The updated RHOMOLO impact assessment of the 2014-2020 European cohesion policy (including REACT-EU).
Date: 2025-07-16
Institutions: Directorate-General for Regional and Urban Policy, García Rodríguez, Abián
Subjects: EU regional policy, distribution of EU funding, economic and social cohesion, impact study, regional development, regional disparity, socioeconomic conditions

ISSN 2529-3303

The updated RHOMOLO 
impact assessment  
of the 2014-2020 
European cohesion policy  
(Including REACT-EU)
Pablo Casas,  
Tryfonas Christou,  
Abián García Rodríguez, 
Tillmann Heidelk,  
Nicholas-Joseph Lazarou, 
Philippe Monfort, 
Simone Salotti

Working Papers

A series of short papers on regional
research and indicators produced
by the Directorate-General for
Regional and Urban Policy

WP 01/2025

Regional and 
Urban Policy

Manuscript completed in December 2024

1st edition

Any question, comment or contribution should be sent to the following address: REGIO-PAPERS@ec.europa.eu

Editor: Jorge Duran Laguna, European Commission, Regional policy

This document should not be considered as representative of the European Commission’s official position. 

Luxembourg: Publications Office of the European Union, 2025

© European Union, 2025

The Commission’s reuse policy is implemented under Commission Decision 2011/833/EU of 12 December 2011 on 
the reuse of Commission documents (OJ L 330, 14.12.2011, p. 39, ELI: http://data.europa.eu/eli/dec/2011/833/oj). 

Unless otherwise noted, the reuse of this document is authorised under the Creative Commons Attribution 4.0 
International (CC BY 4.0) licence (https://creativecommons.org/licenses/by/4.0/). This means that reuse is allowed, 
provided appropriate credit is given and any changes are indicated.

For any use or reproduction of elements that are not owned by the European Union, permission may need to be 
sought directly from the respective rightholders.

Cover image: © Adobe Stock / tonefotografia #351180928

PDF

ISBN 978-92-68-23326-9 ISSN 2529-3303 doi:10.2776/6178022 KN-01-24-028-EN-N

The updated RHOMOLO 
impact assessment of 
the 2014–2020 European 
cohesion policy  
(Including REACT-EU)

Pablo Casas (1), Tryfonas Christou (1), 

Abián García Rodríguez (1), Tillmann Heidelk (2), 

Nicholas-Joseph Lazarou (1), Philippe Monfort (2) and 

Simone Salotti (1) 

(1)  European Commission, Joint Research Centre Seville
(2)  European Commission, Directorate-General for Regional and Urban Policy

2

ABSTRACT 

We analyse the macroeconomic impact of the European cohesion policy investments 
deployed during the 2014–2020 programming period on the basis of simulations 
carried out with the spatial dynamic general equilibrium model called RHOMOLO. We 
use the latest data on actual expenditures including the EUR 50 billion falling under 
the  recovery  assistance  for  cohesion  and  the  territories  of  Europe  (REACT-EU) 
programme extending the response to the COVID-19 crisis. We quantify the direct and 
indirect potential effects of the policy in the NUTS 2 regions of the European Union 
within a 20-year time frame. The results suggest that the impact of the policy is 
sizeable, especially in the less developed regions. Accordingly, regional disparities are 
shown  to  decrease  due  to  the  policy  intervention.  The  investments  have  also  a 
positive impact on EU GDP, which is almost 0.6 % higher in 2023 compared to a 
scenario without cohesion policy.

Keywords: REACT-EU, cohesion policy, regional growth, regional development, general 
equilibrium modelling. 

JEL codes: C68, R13.

Acknowledgments: We thank Javier Barbero, Martin Christensen, Andrea Conte, 
Francesca Crucitti, Francesco Di Comite, Filippo Di Pietro, Jorge Díaz-Lanchas, Olga 
Diukanova, Patrizio Lecca, Giovanni Mandras, Damiaan Persyn, Stylianos Sakkas and 
Yevgeniya Shevtsova for their valuable comments. All remaining errors are our own.

3

ABBREVIATIONS

CF 

CGE 

DG 

Cohesion Fund

computable general equilibrium

Directorate-General

ERDF 

European Regional Development Fund

ESF 

ESIF 

GDP 

GIMF 

GMR 

JRC 

LD 

MD 

European Social Fund

European structural and investment funds

gross domestic product

global integrated monetary and fiscal model

geographic macro regional

Joint Research Centre

less developed region

more developed region

NUTS 

nomenclature of territorial units for statistics

REACT-EU 

recovery assistance for cohesion and the territories of Europe

TFP 

TR 

YEI 

total factor productivity

transition region

Youth Employment Initiative

THE UPDATED RHOMOLO IMPACT ASSESSMENT OF THE 2014–2020 EUROPEAN COHESION POLICY (INCLUDING REACT-EU)4

TABLE OF CONTENTS

Abbreviations 

1. 

2. 

Introduction 

The literature on the impact of cohesion policy on growth and convergence 

3.  Cohesion policy expenditure 2014–2020 

3.1.  Distribution of resources across funds and over time 

3.2.  Distribution of resources across geographical areas and fields of intervention 

4. 

The RHOMOLO model setup and the simulation strategy 

4.1.  Model description  

4.2.  Simulation strategy 

5.  Macroeconomic impact of the 2014–2020 cohesion policy 

5.1.  Impact at the EU level of the 2014–2020 cohesion policy 

5.2.  National and regional impact of the 2014–2020 cohesion policy 

6.  Conclusions 

References 

Annexes 

3

5

6

8

8

10

15

15

15

17

17

18

25

26

32

5

1. 

INTRODUCTION

The main objective of the European cohesion policy is to reduce 
economic  disparities  between  European  regions,  thereby 
promoting development and growth, with a particular focus on 
the less developed areas of the European Union (EU). Cohesion 
policy  is  the  main  investment  policy  of  the  EU  and  the 
evaluation  of  its  economic  impact  on  Member  States  and 
regions has been the subject of a lively academic and policy 
debate (see, among others, von Ehrlich and Overman, 2020; 
Iammarino et al., 2019).

Article 57 of the 2014–2020 common provision regulation (1) 
requires the European Commission to carry out an  ex post 
evaluation of the European Structural and Investment Funds 
(ESIF). The macroeconomic impact assessment of the policy 
presented in this working paper complements the qualitative 
and quantitative work carried out in this context. It updates the 
analysis presented by Crucitti et al. (2022) based on planned 
expenditure  for  2014–2020  by  using  data  on  the  actual 
cohesion policy expenditure that took place between 2014 and 
2023. In addition, it includes the almost EUR 50 billion added in 
the  context  of  the  emergency  response  to  the  social  and 
economic consequences of the COVID-19 pandemic under the 
recovery assistance for cohesion and the territories of Europe 
(REACT-EU (2) programme.

In this paper, we use RHOMOLO, a spatial dynamic computable 
general equilibrium (CGE) developed and maintained by the 
Joint Research Centre (JRC) of the European Commission in 
cooperation  with  the  Directorate-General  for  Regional  and 
Urban  Policy,  to  analyse  the  impact  of  the  policy  on  key 
macroeconomic variables such as the level of economic activity 
or  employment  but  also  on  how  it  affected  the  extent  of 
regional disparities within the EU and the Member States.

Assessing the impact of cohesion policy at macroeconomic level 
is challenging. The interventions financed by cohesion policy 
have both direct and indirect effects on economies, which can 
be difficult to estimate. For instance, programmes implemented 
in  the  main  beneficiary  countries  and  regions  boost  local 
demand, which is partly served by exports from other countries, 
notably the more developed Member States, which therefore 
indirectly benefit from these programmes (see Crucitti et al., 
2023). Cohesion policy also entails long-term effects, which are 
likely to last well after the termination of the programmes and 
which must be accounted for to fully capture the impact of the 
policy. Finally, cohesion policy needs to be financed and the cost 
of the policy should be taken into account.

All these aspects can be addressed in a consistent manner by a 
spatial dynamic CGE model like RHOMOLO to gauge the net 
effect of the policy when it affects the allocation of resources 
across the EU economies. The economic foundations of the 
model are based on the well-established literature on general 
equilibrium models. The model itself is presented in numerous 
articles  contributing  to  this  literature  (see,  among  others, 
Barbero et al., 2024a; Crucitti et al., 2024; Gianelle et al., 2024; 
and Crucitti et al., 2023). The version of the model used here is 
calibrated with data for 2017 (3).

The model captures both demand- and supply-side effects and 
looks at the short- and long-term effects of the policy, taking 
into account the economic characteristics of the regions and the 
interactions between them through interregional trade, along 
with spillovers and mobility of production factors.

The analysis covers all expenditures channelled through the 
European Regional Development Fund (ERDF), the Cohesion 
Fund  (CF),  the  European  Social  Fund  (ESF),  the  Youth 
Employment  Initiative  (YEI),  and  REACT-EU.  The  model 
differentiates the transmission channels activated by different 
types  of  interventions,  such  as  support  to  research  and 
development  (R  &  D),  public  investment,  investments  in 
transport  infrastructure,  subsidies  to  private  investment, 
investments in human capital or labour market interventions. It 
also accounts for the cost of the policy by assuming it to be 
financed by taxes levied in the Member States and regions.

As with any analysis based on economic modelling techniques, 
the results presented here are sensitive to the assumptions 
made in setting up the model, notably that all the money is 
directed towards productive investments and projects. In reality, 
the impact of the policy critically depends on the precise nature 
of the projects that it finances, which partly stems from the 
quality of the institutions governing the implementation of the 
programmes. Project evaluations can provide evidence on such 
aspects but they cannot account for the many indirect effects 
of the interventions and hence inform on the likely net impact 
of the policy. Model simulations should therefore be seen as 
complementary  to  more  granular  and  micro-level  analysis, 
since each approach unveils different elements of how the 
policy produces its impact.

The  report  is  structured  as  follows.  Section  2  briefly 
summarises the literature on the evidence of the impact of 
cohesion policy on economic growth and convergence. Section 3 
describes the financial allocation of the policy for 2014–2020 
and how it is categorised for the RHOMOLO analysis. Section 4 
briefly describes the model. Section 5 presents the results of 
the simulations at both the EU and NUTS (nomenclature of 
territorial units for statistics) 2 levels. Section 6 concludes.

(1)  https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32013R1303.
(2)  REACT-EU extends the crisis-response and crisis-repair measures delivered through the coronavirus response investment initiative and the 
coronavirus response investment initiative plus and constitutes a bridge to the long-term recovery plan. See https://ec.europa.eu/region-
al_policy/funding/react-eu_en for additional details about REACT-EU.

(3)  See García Rodríguez et al. (2023) and Thissen et al. (2019) for details on the calibration process.

THE UPDATED RHOMOLO IMPACT ASSESSMENT OF THE 2014–2020 EUROPEAN COHESION POLICY (INCLUDING REACT-EU)6

2.  THE LITERATURE 

ON THE IMPACT OF 
COHESION POLICY 
ON GROWTH AND 
CONVERGENCE

The  impact  of  cohesion  policy  is  the  subject  of  extensive 
research  and  debate  among  academics  and  policymakers 
(Farole  et  al.,  2011).  Various  studies  have  examined  the 
effectiveness  of  cohesion  policy  in  reducing  economic 
disparities,  fostering  regional  development  and  promoting 
growth across the EU. In this section, we review the existing 
literature on the impact of cohesion policy on such variables, 
focusing on the methodologies, findings and key insights that 
have emerged from previous research. This literature review 
excludes qualitative studies (e.g. case studies) and focuses on 
quantitative methods, both econometric (dealing with ex post 
analyses)  and  modelling  (either  ex  ante   or  ex  post 
evaluations) (4).

One  strand  of  the  econometric  literature  is  based  on  the 
neoclassical  growth  framework  and  uses  econometric 
estimates of beta convergence models, following the method of 
Barro  et  al.  (1991).  Authors  using  this  approach  typically 
estimate cross-country or cross-regional growth regressions 
augmented with cohesion policy variables. This type of evidence 
is rather inconclusive as regards the impact of cohesion policy. 
Dall’Erba  and  Fang  (2017)  provide  a  meta-analysis  of  the 
impact of the ESIF on regional growth, using the results of 17 
different econometric studies published between 1996 and 
2013. The estimated policy impacts range from negative to 
positive, sometimes not significantly different from zero. One 
of the main findings of this meta-analysis is that a learning 
effect may be at work, as more recent estimates tend to find 
more positive results, suggesting that the implementation of 
cohesion policy has become more effective over time. Similarly, 
mixed results on the impact of cohesion policy on growth are 
highlighted by the surveys of Mohl (2016) and Pieńkowski and 
Berkowitz (2016), who also focus on econometric evidence 
based on neoclassical growth (5).

