The impact of cohesion and rural development policies 2007-2013 : model simulations with QUEST III.
Date: 2017-06-02
Institutions: Directorate-General for Regional and Urban Policy
Subjects: econometrics, economic and social cohesion, economic growth, fiscal policy, regional disparity, research and development, rural development

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

WP 05/2017

THE IMPACT OF COHESION 
 AND RURAL DEVELOPMENT 
 POLICIES 2007-2013:

MODEL SIMULATIONS WITH QUEST III

PHILIPPE MONFORT, VIOLETA PICULESCU, ALEXANDRA
RILLAERS, KAI STRYCZYNSKI, JANOS VARGA

Regional and
Urban Policy

> EXECUTIVE SUMMARY

Cohesion policy is the Union's main instrument to promote regional development and reduce disparities 
among the Union's Member States and regions. It concentrates its support in the fields of R&D, support to 
SMEs, education, or transport, telecommunication, social and environmental infrastructure.

Cohesion funding is allocated to all Member States and regions in the EU but relative to their GDP the 
amounts accruing to the less developed ones are much higher than elsewhere. In some countries, it 
represents more than 3% of GDP, financing a substantial part of public investment.

The EU rural development policy supports rural areas in tackling their economic, environmental and social 
challenges. Its main strategic objectives are to foster the competitiveness of agriculture, to ensure the 
sustainable management of natural resources and to promote a balanced territorial development of rural areas.

For the 2007-2013 programming period, the EU allocated €347 billion for cohesion policy and €96 billion 
for rural development. Member States allocations were divided into annual amounts which must be spent 
within two or three years, depending on the country, over the period 2007-2015.

This paper provides an assessment of the potential impact of cohesion and rural development policies for 
the programming period 2007–2013 using QUEST. The model simulates the impact of policy interventions 
on a large number of economic variables relevant to cohesion and rural development policies such as GDP, 
employment, wages, productivity, or corporate investment. 

Simulations show that cohesion and rural development policies affect many key economic variables. Overall, 
the simulations shows that the interventions improve the structure of the EU economies and hence their 
competitiveness. In particular, they have a positive and significant impact on the productivity of factors of 
production,  as  result  of  direct  investment  in  technology  but  also  of  enhanced  business  conditions 
encouraging investment in tangible and intangible assets.

As a result, interventions substantially increased GDP, in particular in the Member States which are the main 
beneficiaries of the policies. The highest impact is found in Hungary (+ 5.3%) and Latvia (+ 5.1%) as well as 
in Poland (+4.3%). The impact is also substantial in EU-15 Member States like Greece (+2.2%), Portugal 
(+1.8%) and Spain (+0.7%) which benefited from support of the Cohesion Fund.

Finally, cohesion and rural development policies yield high value for money. As expected from policies 
supporting investments in key engines of growth, a substantial part of their effects progressively increases 
over time and only emerge in the long run. However, once they produce their full impact, the interventions 
prove to benefit to the whole Union even if they are concentrated in its less developed places.

> Contents

INTRODUCTION 

1.   
2.    The use of models for assessing the impact of cohesion and rural development policies 
3.    Cohesion and rural development policies: coverage and allocations 
4.    The Model 
5.    Model simulations 
5.1  Model elds of intervention 
5.2  Member States policy mix - Impact on GDP 
6.    Sensitivity analysis 
7.    Conclusions  
REFERENCES 
APPENDIX 
A.1  ERDF, CF and ESF Mapping of priority themes into elds of intervention 
A.2  EARDF Mapping of measures into elds of intervention 
A.3 

Impact per eld of intervention 

1
2
4
5
9
11
15
21
23
23
25
25
28
30

1

The Impact of Cohesion and Rural Development Policies 2007-2013: Model
Simulations with QUEST III(cid:31)

Philippe Monforta,

∗, Violeta Piculescua, Alexandra Rillaersa, Kai Stryczynskia, Janos Vargab

aEuropean Commission, Directorate General for Regional and Urban Policy, Brussels, Belgium
bEuropean Commission, Directorate General for Economic and Financial Aﬀairs, Brussels, Belgium

Abstract

For the 2007-2013 programming period, the EU allocated e347 billion for cohesion policy and
e96 billion for rural development. The funds were used to ﬁnance programmes promoting growth
and development across the Union. The paper provides an ex-post impact assessment of these
programmes based on a set of simulations using QUEST, a DGSE model with semi-endogenous
growth. The results show that the interventions contributed to enhance economic performance of
the EU Member States, in particular of the less developed which are the main beneﬁciaries of the
policies. They also suggest that the policies yield good value for money, especially in the long run.

Keywords: Cohesion Policy, Rural Development, endogenous growth, R&D, dynamic general
equilibrium modelling.
JEL code: C53, E62, O30, O41.

(cid:31)The views expressed in this paper are those of the authors and should not be attributed to the European

Commission.

∗Corresponding author.
Philippe.Monfort@ec.europa.eu

European Commission, BU1-03/95, B-1049 Brussels, Belgium.

E-mail:

THE IMPACT OF COHESION AND RURAL DEVELOPMENT POLICIES 2007-20132

cannot provide information on their net global impact. The programmes produce many direct and

indirect eﬀects which implies that in order to fully capture their impact on the EU economies,

one need to use analytical instruments capable of tracking how the policies aﬀect the allocation of

resources in a general equilibrium perspective.

In this paper, the potential impact of cohesion and rural development policies for the program-

ming period 20072013 is assessed using QUEST, a model developed by the Directorate General for

Economic and Financial Aﬀairs of the European Commission. The model simulates the impact

of policy interventions on a large number of economic variables relevant to cohesion and rural

development policies such as GDP, employment, wages, productivity, or corporate investment. This

type of approach allows to examine the outcome of various policy scenario taking into consideration

the manner in which interventions aﬀect the allocation of resources throughout the economy, thus

enabling an analysis of policy impacts at the macroeconomic level.

The paper is organised as follows. Section 2 explains the value added of analysing this type

of policy interventions by means of model simulations. Section 3 gives the scale and scope of the

policies by detailing where and how the ﬁnancial resources involved were spent. Section 4 describes

QUEST and its main features. Section 5 presents the results of the simulations. Finally, section 6

concludes.

2. The use of models for assessing the impact of cohesion and rural development

policies

When looking at the impact of cohesion and rural development policies on macroeconomic

variables such as GDP, employment or productivity, we ﬁrst need to diﬀerentiate between short-term

(mostly demand) and long-term (supply-side) eﬀects.

Short-term eﬀects occur mainly during the period when the programmes are being implemented

in the form of projects on the ground (e.g. road construction, training schemes, etc.). These

interventions boost output and employment (e.g. construction workers, trainers), thereby increasing

income and hence demand, which to some extent translates into another increase in output(so-called

Keynesian multiplier eﬀect).

The policies also enhance the structure of the economies and increase the productivity of its

factors of production. These supply-side eﬀects are likely to progressively build up in time and last

long after the implementation of the interventions is over, therefore only becoming signiﬁcant in

the medium to long-term. For example, the impact of investment in R&D typically takes time

to become apparent but its output gains can be signiﬁcant and continue to increase long after

spending is discontinued.

Second, interventions do not only have direct impacts but also indirect ones. For instance,

projects in the ﬁeld of transport directly boost demand in the short run (e.g. increase in public

consumption) and improve the structure of the economy in the long run, with a combined positive

2

3

impact on GDP. At the same time, these interventions increase labour demand which can fuel wage

and price inﬂation, thereby adversely aﬀecting GDP. These feedback eﬀects are often diﬃcult to

pinpoint.

Cohesion and rural development policies are also likely to generate important spillover eﬀects

and externalities outside the economies directly beneﬁting from the funds. Examples include the

demand expansion in the beneﬁciary country leading to higher exports from other countries, or

R&D innovations in one economy generating technological progress in other economies. Again, such

spillover eﬀects are not accounted for when examining the outcomes of projects.

Third, economic performance is aﬀected by a wide range of other developments which happen

to coincide with the interventions, including other policy actions or changes in the business cycle.

