: contributions by country of origin……………………….…………..……………..19 

Main sectors benefitting from the RRF in Germany…………………………………….…...…………..………..…..20 

Figure   9: 

Figure 10: 

BOX 

Box  

 1: 

The Recovery and Reslience Facility…………..………………………..……………….………………………...…….…..……6 

3 

European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

1. INTRODUCTION 

With  a  budget  of  EUR  650  billion,  amounting  to  around  4%  of  the  2023  EU  GDP,  the  Recovery  and 
Resilience Facility (RRF) has played a central role in the EU’s economic recovery and resilience post-COVID. 
While  implementation  is  still  progressing  rapidly,  several  studies  have  begun  to  discuss  its  impact  
(e.g. Pfeiffer et al., 2023, Bańkowski et al., 2024, Miller et al., 2023, Ramboll et al., 2022), and a Mid-Term 
Evaluation was completed in 2024 to assess progress over its 2020-2026 timeframe. 

Studies available so far tend to provide two complementary yet relatively disconnected sets of results.  
A  first  strand  provides  a  macro-economic  aggregate  view,  looking  at  the  overall  impact  of  the  RRF,  
based  on  broad  assumptions.  A  second  strand  takes  a  micro-economic  perspective,  looking  at  certain 
projects, measures, policy domains or countries, to illustrate specific impacts. 

This  paper  introduces  a  novel  approach  that  bridges  the  gap  between  aggregate  macro-economic 
assessments  and  more  granular  micro-economic  analyses,  by  exploring  the  impact  of  the  RRF  at  
the  sectoral  level.  This  meso-level  perspective  can  enhance  our  understanding  of  how  RRF-funded 
measures translate into real economic outcomes across different segments of the economy. 

To this end, a new sectoral database was developed using artificial intelligence techniques, building on  
the  RRF’s  main  public  reporting  platform,  FENIX.  This  database  classifies  all  reform  and  investment 
measures  undertaken  by  Member  States  under  their  national  Recovery  and  Resilience  Plans  (RRP)  
into  241  categories  of  economic  activities.  These  categories  were  subsequently  grouped  into  64  main 
industries, in line with standard classifications used for statistical and economic analysis in the EU. 

This  dataset  was  then  used  as  input  for  a  macro-economic  analysis  of  the  impact  of  RRF-funded 
investments  using  the  well-established  FIDELIO1  model  of  the  European  Commission.  This  general 
equilibrium model allows to disentangle the direct and spillover impacts of the RRF, analysed as a demand-
driven  shock  and  considering  value  chain  linkages  across  64  sectors  and  bilateral  trade  between  
46 countries. 

This work paves the way for a more systematic and detailed analysis of the macro-economic, sectoral and 
policy impacts of the RRF. This paper presents a first sectoral overview of RRF reform and investment 
measures according to this economic categorisation, for the EU as a whole and for each Member State.  
It then provides estimates of the direct and spillover impacts of RRF investments across the EU, assuming 
the full implementation of the 27 plans and leaving the assessment of the impacts of RRF reforms for 
future  research.  Looking  at  the  case  of  Germany,  our  findings  highlight  the  significance  of  direct  and 
spillover impacts in a deeply integrated Single Market, both in aggregate terms and within specific sectors. 

The  paper  is  structured  as  follows.  Section  2  reviews  the  main  studies  on  the  economic  impacts  of  
the RRF, situating this work within the broader relevant literature. Section 3 introduces the new database 
developed  to  classify  RRF  reform  and  investment  measures  and  presents  a  first  overview.  Section  4 
explains  how  these  data  were  used  to  provide  a  quantification  of  direct  and  spillover  impacts  and  
presents first results. Section 5 discusses the case of Germany by way of illustration. Section 6 concludes 
on the possible use of this work for further analysis and research. 

1 FIDELIO is a dynamic econometric general equilibrium model with a detailed input-output modelling of European industries. 
FIDELIO was developed by the Joint Research Centre (JRC) and is run under a partnership between the JRC and DG GROW 
within the European Commission. As part of this collaboration, which began in 2024, FIDELIO is being used to conduct case 
studies on the effects of industry-relevant policies, as well as to provide ad hoc policy support. FIDELIO is being upgraded 
to include an SME dimension and extend its capabilities in relation to priorities of DG GROW. Rocchi, P., Reynès, F., Hu, J., 
Pedauga, L., Cai, M., Boonman, H., Rueda Cantuche, J.M. (2025) FIDELIO 4 manual: Equations and data sources, JRC Technical 
Report, Publications Office of the EU, Luxembourg. 

5 

 
 
 
 
 
 
 
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

2. 

ANALYSIS OF ECONOMIC IMPACTS OF THE RRF TO DATE 

The  scale  of  the  RRF,  its  new  features,  its  recent  and  fast-paced  implementation,  and  the  breadth  of  
its activities, which vary according to Member States, make it inherently challenging to study and assess. 
Such difficulties are compounded by the fact that the RRF has evolved over time – with significant revisions 
of  plans  and  take-up  of  loans  in  2023  following  the  war  of  aggression  against  Ukraine  –  and  that  a 
substantial share of its more than 2,800 reform and investment measures are still being rolled out, with 
new developments almost on a daily basis (see box below).  

Box 1: The Recovery and Resilience Facility  

The  RRF  is  the  centrepiece  of  NextGenerationEU  (NGEU),  the  EU’s  plan  to  recover  from  the  COVID-19 
pandemic  crisis,  enhance  the  resilience  of  the  Union  and  drive  EU’s  policy  priorities.  At  least  37%  of  
the envelope of each Member State’s Recovery and Resilience Plan (RRP) is allocated to green objectives 
and 20% to digital objectives. The plans include both reform and investment measures, which were agreed 
in  light  of  Country-Specific  Recommendations  adopted  at  EU  level.  Progress  is  assessed  based  on  
the achievement of defined milestones and targets (7,131 in total): one key novelty of the RRF is that  
it functions as a performance-based instrument, with regular disbursements based on proven results. This 
also gives transparency and predictability for actors at all levels. 

The Facility entered into force in February 2021, providing financing for measures that have taken place 
since February 2020, which was the start of the pandemic. The measures supported by the Facility need 
to be completed by 31 August 2026. Each Member State’s financial envelope of grants was calculated 
based on population, inverse GDP per capita and relative unemployment rate, with 30% of it readjusted 
in 2022 to account for real GDP loss in 2020 and 2021. Member States could also request loan support, 
of up to 6.8% of their 2019 GNI, until August 2023 (see Figure 1 for RRF envelopes by Member State). 

Following  Russia’s  war  of  aggression  against  Ukraine,  the  EU  launched  REPowerEU  in  May  2022  
to  diversify  energy  supply,  save  energy  and  step  up  clean  energy  production.  REPowerEU  mobilised  
EUR  19  billion  of  additional  grants  through  the  sale  of  Emission  Trading  System  allowances  and  
EUR 2 billion of funds transferred from the Brexit Adjustment Reserve. 26 Member States revised their 
RRPs to introduce additional REPowerEU chapters. 

After  these  adjustments  and  new  loan  uptakes,  the  RRF  envelope  increased  in  late  2023  from  
EUR 500 billion to now EUR 650 billion, of which EUR 359 billion are grants and EUR 291 billion are loans. 
As of 13 May 2025, disbursements under the RRF stand at EUR 311 billion (48% of the envelope). 30% 
of milestones and targets have been assessed as fulfilled and an additional 22% reported as already 
completed by Member States. 

A  lot  of  information  is  available  publicly,2  yet,  it  can  be  challenging  for  observers  and  researchers  
to extract, structure and analyse the data in a way that enables sophisticated economic assessments. 

2  The  main  website  is:  https://commission.europa.eu/business-economy-euro/economic-recovery/recovery-and-resilience-
facility_en.  Each  national  Recovery  and  Resilience  Plan  provides  a  detailed  description  of  measures  and  corresponding 
milestones and targets for their roll-out and completion. The European Commission assessed these plans and monitors their 
delivery. The Council of the European Union endorsed them and agrees to regular disbursements. A scoreboard of indicators 
was put in place at the request of the European Parliament (see: Recovery and Resilience Scoreboard). Progress with the 
RRF is reported in Annual Reports and regularly discussed within the European Parliament and with the Member States. 

6 

 
 
 
 
 
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

Figure 1: RRF envelopes by Member State (in EUR billion and in % of 2023 GDP) 

Source: European Commission services. 

A  comprehensive  analysis  of  the  RRF  would  need  to  capture  the  impact  of  its  novel  framework,  
which combines both reform and investment measures. This involves assessing not only their individual 
impact but also their interaction, since many reform measures aim to improve the framework conditions  
for investments. In practice, however, assessing the impact of reforms is challenging, as their effects are 
difficult to isolate and typically take time to be captured in economic indicators. Estimating the impact  
of  investments  prior  to  their  full  implementation  is  also  complex,  but  it  is  more  feasible  and  can  be 
approached through ex-ante estimations. 

As a result, economic studies of the RRF so far have typically produced two complementary but rather 
disconnected sets of findings. A first strand adopts a macro-economic aggregate perspective, assessing 
the  impact  of  the  RRF  based  on  broad  assumptions  essentially  linked  to  the  overall  stimulus  effect,  
without capturing the more granular impacts of reform or investment measures. A second strand takes  
a more micro-economic and case-specific approach, focusing on selected projects, measures, policy areas 
or countries to illustrate specific impacts, without providing a comprehensive and systematic analytical 
framework. This paper seeks to bridge the gap between these two approaches by allowing the analysis of 
impacts at sectoral and industry level. This meso-level approach enables a more nuanced understanding 
of how RRF measures affect different parts of the economy, combining the breadth of macro-economic 
modelling with the specificity of sectoral analysis. 