The growth regressions whose results are analysed by these 
authors  have  a  number  of  drawbacks,  mostly  related  to 
endogeneity, model uncertainty (including omitted variables), 

spatial correlation across geographical areas and the presence 
of  poor  quality  controls,  which  can  bias  the  results.  A  key 
problem is that the decision to invest in certain regions depends 
on the gross domestic product (GDP) levels and growth rates of 
the  regions  themselves,  which  by  design  makes  the  policy 
variable negatively correlated with the dependent variable of 
the growth regressions, thereby undermining the robustness of 
the results (Brasili et al., 2023; Berkowitz et al., 2020).

Other  approaches  which  allow  for  spatially  heterogeneous 
results (6), such as counterfactual analyses based on regression 
discontinuity design, have been used to assess the impact of 
cohesion policy on economic growth. Such studies exploit the 
existence of categories of beneficiaries for whom the policy 
injection  differs  significantly  between  regions  that  could 
otherwise be considered relatively similar or changes in aid 
intensity  from  one  programming  period  to  another.  The 
evidence  arising  from  this  literature  generally  indicates  a 
significant and positive impact of cohesion policy in this setting, 
albeit heterogeneously distributed across the EU territories 
(Crescenzi  and  Giua,  2020;  Cerqua  and  Pellegrini,  2018; 
Gagliardi  and  Percoco,  2017;  Ferrara  et  al.,  2017;  Percoco, 
2017;  European  Commission,  2016;  Pellegrini  et  al.,  2013; 
Becker et al. 2010).

Building on the literature on fiscal multipliers (Blanchard and 
Perotti, 2002), some authors have estimated the multipliers 
associated with the EU structural funds, with mixed results 
depending on the time horizon analysed, on the specific funds 
studied and on the geographical level of analysis (Canova and 
Pappa, 2024; Fiuratti et al., 2023; Durand and Espinoza, 2021; 
Coelho, 2019).

A significant part of the literature has convincingly argued that 
there may be key factors that condition the impact of cohesion 
policy on economic growth (Fratesi and Wishlade, 2017), such 
as the quality of institutions (Becker et al., 2013; Rodríguez-
Pose and Garcilazo, 2015; Aiello et al., 2019; Albanese et al., 
2021; Incaltarau et al., 2020; Mendez and Bachtler, 2024), 
fiscal  decentralisation  (Bähr,  2008),  openness  to  trade 
(Ederveen et al., 2006) or the presence of certain assets and 
endowments (Pinho et al., 2015).

Model simulations have also been used to analyse the impact 
of cohesion policy. In general, model-based simulations tend to 
support a significant impact of the policy on key economic 
variables, especially in the main beneficiaries, and on economic 
convergence. In this type of study, it is generally assumed that 
funds are spent efficiently on all projects, which may not be the 

(4)  A review of the literature examining the impact of cohesion policy on variables other than economic growth and regional convergence is 

beyond the scope of this paper. It is worth mentioning that there are studies on the impact of cohesion policy on firm growth (Bachtrögler 
et al., 2020) and on innovative start-ups (Santoleri and Russo, 2025), on the resilience of labour markets (Di Caro and Fratesi, 2023), on 
migration (Cerqua et al., 2022), on the trade-off between employment and productivity (Bourdin et al., 2024), on household inequality 
(Psycharis et al., 2024), and on political outcomes (Rodríguez-Pose and Dijkstra, 2021), among other things.

(5)  Giannini and Martini (2024) study regional convergence in the EU using a machine learning approach and find that cohesion policy has 

played a role in promoting it. They also challenge the existing evidence on regional convergence, thus contributing to the ongoing debate 
on the topic (see, among others, Cavallaro and Villani, 2021; Monfort, 2020; Bisciari et al., 2020; Monfort et al., 2013).

(6)  This can also be done with other methods, such as the geographically weighted regression approach used by Bourdin (2019) or heteroge-

neous coefficient modelling (Amendolagine et al., 2024; Di Caro and Fratesi, 2022).

7

case in all countries and regions. Moreover, the policy injection 
is sometimes measured using data on the ex ante allocation of 
funds across regions and intervention areas, which may differ 
from  the  actual  expenditure  resulting  from  programme 
implementation. Model simulations should therefore be seen as 
estimates of the potential impact of the policy if implemented 
as planned.

A number of papers analyse the impact of the policy at the EU 
or national level using different models such as GIMF (global 
integrated monetary and fiscal model) (Allard et al., 2008) or 
QUEST (Varga and in’t Veld, 2011a and 2011b; Monfort et al., 
2017). However, little work has been done using modelling 
frameworks to generate evidence at the regional level, which is 
mainly devoted to single-region or single-country analyses. For 
example, De la Fuente (2002) assesses the impact of policy on 
growth and convergence in Spanish regions using a supply-side 
model  estimated  with  regional  panel  data  over  a  30-year 
period. Sosvilla-Rivero et al. (2006) use the HERMIN model to 
analyse the impact of structural funds in the Spanish region of 
Castilla la Mancha, Fortuna et al. (2016) use a CGE model to 
analyse the impact of cohesion investments in the Portuguese 
Azores, Garau and Lecca (2015) use a CGE model to study the 
impact  of  R  &  D  structural  funds  in  the  Italian  region  of 
Sardinia, while Arcalean et al. (2012) calibrate a two-region 
endogenous growth model for Portugal.

Multiregional  general  equilibrium  models  also  have  the 
advantage of explicitly accounting for the spillovers generated 
by cohesion policy interventions (7). The RHOMOLO model is 

such  a  model  and  has  been  used  extensively  to  study  the 
impact of cohesion policy. Other multiregional models include 
the CGE model used by Korzhenevych and Bröcker (2020) to 
study the ESIF impact in Poland and the Baltic EU Member 
States, the model used by Mogila et al. (2022) to study the 
impact  of  cohesion  policy  on  intra-country  disparities  in 
Romania, Czechia and Poland (this is a version of the HERMIN 
model, see Bradley and Untiedt, 2009), the GMR (geographic 
macro regional) model used by Varga et al. (2020) to study the 
impact of smart specialisation in Hungary, and the general 
equilibrium model used by Blouri and von Ehrlich (2020) to 
study the impact of 2007–2013 cohesion policy investments.

The RHOMOLO model has been used to study the impact of 
cohesion policy programmes on all NUTS 2 regions in the EU 
over many programming periods. Di Comite et al. (2018) and 
Crucitti et al. (2022) focus on the 2007–2013 and 2014–2020 
investments, respectively, while more recently Christou et al. 
(2023) provided an impact assessment of the planned 2021–
2027 interventions (8). The same model has been used for more 
specific  cohesion-related  analyses  focusing  on  the  equity-
efficiency trade-off of the policy (Barbero et al., 2024a), the 
impact on EU regional disparities (Crucitti et al., 2024), smart 
specialisation (Gianelle et al., 2024; Barbero et al., 2024b; and 
Barbero  et  al.,  2022),  the  magnitude  of  regional  and 
international spillovers (Lecca et al., 2020; Crucitti et al., 2023), 
the influence of the quality of institutions (Barbero et al., 2023) 
and  the  long-term  effects  of  transport  infrastructure 
investment (Persyn et al., 2023).

(7)  There are econometric studies in the literature that account for the existence of spillovers. See, for example, Kisiała and Stępiński (2024), 

Fiaschi et al. (2018), Mohl and Hagen (2010) and Ramajo et al. (2008).

(8)  Christou et al. (2024) combine data for the 2014–2020 and 2021–2027 programming periods in a single simulation analysis (see also 

European Commission: DG Regional and Urban Policy et al., 2024).

THE UPDATED RHOMOLO IMPACT ASSESSMENT OF THE 2014–2020 EUROPEAN COHESION POLICY (INCLUDING REACT-EU)8

3.  COHESION POLICY 
EXPENDITURE  
2014–2020

3.1.  DISTRIBUTION OF RESOURCES  
ACROSS FUNDS AND OVER TIME

For  the  period  2014–2020,  EU  funding  for  cohesion  policy 
amounts to EUR 355 billion, more than 34 % of the total EU 
budget. Around EUR 50 billion has been added as part of the 
emergency response to the social and economic consequences 
of the COVID-19 pandemic under REACT-EU. This brings the 
total amount injected into EU economies through the ERDF, ESF, 
CF and YEI to almost EUR 405 billion (9). Taking into account an 
implementation period of 10 years due to the N + 3 rule (10), 
this corresponds on average to about 0.4 % of EU GDP per year. 
Table 1 provides an overview of the financial allocation by fund.

cumulative expenditure is equal to the total allocation for the 
programming  period  (Annex  1  describes  how  the  data  are 
processed). The assumption of a 100 % absorption rate is in 
line with evidence from previous programming periods. For 
example, for the 2007-2013 period, more than 98.5 % of the 
allocation was spent. For the 2014-2020 period, the absorption 
rate was 96 % as of August 2024, largely due to the very 
significant additional funds injected through REACT-EU during 
programme implementation. However, the amounts not yet 
invoiced to the European Commission generally correspond to 
actions that have already taken place on the ground, so this 
working assumption of a 100 % absorption rate may not be too 
far from reality.

Figure  1 shows the total annual expenditure from 2014 to 
2023, highlighting the increase in annual amounts over time, 
while Figure 2 goes into more detail and shows the same data 
broken down by fund and time. The expenditure data start in 
2016, i.e. the years 2014 and 2015 show zero expenditure. This 
can be explained by the following two factors.

1.  In the first years of the programming period, programmes 

Table 1: Budget by fund (EUR, current prices)

have to be negotiated and adopted before starting.

Fund

CF

ERDF

ESF

YEI

Planned amount (€)

61 434 784 152

230 005 563 849

104 402 027 966

8 960 645 385

Total
Source: DG Regional and Urban Policy, Cohesion Open Data Platform (https://
cohesiondata.ec.europa.eu/2014-2020/ESIF-2014-2020-FINANCES-PLANNED-
DETAILS/e4v6-qrrq).

404 803 021 352

The data used in the analysis refer to expenditure figures up to 
31 December 2023. For the sake of this exercise, we assume 
full  absorption  of  funds,  so  that  by  2023  the  estimated 

2.  The data presented correspond to the reimbursement 
claims submitted to the European Commission by the 
managing authorities in the Member States following the 
start and implementation of projects in the EU. Therefore, 
actual expenditure may in some cases take place prior to 
the reporting year (see European Commission: DG Regional 
and Urban Policy, 2020).

As a result, with the exception of the YEI, the annual share of 
expenditure increases over time. This is also explained by the 
implementation  of  REACT-EU,  which  increased  the  ERDF 
allocation by 14.96 % and the ESF by 23.51 %. 

(9) 

In this analysis, we consider the funds managed under cohesion policy. Therefore, we exclude the Connecting Europe Facility, some of 
the funds transferred outside cohesion policy (e.g. transfers to Directorate-General for Migration and Home Affairs funds, InvestEU), 
instruments managed by the European Commission and EU technical assistance in support of programming. Data for a limited number of 
programmes were also missing at the time of writing. The analysis therefore covers a total amount of around EUR 404 293 374 570.
(10)  The N + 3 rule allows funds to be used up to three years after they have been committed which implies that the programmes are actually 

implemented over a period of 10 years.

Figure 1: Total expenditure (billion EUR), across years; all funds

9

s
e
r
u
t
i
d
n
e
p
x
e
U
E

100

80

60

40

20

0

0.00

0.00

14.64

26.46

44.58

45.42

51.39

57.03

55.68

109.12

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

Source: Cohesion Open Data Platform (https://cohesiondata.ec.europa.eu); calculations by DG Regional and Urban Policy.

Figure 2: Total expenditure (billion EUR), across years and funds

s
e
r
u
t
i
d
n
e
p
x
e
U
E

60

40

20

0

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

CF

ERDF

ESF

YEI

Source: Cohesion Open Data Platform (https://cohesiondata.ec.europa.eu); calculations by DG Regional and Urban Policy.

THE UPDATED RHOMOLO IMPACT ASSESSMENT OF THE 2014–2020 EUROPEAN COHESION POLICY (INCLUDING REACT-EU) 
 
 
10

3.2.  DISTRIBUTION OF RESOURCES 
ACROSS GEOGRAPHICAL AREAS 
AND FIELDS OF INTERVENTION

Investment is concentrated on key areas of intervention to 
promote growth and development. For the purposes of data 
presentation  and  subsequent  modelling  analysis,  we  group 
cohesion  policy  expenditure  into  the  following  six  fields  of 
intervention which are then modelled differently in RHOMOLO 
to reflect the different transmission channels through which 
policy interventions affect the economies of Member States and 
their regions.

1.  RTD. Support for R & D (research, technological 

development and innovation, creation of networks and 
partnerships between companies and/or research 
institutes).