The speciﬁc impact of the policy under analysis can therefore not be identiﬁed by simply looking at

the data included in the national and regional accounts. In order to capture the impact which can

be attributed to the policy, one would need to compare the world as it is with what it would have

been without the policy which obviously cannot be observed in reality.

The use of macroeconomic models allows to partly address these issues. First, models can be

used to simulate the world without the policy and hence provide a solid counterfactual against

which the impact of the policy can be assessed. Second, they allow simulating both the short-term

and long-term impacts of the policy and take the interaction between direct and indirect eﬀects

into account. Third, models account for the spill-over eﬀects and externalities and thereby give

a full assessment of the policy impact. Finally, models help tracing back the eﬀects of the policy

interventions and shed light on the channels through which the policy produces its impact on the

economy.

Macroeconomic models have often been used to assess structural policies and their economy-wide

impacts. Cohesion policy is no exception to this. For years, the Directorate General for Regional and

Urban Policy of the European Commission (DG REGIO) has assessed the impact of its programmes

with the help of models such as HERMIN (Bradley et al., 2003) or EcoMod (Bayar, 2007). QUEST

has also often been used in the past for assessing the impact of cohesion policy. For instance, Varga

and in ’t Veld (2011a) conduct an ex-post impact assessment (i.e. based on actual expenditure)

of the 2000-2006 programmes implemented in the main beneﬁciaries of the policy, namely the 10

Member States which joined the Union in 2004 plus Germany, Greece, Italy, Ireland, Portugal and

Spain while Varga and in ’t Veld (2011b) provide an ex-ante analysis of the policy (i.e. taking

into account the resources allocated by the policy to the Member States rather than the actual

expenditure) covering the same countries plus Bulgaria and Romania. Compared to these earlier

contributions, this paper considers the impact of both cohesion and rural development policies and

takes into account the actual expenditure of the 2007-2013 programmes implemented in all Member

States of the Union. Other institutions have also analysed the EU cohesion policy using economic

models. For instance, based on its multi-region model GIMF (Global Integrated Monetary and

Fiscal) the IMF has assessed the potential impact of the EU cohesion transfer in the new Member

3

THE IMPACT OF COHESION AND RURAL DEVELOPMENT POLICIES 2007-20134

States from 2004 to 2015 (Allard et al., 2008).

Finally, diﬃculties to absorb the funds have sometime been reported in some countries. Since

the analysis presented in this paper is based on the expenditure eﬀectively executed by the Member

States, it implicitly account for this issue. However, some authors (see for instance Ederveen

et al. (2006)) have argued that funding is not always used so as to maximise its impact. The

reasons usually invoked for such sub-optimal use of policy resources are related to possible rent-

seeking activities and diversion due to protectionism and market rigidities. However, in absence

of quantiﬁed evidence concerning the degree to which cohesion and rural development funding is

optimally used, macroeconomic models cannot incorporate this aspect and their results should

therefore be interpreted as providing the potential impact of the policies, i.e. the one that would

emerge if resources are directed to the most eﬀective projects.

3. Cohesion and rural development policies: coverage and allocations

The EU funds covered by this exercise include the ERDF, CF, ESF and the EAFRD. The EU
payments for cohesion and rural development have reached the level of almost e383 billion during
the 2007-2013 programming period, of which 76% are represented by the structural and cohesion

funds (ERDF, CF and ESF). The data used for the simulation of cohesion and rural development

policies is based on several sources as follows.

The EU expenditure for ERDF and CF correspond to advance and interim annual payments

reported in the SFC database of DG REGIO over the period 2007 - October 2015, subject to two

adjustments. First, these data do not give details concerning the categories of expenditure in which

investment took place. However, this information is available from the Work Package 13 of the

ex-post evaluation 2007-2013 (European Commission, 2015) which breakdowns total expenditure

for 2014 into 86 categories covering the areas in which support has been provided (Appendix 1 lists

the 86 categories of expenditure). This was then used to approximate the distribution of the funds

across the categories of expenditure for the other years. Second, at the time they were collected

data on payments (i.e. funding which has been used by the beneﬁciary) was available until early

October 2015. In order to approximate total EU payments until end 2015, we assumed that the

countries which, by October 2015, had not reached an execution rate of 95% would in the end
absorb at most the same level as in year 20144.

Similarly, for the ESF total EU expenditure over the period 2007-2015 is proxied by the advance

and interim annual payments, subject to the assumption on absorption described above. The

distribution of the fund across priority themes within country is approximated by the distribution

of latest decided amounts across these types of expenditure for each country. Data on EAFRD,

provided by the Directorate-General for Agriculture and Rural Development of the European

4The execution rate corresponds to the share of the programmes ﬁnancial allocation which has actually been used

at a given date. It gives an indication on the capacity of the beneﬁciary to absorb the funding available.

4

5

Commission (DG AGRI), refer to payment requests ﬁled until August 2015, broken down by 46

types of expenditure relevant for rural development (Appendix 1 lists the types of expenditure).

For the rural development fund, no further assumption on absorption has been made.

Total actual EU payments and the resulting series for estimated absorption are presented by
country in Table 1, columns (1) and (5)5. The assumption on absorption for cohesion funding
signiﬁcantly increases the level of payments for Germany, Hungary and Romania. Indeed, the

estimation on the total level of payments amounts to 2.5% of actual payments for the period

October-December 2015.

The breakdown of total EU payments by funds reported in Table 1 highlight diﬀerent distributions

across countries. First, shares higher than 50% for ERDF and CF in total payments are observed

in Easter and Central European countries, as well as in Greece, Italy and Portugal. The highest

shares for ESF, on the other hand, are reported in the Netherlands and Belgium (36 and 40%

respectively). For EAFRD, the highest shares are observed in Austria and Ireland (79%), followed

by Luxembourg (67%) and Denmark (55%).

The time proﬁle of EU payments for the four funds combined is shown in Figure 1. The graph

presents the payment proﬁle for two groups of countries: EU-15 (EU members prior to accession in

2004) and EU-12 (EU Members States which joined the EU after 2004).

Overall, the payments made to the two groups of countries are roughly similar (51% of payments

in the EU-15 and 49% in the EU-12) but the time proﬁle of annual payments diﬀers. In the ﬁrst

year, for instance, EU payments were 82% higher in the EU-15 than in the EU-12, this pattern

being maintained until 2014 when the EU annual payments for the EU-12 exceeded the ones in the

EU-15.

The importance of the funds for the Member States economy is better highlighted when expressed

as a percentage of GDP (Figure 2). Across years, cohesion and rural development payments range

between 0.20% (2007) and 0.52% (2013) of GDP in the EU-15. In the EU-12, they range from

0.41% of GDP in 2007 to 2.78% in 2013. Overall, for the EU-27 cohesion and rural development

funding represent between 0.08% and 0.49% of GDP over the period 2007-2015.

In sum, the data on EU payments for the four funds combined highlight the following: 1) the

amounts of total payments to the EU-12 and the EU-15 are roughly equal; 2) relative to the EU-12,

the EU-15 seem to use funding at an earlier stage of the programming period; and 3) the weight of

EU funding is signiﬁcantly higher in the economy of the EU-12.

4. The Model

The model used in this paper is QUEST III which has been developed by DG Economic and

Financial Aﬀairs (DG ECFIN) of the European Commission. The model is regularly used for the

5The amounts reported in Table 1 diﬀer from those mentioned in the introduction because the former correspond
to cumulated expenditures up to 2015 while the latter correspond to the allocation budgeted for in 2006 for the period
2007-2013.

5

THE IMPACT OF COHESION AND RURAL DEVELOPMENT POLICIES 2007-20136

Table 1: EU advance and interim payments 2007- 2015.