7 

 
 
 
 
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

2.1 

OVERVIEW OF MAIN OVERALL AND SPECIFIC STUDIES 

Watt  and  Watzka  (2020)  conducted  one  of  the  earliest  assessments  of  the  RRF's  potential  macro-
economic impact, by using the NiGEM macro-econometric policy model. Their analysis showed substantial 
increases in public capital stocks across the EU, suggesting that the RRF could play a significant role in 
mitigating output losses from the COVID-19 pandemic crisis and in reducing economic divergence within 
the EU. They found an average GDP gain in the order of 0.3% per year (Watt and Watzka, 2020).  

Subsequently,  Pfeiffer  et  al.  (2023)  applied  the  European  Commission’s  QUEST  Dynamic  Stochastic 
General  Equilibrium  (DSGE)  model  to  quantify  the  NGEU’s  spillover  impacts  across  EU  Member  States. 
Their results suggested that spillovers would contribute to approximately one-third of the average GDP 
impact, with  some  highly integrated  economies  deriving  most  of  their  GDP gains from  such  spillovers.  
This  work  also  suggested  that  coordinated  investments  under  the  RRF  would  amplify  growth  effects 
beyond individual recipient countries. 

Building on this work and using updated data, the European Commission’s Mid-Term Evaluation of the RRF 
(European  Commission,  2024)  found  that  NGEU  could  increase  the  EU’s  real GDP  every  year,  reaching  
a peak increase of 1.4% in 2026 and remaining above 0.6% annually until 2032. Approximatively one-
fourth to one-third of this impact is attributed to spillover impacts across Member States. Employment is 
also projected to see a short-run increase of 0.8%. The growth impact, measured as the peak percentage 
increase  in  GDP,  is  expected  to  vary  across  Member  States,  with  Greece  (4.5%),  Croatia  (4%)  and  
Spain (3.5%) estimated to benefit the most relative to the size of their economy. Spillover impacts are 
projected to be particularly pronounced for small open Member States with smaller RRPs. 

The  European  Central  Bank  (ECB)  conducted  several  studies  on  the economic impact  of  the  RRF,  using  
its  EAGLE  (Euro  Area  and  Global  Economy)  DSGE  model  alongside  complementary  semi-structural 
modelling  approaches.  They  analyse  the  RRF  impacts  through  three  main  channels:  fiscal  stimulus, 
structural reforms, and reductions in risk premiums (Bańkowski et al., 2022). In their most recent study 
(Bańkowski  et  al.,  2024),  they  find  that,  under  a  scenario  of  successful  implementation,  NGEU  could 
increase euro area GDP by up to 0.9% by 2026 and 1.2% by 2031. 

In parallel to the growing body of research on the macro-economic impact of RRF investments, increasing 
attention  is  being  devoted  to  the  impact  of  structural  reforms,  the  assessment  of  which  remains  one  
of the key challenges in evaluating the overall effectiveness of the Facility. This challenge is not unique  
to  the  RRF.  Garcìa  et  al.  (2024)  reviewed  existing  tools  to  analyse  the  economic  impact  of  structural 
reforms and concluded that both econometric approaches and macro-economic models have limitations  
in producing a robust quantitative ex-ante assessment of reforms. 

Pfeiffer et al. (2023) employed a model-based benchmarking approach to assess the potential macro-
economic gains from structural reforms across EU Member States. They simulated the effects of closing 
half  of  the  gap  between  national  performances  and  that  of  EU’s  best  performers,  thereby  reflecting 
ambitious reforms  for  countries  with  significant  distance  to  the  frontier.  In  addition,  the  Commission’s 
Directorate-General  for  Economic  and  Financial  Affairs  (2016)  quantified  the  medium-  to  long-term 
macro-economic effects of selected structural reforms in Italy, France, Spain, and Portugal. This approach 
involved translating individual reform measures into changes in the structural indicators of the QUEST 
model, thereby allowing for an estimation of their macro-economic impacts. 

Based  on  a  qualitative  approach,  the  European  Commission’s  Mid-Term  Evaluation  of  the  RRF  
(European Commission, 2024) finds that NGEU has played a crucial role in incentivising Member States  
to  implement  key  reforms,  including  many  of  macro-economic  significance  that  had  long  been 
recommended  under  the  European  Semester  of  economic  policy  coordination.  This  assessment  is 
supported by Bańkowski et al. (2024), who highlight how the RRF has accelerated the implementation of 
structural reforms aligned with EU-level Country-Specific Recommendations. Their analysis suggests that, 
in some cases, the long-term impact of reforms could exceed that of investments. The impact of selected 
reforms has also been explored in specific cases, such as Italy’s judicial system reform (D’Andrea et al., 
2023) and Spain’s labour market reform (Anghel et al., 2023).  

8 

 
 
 
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

Finally,  there  is  a  growing  body  of  research  on  the  micro-economic  impact  of  the  RRF  with  studies 
examining  specific  projects,  country-level  impacts  and  governance  dynamics  shaped  by  the  Facility’s 
performance-based design. For instance, several studies have focused on the climate and energy-related 
impacts of RRF measures (e.g. Hindriks et al., 2022; Ramboll et al., 2022; Miller et al., 2023), while others 
have  assessed  the  contribution  to  the  digital  transition  (Demertzis  et  al.,  2024;  Empoli  et  al.,  2023). 
Additionally, governance-related research has provided valuable insights into the strengths and limitations 
of the RRF implementation framework, with Zeitlin et al. (2024) evaluating its governance model and Begg 
et al. (2024) analysing the implications of performance-based budgeting. 

The European Parliament’s Research Service (2025) recently published a European Impact Assessment  
of the RRF, including a literature review of research papers on key parameters of the RRF.3 These studies 
offer multiple valuable insights into the RRF implementation and effectiveness. 

2.2 

A NEW APPROACH TO ENRICH AND CONNECT MACRO- AND MICRO-ANALYSIS  

Two important lessons can be drawn from the existing and emerging literature on the impact of the RRF. 
First,  a  more  systematic  study  requires  structured,  detailed  and  comprehensive  data,  together  with  
a  modelling  framework  capable  of  processing  such  granular  information.  Second,  any  comprehensive 
assessment must factor in not only the direct impacts of individual plans, but also the spillover impacts: 
even  discounting  for  the  expected  positive  direct  and  spillover  impacts  of  reforms,  the  simultaneous 
implementation of public investment measures across 27 EU Member States within a deeply integrated 
Single Market generates substantial cross-border effects. 

Against this background, and given the advancing implementation of the RRF, it has become important to 
bridge the gap between aggregated macro-economic analyses and targeted, project-specific assessments, 
to offer a more comprehensive and systematic overview of potential impacts. 

As  a  follow-up  to  the  European  Commission’s  Mid-Term  Evaluation  of  the  RRF,  different  additional 
techniques  have  been  tested,  but  they  all  showed  some  limitations.4  However,  with  the  progress  of  
the RRF’s implementation, the availability of new information in FENIX and the increasing performance  
of AI tools, it is becoming possible to create new databases in order to document and analyse the RRF  
in a more systematic and detailed manner. 

3 European Parliament Research Service, (2025), Recovery and Resilience Facility: State of play and way forward - European 
Implementation  Assessment,  available  here  Recovery  and  Resilience  Facility:  State  of  play  and  way  forward -  European 
Implementation Assessment | Think Tank | European Parliament. In addition, the European Court of Auditors has produced 
Special Reports on different features and policy domains of the RRF. 

4 In the lead up to this project, several alternative avenues were explored such as using information regarding the “top 100” 
final  beneficiaries,  data  held  by  national  administrations  and  insights  from  the  European  Public  Tender  Database.  
These options were eventually set aside due to limited data operationability. 

9 

 
 
 
 
 
 
 
 
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

3. 

A NEW DATABASE FOR MACRO-ECONOMIC, SECTORAL 
AND POLICY ANALYSIS 

In  recent  months,  a  new  sectoral  database  was  established  to  categorise  the  2,878  investment  and  
reform  RRF  measures5  into  241  industries,  following  the  standard  NACE  classification  of  Eurostat.6  
This  classification  was  carried  out  using  the  European  Commission’s  internal  AI  tools,  which  leveraged 
textual descriptions of the RRF measures, as detailed in the annexes of the Council Implementing Decisions, 
to categorise the measures by economic sector. To ensure accuracy, the classification underwent three 
rounds of robustness checks by Commission staff. 

The database covers the entire RRF over its full implementation period 2020-2026. Reform and investment 
measures are classified separately. It includes data on actual yearly disbursements over the period 2020-
2024,  as  well  as  projected  yearly  disbursements  for  2025-2026,  disaggregated  at  both  country  and 
measure levels. The cut-off date for the incorporation of amendments to the plans was 15 January 2025, 
but the database can be updated in the future to reflect subsequent revisions. 