2.  AIS. Support to the private sector (support to small and 

medium-sized enterprises, facilitation of credit, support to 
improve tourism and cultural services).

3.  TRSNP. Investment in transport infrastructure.

4.  INFR. Investment in non-transport infrastructure 

(telecommunications, energy, environmental, health and 

social infrastructure), possibly linked to industrial 
processes.

5.  HC. Investment in human capital (education and training, 
active labour market policies and other labour market 
interventions).

6.  TA. Technical assistance (administrative capacity building, 

monitoring and evaluation).

The  distribution  of  funds  between  the  above  fields  of 
intervention varies from region to region and therefore from 
Member  State  to  Member  State,  reflecting  the  policy  mix 
resulting from the design of the programmes. Table 2 provides 
a breakdown of the allocation of funds by Member State and by 
field of intervention. In general, the less developed regions and 
Member States have a higher share of funding allocated to 
transport and other infrastructure-related interventions (TRNSP 
and INF). In contrast, the more developed regions and Member 
States allocate a higher share to support for R & D, the private 
sector and to investment in human capital (RTD, AIS and HC, 
respectively). For example, in Poland 35.4 % of the funds are 
allocated to TRNSP, while in Lithuania 42.3 % are allocated to 
INFR. In contrast, the Netherlands allocates only 0.6 % (lowest 
of all Member States) and 10.0 %, respectively, to these areas, 
but more than 37.4 % to RTD (highest of all Member States) 
and 42.5 % to HC.

Table 2: Cohesion policy allocation by Member State, per field of intervention, % of total allocation

11

Country

RTD

AT

BE

BG

CY

CZ

DE

DK

EE

EL

ES

FI

FR

HR

HU

IE

IT

LT

LU

LV

MT

NL

PL

PT

RO

SE

SI

SK

UK

EU-28
Source: DG Regional and Urban Policy.

21.8

18.8

10.0

9.6

14.1

26.6

35.7

21.1

6.4

10.5

32.2

14.5

9.2

8.8

6.0

9.5

16.7

6.3

15.5

7.1

37.4

16.8

16.6

4.2

25.5

23.2

7.9

23.3

13.6

AIS

18.0

10.2

15.8

7.0

3.9

9.6

15.4

2.1

26.0

6.7

20.3

6.4

15.0

16.2

1.8

12.4

4.3

0.1

4.2

5.3

6.0

2.2

17.2

7.8

11.5

8.7

5.1

13.1

9.1

TRNSP

INFR

HC

TA

Total

3.4

4.7

23.6

13.3

25.7

2.7

2.0

13.9

12.3

6.0

2.6

6.5

12.9

17.3

0.9

7.7

15.4

15.9

23.1

12.3

0.6

35.4

6.4

31.0

4.6

13.3

25.4

5.7

17.0

15.8

14.6

25.6

39.9

36.0

19.5

6.4

43.3

26.9

41.3

5.8

30.9

36.5

33.7

35.3

23.2

42.3

28.1

37.5

40.4

10.0

27.0

24.6

31.9

8.7

30.1

27.2

12.0

28.9

37.8

48.1

22.0

27.5

16.4

38.2

36.6

16.8

25.7

34.1

35.2

38.1

22.6

22.6

54.0

44.7

18.3

48.0

16.6

32.6

42.5

15.5

33.5

22.0

45.9

21.5

30.4

43.4

28.5

3.2

3.5

3.1

2.7

4.0

3.3

3.8

2.9

2.6

1.5

3.7

3.5

3.8

1.4

1.9

2.5

3.1

1.6

3.1

2.3

3.5

3.2

1.8

3.2

3.9

3.3

4.0

2.5

2.8

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

Figure 3 shows the geographical distribution of the funding, 
which reflects the objective of cohesion policy to promote a 
balanced  development  of  the  EU  and  to  reduce  disparities 
between EU regions. A large part of the funding is therefore 
concentrated on less developed regions and Member States. To 
determine eligibility for the 2014–2020 ERDF, cohesion policy 

distinguishes three groups of regions according to their level of 
development. The group of less developed regions (LD) includes 
regions with a GDP per capita of less than 75 % of the EU 
average,  the  transition  regions  (TR)  have  a  GDP  per  capita 
between 75 % and 90 % of the EU average and the more 
developed regions (MD) have a GDP per capita of over 90 % (11).

(11)  These groups have been constituted in 2011 based on the average GDP per capita for the years 2007, 2008 and 2009. The composition 

of the groups has then been frozen, and it is therefore constant throughout the whole period of analysis.

THE UPDATED RHOMOLO IMPACT ASSESSMENT OF THE 2014–2020 EUROPEAN COHESION POLICY (INCLUDING REACT-EU)12

Figure 3: Cohesion policy allocation 2014–2020, % of base year GDP, yearly average

Canarias

Guadeloupe 
Martinique

Guyane

Mayotte Réunion

Açores

Madeira

REGIOgis

Cohesion policy expenditure, 2014 2020

% of base year  GDP (yearly average)

< 0.25

2.00   2.50

0.25   0.50

2.50   3.00

0.50   1.00

> 3.00

1.00   2.00

Sources: Eurostat and DG Regional and Urban Policy.

0

500 km

© EuroGeographics Association for the administrative boundaries

13

For this analysis, all national or multiregional programmes have 
been regionalised at the NUTS 2 level on the basis of their 
geographical coverage and the proportion of the population of 
the beneficiary regions. Around 63 % of the funding is directed 

towards the less developed regions of the EU (Table 3). Aid 
intensity is also highest in these regions, with an average of 
EUR 298.8 per inhabitant per year, more than five times higher 
than in the more developed regions.

Table 3: Population, cohesion policy allocation, and aid intensity, across categories of regions

Share of population* (%)

Share of funding (%)

Aid intensity

Regions 

Less developed

Transition

More developed

24.2

13.6

62.3

62.7

12.5

24.9

100.0
EU-28
(*) 2014-2020 cohesion policy allocation by field of intervention, % of total allocation (*) 2014–2020 average.
NB: Aid intensity is measured in euro per inhabitant and per year.
Sources: Eurostat and DG Regional and Urban Policy.

100.0

292.8

103.9

45.0

112.9

In  some  Member  States,  cohesion  policy  represents  a 
significant injection of funds, up to 1.8 % of base year GDP 
(2017)  on  average  in  Croatia  and  Hungary.  For  some  less 
developed regions, the funding is even higher, for example in 

the Região Autónoma dos Açores in the Azores/Portugal or in 
Észak-Alföld in Hungary, where the policy injection exceeds 4 % 
of GDP per year on average (Table 4).

Table 4: Top 10 regions in terms of cohesion policy funding relative to GDP

Region

Cohesion policy funding, % base year GDP

PT20, Região Autónoma dos Açores

HU32, Észak-Alföld

HU23, Dél-Dunántúl

HU33, Dél-Alföld

HU31, Észak-Magyarország

PL62, Warminsko-Mazurskie

PL81, Lubelskie

PL82, Podkarpackie

PL84, Podlaskie

PL72, Swietokrzyskie
Sources: Eurostat and DG Regional and Urban Policy.

4.2

4.1

3.9

3.6

3.6

3.4

3.2

3.1

3.1

3.0

In terms of the temporal distribution across regions, Figure 4 
shows the total absolute expenditure over time in the three 
groups of regions identified above. The less developed regions 
had higher absolute expenditure in all years, in line with the 
higher  allocation  (see  also  Table  3).  Figure  5  shows  the 
proportions of allocations spent by the regions, indicating the 
absorption rates. For the initial allocations (all funds, excluding 

the REACT-EU allocations), the more and less developed regions 
have the highest absorption rates, while the transition regions 
lag  slightly  behind.  However,  when  initial  allocations  and 
REACT-EU  allocations  are  considered  together,  the  less 
developed regions have the fastest absorption rates, in line with 
the fact that REACT-EU allocations were relatively higher for the 
more developed regions (see Annex 2 for more details).

THE UPDATED RHOMOLO IMPACT ASSESSMENT OF THE 2014–2020 EUROPEAN COHESION POLICY (INCLUDING REACT-EU)14

Figure 4: Total expenditure (billion EUR), across years and regions; all funds

)
s
d
n
u
f

l
l

a
(

s
e
r
u
t
i
d
n
e
p
x
e
U
E

60

40

20

0

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

Less developed

Transition

More developed

Source: Cohesion Open Data Platform (https://cohesiondata.ec.europa.eu); calculations by DG Regional and Urban Policy.

Figure 5: Cumulative annual share of expenditures within categories of regions, across years and regions; all funds

150

100

50

0

s
e
r
u
t
i
d
n
e
p
x
e
U
E

)
e
v
i
t
a
u
m
u
c

l

,

s
d
n
u
f

l
l

a

f
o

l

a
t
o
t

f
o
%

(

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

Less developed:
Transition:
More developed:

Initial allocation
Initial allocation
Initial allocation

REACT-EU
REACT-EU
REACT-EU

NB: Initial allocation sums up to 100 % for each group of regions. The share of REACT-EU is added on top, calculated as the REACT-EU allocation divided by the 
total initial allocation (12).
Source: Cohesion Open Data Platform (https://cohesiondata.ec.europa.eu); calculations by DG Regional and Urban Policy.

(12)  For example: If one of the three groups of regions had an initial allocation total of EUR 210 billion and an additional allocation 

of EUR 10.5 billion in the context of REACT-EU, then the graph will reach 100 % for the initial allocation and 100 × (EUR 210 bil-
lion + EUR 10.5 billion) / EUR 210 billion = 105 % in total, i.e. 100 % + 5 %.

 
 
 
 
 
 
 
 
 
 
15

4.  THE RHOMOLO 

region affect this equilibrium, leading to a redistribution of 
firms across space.

MODEL SETUP AND 
THE SIMULATION 
STRATEGY

4.1.  MODEL DESCRIPTION 

The economic foundations of the spatial dynamic RHOMOLO 
model are based on the well-established literature on CGE 
models.  In  brief  (13),  the  model  is  calibrated  to  a  set  of 
integrated EU regional social accounting matrices (SAMs) for 
the year 2017, which include all the standard information of 
input–output tables on the production and use of goods and 
services, along with information on the secondary distribution 
of income, detailing the roles of labour and households (García 
Rodríguez et al., 2023).

The model economies are divided into ten economic sectors 
(based on NACE (general industrial classification of economic 
activities  within  the  European  Union)  Rev.  2).  Households 
consume  a  fixed  proportion  of  their  income  and  firms  are 
assumed to maximise profits and produce goods and services 
according to a constant elasticity of substitution production 
function. Governments collect revenue and spend it on public 
goods  and  transfers  (both  tax  rates  and  transfers  are 
exogenous in the simulations presented in this paper). Capital 
and labour are used as factors of production (public capital also 
enters the production function as an unpaid factor, following 
Barro, 1990 and Baxter and King, 1993, among others). Trade 
in  goods  and  services  –  within  and  between  regions  –  is 
modelled following Armington (1969) and is assumed to be 
costly, with transport costs increasing with distance (Krugman, 
1991). The valuation of transport costs is based on a transport 
model by Persyn et al. (2020). Regional economies are typically 
more open than national economies due to their smaller size. 
This is accounted for in the model thanks to regional trade 
flows and the relatively high elasticity of substitution between 
domestic  and  imported  goods  and  services  (Németh  et  al., 
2011; Olekseyuk and Schürenberg-Frosch, 2016).

This setup ensures that the model captures the existence of 
interregional spillovers through trade flows and capital mobility, 
leading to endogenous firm location. Trade links imply that 
changes in economic activity in one region trigger changes in 
the regions of trading partners. The model also borrows from 
economic  geography  and  incorporates  a  notion  of  spatial 
equilibrium, in which the balance between agglomeration forces 
(due to increasing returns to firms’ technology) and dispersion 
forces (due to competition between rival varieties) determines 
the location of firms and workers. Policy shocks in a given 

The base year is assumed to correspond to a steady state that 
does  not  change  unless  perturbed  by  the  introduction  of 
exogenous shocks. The interest rate is exogenous to the model 
and fixed at 4 %. RHOMOLO is used for scenario analysis in the 
sense  that  shocks  mimicking  the  effects  of  policies  are 
introduced to perturb the initial steady state calibrated with the 
SAMs, resulting in different values for the model’s endogenous 
variables such as GDP, employment, imports and exports, prices 
and  others.  The  model  is  solved  in  a  recursively  dynamic 
process, where a sequence of static equilibria are linked by the 
law of motion of the state variables. This implies that economic 
agents are not forward-looking and that their decisions are 
based solely on current and past information.

