COMMISSION STAFF WORKING DOCUMENT EVALUATION of the impact of the Common Agricultural Policy on territorial development of rural areas
Date: 2021-12-10
Institutions: Directorate-General for Agriculture and Rural Development, European Commission
Subjects: EAFRD, EAGF, EU direct payments, European Structural and Investment Funds, aid per hectare, case study, regional development, rural development, rural region, single payment scheme

EN 

  EN 

   EUROPEAN COMMISSION  Brussels, 10.12.2021  SWD(2021) 394 final  COMMISSION STAFF WORKING DOCUMENT  EVALUATION  of the impact of the Common Agricultural Policy on territorial development of rural areas {SWD(2021) 398 final}  
 
 
Table of contents 

1. 

2. 

INTRODUCTION ............................................................................................................................ 3 

BACKGROUND OF THE INTERVENTION ............................................................................... 4 

2.1 

Description of the intervention and its objectives .......................................................................... 4 

2.2 

Baseline and points of comparison .................................................................................................. 8 

3. 

IMPLEMENTATION/STATE OF PLAY ...................................................................................... 9 

METHODOLOGY ......................................................................................................................... 16 
4. 
ANALYSIS ...................................................................................................................................... 19 
5. 
Effectiveness .................................................................................................................................... 19 
5.1 
Efficiency ......................................................................................................................................... 42 
5.2 
5.3 
Coherence ........................................................................................................................................ 51 
5.4   Relevance ......................................................................................................................................... 56 
5.5   EU added value ............................................................................................................................... 59 
CONCLUSION ............................................................................................................................... 62 
6. 

4. 

5. 

6. 

7. 

ANNEX 1: PROCEDURAL INFORMATION ............................................................................ 71 

ANNEX 2: STAKEHOLDER CONSULTATION ....................................................................... 78 

ANNEX 3: METHODOLOGY ...................................................................................................... 86 

ANNEX 4: TABLES AND GRAPHS SUPPORTING THE STUDY ....................................... 100 

1 

 
 
 
 
 
 
Glossary  

BPS  

CAP 

Basic Payment Scheme  

Common agricultural policy 

CATS 

Clearance of accounts audit trail system 

CLLD 

Community-led local development 

CMEF 

Common Monitoring and Evaluation Framework 

EAGF 

European Agricultural Guarantee Fund 

EAFRD  European Agricultural Fund for Rural Development 

EFA 

EIP 

ESIF 

EU 

EUR 

Ecological focus area 

European Innovation Partnership 

European structural and investment funds 

European Union 

Euro, Eurozone currency 

FADN 

Farm Accountancy Data Network 

FAO 

GDP 

Food and Agriculture Organization of the United Nations 

Gross domestic product 

GVA 

Gross value added 

ITI 

Integrated territorial investments 

LAGs 

Local action groups 

NGOs 

Non-governmental organisations 

SAPS 

Single Area Payments Scheme 

Definitions 

Balanced  territorial  development  refers  to  territorial  cohesion  and  convergence  (a 
complementary  policy  objective),  aiming  to  address  the  development  gaps  between 
economically  flourishing  regions  and  those  falling  behind,  through  targeted  policy 
interventions and investments. It also refers to the policy aim to improve the working and 
living conditions and economic factors of all Member States and regions.  

Social  inclusion  refers  to  the  living  and  working  conditions  of  people  in  situations  of 
vulnerability,  such  as  the  rural  poor  (farmers  and  non-farmers)  populations  living  in 
remote  rural  areas  (isolation  issue),  rural  women,  young  people,  older  people,  people 
with disabilities, people with a Roma or migration background.  

2 

 
 
 
 
 
 
 
1. 

1. INTRODUCTION 

One  of  the  EU’s  main  objectives  is  to  strengthen  its  economic,  social  and  territorial 
cohesion.  The Treaty on the functioning of the European Union1  requires  it to  promote 
harmonious  development  and  seek  to  reduce  disparities,  with  special  attention  to  rural 
areas.  The  Treaty  also  calls  for  ensuring  a  fair  standard  of  living  for  the  agricultural 
community,  particularly  by  increasing  the  individual  earnings  of  people  engaged  in 
agriculture.  

The  common  agriculture  policy  (CAP)  aims  to  contribute  to  balanced  territorial 
development. It seeks to achieve this by maintaining a diversified agricultural sector on 
the  EU  territory,  by  reducing  the  gap  between  agricultural  incomes  and  those  in  other 
sectors, and by supporting economic development, poverty reduction and social inclusion 
in rural areas, partially financed by the EU rural development policy.  

The  purpose  of  this  evaluation  is  to  assess  the  extent  to  which  CAP  instruments  and 
measures have contributed to balanced territorial development in rural areas. The focus 
here is on socio-economic aspects, including social inclusion.   

The evaluation is relevant for the long-term vision for the EU’s rural areas2. This vision 
aims to enable rural areas to make the most of their potential and support them in facing 
their  own  unique  set  of  issues,  from  demographic  change  to  connectivity,  the  risk  of 
poverty3 and limited access to services. In this regard, the objective of balanced territorial 
development is as relevant as ever.  

In addition, the findings of the evaluation are important to assess the relevant measures of 
the future CAP. The evaluation will be a source of information for Member States in the 
ongoing development  of their national  strategic  plans for the next  CAP  period,  and for 
the Commission in approving the strategic plans. 

The scope of this evaluation covers the full spectrum of relevant instruments set out in 
the  basic  regulations  of  the  CAP  2014-2020:  direct  payments4,  rural  development5,  the 
common  market  organisation6  and  the  Horizontal  Regulation.  These  all  target  the 
aforementioned objectives.  

1      https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:12012E/TXT. 
2       European Commission, A long-term Vision for the EU’s Rural Areas, COM(2021)345 final, EUR-Lex 

3  

- 52021DC0345 - EN - EUR-Lex (europa.eu).     
For the purpose of this evaluation, risk of poverty or social inclusion is defined as ‘the sum of persons  
who are either at risk of poverty, or severely materially and socially deprived or living in a household 
with a very low work intensity’.  

4   Regulation (EU) No 1307/2013 of the European Parliament and of the Council of 17 December 2013 
establishing rules for direct payments to farmers under support schemes within the framework of the 
common agricultural policy, OJ L 347, 20.12.2013, p. 608.                  

5   Regulation (EU) No 1305/2013 of the European Parliament and of the Council of 17 December 2013 
on  support  for  rural  development  by  the  European  Agricultural  Fund  for  Rural  Development 
(EAFRD),  OJ L 347, 20.12.2013, p. 487–548.      

6   Regulation (EU) No 1308/2013 of the European Parliament and of the Council of 17 December 2013 
establishing a common organisation of the markets in agricultural products, OJ L 347, 20.12.2013, p. 
671–854.        

3 

 
 
 
                                                           
The  overall  theoretical  framework  and  reference  points  of  this  evaluation  rely  on  a 
causal  analysis,  which  serves  as  a  scoping  exercise.  The  causal  analysis  encompassed 
three key elements: (i) the development of intervention logics for the CAP measures and 
instruments; (ii) a theory-based impact assessment (via an impact assessment grid); and 
(iii)  a  territorial  distribution  analysis  to  assess  the  regional  allocation  of  CAP  funding 
across Member States. The geographical scope of the evaluation is the European Union 
of 28 Member States, including the United Kingdom as it was a member of the EU at the 
time of the evaluation.  

The  evaluation  covers  the  period  following  the  implementation  of  the  2013  CAP 
reform,  notably  after  1  January  2015  for  direct  payments  and  after  1 January 2014  for 
other measures.  

The  evaluation  encompasses  all  five  evaluation  criteria,  namely  effectiveness, 
efficiency, coherence, relevance and EU added value. 

This  Commission  staff  working  document  is  primarily  based  on  the  corresponding 
external evaluation support study7, but also draws from additional analysis carried out by 
the Commission and external sources.  

2. 

2. BACKGROUND OF THE INTERVENTION 

3. 

2.1 

DESCRIPTION OF THE INTERVENTION AND ITS OBJECTIVES 

The main aim of the 2013 CAP reform was to address the economic, environmental and 
territorial challenges, therefore it proposed three  general objectives for the CAP 2014-
2020: 

1.  Viable  food  production,  with  a  focus  on  agricultural  income,  agricultural 

productivity, and price stability; 

2.   Sustainable management of natural resources and climate action, with a focus on 

greenhouse gas emissions, biodiversity, soil, and water; 

3.   Balanced territorial development, with a focus on rural employment, growth, and 

poverty in rural areas8. 

The  three  general  objectives  align  with  the  more  general  Europe  2020  goals  of  Smart, 
Sustainable,  and  Inclusive  Growth.  The  general  objectives  are  reflected  in  specific 
objectives and several measures under both Pillar I (direct payments and common market 
organisation (CMO)) and Pillar II (rural development) of the CAP, as well as within the 
six priorities defined for the EU’s rural development policy for 2014-20209. Priority 6 of 
the latter on social inclusion, poverty reduction and economic development is particularly 
relevant to balanced territorial development.  

7        https://op.europa.eu/en/publication-detail/-/publication/08e60401-71a0-11eb-9ac9-

01aa75ed71a1/language-en. 

8   Article  110  of  Regulation  (EU)  No  1306/2013  of  the  European  Parliament  and  of  the  Council  of 

17 December 2013. 

9   Article 5 of Regulation (EU) No 1305/2013. 

4 

 
 
 
                                                           
Pillar I specific objectives contributing to balanced territorial development are to: 

improve competitiveness of agricultural sector and enhance share in food chain; 

  maintain market stability on both supplier and demand sides;  
 
  enhance farm incomes; 
  maintain agricultural diversity across the EU. 

All  Pillar  II  priorities  and  focus  areas  are  contributing  to  balanced  territorial 
development: 

  Priority  1:  Knowledge  Transfer  and  Innovation,  with  focus  areas  1A:  Fostering 
innovation,  cooperation  and  the  development  of  the  knowledge  base  in  rural 
areas;  1B:  Strengthening  the  links  between  agriculture,  food  production  and 
forestry  and  research  and  innovation;  1C:  Fostering  lifelong  learning  and 
vocational training in the agricultural and forestry sectors. 

  Priority 2: Farm Viability and Competitiveness, with focus areas 2A: Improving 
the  economic  performance  of  all  farms  and  facilitating  farm  restructuring  and 
modernisation;  2B:  Facilitating  the  entry  of  adequately  skilled  farmers  into  the 
agricultural sector and generational renewal.  

  Priority 3: Food Chain Organisation and Risk Management, with focus areas 3A: 
Improving competitiveness  of primary producers by better integrating them  into 
the agri-food chain; 3B: Supporting farm risk prevention and management. 

  Priority  4:  Restoring,  Preserving  and  Enhancing  Ecosystems,  with  focus  areas 
4A:  Restoring,  preserving  and  enhancing  biodiversity;  4B:  Improving  water 
management; 4C: Preventing soil erosion and improving soil management.  

  Priority  5:  Resource-efficient,  Climate-resilient  Economy,  with  focus  areas  5A: 
Increasing  efficiency  in  water  use  by  agriculture;  5B:  Increasing  efficiency  in 
energy use in agriculture and food processing; 5C: Facilitating the supply and use 
of  renewable  sources  of  energy;  5D:  Reducing  greenhouse  gas  and  ammonia 
emissions from agriculture; 5E: Fostering carbon conservation and sequestration 
in agriculture and forestry. 

  Priority  6:  Social  Inclusion  and  Economic  Development,  with  focus  areas  6A: 
Facilitating diversification, creation and development of small enterprises, as well 
as job creation; 6B: Fostering local development in rural areas; 6C: Enhancing the 
accessibility,  use  and  quality  of  information  and  communication  technologies 
(ICT) in rural areas. 

The CAP delivers concrete actions to address balanced territorial development, including 
the  support  to  social  inclusion.  Together  with  the  cohesion  policy  funds,  including  the 
European  Regional  Development  Fund,  it  has  a  particularly  important  role  in  remote 
areas  (including  outermost  regions  or  areas  with  permanent  natural  or  demographic 
handicaps).  In  addition,  the CAP acts as  the only  fund in  certain  remote areas. Table 1 
presents the list of socio-economic aspects of balanced territorial development addressed 
directly  or  indirectly  by  the  CAP,  as  well  as  those  aspects  better  addressed  by  other 
funds.  It  has  a  particularly  important  role,  as  well  as  the  ERDF,  in  remote  areas 
(including outermost regions or areas with permanent natural or demographic handicaps), 
where it may act as the only fund   

5 

 
 
 
Figure 1 presents the intervention logic of the relevant CAP 2014-2020 instruments and 
measures  addressing  the  general  objective  of  balanced  territorial  development  based 
mainly on the assessment of the relevant evaluation support study. It presents simplified 
versions and assumptions of the ability of the CAP instruments and measures to address 
balanced territorial development. Figure 1 details both: 

o  Pillar  I  instruments,  the  purpose  of  the  intervention/the  expected  patterns  of 
balanced  territorial  development-relevant  impacts  and  the  potential  significance 
or scale of these impacts, and  

o  Pillar II specific objectives (priority), focus areas and measures, starting with the 

most relevant priority, namely priority 6. 

Table 1. The CAP’s role in addressing balanced territorial development - socioeconomic 
aspects 

Direct impact 

Indirect impact 

or 

limited 
No 
interventions under other funds 

impact 

– 

Depopulation/abandonment 

Rural areas  as shock absorber in 
times of crisis; 

Repopulation/in–migration 

Income, 
poverty 
growth, 
reduction,  employment,  business 
creation/maintenance 
/diversification, 
(farming) 

investments 

income,  growth,  poverty,  jobs, 
employment, 
business 
creation/maintenance/ 
diversification, 
(non-farming), labour market 

investments 

Evolution  of  social  rights  and 
systems 
occupational 
safety,  pension  schemes  and 
transfers). 

(e.g. 

Reducing  economic  disparities 
between  farmers  and  between 
farmers  and  other  economic 
actors 

Generational renewal, ageing. 

Reducing  economic  disparities 
between rural and urban areas 

inclusion: 

Social 
vulnerable 
groups,  such  as  women,  specific 
EU 
ethnic 
nationals  and 
third  country 
nationals*. 

groups, 

other 

Gender disparities. 

Access  to  social  and  economic 
infrastructure. 

Supporting  small  investments  in 
remote  areas,  but  not  the  core 
action of the CAP.  

Remoteness, 
housing, availability  

commuting, 

Availability  and  taking  care  of 
social 
capital/fabric:  building 
local  governance/capacities  and 
bottom–up 
participation/approaches 
cooperation). 

(e.g. 

Availability 
research, 
training/advice. 

access 

and 
innovation 

to 
and 

Promoting  natural 
landscape) heritage. 

(including 

life; 
Promoting  qquality  of 
cultural  aspects  of  ‘feeling  left 
behind’/discontent’ 

Source: DG Agriculture and Rural Development 
* The CAP 2014-2020 did not have as objective to address the needs of all vulnerable groups. 

6 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 1. Intervention logic of the CAP measures related to the general objective of balanced territorial development 

Source: DG Agriculture and Rural Development. Note: further details on the intervention logic are presented in Annex 4. 

 
 
 
4. 

2.2 

BASELINE AND POINTS OF COMPARISON 

In  time  of  the  2013  CAP  reform,  an  impact  assessment  accompanied  the  regulation10,  which 
described  the  situation  preceding  the  reform  and  included  medium-term  projections  comparing 
no policy change with policy alternatives. This impact assessment was considered inappropriate 
for a baseline, because the options assessed did not correspond to the final outcome of the reform 
after negotiations with the European Parliament and Council. There was no other objective and 
quantitative  projection  that  could  have  been  used  as  a  baseline.  Thus  where  relevant,  the  pre-
2013 reform situation was used as a benchmark. 

The lack of EU targets or any binding legislative framework, specific to socio-economic aspects 
in  rural  areas,  further  prevented  the  measurement  of  any  concrete  achievements.  In  addition, 
social inclusion was not  at  the heart of the CAP  objectives  at  the time of developing the CAP 
2014-2020.  

Nevertheless, where relevant, the evaluation takes into account the targets on result indicators set 
by Member States to reflect the expected coverage of their CAP interventions programmed under 
Priority  6  (Promote  social  inclusion,  poverty  reduction  and  economic  development  in  rural 
areas).  Other  dimensions  of  balanced  territorial  development  are  not  covered  by  quantified 
targets  in  the  CAP  2014-2020.  The  next  CAP  will  fill  in  this  gap,  notably  adding  quantified 
targets on the direct support redistribution and the support to areas in needs.   

10     https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52011SC1153. 

8 

 
 
 
 
 
                                                           
5. 

3. IMPLEMENTATION/STATE OF PLAY 

The instruments and measures here described are those selected in the study and considered to 
have  a  direct  impact  on  the  general  objective  of  balanced  territorial  development  and 
socioeconomic aspects including social inclusion in rural areas following a theory-based impact 
assessment and territorial distribution analysis. 

Budgetary framework 

The design of the CAP 2014-2020 was influenced by the multiannual financial framework 2014-
2020, with implications on the implementation of the various CAP instruments, including: 

  external  convergence,  to  reduce  the  differences  in  the  level  of  income  support  per 

hectare based on historical differences between Member States; 

  degressivity,  to  improve  the  distribution  of  direct  payments  by  reducing  the  basic 

 

payment above a certain level11;  
flexibility between the two pillars of the CAP, allowing Member States to better target 
the available financial resources in accordance with their particular objectives.  

The implementation choices of Member States in relation to the flexibility mechanism result in a 
net transfer of EUR 3.76 billion to Pillar II12.   

A  transitional  Commission  Regulation  (EU)  No  2220/2020  ensures  continuity  of  the  CAP 
implementation  until  the  new  legal  framework  including  the  future  Strategic  Programme 
Regulation enters into force by the beginning of 2023. 

Pillar I measures targeting the general objective of balanced territorial development13 

Pillar I measures include income support14 via direct payments, which is the core instrument 
of the CAP contributing to its objective of enhancing farm income.  Direct  payments under 
the  CAP  2014-2020  came  into  force  on  1 January 2015,  following  notification  from  Member 
States to the European Commission on their implementation decisions15.  

11   Regulation  (EU)  No  1307/2013  established  a  compulsory  payment  reduction,  according  to  which  Member 
States  have  to  reduce  annual  basic  payments  by  at  least  5%  for  the  share  of  the  payment  amount  exceeding 
EUR 150 000 (with exceptions for Member States using more than 5% of their annual national ceiling to grant 
a redistributive payment). The aid reduction can be mitigated for farms employing paid labour. Member States 
can opt for any reduction percentage up to 100% (capping). The amount reduced is transferred to Pillar II. 
12   As  an  outcome  of  12  Member  States  transferring  funds  from  direct  payments  to  rural  development  (notably 
Belgium,  Czechia,  Denmark,  Germany,  Estonia,  Greece,  France,  Latvia,  Lithuania,  Netherlands,  Romania 
(until  calendar  year  2017)  and  the  UK)  and  five  Member  States  (Croatia,  Hungary,  Malta,  Poland  and 
Slovakia)  shifting  from  their  rural  development  to  direct  payments,  with  Poland,  Hungary  and  Croatia 
transferring the maximum rates applicable (25% for Poland and 15% for Hungary and Croatia). 

13     The  following  instruments  were  selected  on  the  basis  of  theory  based  impact  assessment  described  under 
chapter  4  ‘Methodology’.  Pillar  I’s  direct  payment  for  areas  facing  natural  or  other  specific  constraints  was 
excluded from the shortlist given its limited territorial scope (it is only funded in Denmark and Slovenia). 
14   For  detailed  information  on  the  income  support  measures  of  the  CAP,  see  https://ec.europa.eu/info/food-

farming-fisheries/key-policies/common-agricultural-policy/income-support_en. 

15   The  decisions  related  to  some  payments  could  be  reviewed,  in  particular  the  implementation  of  the 

redistributive payment and the share of the national ceiling used for the young farmer payment. 

9 

 
 
                                                           
Basic  payments  contribute  to  balanced  territorial  development  by  ensuring  a  basic  income 
support  for  farmers  engaged  in  agricultural  activities,  on  the  basis  of  a  basic  payment  scheme 
(BPS) or a single area payment scheme (SAPS). Basic payments are compulsory and account for 
approximately 50% of the total available support, with variable shares at Member State level (see 
Figure  2).  The  10  Member  States  (Bulgaria,  Czechia,  Estonia,  Cyprus,  Latvia,  Lithuania, 
Hungary,  Poland,  Romania  and  Slovakia)  applying  single  area  payment  scheme  until  2014 
maintain the scheme until the end of 202016. 

A 30% share of the budget for direct payments is provided for farmers respecting agricultural 
practices  beneficial  to  the  climate  and  the  environment,  notably  crop  diversification, 
maintenance  of  existing  permanent  grassland,  and  having  an  area  of  ecological  interest.  This 
‘green payment’ is obligatory for Member States and granted as a flat-rate payment per eligible 
hectare declared under the single area payment scheme or according to the value of entitlements 
activated under the basic payment scheme.  

The  young  farmer  payment  is  a  compulsory  scheme  providing  support  for  farmers  up  to 
40 years  of  age  who  begin  their  agricultural  activities,  with  an  additional  25%  of  direct 
payment17  for  a  maximum  of  five  years.  Member  States  may  use  up  to  2%  of  their  national 
envelope to this scheme and most have utilised this maximum rate (or close to that). However, 
six Member States assigned a very small share of up to 0.5% of their national envelope to this 
payment.  

Member States can also allocate 8% to 13% of the their national ceiling for direct payments to 
voluntary  coupled  support  in  certain  sectors  or  regions  where  specific  types  of  farming  or 
specific  agricultural  sectors  that  are  particularly  important  for  economic,  environmental  and/or 
social reasons undergo certain difficulties. Funding can be increased by a further 2% to support 
protein crops in order to maintain the plant protein supply necessary for the feed sector. In claim 
year 2019, the most supported sectors at EU-28 level were: (i) beef and veal (23 Member States, 
about 40% of total voluntary coupled support envelope), (ii) milk and dairy products (19, 21%), 
(iii) sheep and goat meat (21, 13%), (iv) protein crops (16, 11%), (v) sugar beet (11, 4.4%), (vi) 
fruit  and  vegetables  (19,  4.1%),.  Germany  was  the  only  Member  State  not  applying  voluntary 
coupled support in 2019. 

The  CAP  2014-2020  introduced  a  voluntary  redistributive  payment,  which  allows  a  higher 
payment for the first 30 hectares of a farm (or more if the average farm size of a Member State is 
greater  than  30 hectares).  The  purpose  is  to  ensure  a  redistribution  of  income  support  towards 
smaller farms as their income support needs tend to be higher. Although Member States can use 
up to 30% of their national ceiling for this payment, the actual uptake ranges from 2% to 15%. 

16   Member States applying SAPS may grant a complementary, voluntary transitional national aid for farmers in 
the period 2015-2020, in order to avoid an abrupt and important decrease of the transitional aid available until 
2014. Croatia may provide complementary national direct payments. Complementary national direct payments 
may be provided in Bulgaria and Romania in 2015. Bulgaria may, in 2015, also use national direct payments to 
complement payments granted under the crop-specific payment for cotton. 

17   Regulation (EU) 2017/2393 (the so-called Omnibus Regulation) allowed Member States to decide from claim 

year 2018 onwards to increase it up to maximum 50% and for a fixed period of 5 years. 

10 

 
 
                                                           
Nine  Member  States/Regions  (Belgium-Wallonia,  Bulgaria,  Germany,  France,  Croatia, 
Lithuania,  Poland,  Romania  and  UK-Wales)  have  applied  the  redistributive  payment  since 
2014/2015 and Portugal as of 2017.  

Figure 2. Distribution of funds across the direct payment schemes in claim year 2019 

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

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

Basic Payment Scheme
Redistributive Payment
Payment for Areas with Natural Constraint
Crop Specific Payment for Cotton
Small Farmers' Scheme

Single Area Payment Scheme
Greening
Young Farmers' Scheme
Voluntary Coupled Support

Source: European Commission (2020), CAP Output Indicators - Financing the Common Agricultural Policy 

Member  States  could  implement  a  voluntary  small  farmers’  scheme,  which  is  a  simplified 
direct  payment  scheme  replacing  all  other  direct  payments  and  exempts  beneficiaries  from 
greening obligations and cross-compliance penalties to ensure a support specifically designed to 
the smallest farms. Sixteen Member States applied the small  farmers’ scheme in  financial  year 
2019.  

Table 2. Average support per ha for the EU-28 under Pillar I (EUR/ha of total potentially eligible 
area)   

Source:  European  Commission,  Information  System  for  Agriculture  Refund  Expenditure  (AGREX)  and  Clearance  Audit  Trail 
System (CATS) databases. 

Pillar I instruments also include measures under the common organisation of the markets18. 
Market  measures  address  specific  issues  in  certain  EU  agricultural  markets  to  help  producers 
adapt to market conditions and increase competitiveness and sustainability. Market measures are 

18   For  detailed  information  on  the  market  measures  of  the  CAP,  see  https://ec.europa.eu/info/food-farming-

fisheries/key-policies/common-agricultural-policy/market-measures/market-measures-explained_en. 

11 

Financial yearBasic Payment (BPS/SAPS)Redistributive PaymentGreeningYoung Farmer SchemeVoluntary Coupled SupportSmall Farmer SchemeTotal2016137.17.873.52.023.85.7251.42017135.210.173.62.224.47.6254.52018133.810.373.32.425.16.4252.92019130.610.271.93.324.45.5247.3 
 
 
 
                                                           
structured  along  product  types  (i.e.  products  eligible  for  intervention19),  and  programmes  and 
areas of intervention. They contribute to enhance the value added of primary producers and the 
sustainability of the agri-food chain (e.g. fruit and vegetable sector) and income to farmers, to 
their  households  and  exports  (e.g.  wine).  The  market  expenditure  reached  EUR 2.41 billion  in 
2018,  out  of  which  40%  went  to  the  wine  sector  and  35%  to  fruit  and  vegetables.  The  CMO 
funding  is  concentrated  in  the  southern  regions  of  Spain,  and  in  several  regions  in  France  and 
Italy. 

In  the  2014-2019  period  the  total  expenditure  for  the  Pillar  I  instruments  amounts  to  around 
EUR 300  billion  (about  EUR  44  billion  per  year).  Single  area  payment  scheme/basic  area 
payment scheme, greening and voluntary coupled support have the highest total amount. In this 
period  the  funding  available  for  EU  market  policies  (including  the  crisis  reserve  fund)  was 
planned to account for approximately 4% (EUR 17.5 billion) of the total CAP budget.  