3.1 

OVERVIEW OF RRF REFORMS BY KEY POLICY DOMAINS 

We  have  classified  the  1,131  RRF  reform  measures  based  on  the  sector  targeted  by  each  reform.  
For example, if a reform measure aims to reduce CO2 emissions by providing incentives for the purchase 
of electric vehicles, it will be classified under ‘manufacture of motor vehicles’ (NACE code 29.1).7 

Such categorisation builds on the NACE code classification and follows an economic sector perspective.  
It thus differs from other categorisations under the RRF framework such as the use of intervention fields 
for climate and digital tagging, and the pillar reporting system that assigns each measure to policy areas.8  

The reform-related data highlights the broad scope of the RRF (see Figure 2 below). Energy market reforms 
(126),  education  reforms  (105),  the  modernisation  of  the  public  administration  (91)  and  justice,  anti-
corruption and anti-fraud (84) are among the most prominent areas of intervention. 

For  illustrative  purposes,  we  have  regouped  these  areas  into  five  generic  headings:  green;  business; 
institutions;  health  and  social;  and  skills  and  labour.  We  express  the  results  in  percentage  of  the  total 
number of RRF reforms.  

5 Out of the 2,878 measures, 1,131 are ‘reforms’ and 1,747 are ‘investments’. To bring even more precision to the analysis, 
we classified them at sub-measure level, which is also the level at which the costing of the RRPs is done. By our cut-off date 
of mid-January 2025, the Council Implementing Decisions endorsing the 27 plans included 2,878 measures; 4,687 including 
sub-measures. A sub-measure typically refers to a specific investment or reform component, such as a concrete project, 
programme, or legislative action, that contributes to the overall objective of the broader measure. For example, one measure 
in the Austrian plan referring to green investment by enterprises has 6 sub-measures that address different aspects of 
the green transition, such as energy-saving investments, zero emission mobility and the renovation of buildings. 

6 See NACE Rev. 2 - Statistical classification of economic activities - Products Manuals and Guidelines - Eurostat. 

7 In cases where a measure is linked to multiple sectors, a dual classification system was applied (primary and secondary 
NACE codes). see Annex I for more details on the classification.  

8  The  intervention  field  classification  serves  to  define  a  measure’s  climate  or  digital  contribution  under  the  RRF.  This  is 
different from our analysis here: for instance, looking at the ‘water supply and waste category’ (NACE code 35), one may 
find  measures  which  are  classified  as  green  and/or  digital  under  the  RRF  intervention  field  system.  The  pillar  reporting 
system follows yet another – policy – logic: it attributes measures to six main policy areas and it is used to monitor progress 
under the Recovery and Resilience Scoreboard mentioned above. 

10 

 
 
 
 
 
 
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

Figure 2: Overall  distribution  of  reforms by  key  policy  domains,  in  %  of  total  number  of  reforms 
(total = 1,131) 

Skills & labour
13%

Social Housing
1%
Culture
1%

Social (incl. long term and child care)
3%

Social security (e.g. Pensions)
3%

Health & Social
15%

Health
6%

Public procurement 
2%

Implementation of the plan
2%
Public spending reviews
2%

Taxation
5%

Justice, anti-corruption and anti-fraud
7%

Institutions
25%

Source: European Commission services.9 

Labour market 
3%

Energy (incl. permitting)
11%

Education
9%

Green 
30%

Transport
7%

Modernisation of buildings 
(e.g. energy efficiency)
6%

Water supply & waste
3%

Agriculture
2%

Digital & IT sector
5%

Simplification of regulation (cross-cutting)
4%

Research & Development
3%

Professional and technical activities
1%

Public administration
8%

Financial Markets
2%

Business
17%

The policy mix of RRF reforms varies across Member States (see Figure 3 below). Considering only the five 
generic headings, all Member States dedicate a significant share of reforms in their plans to the green 
transition,  with  a  total  of  340  reforms  across  the  EU.  Belgium,  Denmark,  Finland  and  Poland  devote  
the  largest  share  of  their  reforms  to  green  policies.  Other  Member  States,  such  as  Croatia,  Cyprus,  
Hungary  and  Lithuania,  have  most  reforms  related  to  institutions  (including  public  administration,  
justice  and  anti-corruption reforms).  While  all  Member  States  have included  health  and  social  reforms  
in  their plans,  Estonia,  Sweden  and  Portugal  have  allocated  the  highest  share  of reforms  to  this  area. 
Finally, Belgium, Finland, Germany and Slovakia have dedicated a large share of their plans to education 
and labour market reforms. 

9 See Annex I for more details on the classification. 

11 

 
 
 
 
 
 
 
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

Figure 3: Country-level distribution of reforms by key policy domains, in % of total number  
of reforms  

Green

Business

Institutions

Health & Social

Skills & Labour

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

4

6

5

8

7

3

9 5 6
4

9 10

8

9

2

19 11

26

4 9

3

9

20

16

19

17

11

24

2

1

2

5

3

5

2

2

2

5

9

4

3

4

5

4

10

3 2

17 13

3

6 4 18

6 5

4 2

12

12

6

3

22 34

15

4

4

8

18 3 4

11

5

10

6

25

22

2

3 5

3 5

1 4 2 5 5 10

5 14 2 144

2 4 4 10

10 9

16

3

12

9

18

11

14

8

9

6

169

24 4

1 282

21

3

18

196

20

15

11 34

6

340

5

8

26

3

1

4

7

2

8

11

4

8

9

9

Source: European Commission services. 

3.2 

OVERVIEW OF RRF INVESTMENTS BY KEY ECONOMIC SECTORS 

As part of the new sectoral database, we also categorised all RRF investment-related measures according 
to a similar logic as is done with reforms. These include 1,747 investment measures and 212 so-called 
‘costed reforms’10, amounting to the RRF total of EUR 655.5 billion.11  

For the classification of investment measures, we adopted a commodity-based approach, whereby each 
investment measure is assigned to the sector where the money will actually be spent and where demand 
is ultimately created. This means that each measure was classified based on the type of goods or services 
it  is  expected  to  purchase  or  support.  For  example,  if  an  investment  measure  finances  the  building  of  
new schools, the measure will be classified under the ’construction of buildings’ sector.12 

10 A limited number of reforms (212 out of 1,131) include implementation costs. These ‘costed reforms’ appear twice in our 
analysis, as ‘reforms’ and as ‘investments’. Their implementation costs are treated in the same way as investment measures 
and classified accordingly. Their reform component was classified in line with other reforms and the result is reflected in 
Figures 2 and 3 above. 

11 The total amount of the RRF stands at EUR 650.4 billion. A small number of Member States have included additional 
national funds in their plans. This ‘over-subscription’ amounts to EUR 4.8 billion and is added here. 

12 See Annex I for more details on the classification. 

12 

 
 
 
 
 
 
 
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

Figure  4  provides  an  overview  of  the  overall  distribution  of  RRF  funds  by  key  sectors,  based  on  
an illustrative aggregation into 14 headings13.  

Figure 4: Overall distribution of investment measures by key sectors, in % of total RRF budget  

Other sectors
13%

Renovation of 
residential housing
6%

Research & development
6%

Education & training
5%

Electronic products 
(incl. laptops)
8%

Modernisation of non-residential 
buildings (incl. businesses, public 
administration, hospitals, schools)
15%

Construction in electricity & 
telecommunication sectors
9%

Construction of railways 
7%

Digitisation & IT software
10%

Machinery & equipment
4%

Electrical equipment 
(incl. charging stations)
4%

Civil engineering projects (incl. flood protection)
5%

Transport vehicles
5%

Green energy projects for alternative fuels 
(incl. hydrogen)
3%

Source: European Commission services. 

Looking across the 27 EU Member States, the data shows that the sectors standing to benefit the most 
from RRF funding include those related to the green transition, such as the energy-efficient modernisation 
of  buildings,  construction  of  electricity  and  renewable  energy  projects,  railway  infrastructure,  and 
investments in sustainable transport vehicles and electrical equipment (e.g. batteries and charging points). 
Sectors relating to the digital transition also feature prominently, notably IT software, the construction of 
telecommunication infrastructure and the manufacturing of electronic products and electrical equipment. 

The sectoral distribution of RRF funding varies significantly across Member States (see Figure 5 below). 
Yet,  the  modernisation  of  buildings,  as  well  as  the  renovation  and  construction  of  green  and  digital 
infrastructure, consistently emerge as the primary investment areas in most countries. 

13 See Annex I for an explanation of the agregation method. 

13 

 
 
 
 
 
 
 
 
 
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

Figure 5: Sectoral distribution of investment measures by country, in % of national envelope 

Renovation of residential buildings
Construction in electricity & telecommunication sectors
Civil engineering projects (incl. flood protection)
Transport vehicles
Electrical equipment (incl. charging stations)
Other sectors
Education & training

Modernisation of non-residential buildings
Construction of railways
Green energy projects for alternative fuels (incl. hydrogen)
Machinery & equipment
Digitisation & IT software
Electronic products
Research & development

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

Source: European Commission services. 

Looking  at  the  specificities  of  each  country,  we  see  that,  in  Croatia,  Latvia,  Poland  and  Slovenia,  
for  example,  funding  is  highly  concentrated  in  one  sector,  the  construction  sector,  whereas  Spain  and 
Austria exhibit a more even allocation of funds across multiple sectors. In the case of Germany, nearly  
30 percent of national RRP funding is directed towards the construction and automotive sectors, reflecting 
the  strategic importance  of  these  industries. Moreover,  key  areas  such  as  digitisation  and  IT  software, 
electronic products, and research and development feature prominently in Germany’s RRP, underscoring 
their role in fostering technological and industrial transformation. 