4.2.  SIMULATION STRATEGY

In order to simulate the impact of cohesion policy in RHOMOLO, 
each of the six intervention fields defined in Section  3.2 is 
associated with a set of model shocks designed to capture the 
economic transmission mechanisms that it is most likely to 
activate. More specifically, one or more model shocks are used 
to simulate each of the six fields of intervention (each one 
containing  several  of  the  123  spending  categories  of  the 
policies defined by the legislation, see European Union, 2014). 
The model shocks can be divided into demand-side shocks (with 
temporary  effects)  and  supply-side  shocks  (with  more 
permanent structural effects on the economy). The relationship 
between the shocks and the intervention areas is as follows.

1.  RTD. Investment in R & D is modelled as an increase in 
private investment via a reduction in the user cost of 
capital, which temporarily increases the stock of private 
capital (in the production function, the capital–labour 
elasticity of substitution is 0.4, in line with, among others, 
Chirinko, 2008 and Leon-Ledesma et al., 2010). Moreover, 
these investments are assumed to increase total factor 
productivity (TFP) according to an elasticity that ranges 
between 0.01 and 0.04 and depends on the regional R & D 
intensity (the estimates are in line with the existing 
literature, see Männasoo et al., 2018; Kancs and 
Siliverstovs, 2016; Bronzini and Piselli, 2009; Griffith et 
al., 2004).

2.  AIS. Aid to the private sector is modelled as an increase in 
private investment through a reduction in the user cost of 
capital, as for RTD investment, but with no impact on TFP.

3.  TRNSP. Investment in transport infrastructure is assumed 

to have both demand-side and supply-side effects. 
Demand-side effects are generated by the temporary 
increase in government consumption, which accounts for 
the purchase of goods and services needed to build the 
actual infrastructure. On the supply side, these investments 

(13)  A full explanation of its functioning is beyond the scope of this paper (all model equations are presented in Lecca et al., 2018).

THE UPDATED RHOMOLO IMPACT ASSESSMENT OF THE 2014–2020 EUROPEAN COHESION POLICY (INCLUDING REACT-EU)16

are assumed to reduce transport costs, thereby lowering 
the prices of goods and stimulating trade flows (Ignatov, 
2023). The induced reduction is based on the estimates 
obtained with the full transport cost model by Persyn et al. 
(2020) for the 2014–2020 cohesion policy investments in 
transport infrastructure.

4.  INFR. Investment in non-transport infrastructure, such as 
electricity networks, water treatment plants and waste 
management facilities, is modelled as public investment 
when linked to industrial processes and otherwise as 
government consumption (in the latter case there are only 
temporary demand-side effects). In addition to increasing 
demand, public investment also has supply-side effects, as 
it temporarily increases the stock of public capital and thus 
stimulates the production of goods and services. We set 
the output elasticity of public capital at 0.1, in line with 
Ramey (2020) and slightly below the average of 0.12 
found in the meta-study by Bom and Lightart (2014) (14). 
We set the congestion parameter of public capital to 0.5, 
which corresponds to a medium level of congestion 
(Alonso-Carrera et al., 2009; a value of zero would make 
public capital a pure public good).

5.  HC. Investments in human capital are assumed to increase 
demand through government current expenditure. They are 
also assumed to have two alternative supply-side effects, 
depending on the nature of the interventions. On the one 
hand, the categories of expenditure related to human 
capital development, such as training, retraining and 
upskilling, are assumed to increase labour productivity. The 
main assumption behind this effect is the increase in 
productivity caused by an additional year of training, which 
we take from the country-specific estimates of 
Psacharopoulos and Patrinos (2018a, 2018b) and is 
consistent with the evidence from Mincer (1974) type 
estimates in the literature (see also Card, 2001; De la 
Fuente and Ciccone, 2003; and Canton et al., 2018). The 
cost of one year of tertiary education per capita 
(Organisation for Economic Co-operation and Development, 

2018) is used to calculate the amount of training implied 
by the HC funds of cohesion policy. On the other hand, 
interventions aimed at promoting the socioeconomic 
integration of marginalised communities, participation in 
the labour market or the modernisation of labour market 
institutions are assumed to generate an increase in 
aggregate labour supply (in this case, we use the cost of 
one year of secondary education as the cost per pupil for 
the calibration of the shock).

6.  TA. Technical assistance is modelled as a demand-side 

shock increasing current public expenditure with no supply-
side effects.

We also assume that all supply-side effects decay over time. 
Thus, changes in labour productivity, TFP and transport costs 
are all assumed to decay at an annual rate of 5 %. In addition, 
the stocks of private and public capital have a depreciation rate 
of 15 % and 5 %, respectively (a larger depreciation rate of 
private  capital  relative  to  public  capital  is  a  common 
assumption in the literature, see, for example, Bom, 2017). This 
implies that, in the absence of further investment, the structural 
effects associated with the policy gradually disappear and the 
economy returns to its initial steady state.

The model simulations take into account the fact that cohesion 
policy is financed by Member States’ pro rata contribution to 
the EU budget, which is assumed to be proportional to the 
weight  of  their  GDP  in  the  EU  GDP.  The  Member  States’ 
contribution to the part of the EU budget corresponding to 
cohesion policy is assumed to be financed by a lump-sum tax, 
which reduces the disposable income of households, thereby 
negatively  affecting  economic  performance  and  partly 
offsetting the positive effects of the programmes (15). This 
implies that a larger share of Member States’ contributions to 
cohesion policy comes from the more developed parts of the 
EU,  while  the  bulk  of  interventions  take  place  in  its  less 
developed territories. The next section presents the results of 
the  analysis  based  on  the  policy  shocks  introduced  as 
explained above.

(14)  Foster et al. (2023) review the literature and highlight the uncertainty surrounding the magnitude of the output elasticity of public infra-

structure investment.

(15)  This means that, in the model, the EU regions are not constrained to run a balanced budget and can experience either deficits or surpluses. 
The EU budget is exogenously constrained to be balanced, as the amount of spending incurred by regions and which is financed through 
the programmes is repaid with an equal amount of lump-sum transfers from the households.

17

5.  MACROECONOMIC 
IMPACT OF THE  
2014–2020 
COHESION POLICY

5.1. 

IMPACT AT THE EU LEVEL OF THE 
2014–2020 COHESION POLICY

The impact of the policy corresponds to the difference between 
a scenario with no policy (referred to as the baseline) and one 
with  the  shocks  representing  the  policy.  The  results  are 
expressed as a percentage deviation from the baseline. The 
magnitude of the impact depends on the size of the policy 
injection, but also on the composition of the policy mix (which 

varies considerably from one region to another, even within the 
same  Member  States)  and  on  the  characteristics  of  the 
beneficiary  economy,  such  as  its  industrial  structure, 
endowment in physical and human capital or location (which 
determines market access and transport costs).

Figure 6 shows the impact on EU GDP of all the cohesion policy 
interventions  described  above  over  time  (magenta  line), 
together with the time profile of cohesion policy expenditure 
expressed as a percentage of EU GDP (blue bars), from 2014 to 
2045.  The  impact  on  GDP  rises  steadily  during  the 
implementation period, peaking at almost + 0.6 % in 2023. It 
then  gradually  declines  after  the  end  of  programme 
implementation, as the increased private and public capital 
stock depreciates, while the increase in labour productivity and 
TFP, along with the decrease in transport costs, decline. In 2045, 
however, the effects of the policy are still significant as annual 
GDP remains + 0.3 % above its initial level.

Figure 6: Cohesion policy expenditure and GDP impact at the EU level

Source: RHOMOLO simulations.

Table 5 shows the percentage deviations from the baseline for 
some key macroeconomic variables in selected years and the 
cumulative GDP multiplier. The multiplier is the ratio of the 
cumulative change in GDP to the cumulative policy shock. It can 
be  interpreted  as  the  euro  of  GDP  created  for  each  euro 
invested in the policy or the GDP impact per euro spent on the 
policy (broadly speaking, it is a kind of return on the policy 
investment). It increases over time as the GDP impact is positive 
throughout the simulation period, while the policy injection (in 
the  denominator)  stops  increasing  after  the  end  of  the 

implementation period. In the short term, the multiplier at the 
EU level is usually less than one, as the benefits of the policy 
are not yet sufficient to outweigh its costs. However, this soon 
changes and, 25 years after the start of the programme, each 
euro  spent  on  the  policy  has  generated  almost  3  euro  of 
additional GDP. The policy injection also leads to improvements 
in employment, with the impact peaking at + 0.91 % in 2023, 
equivalent to around 2.1 million additional jobs compared to 
the baseline (16).

(16)  The total number of persons employed in the EU-28 in the base year of the model is around 231.83 million (source: Eurostat).

THE UPDATED RHOMOLO IMPACT ASSESSMENT OF THE 2014–2020 EUROPEAN COHESION POLICY (INCLUDING REACT-EU)0.00.10.20.30.40.50.60.70.82014201620182020202220242026202820302032203420362038204020422044Expenditures cohesion policyPercent of GDPImpact on GDP (change from baseline)18

Table 5: Cohesion policy impact in selected years on a selection of macroeconomic variables at the EU level; EU-28

GDP (% change)

GDP Multipliers

Employment (% change)

Employment (change in thousands of people)
Source: RHOMOLO simulations.

Year

2023

2030

2040

2045

0.56

0.69 

0.91

2 104

0.40

1.75 

0.31

715

0.29

2.97 

0.23

537

0.25

3.43 

0.19

447

5.2.  NATIONAL AND REGIONAL IMPACT 

OF THE 2014–2020 COHESION 
POLICY

The model allows results to be obtained at the level of EU NUTS 
2 regions. Figures 7 and 8 show the territorial distribution of the 
GDP impact of the 2014–2020 programmes in 2023 (the end 
of the implementation period) and 2045 (30 years after the 
start of policy implementation). As expected, the GDP impact 
reflects the territorial distribution of the funds, especially in the 
short term, where it is higher in the main beneficiaries of the 
policy, i.e. the eastern European regions, Portugal and Greece. 
By the end of the programmes, GDP is around + 5.5 % higher in 
Croatia than in a scenario without cohesion policy, + 3.9 % in 
Lithuania, + 3.8 % in Latvia and around + 3.7 % in Portugal 
and Greece.

The results show considerable differences between regions 
within some Member States. For example, the impact on GDP 

ranges from + 2.2 % to + 5.3 % in Poland, from + 1.5 % to 
+ 6.6 % in Hungary and from + 1.9 % to + 8.7 % in Portugal.

In the most developed regions of the EU, the short-term impact 
of the policy is more limited and sometimes even negative, as 
many of these regions contribute more to the policy than they 
receive.  However,  once  the  interventions  have  been  fully 
implemented (and financed), in the medium to long term the 
differences  in  terms  of  GDP  impact  between  EU  regions 
diminish and the impact becomes positive in all regions. This is 
partly due to the strong positive spatial spillovers generated by 
the policy, whereby programmes implemented in one region 
have an impact in other regions. These spillovers are mainly due 
to the fact that the main beneficiaries are often small, open 
economies with a narrow industrial base and limited R & D 
capacity. Many goods or services critical to the implementation 
of cohesion policy programmes are not produced domestically 
and therefore have to be imported to a large extent from the 
more developed regions of the EU.

Figure 7: Impact of the 2014–2020 cohesion policy programmes on GDP in NUTS 2 regions in 2023 

19

Canarias

Guadeloupe 
Martinique

Guyane

Mayotte Réunion

Açores

Madeira

Impact of the 2014 2020 cohesion policy programmes on GDP in NUTS 2 regions in 2023

–

REGIOgis

% deviation from baseline

<= 0.1

0.1   0.5

0.5   1

1   1.5

1.5   2

> 2

Source: RHOMOLO simulations.

0

500 km

© EuroGeographics Association for the administrative boundaries

THE UPDATED RHOMOLO IMPACT ASSESSMENT OF THE 2014–2020 EUROPEAN COHESION POLICY (INCLUDING REACT-EU) 
 
20

Figure 8: Impact of the 2014–2020 cohesion policy programmes on GDP in NUTS 2 regions in 2045

Canarias

Guadeloupe 
Martinique

Guyane

Mayotte Réunion

Açores

Madeira

Impact of the 2014 2020 cohesion policy programmes on GDP in NUTS 2 regions in 2045

REGIOgis

% deviation from baseline

<= 0.1

0.1   0.5

0.5   1

1   1.5

1.5   2

> 2

Source: RHOMOLO simulations.

0

500 km

© EuroGeographics Association for the administrative boundaries

21

Figure 9 shows the evolution of the distribution of cumulative 
regional GDP multipliers over time. Moving from the short term 
to the long term, the multipliers tend to move to the right, as 
the combined demand- and supply-side effects of cohesion 
policy generate more than one euro of GDP for each euro spent 
in most EU regions over the 20 years of the simulation. On 
average, the long-term GDP multipliers are higher in regions of 
beneficiary  countries  of  cohesion  policy  than  in  regions  in 

countries that are net contributors to the policy. However, there 
are some exceptions, as some of the highest multipliers are 
found in regions in more developed countries. This is because 
these regions benefit from substantial spillover effects from 
the  rest  of  the  EU,  leading  to  a  significant  impact  on  GDP 
despite low levels of policy investment (the latter being in the 
denominator of the multiplier formula).