Pillar II measures targeting the general objective of balanced territorial development 

The  rural  development  policy  of  the  CAP  is  implemented  over  a  seven-year  period,  via  rural 
development  programmes  designed  by  national  or  regional  Managing  Authorities.  A  total  of 
118 programmes  are  implemented  in  the  28  Member  States,  with  an  EU  funding  of 
EUR 99.6 billion  over  the  2014-2020  period.  Adding  the  co-funding  by  national,  regional  and 
private  resources,  the  total  amount  of  funding  for  the  rural  development  policy  of  the  CAP 
reaches EUR 161 billion for 2014-2020. 

The CAP provides Member States flexibility regarding the type of measures implemented under 
Pillar II, as well as the budget allocated to the various measures. The major difference thus stems 
from the various programming among Member States. On social aspects, the only obligation for 
all Member States was the ring-fencing on LEADER (minimum 5% of rural development fund) 
and  most  Member  States  went  beyond  this  minimum.  The  implementation  of  other  rural 
development measures affecting social sustainability was voluntary for Member States. The way 
in  which  the  CAP  Pillar  II  measures  are  structured  and  implemented  vary  also  significantly 
between  Member  States  resulting  in  an  array  of  different  approaches  and  detailed  operations 
available to beneficiaries. As described in chapter 2.1, the rural development policy of the CAP 
2014-2020  includes  a  number  of  priorities  and  focus  areas  targeting  balanced  territorial 
development of rural areas.  

19     23 products listed under Article 1 Reg. No 1208/2013. 

12 

 
 
                                                           
The most relevant EAFRD measures contributing to balanced territorial development20 are: 

M01: Knowledge transfer and information actions. The measure aims to improve the access 
for farmers, forest holders, persons engaged in the food sector and rural small and medium-sized 
enterprises to technical and economic knowledge and information.  

M02: Advisory services, farm management and farm relief services. Farm advisory services 
help farmers and other actors of rural areas to enhance their sustainable management and overall 
performance of their holding. 

M04:  Investments  in  physical  assets.  The  investment  grant  is  designed  to  improve  the 
economic  and  environmental  performance  of  holdings,  notably  in  a  context  of  short  supply 
chains and local markets.  

M06:  Farm  and  business  development.  The  main  aims  of  the  measure  are  to  encourage  the 
restructuring of the agricultural sector and ensure the viability of new activities (e.g. by means of 
business  start-up  aid  for  young  farmers;  non-agricultural  activities  in  rural  areas  and  the 
development of small farms etc.). 

M07: Basic services and village renewal in rural areas. Investments under this measure focus 
on rural settlements and surrounding cultural and natural heritage.  

M09:  Setting  up  of  producer  groups  and  organisations.  Supports  producer  groups  and 
organisations  (qualifying  as  small  and  medium-sized  enterprises)  in  order  to  face  market 
challenges (adapting production to market requirements, placing goods on the market etc.).  

M10:  Agri-environment-climate.  This  measure  aims  to  preserve  and  promote  ‘agricultural 
practices  that  make  a  positive  contribution  to  the  environment  and  climate’,  by  compensating 
beneficiaries for all or part of the additional costs and income foregone.  

M11:  Organic  farming.  The  measure  foresees  a  support  to  farms  that  convert  or  maintain 
organic farming activity, in  order to  encourage organic farming practices by  groups of farmers 
through collective contracts or co-operation between farmers. 

M12:  Natura  2000  and  Water  Framework  Directive.  The  measure  provides  annual  per 
hectare  compensation  payments  to  farmers  and  foresters  for  the  additional  costs  and  income 
foregone when implementing the Birds and Habitats Directives or (for farmers only) the Water 
Framework Directive. 

M13:  Payments  to  areas  facing  natural  or  other  specific  constraints  (ANC).  The  measure 
intends to encourage farming activity in mountain areas and other areas facing natural or other 

20   The following measures were selected on the basis of theory based impact assessment described under chapter 
4  ‘Methodology’.  The  following  measures  were  excluded  on  the  basis  of  a  weak  theoretical  link  with  the 
socioeconomic aspects, as represented by smaller final scores M03 – Quality schemes, M05  – Investments in 
restoring production potential and preventing damage, M08  - Investments in forest area development, M14 – 
Animal  welfare  and  M20  –  Technical  assistance.  M18  complementary  direct  payments  for  Croatia,  was 
discarded  because  locally  specific,  as  demonstrated  through  the  territorial  distribution,  with  limited 
comparability between Member States.  

13 

 
 
                                                           
specific  constraints  (as  designated  in  Article  32  of  Regulation  (EU)  1305/2013).  The  support 
compensates farmers for the relevant additional costs and income foregone.  

M15:  Forest-environmental  and  climate  services  and  forest  conservation.  Under  this 
measure  Member  States  may  support  public  and  private  forest  owners  who  undertake  forest-
environment and climate commitments on a voluntary basis. 

M16:  Cooperation.  Encourages  cooperation  involving  two  entities,  creation  of  clusters  or 
networks,  as  the  establishment  of  operational  groups  of  the  European  Innovation  Partnership 
(EIP).  These  can  for  instance  take  the  form  of  pilot  projects;  development  of  new  products, 
practices, processes or technologies. 

M17: Risk management. This measure may cover economic losses caused by external market 
effects, outbreak of an animal or plant disease or weather events. 

M19: Support for LEADER and local development. The  LEADER (links between the rural 
economy  and  development  actions)  approach  is  a  compulsory  feature  of  all  rural  development 
programmes.  It is an area-based, bottom-up method of delivering support to local communities 
that design and implement local development strategies themselves.  

As regards in particular the measures in the scope of this evaluation, the declared expenditure for 
the  period  2014-2019  amounts  to  EUR  73.6  billion,  i.e.  91%  of  the  total  EAFRD  public 
expenditure21.  The  measures  with  the  higher  expenditure  are  M13  –  ANC,  M10  –  Agri-
environmental commitments, M11 – Organic and M04 – Investments (see Figure 3). The majority 
of  Pillar  II  measures  are  targeted  on  farmers  and  foresters  needs,  however  some  of  them  have 
broader targets i.e.: M07 – basic services, M19 – LEADER, M04 – investments and M06 – farm 
and business development. 

Figure 3. Declared expenditure for the period 2014-2019 for the relevant EAFRD measures (million 
of EUR) 

Total

P6

P5

P4

P3

P2

0

10.000

20.000

30.000

40.000

50.000

60.000

70.000

80.000

M01 M02 M04 M06 M07 M09 M10 M11 M12 M13 M15 M16 M17 M19

Source: European Commission, DG Agriculture and Rural Development 

Priority 6, targeting more specifically social inclusion and economic development, covered 10% 
of the expenditure in 2014-2018. The Pillar II amount spent on broader rural development is 

21  

Including national co-financing. 

14 

 
 
 
                                                           
modest compared to spending on other priorities (close to 57% on natural resources – Priority 4 
and 19% on competitiveness – Priority 2). In addition, by comparison to 2007-2013, the 2014-
2020 rural development programmes generally offer much smaller allocations of funds towards 
Measure  7  on  ‘Basic  services  and  village  renewal’,  but  increased  funding  to  LEADER.  Total 
public funding between both periods is similar. 

In view of the planned expenditure, over 2014-2019, the realised expenditure is particularly low 
for Priority 6 (34%) and for the measures very relevant to balanced territorial development: M19 
– LEADER (30%), M16 – cooperation (18%) and M02 – advice (21%) (see Table 14 in Annex 
4). The implementation of these more complex instruments take more time than annual payments 
(83% of the planned expenditure is realised for M13 – ANC). The rate of realisation is similar for 
M07 – basic services and M.04 – investments around 40%. The reporting after the projects are 
completed explain in part these low figures. 

In their rural development programmes, Member States set targets for a number of social aspects 
beyond  farming.  These  targets  relate  to  the  rural  population  covered  with  LEADER  strategies, 
job creation, and the development of improved infrastructure and services (including information 
and communication technology).  

The targets  cover: (i) local  development,  notably  via  LEADER (Focus area 6B); (ii) economic 
diversification;  (iii)  the  development  of  small  and  medium-sized  enterprises;  and  (iv)  job 
creation (Focus area 6A).  

Member States must spend a minimum of 5% of Pillar II on LEADER. In 2014-2020, 11% of 
total planned rural-development public expenditure was allocated to  local development notably 
via  LEADER  (focus  area  6B).  Member  States  also  set  a  target  to  cover  more  than  50%  of  the 
rural  population  with  local-development  strategies  by  2023.  This  target  has  already  been 
achieved.  By  contrast,  only  3%  of  public  expenditure  was  allocated  to  job  creation  under 
diversification (focus area 6A). 

On job creation in supported CAP projects, EU targets are relatively small (120 500 jobs in total 
by  2023)  and  progress  towards  the  target  is  slow.  This  is  partly  because  Member  States  only 
notify the jobs created once projects are completed. However, the  various quantitative analyses 
showed a higher impact from the CAP on jobs. 

Few Member States rely on EAFRD support to develop rural access to broadband, hence the low 
target  value  at  EU  level  (16%  of  rural  population  covered  by  improved  information  and 
communication technology supported with the CAP). Nevertheless, where countries make use of 
EAFRD  support,  there  is  a  direct  link  between  the  funded  operations  and  their  impact  (see 
Chapter 5.1). 

15 

 
 
Table 3.  – Progress towards social targets by 2019 

Target indicator 

T21: Percentage of rural population covered by local-development strategies 
(Focus area 6B) 

T20: Jobs created in supported projects (Focus area 6A) 

T23: Jobs created in supported projects (LEADER) (Focus area 6B) 

T22: Percentage of rural population benefiting from improved 
services/infrastructure (Focus area 6B) 
T24: Percentage of rural population benefiting from new or improved 
services/infrastructure (ICT) (Focus area 6C) 

Target 

Progress to 
target 

53% 

118% 

76 430 

44 110 

16% 

23% 

51% 

139% 

5.8% 

66% 

Note: Data are notified by Member States when projects are completed. For long-term investments such as for ICT, progress to 
target is slow. 
Source: DG Agriculture and Rural Development 

6. 

4. METHODOLOGY 

The  evaluation  is  primarily  based  on  the  external  evaluation  support  study  on  territorial 
development22,  complemented  with  internal  (DG  Agriculture  and  Rural  Development)  analysis 
based on more recent data and information (2019 expenditure). It is also supported with relevant 
findings of available external analyses. 

The methodological approach combines theoretical and empirical analysis and includes a variety 
of quantitative and qualitative methods to deal with the complexity and the range of topics under 
the evaluation (see Annex 3 for more information). The evaluation tools and methods included, 
but  were not  limited to: documentary research, literature  reviews, statistical data analysis,  case 
studies  surveys  and  interviews  with  stakeholders,  public  consultations,  etc.  The  collected  data 
was analysed through both quantitative and qualitative methods.  

The  quantitative  analysis  in  the  evaluation  support  study  was  based  on  the  use  of  clusters, 
grouping regions along common characteristics and needs, allowing for a targeted discussion of 
the  effects  of  CAP  funding  on  differentiated  regions’  balanced  territorial  development, 
characteristics,  and  socio-economic  factors.  This  analysis  produced  a  set  of  four  clusters  (see 
Map 2):  

  Cluster 1  –  Diversified rural  and intermediate regions,  featuring ageing societies within 
structurally  well-developed  regions.  These  regions  are  more  often  associated  with  high 
labour costs and strong inter-sectoral competition;  

  Cluster  2  –  Peripheral  rural  and  intermediate  regions,  featuring  very  low  degrees  of 
accessibility.  The  agricultural  sector  is  important  in  these  regions.  These  regions  are 
lagging in terms of productivity and standards of living. Population outflows to wealthier 
regions negatively impact human capital endowments;  

22   Evaluation  support  study  on  the  impact  of  the  CAP  on  territorial  development  of  rural  areas  -  Publications 

Office of the EU (europa.eu). 

16 

 
 
 
                                                           
  Cluster  3  –  Dynamic  rural  and  intermediate  regions,  having  stronger  development 
patterns, however, farming in these regions  faces pressure from the neighbouring urban 
centres by means of land value for purposes other than agriculture;  

  Cluster 4 – Traditional rural and intermediate regions, which are generally  younger and 
feature high employment growth. The regions retain a strong rural character, with large 
NATURA 2000 areas, a large share of the population in rural areas. These regions retain 
a strong and viable agricultural sector.  

Additionally,  also  in  the  framework  of  the  support  study  various  quantitative  analyses  were 
carried out: regression and correlation analyses that included spatial socioeconomic variables; an 
input-output analysis to  identify the effectiveness of Pillar I funding with respect to  generating 
jobs and income within the primary and upstream sectors; an efficiency analysis of CAP funding.  

The  methodological  approach  taken  in  the  JRC  study  for  the  elaboration  of  a  counterfactual 
impact  evaluation  study  provides  causal  estimates  of  the  CAP’s  impact  on  a  set  of  regional 
economic  (GDP  per  capita,  total  gross  value  added  (GVA)  and  employment)  and  agri-sector 
related  economic  outcomes  (GVA  in  the  agri-sector,  employment  in  agriculture,  land  and 
employment productivity)23. The analysis addresses the CAP’s performance at regional level and 
in particular the convergence of rural regions using the Generalised Propensity Score method. 

The JRC analysis is based on a NUTS3 level description of the regional dimension of the CAP in 
the period 2011-201824. This implies a characterisation of: (i) the rural regions, according to their 
economic  aspects  and  in  particular  of  their  agri-sector  and;  (ii)  of  the  regional  CAP  mixes  (of 
Pillar 1 and Pillar 2 groups of measures) in time intervals characterised by the implementation of 
relevant CAP reforms. 

The evaluation benefits as well from various consultations, including, as part of the case study, 
interviews that have been carried out in the context of case studies and addressing various types 
of  key  stakeholders.  The  interviews  conducted  gathered  also  information  from  DG  AGRI 
representatives,  concerned  with  Pillar  I  and  Pillar  II,  on  the  administrative  burden  possibly 
generated by the implementation of CAP instruments and measure. 

Additionally, an online public consultation on A long term Vision for the EU’s Rural Areas25, 
was carried out to gather and analyse the perceptions of Europeans (citizens, companies, NGOs, 
etc.).  A  specific  section  on  the  CAP  and  balanced  territorial  development  was  added  to 
contribute to this evaluation. This section was answered by 864 respondents. A homonym Staff 
Working  Document  was  also  published26.  Other  European  Institutions,  the  Committee  of 

23  Dumangane, M., Freo, M., Granato, S., Lapatinas, A. and Mazzarella, G., An Evaluation of the CAP impact: a 

Discrete policy mix analysis, Publications Office of the European Union, Luxembourg, 2020,JRC125451. 

24  Dumangane, M., Freo, M., Granato, S., Lapatinas, A. and Mazzarella, G., The regional dimension of the CAP: 

2007-2018, Publications Office of the European Union, Luxembourg, 2020, JRC125450. 

25   Stakeholder consultation – synopsis report accompanying the report European Commission, A long-term Vision 

for the EU’s Rural Areas, COM(2021)345 final, EUR-Lex - 52021DC0345 - EN - EUR-Lex (europa.eu). 

26   European  Commission,  A  long-term  Vision  for  the  EU’s  Rural  Areas,  COM(2021)345  final,  EUR-Lex  - 

52021DC0345 - EN - EUR-Lex (europa.eu).     

17 

 
 
                                                           
Regions,  and  the  European  Economic  and  Social  Committees,  also  launched  consultations, 
which results are also relevant for this evaluation. See Annex 2 to know more on the stakeholder 
consultations launched and their outcome. 

Limitation and robustness of findings 

CAP instruments and measures may directly have socioeconomic implications, the magnitude of 
these impacts may be influenced, in a positive and/or negative manner, by the history and socio-
economic  context  of  a  given  region/country.  These  multiple  exogenous  factors  increase 
significantly  the  difficulty  to  assess  the  net-impact  of  the  CAP  on  the  social-economy  of  rural 
areas.  Additionally,  some  CAP  instruments  and  measures  have  a  much  more  direct  and 
observable impacts on this aspect than others.  

The  robustness  of  findings  is  affected  mainly  by  the  availability  of  complete,  detailed  and 
updated  data,  describing  the  socio  economic  status  of  rural  areas.  There  are  several  statistical 
definitions  of  rurality  (notably  rural  areas  and  rural  regions),  not  always  answering  evaluation 
needs requiring thus the development of specific analytical frameworks (see Chapter 5.1.7 Rural 
area definitions). In addition, some variables are available for rural areas, others for rural regions. 
Furthermore, data (or most recent data) is sometimes only available at national level or at a low 
geographic  resolution  (such  as  NUTS1),  which  is  not  sufficient  for  analyses  related  to  the 
territorial differentiation of effects with a high geographic resolution (NUTS3).  

Other  limiting  factors  include  the  large  array  of  interlinked  socio-economic  aspects  and  social 
inclusion  of  specific  vulnerable  groups  and  the  broad  range  of  policies  under  study,  from  the 
CAP, to European structural and investment funds (ESIF) and national/regional policies that, due 
to  the  heterogeneity  of  delivery  mechanisms  and  multi-level  governance  systems  involved,  is 
difficult to investigate. 

Numerous  mitigation  techniques  have  been  applied  in  the  study,  allowing  to  draw  useful 
conclusions. For instance, second-best indicators (in case the thematic coverage is not ensured) 
were used and regional breakdown methods via proxy indicators (when the required geographic 
resolution is not available) were applied. This, however, can mask certain effects that could be 
more clearly singled out if more specialised datasets were available. 

The existence of a low number of regions, which receive no CAP funding and are similar enough 
to be matched with regions obtaining relevant funding, did not allow the contractor which carried 
out the support study, applying counterfactual regression methods based on comparisons with a 
control  group.  The  correlation  analysis  and  regression  analysis  shed  light  on  statistical 
associations  and  provide  insights  into  relationship  not  necessarily  impacts  and  effects.  The 
regression analysis identified only few significant relationships. The interpretation of the results 
is limited by the separate consideration of funding from CAP and European Social Fund (ESF) 
and European Regional Development Fund (ERDF) via two regressions and the fact that many 
impacts of CAP funding may only materialise in the future. Finally, the fact that the dependent 
and explanatory variables cover the same time-period may introduce endogeneity issues.  

18 

 
 
To  circumvent  this  drawback,  the  JRC  developed  specifically  for  this  evaluation  an  analytical 
framework  allowing  for  establishing  causal  links,  distinguishing  between  the  role  of  rural 
development support, direct payments and market expenditure. 

Some interviewees could not reply to questions related to the lack of effectiveness for Pillar I and 
II, due to their specialised ‘sectorial’ knowledge.  

4.  ANALYSIS  

The  evaluation  questions  are  structured  following  the  five  evaluation  criteria:  effectiveness, 
efficiency, relevance, coherence and EU added value. Chapters 5.1 to 5.5 focus on the impact of 
the CAP on balanced territorial development by looking at each of these criteria in turn. 

5.1  EFFECTIVENESS 

The evaluation criterion on effectiveness shows to what extent the set objectives were achieved, 
how  they  were  linked  to  the  specific  intervention,  and  whether  there  were  any  unexpected  or 
unintended  effects.  Questions  related  to  the  effectiveness  of  CAP  instruments  on  balanced 
territorial development in rural areas, are presented in the following order: (i) farm income and 
distribution  of  support;  (ii)  productivity  and  competitiveness;  (iii)  jobs  in  agriculture  and 
generational  renewal;  (iv)  spill-over  effects  into  the  wider  rural  economy;  (v)  social  inclusion; 
and  (vi)  building  human  capital.  The  last  paragraph  addresses  the  issue  of  the  definition  rural 
areas. 

5.1.1 

 Farm income and distribution of support  

The average EU factor income per worker increased by 15% from 2013 to  2019 in real  terms. 
The increase in income was mainly due to major gains in labour productivity, mostly driven by 
an outflow of labour from agriculture. Income by type of region (rural, intermediate and urban) 
also varied.  

Direct  payments  represent  close  to  30%  of  farmers’  income  and  case  study  respondents27 
assessed  them  highly  positive  in  supporting  farm  business  maintenance  and  farm  income 
notably  in  times  of  crisis  (see  Figure  10).  The  effectiveness  of  direct  payments  in  stabilising 
farm  income  and  thereby  supporting  farm  competitiveness  and  economic  viability  was  also 
highlighted in the consultation on the CAP and balanced territorial development.  

However, according to the Implementation Report of the Committee of the Regions (consultation 
of  the  regional  hubs)28  direct  payments  are  not  enough  to  overcome  market  fluctuations  and 
agricultural crises. In this context, this report as well as the case study analysis highlighted also 
the importance of CMO funding in terms of income stabilisation (e.g. during the crisis caused 

27   Respondents  include  rural  development  experts,  public  authorities,  local  municipalities,  farmers,  processors, 

producer organisations and chambers of commerce. 

28   https://cor.europa.eu/en/engage/Documents/RegHub/report-consultation-05-cap-territorial-development.pdf. 

19 

 
 
 
                                                           
by the plant pathogen Xylella fastidiosa in Apulia - Italy).  In crisis, the case study respondents 
deemed also Pillar II support important to farm incomes. 

Reducing the gap between the level of income support received by EU farmers 

The  public  consultation  on  the  CAP  and  balanced  territorial  development  highlighted  that  the 
uneven distribution of resources limits the effectiveness of CAP instruments and measures. The 
EU has introduced in its 2013 CAP reform a mechanism called  external convergence with the 
aim  of  progressively  adjusting  income  support  per  hectare  in  each  Member  State,  upwards  or 
downwards, to bring them closer to EU average for a more balanced income support distribution 
on the EU territory. The gap between direct payments per hectare is progressively decreasing as 
a  result  of  external  convergence.  The  change  between  2015  and  2019  is  in  general  inversely 
proportional  to  the  level  of  direct  payments  per  hectare.  However,  it  should  be  noted  that 
Member  States  may  have  transferred  funds  from  direct  payments  to  rural  development  or  the 
contrary  (as  illustrated  on  the  graph).  The  level  of  direct  payments  decreased  mainly  in  the 
Netherlands, Greece and Belgium, while it increased mainly in Croatia, Malta, the Baltic States 
and  Romania.  In  the  case  of  Malta,  the  change  is  explained  by  the  transfer  from  rural 
development  to  direct  payments,  which  increased  significantly  between  2015  and  2019.  In 
Croatia,  the  change  between  2015  and  2019  is  mainly  explained  by  the  phasing-in,  i.e.  the 
progressive increase of funding after accession (Croatia joined in 2013).  

Figure 4. Average direct payments in 2019 and change between 2015 and 2019 (EUR/ha) 

Note:  Direct  payments  (DP)  per  hectare  of  potentially  eligible  area  (for  the  basic  payment  and  small  farmer  scheme).  The 
change  in average direct  payments per  hectare determined  is  the  result of  external  convergence,  the  transfers  between pillars 
(from  DP  to Rural  Development  (RD)  and  vice-versa),  as  well  as  the  implementation  choices  and  trend  in potentially  eligible 
area. 
Source: DG Agriculture and Rural Development, based on CATS audit database 

20 

 
 
 
The difference in the level of direct payments per hectare between farmers within Member States 
has  reduced  significantly  between  2015  and  2019  as  a  result  of  internal  convergence29. 
18 Member  States  apply  the  Basic  payment  scheme  (BPS)  and  10 Member  States30  keep 
applying  the  Single  area  payment  scheme  (SAPS),  thus  have  a  national  flat  rate  area  payment 
since they joined the EU. Member States applying the BPS have three options: 

(1)  a uniform unit value per hectare for all payment entitlements as from 201531 at national or 

regional level; 

(2)  full convergence of the per hectare value by 201932 at national or regional level; or 
(3)  partial convergence (‘tunnel model’)33: payment entitlements with a value lower than 90% 
(or between 90 and 100%) of the national/regional unit value in 2019 will have their value 
increased during the period 2015-2019 to close at least 1/3 of the gap between their initial 
unit value and the value in 2019. 

As a result of this internal convergence and also changes in Member States’ distribution of direct 
payments34,  the  difference  in  the  level  of  direct  payments  received  by  EU  farmers  reduced 
significantly35: from EUR 1 680/ha in 2015 to EUR 1 240/ha in 2019 in the EU-28. The internal 
convergence  led  to  the  strongest  decline  in  the  difference  between  farmers  in  the  Netherlands 
moving  progressively  to  a  national  flat  rate  by  2019.  The  difference  has  remained  high  (up  to 
EUR 2 000/ha  in  Spain  and  Portugal)  in  certain  Member  States  still  applying  the  historical 
model36. In Member States applying SAPS the difference in level of direct payments per hectare 
is generally low, except in Bulgaria, due notably to the coupled payments distribution. 

29 

https://ec.europa.eu/info/sites/default/files/food-farming-fisheries/key_policies/documents/basic-payment-
scheme_en.pdf. 

30  Bulgaria, Czechia, Estonia, Cyprus, Latvia, Lithuania, Hungary, Poland, Romania and Slovakia. 
31  Germany, Malta, France (Corsica), and the UK (England). 
32   The Netherlands, Austria, Finland and the UK (Scotland and Wales). 
33   Belgium  (Flanders  and  Wallonia),  Denmark,  Ireland,  Greece,  Spain,  France,  Hungary,  Italy,  Luxemburg, 

Portugal, Slovenia, Sweden and the UK (Northern Ireland). 

34   E.g. coupled payments or in France, the reallocation of direct payments from arable land to grassland. 
35   Difference  between  the  first  and  99th  percentiles  of  direct  payments  per  hectare.  The  width  represents  the 

difference in the value of direct payments per hectare received by 98% of EU farmers. 

36   The  regionalisation  model  applied  in  Spain  has  reduced  differences  in  the  level  of  direct  payments  only 

marginally, due to its design.  

21 

 
 
                                                           
Figure  5.  Distribution  of  direct  payments  in  EUR  per  hectare,  difference  between  the  first 
percentile and 99th percentile in 2015 and 2019 

Note: Based on 2018 figure for Slovakia  
Source: DG Agriculture and Rural Development, based on CATS audit database 

In  the  18  Member  States  applying  the  BPS,  the  2013  reform  has  allowed  moving  away  from 
historical references with a certain convergence of direct payments per hectare within Member 
States.  As  a  result  of  both  external  and  internal  convergence,  as  well  as  other  Member  States 
implementation  choices,  accounting  for  total  income  support  (direct  payments  and  aid  to  areas 
facing natural or other specific constraints (ANC)), the differences between regional (Nuts3) per 
hectare levels reduced significantly between 2015 and 2019: 

 
increased by up to 5% in 272 NUTS 3 regions, 
 
increased by more than 5% in 327 NUTS 3 regions, 
  decreased by up to 9.9% in 475 NUTS 3 regions, and 
  decreased by 10% or more in 267 NUTS 3 regions. 