The  above  provides  a  snapshot  of  what  the  new  RRF  sectoral  database  can  show.  In  the  following  
two  Sections  4  and  5,  our  analysis  will  focus  exclusively  on  RRF-funded  investments,  as  represented  
in Figures 4 and 5 above.  We thus set aside the impacts of RRF-funded reform measures (as represented 
in Figures 2 and 3 above), as such analysis goes beyond the scope of this paper. Section 6 will conclude 
by highlighting potential avenues for future research. 

14 

 
 
 
 
 
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

4.  MODELLING STRATEGY AND MACRO-ANALYSIS:  

DIRECT AND SPILLOVER IMPACTS 

4.1 

THE MODELLING STRATEGY 

To  assess  the  macro-economic  direct  and  spillover impacts  of  RRF-funded  investments,  we  have  used  
the FIDELIO model of the European Commission (Rocchi et al., 2025). FIDELIO is based on official statistics 
from EUROSTAT’s FIGARO global input-output (IO) tables, with a breakdown of 64 NACE industries covering 
46 countries (27 Member States, 18 non-EU countries14 and the rest of the world). Annex II provides a 
detailed description of the FIDELIO model, Annex III outlines the key assumptions behind the modelling 
work for this analysis and Annex IV descrives the 64 NACE categories covered in the model. 

Designed for policy impact assessments, the model compares counterfactual and baseline equilibriums to 
assess complex interactions15 between individual economic agents (households, firms and governments) 
across  national  and  international  markets.16  The  results  of  the  model  should  not  be  interpreted  as 
forecasts of future outcomes, but rather as simulation-based assessments of partial potential economic 
impacts. Moreover, the analysis does not cover the impacts of reforms, nor does it evaluate welfare effects  
beyond GDP. The estimates presented here are thus likely to underestimate the full impact of the RRF. 

In this analysis, the RRF is interpreted as an induced investment shock, which translates into an increase 
in investment spending and raises aggregate demand. This rise in demand affects both domestic supply 
and the demand for imported goods. From a geographical perspective, these impacts can be categorised 
into direct and spillover impacts. 

Direct impacts materialise as an increase in production and employment in the industries of the country 
directly receiving the funds. These effects encompass not only the impact on the funded industry, but also 
the  effect  on  other  related industries,  as  higher  activity  generates  additional  demand  for intermediate 
inputs  from  domestic  supplier  industries,  thereby  stimulating  production  and  employment  throughout  
the national supply chain.  

Spillover impacts, in turn, arise from the deep economic integration within the EU, as well as with key non-
EU  trading partners.  The increase in  demand  will  also lead  to  a  higher  demand  for  imported final  and 
intermediate goods. This means that the benefits of the RRF extend beyond national borders, strengthening 
trade and economic linkages among EU Member States and with non-EU countries. 

4.2 

MACRO-ANALYSIS: DIRECT AND SPILLOVER IMPACTS 

The simulations using the FIDELIO model show that, in 2030, 4 years after the completion of the RRF and 
10 years after its launch, the total global cumulative impact of the RRF would amount to EUR 891.7 bn, 
corresponding to a multiplier of 1.36, out of which EUR 345.5 bn are spillovers. Focusing on the EU alone, 
the medium-term impact of the RRF would be EUR 685.1 bn, with EUR 138.8 bn in intra-EU spillovers, 
yielding a multiplier of 1.05.17 

14  United  Kingdom,  United  States,  China,  Mexico,  Canada,  Argentina,  Brazil,  India,  South  Africa,  Saudi  Arabia,  Indonesia, 
Australia, South Korea, Japan, Russia, Switzerland, Norway and Türkye. 

15 Besides spillovers, the model also accounts for rebound effects, while quantifying their impacts on employment, economic 
growth, investments, resource consumption, air emissions and trade balance. 

16 For more details, please see the following link:  https://joint-research-centre.ec.europa.eu/scientific-activities-z/trade-and-
industrial-policy-analysis/industrial-policy/fidelio-model_en. 

17 It is worth noting that the highest multiplier effects are observed for Member States classified as “leaders” and “strong” 
innovators according to the Innovation Scoreboard (2024) published by the Directorate-General for Research and Innovation: 
https://projects.research-and-innovation.ec.europa.eu/en/statistics/performance-indicators/european-innovation-
scoreboard/eis-2024#/eis. 

15 

 
 
 
 
 
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

These  results  are  comparable  to,  yet  slightly  lower  than,  those  reported  under  the  ‘low  productivity’ 
scenario of Pfeiffer et al. (2023). The differences can be explained by different modelling approaches. The 
QUEST model used in Pfeiffer et al. (2023), for instance, applies an aggregate forward-looking modelling 
framework that includes monetary policy and exchange rate effects. The FIDELIO model used in this paper 
follows  a  sectoral  modelling  approach,  with  64  sectors  and  46  countries.  The  data  used  for  the  two 
estimations also differ: this paper analyses the RRF by using precise updated disbursement profiles that 
reflect the backloading of some measures, similarly to Bańkowski et al. (2024), while Pfeiffer et al. (2023) 
analyse the NextGenerationEU initative, which is larger than the RRF, assuming a uniform distribution of 
payments over the entire period.18 Furthermore, both methods rely on different productivity assumptions.19 

Real GDP in the EU is estimated to be, on average, 0.4 percent higher per year between 2020 and 2030 
compared to a scenario without the RRF, with a peak impact of 1.2 percent in 2026. This peak result is 
higher than the result reported by Bańkowski et al. (2024), but in line with the findings of Pfeiffer et al. 
(2023). Importantly, sizable growth effects persist over the long term.  

Analysing the medium-term effects of the RRF, we find that, given the size of their respective RRPs, Italy 
and  Spain  emerge  as  the  two  main  beneficiaries,  with  their  respective  GDP  projected  to  increase  by  
EUR 189.6 bn (8.9% of current GDP) and EUR 142.7 bn (9.5% of current GDP) over the 10-year period. 
These gains are primarily driven by the direct impacts of their national plans, namely the immediate boost 
in production and employment in national recipient industries and the increased demand for inputs from 
domestic  supplier  industries.  Spillover  impacts  from  other  RRPs  contribute  only  to  a  limited  extent  to  
the GDP increase for both countries (see Figure 6 below). 

Figure 6: Direct and spillover impacts on value added relative to RRF envelopes, by country (EUR bn) 

200

180

160

140

120

100

80

60

40

20

0

194,4

13,9

163,0

42,6

6,8

59,8

18,4

6,2

36,6

0,8

28,8

44,9

39,2

23,5

175,7

135,9

Italy

Spain

Source: European Commission services. 

Direct impact

Spillover impact

RRF envelopes

80

70

60

50

40

30

20

10

0

18 In the mid-term review, however, the assumption of a uniform payment profile is relaxed in favour of a more precise 
disbursement schedule 

19 As noted by Pfeiffer et al. (2023), there is no consensus in the literature regarding the productivity effects. To reflect this 
uncertainty, Pfeiffer et al. present two scenarios (high and low productivity), while this paper uses sector-specific productivity 
assumptions, with an average elasticity close to the low-productivity scenario of Pfeiffer et al. (2023). Furthermore, the 
QUEST model treats public capital as a productivity-enhancing, non-rival input (e.g. infrastructure), whereas in the FIDELIO 
model productivity gains arise from the additional value added generated by RRF funds on R&D investments. 

16 

 
 
 
 
 
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

A different picture emerges for the German economy which, due to spillovers from other national plans, 
becomes the third-largest beneficiary of the RRF. Germany is, in fact, the largest recipient of spillovers 
across the EU, with an estimated 42.6 billion in spillovers over a 10-year period (see Figure 6), equivalent 
to 1% of its current GDP (see Figure 7). The total potential macro-economic impact of the RRF in Germany 
is estimated at EUR 66.1 billion (1.6 % of its current GDP), which is double the size of Germany’s RRP.  

The ratio of spillovers to direct impact is also important for other Member States that are highly integrated 
in  the  Single  Market  such  as  the  Netherlands  (EUR  8.8  bn  of  spillovers  or  0.8%  of  GDP),  Austria  
(EUR 5.4 bn of spillovers or 1.1% of GDP) and Denmark (EUR 2.4 bn of spillovers or 0.6% of GDP) where 
spillovers  more  than  double  the  direct  impact.  The  amount  of  spillovers  is  also substantial  for France, 
accounting for 32% of the total impact (EUR 18.4 bn out of EUR 57.5 bn).  

The significance of these spillover effects highlights the importance of the Single Market as an amplifier 
of economic impacts20. When considering spillovers relative to GDP (see Figure 7 below), the results show 
that Slovakia, Slovenia and the Czech Republic are among the EU countries with the highest spillover-to-
GDP ratios, at 1.8%, 1.4% and 1.3%, respectively. This can be attributed to their high degree of integration 
into EU value chains. They are followed by Hungary and Austria, where spillovers amount to 1.2% and 
1.1% of their respective GDP. 

Figure 7: Spillovers as a percentage of 2023 GDP 

2,0%

1,5%

1,0%

0,5%

0,0%

Source: European Commission services. 

The vast majority of RRF funds stimulate economic activities and goods produced within the Single Market. 
Yet, we also observe EUR 206.7 bn of spillovers to non-EU trading partners, an expected outcome given  
the EU’s strong trade integration into global value chains. These include China (26 % of non-EU spillovers), 
the United States (14 %), the United Kingdom (6%), Japan (5%) and Switzerland (4%) (see Figure 8 below).  