Figure 9: Distribution of regional GDP multipliers in 2023 and in 2045

s
n
o
g
e
r

i

f
o

r
e
b
m
u
N

80

70

60

50

40

30

20

10

0

2023
2040

– 2

0

2

4

6

GDP multipliers

Source: RHOMOLO simulations.

The geographical distribution of the macroeconomic impact of 
the policy is evident when looking at the impact per category of 
beneficiary (Figure 10). The impact of the policy is significantly 
higher  in  the  less  developed  regions  than  in  the  other 
categories, both initially and over time. In 2023, the impact on 
GDP is + 3.4 % in the less developed regions, compared with 

+ 0.7 % and + 0.2 % in the transition and more developed 
regions, respectively. The difference between the impact on the 
different  categories  decreases  over  time  but  remains 
significant: + 1.1 % in the less developed regions, + 0.3 % in the 
transition  regions  and  +  0.1  %  in  the  more  developed 
regions in 2045.

THE UPDATED RHOMOLO IMPACT ASSESSMENT OF THE 2014–2020 EUROPEAN COHESION POLICY (INCLUDING REACT-EU) 
 
22

Figure 10: Cohesion policy expenditure and GDP impact by group of regions

Source: RHOMOLO simulations.

The fact that cohesion policy produces most of its impact in the 
less developed regions of the EU demonstrates that it is in line 
with its mandate to strengthen economic and social cohesion 
by reducing disparities in the level of development between 
regions. Our simulation results permit us to investigate the 
impact of the policy on various measures of regional disparities. 
They suggest that the policy reduces regional disparities both 
across and within Member States. At the aggregate EU level, 

the coefficient of variation (17) and the ratio of the 80th to 20th 
percentile values of the regional GDP per capita distribution are 
found to decrease with the implementation of the programmes 
(Figure 11). Both indices reach their minimum value at the end 
of the implementation period. However, 30 years after the start 
of the programmes, GDP per capita dispersion remains lower 
than the initial level.

(17)  Defined as the ratio of the standard deviation regional GDP relative to the mean regional GDP per capita.

0.01.02.03.04.05.06.02014201620182020202220242026202820302032203420362038204020422044Percent of  GDPExpenditures MDExpenditures TRExpenditures LDImpact MD (over baseline)Impact TR (over baseline)Impact LD (over baseline)Figure 11: Cohesion policy impact on the coefficient of variation and on the 80/20 distribution in the EU

23

Coeﬃcient of variation (leﬅ axis)

Ratio D80/D20 (right axis)

Source: RHOMOLO simulations.

The  evidence  presented  in  Figure  11  is  confirmed  by  the 
changes in the Theil index, which shows the largest decrease, 
– 4.0 %, at the peak of the policy’s impact on GDP in 2023 (see 
Table  6)  ( 18).  Both  the  between  and  the  within  country 
components  of  the  index  decline,  implying  that  disparities 
within  Member  States  are  reduced,  along  with  disparities 

between Member States. 10 years after the start of the policy, 
disparities are nearly 4 % lower than the initial level coinciding 
with  the  year  of  maximum  GDP  impact.  Thereafter  the 
reductions are slowly extinguishing: in 2030 and 2045 they are 
2.5 % and 1.15 % lower than the initial level, respectively.

Table 6: Cohesion policy impact on the Theil Index

Change in 2023

Change in 2030

Change in 2040

Change in 2045

Within

Between 

- 2.11 %

- 4.79 %

- 1.28 %

- 3.01 %

- 0.62 %

- 1.76 %

Overall
 Source: RHOMOLO simulations. Only countries with more than four NUTS 2 regions are reported to enable the calculations of the Theil index.

- 3.99 %

- 1.42 %

- 2.49 %

- 0.5 %

- 1.4 %

- 1.15 %

In order to understand the magnitude of changes in disparities 
within and between Member States, we report in Table 7 the 
maximum observed difference from the baseline in selected 
percentiles of regional GDP per capita (this occurs in 2023). For 
almost all Member States, there is a positive impact on the 
lowest 10 % of the GDP per capita distribution, with an average 
increase of almost + 0.5 %. The impact is more pronounced in 
net beneficiary countries. In the higher parts of the distribution, 

the value of the percentiles tends to fall, although the evidence 
on changes in the income of the median regions within each 
country is mixed. The richest regions of both net contributors 
and net recipients (at the 90th percentile) experience a decline 
of – 0.9 % on average. Overall, the country-level evidence on 
the distributional impact of the policy suggests that it reduces 
internal regional disparities.

(18)  The index is calculated as: 

 , where the first term of the formula represents the within part of the decom-
position and is the weighted averages of the Theil index of each Member State. The second term is the between component of the Theil 
index and represents the component of regional disparities that depends on disparities across countries. Sj are weights and are computed 
as the ratio between the country average of income per head, y, and its EU average. Source: OECD (2016).

ln ( ̅ ) +

 ln ( ̅ )

ℎ =

 ∑

∑

1

1

THE UPDATED RHOMOLO IMPACT ASSESSMENT OF THE 2014–2020 EUROPEAN COHESION POLICY (INCLUDING REACT-EU)2.522.532.542.552.562.572.580.5860.5880.590.5920.59420142017202020232026202920322035203820412044 
 
 
̅
24

Table 7: Regional income distribution by country (% change with respect to baseline)

Member State

Δ% p10

Δ% p50

Δ% p90

AT

BE

BG

CZ

DE

DK

EL

ES

FI

FR

HU

IT

NL

PL

PT

RO

SE

0.235

0.130

0.428

0.304

0.407

0.062

1.317

0.368

0.153

0.182

1.826

1.115

0.051

1.109

0.446

0.326

0.057

- 0.025

- 0.064

0.279

0.237

- 0.015

- 0.009

- 0.737

- 0.048

- 0.023

0.017

- 0.138

- 0.234

0.004

- 0.598

2.793

0.169

- 0.028

- 0.045

- 0.097

- 1.223

- 1.815

- 0.168

- 0.033

- 1.493

- 0.581

- 0.150

- 0.108

- 4.700

- 0.613

- 0.072

- 0.534

- 2.717

- 1.213

- 0.209

UK
Source: RHOMOLO simulations. Only countries with more than four NUTS 2 regions are reported to enable the calculations of percentile values.

- 0.025

- 0.002

- 0.061

25

6.  CONCLUSIONS

Assessing  the  macroeconomic  impact  of  cohesion  policy  is 
challenging. The policy has effects well beyond those directly 
linked to the interventions, and many of these are fully realised 
only in the long run as the policy affects the structure of the 
beneficiary economies. The cost of financing the policy must 
also be taken into account to capture the net effects of the 
policy.  While  programme  data  and  outcome  indicators  are 
essential  to  monitor  the  progress  of  implementation  and 
effectiveness, they are not sufficient to properly assess the 
impact of the policy on overall economic aggregates such as 
GDP, employment or regional disparities. However, this must be 
considered when evaluating the policy and the extent to which 
it has achieved its overarching objectives.

In  this  analysis,  we  used  RHOMOLO,  a  dynamic  general 
equilibrium  model,  to  estimate  the  potential  impact  of  the 
2014–2020 programmes financed by the three main cohesion 
policy funds, the YEI and REACT-EU. The results suggest that 
cohesion policy interventions have a positive impact on the EU 
economy.  The  EU  GDP  simulated  under  the  ‘with  cohesion 

policy’ scenario is up to + 0.6 % higher compared to the one 
obtained under the ‘without cohesion policy’ scenario. In the 
long run, the policy investments generate positive returns, with 
a 25-year GDP multiplier of almost three (equivalent to an 
annual rate of return of around 4 %). The impact of the policy 
is particularly high in the less developed regions of the EU, 
which  are  its  main  beneficiaries.  It  is  lower  in  the  more 
developed Member States and regions, but in the long run the 
impact is positive even in net contributors to the policy. This is 
partly due to the interregional spillover effects of the policy, 
whereby measures implemented in one region also benefit 
other regions in the EU, especially those with strong trade links 
to the main beneficiaries.

The  GDP  impacts  and  multipliers  are  greater  in  the  less 
developed regions of the EU, which are the main target of the 
policy, with GDP in these regions being around + 3.3 % higher 
than the baseline at the end of the implementation period, 
compared with + 0.7 % and + 0.2 %, respectively, in transition 
and  more  developed  regions.  This  suggests  that  the 
interventions  have  contributed  to  reducing  or  limiting  the 
increase in regional disparities, both at the EU level and within 
most Member States.

THE UPDATED RHOMOLO IMPACT ASSESSMENT OF THE 2014–2020 EUROPEAN COHESION POLICY (INCLUDING REACT-EU)26

REFERENCES

Aiello,  V.,  Reverberi,  P.  M.  and  Brasili,  C.  (2019),  ‘Regional 
diversity in experiences of cohesion policy: The cases of Emilia-
Romagna and Calabria’, Papers in Regional Science, Vol. 98, 
Issue  6,  Elsevier,  pp.  2275–2294,  https://doi.org/10.1111/
pirs.12461.

Albanese, G., de Blasio, G. and Locatelli, A. (2021), ‘Does EU 
regional policy promote local TFP growth? Evidence from the 
Italian  Mezzogiorno’,  Papers  in  Regional  Science,  Vol.  100, 
Issue  2,  Elsevier,  pp.  327–349,  https://doi.org/10.1111/
pirs.12574.

Allard, C., Choueiri, N., Schadler, S. M. and Van Elkan, R. (2008), 
‘Macroeconomic effects of EU transfers in new Member States’, 
IMF Working Papers, WP/08/223, International Monetary Fund, 
https://www.imf.org/external/pubs/ft/wp/2008/wp08223.pdf.

Alonso-Carrera, J., Freire-Serén, M. J. and Manzano, B. (2009), 
‘Macroeconomic effects of the regional allocation of public 
capital  formation’,  Regional  Science  and  Urban  Economics, 
Vol. 39, Issue 5, Elsevier, pp. 563–574, https://doi.org/10.1016/j.
regsciurbeco.2009.02.006.

Amendolagine, V., Prota, F. and Serlenga, L. (2024), ‘The impact 
of European cohesion policy: A spatial perspective’, Journal of 
Economic Geography, Vol. 24, Issue 4, Oxford University Press, 
pp. 477–494, https://doi.org/10.1093/jeg/lbae006.

Arcalean, C., Glomm, G. and Schiopu, I. (2012), ‘Growth effects 
of spatial redistribution policies’, Journal of Economic Dynamics 
and Control, Vol. 36, Issue 7, Elsevier, pp. 988–1008, https://doi.
org/10.1016/j.jedc.2012.01.004.

Armington,  P.  S.  (1969),  ‘A  theory  of  demand  for  products 
distinguished by place of production’, IMF Staff Papers, Vol. 16, 
No 1, International Monetary Fund, pp. 159–178, https://doi.
org/10.5089/9781451956245.024.

Bachtrögler, J., Fratesi, U. and Perucca, G. (2019), ‘The influence 
of the local context on the implementation and impact of EU 
cohesion  policy’,  Regional  Studies,  Vol.  54,  No  1,  Taylor  & 
Francis Group, pp. 21–34, https://doi.org/10.1080/00343404.2
018.1551615.

Bähr, C. (2008), ‘How does sub-national autonomy affect the 
effectiveness of structural funds?’, Kyklos, Vol. 61, No 1, John 
h t t p s : / / d o i .
Wi l e y   &   S o n s   L t d ,   p p .  3 – 1 8 ,  
org/10.1111/j.1467-6435.2008.00389.x.

Barbero, J., Diukanova, O., Gianelle, C., Salotti, S. and Santoalha, 
A. (2022), ‘Economic modelling to evaluate smart specialisation: 
An analysis of research and innovation targets in southern 
Europe’,  Regional  Studies,  Vol.  56,  Issue  9,  European 
Commission: Joint Research Centre, Seville, Spain, pp. 1496–
1509, http://dx.doi.org/10.13140/RG.2.2.22073.31846.

Barbero, J., Christensen, M., Conte, A., Lecca, P., Rodríguez-Pose, 
A. and Salotti, S. (2023), ‘Improving government quality in the 
regions of the EU and its system-wide benefits for cohesion 
policy’,  JCMS:  Journal  of  Common  Market  Studies,  Vol.  61, 
Issue  1,  John  Wiley  &  Sons  Ltd,  pp.  38–57,  https://doi.
org/10.1111/jcms.13337.