Country 

Total actual 
payments 
(all funds) 
 2007-Oct 
2015  
(mio euro) 
(1) 

AT 

BE 

BG 

CY 

CZ 

DE 

DK 

EE 

ES 

FI 

FR 

GR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

RO 

SE 

SI 

SK 

UK 

5103 

2323 

7639 

704 

24082 

32120 

1024 

3957 

36026 

3661 

19120 

21735 

23977 

3170 

29967 

8202 

142 

5339 

755 

2060 

76075 

24391 

20094 

3464 

4659 

10087 

13006 

Total 

382881 

Share in Total Payments  
(col. 1) 
(%) 

ERDF+CF  ESF 

EAFRD
* 

(2) 

0.11 

0.40 

0.55 

0.62 

0.75 

0.45 

0.24 

0.72 

0.63 

0.25 

0.37 

0.68 

0.72 

0.11 

0.51 

0.67 

0.17 

0.70 

0.79 

0.36 

0.70 

0.57 

0.52 

0.25 

0.65 

0.70 

0.36 

0.58 

(3) 

0.10 

0.40 

0.14 

0.16 

0.13 

0.27 

0.21 

0.09 

0.16 

0.16 

0.26 

0.17 

0.12 

0.11 

0.20 

0.12 

0.16 

0.10 

0.12 

0.36 

0.12 

0.27 

0.11 

0.19 

0.15 

0.11 

0.29 

0.17 

(4) 

0.79 

0.21 

0.30 

0.22 

0.12 

0.28 

0.55 

0.18 

0.21 

0.59 

0.37 

0.15 

0.16 

0.79 

0.29 

0.22 

0.67 

0.20 

0.10 

0.29 

0.17 

0.17 

0.37 

0.56 

0.20 

0.18 

0.35 

0.24 

Estimated 
payments  
(all funds) 
2007-2015**  
(mio euro) 

(5) 

5103 

2337 

7669 

737 

24082 

33265 

1048 

3957 

36026 

3671 

19710 

22529 

26590 

3185 

30284 

8202 

143 

5358 

856 

2152 

76841 

24400 

22259 

3469 

4716 

10284 

13472 

392345 

 Source: DG REGIO. EAFRD data refers to requests for payments until August 2015.
** Estimated absorption until end 2015.

analysis of key ﬁscal and monetary policy scenarios or for assessing the impact of the structural

reforms. For the analysis of cohesion policy and rural development, we adopt the R&D version of

QUEST III which is a dynamic stochastic general equilibrium (DGSE) model with semi-endogenous

6

  
7

Figure 1: Time pattern of EU payments, all funds (million euros).

Period: 2007-2015 (mio euros) 
EU-15 
EU-12 
EU-27 
Funds: ERDF, CF, ESF, EARDF 

200,793 
191,552 
392,345 

40000

35000

30000

25000

20000

15000

10000

5000

0

EU-15

EU-12

2007

7015

3849

2008

9871

8221

2009

17324

16388

2010

23879

20019

2011

26980

24461

2012

30024

27090

2013

34384

31299

2014

29044

32474

2015*

22273

27751

Source: DG REGIO. *Totals for ERDF, CF and ESF in 2015 are estimated until end-year; EAFRD payment requests
data until August 2015.

growth based on Jones (1995). The model belongs to the class of New-Keynesian DGSE models that

are now widely used in economic policy institutions. It provides a fully micro-founded, integrated

and optimization-based representation of the economies of the Member States. Results are produced

at the national level on a wide set of economic variables such as for instance GDP, employment,

wages, investment or productivity.

QUEST is structured around building blocks which represent the behaviour of fundamental

economic agents and interactions. The model describes the dynamics of the system in a general

equilibrium framework where changes in the conditions for a particular block are transmitted to

the other blocks though various market interactions. The model, the underlying assumptions and

the calibration procedure are fully described in Roeger et al. (2008) and D’Auria et al. (2009).

The model features a R&D sector and two types of ﬁrms: the R&D sector produces designs by

using high skilled labour as well as the domestic and foreign knowledge embodied in the existing

stock of designs. Households buy the patents of designs from the R&D sector and license them

to ﬁrms producing intermediate goods. These ﬁrms are monopolistically competitive and produce

varieties of intermediate products using physical capital rented from households. Each from must

7

THE IMPACT OF COHESION AND RURAL DEVELOPMENT POLICIES 2007-2013 
8

Figure 2: Time pattern of EU payments, all funds (as % of GDP).

)

%

(
e
r
a
h
s
e
g
a
r
e
v
A

3.00

2.50

2.00

1.50

1.00

0.50

0.00

2007

2008

2009

2010

2011

2012

2013

2014

2015*

EU-15

EU-12

EU27

0.10

0.41

0.24

0.13

0.75

0.41

0.23

1.97

1.00

0.33

2.22

1.17

0.37

2.22

1.19

0.47

2.57

1.40

0.52

2.78

1.52

0.42

2.74

1.45

0.20

2.12

1.05

Source: DG REGIO. *Totals for ERDF, CF and ESF in 2015 are estimated until end-year; EAFRD payment requests
data until August 2015.

acquire a design in order to start operating. Finally, producers of ﬁnal goods use a combination of

intermediate goods and three types of labour - low, medium and high-skilled. As in Romer (1990),

the main engine of endogenous growth consists in the accumulation of knowledge which enhances

the capacity of the research industry to produce new designs, eventually leading to increase the

number of varieties in the intermediate goods sector and hence the productivity in the ﬁnal goods

sector.

Capital is rented by ﬁrms in exchange of interest (or dividends) which are key components

of the capital cost. Labour is hired from households against a wage rate which, together with

the level of employment, is determined on the labour market. The productivity of ﬁrms is also

positively aﬀected by the stock of public capital which is provided by the government through

public investment. The government also raises taxes which are used to ﬁnance its consumption and

investment expenditure.

Households can accumulate human capital in the three types of labour skills by participating in

education. During the time spent in schooling, individuals are not employed and as a result, they

are not included in the labour supply. Nevertheless, the accumulation of human capital increases

labour productivity over time.

Varga and in ’t Veld (2011a,b) extended the model with multicountry-setting, including the 27

8

 
 
 
 
9

Member States, the rest of the world. The individual country blocks are linked through international

trade. The model also allows for international R&D spillovers in order to capture the fact that

technology is not fully appropriable and that innovation can be absorbed by non-innovative agents

(e.g. through imitation). Support to R&D in one country will therefore also have a positive impact

on the level of technology in the rest of the EU. In this respect, the model takes into account the

fact that programmes implemented in a particular Member States produce an impact in the other

countries by aﬀecting the intensity of trade and/or knowledge ﬂows. The model takes into account

the fact that cohesion and rural development policies are ﬁnanced by contributions of the Member

States to the community budget. It assumes that the contribution of each Member State to the

community budget is proportional to its GDP and is ﬁnanced by adapting VAT taxes. Taxes are

distortionary and their increase adversely aﬀects economic performance. This negative eﬀect partly

oﬀsets the positive impact of the programmes.

The model allows to consider a wide range of policy interventions, some of which being closely

related to cohesion and rural development policies. Support to R&D is assumed to facilitate the

adoption of innovation by reducing the cost of producing new processes. The government can also

help ﬁrms by providing subsidies (modelled as reductions in ﬁxed costs) or by easing their access to

ﬁnance, thereby reducing the cost of capital and encouraging investments. The government plays

another key role by providing public infrastructure which contributes to building up the stock of

public capital without which ﬁrms cannot operate. Public interventions in the ﬁeld of education

increase the eﬃciency of the schooling system in enhancing human capital which, by increasing

labour productivity, contributes to increasing competitiveness and wages.

In general, the analysis is conducted by simulating and comparing two scenarios. The baseline

scenario relies on the natural trend in the economy, excluding any policy intervention. The second

scenario includes the policy interventions for cohesion and rural development. By comparison with

the baseline, it allows to isolate the impacts of the policy on the economy. For a given variable,

the diﬀerence between the values obtained under the two scenarios is interpreted as the impact
attributable to the policy. It is expressed as a percentage deviation from the baseline6. The model
has been calibrated based on 2010 data and hence accounts for the particular conditions of the EU

economies at the time of implementing the 2007-2013 programmes.