20 See for example Fontagné and Yotov (2025, forthcoming in SMEP): “Assessing the Effectiveness and Potential of the 
Single Market for New and Existing Members: New Methods and Measures”, which shows that on average, the Single Market 
has led to about 63% increase in trade among EU Member States. 

17 

 
 
 
 
 
 
 
 
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

Figure 8: Non-EU spillovers: share of spillovers going to non-EU countries (total = EUR 206.7 bn) 

South Korea
3%

Indonesia
3%

Switzerland
4%
Japan
5%

China
26%

UK
6%

USA
14%

Rest of the World
39%

Source: European Commission services21 

China’s share in non-EU spillovers also reflects the focus of the RRF on the green and digital transition. 
Most  non-EU  spillovers  stem  from  increased  demand  for  inputs  in  high-tech  and  medium-high-tech 
industries, such as the manufacture of computer, electronic and optical products, basic metals, as well as 
machinery  and  equipment.  This  corresponds  to  the  direction  of  many  RRF  measures,  which  drive  up 
demand for products such as solar panels, wind turbines and batteries, for which China currently holds  
a dominant position in global production and market shares22. These findings are thus a further illustration 
of the EU’s external dependencies within sensitive industrial ecosystems23 (see Arjona et al. (2023)). 24 

21 The category “rest of the world” includes also Türkiye, Canada, Mexico, Norway, Brazil, Saudi Arabia, Russia, Australia, 
India, South Africa and Argentina. 

22  See  for  example  Vandermeeren  (2024):  “Understanding  EU-China  exposure”,  which  highlighted  that  China  has  a  very 
strong position in global production capacities for clean tech products and global market shares in electronic equipment and 
parts. 

23  Sensitive  industrial  ecosystems  cover  important  sectors  such  as  energy-intensive  industries,  health,  as  well  as  those 
related to the green and digital transition, which are all important beneficiaries under the RRF. 

24 According to the work of Arjona et al. (2023), China is the primary source for approximately 31% of products in sensitive 
industrial ecosystems. These goods are essential to the EU’s twin transition, encompassing both raw materials, such as rare 
earths, magnesium and manganese, as well as advanced products like solar panels and laptops. Notably, around 80% of EU 
dependencies on China are subject to ‘choke points’, where production is concentrated in a few countries that are central 
nodes in global trade networks. 

18 

 
 
 
 
 
 
 
 
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

5.      THE CASE OF GERMANY 

As mentioned above, the German economy stands out as the largest beneficiary of RRF-induced spillovers, 
with more than two-thirds of its potential GDP gains originating from the delivery of other countries’ RRP. 
With  our  work,  it  is  possible  to  trace  the  origin  of  such  spillovers,  and  Figure  9  (see  below)  shows  
that the main sources are Spain (25.7% of the total spillovers to Germany), Italy (24.6%), Poland (12.6%),  
France (6.5%) and Romania (5.5%).  

Figure 9: RRF spillovers to Germany: contributions by country of origin  

Source: European Commission services. 

The significant spillover impacts observed for Germany stem from the deep integration of its industrial 
sectors with the rest of the EU economy, in particular Spain and Italy. At the core of the German economy 
lies  a  globally  integrated  manufacturing  sector,  with  industries  such  as  automotive,  machinery  and 
electrical equipment, that is deeply embedded in complex EU-wide and global value chains. With this work, 
it  is  possible  to  show  a  sectoral  breakdown  of  the  impact  of  RRF  funds,  which  sheds  further  light  on  
the central role of Germany as an economic powerhouse and partner (see Figure 10 below). 

In  Germany,  four  manufacturing  sectors  are  expected  to  benefit  the  most  from  RRF  spillovers  from  
the rest of the EU: the manufacture of machinery and equipment (EUR 4.7 bn), the manufacture of motor 
vehicles  (EUR  3.4  bn),  the  manufacture  of  computer,  electronic  and  optical  products  (EUR  2.9  bn)  and  
the manufacture of electrical equipment (EUR 2.7 bn).  

19 

 
 
 
 
 
 
 
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

Figure 10: Main sectors benefitting from the RRF in Germany (EUR bn)  

C29 - Manufacture of motor vehicles, 
trailers and semi-trailers

F - Construction

G46 - Wholesale trade, except of 
motor vehicles and motorcycles

J62- 63 - Computer programming, 
consultancy / Information service activities

C26 - Manufacture of computer, 
electronic and optical products

C27 - Manufacture of electrical 
equipment

C28 - Manufacture of machinery and 
equipment
6,0
5,5
5,0
4,5
4,0
3,5
3,0
2,5
2,0
1,5
1,0
0,5
0,0

1
0
A

2
0
A

3 B
0
A

6
1
C

7
1
C

8
1
C

9
1
C

0
2
C

1
2
C

2
2
C

3
2
C

2
1
T
0
1
C

5
1
T
3
1
C

4
2
C

5
2
C

6
2
C

7
2
C

8
2
C

9
2
C

0
3
C

2
3
_
1
3
C

3
3
C

5
3
D

6
3
E

9 F
3
T
7
3
E

5
4
G

6
4
G

7
4
G

9
4
H

0
5
H

1
5
H

2
5
H

3 I
5
H

8
5
J

1
6
J

0
6
_
9
5
J

3
6
_
2
6
J

4
6
K

5
6
K

6 L
6
K

0
7
_
9
6
M

1
7
M

2
7
M

3
7
M

5
7
_
4
7
M

7
7
N

8
7
N

9
7
N

4
8
O

5
8
P

6
8
Q

2
8
T
0
8
N

8
8
_
7
8
Q

2
9
T
0
9
R

3
9
R

4
9
S

5
9
S

6 T
9
S

Source: European Commission services (see Annex IV for the description of the NACE codes). 

Direct Impact

Spillover Impact

In addition to significant spillover impacts, the direct impact of the German RRP on these sectors further 
reinforces  their  status  as  important  beneficiaries  of  the  RRF.  Moreover,  the  construction  sector  
(housing,  buildings,  infrastructure)  and  activities  related  to  computer  programming,  consultancy,  and 
information  services  also  emerge  as  significant  beneficiaries,  with  the  latter  benefiting  primarily  from 
Germany’s national plan. All these sectors are critical to the German economy, driving both innovation and 
production  in  areas  essential  for  the  green  and  digital  transition,  such  as  the  renovation  of  buildings,  
the manufacturing of electric vehicles, and the production of components for IT equipment. Interestingly, 
many of the sectors benefitting the most from the RRF in Germany are currently undergoing a significant 
economic transformation25. 

An additional way to exploit the sectoral database is to zoom in on individual sectors in order to estimate 
more closely RRF deliverables. For instance, looking at the automotive sector (NACE 29.1), the database 
makes it possible to identify specific measures supporting the uptake of electric vehicles (EVs). Combined 
with a reading of the measure description, it is then possible to compute the number of EVs financed or 
subsidised by the RRF overall: more than 1.9 million across the EU. Based on the 2023 market share of 
German-made EVs within the EU, this corresponds to around 760 000 EVs manufactured in Germany. To 
give an order of magnitude, this is around 80% of all EVs produced in Germany in the year 2023.26  

Looking at a related sector, that of charging stations (NACE 27.9), which is critical to support the transition 
to clean mobility, we find that the RRF is contributing significantly to the deployment of such infrastructure, 
with over 1.36 million public and private charging stations funded by the RRF across the EU, including  
691 500 stations installed in Germany alone. For context, the total number of public charging stations 
available across the EU stood at 691 000 in the first quarter of 2024.27 

25  See  Heikkonen,  Listl,  and  Reuter  (2025)  “Mapping  the  impact  of  industrial  decline  on  European  regions”.  
DG GROW Economic Briefs. 

26    Based  on  EUROSTAT  data,  966,378  EVs  were  produced  in  Germany  in  2023  and  608,250  EVs  in  2022.  Based  on 
EUROSTAT and DESTATIS data, the share of German-made EVs within the EU is equal to 39.5% for 2023. 

27 Source: European Alternative Fuels Observatory. 

20 

 
 
 
 
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

6. 

 CONCLUSION 

This  work  largely  confirms,  reinforces  and  refines  previous  estimates  of  macro-economic  impacts  of  
the  RRF  carried  out  by  European  Commission  services,  which  provided  first  orders  of  magnitude  and 
underlined the importance of spillover effects. The RRF is having a significant impact across the EU and 
all Member States stand to benefit, to an extent which may go well beyond their national envelopes. 

Our simulations indicate that the German economy is the largest beneficiary of spillovers within the EU, 
with  more  than  two-thirds  of  its  potential  medium-term  GDP  gains  driven  by  the  implementation  of  
the  RRF  in  other  EU  Member  States.  The  German  sectors  that  benefit  the  most  from  RRF-funded 
investments across the EU are the manufacturing sector, including the automotive, machinery, electrical 
and electronic equipment industries, as well as the construction and IT sectors. 

The  approach  developed  here  provides  new  data,  findings  and  techniques  that  open  up  new  avenues  
to characterise and visualise the RRF’s economic significance. It can pave the way for further, more detailed 
and sophisticated research, whether for the RRF as a whole, for individual countries or at a sectoral level. 
Building on this work, several further applications could be envisaged. 