Barbero, J., Christou, T., Crucitti, F., Rodríguez, A. G., Lazarou, N. 
J., Monfort, P., and Salotti, S. (2024a), ‘A spatial macroeconomic 
analysis of the equity-efficiency trade-off of the European 
cohesion policy’, Spatial Economic Analysis, Vol. 19, Issue 3, 
Taylor & Francis Group, pp. 394–410, https://doi.org/10.1080/1
7421772.2024.2306948.

Barbero, J., Diukanova, O., Gianelle, C., Salotti, S. and Santoalha, 
A. (2024b), ‘Technologically related diversification: One size 
does not fit all European regions’,  Research Policy, Vol.  53, 
Issue  3,  104973,  Elsevier,  https://doi.org/10.1016/j.
respol.2024.104973.

Barro, R. J. (1990), ‘Government spending in a simple model of 
endogenous growth’, Journal of Political Economy, Vol. 98, No 5, 
The University of Chicago Press, pp. S103–S125, https://www.
jstor.org/stable/2937633.

Barro,  R.J.,  Sala-i-Martin,  X.,  Blanchard,  O. J. and  Hall,  R. E. 
(1991), ‘Convergence across states and regions’,  Brookings 
Papers on Economic Activity, No 1, The Johns Hopkins University 
Press, pp. 107–182, https://doi.org/10.2307/2534639.

Baxter,  M.  and  King,  R.  G.  (1993),  ‘Fiscal  policy  in  general 
equilibrium’,  American  Economic  Review,  Vol.  83,  No  3, 
American Economic Association, pp. 315–334, https://www.jstor.
org/stable/2117521.

Becker, S. O., Egger, P. H. and von Ehrlich, M. (2010), ‘Going 
NUTS:  The  effect  of  EU  structural  funds  on  regional 
performance’, Journal of Public Economics, Vol. 94, Issues 9–10, 
Elsevier,  pp.  578–590,  https://doi.org/10.1016/j.
jpubeco.2010.06.006.

Becker, S. O., Egger P. H. and von Ehrlich, M. (2013), ‘Absorptive 
capacity and the growth and investment effects of regional 
transfers: A regression discontinuity design with heterogeneous 
treatment  effects’,  American  Economic  Journal:  Economic 
policy, Vol. 5, No 4, American Economic Association, pp. 29–77, 
https://www.jstor.org/stable/43189353.

Berkowitz, P., Monfort, P. and Pieńkowski, J. (2020), ‘Unpacking 
the  growth  impacts  of  European  Union  cohesion  policy: 
Transmission  channels  from  cohesion  policy  into  economic 
growth’, Regional Studies, Vol. 54, Issue 1, Taylor & Francis 
Group,  pp.  60–71,  https://doi.org/10.1080/00343404.2
019.1570491.

Bisciari,  P.,  Essers,  D.  and  Vincent,  E.  (2020),  ‘Does  the  EU 
convergence machine still work?’, NBB Economic Review, June, 
National Bank of Belgium, pp. 1–42, https://econpapers.repec.

27

org/article/nbbecrart/y_3a2020_3am_3ajune_3ai_3ai_3ap
_3a23-64.

Association,  jvae031,  Oxford  University  Press,  https://doi.
org/10.1093/jeea/jvae031.

Blanchard,  O.  and  Perotti,  R.  (2002),  ‘An  empirical 
characterization  of  the  dynamic  effects  of  changes  in 
government  spending  and  taxes  on  output’,  The  Quarterly 
Journal of Economics, Vol. 117, No 4, Oxford University Press, 
pp. 1329–1368, https://www.jstor.org/stable/4132480.

Canton, E., Thum-Thysen, A. and Voigt, P. (2018), ‘Economist’s 
musing on human capital investment: How efficient is public 
spending  on  education  in  EU  Member  States?’,  European 
Economy Discussion Papers, No 081, Publications Office of the 
European Union, Luxembourg, https://doi:10.2765/478780.

Blouri, Y. and von Ehrlich, M. (2020), ‘On the optimal design of 
place-based policies: A structural evaluation of EU regional 
transfers’,  Journal  of  International  Economics,  Vol.  125, 
1 0 3 3 1 9 ,  
h t t p s : / / d o i . o r g / 1 0 . 1 0 1 6 / j .
jinteco.2020.103319.

E l s e v i e r,  

Bom, P. R. D. (2017), ‘Factor-biased public capital and private 
capital  crowding  out’,  Journal  of  Macroeconomics,  Vol.  52, 
Elsevier,  pp.  100–117,  https://doi.org/10.1016/j.
jmacro.2017.03.002.

Bom, P. R. and Lightart, J. E. (2014), ‘What have we learned 
from three decades of research on the productivity of public 
capital?’, Journal of Economic Surveys, Vol. 28, Issue 5, John 
Wiley  &  Sons  Ltd,  pp.  889–916,  https://doi.org/10.1111/
joes.12037.

Bourdin, S. (2019), ‘Does the cohesion policy have the same 
influence on growth everywhere? A geographically weighted 
regression approach in central and eastern Europe’, Economic 
Geography, Vol. 95, Issue 3, Taylor & Francis Group, pp. 256–
287, https://doi.org/10.1080/00130095.2018.1526074.

Bourdin, S., Destefanis, S., Coppola, G. and Kubara, M. (2024), 
‘What  are  the  impacts  of  cohesion  policy  on  the  trade-off 
between  employment  and  productivity?’,  Regional  Studies, 
Taylor & Francis Group, pp. 1–16, https://doi.org/10.1080/0034
3404.2024.2379444.

Bradley, J. and Untiedt, G. (2009), ‘Analysis of EU cohesion 
policy 2000–2006 using the CSHM: Aggregate impacts and 
inter-country  comparisons’,  HERMIN  Economic  Papers, 
No 2-2012.

Brasili, C., Calia, P. P. and Mogila, Z. (2023), ‘Cohesion policy 
impact on regional development – Cross-sectional and panel 
data-based spatial analysis’, in: Capello, R. and Conte, A. (eds), 
Cities  and  Regions  in  Transition,  Francoangeli,  Milan,  Italy, 
pp. 225–244, https://hdl.handle.net/11585/950433.

Bronzini, R. and Piselli, P. (2009), ‘Determinants of long-run 
regional productivity with geographical spillovers: The role of 
R  &  D,  human  capital  and  public  infrastructure’,  Regional 
Science  and  Urban  Economics,  Vol.  39,  Issue  2,  Elsevier, 
h t t p s : / / d o i . o r g / 1 0 . 1 0 1 6 / j .
p p . 
regsciurbeco.2008.07.002.

1 8 7 – 1 9 9 ,  

Canova, F. and Pappa, E. (2024), ‘The macroeconomic effects of 
EU regional structural funds’, Journal of the European Economic 

Card, D. (2001), ‘Estimating the return to schooling: Progress on 
some persistent econometric problems’, Econometrica, Vol. 69, 
No 5, The Econometric Society, pp. 1127–1160, https://www.
jstor.org/stable/2692217.

Cavallaro, E.  and  Villani, I.  (2021), ‘Club convergence in  EU 
countries’, Journal of Economic Integration, Vol. 36, No 1, Center 
for  Economic  Integration,  Sejong  University,  pp.  125–161, 
https://www.jstor.org/stable/26985579.

Cerqua, A. and Pellegrini, G. (2018), ‘Are we spending too much 
to grow? The case of structural funds’,  Journal of Regional 
Science, Vol. 58, Issue 3, John Wiley & Sons Ltd, pp. 535–563, 
https://doi.org/10.1111/jors.12365.

Cerqua, A., Pellegrini, G. and Tarola, O. (2022), ‘Can regional 
policies shape migration flows?’, Papers in Regional Science, 
Vol. 101, Issue 3, John Wiley & Sons Ltd, pp. 515–537, https://
doi.org/10.1111/pirs.12670.

Chirinko, R. S. (2008), ‘σ: The long and short of it’, Journal of 
Macroeconomics, Vol. 30, Issue 2, Elsevier, pp. 671–686, https://
doi.org/10.1016/j.jmacro.2007.10.010.

Christou, T., Crucitti, F., Garcia Rodriguez, A., Lazarou, N., Monfort, 
P.  and  Salotti,  S.  (2023),  ‘The  RHOMOLO  ex-ante  impact 
assessment  of  2021–2027  cohesion  policy’,  Territorial 
Development  Insights  Series,  JRC133844,  European 
Commission:  Joint  Research  Centre,  Seville,  Spain,  https://
econpapers.repec.org/RePEc:ipt:iptwpa:jrc133844.

Christou,  T.,  Garcia  Rodriguez,  A.,  Heidelk,  T.,  Lazarou,  N., 
Monfort, P. and Salotti, S. (2024), ‘A RHOMOLO assessment of 
2014–2027 cohesion policy’, Territorial Development Insights 
Series,  JRC136790,  European  Commission:  Joint  Research 
Centre,  Seville,  Spain,  https://econpapers.repec.org/paper/
iptiptwpa/jrc136790.

Coelho,  M.  (2019),  ‘Fiscal  stimulus  in  a  monetary  union: 
Evidence from Eurozone regions’, IMF Economic Review, Vol. 67, 
No 3, Palgrave Macmillan Journals, pp. 573–617, https://www.
jstor.org/stable/48742532.

Commission Implementing Regulation (EU) No 215/2014 of 
7 March 2014 laying down rules for implementing Regulation 
(EU) No 1303/2013 of the European Parliament and of the 
Council  laying  down  common  provisions  on  the  European 
Regional Development Fund, the European Social Fund, the 
Cohesion  Fund,  the  European  Agricultural  Fund  for  Rural 

THE UPDATED RHOMOLO IMPACT ASSESSMENT OF THE 2014–2020 EUROPEAN COHESION POLICY (INCLUDING REACT-EU)28

Development and the European Maritime and Fisheries Fund 
and laying down general provisions on the European Regional 
Development Fund, the European Social Fund, the Cohesion 
Fund and the European Maritime and Fisheries Fund with regard 
to methodologies for climate change support, the determination 
of milestones and targets in the performance framework and 
the nomenclature of categories of intervention for the European 
Structural and Investment Funds, OJ L 69, 8.3.2014, p. 65, ELI: 
http://data.europa.eu/eli/reg_impl/2014/215/oj.

Crescenzi,  R.  and  Giua,  M.  (2020),  ‘One  or  many  cohesion 
policies of the European Union? On the differential economic 
impacts of cohesion policy across Member States’, Regional 
Studies, Vol. 54, Issue 1, Taylor & Francis Group, pp. 10–20, 
https://doi.org/10.1080/00343404.2019.1665174.

Crucitti, F., Lazarou, N. J., Monfort, P. and Salotti, S. (2022), ‘The 
RHOMOLO impact assessment of the 2014–2020 cohesion 
policy in the EU regions’, JRC Working Papers on Territorial 
Modelling and Analysis, No 01/2022, JRC128208, European 
Commission:  Joint  Research  Centre,  Seville,  Spain,  https://
econpapers.repec.org/RePEc:ipt:termod:202201.

Crucitti, F., Lazarou, N., Monfort, P. and Salotti, S. (2023), ‘Where 
does  the  EU  cohesion  policy  produce  its  benefits?  A  model 
analysis of the international spillovers generated by the policy’, 
Economic Systems, Vol. 47, Issue 3, 101076, Elsevier, https://
doi.org/10.1016/j.ecosys.2023.101076.

Crucitti, F., Lazarou, N. J., Monfort, P. and Salotti, S. (2024), ‘The 
impact  of  the  2014–2020  European  structural  funds  on 
territorial cohesion’, Regional Studies, Vol. 58, Issue 8, Taylor & 
Francis Group, pp. 1568–1582, https://doi.org/10.1080/003434
04.2023.2243989.

Dall’Erba, S. and Fang, F. (2017), ‘Meta-analysis of the impact 
of  European  Union  structural  funds  on  regional  growth’, 
Regional Studies, Vol. 51, Issue 6, Taylor and Francis Group, 
pp.  822–832,  https://doi.org/10.1080/00343404.2
015.1100285.

De la Fuente, A. (2002), Does Cohesion Policy Work? Some 
general considerations and evidence from Spain, Institute for 
Economic  Analysis  –  Spanish  Higher  Council  for  Scientific 
Research (IAE-CSIC), Barcelona, Spain, https://econpapers.repec.
org/RePEc:aub:autbar:563.03.

De la Fuente, A. and Ciccone, A. (2003), Human capital in a 
global and knowledge-based economy – Final report, Office for 
Official  Publications  of  the  European  Communities, 
Luxembourg,  https://op.europa.eu/publication-detail/-/
publication/5fcdeb9d-1724-45b6-8608-49be429cd9b4.