This led to a re-balancing of the level of support per hectare on the EU territory. However, some 
significant differences remain in Member States applying partial convergence (i.e. France, Italy, 
Spain, Portugal, see Figure 26 in Annex 4). In addition, it is also important to note that because 
of  very  different  farm  structures  and  income,  a  similar  level  of  support  per  hectare  does  not 
translate  on  similar  support  per  farm  or  worker  and  high  income  support  per  hectare  does  not 
necessarily mean a high share of support in income and vice versa. 

22 

 
 
 
Map 1. Change in income support (direct payments and ANC) 2015-2019 in %37 

Note: see also see Figure 25 and Figure 26 in Annex 5 
Source: DG Agriculture and Rural Development on the basis of the CATS database 

Redistribution of support to smaller farms38 

Most direct payments are paid per hectare and the level of basic income support is converging 
towards closer levels, thus by definition larger farms receive more payments in absolute value. It 
explains  why  several  interviewees  during  case  studies  pointed  that  the  nature  and  structure  of 
area-based  payments  (BPS/SAPS)  favour  large-scale  farms  and  more  competitive  areas39.  The 
unfair  distribution  of  support  can  also  be  considered  to  have  implications  on  the  feeling  of 
being  left  behind,  argument  which  was  supported  by  public  authorities,  rural  development 
experts, farmers, and producer organisation.  

By contrast, in the Netherlands (applying full convergence), case study respondents consider that 
direct  payments  reduced  economic  disparities  between  small  and  mid-sized  and  large-scale 

37   The  map  does  not  include  the  outermost  regions  of  the  European  Union  (French  Guiana,  Guadeloupe, 

Martinique, Mayotte, Reunion Island, Saint-Martin, Azores, Madeira and the Canary Islands). 

38   Farm size can be measured by physical size (utilised agricultural area) or by economic size (standard output). 

On average, the higher the farm size, the higher the average income. 

39   For example, in Spain, the current regionalised support system, in which significant differences between areas 
inherited from the rights acquired in previous reforms still persist, results in more competitive areas receiving a 
higher amount of basic payment and conversely, pushing the abandonment of activities in the ‘losing’ areas. 

23 

 
 
 
 
 
                                                           
farms. This opinion is largely shared by respondents40 to the case studies (see Figure 10), which 
shows that there are more respondents pointing at the positive contribution of direct payments in 
reducing economic disparities than at the negative one.  

Redistribution of support towards smaller farms was one of the CAP’s objectives to address their 
higher income support needs. For this, Member States could choose to implement a redistributive 
payment41 to smaller farmers, as well as capping and degressivity of direct payments per farm.  

In 2016-2018, smaller farms in the EU (below 30 ha) received direct payments per hectare 
25%  higher  than  EU  average42.  This  contributes  to  address  their  higher  need  for  income 
support,  but  only  in  part,  as  on  EU  average,  farms  below  30  ha  had  an  income  40%  below 
average  over  this  period  (see  Figure  27  in  Annex  4).  Farms  small  in  physical  size  often  group 
together  farms  producing  high  value  added  products  not  always  eligible  to  decoupled  direct 
payments  (i.e.  farms  specialised  in  horticulture  and  vineyards,  intensive  livestock  farms,  many 
small farms in Romania or part time farms). Comparing farms according to their economic size 
is more relevant in this context. 

In  terms  of  economic  class,  the  smallest  class  (below  EUR 8 000  standard  output)  received 
EUR 244/ha in 2016-2018, which is below EU average of EUR 260/ha. This result is driven by 
the over representation of Romania43 in this class (57% of the represented farms).  

Nevertheless,  as  a  result  of  the  reform,  the  level  of  direct  payments  per  hectare  in  this 
smallest economic class increased by 17% compared to 2011-2013. The increase was also very 
significant  for  farms  between  EUR 8 000  and  EUR 50 000.  While  for  farms  with  an  economic 
size  between  EUR 100 000  and  EUR 500 000  (concentrating  close  to  40%  of  direct  payments) 
the  per  hectare  payment  declined  by  5%  with  the  reform  (see  Figure  6).  However,  income  of 
smaller farms remains significantly lower than average and the share of direct payment support 
in  income  is  significantly  higher  for  the  smallest  economic  farm  size  (48%)  compared  to  the 
largest farms (18%). 

40   85 respondents from the categories public authority, rural development expert, farmer, processor and producer 

organisation. 

41   10 Member States have applied the redistributive payment (Belgium  - Wallonia, Bulgaria, Germany, France, 
Croatia,  Lithuania,  Poland,  Romania,  UK  –  Wales  and  Portugal).  The  amount  of  the  top-up  payment  per 
hectare varies (in 2015, they ranged from EUR 25 in France to EUR 127 in Wallonia). 

42   According  to  FADN,  professional  farms  with  less  than  30 ha  received  EUR 325/ha  of  support  in  2016-2018 

43  

compared to EUR 260/ha on average. Higher coupled payments explain a large share of the difference. 
In  2019,  direct  payments  per  hectare  averaged  EUR 200  in  Romania,  well  below  the  EU  average  of 
EUR 290/ha.  

24 

 
 
                                                           
Figure 6.  Income and direct payments per hectare by economic farm size in 2016-201844 

Income per worker (EUR) and share of direct payments 
(DP) in income (%) 

Direct payments per hectare (EUR/ha) 

Note: Economic size classes: (1) EUR 2 000 – < 8 000; (2) EUR 8 000 – < 25 000; (3) EUR 25 000 – < 50 000; (4) EUR 50 000 
– < 100 000; (5) EUR 100 000 – < 500 000; (6) > EUR 500 000. From 2018, the first economic size class includes only farms 
from EUR 4 000 to EUR 8 000. The income indicator used is the farm net value added per full time equivalent. 
Source: DG Agriculture and Rural Development, based on FADN data 

Ranking farmers according to income level (i.e. need for support), farmers with the highest level 
of income per worker received more direct payments per worker in 2016-2018 (see Figure 28 in 
Annex  4).  However,  due  to  the  redistribution  of  support  with  the  2013  reform,  the  level  of 
support for farmers with the highest income decreased to the benefit of farmers with lower 
income.  Compared  to  the  period  2011-2013,  direct  payments  in  2016-2018  for  the  10%  farms 
with the lowest income increased by EUR 535 per worker and decreased for the farms with the 
highest income by EUR 2 577 per worker, rebalancing the support towards farmers most in need. 
Despite  this  redistribution  of  support,  income  of  the  10%  farms  with  the  lowest  income  was 
negative in 2016-2018, highlighting the need for these farms to increase productivity45 as well as 
market revenues. Many of those farms are located in Romania, Poland and Greece and they are 
often  specialist  cereals,  oilseeds  and  protein  crops,  mixed  crops  and  livestock  and  specialist 
cattle  farms.  For  the  10%  farms  with  the  highest  income,  direct  payments  represent  less  than 
20% of their income. There is  a high concentration of high-income farms in  Denmark  and the 
Netherlands. 

Redistribution of support to areas in need 

Direct  payments  and  Pillar  II  support  represent  close  to  50%  of  farmers  income  in  mountain 
areas and CAP funding is deemed important in underpinning farm viability in the most marginal 

44   The figure presents the 2016-2018 average to smooth market effects on income. The income indicator used is 
the farm net value added per agricultural work unit = amount available to remunerate all factors of production 
(land, labour and capital, both external and own factors). 

45   E.g. via economies of scale to reduce costs or change to a production system raising more income. 

25 

 
 
 
 
 
                                                           
and remote  rural  areas46. As concluded by  The challenge of  land  abandonment  after 2020 and 
options  for  mitigating  measures  of  rural  areas47  farms  in  remote  areas  are  important  for  the 
provision  of  ecosystem  services,  thus  the  importance  to  maintain  farms  in  many  remote  areas 
threatened by land abandonment. 

On EU average, farm income is lower in areas facing natural or other specific constraints (ANC) 
than  in  non-ANC.  Pre-reform  (2011-2013)  direct  payments  per  hectare  were  lowest  in  not-
mountain areas facing other constraints and highest in areas facing no constraints (see Figure 32 
in Annex 4). The reform led to a redistribution of support to areas facing constraints. With an 
increase of EUR 18/ha in mountain  areas, the level  of direct  payments  (around EUR 280/ha in 
2016-2018) is now higher in mountain areas than in areas facing no constraints. In areas facing 
other constraints, the increase of EUR 8/ha was not enough to compensate for the gap in direct 
payment  support.  In  these  regions,  the  gap  is  closed  thanks  to  the  additional  income  support 
(ANC), bringing total income support to around EUR 270/ha as in plains.  

In  mountain  areas,  adding  ANC  support,  the  level  of  income  support  reached  close  to 
EUR 380/ha in 2016-2018. Farmers in mountain areas receive also more other rural development 
subsidies  (e.g.  national  top-ups,  agri-environment-climate  commitments).  The  high  level  of 
subsidies48 does not compensate fully for the income gap with plains though, but it explains in 
part  why  the  income  per  worker  in  mountain  areas  is  higher  than  in  areas  facing  other 
constraints. In 2016-2018, the income per worker in mountain areas was 18% below income in 
areas  facing  no  constraints,  while  in  areas  facing  other  constraints  it  was  25%  below.  Without 
this high level of operating subsidies, the income gap would be much larger.  

46   Vigani,  M.  and  Dwyer,  J.  (2020)  The profitability  and  efficiency  of  High  Nature  Value  marginal  farming  in 

England. Journal of Agricultural Economics, vol.71 (2) pp.439-464.  

47  https://www.europarl.europa.eu/RegData/etudes/STUD/2020/652238/IPOL_STU(2020)652238(SUM01)_EN.pdf 
48  
In mountain areas and in areas facing other constraints, operating subsidies (direct payments, ANC support and 
other rural development subsidies) represent around half of farmers’ income, while it corresponds to only one 
third in non-ANC. 

26 

 
 
                                                           
Figure 7.  Share of operating subsidies in FNVA by ANC class in 2016-2018 

Note: ANC = support to farms in areas facing natural or other specific constraints; FNVA = farm net value added per full 
time equivalent = amount available to remunerate all factors of production (land, labour and capital, both external and 
own factors); other RD: rural development measures other than ANC (including national top-ups and agri-environment-
climate commitments, but excluding investments supports). 
Source: DG Agriculture and Rural Development, based on FADN data 

Reducing the gap in income with the rest of the economy 

In 2019, agricultural income was still less than half the wage in the rest of the economy, although 
CAP support contributed to reducing significantly this income gap since the 1990s49. In addition, 
the average income gap diminished further by approximately 2% (Figure 5) between 2010-2012 
and  2017-2019.  In  three  Member  States  only  (Spain,  Czechia  and  Slovakia),  the  average 
agricultural income was above the wage in the rest of the economy.  

Moreover, in 2019, the average income in predominantly rural regions reached 77% of the EU 
average income. It represents a 9 percentage points increase compared to 2013, meaning that the 
income gap with urban areas decreased significantly50. 

Nevertheless,  the  case  study  analyses  underlined  that  climate  externalities,  changing  consumer 
preferences  and  price  volatility  continue  to  detrimentally  affect  farm  viability  and  thus  farm 
maintenance on the EU territory.  

49   EU  regular  economic  report  4,  Thinking  CAP,  Supporting  agricultural  jobs  and  incomes  in  the  EU,  World 

Bank (2018). 
 Source: Eurostat (online data table: urt_10r_3gdp). Data for France and United Kingdom are not  available. 

50  

27 

 
 
 
                                                           
Figure 8. Income GAP between agriculture and the overall economy, 2017-2019 average 

Source: DG Agriculture and Rural Development on the basis of Eurostat data51 

5.1.2 Productivity and competitiveness 

Lack  of  agricultural  productivity  and  poor  competitiveness  of  the  agricultural  sector  were  the 
issues  most  frequently  cited  by  case  study  interviewees  from  the  categories  ‘public  authorities 
and  rural  development  experts’.  Physical  limitations  (i.e.  inhospitable  climate,  or  suboptimal 
topography)  are  weakening  agricultural  competitiveness  in  rural  regions  (e.g.  Spain).  In  other 
case study regions (e.g. Saxony-Anhalt), the relatively lower labour productivity and economic 
efficiency of the primary sector are important issues. These regions are generally characterised 
by  strong  migratory  patterns  and  subsequent  shortages  of  skilled  labour  across  the  economic 
sectors, not only in agriculture. 

Case  study  analyses  highlighted  the  important  contribution  to  farm-level  productivity  growth 
stemming  from  M04  –  investments  and  M06  –  farm  and  business  development.  In  peripheral 
regions (cluster 252) and dynamic regions (cluster 353), the quantitative analysis demonstrated that 
M04 – investment funding enabled farm production capital and water conservation investments. 
Similarly,  M01  –  knowledge  transfer,  M02  –  advisory  services  and  M16  –  cooperation  are 

51  

 Based on the online tables aact_eaa01, aact_ali01, nama_10_a10, nama_10_a10_e. 

52  Cluster 2 contains the peripheral rural and intermediate regions which feature very low degrees of accessibility. 
The  agricultural  sector  is  important  in  these  regions.  These  regions  are  lagging  in  terms  of  productivity  and 
standards of living. Population outflows to wealthier regions negatively impact human capital endowments. 
53  Dynamic rural regions (cluster 3) have stronger development patterns, however, farming in these regions faces 
pressure from the neighbouring urban centres by means of land value for purposes other than agriculture. 

28 

 
 
 
 
                                                           
considered as beneficial to modernisation and farm-based productivity in several case studies54. 
Case study analysis highlighted the importance of CMO funding for farms in terms of improved 
competitiveness of specific sectors such as wine (e.g. in Spain). The modernisation of the wine 
sector fosters the development of specialised companies and the creation of jobs that affect not 
only the producer and processing sector, but also related industries along value chains. 

In addition, the JRC counterfactual analysis of the effects of the CAP55 carried-out in the context 
of  this  evaluation,  confirms  that  CAP  support  (market  expenditure,  direct  payments  and  rural 
development  support)  contributed  to  improve  labour  productivity  across  time  in  agriculture, 
allowing  the  freed  labour  force  to  grasp  labour  opportunities  in  other  sectors  of  the  economy. 
Freed labour migrated in part to urban regions where there were more opportunities.  

This  increase  in  labour  productivity  was  accompanied  by  an  increase  in  agricultural  land 
productivity,  as  CAP  support  incentivized  to  farm  modernisation  (trough  mechanisation  e.g.), 
leading to economies of scale, better farm economic results and labour outflow from agriculture. 
Nevertheless,  the  analysis  done  by  the  JRC  shows  that  without  the  CAP,  that  supported  the 
maintenance of farms in most remote areas, this labour outflow would have been bigger towards 
urban areas.  

5.1.3 Jobs in agriculture and generational renewal 

Overall  labour  input  in  EU  agriculture  at  9.1  million  full-time  equivalents  in  2019  has  been 
falling, although it now seems to be levelling off (from -3.9% per year in 2005-2011 to -1.25% 
per year in 2011-2019).  

Public authorities and rural  development  experts, surveyed in  the case studies, highlighted that 
generational  renewal  in  agriculture  is  hampered  in  part  by  land  pressures.  In  some  areas,  the 
increase in farm size makes new entry into farming too costly for young and smaller farmers. In 
addition, the reduction in the availability of skilled workers (especially for seasonal production) 
is an issue.  

Respondents  from  the  categories  public  authority,  rural  development  expert,  farmer,  processor 
and  producer  organisation  consider  basic  income  support  to  provide  positive  impacts  to 
generational  renewal.  The  Implementation  Report  of  the  Committee  of  the  Regions 
(consultation  of the  regional  hubs)56, assessed that payments  to  young farmers  (Pillar  I) are an 
incentive  to  generational  renewal  but  not  sufficient.  The  evaluation  of  the  CAP  impact  on 
generational renewal57, showed that the support to young-farmers via direct payments and rural 
development  imply  the  highest  potential  increase  in  the  number  of  young  farmers  in  less 

54   Castilla-La Mancha, Auvergne, Southwest. 
55   Dumangane, M., Freo, M., Granato, S., Lapatinas, A. and Mazzarella, G., An Evaluation of the CAP impact: a 
Discrete policy mix analysis, Publications Office of the European Union, Luxembourg, 2020,JRC125451. 

56   https://cor.europa.eu/en/engage/Documents/RegHub/report-consultation-05-cap-territorial-development.pdf 
57    https://op.europa.eu/en/publication-detail/-/publication/4bd0b0a2-0503-11ea-8c1f-01aa75ed71a1/language-

en/format-PDF. 

29 

 
 
                                                           
developed  areas  with  many  small  farmers.  The  World  Bank  report58  also  found  a  significant 
positive  association  between  agricultural  employment  and  decoupled  Pillar  I  and  Pillar  II 
payments.  However,  wider  socio-economic  conditions  greatly  affect  the  relative  attraction  of 
farming for young people in many Member States. 

Moreover,  the  cluster  analysis,  carried-out  for  this  evaluation,  demonstrated  that  diversified 
regions (cluster 159) and dynamic regions (cluster 3), receiving high direct payments, CMO, M04 
– investments and M06 – farm and business development funding, are positively associated with 
growth in the size of agricultural labour force between 2014 and 2017. 

Interviewees confirmed that Pillar I funding enables farms to retain labour force in agriculture. 
The counterfactual exercise of the JRC showed that all CAP support and in particular CMO and 
direct  payments  had  a  large  causal  impact  on  safeguarding  employment  in  agriculture.  In 
addition,  the  World  Bank  report  highlights  profitable  and  productive  farming  as  a  catalyst  in 
many rural areas for driving people on to better jobs, higher wages and an improved quality of 
life.  However,  employment  opportunities  created  in  the  agricultural  sector  are  not  necessarily 
conducive to long-term improvements in the standards of living. Case study respondents (Spain 
and  Greece)  highlighted  seasonality  and  low  payment  as  negative  factors  of  agricultural 
the 
work.  The 
overrepresentation of women in precarious jobs are also highlighted by the European Economic 
and Social Committee in its Evaluation of CAP impact on territorial development of rural areas. 
As elaborated in the World Bank report, large differences in farm income among Member States 
result  in  seasonal  migration  of  workers  from  low  to  high  wage  regions,  and  seasonal  workers 
from outside EU provide labour force on a temporary basis.  

risk  of  exploitation  and  underpayment  of  seasonal  workers,  and 

5.1.4 Spill-over effects into the wider rural economy 

Employment in rural areas 

Primary  agriculture  still  provides  work  for  roughly  20  million  people  (both  full  time  and  part 
time)60.  Furthermore,  together  with  food  processing,  food  retail  and  food  services, 
agriculture makes up a sector providing around 40 million jobs in the EU61. In addition, in 
2019, the employment rate for the age group 15-64 reached 69% in rural areas in the EU-28, 6 pp 
higher then pre-CAP reform level in 2013, thus closing the gap with the employment rate over 
the whole territory. 

The expenditure of agricultural sector financed with Pillar I funding, such as the purchase of seed 
or fodder, fuel, chemical products or food processing, contributes to supporting employment in 
the  respective  industries.  According  to  the  input-output  analysis,  direct  payments  created  or 

58   EU  regular  economic  report  4,  Thinking  CAP,  Supporting  agricultural  jobs  and  incomes  in  the  EU,  World 

Bank (2018). 

59   Cluster  1  is  characterised  by  diversified  rural  and  intermediate  regions,  featuring  ageing  societies  within 
structurally well-developed regions. These regions are more often associated with high labour costs and strong 
inter-sectoral competition. 
Jobs in agriculture in 2016 according to ESTAT Farm Structure survey. 

60  
61   The food supply chain factsheet, updated with the latest figure on number of jobs in agriculture. 

30 

 
 
                                                           
safeguarded 5.2 million jobs in the agricultural and related upstream sectors between 2015 and 
2018, out  of which 2.4 million jobs  in the agricultural  sector solely (Figure 9). The number of 
safeguarded  employment  depends  on  the  choice  and  mechanisms  in  the  regional  and  national 
implementation of the CAP: most funding is targeted at farmers with only indirect targeting of 
the wider rural population62. 

In addition, the JRC counterfactual analysis of the effects of the CAP carried-out in the context 
of  this  evaluation,  shows  that  CAP  support  (market  expenditure,  direct  payments  and  rural 
development support) contributed to a significant total employment growth in predominantly 
rural  areas,  with  effects  increasing  over  time  and  no  significant  differences  in  the  role  of  the 
various instruments. 

The  synthesis  of  the  evaluation  components  of  the  enhanced  Annual  Implementation  Report 
201963, further demonstrates the effects of M01 - knowledge transfer, M04 - investments, M06 - 
farm and business development and M19 – LEADER in improving employment opportunities 
in rural areas (job maintenance and creation). France showed that ANC payments contributed to 
maintain grazing farms and associated jobs. Similar effects were reported in France for support 
to  organic  farming  and  agri-environment-climate  actions.  Rural  development  programmes 
support  also  the  diversification  of  small  enterprises  in  rural  areas,  especially  through  farm 
diversification (e.g. Austria64) or the creation of new businesses in non-agricultural sectors (e.g. 
Estonia,  Italy,  Slovakia).  However,  the  assessments  also  concluded  that  the  rural  development 
programmes’  contribution  to  raising  employment  in  rural  areas  was  not  fully  sufficient  to 
compensate for the loss of jobs in the agricultural sector and rural areas (e.g. France, Luxemburg, 
Latvia). The European Economic and Social Committee, in its Evaluation on the CAP’s impact 
on  territorial  development  of  rural  areas  confirmed  that  CAP  measures  did  not  impact 
employment in rural areas as positively as most countries needed. 

Gross Value Added growth in the primary sector and related sectors 

According to the input-output analysis, approximately EUR 100 billion of the receipts of farmers 
coming  from  direct  payments  funding  were  spent  on  goods  and  services  produced  in  domestic 
rural  regions  between  2015  and  2018  (see  Figure  9).  Out  of  this,  EUR 45  billion  were  spent 
within the agricultural sector, while EUR 55 billion contributed to value-added creation outside 
agriculture  in  the  upstream  sectors.  In  total,  EUR  36  billion  were  spent  on  manufactured 
products such as fodder and EUR 16.5 billion on wholesale trade. Domestic rural regions, i.e. the 
regions  situated  in  the  same  Member  State  as  where  the  funding  was  induced,  are  the  main 
recipients  of the re-invested money (domestic rural  regions  make up half  of the expenditure in 
food, fodder and wholesale trade).  

62   Vigani, M., Powell, J., & Dwyer, J. (2019).  CAP and Rural Jobs: Analysis of Studies in M Davidova, S. M. 

(eds). Rural Policies and Employment: Transatlantic Experiences. World Scientific. (pages 111–129).  

63   Summary  Report  –  Synthesis  of  the  evaluation  components  of  the  enhanced  Annual  Implementation  Report 

2019: Chapter 7. 

64   Austria  supported  265  farm  diversification  projects  such  as  catering,  farm  holidays,  green  care  or 

processing/direct marketing. 

31 

 
 
                                                           
Figure 9. Effects of direct payments in agricultural and upstream sectors (2015-2018)65 

Number of created or safeguarded jobs 

Gross value added (EUR million) 

Source: Evaluation support study  

In addition, the JRC counterfactual analysis of the effects of the CAP carried-out in the context 
of  this  evaluation,  confirms  that  CAP  support  (market  expenditure,  direct  payments  and  rural 
development support) contributed to the increase in gross value added in agriculture, with effects 
increasing over time. This translated into a growth of gross value added in rural regions and on 
the whole territory. This effect benefited the regions with relatively stronger  rural development 
support, such as regions in central and eastern part of Europe and in Portugal. 

The cluster analysis showed that by improving economic opportunities and attractiveness in rural 
regions,  M01  –  knowledge  transfer,  M02  –  advisory  services  and  M16  -  cooperation  foster  a 
more vibrant  local  economy with  spill-overs. This  is  observed to  a stronger degree (change in 
value added in primary sector and in wider economy) among the lesser developed peripheral 
and traditional regions (cluster 2 and 4). 

Enhanced quality of life in rural areas 

According  to  the  Eurobarometer  50466,  the  majority  of  Europeans  consider  that  infrastructure 
and services are good in rural areas in their country, with proportions varying between 82% for 
the environment and landscape and 51% for access to high-speed internet connections; however, 
only a minority hold  this  view about  job  opportunities (37%). Compared with  ten  years ago, a 
majority  says  that  things  have  improved  in  terms  of  access  (55%)  and  transport  infrastructure 
connecting  to  cities  (37%)  conversely,  a  majority  thinks  that  things  have  got  worse  for  job 
opportunities (42%) and health services (36%). 

65   The analysis differentiates between domestic regions (regions situated in the same Member State as where the 
funding  was  induced)  and  other  European  regions  (regions  situated  in  other  Member  States  than  where  the 
funding was induced). The sub-category ‘others’ refers to economic activities, such as legal or health  services 
or  manufacturing  of  electronic  goods.  These  were  grouped  into  one  category  due  to  their  lower  individual 
importance to the agricultural sector. 

66   https://data.europa.eu/euodp/en/data/dataset/S2229_93_2_504_ENG. 

32 

01 000 0002 000 0003 000 0004 000 0005 000 000Domestic RURAL andINTERMEDIATE regionsDomestic URBAN regionsOther European RURALand INTERMEDIATE regionsOther European URBANregionsEmployment effects in input sectors to the agricultural sector Effects of selected instruments of CAP Pillar I,Number of employees compensated  per sectorOthersN77 Rental and leasing activitiesK64-Financial service activities, exceptinsurance and pension fundingH 49 Land transport and transport via pipelinesG47-Retail trade, except of motor vehicles andmotorcyclesG46-Wholesale trade, except of motor vehiclesand motorcyclesF ConstructionD35 Electricity, gas, steam and air conditioningsupplyC33 Repair and installation of machinery andequipmentC20-Manufacture of chemicals and chemicalproductsC10T12-Manufacture of food products incl.FodderA01-Crop and animal production, hunting andrelated020 00040 00060 00080 000100 000Domestic RURALregionsDomesticINTERMEDIATE andURBAN regionsOther EuropeanRURAL regionsOther EuropeanINTERMEDIATE andURBAN regionsGross Value Added in input sectors to the agricultural sector Effects of selected instruments of CAP Pillar I,MEUR per sectorOthersN77 Rental and leasing activitiesK64-Financial service activities, exceptinsurance and pension fundingH 49 Land transport and transport via pipelinesG47-Retail trade, except of motor vehicles andmotorcyclesG46-Wholesale trade, except of motor vehiclesand motorcyclesF ConstructionD35 Electricity, gas, steam and air conditioningsupplyC33 Repair and installation of machinery andequipmentC20-Manufacture of chemicals and chemicalproductsC10T12-Manufacture of food products incl.FodderA01-Crop and animal production, hunting andrelated 
 
 
 
 
                                                           
Among  the  social  aspects,  respondents  deem  the  role  of  Pillar  II  support  (see  Figure  10)  as 
particularly positive in improving access to local infrastructure and services. 