5. Model simulations

As explained above, investments ﬁnanced by cohesion and rural development policies are broken

down in 86 and 46 categories of expenditure respectively. For the purpose of the simulations, these

categories have been used to group investments into ﬁve ﬁelds of intervention which are relevant

6The baseline is established on the basis of assumptions concerning the trends of key variables which is common
practice in economic modelling. The results, which correspond to the diﬀerence between the baseline and the
’with-policy’ scenarios, are independent from the baseline.

9

THE IMPACT OF COHESION AND RURAL DEVELOPMENT POLICIES 2007-201310

for the model: infrastructure, human capital, research and development (R&D), aid to private

sector, and technical assistance and other interventions. Appendix 1 shows how the categories of

EU investments are mapped into the ﬁve ﬁelds of intervention. The corresponding policy mix for

each Member States is displayed in Table 2 below.

Table 2: Distribution of EU expenditure per ﬁelds of intervention (% of total expenditure).

Research and Development 

AT  

BE  

BG  

CY  

CZ  

DE  

DK  

EE  

EL  

ES  

FI  

FR  

HU  

IE  

IT  

LT  

LU  

LV  

MT  

NL  

PL  

PT  

RO  

SE  

SI  

SK  

UK  

EU-27  

4.2 

8.3 

2.4 

4.3 

9.8 

12.1 

12.5 

12.0 

3.6 

9.7 

9.9 

8.1 

3.4 

2.9 

13.2 

8.5 

7.3 

12.1 

6.5 

14.1 

9.7 

13.0 

2.4 

9.0 

14.4 

6.4 

9.2 

8.9 

Aid to Private 
Sector 
72.9 

36.0 

29.0 

42.9 

20.6 

31.4 

45.5 

22.0 

28.0 

23.6 

59.0 

38.2 

26.8 

68.5 

33.4 

25.2 

60.9 

23.7 

20.3 

29.4 

18.5 

22.2 

27.9 

53.5 

27.5 

21.8 

43.2 

Infrastructure  Human Capital 

8.0 

6.2 

48.6 

30.4 

50.8 

24.3 

9.0 

54.1 

45.6 

44.2 

9.6 

17.5 

52.0 

14.4 

26.9 

50.5 

11.0 

50.1 

59.4 

13.5 

54.3 

32.7 

42.8 

10.4 

39.8 

53.7 

11.0 

11.3 

47.2 

12.3 

18.2 

15.4 

29.8 

27.9 

10.1 

19.9 

19.7 

18.0 

32.3 

13.3 

12.3 

22.7 

10.6 

17.7 

10.9 

10.5 

39.7 

14.0 

29.1 

19.5 

23.1 

15.6 

14.5 

34.1 

Technical 
Assistance and Other 
3.5 

2.4 

7.8 

4.2 

3.4 

2.5 

5.1 

1.8 

2.9 

2.7 

3.5 

3.8 

4.5 

1.9 

3.9 

5.1 

3.0 

3.1 

3.2 

3.3 

3.5 

3.0 

7.5 

4.0 

2.7 

3.7 

2.5 

27.9 

39.5 

20.1 

3.6 

Source: DG REGIO calculations.

At the EU-27 level, the highest share of payments goes to infrastructure (39.5%), followed by

aid to the private sector (27.9%) and support for the development of human capital (20.1%). The

share of infrastructure is generally higher in the EU-12 Member States compared with the EU-15

where payments in support to human capital and to the private sector is relatively more important.

Results are ﬁrst presented for each type of intervention separately (section 5.1) and then for

10

 
 
11

the full policy package where all interventions are considered together (section 5.2). The ﬁgures

are reported either for the time horizon 2007-2023 or for two points in time, 2015 and 2023. The

year 2015 marks the end of the implementation period and can be considered as a threshold for the

short to medium run. However, as already mentioned the eﬀects of cohesion and rural development

policies are likely to fully materialize with a lag and results are therefore also reported for 2023

which, 10 years after the end of the programming period, is chosen to represent the long run.

5.1. Model ﬁelds of intervention

Infrastructure

Infrastructure includes investments in transport, telecommunications, energy, environmental

and social infrastructure. These investments are modelled either as government investment (e.g.

motorways, railways, infrastructure related to ICT, energy infrastructure, or management and

distribution of water) or government consumption (e.g. promotion of biodiversity and nature

protection or risk prevention). The ﬁrst type accounts for more than 91% of the total infrastructure
expenditure of cohesion and rural development policies7.

Government investment is part of ﬁnal demand for goods and services and as such the inter-

ventions in the ﬁeld of infrastructure falling in this category have a strong short run demand-side

eﬀect during the period of implementation. Government investment has also a supply-side eﬀect as

it contributes to building up public capital which in turn raises factor productivity. This mostly

occurs in the medium to long run when the output enhancing eﬀects of infrastructure investment

become stronger. When investment is discontinued, the productivity eﬀect slowly declines due

to the depreciation of public capital. In the short run, government investment can also partly

crowd-out private investment, although this eﬀect proves rather modest (see Appendix 2).

Accordingly, the impact of investment in this type of infrastructure materialises as soon as

projects are implemented (due to the short run demand side eﬀect of the interventions). They also

have a long run eﬀect linked to the increase in productivity they generate which continues after the

implementation period.

Government consumption is also a component of ﬁnal demand but it is not expected to have a

long-lasting impact on the structure of the economies. As such, interventions of this type only have

a short run demand eﬀect which appears during the implementation period.

Figure 3 shows the impact on EU-27 GDP of interventions in the ﬁeld of infrastructure between

2007 and 2023. The drop in 2015 corresponds to the termination of the programmes after which

only the long run supply side eﬀects of the interventions are maintained.

Human capital

7This classiﬁcation is disputable and an alternative scenario has been tested where environmental infrastructure is
included in the group of infrastructure considered as government consumption. The results of this alternative scenario
are quite similar in nature and are available upon request.

11

THE IMPACT OF COHESION AND RURAL DEVELOPMENT POLICIES 2007-201312

Figure 3: Cohesion and rural development policies investment in infrastructure, impact on GDP, 2007-2023 (percentage
deviation with respect to baseline).

e
n
i
l
e
s
a
b
m
o
r
f
e
c
n
e
r
e
f
f
i
d
%

,

P
D
G
n
o
t
c
a
p
m

I

2.50

2.00

1.50

1.00

0.50

0.00

EU-12

EU-15

EU-27

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

Source: QUESTIII simulations.

Investments in human capital include all spending on educational and vocational training as

well as more generally deﬁned labour market interventions. These interventions are modelled as

enhancing human capital for each group of skill, which increases labour productivity. This in turn

translates into higher real wages and stimulates investment (although this eﬀect comes at a later

stage, see Appendix 2). These interventions also increase productivity in the R&D sector which

fosters the production of patents and hence eventually raises total factor productivity. The eﬀects

of training on average skill eﬃciencies take time to build up, taking into account cohort eﬀects.

Accordingly, the gains in GDP are only becoming apparent in the long run but they are signiﬁcant

and highly persistent (see Figure 4) due to the fact that they positively aﬀect the main engines of

long run growth in the model, i.e. accumulation of human capital (direct eﬀect) and of physical

capital and technology (indirect eﬀect). However, the impact progressively fades out in proportion

to the exit rate of working age population in the long run.

Research and Development (R&D)

Support to R&D includes all spending on research, technological development and innovation,

including the establishment of networks and partnerships between businesses and/or research

institutes. In the model, this is captured as reductions in ﬁxed costs for ﬁrms engaged in R&D and

reductions in intangible capital costs. Facilitating the production of innovative processes, which in

the model is reﬂected by the increase in the number of patents (see Appendix 3), directly boosts

total factor productivity. Increases in R&D activities lead also to reallocate high skilled workers

away from the production of ﬁnal goods, which explains why they can have an initial negative

12

 
 
 
 
 
 
 
Figure 4: Cohesion and rural development policies investment in human capital, impact on GDP, 2007-2023 (percentage
deviation with respect to baseline).