Di Caro, P. and Fratesi, U. (2022), ‘One policy, different effects: 
Estimating the region-specific impacts of EU cohesion policy’, 
Journal of Regional Science, Vol. 62, Issue 1, John Wiley & Sons 
Ltd, pp. 307–330, https://doi.org/10.1111/jors.12566.

Di Caro, P. and Fratesi, U. (2023), ‘The role of cohesion policy for 
sustaining the resilience of European regional labour markets 
during different crises’,  Regional Studies, Vol. 57, Issue 12, 
Taylor & Francis Group, pp. 2426–2442, https://doi.org/10.108
0/00343404.2022.2118252.

Di Comite, F., Lecca, P., Monfort, P., Persyn, D. and Piculescu, V. 
(2018),  ‘The  impact  of  cohesion  policy  2007–2015  in  EU 
regions: Simulations with the RHOMOLO interregional dynamic 
general equilibrium model’, JRC Working Papers on Territorial 
Modelling and Analysis, No 03/2018, JRC114044, European 
Commission:  Joint  Research  Centre,  Seville,  Spain,  https://
econpapers.repec.org/paper/ipttermod/201803.

Durand, L. and Espinoza, R. (2021), ‘The fiscal multiplier of 
European structural investment funds: Aggregate and sectoral 
effects with an application to Slovenia’, IMF Working Papers, 
WP/21/118,  International  Monetary  Fund,  https://doi.
org/10.5089/9781513573762.001.

Ederveen, S., De Groot, H. L. and Nahuis, R. (2006), ‘Fertile soil 
for structural funds? A panel data analysis of the conditional 
effectiveness of European cohesion policy’, Kyklos, Vol.  59, 
Issue  1,  John  Wiley  &  Sons  Ltd,  pp.  17–42,  https://doi.
org/10.1111/j.1467-6435.2006.00318.x.

European Commission (2016), Ex Post Evaluation of the ERDF 
and CF: Key outcomes of Cohesion Policy in 2007-2013, Work 
package 14c: Regression discontinuity design and Work package 
14d:  Propensity  score  matching.  Directorate-General  for 
Regional and Urban Policy.

European Commission: DG Regional and Urban Policy (2020), 
Test and Update of Regionalised ESIF Payments 1989-2018 – 
Final  report,  CCI  Contract:  2018CE16BAT115,  Publications 
Office of the European Union, Luxembourg, https://data.europa.
eu/doi/10.2776/569313.

European Commission: DG Regional and Urban Policy, Laguna, 
J. D., D’Apice, P., Grzegorzewska, M., De Franceschi, F., Brons, M., 
Maes, J., De Dominicis, L., Pesaresi, E., Gianelle, C., Heidelk, T., 
Monfort, P., Walsh, J. (2024), Ninth Report on Economic, Social 
and Territorial Cohesion, Publications Office of the European 
U n i o n ,  
h t t p s : / / d a t a . e u r o p a . e u /
doi/10.2776/264833.

L u x e m b o u r g ,  

Farole, T., Rodríguez-Pose, A. and Storper, M. (2011), ‘Cohesion 
policy in the European Union: Growth, geography, institutions’, 
JCMS: Journal of Common Market Studies, Vol. 49, Issue 5, John 
Wiley  &  Sons  Ltd,  pp.  1089–1111,  https://doi.
org/10.1111/j.1468-5965.2010.02161.x.

Ferrara, A. R., McCann, P., Pellegrini, G., Stelder, D. and Terribile, 
F.  (2017),  ‘Assessing  the  impacts  of  cohesion  policy  on  EU 
regions: A non-parametric analysis on interventions promoting 
research and innovation and transport accessibility’, Papers in 
Regional Science, Vol. 96, Issue 4, Elsevier, pp. 817–841, https://
doi.org/10.1111/pirs.12234.

Fiaschi,  D.,  Lavezzi,  A.  M.  and  Parenti,  A.  (2018),  ‘Does  EU 
cohesion policy work? Theory and evidence’, Journal of Regional 
Science, Vol. 58, Issue 2, John Wiley & Sons Ltd, pp. 386–423, 
https://doi.org/10.1111/jors.12364.

Fiuratti, F., Nikolova, D., Pennings, S. and Schiffbauer, M. (2023), 
‘Are regional fiscal multipliers on EU structural and investment 
fund spending large? A reassessment of the evidence’, Policy 
Research Working Papers, No 10658, World Bank Group, https://
openknowledge.worldbank.org/handle/10986/40870.

Fortuna, M., Silva, F. and Medeiros, A. (2016), ‘A CGE approach 
to measuring the impacts of EU structural funds in a small open 
economy’, Papers in Regional Science, Vol. 95, Issue 3, Elsevier, 
pp. 507–539, https://doi.org/10.1111/pirs.12137.

Foster, V., Gorgulu, N., Jain, D., Straub, S. and Vagliasindi, M. 
(2023),  ‘The  impact  of  infrastructure  on  development 
outcomes: A meta-analysis’, Policy Research Working Papers, 
N o  1 0 3 5 0 ,   Wo r l d   B a n k   G r o u p ,  
h t t p s : / / d o i .
org/10.1596/1813-9450-10350.

Fratesi, U. and Wishlade, F. G. (2017), ‘The impact of European 
Cohesion Policy in different contexts’, Regional Studies, Vol. 51, 
Issue 6, Taylor & Francis Group, pp. 817–821, https://doi.org/10
.1080/00343404.2017.1326673.

Gagliardi, L. and Percoco, M. (2017), ‘The impact of European 
cohesion policy in urban and rural regions’, Regional Studies, 
Vol. 51, Issue 6, Taylor & Francis Group, pp. 857–868, https://
doi.org/10.1080/00343404.2016.1179384.

Garau, G. and Lecca, P. (2015), ‘The impact of regional R & D 
subsidy  in  a  computable  general  equilibrium  model’, 
International Regional Science Review, Vol. 38, Issue 4, Sage 
P u b l i c a t i o n s ,  
h t t p s : / / d o i .
org/10.1177/0160017613485758.

3 1 9 – 3 5 7 ,  

p p . 

García Rodríguez, A., Lazarou, N., Mandras, G., Salotti, S. and 
Thissen, M. (2023), ‘A NUTS-2 inter-regional system of social 
accounting matrices for the European Union with 2017 data: 
Construction, and calibration of RHOMOLO V4’, JRC Working 
Papers  on  Territorial  Modelling  and  Analysis,  No  01/2023, 
JRC132883,  https://publications.jrc.ec.europa.eu/repository/
handle/JRC132883.

Gianelle, C., Guzzo, F., Barbero, J. and Salotti, S. (2024), ‘The 
governance  of  regional  innovation  policy  and  its  economic 
implications’, The Annals of Regional Science, Vol. 72, Springer 
Publishing Company, pp. 1231–1254, https://doi.org/10.1007/
s00168-023-01241-2.

Giannini, M. and Martini, B. (2024), ‘Regional disparities in the 
European  Union.  A  machine  learning  approach’,  Papers  in 
Regional Science, Vol. 103, Issue 4, 100033, Elsevier, https://doi.
org/10.1016/j.pirs.2024.100033.

29

Griffith, R., Redding, S. and Reenen, J. V. (2004), ‘Mapping the 
two faces of R & D: Productivity growth in a panel of OECD 
industries’, The Review of Economics and Statistics, Vol. 86, 
Issue 4,  The  MIT  Press,  pp. 883–895,  https://www.jstor.org/
stable/40042976.

Iammarino,  S.,  Rodriguez-Pose,  A.  and  Storper,  M.  (2019), 
‘Regional  inequality  in  Europe:  Evidence,  theory  and  policy 
implications’, Journal of Economic Geography, Vol. 19, No 2, 
Oxford University Press, pp. 273–298, https://doi.org/10.1093/
jeg/lby021.

Ignatov, A. (2023), ‘European highway networks, transportation 
costs,  and  regional  income’,  Regional  Science  and  Urban 
Economics, Vol. 104, 103969, Elsevier, https://doi.org/10.1016/j.
regsciurbeco.2023.103969.

Incaltarau,  C.,  Pascariu,  G.  C.  and  Surubaru,  N.  C.  (2020), 
‘Evaluating the determinants of EU funds absorption across old 
and new Member States – The role of administrative capacity 
and political governance’, JCMS: Journal of Common Market 
Studies, Vol. 58, Issue 4, John Wiley & Sons Ltd, pp. 941–961, 
https://doi.org/10.1111/jcms.12995.

Kancs,  D.  and  Siliverstovs,  B.  (2016),  ‘R & D  and  nonlinear 
productivity growth’, Research Policy, Vol. 45, Issue 3, Elsevier, 
pp. 634–646, https://doi.org/10.1016/j.respol.2015.12.001.

Kisiała, W. and Stępiński, B. (2024), ‘European Funds and the 
dynamics of economic growth among EU regions: A spatial 
modelling approach’, Quaestiones Geographicae, Vol. 43, No 2, 
S c i e n d o ,   p p .  6 7 – 8 0 ,  
h t t p s : / / d o i . o r g / 1 0 . 1 4 7 4 6 /
quageo-2024-0020.

Korzhenevych, A. and Bröcker, J. (2020), ‘Investment subsidies 
and regional welfare: A dynamic framework and its application 
to  the  European  regional  policy’,  Regional  Studies,  Vol.  54, 
Issue 9, Taylor & Francis Group, pp. 1262–1274, https://doi.org
/10.1080/00343404.2019.1702157.

Krugman,  P.  (1991),  ‘Increasing  returns  and  economic 
geography’, Journal of Political Economy, Vol. 99, No 3, The 
University  of  Chicago  Press,  pp.  483–499,  http://dx.doi.
org/10.1086/261763.

Lecca P., Barbero J., Christensen M.A., Conte A., Di Comite F., 
Diaz-Lanchas J., Diukanova O., Mandras G., Persyn D., Sakkas S. 
(2018), ‘RHOMOLO V3: A spatial modelling framework’, JRC 
Technical Reports, JRC111861, EUR 29229 EN, Publications 
Office of the European Union, Luxembourg, https://data.europa.
eu/doi/10.2760/671622.

Lecca, P., Christensen, M., Conte, A., Mandras, G. and Salotti, S. 
(2020), ‘Upward pressure on wages and the interregional trade 
spillover effects under demand-side shocks’, Papers in Regional 
Science, Vol. 99, Issue 1, John Wiley & Sons Ltd, pp. 165–182, 
https://doi.org/10.1111/pirs.12472.

THE UPDATED RHOMOLO IMPACT ASSESSMENT OF THE 2014–2020 EUROPEAN COHESION POLICY (INCLUDING REACT-EU)30

Leon-Ledesma,  M.  A.,  McAdam,  P.  and  Willman,  A.  (2010), 
‘Identifying the elasticity of substitution with biased technical 
change’, American Economic Review, Vol. 100, No 4, American 
Economic Association, pp. 1330–1357, https://doi.org/10.1257/
aer.100.4.1330.

Németh, G., Szabó, L. and Ciscar, J. C. (2011), ‘Estimation of 
Armington elasticities in a CGE economy–energy–environment 
model for Europe’, Economic Modelling, Vol. 28, Issue 4, Elsevier, 
h t t p s : / / d o i . o r g / 1 0 . 1 0 1 6 / j .
p p .  
econmod.2011.03.032.

1 9 9 3 – 1 9 9 9 ,  

Männasoo, K., Hein, H. and Ruubel, R. (2018), ‘The contributions 
of human capital, R & D spending and convergence to total 
factor productivity growth’, Regional Studies, Vol. 52, Issue 12, 
Taylor & Francis Group, pp. 1598–1611, https://doi.org/10.108
0/00343404.2018.1445848.

Mendez, C. and Bachtler, J. (2024), ‘The quality of government 
and administrative performance: Explaining cohesion policy 
compliance, absorption and achievements across EU regions’, 
Regional  Studies,  Vol.  58,  Issue  4,  Taylor  &  Francis  Group, 
pp.  690–703,  https://doi.org/10.1080/00343404.2
022.2083593.

Mincer, J. A. (1974), ‘Schooling and earnings’, in:  Schooling, 
Experience,  and  Earnings,  National  Bureau  of  Economic 
Research, pp. 41–63, https://www.nber.org/books-and-chapters/
schooling-experience-and-earnings.

Mogila, Z., Miklošovič, T., Lichner, I., Radvanský, M. and Zaleski, J. 
(2022), ‘Does cohesion policy help to combat intra-country 
regional  disparities?  A  perspective  on  central  European 
countries’, Regional Studies, Vol. 56, Issue 10, Taylor & Francis 
Group, pp. 1783–1795, https://doi.org/10.1080/00343404.2
022.2037541.

Mohl, P. (2016), ‘Econometric evaluation of EU cohesion policy: 
A survey’, in: Empirical Evidence on the Macroeconomic Effects 
of EU Cohesion Policy, Springer Gabler, Wiesbaden, Germany, 
pp. 7–35, https://doi.org/10.1007/978-3-658-13852-3_2.