Figure 10. Impact of the CAP on socio-economic aspects 

Direct payments67  

Rural development measures68 

Source:  Evaluation  support  study;  N=85  respondents  from  the  categories  public  authority,  rural  development  expert,  farmer, 
processor and producer organisation 

According  to  the  Implementation  Report  of  the Committee  of  the Regions  (consultation  of  the 
regional hubs)69, none of the measures of the European Direct Payment Regulation is considered 
to  have  a  significant  effect  on  the  challenges  posed  by  sub-optimal  infrastructures  and 
services70. Over 50% of respondents  to  the public consultation  of the long-term  Vision for the 
EU’s  rural  areas71  also  stated  that  infrastructure  is  the  most  pressing  need  for  rural  areas. 
Similarly, around 43% of respondents reported the lack of basic services in rural regions, with no 
major difference between stakeholder group. 

67   Respondents provided a score for each direct payment instrument. Therefore, the sample size is 85, while the 
total count is representative of the sample size n, multiplied by the number of instruments grouped (n*6=510). 
The  frequency  of  allocation  of  non-applicable  (n/a)  indicating  that  a  given  instrument  has  a  non-applicable 
effect on an aspect ranges from 225 to 285 for this question and instruments. 

68   M01  –  knowledge  transfer,  M02  –  advisory  services,  M04  –  investments,  M06  –  farm  and  business 
development,  M07  –  basic  services,  M09  –  producer  groups  and  organisation,  M10  –  agri-environmental 
climate, M11 – organic farming, M12 – NATURA 2000, M13 – payments to ANC, M15 – forest-environment 
and  climate,  M16  –  cooperation,  M17  –  risk  management,  M19  –  LEADER.  Pillar  II  results  represent 
responses across five bundles of Pillar II measures (together bundles account for all measures of interest: M01, 
M02, M04, M06, M07, M09, M10, M11, M12, M13, M15, M16, M17, M19). Respondents provided a score 
for  each  bundle  of  measures.  Therefore,  the  sample  size  is  85,  while  the  total  count  is  representative  of  the 
sample size  n, multiplied by the number of measure bundles (n*5=425). The frequency of allocation of non-
applicable (n/a) indicating that a given instrument has a non-applicable effect on an aspect ranges from 220 to 
253 for this question and measures. 

69   https://cor.europa.eu/en/engage/Documents/RegHub/report-consultation-05-cap-territorial-development.pdf. 
70   https://cor.europa.eu/en/engage/Documents/RegHub/report-consultation-05-cap-territorial-development.pdf. 
71  

 See footnote 25 

33 

 
 
 
 
                                                           
LAG  managers  and  public  authorities  frequently  cited  M07  –  basic  services  and  M19  – 
LEADER,  when  discussing  the  effect  of  the  CAP  on  the  provision  of  social  services  in  rural 
regions, however, effectiveness of CAP funding is often reported to be indirect.  

Some  key  sectors  in  terms  of  potential  growth  for  rural  areas  include  tourism  (over  40%  of 
accommodation (beds) in the EU (2019) are located in rural areas72) and the renewable energy 
sector.  A  large  number  of  rural  development  programmes  contributed  to  the  development  of  a 
broad variety of services and local infrastructures, notably for tourism, and thereby increasing 
the  accessibility  and  mobility  in  rural  areas  (e.g.  biking  paths).  Projects  of  basic  services  and 
village renewal played an important role in the development of local infrastructure, with a focus 
on energy efficiency and renewable energy (e.g. Spain)73. 

Overall, in traditional regions (cluster 474) the regression analysis indicates positive associations 
between M07 – basic services, M19 – LEADER, M04 – investment and M06 – farm and business 
development funding and change in doctors per 100 000 inhabitants. This is valid also for high 
related ESIF funding regions (predominantly regions in southern and central Europe). Improving 
access  to  healthcare  and  mental  health  services  and  services  for  migrant  workers,  is  deemed 
important  by  public  authorities  and  rural  development  experts  from  Bulgaria,  Ireland  and 
Estonia. 

Broadband  is  available  to  98%  of  Europeans  and  80%  of  European  homes  can  access  fast 
broadband  (at  least  30  Mbps)75.  However,  in  rural  areas,  less  than  60%  of  households  have 
access to fast broadband. In 2017, the Commission launched an action plan for rural broadband. 
This  contained  a  coordinated  set  of  actions  with  concrete  deadlines  to  ensure  that  the  specific 
difficulties in rolling out broadband in rural areas were addressed, thus helping to overcome the 
rural-urban  digital  gap.  Broadband  connectivity  is  supported  by  M07  –  basic  services  (in 
particular  M7.3  –  broadband  infrastructure76).  Member  States  (e.g.  Greece  and  Spain) 
strategically  decided  to  restrict  the  availability  of  this  measure  to  certain  territories  that  are 
particularly  affected  by  remoteness  and  commuting  challenges.  The  evaluation  of  the  CAP  on 
generational  renewal77  highlighted  that  with  EAFRD  in  Lithuania,  next-generation-access 
broadband  in  rural  areas  increased  from  15.6%  to  28.7%  between  2015  and  2019,  while  in 

72  

 Source: Eurostat (online data table: tour_cap_natd). 

73   Summary  Report  –  Synthesis  of  the  evaluation  components  of  the  enhanced  Annual  Implementation  Report 

2019: Chapter 7. 

74   Traditional  rural  and  intermediate  regions  (cluster  4)  are  generally  younger  and  feature  high  employment 
growth.  The  regions  retain  a  strong  rural  character,  with  large  NATURA  2000  areas,  a  large  share  of  the 
population in rural areas. These regions retain a strong and viable agricultural sector.   

75     European Commission, Digital Scoreboard https://ec.europa.eu/digital-singlemarket/digital- scoreboard. 
76  M7.3  –  broadband  infrastructure,  including  creation,  improvement  and  expansion,  passive  infrastructure  and 

access to broadband and public e-government. 

77   https://op.europa.eu/en/publication-detail/-/publication/4bd0b0a2-0503-11ea-8c1f-01aa75ed71a1/language-

en/format-PDF. 

34 

 
 
                                                           
Sweden it went up from 13.9% to 40.9%. These findings align with the improvement of 56.4% 
rural area connection pointed by a recent EC study78. 

This is further complemented by the Annual Implementation Report 201979, according to which, 
although the level of expenditure for the expansion of broadband and better use of information 
and communication technology in  rural  areas was overall low across the  Member States, more 
significant  progresses  could  be  observed  in  Germany,  Finland,  France,  Italy  and  Sweden.  It  is 
worth  noticing  that  in  some  Member  States,  rural  development  support  to  broadband 
complements  other national  digital  strategies  (e.g. Austria,  Italy, Spain),  with  the possibility to 
achieve potential synergies. 

The  European  Economic  and  Social  Committee,  in  its  Evaluation  on  the  CAP’s  impact  on 
territorial development of rural areas, concluded that CAP measures helped people stay in the 
countryside and fight depopulation. Case study respondents deemed direct payments as having 
positive  effects  on  many  social  aspects  such  as  reducing  depopulation  and  prevention  of 
abandonment,  although  mostly  addressing  farmers.  The  quantitative  analysis  (see  Annex  4) 
suggest that the small farmers’ scheme in the EU and M06 – farm and business development in 
dynamic regions (cluster 3) are not enough to prevent/reduce out-migration80. However, not all 
Member States have implemented these schemes and without these instruments the effect on out-
migration  would  have  been  even  larger.  In  particular  for  dynamic  regions,  the  pull-factor  of 
neighbouring  urban  areas  may  outweigh  the  sustaining  support  for  farm  and  business 
development.  

Concerning  agri-environment-climate  commitments81  and  support  to  ANC,  Estonian  case  study 
findings  underlined  the  importance  of  these  measures  in  retaining  inhabitants  in  rural  areas 
(primarily  via  sustained  environmental  quality).  The  Bulgarian  case  study  also  underlined  the 
impact  of  these  measures  in  allowing  farms  to  diversify  to  organic  production,  which  can 
contribute to valuable local employment opportunities and counteracting local poverty.  

Most respondents to the consultation on the long-term Vision for the EU’s rural areas82 (2 219, 
95%) consider that the importance of ‘Landscape, countryside, biodiversity and wildlife habitat’ 
will  increase  in  the  next  years  (with  no  relevant  difference  in  choices  by  stakeholder  groups). 
However,  as  indicated  in  the  study  on  ‘the  challenge  of  land  abandonment  after  2020  and 
options for mitigating measures83’ around 30% of agricultural areas in the EU are under at least 
moderate risk of land abandonment and effective agricultural land abandonment in the EU-27 

78   European Commission. Digital Economy and Society Index. DESI individual indicators – 1b1 Fast BB (NGA) 

coverage [desi_1b1_fbbc]. 

79   Summary  Report  –  Synthesis  of  the  evaluation  components  of  the  enhanced  Annual  Implementation  Report 

2019: Chapter 7. 

80   The net migration rate is the difference between the number of immigrants (people coming into an area) and 

the number of emigrants (people leaving an area) throughout the year. 

81   M10 – agri-environmental climate, M11 – organic farming, M12 – NATURA 2000. 
82   See footnote 25.   
83  

 https://www.europarl.europa.eu/RegData/etudes/STUD/2020/652238/IPOL_STU(2020)652238_EN.pdf. 

35 

 
 
                                                           
might total 5 million ha by 2030. As demonstrated by the Scenar 2030 study84, the CAP helps 
mitigating land  abandonment. Without  CAP,  the decrease in  agricultural production would not 
be  significant,  however,  a  pronounced  reduction  in  land  use  would  occur.  This  would  affect 
territorial  balance,  with  marginal  areas  being  further  marginalised  or,  at  worst,  abandoned, 
possibly leading to environmental degradation, with fewer jobs, and intensive agricultural areas 
being further concentrated. 

The impact of CAP policy tools on land use changes, production concentration and abandonment 
trends  differ  between  farm  types  and  production  groups.  Key  policy  tools  to  minimise  these 
impacts  of  external  drivers  of  land  abandonment  (climate  change,  globalisation,  health  crises) 
include  improving  farming  conditions,  supporting  areas  with  natural  constraints,  forestry  and 
agri-environmental measures and support to rural communities. 

New approaches to territorial development are on the rise that place a greater emphasis on social 
and environmental objectives and on the territorial anchorage of economies, including a concern 
on  resilience85.  The  ROBUST  project86  identified  five  domains  where  innovative  approaches 
have the potential to  enhance rural-urban synergies: social services (focus on social welfare, 
services,  accessibility);  social  and  spatial  proximity  relations  (reduction  of  physical  and  social 
distancing  through  e.g.  short  value  chains;  circularity  (closing  loops);  green  economy 
(rewarding beneficial ways to deliver ecosystem services) and culture and heritage. 

5.1.5 Social inclusion 

This concept refers to the living and working conditions of people in situations of vulnerability 
(such  as  rural  poor  population  living  in  remote  rural  areas,  rural  women,  young,  elderly, 
disabled,  low  skilled,  ethnic  groups,  third  country  nationals).  The  share  of  rural  population  at 
risk of poverty  and social exclusion, which varies from  15% in  Austria to  over 40% in  Malta 
and  some  eastern  Member  States  (e.g.  Bulgaria),  has  reduced  since  2008.  These  differences 
mostly stem from the diverse socioeconomic aspects in Member States, related to lower salaries, 
unemployment and higher living costs.  However, in wealthier countries, rural areas tend to have 
lower shares than urban areas, whereas in eastern and southern Member States, rural areas have 
much higher rates of population at risk of poverty. 

Research  has  highlighted  the  potential  positive  role  of  CAP  measures  and  instruments  – 
particularly those in Pillar II – to address social and economic needs in rural areas (i.e. tackling 
social exclusion and promoting social capital  and enhance quality of life)87. However, Pillar  II 
funding directly targeted at social issues is relatively small. Less than 15% of EAFRD support is 
targeted  at  rural  areas,  while  not  being  directly  linked  to  agriculture  and  forestry,  through 

84   R.  M’barek  et  al,  J,  Scenar  2030  -  Pathways  for  the  European  agriculture  and  food  sector  beyond  2020 

(Summary report), 2017, JRC109053. 

85   See footnote 2. 
86   ROBUST website, www.rural-urban.eu. 
87   Copus, A.K., de Lima, P., Kahila, P., Kovacs, K., Maloutas, T., Shucksmith, M. and Weck, S. (2014) TiPSE 

final report: summary. ESPON studies, European Commission.  

36 

 
 
                                                           
LEADER  and  M07  –  Basic  services88.  In  addition,  for  some  elements  key  for  farmers  welfare 
(e.g. pension schemes, social transfers), EU policies have a limited influence, as responsibilities 
lie within the core competence of Member States.  

Half of CAP recipients receive less than EUR 1 250 per year, the distribution of CAP support is 
thus  very  inclusive.  As  indicated  by  respondents  in  the  case  studies,  direct  payments  have 
positive impacts on farm poverty reduction. This is in line with the results of the World Bank 
report89 according to which the CAP reaches poorer regions within the EU Member States and 
decoupled Pillar I and Pillar II payments are associated with poverty reduction and decrease in 
inequality at regional (sub-national) levels. Although the number of people living below national 
poverty  rate  has  decreased,  various  Member  States  stated  that  their  rural  development 
programme does not pursue a specific social policy agenda to reduce poverty in rural areas (e.g. 
Austria, Germany)90.  

According  to  case  study  respondents,  Pillar  I  instruments  mostly  result  in  significant  socio-
economic benefits  for the farmer/farming household,  and to  a lesser degree  young people and 
rural  populations  in  remote  areas.  Case  study  respondents  deemed  direct  payments  as  having 
positive effects on social rights and systems, innovation, capacity building and cultural heritage 
(see  Figure  10).  However,  people  in  situations  of  vulnerability,  i.e.  third  country  nationals, 
other  EU  nationals,  ethnic  groups,  and  disabled  people  are  most  poorly  targeted  by  Pillar  I 
instruments.  

Pillar II measures provide strong socio-economic benefits to farmers and farming household and 
also  to  multiple  target  groups:  rural  young  people,  low-skilled/unemployed  people  and 
population  in  remote places. Pillar  II measures, are thus  considered more  effective in  targeting 
people in situations of vulnerability (including the low skilled and unemployed, rural women and 
rural young people) than Pillar I instruments, however most CAP funding is disbursed via Pillar 
I, thereby limiting Pillar II effects. 

In its Evaluation on the CAP's impact on territorial development of rural areas91, the European 
Economic  and  Social  Committee  confirmed  that  social  inclusion  of  vulnerable  groups  into 
agricultural activities was not sufficiently ensured, even if farming has always been able to offer 
employment to vulnerable people who experience difficulties in finding work.  

88   See footnote 2. 
89   EU  regular  economic  report  4,  Thinking  CAP,  Supporting  agricultural  jobs  and  incomes  in  the  EU,  World 

Bank (2018). 

90   Summary  Report  –  Synthesis  of  the  evaluation  components  of  the  enhanced  Annual  Implementation  Report 

2019: Chapter 7. 

91   Evaluation  on  the  CAP's  impact  on  territorial  development  of  rural  areas  (Information  report)  -  European 

Economic and Social Committee (europa.eu).  

37 

 
 
                                                           
Figure 11: Socio-economic benefits of CAP measures on target groups 

Impact of Pillar I (n=104) 92 

Impacts of Pillar II93 (n=85) 

Source: Evaluation support study 

Case  studies  underlined  the  importance  of  M19  -  LEADER  and  the  Community-led  Local 
Development (CLLD) approach in strengthening the social fabric in rural areas. The presence 
of  NGOs  working  directly  with  ROMA  communities  and  targeting  information  sessions  for 
potential  Roma  beneficiaries  helped  LAGs  to  foster  their  social  inclusion  strategies  targeted  at 
ROMA  populations94.  M16  -  cooperation,  when  targeted,  can  enable  socially  disadvantaged 
groups to  gain  confidence, skills  and technical  advice to  access  investment  measures  (included 
via financial instruments). Moreover, M07 - basic services can also help a broader range of rural 
stakeholders,  with  other  ESIF  (e.g.  ESF)  playing  a  complementary  role  when  combined  in 
community-led Local  Development. Case study respondents  also  attributed a high effectiveness 
of  M04  –  investments  and  M06  –  farm  and  business  development  funding  in  improving  farm-
level productivity, diversification and in terms of social development. 

The Greek case study refers to ROMA populations in the context of their important role in the 
fruit-picking sector. The integration of non-EU immigrants is reported by several interviewees 
(farmer, processor, and producer organisation) as an issue in the examined Greek regions, where 
long-established  immigrants  (mostly  from  Asian  countries)  are  reportedly  still  not  well 
integrated. In turn, in Italy (Emilia-Romagna), where workers on dairy farms come mainly from 
India and Pakistan, no social inclusion issues have been reported, but rather, problems linked to 
administrative procedures (i.e. visas). 

92   The total count, n, is representative of the sample size (104), two respondents did not provide replies for this 
question.  The  frequency  of  allocation  of  non-applicable  (n/a)  indicating  that  a  given  instrument  has  a  non-
applicable  effect  on  an  aspect  ranges  from  44  to58.  Respondents  from  the  categories  public  authority,  rural 
development expert, farmer, processor, producer organisation, NGO, civil group and rural resident. 

93   The  figure  represents  individual  responses  for  all  respondents  from  the  categories  public  authority,  rural 
development  expert,  farmer,  processor  and producer  organisation.  The  total  count,  n,  is representative  of  the 
sample size (85). The frequency of allocation of non-applicable (n/a) indicating that a given instrument has a 
non-applicable effect on an aspect ranges from 35 to 46. Respondents are from the categories public authority, 
rural development expert, farmer, processor and producer organisation. 
Integro Association (2019) Community-led local development for Roma Inclusion. 

94  

38 

 
 
 
 
                                                           
The  problem  of  limited  employment  opportunities  for  young  people  living  in  rural  areas  has 
been  highlighted  in  multiple  case  studies  (Austria,  Spain,  Bulgaria,  Greece,  France,  Czechia). 
Besides  employment,  rural  youth  may  also  leave  rural  areas  because  of  limited  access  to 
infrastructure (e.g. sports, recreation) and higher education possibilities outside rural areas. The 
Irish case study highlights social inclusion needs of the rural elderly, many of whom live with 
poverty, isolation, and poor housing conditions. 

Despite of the enormous progress that has been accomplished in the field of equal opportunities 
for  women,  the  evidence  points  at  the  disadvantaged  situation  of  women  in  farming  and  those 
living in rural areas. Recent studies showed that women manage only 30% of farms across the 
EU95,  and  when  they  do,  farms  tend  to  be  smaller,  with  lower  incomes,  and  less  access  to 
financial  support  (loans).  Furthermore,  the  EU  Farm  Economics  overview  analysis  also  found 
that on average, farms run by women had a lower income per annual work unit (38% lower than 
those run by men). The income gap by gender concerns all types of farming, with the biggest gap 
observed in dairy and field crop. This points at a gender imbalance in the sector, that is further 
confirmed in some case studies i.e. Tyrol, where women inclusion in the social fabric decrease 
with rurality and remoteness of the areas. It is also partly evidenced by women limited access to 
vocational  training  and  certifications  and  by  Italian  case  study  regions,  where  women  are 
particularly affected by long-term and hidden unemployment.  This has to do with lack of equal 
opportunities, training and resources, women being forced to become caretakers of their families 
or  communities96  (due  to  lack  of  adequate  social  support  services  and  infrastructures),  among 
other issues. 

Overall, results from the regression analysis suggest that funding for various Pillar II measures 
are associated with better general labour market inclusion of women in dynamic regions (cluster 
3). Spain (Castilla La Mancha) has implemented a strategy to address the demographic challenge 
(Estrategia  Nacional  frente  al  Reto  Demográfico  2017),  through  the  implementation  of  the  ITI 
(integrated  territorial  investment)  delivery  mechanism,  where  EAFRD,  ERDF  and  ESF  have 
been  linked  to  territorial  objectives  and,  more  widely,  also  to  the  inclusion  of  certain  areas  or 
groups, such as youth and women. 

Respondents  from  the  categories  public  authority,  rural  development  expert,  farmer,  processor 
and  producer  organisation  consider  BPS/SAPS  to  provide  some  positive  impacts  to  reducing 
gender disparities. 

5.1.6 Building human capital 

Pillar  II  measures  M01  –  knowledge  transfer  and  M02  –  advisory  services  are  territorially 
widespread  across  EU  rural  areas  and  play  an  important  role  in  supporting  farm-level 
investments in human capital. M16 - cooperation is similarly considered important to address 

95   Franic,  R.;  Kovacicek,  T.  (2019)  The  professional  status  of  rural  women  in  the  EU.  Policy  Department  for 

Citizens’ Rights and Constitutional Affairs Directorate General for Internal Policies of the Union. 

96   This in turn may leave them exposed to vulnerable situations (such as no access to social protection or maternity 

benefits, in some cases). 

39 

 
 
                                                           
farm  innovation  needs.  In  terms  of  enabling  access  to  research  and  innovation,  case  study 
respondents also assessed young farmers’ payments as having moderate to positive impacts. 

Following  the  regression  analysis,  in  diversified  regions  (cluster  1)  high  M07  –  basic  services 
and M19 – LEADER funding is positively associated with the change in the training rate as are 
regions  with  high  funding  in  M01  –  knowledge  transfer,  M02  –  advisory  services,  and  M16  - 
cooperation.  The  same  trend  is  observed  in  relation  to  comparable  ESIF  expenditure.  M10  – 
agri-environmental climate, M11 – organic farming, M12 – NATURA 2000, M13 – payments to 
ANC  funding  are  positively  associated  with  growth  rates  in  the  share  of  the  population  in 
training courses. However, this is not observed for high funding from related ESIF expenditure. 

Regression  analysis  shows  that  peripheral  regions  (cluster  2)  with  high  Pillar  I  direct  payment 
and  Common  Market  Organisation  funding  are  positively  associated  with  the  development  of 
secondary educational attainment between 2014 and 2017. This is potentially a spill-over from 
improved farm viability: with the economic viability of the farm ensured, education may be more 
accessible for farm workers, managers and people employed in businesses along the value chain. 
In  dynamic  regions  (cluster  3),  high  M01  –  knowledge  transfer,  M02  –  advisory  services,  and 
M16 – cooperation funding is positively associated with changes in the share of the population 
with tertiary education.  

Building governance capacities and bottom-up participatory approaches are to some extent 
addressed through the delivery mechanism of LEADER/CLLD, via its specific characteristics of 
local  embeddedness,  encouraging  participation  of  rural  society  in  its  own  development. 
According to the consultation on the long-term Vision for the EU’s rural areas97, a large majority 
of respondents (1 582, 68%) indicated that the most efficient way of involving rural people in the 
public  debate  is  to  organise  events  in  rural  areas.  Many  Member  States  reported  increased 
participation  of  the  rural  population  through  various  activities  organised  by  the  LAGs,  both  in 
design and implementation of local development strategies98. In addition, M07 – basic services is 
also deemed to play an impactful role in strengthening social cohesion and fabric.  

Overall, the CAP contributes to balanced territorial development via poverty reduction and social 
inclusion in rural areas, as it narrows the economic disparities between less and more developed 
regions,  with  a  focus  on  remote  areas.  The  effects  of  CAP  funding  depend  on  the  measure  or 
instrument  under  consideration,  and  rural  region  characteristics,  particularly  the  level  of 
structural  development.  CAP  impacts  on  social  inclusion  are  larger  in  regions  in  which  the 
agricultural sector is more important. By contrast, in more structurally developed regions, spill-
overs into the wider rural population are generally lower. 

5.1.7 Rural area definitions 

There  is  no  single  understanding  of  what  ‘rurality’  means,  as  there  are  several  statistical 
definitions.  The  Commission  Communication  on  ‘A  long  term  Vision  for  the  EU’s  Rural 

97   See footnote 25. 
98   Summary  Report  –  Synthesis  of  the  evaluation  components  of  the  enhanced  Annual  Implementation  Report 

2019: Chapter 7. 

40 

 
 
                                                           
Areas’99, investigated rurality at three geographical levels. The most detailed geographical level 
consists of 1 km² grid cells, followed by the local administrative unit (LAU) or municipality 
level and finally the NUTS-3100 level regions. The classification of LAUs is called the degree of 
urbanisation and the classification of NUTS-3 level regions refers to the urban-rural regional 
typology.  The  amount  of  land  classified  as  ‘rural’  will  differ  significantly  between  the  three 
geographical scales. In Spain, the share of land covered by rural grid cells is 98%, by rural areas 
is 90% and 17% by predominantly rural regions. The impact on population is more limited, but 
still significant. It changes from 17% of the Spanish population in rural grid cells, to 27% in rural 
areas and 4% in predominantly rural regions.  

These statistical definitions do not always fit policy needs, given that there is also agriculture in 
urban areas. In addition, needs are higher in remote areas (far from cities) with lower population 
density and lower access to services and infrastructure.  

This  issue  of  rural  areas  definition  is  approached  by  considering  two  different  aspects:  the 
implementation of rural development programmes at national (regional) level and the monitoring 
and evaluation purposes at EU level. 

Implementation of the CAP 

Member States have the flexibility to define the rural areas targeted by each Rural Development 
Programme  (according  to  Article  50  of  Regulation  (EU)  No  1305/2013).  Quite  a  few  Member 
States and regions differentiate this rural area demarcation even by measure of the EAFRD. The 
following table shows the various rural area definitions used in the case studies.  

Table 4. Typologies of rural area definitions 

Typology of definitions 

Corresponding case studies 

Definitions considering population data 

Definitions  considering  socio-economic  and 
territorial 
issues  (e.g.  population  migration, 
unemployment  rate  and  average  monthly  gross 
income) 

Definitions  per  EU 
typologies  (urban-rural 
typology  and  degree  of  urbanisation)  /  OECD 
definition of rural areas 

Definitions ‘other than’, i.e. as opposed to urban 
areas 

Bulgaria,  Czechia,  Germany,  Estonia, 
Greece, Italy, Netherlands, Austria,  

Estonia, Spain, France, Italy 

Czechia, Greece, Austria 

Estonia, Ireland, Greece, France, Poland  

Definitions as per measure-specific delineations  Bulgaria,  Czechia,  Germany,  Greece,  Spain, 

Poland  

Source: Evaluation support study  

99   See footnote 2. 
100   NUTS - Nomenclature of territorial units for statistics. 

41 

 
 
                                                           
According  to  the  support  study,  definitions  considering  socio-economic  and  territorial  issues 
may  be  considered  as  being  the  most  comprehensive  and  the  most  tailored  to  the  needs  and 
specificities of the rural areas. 