13

1.80

1.60

1.40

1.20

1.00

0.80

0.60

0.40

0.20

0.00

e
n
i
l
e
s
a
b
m
o
r
f
e
c
n
e
r
e
f
f
i
d
%

,

P
D
G
n
o
t
c
a
p
m

I

EU-12

EU-15

EU-27

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

-0.20

Source: QUESTIII simulations.

impact on GDP in the short run (see Figure 5). However, in the long term the positive eﬀects on

output dominate. As they stimulate the endogenous growth mechanism at work in the model, the

impact of investments in R&D tend to strengthen over time, long after the end of the programmes.

Accordingly, the eﬀects of this type of interventions takes time to become apparent but the output

gains are signiﬁcant and continue to increase long after spending is discontinued.

Aid to private sector

Aid to private sector includes interventions such as support to small and medium sized enterprises,

facilitation to credit, assistance to improve tourism services and cultural investments. It includes

also various types of support to rural development by the EAFRD. Part of the interventions is

modelled as reductions of ﬁxed costs or of capital costs for tangible capital in the ﬁnal goods

sector, while other interventions are considered as government consumption. The impact of aid to

private sector on GDP over time is shown in Figure 6. Aid to private sector triggers increases in

private investment (see Appendix 2) and hence accelerates the pace of physical capital accumulation.

Other interventions, modelled as increasing government consumption (e.g. in the area of natural or

cultural heritage), produce their impact mostly in the short run as they correspond to a subsidy

provided during the implementation period.

Technical assistance and other interventions

Technical assistance includes investments for building administrative capacity, monitoring and

evaluation, as well as various compensations for speciﬁc territories. It is modelled as government

spending with immediate eﬀects in the short run. This category of intervention is generally modest

13

THE IMPACT OF COHESION AND RURAL DEVELOPMENT POLICIES 2007-2013 
 
 
 
 
 
 
14

Figure 5: Cohesion and rural development policies investment in R&D, impact on GDP, 2007-2023 (percentage
deviation with respect to baseline).

0.80

0.70

0.60

0.50

0.40

0.30

0.20

0.10

0.00

e
n
i
l
e
s
a
b
m
o
r
f
e
c
n
e
r
e
f
f
i
d
%

,

P
D
G
n
o
t
c
a
p
m

I

EU-12

EU-15

EU-27

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

-0.10

Source: QUESTIII simulations.

Figure 6: Cohesion and rural development policies aid to private sector, impact on GDP, 2007-2023 (percentage
deviation with respect to baseline).

e
n
i
l
e
s
a
b
m
o
r
f
e
c
n
e
r
e
f
f
i
d
%

,

P
D
G
n
o
t
c
a
p
m

I

0.50

0.45

0.40

0.35

0.30

0.25

0.20

0.15

0.10

0.05

0.00

EU-12

EU-15

EU-27

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

Source: QUESTIII simulations.

(see Table 2). For the sake of conciseness, although being included in the simulations, the impact of

these interventions is not discussed further.

14

 
 
 
 
 
 
 
 
 
 
 
 
 
 
15

5.2. Member States policy mix

Impact on GDP

Figure 7 shows the net eﬀect on GDP of EU cohesion and rural policies combining investments

in the ﬁve ﬁelds of intervention during the period 2007-2015. Results are reported for each Member

State together with the average for the EU-15, the EU-12 and the EU-27 for the years 2015 and

2023.

Figure 7: Impact on GDP of cohesion and rural development policies, 2015 and 2023 (percentage deviation with
respect to baseline).

6
5
4
3
2
1
0

U
L

K
D

L
N

K
U

E
S

E
B

R
F

E
I

I
F

E
D

T
I

Y
C

T
A

S
E

T
M

I
S

T
P

R
G

G
B

K
S

Z
C

O
R

E
E

T
L

U
H

V
L

L
P

5
1
-
U
E

2
1
-
U
E

2015

2023

Source: QUESTIII simulations.

In the EU-12, the impact of the interventions is signiﬁcant both in the short and in the long run.

For example, at the end of the implementation period (2015), GDP in Hungary is more than 5%

higher thanks to the policies. The impact is somewhat lower in 2023, GDP being 4.4% higher than

in the baseline scenario. In Poland, the impact is also high and contrary to Hungary, it strengthens

between 2015 and 2023, increasing from 4.3% to 5.6%. This is partly due to the stronger emphasis

of the Polish programmes on investments in human capital and R&D which produce most of their

eﬀects in the long run. On average, the impact in the EU-12 is around 4% above baseline both in

the short and the long run.

In the EU-15, the impact of cohesion policy and rural development policies is smaller during

the implementation period but it strengthens over time. It is the highest in the Member States

which beneﬁt from the Cohesion Fund, in particular in Greece (2.2% in 2015 and 2.8% in 2023)

and Portugal (1.8% in 2015 and 2.6% in 2023). Interestingly, the impact remains positive even

in Member States which are net contributors to the policies. This suggests that the negative

15

THE IMPACT OF COHESION AND RURAL DEVELOPMENT POLICIES 2007-2013 
16

eﬀect of increasing taxes is more than compensated by the boost in productivity brought by the

interventions, which is particularly true in the long run. The smaller magnitude of the impact in

the EU-15 directly follows from the fact that, compared to the EU-12, the allocation accruing to

the States is much lower relative to the size of their economies. However, in absolute terms the
gains are rather comparable. Based on the multipliers reported at the end to this section, the e201
billion invested in cohesion and rural policies in the EU-15 have the potential to generate a gain of
e134 billion by 2015 and of e548 billion by 2023. In comparison, for the EU-12 the investment of
e192 billion would generate a gain of e173 billion by 2015 and of e538 billion by 2023.

Given their orientation towards structural change, cohesion policy and rural development policies

need time to generate signiﬁcant gains. Sizeable impacts of the interventions materialise with a

lag, most often long after the programmes are terminated. In the short run, a substantial part of

the impact stems from the increase in demand, partly crowded-out through increases in wages and

prices. In the long run, productivity enhancing eﬀects of the interventions generate increases in

GDP free of inﬂationary pressures. Figure 8 shows the impact of the policies for the EU-12, EU-15
and EU-27 from 2007 to 20238.

Figure 8: Cohesion and rural development policies impact on GDP, 2007-2023 (percentage deviation with respect to
baseline).

e
n
i
l
e
s
a
b
m
o
r
f
e
c
n
e
r
e
f
f
i
d
%

,

P
D
G
n
o
t
c
a
p
m

I

4.50

4.00

3.50

3.00

2.50

2.00

1.50

1.00

0.50

0.00

EU-12

EU-15

EU-27

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

Source: QUESTIII simulations.

As the time proﬁle of the impact changes signiﬁcantly from one ﬁeld of intervention to another

8Note that in general, the impact obtained here is higher than in Varga and in ’t Veld (2011b) due to the fact that
this analysis incorporates rural development funds, which makes the size of the policy injections higher by around
25% and that it covers the programmes implemented in all Member States instead of those implemented in the main
beneﬁciaries only.

16

 
 
 
 
 
 
 
17

(see Figures 3 to 6), the impact of the policies at a given date not only depends on the amount

of resources injected in the economy but also on how they are distributed among these ﬁelds. In

particular, countries which invest heavily in R&D and human capital are more likely to see the

impact of the interventions emerge in the long run while countries heavily investing in infrastructure

already beneﬁt from the interventions in the short run. This is conﬁrmed by the strong correlation

between the share of the funding invested in R&D and human capital on the one hand and the

diﬀerence between the magnitude of the impact in the short run and in the long run, the former

explaining more than 52% of the cross-country variance of the latter. Also, the impact in Member

States which are net contributors is likely to be higher in the long run because there is no need to

raise additional distortionary taxes once the programmes are terminated.

As an illustration, Figure 9 shows the time proﬁle of the policy impact on GDP for the

Netherlands (net contributor investing heavily in R&D and human capital) and Romania (net

beneﬁciary investing heavily in infrastructure). In Romania, the positive eﬀects of the policies

already materialise at the beginning of the implementation period while in the Netherlands they

only become positive from 2013 onwards.

Figure 9: Cohesion and rural development policies impact on GDP, 2007-2023 (percentage deviation with respect to
baseline).