First, we could dive deeper into the various Member States and propose more in-depth sectoral analysis. 
We could also take a thematic approach across countries and focus on specific industries, as was briefly 
done above for the automotive sector. Such analyses could provide a more detailed understanding of how 
RRF-funded  investments  shape  sectoral  dynamics  across  economies,  offering  deeper  insights  into  the 
spillover effects within the Single Market and the value chain linkages between Member States. 

Second, a common challenge in impact assessments has been and remains the evaluation of reforms.  
By  using  this  sectoral  database  and  linking  sectoral  data  on  reforms  with  macro-economic  modelling,  
new research could offer valuable insights into the impacts of RRF-funded reforms. 

Third,  a  key  feature  of  the  RRF  is  the  interplay  between  reform  and  investment  measures,  and  
the  potential  benefits  arising  from  their  combined  implementation.  Leveraging  the  sectoral  database,  
a  deeper  analysis  could  be  conducted  to  assess  synergies,  such  as  the  role  of  reforms  in  unlocking 
investments, with a focus on their combined structural impacts. Looking for instance at the energy sector, 
it  should  be  possible  to  estimate  how  the  126  reforms  targeting  the  energy  sector  have  facilitated  
the large amount of RRF investments in the sector, and how this interaction has contributed to transform 
the EU energy market. At a macro level, we could discuss how the RRF has helped not only to preserve  
the EU Single Market in  the aftermath of COVID-19, but also the extent to which  it has contributed to  
its transformation and deepening through the development of (new) value chains. 

Fourth, subject to legal and technological feasibility, further research leveraging AI tools could support  
the development of firm-level databases, compiling data on companies that have received RRF funding, 
including the amounts disbursed. More than 200,000 documents have already been submitted as part of 
RRF payment requests. Extracting relevant information from these documents could enable more detailed 
analysis of the characteristics of beneficiary firms, such as their geographic location and whether they are 
primarily  large  exporting  champions,  small  and  medium-sized  enterprises,  or  start-ups.  Moreover,  
this  could  enable  the  study  of  the  RRF’s  impacts  on  key  firm-level  indicators,  including  profitability, 
research and development, and employment. This line of research would complement macro- and sectoral 
analyses, by offering a more granular view of impacts on the ground. 

Fifth, when estimating impacts via the FIDELIO model, this paper assumes full implementation of the 27 
national plans, by way of benchmark. The sectoral database can easily be updated in case of changes to 
the  plans,  and  impact  estimates  can  be  adjusted  accordingly.  Alternative  absorption  scenarios  will  be 
explored in a forthcoming companion paper by the Joint Research Centre. 

Sixth, these techniques can serve to document the impacts of the RRF and improve transparency about 
results, for instance by estimating concrete achievements and deliverables (as was done briefly above 
for the case of electric vehicles), or by providing further information on beneficiaries at firm level. 

Finally, this work benfitted from the swift pace of IT and AI developments. Some further lessons could be 
drawn for the design of future data collection frameworks related to policy instruments combining reforms 
and investments, as a way to support evidence-based policy-making. 

21 

 
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

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European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

ANNEX I: CLASSIFICATION METHODOLOGY 

We have developed a new and unique database that classifies all 4,687 measures and submeasures in 
the  national  Recovery  and  Resilience  Plans  into  241  industries,  in  line  with  Eurostat’s  standard  NACE 
classifications. This classification was performed by using the European Commission’s internal AI tools. 
Specifically, the AI model was prompted with the textual descriptions of the RRF measures contained in 
the annexes to the Council Implementing Decisions and generated corresponding NACE codes. As the model 
had  been  previously  trained  to  recognise  NACE  classifications,  no  additional  information  on  the  NACE 
coding system was required and the prompt used was very simple: “Classify the following measure by 
NACE  code  providing  a  primary  and  a  secondary  code.  Take  into  account  the  measure  name  and  the 
measure description”. The AI-generated classifications were then subjected to three rounds of robustness 
checks by Commission staff to ensure accuracy and consistency. Reform and investment measures were 
classified separately. 

For investments, our classification follows a “commodity” approach rather than an “investment” approach. 
This means that each measure is categorised according to the sector where it generates demand, rather 
than the sector that oversees the measure or ultimately benefits from the investment. For example: 

•  An investment of EUR 10 million under the RRP of Country Y, aimed at developing a web 
portal  for  the  national  administration,  is  classified  under  computer  programming, 
consultancy,  and  related  activities  sector  (NACE  code  62)  rather  than  public 
administration and defence sector (NACE code 84). 

This approach maximises the use of available information by directly linking investments to the sectors 
where they create demand. By contrast, if we were to classify measures based on the overseeing sector, 
for  instance  the  ‘public  administration  and  defence  sector’  in  this  example,  we  would  need  to  rely  on 
generalised  allocation  rules  for  investments  of  the  overseeing  sector.  These  rules  estimate  how 
investments (in IT equipment, software, construction, etc.) are typically distributed within this sector, based 
on historical data. However, this approach often results in less precise information about which parts of 
the economy are directly impacted. By instead classifying each measure according to the specific goods 
or  services  it  finances,  we  can  more  accurately  identify  the  sectors  where  demand  will  increase.  
This method provides a clearer and more targeted picture of how RRF investments affect different areas 
of the economy, ensuring a more reliable assessment of their economic impact. 

An  additional  challenge  in  classifying  the  measures  arises  when  a  single  measure  is  linked  to  multiple 
sectors. To address this, we have created two classification categories: primary NACE code and secondary 
NACE code. The most significant expenditure within a measure is assigned as the primary NACE code, while 
smaller expenditures are assigned as secondary NACE codes. For example: 

If country X’s RRP invests EUR 200 million in Measure M, which includes €150 million for constructing 
a new railway network and EUR 50 million for acquiring two new trains, the classification must reflect 
these distinct components. In this case: 

• 

• 

The construction of the railway network would be classified under construction of railways 
(NACE code 42.12). 
The  acquisition  of  new  trains  would  be  classified  under  manufacture  of  railway 
locomotives and rolling stock (NACE code 30.2). 

24 

 
 
 
 
 
 
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

The classification of investments was limited to a maximum of two sectors since very few investment 
measures  generate  demand  across  more  than  two  sectors.  A  majority  (54%)  is  found  to  impact  only  
one sector.  

For  reforms,  our  classification  follows  a  similar  but  different  approach.  Each  reform  (sub)measure  is 
categorised based on the sector targeted by the reform.  For example:  

• 

If  the  objective  of  reform  T  in  the  RRP  of  country  X  is  the  reduction  of  bureaucracy  by 
eliminating double and multiple reporting, we classify this measure under regulation and 
contribution to more efficient operation of business (NACE code 84.13). 

•  Similarly, if the aim of reform J is to increase the collection rates for beverage packaging,  
we will classify this measure under recovery of sorted materials (NACE code 38.32). 

If a reform measure is linked to multiple sectors, the primary NACE code is assigned to the sector primarily 
targeted  by  the  reform.  Sectors  more  indirectly  targeted  are  assigned  to  the  secondary  NACE  code 
category. For example: 

If the objective of reform E is the expansion of renewable energy in electricity supply this measure 
could be classified under either: 

•  Electricity power generation, transmission and distribution (NACE code 35.1), or  
•  Manufacture  of  machinery  and  equipment  (NACE code  28),  as  it will  also impact  the 

production of solar panels and wind turbines.  

In such cases, we allocate the primary NACE code to the industry which is the primary target of the 
reform (Electricity power generation, transmission and distribution) and the secondary NACE 
code to the industry which is more indirectly targeted by the reform Manufacture of machinery 
and equipment (NACE code 28). 

The  classification  of  reforms  has  also  been  limited  to  a  maximum  of  two  sectors  per  measure,  since  
no reform measure was found to target more than two economic sectors. A majority of measures (63%) 
is found to target only one economic sector.  

To ensure clarity and analytical focus, the description of the database above, as well as Figures 2, 3, 4 and 
5, rely exclusively on the primary NACE codes. Likewise, the subsequent analysis only uses the primary 
NACE code category for now. Secondary NACE codes will be considered, for example, in a companion paper 
by the JRC, which will include robustness checks based on alternative weighting schemes for primary and 
secondary classifications in the estimations. 

For visualisation purposes, some NACE categories have been regrouped in Figures 2 and 3, and separately 
in Figures 4 and 5. The details of these groupings are provided below. 

25 

 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

Overall distribution of reforms by key policy domains (Figures 2 and 3) 

Broader policy domains  
(Figure 3) 

Green 

Business 

Institutions 

Health & social 

Skills & labour 

Policy domains 
(Figure 2) 

NACE codes 

35 
29, 49, 50, 51, 52 
41, 42, 43 
36, 38, 39 
1, 2, 3 
5, 6, 9, 91.04 
61, 62, 63 

Energy  
Transport 
Modernisation of buildings 
Water supply & waste 
Agriculture  
Other (green) 
Digital & IT sector 
Simplification of regulation (cross-cutting)  84 (sub-categories) 
Research & Development 
Professional and technical activities  
Financial markets  
Other (business) 

Public administration 
Justice, anti-corruption and anti-fraud 
Taxation 
Public spending reviews 
Implementation of the plan 
Public procurement 
Other (institutions) 
Health  
Social security (e.g. pensions) 
Social (incl. long term and child care) 
Culture 
Social Housing 
Other (health & social) 
Education 
Labour market 

72, 77 
69, 70, 71, 74 
64, 65, 66 
10, 16, 20, 21, 25, 26, 28, 47, 
55, 79 
102 
84 (sub-categories) 
84 (sub-categories) 
84 (sub-categories) 
84 (sub-categories) 
84 (sub-categories) 
84.21 
86 
84.3 
87, 88 
59, 60, 90, 91, 93 
68 
2, 84.2, 84.25 
85 
84 (sub-categories) 

Note: NACE code 84 was split based on the CID description to match the categories above.  