Most  of  the  rural  development  programmes  reviewed  in  the  support  study  include,  besides  a 
main definition of rural  areas,  several  measures-specific definitions  and  exceptions that mostly 
lead to an increase of the scope of the territory defined as rural. 

The definition of rural areas has direct consequences on the targeting and allocation of Pillar II 
funding  in  certain  rural  areas,  which  also  requires  the  coordination  of  public  support,  notably 
from  other  European  structural  and  investment  funds.  Some  Member  States  (e.g.  Greece  and 
Spain)  strategically  decide  to  restrict  availability  of  some  measures  (e.g.  M7.3  –  broadband 
infrastructure))  to  certain  territories  that  are  particularly  affected.  Member  State-specific 
definitions  which  include  demographic  criteria  suggest  that  CAP  Pillar  II  measures  are  being 
targeted to areas facing demographic challenges.  

Continuity between programming periods is also important. In certain cases (Estonia and Italy), 
the  definitions  of  rural  areas  applied  in  this  current  programming  period  are  in  line  with  and 
updated from the definitions applied in the 2007-2013 period.  

Definitions and monitoring and evaluation purposes 

The  multiple  definitions  make  EU  policy  making,  monitoring  and  evaluation  difficult.  All 
definitions contain ‘grey zones’ such as intermediate or semi-dense areas. All are based solely on 
population  /  population  density  criterion  failing  to  correctly  identify  EU  rural  territories 
characteristics and functionalities (e.g. distance to cities).  

There is a need to clarify the degree of rurality and to create a common ground with others EU 
policies (by using also population criteria), while in the meantime address the CAP and rurality 
analytical needs correctly. For example, the JRC evaluation exercise was based on a EU NUTS3 
level description of the regional dimension of the CAP in the period 2011-2018. This required a 
characterisation  of  the  rural  regions,  according  to:  (i)  a  multidimensional  approach  including 
several  economic  aspects;  and  (ii)  the  characteristics  and  contribution  of  the  agri-sector  in  the 
rural economy.  

Investigating specific aspects of rurality might require sometimes the development of a dedicated 
analytical  framework,  as  done  by  the  JRC  in  the  context  of  this  evaluation.  Nevertheless,  for 
monitoring  and  evaluation  purposes  a  single  harmonised  definition  for  identification  of 
functional rural territories could be useful. In addition, a shared territorial definition of functional 
rural  areas  would  be  important  to  ensure  a  higher  level  of  complementarity  among  all  funds 
(CAP and European structural and investment funds).  

5.2  EFFICIENCY 

The  evaluation  assesses  the  efficiency  of  the  CAP  measures  and  instruments’  contribution  to 
achieving  balanced  territorial  development,  particularly  focusing  on  the  social  and  economic 

42 

 
 
aspects,  as  well  as  on  the  administrative  burden  and  costs  incurred  at  the  different  levels  of 
implementation in relation to the benefits achieved.  

A  pure  cost-benefit  analysis  of  the  measures  and  instruments  has  not  been  possible  given  the 
complexity  and  number  of  administrations  involved,  as  well  as  the  differences  in  management 
and implementation  of the CAP  measures.  The  assessment consists in  a quantitative efficiency 
analysis of relevant CAP output indicators in 2015-2018 in comparison with the inputs, i.e. the 
funding  to  the  relevant  measures.  Due  to  the  absence  of  quantified  information  about  several 
benefits  of  the  measures,  the  efficiency  assessment  could  be  done  only  to  a  limited  extent. 
Therefore,  the  case  studies  form  the  other  main  foundation  in  respect  to  the  analysis,  as  they 
identify  the  obstacles  to  efficiency  as  experienced  by  managing  authorities,  beneficiary  groups 
and  stakeholder  organisations.  To  complete  the  analysis  available  local  data  on  administrative 
costs  were  compared  with  the  estimated  value  of  outputs/results  or  impacts,  including 
programme expenditure.   

Instruments outreach per euro spent 

From  the  quantitative  analysis  of  input/output  ratio  for  funds,  Pillar  I  instruments  are  judged 
relatively  ‘efficient’  as,  overall,  across  the  EU  territory,  the  funding  is  much  smaller  than  the 
value-added of the funded sector (i.e. Pillar I spending is much lower than the gross value added 
from primary sector). In general, at NUTS3 level, areas with high agricultural activity are more 
‘efficient’ in their use of Pillar I funds (see Map 2 in Annex 4). This is particularly true in Italy, 
Finland,  Sweden,  Hungary,  and  the  Baltic  countries,  whereas  France,  Greece,  Spain,  Poland, 
Germany  Austria  and  Bulgaria  have  more  areas  with  CAP  funding  closer  to  the  sector  value 
added. The lowest efficiency scores are found in urban areas with minimum agricultural activity. 

Although  data  was  not  available  for  all  rural  development  programmes  for  the  number  of 
training days, there is nonetheless significant variety in the ratio of CAP spend per amount of 
training  delivered.  Higher  efficiency  ratios  (more  than  600  days  per  million euro  of  spending) 
were found in territories with high proportions of farming population or activities (e.g. Greece, 
Spain,  Ireland,  Scotland,  parts  of  Italy,  as  well  as  in  Croatia,  Latvia,  Flanders  and  South 
Germany).  More  central  Member  States  and  Member  States  with  more  developed  secondary 
sectors such as the Netherlands, Denmark and Austria, had lower ratios, suggesting that the types 
of  training  funded  in  these  cases  were  more  expensive,  per  training  day  delivered.  The  lowest 
scores  (below  100  days  of  training  per  million  euros)  appear  in  Lombardy,  Lazio,  Denmark, 
some areas in Germany, the Netherlands, Czechia, Hungary, Cyprus and Estonia. However, it is 
difficult  to  draw  strong  conclusions  from  these  variations  because  of  the  potentially  wide 
variation in the types of training offered, in each case.  

Comparing the number of holdings participating in support schemes –, with the spending on M09 
–  setting-up  of  producer  organisations,  M16  –  cooperation  and  M17  –  risk  management  (see 
Map 3 in Annex 4), shows that the ratio of participants to funding input is relatively high (over 
80  holdings  per  million  euro  of  expenditure)  in  many  Member  States  (including  Portugal, 
Bulgaria,  Cyprus,  Romania,  Poland,  Latvia  and  some  regions  of  Italy).  The  ratio  is  lower  in 
Finland, Estonia, Austria and Hungary (between 20 and 80) and it appears to be very low (below 

43 

 
 
20)  in  most  scarcely  populated  areas.  This  indicates  that  it  is  particularly  costly  to  establish 
effective cooperation and joint working between farmers when the farms are not so closely co-
located, which raises questions also about the efficiency of the funding in those situations. 

The European Innovation Partnership (EIP) intends to enhance human and social capital and 
promote innovation in agricultural practices, with direct or indirect economic benefits. Overall, 
the data on groups supported, actions initiated and actors involved show that by September 2021 
funding  for  2 085  completed  operational  groups  has  been  provided  under  this  measure,  in  the 
current programming period. The highest numbers of groups per million  euros of spending are 
noted  in  Ireland,  central  Germany  and  some  regions  of  Spain,  suggesting  good  efficiency  in 
using this measure, in these territories.  

Improved  services  and  infrastructure  is  key  in  the  pursuit  of  urban-rural  territorial  balance, 
because they make areas more attractive to younger people and families, and allow inhabitants of 
rural  areas  to  improve  their  living  conditions.  The  analysis  of  the  population  benefiting  from 
improved services/infrastructures (IT or others) against expenditure on M07 – basic services 
(see Map 5 in Annex 4) shows that in all Member States which use the relevant CAP measure the 
whole population benefits from rural development investments, although the number per unit of 
spend  varies  significantly  between  programme  areas.  In  some  particularly  remote  areas  with 
natural  constraints  or  in  Member  States  with  an  ageing  rural  population  (e.g.  Greece,  Ireland, 
Scotland,  Castilla  y  Leon)  the  measure  reaches  fewer  people  per  million  euros  of  expenditure 
(less than 500 people). The outreach per unit of spent is highest (above 12 000 people) in rural 
areas that benefit already from better infrastructure (e.g. in Galicia, Catalonia, Andalusia, Haute-
Normandie, four regions in Germany, Finland and Flanders). The ratio is higher also in Member 
States  that  report  younger  rural  populations  and  lower  rural  service  provisions  (e.g.  Bulgaria, 
Poland). The calculations suggest that although needs for rural infrastructure and services depend 
also  on  cultural  and  local  factors  that  go  beyond  the  scope  of  CAP  funding,  such  as  health 
services and education, relatively small expenditure on M07 – basic services can benefit a large 
number of people in rural communities in terms of improved accessibility and opportunities. 

Considering the population covered by local action groups (LAG) (Map 8 and Map 9 in Annex 4), 
France, Czechia, Slovakia and Hungary have the greatest reach per LEADER group (more than 
80 000  people  covered).  The  majority  of  the  rural  areas  score  between  20 000  and 
80 000 persons  per  LAG,  while  Sweden,  Estonia,  Lithuania,  East  Germany,  central  Italy  and 
central  Spain  (which  include  areas  of  low  population  density)  score  below  20 000.  As  for  the 
number  of  LEADER  projects  funded,  LAGs  in  Sweden,  Denmark,  Estonia,  Latvia,  Denmark, 
England,  Czechia  and  Northwest  Spain  are  funding  many  projects  (more  than  10  per  LAG), 
while the majority of other areas fund between 2 and 10 projects. South Spain, Italy, Lithuania, 
central  France  and  Wales  had  difficulties  establishing  LAG  projects  and  they  funded  0  or  1 
project  only.  These  indicators  suggest  that  LAGs  are  succeeding  in  reaching  rural  people 
and  funding  projects  throughout  the  EU  territory  with  a  few  exceptions  mainly  in  Italy, 
Spain and the Baltic countries. It is important to mention nevertheless that these indicators do 
not show the scale of LAG projects nor their ultimate impacts, so it is not possible to conclude 

44 

 
 
that  the  territories  with  lower  scores  for  these  indicators  will  have  lower  LEADER  impact101, 
overall. 

Administrative burden 

The  evidence  presented  in  case  studies  highlight  common  issues  concerning  the  efficiency  of 
CAP  measures  and  instruments  in  achieving  social  outcomes.  A  principal  point  concerns  the 
accessibility of funding to all groups, which is affected by a range of issues including targeting, 
eligibility  and  selection  criteria  and  the  indirect  effects  of  controls  and  administrative 
requirements on potential beneficiaries, and on other actors who support or facilitate their access 
to CAP funds. Case studies referred to e.g. lack of clarity regarding eligibility criteria (e.g. M11 
–  organic  farming)  and  to  inappropriate  criteria  (e.g.  the  requirement  for  plant  cover  in 
mountainous areas for some direct payments some heavy conditions linked to M06 – setting-up). 
Only two case study interviews (Ireland and Saxony-Anhalt) found that CAP funding under both 
pillars is  accessible to  most  farmers.  In most case studies, stakeholders shared the opinion that 
some types of CAP funding are complex to access. This means that farmers who lack confidence 
or financial or management capacity may decide not to apply for funds, or they may feel obliged 
to seek professional help to gain such access (Auvergne – both for Pillars; Italy, Poland, Czechia, 
Bulgaria for Pillar II and especially for LEADER in Austria).  

On Pillar I instruments, interviewees did not mention as many sources of administrative burden 
as  for  the  rural  development  measures.  However,  they  reported  difficulties  to  implement  in 
practice  the  greening  schemes.  Most  importantly,  they  caused  high  administrative  costs102  of 
demarcation and control due to some overlapping with cross-compliance and agri-environmental 
measures.  

Public  authorities  also  signalled  the  lack  of  technical  assistance  fund  under  CMO  policy 
instruments  as  a  strong  limitation  for  public  authorities  providing  technical  assistance  to 
producer  organisations’  programmes  (Emilia-Romagna).Nevertheless,  many  case  studies  report 
that Pillar I aid is quite efficiently delivered, with relatively low administrative burden and 
swift application and payment processes. Online applications, as well as quasi-automatic systems 
managed  by  the  paying  agencies  seem  to  improve  efficiency  in  general,  due  to  which 
beneficiaries do not have to spend much time to make their claims (Austria, Estonia). However, 
they  can  be  relatively  complex  for  smaller  farmers  or  farmers  without  adequate  computer 
knowledge or equipment to deal with them (Saxony-Anhalt).  

As  regards  cost-effectiveness,  Pillar  I  aid  is  seen  as  not  particularly  targeted  towards  the 
social  aspects  of  balanced  territorial  development  and  the  needs  of  vulnerable  or  socially 
excluded rural population (supported by public administration, experts and NGO stakeholders). 
Most  case  study  interviewees  stated  that  most  Pillar  I  aid  is  delivered  as  single  area  payment 
scheme/basic  payment  scheme,  most  of  which  goes  to  areas  and  territories  where  farms  are 

101   A LEADER evaluation is scheduled to occur in the coming year. 

102   The Greek case study evidences suggest an increase of approximately one third of administrative costs deriving 
from the introduction of greening provisions, the added heterogeneity from increased tailoring of policies and 
the modernisation of IT systems and digitalisation of controls. 

45 

 
 
                                                           
productive  and  well-structured.  This  is  also  supported  by  evidence  from  literature  and  it  is 
explained by the particular historical development and evolution of CAP support focused on key 
productive  sectors  (arable  crops,  beef  and  dairy)103.  Anyhow,  interviewees  acknowledged  also 
that supporting successful, large economic actors to become more competitive can clearly bring 
additional  economic  benefits  to  a  territory,  beyond  the  immediate  impact  on  large  farms 
themselves.  

Moreover,  Pillar  I  aid  is  also  found  to  be  essential  to  sustain  farming  incomes  and  rural 
life/farming in the most remote and marginal areas where it is known that local communities 
face  elements  of  social  exclusion  arising  from  remoteness;  and  where  the  land  would  be 
abandoned  or  it  would  be  taken  for  development  (peri-urban  areas  under  pressure  where 
agricultural incomes remain lower than most others). In Estonia, financial instruments are cited 
by  public  authorities  as  a  good  example  of  an  efficient  CAP  instrument  which  could  become 
more important in future. In Spain, rural experts noted that although more CAP funding goes to 
the  more  affluent  farming  areas,  which  is  discouraging  for  young  people  who  come  from 
marginal farming areas, the integrated territorial investment funding tool of the ESIF104 acts as a 
corrective factor, preventing the most dynamic areas from concentrating the aid of the different 
European Investment Funds (including EAFRD)105. In Italy, the routing of CAP funding through 
the  special  market  provisions  for  the  dairy  sector  is  noted  as  a  particularly  efficient  way  of 
operating  because  producer  organisations  take  much  of  the  administrative  and  financial  risk 
away from individual farmer beneficiaries, in dealing with access to CAP funds.  

As for Pillar II funding, the funds are frequently difficult to obtain due to the principles of 
public funding that were not specifically designed as part of the CAP. The public procurement 
principle is cited several times by public authorities and farmers/farmer organisations (Czechia, 
Poland,  Bulgaria)  as  a  cause  of  very  inefficient  delivery  either  because  beneficiaries  have 
difficulties finding three sources for the services they need, and/or because the lowest price is not 
always the most reliable provider  and quality can then suffer, or because the service providers 
who  do  not  get  awarded  the  work  appeal  against  the  decisions  made.  This  leads  to  lengthy 
commissioning  processes  and  complaints  which  require  follow  up  or  redress,  taking  many 
months to resolve.  

In the framework of the case studies, public officials mostly judged the EU framework of Pillar 
II as constraining due to: 

  Too  detailed  rules  defining  the  different  measures,  which  restrict  the  Member  State’s 

freedom of choice (Estonia, France); 

103   Chartier,  C.,  Cronin,  E.,  Zondag,  M.J.,  Jongeneel,  R.  and  Hart,  K.  (2016)  Mapping  and  analysis  of  the 
implementation  of  the  CAP.  Report  to  DG  Agriculture  and  Rural  Development,  European  Commission, 
Brussels. 

104   ITI is a territorial delivery mechanism set out in the Common Provisions Regulation (EU) No1303/2013, and 

can be applied in all ESIF policy. 

105   Ferry, M. (2019) Integrated Territorial Investments as an effective tool of the cohesion policy. Report to the 

Committee of the European Parliament on Budgetary Control. IP/D/CONT/IC/2018-156, Brussels. 

46 

 
 
                                                           
  The  allocation  of  EAFRD  support  by  priorities  and  focus  areas  that  creates  a  higher 
burden  on  the  financial  monitoring  and  reallocation  among  measures  and  sub-measures 
during the programming period (Italy); 

  The existence of different rules in EAFRD and in other ESIF, which makes working at 

local level for LEADER groups (Tyrol); 

  The  burden  of  transposition  and  implementation  of  new  and  more  complex  rules  and 
measures  during  changes  between  one  programming  period  and  another  (including 
the introduction of different contribution rates for different measures).  

Moreover,  under  Pillar  II,  and  including  LEADER,  the  fact  that  beneficiaries  have  to  bear  the 
costs  of  investment  up-front  and  can  be  refunded  only  once  work  is  completed  can  present 
challenges.  Case  studies  in  France  and  Italy  reported  that  the  approval  of  projects  take  a  very 
long time and to receive payments can take even longer (at least one year, often two). This is a 
significant disincentive to take up the funding and it may impose further costs upon beneficiaries 
(e.g. interests on loans).In addition, several case study interviewees (Poland, Italy and Germany) 
pointed out that although EU Regulations alone are not so complex, it is the combination of these 
regulations with national or regional legislation and procedures that creates complexity. 

Many  experts,  farmers  and  stakeholder  organisations  state  that  Pillar  II  funds  tend  to  be 
accessible mainly for bigger or successful farm businesses, rather than smaller farms or those 
that are financially vulnerable (Czechia, Auvergne, Poland, Emilia Romagna, Puglia106, Castilla-
La  Mancha  and  Ireland).  The  support  for  investments  appears  to  cause  more  costs  and 
barriers especially for small farms. Time spent on an investment project and a business plan, 
complicated  and  constraining  eligibility  and  selection  criteria  and  the  process  of  approval  and 
implementing  projects  imply  much  higher  administrative  costs  than  direct  payments  and  area-
based payments. Private transaction costs for small farms are increased by many factors, such as 
lack  of  knowledge  and  information,  need  of  initial  investment,  costs  of  advisors,  etc.  The 
complexity of eligibility criteria is raised in case studies in relation to several measures (M11 – 
organic farming, M6.1 – setting-up of young farmers and M02 – advisory services)107.  

In addition, the implementation choices of managing authorities also lead to  reducing measure 
accessibility to beneficiaries with greater social needs, such as those with low incomes, poor 
education,  etc.  This  issue  of  cost-effectiveness  is  similar  to  the  concerns  raised  in  respect  of 
decoupled payments. However, whereas the efficiency of direct payments could be enhanced by 

106   In  Puglia,  for  all  investments  above  EUR  150 000,  regional  calls  imposed  a  condition  that  the  farmer  must 
attach to the application a bank decree of loan approval or a bank certificate of financial viability. According to 
farmer  organisations  and  public  authorities,  this  condition  made  many  farms  give  up  trying  to  access  the 
support, since they were already suffering from debts and reductions of income. 

107   In the Apulia case study, a significant barrier is the high cost of renovating olive trees or reconversion toward 
other  agricultural  activities  for  olive  growers  and  olive  oil  processors  in  the  most  marginal  areas  and  less 
accessible  to  mechanisation.  In  addition,  there  is  also  a  case  of  ‘gold-plating’  effect,  since  approval  of 
applications  require  two  additional  elements:  permissions  to  remove  olive  trees  in  landscape  protected  areas 
and  to  change  plant  density,  and  a  bank  certificate  of  financial  viability  for  all  investments  above 
EUR 150 000.  These  two  conditions  have  encumbered  many  farms,  which  are  already  affected  by  debt  and 
income reduction, in accessing support. 

47 

 
 
                                                           
explicit targeting of these instruments to socially-disadvantaged groups, it would be less feasible 
for Pillar II investment aids because the EAFRD regulation requires that beneficiaries have the 
competence to deliver the outputs to be funded. Therefore, other measures (knowledge exchange, 
advice,  training  or  capacity  building)  could  be  used  to  enable  socially  disadvantaged  target 
groups  to  gain  confidence,  skills  and  technical  advice  to  access  to  investment  measures.  In 
addition,  to  contrast  or  lower  the  effects  of  such  administrative  burdens  some  Member  States 
have  developed  some  very  efficient  mechanisms.  In  Austria  and  Italy,  for  example,  the  de 
minimis  support108  is  meant  to  increase  the  inclusion  of  small  farmers.  This  scheme  has  no 
additional public costs contributes to reducing private access costs for the smallest farms. 

Most case studies viewed the implementation of LEADER as overly complex, administratively 
heavy  and  thus  inaccessible  to  smaller  groups,  groups  in  situations  of  vulnerability  or 
associations  (Austria,  Ireland109,  Saxony-Anhalt110  and  Poland).  Beneficiaries  in  situations  of 
vulnerability are the ones who are more likely to resign from funding given that they have fewer 
resources to fulfil all requirements as well as to face the drawbacks during the projects’ runtime, 
the lack of advance payments and the cost of monitoring and control. This issue is reinforced by 
the problem common to several Member States concerning the balance between animation and 
administrative  activities:  funds  received  for  LAG’s  running  costs  are  not  enough  to  balance 
administrative  costs  involved  in  promoting  projects,  assessing  applications  and  controls.  This 
hinders many innovative projects which could produce good results for the local community and 
contributes  to  the  increasing  gap  between  different  social  groups.  In  addition,  administrative 
rules are perceived too rigid for LAGs which instead need more flexibility in order to adapt the 
available  measures  to  needs  of  territories  where  they  operate  (France).  However,  no  major 
obstacles for LEADER implementation were reported in Estonia, Czechia, Italy and Spain. 

An  important  point  voiced  by  several  case  study  interviewees  (experts  and  public  authorities) 
concern  the  low  additionality  of  Pillar  II  investment  measures  (M04  -  investments,  M06  – 
farm and business development and M07 – basic services) stemming from the implementation, 
eligibility and selection criteria choices by managing authorities. Due to the fear of disallowance 
in  relation  to  funding  for  investments,  managing  authorities  tend  to  refrain  from  supporting 
multi-purpose  or  novel  projects  that  do  not  easily  fit  pre-determined  categories  or  assessment 
criteria for measures or sub-measures as set out in the relevant rural development programmes. 
Thus, efficiency becomes the enemy of effectiveness – the projects that are simplest to deal with 

108   The  de  minimis  support  of  EUR  400-600  per  year  for  farms  under  six  hectares  (the  support  is  modulated 
according to the farm size), whose main objective is to compensate the inequality of area-based payments of 
Pillar I. 

109   In  Ireland,  the  efficiency  of  the  measure  seems  to  have  improved  since  steps  were  taken  to  simplify 
implementation but, there are still long delays (12 weeks long in the best case scenarios) in project approval 
and  payments,  as  this  is  now  done  through  local  councils  (public  authorities),  whereas  previously  it  was 
organised directly between LAGs and the paying agency. 

110   The  case  study  in  Saxony-Anhalt  revealed  that  although  LAGs  supported  projects  fostering  social  inclusion 
through integration and welcome projects for refugees and unaccompanied minors, the LAGs are nevertheless 
restricted in their awarding competences. They act more as advisors to applicants and intermediaries between 
the awarding authority and the local beneficiaries, rather than taking a lead role in implementation. With such a 
‘top-heavy’ structure, LAGs are not competitive in attracting innovative projects. 

48 

 
 
                                                           
and  therefore  most  likely  to  be  funded,  are  those  which  offer  nothing  really  new  or  have  low 
additionality  (this  point  was  noted  by  experts  and  NGOs  in  Poland,  Austria,  France  and 
Czechia).  

The  degree  of  administrative  burden  related  to  the  capacity  of  the  delivery  structure 
depend a lot on the personnel involved in the assessment and approval of applications. Several 
case  studies  highlighted  the  importance  of  experienced  personnel  who  understand  how 
operational  decisions  affect  measure  efficiency  and  effectiveness  (Estonia,  Ireland  and  Italy). 
Managing demand is noted as a particular challenge for Pillar II CAP funds and measures. For 
M04 – investment, periodic calls often elicit demand, which significantly  exceeds the available 
funding  in  the  call.  This  creates  a  heavy  workload111  for  administrative  personnel  who  has  to 
determine which applications should succeed and which should be refused.  

Case  study  respondents  mainly  in  regionalised  countries  underlined  a  relevant  administrative 
burden  related  to  the  coordination/governance  structures  due  to  the  many  different 
institutional  actors  and  administrative  layers  involved  in  the  implementation.  The  rural 
development  programmes  are  mostly  under  the  responsibility  of  regional  managing  authority, 
while  direct  payments  are  managed  at  national  level.  These  different  levels  of  responsibilities 
concerning  two  Pillars  and  different  instruments  within  the  same  Pillar,  raise  questions  for  the 
future  programming  period,  where  more  centralisation  of  the  programme  design  and 
management, as well as data integration at national level are foreseen.  

The  analysis  found  that  the  heterogeneity  of  potential  applicants  can  also  be  a  source  of 
potential burden, given the need to cover multiple different structural needs and heterogeneous 
demand  of  support.  Several  Italian  rural  development  programmes  envisage  the  possibility  of 
combining  different  measures  in  ‘supply  chains  integrated  projects’112,  which  are  a 
combination of individual projects submitted within a common framework (in specific territories 
like protected designation of origin areas) by a group of partners and aim to improve the supply 
chain economic performance.  

Another  approach  to  lower  administrative  burden  consists  in  grouping  and  implementing 
multiple  measures  together  as  a  ‘package  of  measures’,  to  deal  with  different  farm  needs 
through a whole-farm vision. A good example is the package of measures envisaged for young 
farmers to promote setting-up and investment support. This solution reduces the public costs of 
assessment, approval, monitoring of the responsible administration  

111   As  reported  in  the  case  study  for  Apulia,  when  staffing  levels  are  too  low  and  criteria  and  priority  scoring 
systems  not  simple  to  apply,  a  large  backlog  of  applications  can  result,  leading  to  long  delays  in  measure 
outturn. 