Romania 

The Netherlands 

4

3.5

3

2.5

2

1.5

1

0.5

e
n
i
l
e
s
a
b
m
o
r
f
e
c
n
e
r
e
f
f
i
d
%

,

P
D
G
n
o
t
c
a
p
m

I

0
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

-0.5

-1

Source: QUESTIII simulations.

Impact on real wages

e
n
i
l
e
s
a
b
m
o
r
f
e
c
n
e
r
e
f
f
i
d
%

,

P
D
G
n
o
t
c
a
p
m

I

0.15

0.1

0.05

0
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

-0.05

-0.1

In QUEST, the impact of cohesion and rural development investments on the labour market are

primarily reﬂected through their eﬀects on labour productivity and real wages. Figure 10 shows the

net impact of the policies on real wages for years 2015 and 2023.

According to the simulations, the largest eﬀects are expected in the EU-12 Member States and

Portugal. For all countries, the impact on real wages persist at comparable levels between the two

reference years. By 2023, cohesion and rural policies programmes could contribute to increase real

wages by almost 3.3% in the EU-12 and by around 1.0% in the EU-27.

Impacts on total factor productivity and investment

The impact of cohesion and rural development policies is also apparent on other key macroe-

conomic variables such as total factor productivity (TFP) or private investment (Figures 11 and

12).

17

THE IMPACT OF COHESION AND RURAL DEVELOPMENT POLICIES 2007-2013 
 
 
 
 
 
 
 
 
 
 
 
 
 
18

Figure 10: Impacts on real wages of cohesion and rural development policies, 2015 and 2023 (percentage deviation
with respect to baseline).

6

4

2

0

U
L

L
N

K
D

E
B

R
F

K
U

E
S

T
I

I
F

E
D

E
I

T
A

Y
C

S
E

T
M

R
G

I
S

K
S

Z
C

T
P

G
B

O
R

E
E

U
H

T
L

L
P

V
L

5
1
-
U
E

2
1
-
U
E

2015

2023

Source: QUESTIII simulations.

The increase in TFP attributable to cohesion and rural development policies is particularly high

in the EU-12, peaking to 4.2% in 2015. By comparison, the average increase in TFP for the EU-15

in the same year is around 0.4%. The impact on private investment is to a large extent indirect

as it mainly reﬂects the improvement of the business environment, notably the increases in factor

productivity triggered by the interventions. However, as highlighted above these eﬀects take time

to fully materialise and, while in the ﬁrst place private investment may be partly crowded out by

the interventions, the positive impact of the policies appears in the medium to long run. In 2023,

for instance private investments in the EU-12 increases by 2.3% due to policies interventions while

the increase in the EU-15 is 0.47%.

Impact on trade balance

The impact on the country trade balance diﬀers between the EU-12 and the EU-15. For most

Member States in the ﬁrst group, the programmes tend to deteriorate the trade balance due mainly

to the increase in economic activity generated by the interventions which triggers an increase in

imports. For other Member States, mostly located in the EU-15, the interventions have a positive

eﬀect on the trade balance which to a large extent reﬂects the fact that a signiﬁcant part of the

increases in imports in the EU-12 originates from the EU-15. This corresponds to a trade spill-over

eﬀect through which programmes implemented in the main beneﬁciaries of the policies also produce

a positive impact in the other Member States.

Cumulative multipliers - Impact per euro spent

18

 
Figure 11: Cohesion and rural development policies on total factor productivity, 2015 and 2023 (percentage deviation
with respect to baseline).

19

8

6

4

2

0

L
N

U
L

K
D

K
U

E
S

E
B

I
F

E
D

R
F

E
I

T
A

T
I

Y
C

S
E

T
M

R
G

I
S

K
S

G
B

E
E

T
P

O
R

Z
C

T
L

U
H

L
P

V
L

5
1
-
U
E

2
1
-
U
E

2015

2023

Source: QUESTIII simulations.

Figure 12: Cohesion and rural development policies impact on private investment, 2015 and 2023 (percentage deviation
with respect to baseline).

4

3

2

1

0

U
L

K
D

K
U

R
F

E
B

L
N

T
I

E
S

E
I

I
F

Y
C

E
D

S
E

T
A

T
M

I
S

K
S

T
P

Z
C

L
P

G
B

O
R

U
H

T
L

R
G

V
L

E
E

5
1
-
U
E

2
1
-
U
E

2015

2023

Source: QUESTIII simulations.

As mentioned above, the impact in each Member States is directly related to the size of the

ﬁnancial support it receives from cohesion and rural development policies. In order to better capture

19

THE IMPACT OF COHESION AND RURAL DEVELOPMENT POLICIES 2007-2013 
 
20

Figure 13: Impacts of cohesion and rural development policies on Trade Balance as % of GDP, 2015 and 2023
(percentage deviation with respect to baseline).

0.5

0

-0.5

-1

-1.5

V
L

E
E

T
L

U
H

G
B

O
R

T
P

R
G

Z
C

I

K S
S

L
P

S
E

T
A

E
D

T
I

I
F

E
I

E
S

R
F

K
U

L
N

E
B

K
D

Y
C

U
L

T
M

5
1
-
U
E

2
1
-
U
E

Source: QUESTIII simulations.

2015

2023

the eﬀectiveness of the interventions, the results of the simulation can be used to calculate the

impact, say on GDP, per euro spent which corresponds to the ratio between the cumulated impact

on GDP up to a given year and the cumulated amounts spent up to the same year. This is referred

as the cumulative multiplier whose value indicates the accumulated GDP eﬀect over the speciﬁed

period.

Table 3 reports the values of the cumulative multipliers for the EU-12, EU-15 and EU-27. By

the end of the implementation period, the cumulative multiplier for the EU-27 is estimated at

around 0.78. The values of the multipliers increase signiﬁcantly in the long run. Again, this is due

to the fact that many interventions continue to produce their eﬀects long after they are terminated

which implies that in the long run, positive impact on GDP continues to build up after spending has

been stopped. By 2023, cumulative multiplier obtained for the EU-27 reaches 2.74 which suggests

that the eﬀorts of the Union to allocate resources to cohesion and rural policies generate a potential

beneﬁt for all the members of the EU. Note that these ﬁgures are in line with those obtained in

previous analysis with QUEST (see Varga and in ’t Veld (2011a,b)). Over a period of 17 years, such

a multiplier corresponds to an annual average return of around 6% which is good value for money
but still below usual long run returns on private investment which makes sense for public policies9.

9For instance, between 1995 and 2015 the compound annual growth rate of the SP 500 is 11.3%.

20

 
21

Table 3: Cumulative multipliers, EU-15, EU-12 and EU-27, 2015 and 2023.

2015 

2023 

EU-12 

EU-15 

EU-27 
 Source: QUESTIII simulations.

6. Sensitivity analysis

0,90 

0,67 

0,78 

2,80 

2,73 

2,74 

The results obtained from the simulations partly depend on the values selected for a number

of key parameters. These values are based on a review of trustworthy empirical analysis and

correspond to what is commonly assumed in the economic literature. However, estimations for

some parameters sometime signiﬁcantly vary from one source to the other and the value selected for

modelling purposes corresponds to an average hiding a number of quite diﬀerent estimates. This is
for instance the case for the output elasticity of public capital (αG).

Since the seminal works of Aschauer (1989a,b), a huge body of literature has developed on the

economic impact of public infrastructure. The question has led to an intense methodological debate

as the exercise of estimating the link between public infrastructure and economic performance

proved to include many diﬃculties, from simultaneity among the variables considered in the analysis

to reverse causality, or uncertainties concerning the identiﬁcation of exogenous shocks in VAR-based

analysis (see for instance Kamps (2005) or Pereira and Andraz (2012)).

Not surprisingly, if most contributions conclude to a positive and signiﬁcant impact of public

infrastructure on economic performance in the EU, there is no consensus concerning the quantiﬁcation

of this link (see for instance the recent surveys by Pereira and Andraz (2013), De la Fuente (2010)

or Romp and de Haan (2007)). For example, De la Fuente (2010) makes a selection of the analyses
which are most likely to produce comparable and plausible results for the EU10. Across the studies
reviewed, the estimates of the elasticity of output to public infrastructure averages to around 0.13

for Spain and 0.18 for regional studies covering other countries. However, dispersion around these

averages is quite high with t-stat of 3.99 for Spain and 9.19 for regions of other countries.