Overall distribution of investment measures by key sectors (Figures 4 and 5) 

Key sectors 

NACE codes 

Renovation of buildings  
Construction in electricity & telecommunications sectors 
Construction of railways 
Civil engineering projects (incl. flood protection) 
Green energy projects for alternative fuels 
Transport vehicles 
Electrical equipment (incl. charging stations) 
Machinery & equipment 
Digitalisaion & IT software 
Electronic products (incl. laptops) 
Education & training 
Research & development 

41, 43* 
42.22 
42.12 
42.1, 42.11, 42.13, 42.9, 42.91, 42.99 
42.21 
29, 30 
27 
28 
62, 63 
26 
85 
72 

Note: Renovation of buildings (codes: 41, 43) were further split into renovation of residential housing and non-residential 
buildings (incl. businesses, public administration, hospitals, schools) based on the CID description of each measure.  

26 

 
 
 
 
 
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

ANNEX II: THE FIDELIO MODEL 

The  FIDELIO  model  is  a  multi-country,  multi-sector  dynamic  econometric  general  equilibrium  model, 
developed  by  the  Joint  Research  Centre  of  the  European  Commission  for  policy  impact  assessment, 
providing industrial-, country-, and time-specific simulations (Rocchi et al., 2025, Rocchi et al., 2019).  

The  model  is  based  on  official  Eurostat’s  statistics  from  the  FIGARO  database,  covering  64  industries 
(NACE  Rev.  2)  and  45  countries  (EU-27  Member  States,  its  main  18  non-EU  trading  partners,  and  an 
aggregate region representing the rest of the world (Remond-Tiedrez and Rueda-Cantuche, 2019)). The 
and other data come from Eurostat and the OECD official statistics. 

The FIDELIO model has been used to support policy analysis related to topics such as the economic impact 
of state aid on the green transition and the effects on the EU Single Market (Rueda-Cantuche, et al., 2024), 
for  calculating  the  rebound  effects  of  food  waste  prevention  (Albizzati  et  al.,  2022),  to  analyse  the 
employment  and  value  added  impacts  of  the  stricter  energy  efficiency  requirements  for  dishwashers, 
washing  machines,  and  washer  dryers  (Rocchi  et  al.,  2019),  to  evaluate  the  potential  macro-economic 
benefits  of  eliminating  the  regulatory  restrictiveness  in  business  services  in  the  European  countries 
(Barbero et al., 2022), and to estimate the macro-economic impact of US trade tariff increases on steel 
and aluminium (Salotti et al., 2019).  

Modular framework 

FIDELIO is primarily a demand-driven28 input-output based model, which is structured into four modules 
that can be activated and deactivated as desired depending on the case study. This modular structure 
enhances the ability to decompose model outcomes, with greater or lesser degree of sophistication, thus 
improving transparency and facilitating the interpretation of simultaneous economic impacts within a CGE 
framework across industries and countries, as described below. 

Module 1: Standard input-output module (direct and spillover impacts) 

This module is the core of the FIDELIO model and it is used for standard input-output analysis. It applies 
the Leontief model, which assumes exogenous components of final use and production technology. The 
model  captures  inter-industry  spillovers  impacts  from  intermediate  consumption  and  allows  for  the 
tracking  of  economic  interdependencies  among  industries,  both  domestically  and  across  different 
countries. The results of this module focus on the direct and spillover impacts of changes in the final use 
of products. 

Module 2: Investment input-output module (investment multiplier impacts) 

The  investment  input-output  module  goes  beyond  the  basic  input-output  analysis  by  considering  the 
impacts of investments. This module endogenises investors and activates capital accumulation. This allows 
analysts  to  separate  investment  impacts  from  inter-industry  spillovers  impacts,  making  it  particularly 
relevant for policy studies that aim to influence investment decisions.  

Module 3: National accounting matrix module (distributional impacts) 

This module includes the data displayed in a national accounting matrix (also sometimes known as a social 
accounting matrix), which endogenises final use (households and governments) and income distribution. 
This module captures the behaviour of all economic agents, enabling the model to calculate the income 
distributional  impacts  of  EU  policies.  It  enriches  the  range  of  analyses  that  FIDELIO  can  support  by 
addressing redistributive impacts. 

28 The model can also model supply shocks, such as the evaluation of the impacts of state aid in specific industries (Rueda-
Cantuche et al., 2024) or the impacts derived from green and digital transitions (e.g. greener technologies). 

27 

 
 
 
 
  
 
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

Module 4: Full econometric and price module (substitution and rebound impacts) 

The full model includes all econometric components, the dynamic29 nature of the model and endogenous 
prices. It introduces substitution and rebound impacts between different factor inputs, goods, and between 
imported and domestically produced goods. Although this module provides the most realistic scenarios, it 
involves  complex  interactions  and  makes  the  results  more  challenging  to  interpret.  Nonetheless,  the 
dissociation of price effects from other effects is possible by comparing it with previous modules, a unique 
feature of the FIDELIO model. This is the module used in the analysis of the economic impacts of RRF 
funds. 

The  four  modules  can  be  activated  or  deactivated,  enabling  a  gradual  transition  from  a  standard 
multiregional  input-output  model30  to  a  full  econometric  input-output  model31,  passing  through  a  CGE 
models.  32  This  approach  effectively  combines  econometric  techniques  with  two  widely  used  impact 
assessment models, i.e. input-output models (IO) and computable general equilibrium (CGE) models, which 
extend  IO  analysis  by  incorporating  additional  economic  agents  (e.g.  institutional  sectors:  households, 
government, etc.), secondary income accounts and price adjustment mechanisms in the short and long run 
via econometric methods (Koks et al., 2016).  

Model structure 

FIDELIO is structured in four different blocks: production (firms); trade; government, households and prices. 

Production block   

The production block is defined by firms that adjust their production to meet the total demand for domestic 
products.  This  demand  includes  several  components:  intermediate  inputs,  investment,  changes  in 
inventories, household consumption, government consumption, and foreign demand. Firms use production 
functions  with  nested  constant  elasticities  of  substitution  (CES)  to  model  substitution  among  different 
inputs, such as energy and materials. This block is crucial for understanding how firms optimise production 
and respond to both domestic and international market conditions. 

Trade 

The trade block endogenises exports, which are considered as the mirror flow of imports as shown by the 
FIGARO database. Bilateral export transactions are calculated based on bilateral imports through a two-
step process. First, the model determines the share of imports in the total demand for each commodity. It 
then allocates imports to the country of origin. Armington elasticities guide import substitution between 
domestic and imported goods, considering differences due to consumer preferences or historical positions. 
This aspect allows FIDELIO to analyse trade dependencies and flows between countries. 

29 The dynamic nature of the FIDELIO model solves recursively baseline and simulation runs over time. The model's dynamics 
is  partially  driven  by  the  evolution  of  variables  such  as  population  growth  and  capital  accumulation.  Furthermore,  the 
FIDELIO model integrates neo-Keynesian principles to enhance its representation of economic behaviour. 

30  A  standard  multi-country  input-output  model  captures  interdependencies  among  different  countries and  industries  by 
tracking the flow of goods and services across economies. The model is static and it is based on national accounts data and 
international trade statistics. Prices do not react to supply and demand shocks and therefore, IO models tend to be used for 
short-run impacts. 

31  The  integration  of  econometric  techniques  in  input-output  models  and  CGE  models  allow  them  to  have  dynamic 
behavioural responses, price adjustment mechanisms and structural changes over time, making the models more flexible in 
capturing economic fluctuations and policy impacts. 

32  Computable  general  equilibrium  (CGE)  models  assume  that  agents  (households,  firms,  governments)  make  decisions 
based on optimising behaviour, leading to more complex interactions. CGE models make price formation a central aspect of 
the analysis while input-output models do not. 

28 

 
 
 
 
 
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

Government 

In  the  government  block,  the  government  generates  revenue  from  multiple  sources:  operating  surplus, 
taxes on production, taxes on products, social security contributions, and household income taxes. These 
revenues  finance  government  expenditure,  which  includes  interest,  capital  formation,  transfers  to 
households, and government consumption. The model calculates the budget balance, measured by the 
difference between revenue and expenditure, influencing public debt levels. By capturing governmental 
activities, this block describes how policy decisions affect and are influenced by fiscal health. 

Households 

Households  receive  income  through  wages,  operating  surplus  shares,  property  income,  and  transfers 
(governmental and non-governmental), after deducting taxes and social security contributions. Disposable 
income  is  then  divided  between  consumption  and  savings,  with  the  model  considering  factors  such  as 
unemployment  rates,  interest  rates,  and  debt-to-GDP  ratios  that  affect  the  households’  propensity  to 
consume or save.  

Once  the  total  level  of  household  consumption  is  determined,  the  FIDELIO  model  describes  how  this 
expenditure  is  optimally  allocated  across  different  commodities  to  maximise  utility  under  an  income 
constraint.  The  allocation  of  consumption  across  commodities  can  be  implemented  using  two33 
approaches, i.e. either a linear expenditure system (LES) or an almost ideal demand system (AIDS). 