112   These partners sign a supply chain contract in which they commit to undertake investments both in farm assets 
and  machinery, as  well as in  dairy structures. This approach implies additional transaction costs both for the 
public  sector  (deriving  from  the  need  to  aiding,  assessing  and  monitoring  integrated  projects)  and  for  the 
private side (deriving from the need to cooperate, design more complex projects, reaching an agreement for the 
final contract, monitoring and coordinating the whole project). However, there are relevant economies of scale 
for the public authority in assessing the whole set of projects, learning effects, better quality of projects’ design 
and more internal coherence of the whole set of individual projects. 

49 

 
 
                                                           
The main types of administrative burden for both beneficiaries and administrations in relation to 
the implementation of the CAP instruments and measures are also highlighted by the respondents 
to the open public consultation on the long-term vision for the EU’s rural areas. When it comes 
to administrations, 28% of respondents find that the main cost is the complexity of management 
in  the  administrative  system,  followed  by  human  and  financial  resources  (22%),  frequency  of 
policy  change  (18%),  cost  of  administrative  control  for  programme  management  (16%),  and 
complexity due to a decentralised administrative system (12%).   

As  regards  administrative  burdens  for  beneficiaries,  the  main  burden  is  identified  as  the 
complexity of submitting an aid application (36%), followed by the time required to receive the 
payment after submitting the request for aid (25%) the time and effort required for administrative 
control (21%), and the frequency of policy change (18%).  

Figure 12. Responses to OPC on the main aspects of administrative cost/burden  

Source: Consultation on CAP and balanced territorial development 

Administrative costs 

From  the  study  on  the  administrative  burden  arising  from  the  CAP113,  the  estimated 
administrative  costs  of  delivering  the  CAP  funds  to  all  beneficiaries  of  the  integrated 
administration  and  control  system  (IACS)  represent  around  3.5-3.9%  of  the  total  funding 

113  ECORYS  (2018)  Analysis  of  administrative  burden  arising  from  the  CAP.  https://op.europa.eu/en/publication-

detail/-/publication/dabd45ab-9baf-11e9-9d01-01aa75ed71a1. 

50 

 
 
 
 
                                                           
delivered,  on  average,  across  all  EU  Member  States.  However,  this  figure  varies  considerably 
between individual instruments and measures in both Pillars of the CAP, and between Member 
States and regions. In general, Pillar II measures involve rather higher administrative costs than 
Pillar  I  instruments.  More  importantly,  the  study  notes  that  the  administrative  impact  is 
disproportionately  high  for  smaller  Member  States  and  that  the  main  costs  are  associated  with 
management and controls (74% of the estimated total administrative costs), as opposed to set-up 
and running costs (at 26%). The administrative task taking the largest share of overall costs was 
identified  as  the  verification  of  projects  undertaken  by  beneficiaries,  i.e.  an  element  of  the 
financial controls.  

In  addition,  the  study  of  ESIF  administrative  costs114  estimated  that  the  costs  of  delivery  for 
Pillar  II  funds  were  the  highest  of  the  main  ESIF,  and  involved  the  greatest  workload: 
EUR 83 100  and  2.18  annual  work  units  of  labour  input  per  million  euros  of  spending,  i.e. 
administrative  costs  around  8%  of  total  programme  spending.  Paying  agencies  incur  a 
particularly  high  share  of  these  costs  due  to  the  comparatively  high  level  of  administrative 
checking (100%) that they perform on EAFRD-funded projects.  

5.3 Coherence 

The evaluation assesses whether the CAP measures and instruments within the CAP 2014-2020 
deliver  a  coherent  contribution  to  balanced  territorial  development  in  rural  areas  regarding  the 
social and economic aspects. It analyses whether they are consistent with and complement each 
other,  or  whether  there  are  conflicting  objectives  and/or  incentives  within  the  CAP  (internal 
coherence)  and/or  with  respect  to  other  EU  or  national  policies  (external  coherence)  that  may 
compromise the effectiveness and/or efficiency of the relevant measures under evaluation.  

5.3.1 Internal coherence of the CAP measures in addressing balanced territorial development 

Most case study (Austria, Bulgaria, Czechia, Estonia, Germany, France) interviewees retain that 
instruments  and  measures  within  Pillar  I  and  Pillar  II  are  coherent  and  playing 
complementary roles to each other. Most case study interviewees also retained that the role of 
the  whole  set  of  CAP  instruments  turned  to  be  decisive  to  support  income  and  investment 
capacities  of  the  concerned  farms115.  This  maintained  the  viability  of  farming  activities.  In 
Spain,  rural  development  experts  highlighted  that  complementary  support  from  a  set  of  direct 
payments turned out to be essential to the economic viability of less dynamic types of farming, 
especially extensive and dry farms (e.g. in the cereals sector).  

114   https://ec.europa.eu/regional_policy/en/information/publications/studies/2018/new-assessment-of-esif-

administrative-costs-and-burden. 

115   A good example for complementarity was highlighted in the Saxony-Anhalt case study: the financial support 
that farmers receive via direct payments and rural development measures has spill-overs into the general rural 
population,  such  as:  employment  maintenance,  income  transfer  via  land  leases  (generally,  to  the  elderly  and 
large  land  owners)  and  business  maintenance.  In  addition,  they  ease  generational  renewal  since  new  farm 
managers  obtain  fundamental  income  support  from  the  CAP  (especially  young  farmer  direct  payments  and 
M06 – setting up of young farmers). It is also a vital income source for farmers since bank loans remain less 
effective than CAP support in financing farmers with low capital. 

51 

 
 
                                                           
The  complementarity  among  the  Pillar  I  instruments,  has  been  crucial  to  provide  business 
certainty and financial liquidity in times of crisis to  cover two main productions  costs:  land 
leases and labour costs. The bundle of instruments within the direct payments has strong impacts 
on farm incomes in the agricultural sector, which is re-invested in rural and intermediate regions, 
thus  they  have  a  strong  role  in  fostering  balanced  territorial  development.  The  other  positive 
social  and  economic  impacts  mentioned  are  the  reduction  of  de-population  and  abandonment, 
maintenance of the natural and cultural heritage of rural areas and partly supporting generational 
renewal.  

In Austria, the complementarity results also from the fact that the same ministry coordinates both 
pillars, which simplifies the process and communication in general.  

The  case  studies  revealed  synergistic  relations  between  Common  Market  Organisation 
instruments and rural development measures. In Castilla-La Mancha, the CAP had a relevant 
impact on the wine sector through a combination of CMO support (national support programme 
for  the  wine  sector)  and  a  series  of  rural  development  measures  (M03  –  quality  schemes  for 
agricultural products; M09 - creation of producer groups; M16 – cooperation projects; M19.2 
creation of the Wine Route). The CAP has fostered investment in wineries, thus improving the 
sector’s competitiveness. The actions promoted have also favoured better commercialisation and 
openness  to  the  foreign  market.  The modernisation of the sector, in  turn, influenced the whole 
economy  of  the  rural  area.  It  involves  structural  investments  into  physical  capital  on  farms,  as 
well  as  in  wineries,  and  fosters  the  development  of  specialised  companies  and  the  creation  of 
jobs. In short, it generates a greater dynamism that affects not only the producer and processing 
sector,  but  also  related  industries.  Furthermore,  the  wine  sector  affects  the  settlement  of  the 
population in the territory, especially in the central zone of the region, and plays a role in the soil 
protection against erosion and desertification. 

Synergies  between  CAP  instruments  may  sometimes  foster  indirect  impacts  on  social 
inclusions  in  rural  areas  where  migrants  from  third  countries  represent  a  relevant  share  of  the 
agricultural  labour  force.  In  Emilia-Romagna,  the  synergies  between  the  two  pillars’  funding 
helped to carry out structural change in the milk and cheese sector, hiring numerous Indians and 
Pakistani.  They  contributed  to  the  stabilisation  of  incomes  in  the  dairy  sector  and  at  the  same 
time  their  living  standards  are  a  direct  effect  of  the  policy  targeting  the  supply  chain  in  the 
territory.  Immigrants  also  contributed  to  entrance  of  new  population  in  mountain  areas  in  the 
Reggio  and  Modena  provinces.  Social  integration  has  not  been  a  problem,  and  many  local 
schools survive thanks to them.  

The  evaluation  also  revealed  few  cases  of  no  interactions  or  even  negative  interactions 
between Pillar I instruments and Pillar II measures: 

  Although  smaller  farms  receive  higher  direct  payments  per  hectare,  they  receive  less 
direct  payments  in  absolute  value  than  medium-large  farms,  while  they  have  higher 
needs, thus direct payments do not fully compensate for the economic disparities. Rural 
development  measures  do  not  counterbalance  this  effect,  as  they  do  not  particularly 
contribute  to  helping  with  the  structural  constraints  of  smaller  farms.  For  example,  in 

52 

 
 
Ireland  despite  Pillar  I  measures  effect  supporting  local  economies,  large  parts  of  rural 
Ireland services are declining and young people are migrating to the urban areas. 

  Community-led  local  development  LEADER  implementation  broadens  the  target  group 
of the funding by addressing deficits in non-farming parts of rural society. However, in 
practice, this funding is thematically detached from the rest of Pillar II and from Pillar I. 
The Saxony-Anhalt case study emphasised this deficit of interaction. 

5.3.2  External  coherence  of  the  CAP  measures  with  other  EU  policies  in  addressing  balanced 
territorial development 

Close  to  15%  of  EAFRD  support  is  targeted  at  rural  areas,  while  not  being  directly  linked  to 
agriculture and forestry, through LEADER and M07 – Basic services. The investments in basic 
services  and  village  renewal  in  rural  areas  often  consist  in  small-scale  investments  (like  agri-
tourism,  small-scale  manufacturing  or  points  of  sale  for  local  or  farm  products)  which  often 
benefit  farming  families  directly.  Similar  investments  can  be  funded  under  the  European 
Regional  Development  Fund  (ERDF),  and  in  some  cases  by  the  Cohesion  Fund.  There  are  no 
strict  demarcation  rules,  but  the  EAFRD  Regulation  refers  explicitly  to  the  funding  of  ‘small-
scale infrastructures’, with the exceptions of investments in broadband and renewable energy, for 
which  Member  States  have  to  ensure  demarcation  and  complementarity  of  support  available 
under different Funds of the Union. The same approach applies as regards other support to non-
agricultural activities, which can also be funded by the EAFRD as well as the ERDF/Cohesion 
Fund. Multiple funding for these types of investments is chosen particularly by Member States or 
regions which have limited ERDF/Cohesion fund funding or particularly high needs to develop 
their rural infrastructures, which can lead to certain overlaps in funding. 

Operations funded by EAFRD are with few exceptions of a small-scale character. In comparison, 
Cohesion  Policy  is  able  to  finance  larger  scale  investments,  in  line  with  its  focus  on  broader 
territorial  development  aims  (e.g.  linked  to  connectivity,  job  creation  and  economic  growth), 
although it may also support smaller-scale actions. Cohesion Policy may therefore be less likely 
to  support  the  type  of  projects  supported  by  the  EAFRD  (in  particular  the  ones  in  remote  and 
sparsely  populated  rural  areas  facing  particular  challenges,  and  support  under  the  LEADER 
approach)  due  to  its  focus  on  the  overall  development  potential  of  regions  that  tends  to 
concentrate  on  more  populated  areas,  including  urban.  To  avoid  possible  ‘funding  gaps’  in 
disadvantaged territories, the coordination and understanding of EU funds is essential to ensure 
that  funds  are  mobilised  in  full  complementarity116.  Nevertheless,  complementarity  and 
coherence are not always perceived positively by citizens and stakeholders, which is also shown 
by the results of the consultation (Figure 13). The perception of coherence between the CAP and 
other EU policies is  rather mixed, with a similar rate of positive and negative answers, and no 
significant  distributional  difference  between  stakeholder  groups,  age,  gender  or  whether  they 
lived in urban or rural areas. 

116   See footnote 2. 

53 

 
 
                                                           
Figure  13.  Extent  to  which  CAP  instruments  and  measures  concerning  balanced  territorial 
development of EU rural areas deliver a coherent and complementary contribution with other EU 
funds, and/or national, regional/local 

Source: Consultation on CAP and balanced territorial development  

Relations  of  complementarity  prevail  in  most  of  the  case  studies  between  the  CAP  and  the 
European  Structural  and  Investment  Funds  (European  Regional  Development  Fund  (ERDF), 
European Social Fund (ESF), Cohesion Fund) and there are no particular problems of overlaps or 
major inconsistencies.  

In general, the different instruments and measures of the CAP are necessarily coordinated with 
other policies, relying on the general programming document of the partnership agreement, in 
which the objectives and strategies of the different policies are harmonised. However, as all case 
studies showed, the harmonisation in most cases means a concrete division of tasks among the 
separate ESIF to avoid overlap within the same category of interventions, sometimes at the cost 
of a higher level of complementarity. Therefore,  overall, the major risk is not funding overlap, 
but rather funding gaps.  

In some case study areas, there are more substantial relations of complementarity, in particular 
when  ESIF  operational  programmes  clearly  state  that  rural  areas  are  beneficiaries  of  specific 
interventions.  

In  Czechia,  for  example,  interesting  complementarities  are  mentioned  in  improvements  in 
transport  infrastructure.  Here,  ERDF  multiplies  the  effects  of  investments  into  agricultural  and 
infrastructure.  Similar 
other  enterprises 
complementarities occur in  this country in  the liveability of rural  areas, through higher quality 
and accessibility of public services (funded by ERDF and ESF). 

in  rural  areas  served  by 

improved 

transport 

In Spain, the operational programme of the ESF refers in many cases to rural areas and address 
the  importance  of  socio-economic,  demographic,  and  territorial  characteristics  of  the  most 

54 

 
 
 
vulnerable areas of the region. This allows the ESF to meet the needs and challenges of rural and 
dispersed  areas.  The  operational  programme  of  the  ERDF  identifies  rural  areas  (and  allocate 
public resources to them) as the main priority for development. This includes the deployment of 
broadband and high-speed networks, the development of ICT to modernise public services and 
provide  e-health  or  e-education  services  in  rural  areas,  and  the  diversification  of  the  tourism 
sector (e.g. via hunting, oenology, literary routes, or rural tourism). 

Stronger linkages of synergy occur when the coherence of policy measures is more structured in 
local integrated schemes and joint actions of different funds, where EAFRD has to work together 
with  European  Regional  Development  Fund  and  European  Social  Fund.  It  is  the  case  of 
community-led  local  development  in  Tyrol,  Saxony-Anhalt  and  Czechia,  which  supports, 
through  a  good  integration  of  fund,  small  scale,  and  pilot/innovative  solutions,  in  rural  areas, 
which also benefit from interventions of ESIF.  

Strong  synergic  relations  also  exist  between  EAFRD  and  other  ESIF  for  balanced  territorial 
development, such as the specific schemes for mountain/peripheral/lagging behind territories, at 
sub-regional  scale.  Their  aim  is  fighting  de-population  and  territorial  disparities  in  services 
provision. In Italy, the National Strategy for Inner Areas 2014-2020 started in 2014; in Castilla-
La  Mancha,  there  is  a  similar  approach  in  the  2017  strategy  to  address  the  demographic 
challenge. In each scheme, the single funds cover specific tasks (coherent with functions given 
by  EU  regulations)  and  converge along shared objectives. Methodologically, both  schemes are 
carried  out  through  territorial  or  local  development  strategies  (the  Italian  case),  designed  and 
managed by local partnerships, using one of the ESIF territorial delivery mechanisms (Integrated 
Territorial Investments - the Spanish case study, Community-Led Local Development) or other 
national integrated approaches. 

Despite  the  common  programming  framework,  some  case  studies  highlighted  that  there  is  no 
mechanism  enabling  different  policies  to  be  coordinated  and  integrated  at  local  and  regional 
level (Estonia).  

5.3.3  External  coherence  of  the  CAP  measures  with  national/regional  policies  in  addressing 
balanced territorial development 

Coherence with national/regional policies is challenging to evaluate due to the broad spectrum of 
action of national/regional policies, but no major incoherence has been found. 

In  some  countries,  the  role  of  national  funds  is  decreasing  in  financial  terms.  It  implies  an 
increasing role of CAP resources, to compensate for the need of public actions in crucial fields of 
intervention. In Estonia, the public intervention in rural areas mainly relies on EU instruments, 
but  there  are  also  few  national  interventions.  As  a  result,  CAP  tend  to  cover  the  needs  that 
national polices are unable to cover. This is not always the case as seen in Figure 13, where the 
perceptions of respondents is that the complementarity between national and regional policies in 
addressing balanced territorial development depends upon territorial specificities, and the role of 
specific national policies on supporting rural areas. 

In  other  cases,  the  function  of  national  policies  is  complementary  since  it  counterbalances  the 
lack of CAP instruments or covers the needs of beneficiaries that cannot be covered by the CAP 

55 

 
 
instruments.  This  concerns  particularly  some  vulnerable  groups,  whose  needs  might  be  much 
better addressed by the other EU or national policies, thus they play a significant role.  

For  example,  in  Tyrol,  the  specific  national  scheme  introduces  a  positive  preference  for  small 
farms and compensate small farms placed in unfavourable locations (like mountains with fields 
in slopes). 

In Ireland, the government announced in May 2018 a new EUR 1 billion rural regeneration and 
development fund to combat rural depopulation and regenerate rural areas over the period 2019 
to  2027.  Key  objectives  are  to  address  de-population  in  small  rural  towns,  villages  and  rural 
areas  and  help  achieve  ‘Strengthened  Rural  Economies  and  Communities’,  one  of  the  national 
strategic  outcomes  of  the  national  planning  framework.  These  additional  funds  aim  to 
complement EU programmes, and strengthen global impacts of national and European funds in 
rural areas. 

5.4 RELEVANCE 

Relevance is the extent to which the objectives of an intervention are pertinent to current needs, 
problems and issues. The evaluation assesses the relevance of the CAP instruments and measures 
fostering balanced territorial development in relation to the actual needs of rural areas and rural 
population groups. 

Most case study respondents  see the  CAP  as  an exclusively  agricultural  policy instrument  that 
addresses  rural  needs  related  to  economic  growth  and  development,  rather  than  social 
needs.  The  results  of  the  input-output  and  the  regression  analyses,  as  well  as  the  case  study 
findings and JRC analysis show that both Pillar I and II funding are effective in stimulating the 
local economic performance (particularly in developed peripheral rural regions and traditional 
rural  regions),  indicating  high  relevance  in  bolstering  gross  value  added  and  safeguarding 
employment  in  rural  areas.  Direct  Payments  and  the  CMO  are  particularly  highly  relevant  to 
economic needs through their targeting of farm-based needs, such as income stability and farm 
viability.  Although  these  instruments  are  primarily  targeting  the  farming  sector,  the  analysis 
suggests  the  existence  of  spill-overs117  of  Pillar  I  funding  to  sectors  outside  of  agriculture, 
especially  in  less  developed  rural  regions,  which  shows  the  importance  of  measures  which 
positively affect economic development. 

The  high  relevance  of  Pillar  I  instruments  is  also  reflected  in  the  results  of  the  open  public 
consultation  on  the  long-term  Vision  for  the  EU’s  Rural  Areas  (found  negatively  relevant  by 
only  10%  of  the  respondents,  most  of  them  of  the  stakeholder  category  ‘citizens’,  over  two 
thirds).  As  regards  economic  priorities  in  rural  areas,  most  respondents  to  the  consultation 
perceive the creation of new job opportunities, as well as the need to foster entrepreneurship and 
innovation  to  boost  employment  as  essential  needs.  Moreover,  a  large  share  of  respondents 

117 Given the nature of rural regions and rural economies, when economic development needs in the primary sector 
are addressed, secondary positive effects in terms of social development are often observed. 

56 

 
 
                                                           
indicate  infrastructure  and  digital  connectivity  as  the  most  urgent  needs  to  be  addressed  in 
terms of balanced territorial development for the EU’s rural areas. 

Case  study  respondents  ranked  young  farmers’  and  basic  payments  as  highly  relevant  in 
addressing  local  needs,  as  was  seen  in  chapter  5.1.  Basic  payments  are  perceived  to  have  the 
strongest  impacts  on  farm  economics  (such  as  farm  viability).  However,  due  to  the  lack  of 
targeting  and  to  limited  redistribution  of  support,  basic  payments  contribute  less  to  reduce 
income disparities between farms and to address social issues. Nevertheless, basic payments are 
deemed relevant to reduce depopulation and land abandonment, supporting generational renewal, 
reducing poverty, gender disparities and maintaining cultural and natural heritage.  

As highlighted also  by the European Economic  and Social Committee in its  Evaluation  on the 
CAP's impact on territorial development of rural areas, the CAP is the key relevant policy for 
funding  and  maintaining  employment  in  agriculture.  Agriculture  is  multifunctional  though, 
and other existing funding programmes are  complementary to  the CAP  in order to  support the 
territorial  development  of  rural  areas.  In  addition,  jobs  created  and  retained  by  CAP  spending, 
are  not  necessarily  high-quality  jobs  especially  for  seasonal  workers  (case  study  findings  in 
Peloponnese and Castilla-la-Mancha). Furthermore, case studies also indicate that in rural areas, 
the employment opportunities are not always attractive enough to halt emigration into wealthier 
regions. Thus the high relevance of dedicated CAP support to attract and maintain employment 
among young people (such as M06 –  business development and M19 – LEADER). 

The most important social needs across the case study regions are: (i) access to social services 
and healthcare facilities; (ii) access to transportation infrastructure118 to reduce remoteness; (iii) 
fighting outmigration and poverty; (iv) creating employment; and (v) improve age demographics. 
As showed in the effectiveness chapter, the CAP contributes to reduce poverty in rural areas and 
limit labour outflow from agriculture while supporting employment in rural areas. However, the 
majority of respondents  retain that the CAP has very limited or no impact  on delivering social 
services. 

Only M07 – basic services, M01 – knowledge exchange and M19 – LEADER are highlighted 
as  highly  relevant  in  targeting  social  disparities  and  for  the  provision  of  social  services,  thus 
proofing  balanced  territorial  development.  This  support  particularly  focus  on  regions  and 
populations that may otherwise be left behind. LEADER, in general, is described as best practice 
by many  case study respondents  that recommend it to  be further disseminated to  other regions 
and aspects of CAP funding. However, many interviewees (Austria, Ireland, Saxony-Anhalt and 
Poland)  retain  the  importance  of  simplifying  the  LEADER  application  processes  to  engage  a 
wider range of participants and create more inclusiveness. 

Several  research  projects  funded  under  Horizon  2020,  such  as  SIMRA,  RURACTION  and 
RURITAGE, highlight the key  potential that  social capital  (including norms and values, trust, 

118   Pillar II has funded investments in road infrastructure in several Member States, enhancing the connectivity to 
rural areas. However, the case study findings suggest that such large-scale infrastructural improvements have 
not been equally effective across all Member States, which have funded these measures, as road networks and 
rail services continue to be poor in rural areas of some eastern and central Member States. 

57 

 
 
                                                           
networks),  social  innovation119  and  social  entrepreneurship  can  play  in  strengthening  the 
capacity  of  rural  communities  to  address  their  challenges.  SIMRA  researchers  highlighted  that 
instruments that support cooperation between actors in developing practical innovations together, 
in  flexible  ways,  are  interesting  to  support  social  innovation.  Within  CAP  measures,  they 
highlighted LEADER, the cooperation measure (M16) and the emergent policies such as those to 
support  Smart  Villages  and  some  EIP-AGRI  actions  as  particularly  relevant.  They  argue 
however  that  these  measures  would  need  ring-fenced  funding  for  the  full  potential  of  social 
innovation  to  be  tapped120.  Pisani  et  al.  also  evidenced  positive  impact  of  LEADER  on  social 
capital in several Italian LAGs121. 

The  results  of  the  Open  Public  Consultation  show  additional  needs  for  rural  areas,  related  to 
policy and governance, such as the (i) need for a place-based approach to address development 
challenges in  rural  areas, (ii) the need for  more  political  cooperation  between local,  regional, 
national, and international levels and (iii) the need for multilevel governance. These aspects are 
closely connected to the principles of LEADER, thus suggesting that the instrument corresponds 
to real needs of rural communities and is, hence, highly relevant. 

As regards social inclusion, generally, the interviewees highlight the poor targeting and thus the 
poor  relevance  of  CAP  support  (see  Figure  11  in  Chapter  5.1)  to  the  needs  of  third  country 
nationals,  other  EU  nationals,  ethnic  groups,  and  disabled.  By  contrast,  CAP  support  is  more 
relevant  for  farmers,  rural  young  people  and  population  in  most  remote  areas.  Particularly  the 
small and young farmers schemes help include farmers in disadvantageous situations, however, 
as the case study findings concluded, the effectiveness of the funding does not necessarily spill 
over  to  marginalised  rural  groups.  In  addition,  in  general  CAP  funding  contributes  to  retain 
existing labour rather than providing job opportunities. 

According to the effectiveness analysis, Pillar II measures are more relevant to target the needs 
of  vulnerable  groups  (including  the  low  skilled  and  unemployed,  rural  elderly  people,  rural 
women and disabled people) than Pillar I. However, the relevance of Pillar II in addressing the 
needs of vulnerable ethnic groups, other EU nationals and third country nationals, remains also 
low. The European Economic and Social Committee in  its  Evaluation  on the CAP's impact  on 
territorial development  of  rural areas, highlights  that the social inclusion of vulnerable groups 
into  agricultural  activities  is  not  always  sufficiently  ensured,  even  if  farming  has  always  been 
able to offer employment to vulnerable people who experience difficulties in finding work. 

In  addition,  studies  on  the  CAP  and  women’s  inclusion  indicate  that  the  CAP’s  relevance  in 
furthering  the  economic  inclusion  of  women  farmers  is  low.  A  lack  of  explicit  targeting  of 

119   Defined by SIMRA as ‘the reconfiguring of social practices, in response to societal challenges, which seeks to 
enhance outcomes on societal well-being and necessarily includes the engagement of civil society actors’. 

120   SIMRA Policy brief: How policy can help bring about social innovation in rural areas?, 2020. 
121   Pisani, E., Aguanno, M. The assessment of the added value of LEADER/CLLD as improved social capital in 
the  LAG  Prealpi  &  Dolomiti  (Italy),  presentation  at  Good  Practice  Workshop:  ‘Showing  the  added  value  of 
LEADER/CLLD through evaluation’, 2018. 