For these simulations, we therefore chose a rather conservative approach setting the output

elasticity of public capital to 0.10. However, given the uncertainty surrounding the estimation of
this parameter, we examine how results change with values of αG set to 0.075 and 0.125. As shown
in Figure 14, results are quite sensitive to the value selected for this elasticity. For example, while
the the impact of programmes on EU-12 GDP by 2015 is 4.05% above baseline when αG is set to

10These correspond to studies relying on regional data of the same country. Indeed, diﬀerences in the econometric
speciﬁcations adopted, sample composition and lack of data homogeneity make cross-country studies diﬃcult to use.

21

THE IMPACT OF COHESION AND RURAL DEVELOPMENT POLICIES 2007-2013 
22

0.10, it is 3.5% for αG = 0.075 and 4.61% for αG = 0.125. However, the results do not change in
nature and the conclusions drawn from the analysis remain valid.

Figure 14: Impacts of cohesion and rural development policies on EU-12 GDP for alternative output elasticities of
public capital.

e
n
i
l
e
s
a
b
m
o
r
f
e
c
n
e
r
e
f
f
i
d
%

,

P
D
G
n
o
t
c
a
p
m

I

6.00

5.00

4.00

3.00

2.00

1.00

0.00

αG= 0.125 

αG= 0.10 

αG= 0.075 

Source: QUESTIII simulations.

Another source of uncertainty is the parameter corresponding to the Frisch elasticity of labour

supply which is included in households’ preferences. A large literature provides estimates of the

responsiveness of individual labour supply to wage changes but the range of the results obtained is

quite large and depends on the data used, the approach selected (e.g. micro vs macro data) or the

time, place and population for which the analysis is conducted. Estimates of the Frisch elasticity for

men usually range from zero to 0.8. As highlighted by Reichling and Whalen (2017), some authors

have found relatively small values between 0 and 0.5 (e.g. Blundell et al. (2014), French (2005),

Altonji (1986), or MaCurdy (1981)) while others have estimated larger elasticities between 0.5 and

0.8 (Ziliak and Kniesner (2005), Pistaferri (2003), Lee (2001), or Angrist (1991)). Keane (2011)

surveys a substantial part of the literature and reports estimates with a range from zero to 0.7 and

a median value of 0.2.

For the present simulations, a value of 0.2 has been selected but in order to check the sensitivity

of the results to this parameter, values of 0.1 and 0.3 have been used as lower and upper bound

alternatives. The results suggest that the values chosen for the labour supply elasticity only have a

marginal inﬂuence on the results.

22

 
 
 
 
 
 
 
23

7. Conclusions

This paper provides an assessment of the programmes implemented under the EU cohesion and

rural development policies during the period 2007-2013. It analyses their impact on the European

economy based on a set of simulations conducted with QUEST, a dynamic stochastic general

equilibrium model with endogenous growth and human capital accumulation. The results show

that in general, cohesion and rural development policies brought signiﬁcant gains and contributed

to enhance the structure and the performance of the EU Member States economies.

Interventions substantially increased GDP, in particular in the Member States which are the

main beneﬁciaries of the policies. The results suggest that, following the implementation of the

programmes, GDP in 2015 was on average 4.1% higher in the Member States which joined the

Union after 2004. The highest impact is found in Hungary (+ 5.3%) and Latvia (+ 5.1%) as well as

in Poland (+4.3%). In the EU-15, the impact is more modest but it remains substantial for some

Member States like Greece (+2.2%), Portugal (+1.8%) and Spain (+0.7%) which beneﬁted from

support of the Cohesion Fund.

For some ﬁelds of intervention, the impact takes time to materialise and continues to build up

long after the termination of the programmes. This is particularly the case for interventions in the

ﬁelds of R&D and human capital for which most of the eﬀects come through in the long run when

the productivity enhancing eﬀects become gradually stronger.

Cohesion and rural development policies also aﬀect many other key economic variables. Overall,

the simulations shows that the interventions improve the structure of the EU economies and hence

their competitiveness. In particular, they have a positive and signiﬁcant impact on the productivity

of factors of production, as result of direct investment in technology but also of enhanced business

conditions encouraging investment in tangible and intangible assets.

Finally, cohesion and rural development policies yield high value for money. As expected

from policies supporting investments in key engines of growth, a substantial part of their eﬀects

progressively increases over time and only emerge in the long run. However, once they produce their

full impact, the interventions prove to beneﬁt to the whole Union even if they are concentrated in

its less developed places.

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Lee, C. (2001). Finite Sample Bias in IV Estimation of Intertemporal Labor Supply Models: Is the Intertemporal

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Labor Economics, 23 (5), 769–796.

24

Appendix 1 - ERDF, CF and ESF Mapping of priority themes into ﬁelds of interven-

25

tion

Category 

1.  R&TD activities in research centres  

2.  R&TD infrastructure and centres of competence in a specific technology 

3.  Technology transfer and improvement of cooperation networks 
4.  Assistance to R&TD, particularly in SMEs (including access to R&TD services in research 

centres) 

5.  Advanced support services for firms and groups of firms 
6.  Assistance to SMEs for the promotion of environmentally-friendly products and production 

processes 

7. 

Investment in firms directly linked to research and innovation 

8.  Other investment in firms  

9.  Other measures to stimulate research and innovation and entrepreneurship in SMEs 

10.  Telephone infrastructures (including broadband networks) 

11.  Information and communication technologies 

12.  Information and communication technologies (TEN-ICT) 

13.  Services and applications for citizens (e-health, e-government, e-learning, e-inclusion, etc.) 

14.  Services and applications for SMEs (e-commerce, education and training, networking, etc.) 

15.  Other measures for improving access to and efficient use of ICT by SMEs  

16.  Railways 

17.  Railways (TEN-T) 

18.  Mobile rail assets 

19.  Mobile rail assets (TEN-T) 

20.  Motorways 

21.  Motorways (TEN-T) 

22.  National roads 

23.  Regional/local roads 

24.  Cycle tracks 

25.  Urban transport 

26.  Multimodal transport 

27.  Multimodal transport (TEN-T) 

28.  Intelligent transport systems 

29.  Airports 

30.  Ports 

31.  Inland waterways (regional and local) 

32.  Inland waterways (TEN-T) 

33.  Electricity 

34.  Electricity (TEN-E) 

35.  Natural gas 

36.  Natural gas (TEN-E) 

25

FoI 

RTD 

RTD 

RTD 

RTD 

AIS 

AIS 

RTD 

AIS 

RTD 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

THE IMPACT OF COHESION AND RURAL DEVELOPMENT POLICIES 2007-2013 
26

37.  Petroleum products 

38.  Petroleum products (TEN-E) 

39.  Renewable energy: wind 

40.  Renewable energy: solar  

41.  Renewable energy: biomass 

42.  Renewable energy: hydroelectric, geothermal and other 

43.  Energy efficiency, co-generation, energy management 

44.  Management of household and industrial waste 

45.  Management and distribution of water (drink water) 

46.  Water treatment (waste water) 

47.  Air quality 

48.  Integrated prevention and pollution control  

49.  Mitigation and adaption to climate change 

50.  Rehabilitation of industrial sites and contaminated land 

51.  Promotion of biodiversity and nature protection (including Natura 2000) 

52.  Promotion of clean urban transport  

53.  Risk prevention (...) 

54.  Other measures to preserve the environment and prevent risks 

55.  Promotion of natural assets 

56.  Protection and development of natural heritage 

57.  Other assistance to improve tourist services 

58.  Protection and preservation of the cultural heritage 

59.  Development of cultural infrastructure 

60.  Other assistance to improve cultural services 

61.  Integrated projects for urban and rural regeneration 
62.  Development of life-long learning systems and strategies in firms; training and services for 

employees  

63.  Design and dissemination of innovative and more productive ways of organising work 
64.  Development of special services for employment, training in connection with restructuring of 