Prices 

The  price  block  in  the  FIDELIO  model  serves  to  incorporate  general  equilibrium  effects  into  the  system  by 
representing a comprehensive set of prices, divided into two categories: prices constructed through accounting 
equations (accountancy prices) and behavioural prices (production prices, interest rates and wages).  

Accountancy prices are derived from accounting relationships and include the consumer price index (HCP), which 
is a weighted average of commodity prices. Besides, the purchaser’s price of a commodity is calculated as a 
mix of the basic price, transportation, trade margins, and taxes minus subsidies. The accountancy prices are 
derived from straightforward accounting identities, not influenced by economic behaviours.  

In contrast, behavioural prices, such as the basic price, are derived from rational economic behaviour and 
are crucial to determine, for instance, one of the components of the purchaser’s prices (accountancy price). 
There are three main behavioural prices in FIDELIO: production prices, interest rates and wages.  

•  Production  prices  are  set  by  producers  aiming  to  maximise  profits  in  an  oligopolistic  market 
structure  (à  la  Cournot).  They  consider  competitors' prices  and  assume a  negative  relationship 
between price and demand, determining optimum prices as a mark-up over marginal costs.  

• 

Interest rates are modelled following the Taylor rule, which emphasises the role of the central 
bank in setting interest rates based on inflation and unemployment rather than simply aligning 
savings  and  investment.  This  reflects  a  more  realistic  depiction  of  modern  economies,  where 
central banks have a significant impact on the supply of credit beyond immediate savings, thereby 
maintaining monetary equilibrium. The cost of capital is defined similarly across industries, using 
a standard specification of the user cost of capital, which includes depreciation and interest rates.  

•  Wages can be represented using either the Phillips curve or the Wage Setting (WS) curve, each 
impacting the equilibrium unemployment rate differently. The model allows for nominal wages to 
depend on expected consumption prices, labour productivity, and unemployment rates, as per a 
chosen curve specification. The Phillips curve does not necessitate unit wage indexation on prices 
and productivity, contrary to the WS curve requirements for long-term steady states. 

33 Under a LES, household consumption of a commodity is modelled by taking into account a fixed minimum purchase level 
for necessities, distinguishing between essential and luxury goods. Under an AIDS model (Mongelli et al., 2010), FIDELIO 
offers more flexibility by allowing consumption shares to be influenced by both relative prices and income levels. The AIDS 
model  calculates  consumption  shares  as  a  function  of  price  and  income,  thus  allowing  for  a  wider  range  of  consumer 
behaviours. This model is econometrically estimated. 

29 

 
 
 
 
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

ANNEX III: MAIN MODELLING ASSUMPTIONS  

In  our  analysis, we  simulated  the  impact  of  a  demand-driven  investment34  shock  of  EUR 655.5  billion, 
comprising  EUR  364.5 billion  in  non-repayable  grants35 and  EUR  291  billion in  loans,  financed  through  
the RRF over the 2020-2026 period. Since loans and grants may exhibit distinct macro-economic impacts, 
they  have  been  modelled  differently,  as  suggested by Bankowski et  al.  (2022)  and  Canova  and  Pappa 
(2025).  Grants  are  treated  as  non-debt-creating  transfers  for  recipient  Member  States,  whereas  loans  
are  modelled  as  repayable  over  a  period  of  up  to  30  years  at  a  fixed  annual  interest  rate  of  3.15  %,  
in line with the ECB's main refinancing operations rate.  

For analytical purposes, and in line with Pfeiffer et al. (2023), and Barbero et al. (2024), we assume that 
RRF grants are financed through long-term EU debt. The repayment of this debt is covered by Member 
States’ contribution to the EU budget, which are, in turn, funded via lump sum taxes.  Repayments are 
scheduled to begin at the end of the current multiannual financial framework in 2027 and and will continue 
on a linear path until 2058. Under this approach, households bear the cost of the grants, which reduces 
their  disposable  income  and  negatively  affects  their  consumption  and  savings  behaviour,  partially 
offsetting the RRF positive economic effects. In contrast, loans, while also injecting funds into the economy, 
are modelled to reflect their impact on public debt. Since they require repayment, loans raise future fiscal 
obligations, potentially creating budgetary constraints that may limit their long-term economic stimulus 
effect to a greater extent than grants..36 

By introducing RRF funds as an exogenous investment shock, the model allows us to trace the transmission 
of  economic  impacts  across  sectors  and  countries,  including  productivity  gains.  These  extra  gains  are 
derived from those RRF funds that allow developing and/or upgrading new technologies, products, and 
services,  thereby  contributing  to  long  term  economic  growth  across  the  EU  (Pfeiffer  et  al.  (2023), 
Bankowski et al. (2024). In FIDELIO, productivity gains come from the extra value added generated by the 
new RRF funds on R&D investments. This effect is econometrically estimated using detailed information 
by country and industry on the R&D investments and their subsequent extra value added impacts. 37 

Finally, we assume full implementation of the 27 national plans, as of mid-January 2025. The database 
already accounts for past amendments and updates of plans prior to that date and it can be updated to 
reflect possible future changes to plans. A forthcoming companion paper by the Joint Research Centre will 
discuss alternative absorption scenarios. 

34 According to the system of national accounts, investment grants consist of capital transfers made by governments or 
international  organisations  to  other  institutional  units  to finance  all  or  part  of  the  costs  of  their  acquiring  non-financial 
assets (par. 11.247, 2025 System of National Accounts, UN). 

35 The RRF includes EUR 359 billion in grants, as well as EUR 5.5 billion in nationally financed oversubscription, which is 
treated as grant-equivalent for estimation purposes. 

36 The economic impact of RRF grants and loans is expected to differ significantly, not only because of their distinct financing 
structures, but also due to the different purposes they serve. The FIDELIO simulation framework captures these differences 
by using detailed expenditure data disaggregated by sector for both grants and loans. This granularity allows for a more 
refined and accurate assessment of their respective impacts, avoiding reliance on broad or simplifying assumptions. 

37 The productivity effects are based on the estimates by Pedauga et al. (2025), who, similarly to Moncada-Paternò-Castello 
et al. (2020), use historical data from the Community Innovation Survey to estimate returns on R&D investments. Expected 
sectoral  R&D  intensity  is  derived  from  the  GERD  database,  while  the  econometric  methodology  follows  established 
approaches,  including  those  of  Papke  and  Wooldridge  (2008)  and  Delgado  et  al.  (2018).  The  resulting  sector-specific 
elasticities  vary  significantly,  with  for  example,  higher  values  for  high-tech  sectors  (0.161)  and  lower  ones  for  the 
construction sectors (0.0083). When combined with the actual sectoral distribution of RRF investments, the average elasticity 
is 0.028 which is close to the low productivity scenario elasticity of Pfeiffer et al. (2023). 

30 

 
 
 
 
 
 
 
 
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

ANNEX IV: FIDELIO NACE CODES  

For the economic analysis based on the FIDELIO model, we have merged the 241 NACE categories into  
the following 64 categories: 

NACE A*64 

NACE A*64 Name 

A01 

A02 

A03 

B 

Crop and animal production, hunting and related service activities 

Forestry and logging 

Fishing and aquaculture 

Mining and quarrying 

C10T12 

Manufacture of food products, beverages and tobacco products  

C13T15 

Manufacture of textiles, wearing apparel and leather products 

C16 

C17 

C18 

C19 

C20 

C21 

C22 

C23 

C24 

C25 

C26 

C27 

C28 

C29 

C30 

Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials 

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 

C31_32 

Manufacture of furniture; other manufacturing 

C33 

D35 

E36 

Repair and installation of machinery and equipment 

Electricity, gas, steam and air conditioning supply 

Water collection, treatment and supply 

E37T39 

Sewerage; waste collection, treatment and disposal activities; materials recovery; remediation activities and other waste management 
services  

F 

G45 

G46 

G47 

Construction 

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 

31 

 
 
 
 
 
European Economy Discussion Paper Economic Impacts of the Recovery and Resilience Facility 

H49 

H50 

H51 

H52 

H53 

I 

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 

J58 

Publishing activities 

J59_60 

Motion  picture,  video  and  television  programme  production,  sound  recording  and  music  publishing  activities;  programming  and 
broadcasting  

J61 

Telecommunications 

J62_63 

Computer programming, consultancy and related activities; information service activities 

K64 

K65 

K66 

L 

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 

M69_70 

Legal and accounting activities; activities of head offices; management consultancy activities 

M71 

M72 

M73 

Architectural and engineering activities; technical testing and analysis 

Scientific research and development 

Advertising and market research 

M74_75 

Other professional, scientific and technical activities; veterinary activities 

N77 

N78 

N79 

Rental and leasing activities 

Employment activities 

Travel agency, tour operator reservation service and related activities 

N80T82 

Security  and  investigation  activities;  services  to  buildings  and  landscape  activities;  office  administrative,  office  support  and  other 
business support activities 

O84 

P85 

Q86 

Public administration and defence; compulsory social security 

Education 

Human health activities 

Q87_88 

Social work activities 

R90T92 

Creative, arts and entertainment activities; libraries, archives, museums and other cultural activities; gambling and betting activities 

R93 

S94 

S95 

S96 

T 

U 

Sports activities and amusement and recreation activities 

Activities of membership organisations 

Repair of computers and personal and household goods 

Other personal service activities 

Activities of households as employers; undifferentiated goods- and services-producing activities of households for own use 

Activities of extraterritorial organisations and bodies 

32 

 
 
 
 
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