58 

 
 
                                                           
women’s needs122 and a significant gender imbalance among farm managers (only 30% of farms 
are  managed  by  women  across  the  EU123)  in  a  traditionally  male  sector  reduces  the  CAP’s 
relevance in terms of fostering social inclusion. 

5.5 EU ADDED VALUE 

EU  added  value  is  considered  to  be  the  value  resulting  from  measures  and  policy  instruments 
undertaken  within  the  framework  of  the  CAP  at  EU  level,  which  is  additional  to  the  value  in 
terms  of  balanced  territorial  development  that  would  have  resulted  from  public  authorities 
applying similar policy instruments at solely regional or national level.  

The  analysis  suggest  that  the  CAP  plays  a  relevant  role  in  respect  of  balanced  territorial 
development  across  the  EU  in  general.  However,  the  impacts  of  the  CAP  vary  strongly  by 
territory  according to  the wider socio-economic conditions.  The CAP  framework also  provides 
added value by  allowing for Member States  to  select  and implement  the  measures which most 
accurately target their needs, thus addressing the differences in country needs.  

The quantitative analysis suggests that CAP funding helps to keep people farming as it creates or 
maintains  a  significant  proportion  of  rural  agricultural  jobs,  as  well  as  a  smaller  share  of  rural 
non-farming  jobs,  in  most  regions.  These  results  go  in  line  with  the  conclusions  on  the 
consultation  on  the  CAP  and  balanced  territorial  development,  where  a  large  majority  of 
respondents highlighted that the EU improves the viability of rural economies (Figure 14) with 
no major difference in the responses by stakeholders. 

Case studies, literature review and interviews at EU level, suggest that CAP funding drives farm 
enlargement. Economies of scale are key to increase farm productivity and thus farm profitability 
and viability. However, an accelerated growth rate of larger farms in some regions creates out-
competition  of  smaller  farms,  reduces  farm  labour  availability  and  in  effect  reduces  balanced 
territorial development within these territories.  

In  diverse  rural  economies, farm businesses may  be so interdependent  with  other sectors e.g. 
tourism and hospitality,  food processing and marketing, and construction, for their income and 
growth, that the socio-economic impacts of CAP spending will depend upon what is happening 
in these other sectors too. Rural area accessibility and quality of services affect attractiveness to 
young  families  so  more  remote  areas  will  lose  young  people,  unless  these  aspects  can  be 
improved. CAP  funding  plays a significant  role here, but  other EU funds  and national  policies 
are  key  as  the  CAP  alone  cannot  overcome  these  multiple  challenges.  In  remote  areas  where 
agriculture  is  very  significant,  farm  jobs  and  incomes  are  declining  and  farm  employment 
(outside  the  family)  is  often  casual  or  temporary.  Thus,  investment  beyond  agriculture  is 
necessary to address balanced territorial development. Nevertheless, CAP funding for agriculture 

122   EIGE  (2019).  Gender  budgeting.  Mainstreaming  gender  into  the  EU  budget  and  macroeconomic  policy 

framework. 

123   Franić, R. and Kovačićek, T. (2019), The professional status of rural women in the EU, Directorate General for 
Internal  Policies  of  the  Union,  Policy  Department  for  Citizens’  Rights  and  Constitutional  Affairs,  European 
Parliament: Brussels. 

59 

 
 
                                                           
is beneficial and is often cited by stakeholders, public authorities and beneficiaries as essential, 
compared to no EU funding.  

Figure 14. Most essential benefits of the CAP in terms of balanced territorial development of EU 
rural areas that cannot be achieved by the Member States/sectors acting on their own 

Source: Consultation on CAP and balanced territorial development 

Evidences  from  both  analysis  and  the  case  studies  highlighted  that  in  more  remote  areas,  the 
lack of employment choice is also detrimental to overall economic welfare. Jobs in the primary 
sector  do  not  necessarily  offer  attractive  working  conditions,  but  in  these  areas  alternative 
employment opportunities are more difficult to access. The conditions of instability, seasonality 
and low wages of temporary work are not attractive for people to settle/stay. In less structurally 
developed  regions,  the  continued  existence  of  farms,  supported  by  CAP  funding,  vitally 
contributes  to  local  purchasing  power  and  offers  local  employment.  The  CAP  ensures  the 
sustainability of poorer rural regions, especially those located in remote areas. Thus, the CAP is 
deemed  to  make  an  important  economic  and  social  contribution  in  those  areas.  Even  though 
poorer  regions  could  receive  support  at  national  level,  there  do  not  seem  to  be  other  similar 
instrument able to ensure farm and rural development like the CAP, especially in remote areas. 

Competition  around and, especially in  more economically developed  regions within a Member 
State,  depresses  the  aggregate  output  of  the  primary  sector.  Increased  labour  competition 
between the primary sector and other sectors arises due to reduced competitiveness of farms in 
comparison to other sectors, in these areas. This is sometimes further aggravated by increasing 
emigration  to  regions  with  better  standards  of  living,  such  as  urban  areas  and  more  developed 
regions of the EU.  

Especially in the more developed rural and intermediate regions, the agricultural sector continues 
to be less productive per worker and reliant on income support for its economic viability. This 
also constrains the spill-overs of the funding into the wider rural economy: employment is more 
attractive  in  other  sectors.  As  such,  in  the  more  structurally  developed  and  populous  rural  and 

60 

 
 
 
intermediate regions of Europe, the contributions of the CAP to fostering economic development 
outside of the sector are limited, although some positive social contributions  are indicated  (via 
enhanced quality of life). 

CAP Pillar I funding is deemed very beneficial in terms of crisis support and largely beneficial 
in  terms  of  general  farm  viability.  Farmer  interviewees  in  all  case  studies  state  that:  (i)  their 
economic  situation  would  be  significantly  worse  without  direct  payments  or  the  CMO 
framework;  and  (ii)  these  funds  help  to  maintain  existing  structures  and  prevent  land 
abandonment. However, the evidence suggests that the redistribution of basic income support to 
smaller  farms  is  not  sufficient  to  reduce  economic  disparities,  on  the  contrary  the  current 
distribution of support increases existing economic differences between smaller and larger farms.  

CAP Pillar II funding can also have similar negative impact: in the evaluation of efficiency, a 
view  commonly  expressed  by  experts  and  rural  stakeholders  is  that  larger  and  more 
economically successful businesses (farms and non-farms) profit more often from the delivery of 
Pillar II payments because they are perceived as  less accessible for smaller and more marginal 
ones. This effect is mainly linked to administrative issues.  

EU  interviews  and  some  case  study  evidence  highlighted  that  Pillar  II  support  provides  EU 
added  value  in  LEADER.  LEADER’s  innovative  character  would  not  have  been  implemented 
without the EU. As noted in all the case studies, LEADER touches especially the social fabric of 
rural areas and supports social aspects of local development. Developments like this might have 
happened  without  EU  support  (especially  in  those  areas  where  social  capital  was  already 
established),  but  probably  not  everywhere.  Furthermore,  LEADER  was  recurrently  highlighted 
in the consultation as a tool to align local needs with EU objectives, ensuring that local needs and 
opinions are taken into account. LAGs have expanded the capacity of rural communities across 
Europe to resolve problems in a bottom-up way through innovation and co-operation. The same 
holds  true  for  the  transnational  cooperation  aspect  within  LEADER,  which  creates  EU  value- 
added and is supported by EU level activities of the European Network for Rural Development.  

In  a  similar  way,  some  case  study  interviewees  (e.g.  managing  authorities  from  Estonia  and 
Ireland)  mention  EIP-AGRI  as  offering  EU  added  value  because  this  initiative  would  not 
otherwise  have  happened  in  these  territories.  Notable  examples  of  effective  communities  of 
learning  have  also  been  generated  by  the  EIP-AGRI  in  France,  Germany,  Estonia,  Italy  and 
Ireland,  in  line  with  the  results  from  the  study  on  knowledge  exchange124.  This  initiative  is 
recognised to be still in its infancy as a mechanism for socio-economic benefit in rural Europe, 
with  no  measurable  impacts  yet.  Nevertheless,  its  positive  potential  is  acknowledged  by  case 
study interviewees including farmer representatives, experts and public authorities. 

Public administration interviewees in case studies generally reported EU added value from CAP 
funding  overall,  predicting  greater  poverty  and  rural  decline  if  CAP  funding  were  not 
available. This was reported especially in Greece, Czechia, Bulgaria, Austria, Ireland, Apulia and 
Auvergne.  

124   Evaluation support study on the CAP's impact on knowledge exchange and advisory activities, ADE, CCRI & 

OIR (2020). 

61 

 
 
                                                           
There are examples in case studies of CAP Pillar II measures and Common Market Organisation 
instruments  having  a  positive  impact  upon  social  inclusion.  Most  important  in  this  regard  is 
LEADER,  but  crisis  measures  and  collective  actions  to  strengthen  producers’  influence  in  the 
value chain are also relevant, under the CMO and other Pillar I provisions, and there is evidence 
in Ireland and Estonia pointing to a high potential for EIP-AGRI to build enhanced social capital, 
with  knock-on  benefits  for  social  inclusion,  but  it  is  too  early  to  identify  clear  impacts  in  this 
respect.  

Nevertheless,  the  impact  of  these  measures  and  instruments  is  constrained  by  overall  limited 
CAP  expenditure  on  social  goals  in  rural  areas,  by  comparison  with  CAP  funds  devoted  to 
other goals, in a context of other ESIF funding for social inclusion hardly reaching the rural areas 
according to expert and stakeholder interviewees. 

In  overview,  by  its  very  nature,  the  CAP  funding  represents  a  vehicle  for  allocating  common 
resources between Member States and widely spread regions of the EU, in ways that can support 
balanced  territorial  development  and  that  would  not  be  possible  using  national  funds  alone. 
Therefore,  to  the  extent  that  the  CAP  supports  balanced  territorial  development  at  a  trans-
national level, this clearly offers EU added value. This positive role is particularly evidenced in 
Europe’s  marginal  and  more  remote  areas.  Without  CAP  funding  it  is  widely  held  by  many 
interviewees  from  the  public  administration,  farmers’  organisations,  and  by  experts  and 
stakeholders that marginal areas of the EU would be in a worse economic and social situation, 
notwithstanding the efforts of national and regional policies.  

5.  CONCLUSION 

This  evaluation  set  out  to  assess  how  CAP  policy  is  contributing  to  balanced  territorial 
development. The measures covered by the evaluation are the relevant instruments set out in the 
basic  regulations  of  the  CAP  2014-2020  regarding  direct  payments,  rural  development,  the 
common market organisation, and the Horizontal Regulation. 

The  evaluation  is  primarily  based  on  the  evaluation  support  study  on  balanced  territorial 
development,  complemented  by  additional  analysis  from  the  Commission,  including  a 
counterfactual analysis by the Joint Research Centre, and reports from the European Committee 
of the Regions and the European Economic and Social Committee. This increased the robustness 
of  the  conclusions,  circumventing  some  of  the  limitations  met  during  the  drafting  of  the 
evaluation  support  study,  such  as  the  narrow  observation  period  and  the  lack  of  established 
causality between the CAP and the effects on employment and growth.  

Effectiveness  

CAP  instruments  and  measures  are  considered  overall  to  be  effective  in  contributing  to 
balanced  territorial  development  in  EU  rural  areas.  Two  rural  development  measures, 
LEADER  (M19)  and  basic  services  and  village  renewal  in  rural  areas  (M07),  are  considered 
particularly  effective in  contributing to  balanced territorial  development  as they  are targeted at 
the wider rural population.  

62 

 
 
Direct  payments,  together  with  market  measures  (Pillar  I)  and  support  to  areas  facing  natural 
constraints  (Pillar  II),  are  effective  instruments  that  positively  contribute  to  stabilising  farm 
income,  especially  in  times  of  crisis,  and  thereby  support  farm  viability  and  maintenance.  In 
addition, several CAP instruments, such as investment support, start-up aid, direct payments and 
support  to  knowledge  transfer  and  innovation,  contribute  to  farm  modernisation  and 
productivity growth. 

However,  the  uneven  distribution  of  direct  payments  is  highlighted  as  a  limitation  to  the 
CAP’s  effectiveness  in  reducing  economic  disparities  between  farmers.  The  most  recent  CAP 
reform led to a significant redistribution of direct payments between Member States (as a result 
of  the  mechanism  of  external  convergence)  and  between  farmers  (as  a  result  of  internal 
convergence),  Smaller  farmers  were  the  most  notable  beneficiaries  (as  a  result  of  the  various 
mechanisms of redistribution). The level of direct payments per hectare received by farmers of 
the  smallest  economic  size  class  (below  EUR 8 000  of  standard  output)  increased  by  17%  in 
2016-2018 compared to 2011-2013. In addition, the level of support for farmers with the highest 
income decreased, to the benefit of farmers with lower incomes.  

Nevertheless,  the  difference  in  per  hectare  level  of  income  support  between  farmers  is  still 
significant: in the EU-28, in 2019 the difference between the level of direct payments received 
by the 1% of EU farmers receiving  the  least  support and the 1% receiving the highest  support 
was  EUR  1 240/ha.  This  difference  remains  particularly  high  in  Member  States  such  as  Spain 
and  Portugal,  which  still  apply  a  model  relying  strongly  on  historic  references,  while  having 
numerous intensive livestock producers and olive growers. 

Direct  payments  and  Pillar  II  support  represent  close  to  50%  of  farmers’  income  in  mountain 
areas,  and  CAP  funding  underpins  farm  viability  in  the  most  marginal  and  remote  rural 
areas. The reform led to a redistribution of support to areas facing constraints. The level of 
direct  payments  (around  EUR 280/ha  in  2016-2018)  is  now  higher  in  mountain  areas  than  in 
areas facing no constraints. The high level of total income support does not compensate fully for 
the income gap with plains, but it explains in part why the income per worker in mountain areas 
is higher than in areas facing other constraints.  

CAP support is mainly targeted at farming, but it has clear and significant spillover effects into 
the wider rural economy, notably because it is boosting local expenditure. CAP interventions in 
less developed regions are associated with better economic and social performance in the wider 
rural economy than in more developed regions. In addition, the JRC analysis carried out as part 
of  this  evaluation  demonstrated  the  contribution  of  CAP  funding  to  generating  gross  value 
added and employment in rural areas across the whole EU territory.  

In addition, with half of the recipients receiving less than EUR 1 250 per year, the distribution of 
CAP  support  is  very  inclusive.  The  World  Bank  demonstrated  that  the  CAP  is  contributing  to 
reducing  poverty  and  the  income  gap  between  agriculture  and  the  rest  of  the  economy. 
Moreover,  the  input-output  analysis  showed  that  funding  under  Pillar  I  safeguards  and  creates 
approximately 5.2 million jobs in agriculture and related upstream sectors. The agri-food sector 
provides more than 40 million jobs in the EU. The CAP actually contributes to keeping people in 

63 

 
 
rural  areas,  although  it  is  not  sufficiently  contributing  to  creating  new  job  opportunities. 
However, the sometimes-poor working conditions of seasonal workers in agriculture have led to 
calls  for  granting  CAP  payments  conditional  on  compliance  with  legislation  on  working 
conditions.  

CAP  support  to  young  farmers  is  an  incentive  to  generational  renewal,  but  not  sufficient  to 
address  the  main  entry  barriers  into  farming,  which  are  access  to  land  and  capital,  and  the 
attractiveness of rural areas. Wider economic conditions greatly affect the relative attraction of 
farming  for  young  people  in  many  Member  States.  On  the  low  attractiveness  of  rural  areas 
(notably  due  to  poor  services),  the  CAP  contributes  to  improved  infrastructure,  services  and 
connectivity,  especially  in  remote  areas  or  in  territories  not  always  well  covered  by  other  EU 
funding. Without the CAP, depopulation and land abandonment would be worse. However, the 
CAP alone cannot solve all the issues, particularly as it is mainly targeted at farmers and because 
rural  development  funding  is  anyhow  rather  small.  In  addition,  access  to  land  is  mainly 
determined by national regulations. 

According to respondents, Pillar II instruments have more potential than Pillar I to address the 
needs of women, specific ethnic groups, other EU nationals and third-country nationals. Overall, 
the  effectiveness  of  CAP  instruments  and  measures  as  regards  the  social  inclusion  of  most 
vulnerable groups is not sufficiently ensured. This is notably due to a lack of targeting. 

The  creation  of  human  capital  is  supported  by  the  CAP  measures  on  knowledge  transfer  and 
advisory  services,  although  not  at  its  full  potential  due  to  relatively  low  uptake.  The 
cooperation  measure  EIP  effectively  promotes  innovation  and  enhances  human  and  social 
capital. Moreover, the delivery mechanism of the LEADER, based on bottom-up participatory 
approaches, contributes most to balanced territorial development via its specific characteristics 
of local embeddedness and encouraging rural society’s participation in its own development.  

Efficiency 

The  relevant  CAP  instruments  and  measures  are  delivered  with  reasonable  efficiency 
overall, but specific issues of inefficiency arise in respect of both pillars.  

The targeting of Pillar I is not necessarily conducive to balanced territorial development outside 
the  agricultural  sector.  Interviewees  argued  that  most  Pillar  I  aid  per  farm  goes  to  areas  and 
territories where farms are productive and well-structured (including large size-farms). 

However, from the quantitative analysis of input/output ratio for funds, Pillar I instruments are 
judged relatively ‘efficient’. This is because overall the funding is much smaller than the gross 
value added of the funded sector across the EU territory. In general, areas with high agricultural 
activity are more ‘efficient’ in their use of Pillar I funds. The lowest efficiency scores are found 
in urban areas with minimum agricultural activity. 

The  cost-effectiveness  of  CMO  instruments  seems  favourable  and  less  biased  towards  specific 
farm-sizes than direct payments. In addition, as highlighted in the case studies, these instruments 
have  good  results  in  terms  of  producers’  involvement,  stabilisation  of  incomes  and  better 
governance  of  the  whole  supply  chain,  and  thus  they  contribute  to  the  local  economy.  As 

64 

 
 
delivery of funding  goes via intermediary bodies to  farmers, the costs of public administration 
for these instruments are quite low. 

On  Pillar  II,  it  is  particularly  costly  to  establish  effective  cooperation  (M09  –  setting-up  of 
producer  organisations,  M16  –  cooperation  and  M17  –  risk  management)  and  joint  working 
between farmers when the farms are not closely co-located. This is demonstrated by comparing 
the number of holdings participating in support schemes requiring farmers’ cooperation with the 
spending.  

A relatively small expenditure on M07 – basic services can benefit a large number of people in 
rural communities in terms of improved services/infrastructure (IT or other) and opportunities. 
In  some  particularly  remote  areas  with  natural  constraints  or  in  areas  with  an  ageing  rural 
population,  the  measure  naturally  reaches  fewer  people  per  million  euro  of  expenditure. 
Similarly,  areas  of  low  population  density  score  lower  in  terms  of  coverage  by  local  action 
groups  (LAG).  Moreover,  the  number  of  LEADER  projects  funded  suggests  that  LAGs  are 
succeeding in reaching rural people throughout the EU territory, with a few exceptions, mainly in 
Italy, Spain and the Baltic countries.  

The administrative burden is the main factor limiting the CAP instruments’ ability to achieve 
balanced territorial development, for both beneficiaries and the administration.  

Overall, Pillar I aid is quite efficiently delivered, with relatively low administrative burden and 
swift application and payment processes. However, on cost-effectiveness, Pillar I aid is seen as 
not  particularly  targeted  towards  the  social  aspects  of  balanced  territorial  development  and 
the needs of vulnerable or socially excluded rural populations.  

Pillar II funds are frequently made more difficult to deliver, notably by rules deriving from 
principles  of  public  funding  that  were  not  specifically  designed  within  the  CAP  (e.g.  public 
procurement) and which appear to be applied in a way that is not appropriate to the delivery of 
rural development measures.  

In  addition,  beneficiaries  of  Pillar  II  measures  such  as  investment  and  knowledge  support, 
LEADER and cooperation projects often emphasise the responsibility of managing authorities 
in transposing EU rules in complex implementing processes, which are lengthy and difficult 
(i.e. costly and requiring complex skills) to follow. Often beneficiaries have to bear the costs of 
investment up-front, which can only be refunded once the work is completed. The approval of 
projects can take a very long time and receiving payments can take even longer (at least one 
year, often two). This reduces the accessibility of Pillar II measures to socially and economically 
disadvantaged  beneficiary  groups.  Moreover,  support  for  investments  appears  to  cause  more 
costs and barriers, especially for small farmers.   

Another  concern  for  investment  measures  (M04  –  investments,  M06  –  farm  and  business 
development  and  M07  –  basic  services)  stems  from  managing  authorities’  fear  of  the  funding 
being disallowed. They thus refrain from supporting multi-purpose or novel projects that do not 
easily fit pre-determined categories or assessment criteria. Efficiency can become the enemy of 
effectiveness – the projects that are simplest to deal with and therefore most likely to be funded 
are those which offer nothing really new or have low additionality.  

65 

 
 
Nevertheless,  in  some  case  studies  and  for  some  measures,  managing  authorities  and  private 
stakeholders established innovative mechanisms to improve the cost-effectiveness of CAP rural 
development aids. Such mechanisms include relying on better policy targeting, good cooperation 
and  knowledge  exchange  among  public  and  private  actors,  and  integration  of  different  policy 
measures in a ‘package of measures’ tailored to the different local farm needs. These solutions 
reduce the public costs of assessment, approval, monitoring of the responsible administration, as 
well as the transaction costs for private operators such as small farmers. As a result they achieve 
better results in terms of balanced territorial development and social inclusion.   

As  concerns  the  burden  faced  by  administration  staff,  the  types  of  burden  more  frequently 
cited by  respondents  to  the consultation  on the long-term vision  for rural  areas include  limited 
human  and  financial  resources,  frequent  policy  changes,  cost  of  management,  and  an  intricate 
decentralised  administrative  system.  Several  case  studies  highlighted  the  importance  of 
experienced personnel who understand how operational decisions affect measure efficiency and 
effectiveness.  Case  study  respondents,  mainly  in  regionalised  countries,  underlined  that  there 
was significant administrative burden related to the coordination/governance structures. This 
was  due  to  the  many  different  institutional  actors  and  administrative  layers  involved  in  the 
implementation.  

Coherence 

The internal coherence of the CAP measures in addressing balanced territorial development is 
considerable.  Instruments  and  measures  within  Pillars  I  and  II  play  complementary  roles, 
notably  in  supporting  farm  income,  young  farmers  and  the  investment  capacities  of  farmers. 
There  are  synergies  between  CMO  instruments  and  rural  development  measures,  generating 
greater dynamism in rural economies.  

On  the  consistency  of  the  CAP  measures  with  other  EU  policies,  especially  the  European 
Regional  Development  Fund,  European  Social  Fund  and  Cohesion  funds,  relations  of 
complementarity prevail in most of the case studies. This complementarity relies greatly on the 
partnership agreement covering the five European structural and investment funds (ESIFs).  

Consistency  with  national  regional  policies  could  be  improved  through  more  coordination 
between national, regional and local actors. This conclusion is supported by the outcome of the 
consultation on A long term Vision for the EU’s Rural Areas125. For example, even though there 
is complementarity between EAFRD and other ESIFs on broadband, higher complementarity is 
found only when there is adequate cooperation at national and regional level.  

Relevance 

The CAP addresses rural needs related to economic growth and development, rather than 
social  needs.  Both  Pillar  I  and  II  funding  are  effective  in  stimulating  local  economic 
performance  (particularly  in  developed  peripheral  rural  regions  and  traditional  rural  regions), 
indicating high relevance in bolstering gross value added and safeguarding employment in rural 

125 See footnote 25. 

66 

 
 
                                                           
areas. Basic payments are highly relevant in terms of addressing farm economics such as farm 
viability.  However,  due  to  the  lack  of  targeting  and  limited  redistribution  of  support,  they 
contribute  less  to  reducing  income  disparities  between  farms  and  to  addressing  social  issues. 
Nevertheless, basic payments are also relevant in reducing depopulation and land abandonment, 
supporting  generational  renewal,  reducing  poverty  and  gender  disparities,  and  maintaining 
cultural and natural heritage.  

As rural development measures offer a wide range of types of support that can be  targeted at 
different  local  situations,  they  are  well  designed  to  remove  structural  factors  of  relative 
weakness and tackle uneven economic development in the farm sector.  

The CAP is the key relevant policy for funding and  maintaining employment in agriculture. 
However,  jobs  created  and  retained  by  CAP  spending  are  not  necessarily  high-quality  jobs, 
especially for seasonal  workers,  and employment  in  rural  areas is  not  attractive enough to  halt 
emigration  to  wealthier  regions.  Moreover,  CAP  support  has  high  relevance  for  generational 
renewal, notably by supporting the economic viability of young farmers, but it is not sufficient 
on its own to attract new young people. 

M07  –  basic  services  and  M19  –  LEADER,  with  particular  focus  on  regions  and populations 
that may otherwise be left behind, are highly relevant in targeting social disparities and for the 
provision of social services, thus demonstrating balanced territorial development.  

The  SIMRA  research  project  highlighted  LEADER,  the  cooperation  measure  (M16),  emergent 
policies  such  as  those  to  support  smart  villages  and  some  EIP-AGRI  actions  as  particularly 
relevant in supporting social innovation. 

As  regards  social  inclusion,  CAP  support  in  general  is  poorly  targeted  at  the  needs  of  third-
country  nationals,  ethnic  groups,  people  with  disabilities  and  not  sufficiently  relevant  to  them. 
By  contrast,  CAP  support  is  more  relevant  for  farmers,  rural  young  people  and  population  in 
most remote areas.  

Pillar II measures are more relevant than Pillar I measures when it comes to targeting the needs 
of  vulnerable  groups  (including  the  low-skilled  and  unemployed,  older  people  in  rural  areas, 
rural women and people with disabilities). However, with a lack of explicit targeting of women’s 
needs  and  a  significant  gender  imbalance  among  farm  managers,  the  CAP’s  relevance  in 
furthering the economic inclusion of women farmers is low. 

EU added value 

Overall,  the  CAP  instruments  and  measures  provide  EU  added  value,  maintaining  and/or 
creating a large share of rural agricultural jobs, as well as providing positive indirect effects on 
the creation of non-agricultural  jobs  in rural  areas. Therefore, the CAP  plays a relevant  role  in 
balanced territorial development across the EU, and greater poverty and rural decline would 
happen if CAP support was not available.  

This is especially the case in remote rural areas. There, by supporting the continued existence 
of farms, the CAP is of vital importance as it contributes to local purchasing power and offers 
local employment. In regions with a diverse rural economy (with a high interlinkage between 

67 

 
 
economic sectors), other national and EU funds are necessary on top of the CAP to address the 
multiple challenges these regions face (accessibility, quality of services, attractiveness for young 
people, etc.) and to achieve balanced territorial development. The CAP’s effects are also limited 
in more economically developed regions, though some positive social effects are highlighted in 
some case study countries. 