sectors 

65.  Modernisation and strengthening labour market institutions 

66.  Implementing active and preventive measures on the labour market 

67.  Measures encouraging active ageing and prolonging working lives 

68.  Support for self-employment and business start-up 
69.  Measures to improve access to employment and increase sustainable participation and progress 

of women 

70.  Specific action to increase migrants' participation in employment ... 

71.  Pathways to integration and re-entry into employment for disadvantaged people ... 

72.  Design, introduction and implementing of reforms in education and training systems ... 

26

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

AIS 

AIS 

AIS 

AIS 

AIS 

AIS 

AIS 

HC 

HC 

HC 

HC 

HC 

HC 

HC 

HC 

HC 

HC 

HC 

 
73.  Measures to increase participation in education and training throughut the life-cycle ... 
74.  Developing human potential in the field of research and innovation, in particular through post-

graduate studies 

75.  Education infrastructure  

76.  Health infrastructure 

77.  Childcare infrastructure  

78.  Housing infrastructure 

79.  Other social infrastructure 
80.  Promoting the partnerships, pacts and initiatives through the networking of relevant 

stakeholders 

81.  Mechanisms for improving good policy and programme design, monitoring and evaluation ... 

82.  Compensation of any additional costs due to accessibility deficit and territorial fragmentation 

83.  Specific action addressed to compensate additional costs due to size market factors 

84.  Support to compensate additional costs due to climate conditions and relief difficulties 

85.  Preparation, implementation, monitoring and inspection  

86.  Evaluation and studies; information and communication 

27

HC 

HC 

INFR 

INFR 

INFR 

INFR 

INFR 

TA 

TA 

TA 

TA 

TA 

TA 

TA 

27

THE IMPACT OF COHESION AND RURAL DEVELOPMENT POLICIES 2007-2013 
28

Appendix 2 - EARDF Mapping of measures into ﬁelds of intervention

Category 

111. Vocational training and information actions 

112. Setting up of young farmers 

113. Early retirement 

114. Use of advisory services 

115. Setting up of management, relief and advisory services 

121. Modernisationof agricultural holdings 

122. Improvement of the economic value of forests 

123. Adding value to agricultural and forestry products 

124. Cooperation for development of new products, processes and technologies 

125. Infrastructure related to the development and adaptation of agriculture and forestry 

126. Restoring agricultural production potential 

131. Meeting standards based on EU legislation 

132. Participation of farmers in food quality schemes 

133. Information and promotion activities 

141. Semi subsistence farming 

142. Producer groups 

143. Providing farm advisory and extension services 

144. Holdings undergoing restructuring due to a reform of a common market organisation 

211. Natural handicap payments to farmers in mountain areas 

212. Payments to farmers in areas with handicaps, other than mountain areas 

213. Natura2000 payments and payments linked to Dir. 2000/60/EC 

214. Agri-environment payments 

215. Animal welfare payments 

216. Non-productive investments 

221. First afforestation of agricultural land 

222. First establishment of agro-forestry systems on agricultural land 

223. First afforestation of non 

224. Natura2000 payments 

225. Forest environment payments agricultural land 

226. Restoring forestry potential and introducing prevention actions 

227. Non productive investments environment payments 

311. Diversification into non agricultural activities 

312. Support for business creation and development 

313. Encouragement of tourism activities 

321. Basic services for the economy and rural population 

322. Village renewal and development 

28

FoI 

HC 

AIS 

INFR 

AIS 

AIS 

AIS 

AIS 

AIS 

RTD 

INFR 

AIS 

AIS 

AIS 

AIS 

INFR 

AIS 

AIS 

AIS 

AIS 

AIS 

INFR 

INFR 

INFR 

TA 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

INFR 

AIS 

AIS 

AIS 

INFR 

INFR 

 
323. Conservation and upgrading of the rural heritage 

331. Training and information 
341. Skills acquisition and animation measure for preparing and implementing a local development 

strategy 

411. Competitiveness 

412. Environment/land management 

413. Quality of life/diversification 

421. Implementing cooperation projects 

431. Running the LAG, skills acquisition, animation 

511. Technical assistance 

611. Complimentary direct payments 

29

INFR 

TA 

TA 

AIS 

INFR 

INFR 

TA 

TA 

TA 

TA 

29

THE IMPACT OF COHESION AND RURAL DEVELOPMENT POLICIES 2007-2013 
30

Appendix 3 - Impact per ﬁeld of intervention

Figure A31: Infrastructure

GDP 

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Government.investment 

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Investment 

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Capital.public 

0.4
0.3
0.2
0.1
0

11

6

1

-4

0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3

00.511.522.533.5

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

0.1

0.05

0

Capital 

-0.05

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Source: QUESTIII simulations.

30

 
 
 
 
 
 
 
 
 
 
31

TFP 

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Consumption 

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Government.consumption 

0.4
0.3
0.2
0.1
0

0.4
0.3
0.2
0.1
0
-0.1
-0.2

0.4
0.3
0.2
0.1
0

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Source: QUESTIII simulations.

31

THE IMPACT OF COHESION AND RURAL DEVELOPMENT POLICIES 2007-2013 
 
 
 
 
32

Figure A32: Human capital

0.3
0.2
0.1
0
-0.1

GDP 

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Real.wages 

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Consumption 

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Investment 

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

0.5
0.4
0.3
0.2
0.1
0

0.3
0.2
0.1
0

0.3

0.2

0.1

0

-0.1

-0.2

0.3

0.2

0.1

0

TFP 

-0.1

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Source: QUESTIII simulations.

32

 
 
 
 
 
 
33

Figure A33: R&D and innovation

0.1

0.05

0

GDP 

-0.05

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

0.1

0.05

0

-0.05

Investment 

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Patents 

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

TFP 

1.6
1.1
0.6
0.1
-0.4

0.1

0.05

0

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Source: QUESTIII simulations.

33

THE IMPACT OF COHESION AND RURAL DEVELOPMENT POLICIES 2007-2013 
 
 
 
 
34

Figure A34: Aid to private sector

GDP 

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Investment 

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Capital 

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Government.consumption 

0.1

0.05

0

0.1

0.05

0

0.1

0.05

0

0.1

0.05

0

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Source: QUESTIII simulations.

34

 
 
 
 
 
 
 
35

TFP 

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Exports 

0.1

0.05

0

-0.05

0.1

0.05

0

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Source: QUESTIII simulations.

35

THE IMPACT OF COHESION AND RURAL DEVELOPMENT POLICIES 2007-2013 
 
 
 
36

Figure A35: Policy mix

GDP 

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Investment 

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Government.investment 

1

0.5

0

0.06
-0.01
-0.08
-0.15
-0.22
-0.29
-0.36
-0.43
-0.5

11

6

1

-4

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Consumption 

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Real wages 

0.1
0.05
0
-0.05
-0.1
-0.15
-0.2

1.5
1
0.5
0

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Source: QUESTIII simulations.

36

 
 
 
 
 
 
 
 
 
37

2
1.5
1
0.5
0

1

0.5

0

Patents 

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

TFP 

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Trade Balance (% of GDP) 

0.20

0.00

-0.20

-0.40

-0.60

-0.80

-1.00

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Trade balance EU27 -0.01 -0.01 -0.01 0.01 0.01 0.02 0.02 0.03 0.04 0.04 0.02 0.01 0.01 0.01

Trade balance EU-15 0.02 0.04 0.06 0.07 0.08 0.09 0.10 0.11 0.10 0.05 0.03 0.03 0.03 0.03

Trade balance EU-12 -0.40 -0.60 -0.81 -0.80 -0.82 -0.85 -0.92 -0.89 -0.70 -0.02 -0.05 -0.12 -0.15 -0.17

Source: QUESTIII simulations.

37

THE IMPACT OF COHESION AND RURAL DEVELOPMENT POLICIES 2007-2013 
 
 
 
K
N
-
A
K
-
1
7
-
0
0
2
-
E
N
-
N

© European Union, 2017 
Reuse is authorised provided the source is acknowledged.

ISBN 978-92-79-69338-0
ISSN 2529-3303 
doi:10.2776/521934