Mohl, P. and Hagen, T. (2010), ‘Do EU structural funds promote 
regional  growth?  New  evidence  from  various  panel  data 
approaches’, Regional Science and Urban Economics, Vol. 40, 
Issue  5,  Elsevier,  pp.  353–365,  https://doi.org/10.1016/j.
regsciurbeco.2010.03.005.

Monfort, P. (2020), ‘Convergence of EU regions redux: Recent 
trends in regional disparities’, Working Papers, WP 02/2020, 
Publications Office of the European Union, Luxembourg, https://
data.europa.eu/doi/10.2776/27556.

Monfort,  M.,  Cuestas,  J.  C.  and  Ordonez,  J.  (2013),  ‘Real 
convergence in Europe: A cluster analysis’, Economic Modelling, 
Vol.  33,  Elsevier,  pp.  689–694,  https://doi.org/10.1016/j.
econmod.2013.05.015.

Monfort, P., Piculescu, V., Rillaers, A., Stryczynski, K. and Varga, J. 
(2017), ‘The impact of cohesion and rural development policies 
2007–2013: Model simulations with QUEST III’, Working Papers, 
WP  05/2017,  Publications  Office  of  the  European  Union, 
Luxembourg, https://data.europa.eu/doi/10.2776/521934.

Organisation  for  Economic  Co-operation  and  Development 
(2018), Education at a Glance 2018: OECD indicators, OECD 
Publishing, Paris, https://doi.org/10.1787/eag-2018-en.

Olekseyuk,  Z.  and  Schürenberg-Frosch,  H.  (2016),  ‘Are 
Armington elasticities different across countries and sectors? A 
European study’, Economic Modelling, Vol. 55, Elsevier, pp. 328–
342, https://doi.org/10.1016/j.econmod.2016.02.018.

Pellegrini, G., Terribile, F., Tarola, O., Muccigrosso, T. and Busillo, 
F. (2013), ‘Measuring the effects of European regional policy on 
economic growth: A regression discontinuity approach’, Papers 
in Regional Science, Vol. 92, Issue 1, Elsevier, pp. 217–233, 
https://doi.org/10.1111/j.1435-5957.2012.00459.x.

Percoco, M. (2017), ‘Impact of European cohesion policy on 
regional  growth:  Does  local  economic  structure  matter?’, 
Regional  Studies,  Vol.  51,  Issue  6,  Taylor  &  Francis  Group, 
pp.  833–843,  https://doi.org/10.1080/00343404.2
016.1213382.

Persyn, D., Díaz-Lanchas, J. and Barbero, J. (2020), ‘Estimating 
distance and road transport costs between and within European 
Union regions’, Transport Policy, Vol. 124, Elsevier, pp. 33–42, 
https://doi.org/10.1016/j.tranpol.2020.04.006.

Persyn, D., Barbero, J., Díaz-Lanchas, J., Lecca, P., Mandras, G. et 
al.  (2023),  ‘The  ripple  effects  of  large-scale  transport 
infrastructure investment’, Journal of Regional Science, Vol. 63, 
Issue  4,  Wiley  Periodicals  LLC,  pp.  755–792,  https://doi.
org/10.1111/jors.12639.

Pieńkowski,  J.  and  Berkowitz,  P.  (2016),  ‘Econometric 
assessments of Cohesion Policy growth effects: How to make 
them more relevant for policymakers?’, in: EU Cohesion Policy, 
Routledge, pp. 55–68, http://doi.org/10.4324/9781315401867.

Pinho,  C.,  Varum,  C.  and  Antunes,  M.  (2015),  ‘Under  what 
conditions  do  structural  funds  play  a  significant  role  in 
European  regional  economic  growth?  Some  evidence  from 
recent panel data’, Journal of Economic Issues, Vol. 49, No 3, 
Taylor & Francis Group, pp. 749–771, https://doi.org/10.1080/0
0213624.2015.1072382.

Psacharopoulos,  G. and  Patrinos,  H. A.  (2018a),  ‘Returns  to 
investment  in  education:  A  decennial  review  of  the  global 
literature’,  Education  Economics,  Vol.  26,  Issue  5,  Taylor  & 
Francis Group, pp. 445–458, https://doi.org/10.1080/09645292
.2018.1484426.

Psacharopoulos,  G. and  Patrinos,  H. A.  (2018b),  ‘Returns  to 
investment  in  education:  A  decennial  review  of  the  global 

31

literature’, Policy Research Working Papers, No 8402, World 
Bank  Group,  http://documents.worldbank.org/curated/
e n / 4 4 2 5 2 1 5 2 3 4 6 5 6 4 4 3 1 8 /
Returns-to-investment-in-education-a-decennial-review-of-
the-global-literature.

Psycharis, Y., Tselios, V. and Pantazis, P. (2024), ‘Cohesion policy 
and household income inequality: evidence from the Greek 
regions’,  The  Annals  of  Regional  Science,  Vol.  73,  Springer 
Publishing Company, pp. 1663–1591, https://doi.org/10.1007/
s00168-024-01304-y.

Ramajo, J., Márquez, M. A., Hewings, G. J. and Salinas, M. M. 
(2008), Spatial heterogeneity and interregional spillovers in the 
European Union: Do cohesion policies encourage convergence 
across regions?, European Economic Review, Vol. 52, Issue 3, 
Elsevier,  pp.  551–567,  https://doi.org/10.1016/j.
euroecorev.2007.05.006.

Ramey,  V. A.  (2020),  ‘The  macroeconomic  consequences  of 
infrastructure  investment’,  NBER  Working  Paper  Series, 
No 27625, National Bureau of Economic Research, https://doi.
org/10.3386/w27625.

Rodríguez-Pose, A. and Dijkstra, L. (2021), ‘Does cohesion policy 
reduce EU discontent and Euroscepticism?’, Regional Studies, 
Vol. 55. Issue 2, Taylor & Francis Group, pp. 354–369, https://
doi.org/10.1080/00343404.2020.1826040.

Rodríguez-Pose,  A.  and  Garcilazo,  E.  (2015),  ‘Quality  of 
government  and  the  returns  of  investment:  Examining  the 
impact of cohesion expenditure in European regions’, Regional 
Studies, Vol. 49, Issue 8, Taylor & Francis Group, pp. 1274–
1290, https://doi.org/10.1080/00343404.2015.1007933.

Santoleri,  P.  and  Russo,  E.  (2025),  ‘Spurring  subsidy 
entrepreneurs’,  Research  Policy,  Vol.  54,  Issue  1,  105128, 
Elsevier, https://doi.org/10.1016/j.respol.2024.105128.

Sosvilla-Rivero, S., Bajo-Rubio, O. and Díaz-Roldán, C. (2006), 
‘Assessing the effectiveness of the EU’s regional policies on real 
convergence:  An  analysis  based  on  the  HERMIN  model’, 
European Planning Studies, Vol. 14, Issue 3, Taylor & Francis 
G r o u p ,  
h t t p s : / / d o i .
3 8 3 – 3 9 6 ,  
p p . 
org/10.1080/09654310500420933.

Thissen,  M.,  Ivanova,  O.,  Mandras,  G.  and  Husby,  T.  (2019), 
‘European NUTS 2 regions: Construction of interregional trade-
linked Supply and Use tables with consistent transport flows’, 
JRC  Working  Papers  on  Territorial  Modelling  and  Analysis, 
No  01/2019,  JRC115439,  European  Commission:  Joint 
Research Centre, Seville, Spain, https://publications.jrc.ec.europa.
eu/repository/handle/JRC115439.

Varga, J. and in’t Veld, J. (2011a), ‘A model-based analysis of 
the  impact  of  cohesion  policy  expenditure  2000–06: 
Simulations  with  the  QUEST  III  endogenous  R  &  D  model’, 
Economic Modelling, Vol. 28, Issues 1–2, Elsevier, pp. 647–663, 
https://doi.org/10.1016/j.econmod.2010.06.004.

Varga, J. and in’t Veld, J. (2011b), ‘Cohesion policy spending in 
the new Member States of the EU in an endogenous growth 
model’, Eastern European Economics, Vol. 49. Issue 5, Taylor & 
Francis  Group,  pp.  29–54,  https://doi.org/10.2753/
EEE0012-8775490502.

Varga, A., Szabó, N. and Sebestyén, T. (2020), ‘Economic impact 
modelling  of  smart  specialization  policy:  Which  industries 
should prioritization target?’, Papers in Regional Science, Vol. 99, 
Issue 5, John Wiley & Sons Ltd, pp. 1367–1389,  https://doi.
org/10.1111/pirs.12529.

Von Ehrlich, M. and Overman, H. G. (2020), ‘Place-based policies 
and  spatial  disparities  across  European  cities’,  Journal  of 
Economic  Perspectives,  Vol.  34,  No  3,  American  Economic 
Association, pp. 128–149, https://doi.org/10.1257/jep.34.3.128.

THE UPDATED RHOMOLO IMPACT ASSESSMENT OF THE 2014–2020 EUROPEAN COHESION POLICY (INCLUDING REACT-EU)32

ANNEXES

ANNEX 1. 

DATA PROCESSING

The process to obtain the input data for the analyses consists 
of the following four steps.

1.  The latest raw data are accessed from https://

cohesiondata.ec.europa.eu/d/3kkx-ekfq. The raw data 
contain yearly cumulative aggregate financial amounts 
and are recorded at the programme level with information 
on fields of intervention, category of region and specific 
objectives, among other elements.

2.  The financial amounts indicated in the raw data are linked 
to the regions of the EU, at the NUTS 2 level. On the one 
hand, the data are linked using a dedicated linked dataset 
(https://cohesiondata.ec.europa.eu/d/466c-pqi8). On the 
other hand, for programmes that span more than one 
region, the financial amounts are allocated pro rata to the 
relevant regions. The latter takes into account the category 
of region which the programmes target. Overall, this step 

yields insights concerning the financial intervention, per 
region, for each intervention field.

3.  The data are aggregated at the level of analysis, with one 
observation per year, NUTS 2 region and model shock (see 
Section 4 on the RHOMOLO model). Further, the yearly 
cumulative aggregate financial amounts are adjusted to 
reflect yearly financial amounts by taking the difference 
between subsequent years.

4.  The financial amounts reflect reports by national/regional 
managing authorities with regards to the implemented 
programmes and the related expenditures. In the reports, 
managing authorities indicate the financial amounts which 
are eligible for reimbursement by the EU. This yields two 
cases. First, the managing authorities of a country may 
report more than 100 % of the financial amounts allocated 
to the country in the multiannual financial framework. In 
this case the financial allocation is capped at 100 %, in line 
with the budgetary procedures in place. Second, for some 
countries the data reflect absorption rates below 100 %. 
Based on experience from previous programming periods, 
full absorption is expected to be achieved. The gap 
amounts are allocated to the year 2023.

33

ANNEX 2. 
ABSORPTION OVER TIME

ALLOCATIONS AND 

Figure A1: Annual share of expenditures within categories of regions, across years and regions; all funds

40

30

20

10

0

s
e
r
u
t
i
d
n
e
p
x
e
U
E

)
s
n
o
g
e
r

i

n

i

s
d
n
u
f

l
l

a

f
o

l

a
t
o
t

f
o
%

(

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

Source: Cohesion Open Data Platform (https://cohesiondata.ec.europa.eu); calculations by DG Regional and Urban Policy.

Less developed

Transition

More developed

Figure A2: Cumulative annual share of expenditures within categories of regions, across years and regions; all funds

100

80

60

40

20

0

s
e
r
u
t
i
d
n
e
p
x
e
U
E

)
e
v
i
t
a
u
m
u
c

l

,

s
d
n
u
f

l
l

a

f
o

l

a
t
o
t

f
o
%

(

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

Less developed:
Transition:
More developed:

Initial allocation
Initial allocation
Initial allocation

REACT-EU
REACT-EU
REACT-EU

NB: Figure A2 builds on Figure A1. Joint allocation sums up to 100 % for each group of regions.
Source: Cohesion Open Data Platform (https://cohesiondata.ec.europa.eu); calculations by DG Regional and Urban Policy.

THE UPDATED RHOMOLO IMPACT ASSESSMENT OF THE 2014–2020 EUROPEAN COHESION POLICY (INCLUDING REACT-EU) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Getting in touch with the EU

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

On the phone or in writing
Europe Direct is a service that answers your questions about the European Union. You can 
contact this service: 

 — by freephone: 00 800 6 7 8 9 10 11 (certain operators may charge for these calls), 
 — at the following standard number: +32 22999696, 
 — via the following form: european-union.europa.eu/contact-eu/write-us_en.

Finding information about the EU

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

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

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

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