Lessons learnt 

Despite  the  redistribution  mechanisms  introduced  in  the  2014-2020  CAP,  the  distribution  of 
support  is  still  uneven  and  significant  economic  disparities  remain  between  farmers  and 
territories.  

There  is  room  to  improve  the  targeting  of  CAP  support  to  vulnerable  groups  and  smaller 
beneficiaries. Through a new delivery model and a new objective and higher ambition in terms 
of  social  inclusion,  the  CAP  could  have  more  quantified  targets  on  social  aspects,  especially 
through social conditionality and gender equality.  

The active participation of a wide range of stakeholders  in  the design  of the CAP  could  help 
address  the  specific  characteristics  of  rural  areas.  This  could  also  improve  the  perceptions  of 
CAP effects. 

Access  to  advice,  training,  and  greater  capacity  building  could  be  used  to  enable  socially 
disadvantaged  target  groups  to  gain  confidence  and  skills,  access  to  financial  instruments  and 
technical advice to increase their access to CAP measures. 

The potential of measures was not always fully exploited because of the administrative burden 
and costs to access the support.  

The  reduction  of  eligibility  criteria  and  EU  requirements  could  allow  Member  States  to 
redesign  implementation  processes  (and  potentially  reduce  their  complexity)  and  could  reduce 
the fear of funding disallowance. This could  enable more innovative, multi-purpose projects to 
emerge, and could reduce the administrative costs for smaller Member States. 

These  different  levels  of  responsibilities  concerning  ‘two  pillars’  and  different  instruments 
within the same  pillar raise questions for the  future programming period,  which is  expected to 
feature more centralisation of programme design and management, as well as data integration at 
national  level.  In  this  context,  more  transversal  knowledge  of  the  CAP  among  competent 
authorities could improve coordination between the Funds and administrations. 

The  presence  of  experienced  personnel  who  understand  the  CAP’s  multiple  components, 
together with well-functioning CAP networks, are crucial in enhancing the institutional capacity 
of public authorities and in achieving efficient public administration. 

The  CAP  alone  cannot  address  all  challenges  faced  by  rural  areas.  Strategic  and  integrated 
approaches  within  the  CAP  and  with  other  funds  would  better  help  to  achieve  balanced 
territorial development especially in remote areas. This calls for strong coordination between EU 
funds.  

68 

 
 
 
There  is  considerable  scope  for  Member  States  to  learn  from  good  practices.  Creative  and 
successful  application  of  measures  relies  on  better  policy  targeting,  cooperation  among  actors 
and integration of different policy measures, while lowering burdens. 

To target the territories in greatest need, Member States need to be able to identify specifically 
the  rural  areas  they  want  to  target.  However,  for  monitoring  purposes  a  harmonised  approach 
towards  achieving  a  single  definition  of  functional  rural  areas  at  EU  level  would  be  useful. 
The current lack of basic data for many topics using the single definition of rural hinders policy 
steer and monitoring. 

Although  the  evaluation  comes  late  in  the  policy  discussions  on  the  post-2020  CAP,  its 
conclusions validate relevant policy elements proposed for the post-2020 CAP. These include the 
need  for  a  more  strategic  approach  to  improve  targeting,  consistency  of  approach  and  overall 
performance, as well as greater flexibility for Member States with the necessary safeguards. Key 
aspects related to balanced territorial development of the post-2020 CAP include the following:  

 

  Direct support continues supporting farm income throughout the EU territory. 
  A mandatory  redistribution of 10% of Member States’ direct  payments  towards smaller 
farms.  However,  reduction  and  capping  of  large  amounts  of  direct  support  to  the  same 
beneficiary remains voluntary for Member States.  
Increasing the attractiveness of rural areas will not be achieved without integration with 
national policies. In particular, the policy for generational renewal in agriculture must be 
based on such an integrated approach, aided by substantial CAP financial resources.  
  Reflecting  the  evaluations  and  increasing  societal  concerns,  CAP  support  will  –  for  the 
first time – be linked to farmers’ compliance with EU basic social and labour rights for 
farm workers.  

The many lessons learnt in respect of territorial development are now reflected in the Long-term 
vision for Rural Areas, which in turn, provides an additional perspective for the new CAP. One 
of the actions of the EU rural action plan underpinning this vision relates to the definition of the 
concept of functional rural areas. 

The Commission proposal also introduced a performance monitoring and evaluation framework. 
The  framework  includes  a  set  of  common  indicators,  data  collection  and  regular  reporting  on 
performance,  monitoring  and  evaluation  activities  to  help  the  CAP  move  from  compliance  to 
results and to better explain the CAP’s achievements. In addition, Member States should define 
quantified targets for all result indicators.  

To  better  assess  the  impact  of  the  CAP  on  gender  equality,  a  breakdown  by  gender  of  the 
reported  number  of  CAP  beneficiaries  and  young  farmers  setting  up  with  CAP  support  was 
added  to  the  new  performance  monitoring  and  evaluation  framework  (PMEF).  To  assess  the 
fairness of the redistribution of support to  smaller farmers and  areas in  needs, two results and 
one impact indicators were added. Several indicators to measure the coverage of CAP action on 
social inclusion and smart village strategies were also added.  

69 

 
 
The  findings  of  the  evaluation  will  inform  the  Commission’s  involvement  and  support  to  the 
Member  States  in  the  context  of  the  ongoing  development  of  strategic  plans  for  the  next  CAP 
period.  

70 

 
 
 
 
4. 

ANNEX 1: PROCEDURAL INFORMATION 

1.  Lead DG, Decide Planning/CWP references 

The  evaluation  was  approved  in  the  European  Commission  planning  of  legal  initiatives,  under 
the Decide planning reference: PLAN/2019/5259. The evaluation was organised and conducted 
by the Directorate-General for Agriculture and Rural Development (DG AGRI), according to its 
evaluation and studies plan. 

2.  Organisation and timing 

This  was  a  policy  evaluation  project  included  in  the  Directorate-General  for  Agriculture  and 
Rural  Development  evaluation  plan  2016-2020.  It  followed  the  Better  Regulation  Guidelines 
with regard to evaluations. The evaluation work was carried out through an external evaluation 
support  study,  contracted  through  a  service  request  under  a  framework  contract,  conducted  in 
line with the internal procedure of the Directorate-General for the organisation and management 
of policy  evaluations  carried out  by external  contractors. The project  was supervised under the 
technical as well as the contractual management of unit C.4  - Monitoring and Evaluation, with 
the  involvement  of  representatives  of  8  units  of  the  Directorate-General  and  of  6  Directorates-
General as members of an inter-service steering group. 

The inter-service steering group was set up by the Commission in April 2019, with a mandate to 
finalise the roadmap, provide information,  prepare the terms  of reference, monitor the work of 
the  external  study  team,  discuss  and  give  advice  on  the  approval  of  the  deliverables  and  to 
comment on the draft evaluation Staff Working Document. 

The  inter-service  steering  group  included  members  of  the  Secretariat-General  of  the 
Commission, the Joint Research Centre and Directorates-General for Environment, Research and 
Innovation,  European  Statistics,  Economic  and  Financial  Affairs,  The  steering  group  held  its 
kick-off meeting in September and held nine meetings throughout the project. 

The  roadmap  was  published  on  17  May  2019  and  set  out  the  context,  scope  and  aim  of  the 
exercise.  The  roadmap  presented  the  questions  to  be  addressed  under  the  five  evaluation 
categories  of  effectiveness,  efficiency,  relevance,  coherence  and  EU  added  value.  During  the 
public  consultation  period  feedback  was  received  from  10  stakeholders  (one  feedback  resulted 
from  the  consultation  EU farm policy  —  evaluation of its impact  on knowledge  exchange  and 
advisory activities). 

The evaluation support study carried out by the external contractor started in August 2019. The 
final  deliverable  was  received  on  27  November  2020.  The  inter-service  steering  group  for  the 
external  evaluation  study  carried  out  a  quality  assessment  of  the  external  report  of  the 

71 

 
 
 
contractor of this evaluation126, in particular of the quality of the methodology, the reliability 
of the data and the robustness of the analysis and findings.  

It  judged  that  the  report  could  be  approved  as  it  complied  fully  with  the  conditions  of  the 
contract  and  relevant  professional  evaluation  standards.  The  assessment  highlighted,  among 
others,  the  following  main  elements:  on  the  basis  of  a  theoretical  analysis,  quantitative  and 
qualitative analysis have been carried out, with a prevalence of the second kind of analysis. Lack 
of a counterfactual analysis does not allow to have clear proof of causal chains. Cluster analyses, 
the  correlation  and  regression  analyses  incorporate  a  territorial  dimension  and  provide  insight 
into relationship. As regards the regression analysis the fact that the dependent and explanatory 
variables are measured in the same time-period may introduce endogeneity issues. The results of 
this support study are interesting and constitute a basis towards future evaluations on this subject. 

3.  Exceptions to the Better Regulation Guidelines 

There  was  an  exception  in  relation  to  the  need  to  organise  a  dedicated  open  public 
consultation  as  part  of  this  evaluation,  as  an  open  public  consultation  was  conducted  in  the 
context  of  the  long-term  Vision  for  the  EU’s  rural  areas127.  That  public  consultation  was  held 
between 7 February and 30 November 2020 and included a specific set of questions on the CAP 
and balanced territorial development (see Annex 2). 

4.  Consultation of the Regulatory Scrutiny Board 

The  Regulatory  Scrutiny  Board  scrutinised  the  evaluation  report  at  a  meeting  on  9 June 2021, 
and  provided  a  positive  opinion.  However,  the  Board  considered  that  the  report  should  be 
improved regarding the following aspects. 

Regulatory Scrutiny Board remark 

Considerations for the report 

1)  What  is  the  reference  point  of  analysis 
of  the  report?  What  should  have  been 
achieved by now to consider the measures 
successful? What is the causal relationship 
the 
between 
and 
implemented 
instruments, and the achieved results? 

problems, 
set  of  measures 

identified 

The evaluation uses the pre-2013 reform situation as 
a  benchmark,  on  the  basis  of  the  most  recent  data 
available  up  to  2013.  This  allows  for  an  assessment 
of  the  evolution  of  the  relevant  indicators  from  the 
benchmark to the most recently available data.  
The impact assessment accompanying the 2014-2020 
CAP  proposal  was  considered  inappropriate  for  a 
baseline,  because  the  options  assessed  did  not 
correspond  to  the  final  outcome  of  the  reform  after 
negotiations  with 
the  European  Parliament  and 
Council.  There  was  no  other  objective  and 
quantitative projection that could have been used as a 
baseline, 
the 
medium-term projections of agricultural markets and 

the  annual  updates  of 

including 

126   https://ec.europa.eu/info/food-farming-fisheries/key-policies/common-agricultural-policy/cmef/rural-

areas/impact-cap-territorial-development-rural-areas-socioeconomic-aspects_en. 

127 See footnote 2. 

72 

 
 
                                                           
income produced by DG AGRI (with JRC-IPTS). 
As  regards  what  should  have  been  achieved  by  now 
to  consider  the  measures  successful,  first  balanced 
territorial  development  is  a  very  broad  concept  and 
social  inclusion  was  not  at  the  heart  of  CAP 
objectives  at  the  time  of  developing  the  CAP  2014-
2020.  This  is  why,  when  developing  the  CAP 
proposal 2021-2027, social sustainability was given a 
more prominent role.  
Second,  there  are  a  few  quantified  targets  for  rural 
development  in  link  to  Priority  6  (Promote  social 
inclusion, 
economic 
development  in  rural  areas),  as  illustrated  in  the 
evaluation.  Other  dimensions  of  balanced  territorial 
development are not covered by quantified targets in 
the  CAP  2014-2020.  The  next  CAP  will  fill-in  this 
gap  adding  notably  quantified  targets  on  the  direct 
support  redistribution  and  the  support  to  areas  in 
needs.  
The  intervention  logic  was  completed  to  highlight 
better  the  causal  relationships.  In  addition,  the  JRC 
developed  a  causal  analysis  for  this  evaluation  to 
assess  the  impact  of  the  CAP  on  gross  value  added 
and employment. 

reduction 

poverty 

and 

2) What are the differences in procedures, 
implementation  conditions  and  role  of 
managing  authorities  among  Member 
States? To what extent do these affect the 
success of the interventions? 

The  CAP  provides  Member  States 
flexibility 
regarding  the  type  of  measures  implemented  under 
Pillar II, as well as the budget allocated to the various 
measures.  The  major  difference  stems  thus  from  the 
various  programming  among  Member  States.  On 
social  aspects,  the  only  obligation  for  all  Member 
States  was  the  ring-fencing  on  LEADER  (minimum 
5%  of  rural  development  fund)  and  most  Member 
this  minimum.  The 
States  went 
implementation of other rural development measures 
affecting  social  sustainability  and  the  redistribution 
of  direct  payments  towards  smaller  farms  was 
voluntary for Member States. 
The  way  in  which  the  CAP  Pillar  II  measures  are 
structured  and  implemented  varied  also  significantly 
between  Member  States  resulting  in  an  array  of 
different approaches and detailed operations available 
to beneficiaries. Several examples are provided in the 

beyond 

73 

 
 
 
3)  What  are  the  costs  in  relation  to  the 
the 
perceived 
benefits?  What 
the 
administrative  burdens  related 
the 
structures?  What 
governance 
simplification 
reduction 
and  burden 
potential for future action? 

are 
to 
is 

evaluation report.  
Implementation  choices  that  clearly  affected  the 
success  of  CAP  Pillar  II  measures,  quoted  in  the 
evaluation, were for example the public procurement 
rules,  the  complexity  to  access  the  support  and  the 
lack of readiness for new and multi-purpose projects, 
that  do  not  necessarily  fit  predetermined  assessment 
criteria  and  categories.  The  public  procurement 
principle  was  cited  several  times  as  a  cause  of  very 
inefficient  delivery  either  because  the  beneficiaries 
have difficulties finding three sources for the services 
they need, and/or because the lowest price provider is 
not always the most reliable one and quality can then 
suffer,  or  because  the  service  providers  who  do  not 
get  awarded  the  work  appeal  against  the  decisions 
made. On the complexity of support claim, it mainly 
discarded beneficiaries with higher social needs. 

(and 

involved 

indicators  (2015-2018) 

A  pure  cost-benefit  analysis  of  the  measures  and 
instruments  is  impossible  given  the  complexity  and 
number  of  administrations 
the 
differences  in  management  and  implementation  of 
CAP  measures).  Nevertheless, 
the  assessment 
consists  in  a  quantitative  efficiency  analysis  of 
in 
relevant  CAP  output 
comparison  with  the  inputs,  i.e.  the  funding  to  the 
relevant  measures.  Due  to  the  absence  of  quantified 
information  about  several  benefits  of  the  measures, 
the  efficiency  assessment  could  be  done  only  to  a 
limited extent.  
In addition, the evaluation also refers to the results of 
the  study  on  the  administrative  burden  arising  from 
the  CAP.  The  estimated  administrative  costs  of 
delivering  the  CAP  funds  to  all  beneficiaries  of  the 
integrated  administration  and  control  system  (IACS) 
represent  around  3.5-3.9%  of  the  total  funding 
delivered, on average, across all EU Member States. 
However,  this  figure  varies  considerably  between 
individual instruments and measures in both Pillars of 
the CAP, and between Member States and regions. In 
general,  Pillar  II  measures  involve  rather  higher 
administrative  costs  than  Pillar  I  instruments.  More 
importantly,  the  study  notes  that  the  administrative 
impact is disproportionately high for smaller Member 

74 

 
 
involved 

the  main  ESIF,  and 

States  and  that  the  main  costs  are  associated  with 
management and controls (74% of the estimated total 
administrative  costs),  as  opposed  to  set-up  and 
running costs (at 26%).  
In addition, the evaluation refers also to the study of 
ESIF  administrative  costs,  which  estimated  that  the 
costs  of  delivery  for  Pillar  II  funds  were  the  highest 
the  greatest 
of 
workload: EUR 83 100 and 2.18 annual work units of 
labour  input  per  million  euros  of  spending,  i.e. 
administrative  costs  around  8%  of  total  programme 
spending.  Paying  agencies  incur  a  particularly  high 
share  of  these  costs  due  to  the  comparatively  high 
level  of  administrative  checking  (100%)  that  they 
perform on EAFRD-funded projects. 
As the evaluation highlighted that  the administrative 
burden  related  to  governance  structures  has  arisen 
when  in  the  implementation  of  CAP  measures 
a  number  of 
(mostly 
the  RDP  measures) 
administrative  organisations  and 
institutions  are 
involved  at  different  levels.  This  is  typical  in  both 
centralised 
regionalised  countries 
(Spain,  Italy,  Germany).  This  burden  was  especially 
relevant  when  CAP  measures  were  managed  at 
(some  measures 
different  administrative 
managed at regional level, some at national level and 
some  others  at  NUTS3  level),  e.g.  in  the  case  of 
France,  as  well  as  when  direct  payments  and  rural 
development  programmes  are  managed  at  national 
and regional level, respectively (ES, DE, IT).  
The  burden  arising  from  the  transposition  into 
Member  State  reality  of  eligibility  criteria  and 
requirements defined at  EU level can be lowered by 
reducing  EU  requirements  in  the  legal  basis  (as 
proposed in the new CAP). This was mentioned also 
in the lessons learnt on administrative burden. 
Further  coordination  between  administrations  and  a 
better  understanding  of  the  CAP  could  reduce  this 
burden  on  not  just  the  beneficiaries  but  also  on  the 
administrations.  Furthermore,  a  reduction  of  the 
public procedures not envisioned as part of the CAP 
requirements  could  reduce  costs  for  administrations 
in management of applications, which would also be 

(France)  and 

levels 

75 

 
 
are 

the 
or 

synergies 
overlaps 

and 
4)  What 
and 
complementarities, 
inconsistencies  between  CAP  and  other 
EU  policies?  Where  is  the  CAP  best 
placed 
to  a  balanced 
territorial  development?  What  prevents 
stronger  synergies  between  EU  structural 
funds?  Which  obstacles  prevent  effective 
cooperation at national and regional level?   

to  contribute 

positive  for  beneficiaries.  The  spread  of  good 
practices  among  managing  authorities,  such  as  the 
‘package of measures’ (used in Italian regions) could 
also  help  governments  to  reduce  burden  (and  for 
beneficiaries). 

all 

case 

Funds 

studies 

showed, 

Investment 

that  as  regards 

The  evaluation  concluded 
the 
coherence between CAP and the European Structural 
and 
(European  Regional 
Development  Fund  (ERDF),  European  Social  Fund 
(ESF),  Cohesion  Fund  (CF))  there  are  no  particular 
problems of overlaps or major inconsistencies.  
In  general,  the  coordination  of  different  instruments 
and measures of the CAP with other policies rely on 
partnership  agreements,  in  which  the  objectives  and 
strategies  of  the  different  policies  are  harmonised. 
However,  as 
the 
harmonisation  in  most  cases  means  a  concrete 
division  of  tasks  among  the  separate  ESIF  to  avoid 
overlap within the same category of interventions, at 
the  cost  of  a  higher  level  of  complementarity. 
Overall,  the  major  risk  is  not  funding  overlap,  but 
rather funding gaps.  
In  some  case  study  areas,  there  are  more  substantial 
relations  of  complementarity,  in  particular  when 
ESIF operational  programmes  clearly state that rural 
areas  are  beneficiaries  of  specific  interventions,  as 
illustrated  in  the  evaluation  (e.g.  in  Czechia,  for 
transport infrastructure and liveability of rural areas).  
Stronger 
the 
coherence  of  policy  measures  is  more  structured  in 
local integrated schemes and joint actions of different 
funds,  where  EAFRD  has  to  work  together  with 
ERDF and ESF (e.g. in the case of some community-
led local development).  
Only  few  case  studies,  referenced  in  the  evaluation, 
emphasised that although there is no evident conflict 
between  the  ESIF,  there  is  rather  a  lack  of  more 
concrete  and  effective  mechanisms  of  policy 
integration between the EAFRD and the other ESIF. 
Still, at the same time, there is no mechanism in the 
programming  framework  enabling  different  policies 
to be coordinated and integrated at local and regional 
level. 

linkages  of  synergy  occur  when 

76 

 
 
the  existing  monitoring  and 
5)  Are 
reporting  arrangements  appropriate 
in 
view  of  the  identified  weaknesses  as  for 
the absence of clear success indicators and 
targets,  missing  data,  the  lack  of  regional 
granularity  of 
socio-economic 
some 
indicators  and  the  non-uniform  definition 
of ‘rurality’? 

6)  Why  does  the  report  not  give  a 
balanced  and  granular  account  of  the 
stakeholder  views,  including  dissenting 
ones?   

it 

that 

The lack of uniform definition of ‘rurality’ is clearly 
an  issue  for  policy  evaluation  and  policy  design,  as 
indicated  in  the  evaluation.  Evaluators  and  the  JRC 
circumvented  the  issue  by  developing  their  own 
definition,  best  suited  to  their  analysis.  In  addition, 
the Commission is now working on the definition of 
‘functional rural areas’ in the framework of the Long 
Term Vision for Rural Areas.  
Moreover, 
is 
the  evaluation  concludes 
important  for  Member  States  to  keep  flexibility  for 
the definition of the targeted area. 
The current indicators include some quantified targets 
(such  as  number  of  jobs)  related  to  the  social 
dimension of the CAP, but these do not cover all the 
elements of balanced territorial development (such as 
direct  payment  redistribution  to  smaller  farmers  and 
areas  in  needs,  social  inclusion,  etc).  However,  the 
data  collected  for  audit  purposes,  currently  allows 
assessing the fairness of direct payments distribution. 
In  addition,  FADN  data  allows  for  a  thorough 
analysis  of  the  distribution  of  support  to  smaller 
farmers,  farmers  in  areas  facing  natural  constraints, 
farmers  with  smaller  income…  as  illustrated  in  this 
report. 
In  addition,  some  social  aspects  covered  in  the 
evaluation  go  beyond  the  boundaries  of  CAP  2014-
2020 objectives explaining in part the lack of targets. 
This  is  why  it  is  key  to  be  able  to  use  statistics  and 
develop targeted analytical frameworks. To that end, 
ESTAT  statistics  are  essential  and  the  evaluation 
made  clear  that  a  greater  availability  of  socio-
economic  data  would  be  desirable  (especially  at 
similar geographic level). 

Stakeholder views have not always been presented in 
the  evaluation  support  study  in  a  granular  way, 
therefore the evaluation report tried to give an overall 
view  of  the  majority  of  stakeholder’s  position.  It  is 
important  to  note  that  by  the  time  of  finalising  the 
draft evaluation presented to the RSB, the analysis of 
the  results  of  the  consultation  on  Long-term  vision 
for  Rural  Areas  by  stakeholder  group  was  not 
finalised. These results were added to the evaluation, 
between 
however 

differences 

significant 

no 

77 

 
 
stakeholder,  age  and  gender  group  responses  were 
found. 

5.  Evidence, sources and quality 

An external and independent evaluation support study provides the basis for the evaluation, 
as  presented  in  this  document.  The  study  was  carried  out  by  ADE  SA,  CCRI,  ÖIR  GmbH 
following  the  signature  of  the  contract  by  ADE  on  16  August  2019  and  concluded  on  27 
November 2020 with the receipt of the final deliverable. The limitations are clearly explained in 
chapter IV (methodology) and some specific limitations are also further explained in annex data 
sources  described  in  chapter  4  and  Annex  3,  which  are  clearly  specified  and  referenced  in  the 
relevant chapters of the evaluation support study.  

Additionally, the evaluation also builds upon a JRC study aiming to estimate the causal impacts 
of the CAP, as explained in chapter 4. 

5. 

ANNEX 2: STAKEHOLDER CONSULTATION 

A number of consultation activities were carried out at different points in time for the elaboration 
of the evaluation, including consultations by the Commission and surveys and interviews in the 
context of this study, carried out by the European Commission along with other EU institutions. 

  specifically  concerning  the  support  study  (feedback  on  the  roadmap,  case  study 
interviews) and the evaluation (the open public consultation on the long-term Vision for 
the EU’s rural areas); 

  wider consultations of the European Commission: Special Eurobarometer 504; 
  consultations by other EU  Institutions (Committee of Regions, European Economic and 

Social Committee). 

The consultation actions concerning the support study 

The  roadmap  of  the  support  study  was  published  on  the  Europa  website128  under  ‘published 
initiatives’:  EU  farm  policy  –  impact  on  society  and  the  economy  in  rural  areas  in  order  to 
receive feedback during the period 17.05.2019 - 14.06.2019. 

A  total  of  10  contributions  were  gathered  (one  stemming  from  the  feedback  received  on  the 
impact  of  the  CAP  knowledge  exchange  and  advisory  activities).  The  following  stakeholder 
groups  participated:  EU  Citizens  –  individuals  anonymous  not  involved  in  farming/farmer  (2); 
Private Companies (2); NGOs (4); Association of producers (2). 

Overall, the feedback received highlighted the relevance of the following elements: 

- LEADER and the relevant budget allocation and, more in general, bottom-up approaches; 
- Coordination of the EU funds.  

128  https://ec.europa.eu/info/law/better-regulation/initiatives/ares-2019-2133696/feedback_en?p_id=5512958. 

78 

 
 
 
 
                                                           
-  Other  issues  were  highlighted:  the  relevance  of  farm  advisory  services,  short  supply  chains, 
local markets. 
These  and  other  elements  were  discussed  with  the  ISSG  and  taken  into  consideration  in  the 
technical specification of the study to which this evaluation refers.  

Consultation on the long-term Vision for the EU’s rural areas  

A  broad  public  consultation  aimed  at  all 
to 
30 November 2020  via  EU  survey  and  gathered  the  perceptions  and  views  of  Europeans  on  a 
range  of  issues  including  an  optional  set  of  questions  on  the  Common Agricultural  Policy  and 
territorial development. 

took  place  from  7 

interested  parties 

Regarding the optional set of questions on the Common Agricultural Policy and territorial 
development, 864 respondents  were registered.  The following stakeholder groups participated: 
EU  citizens  (460  responses,  53%  of  total  share);  Businesses  and  associations  (107,  12%); 
Academic/Research  Institutions  (49,  6%);  Public  authorities  (79,  9%);  NGOs  (81,  9%);  Rural 
Development Networks (64, 7%); Other (24, 3%)  

Half  of  the  responses  were  provided  by  respondents  coming  from  five  countries:  Spain  (121 
responses),  Germany  (102),  Austria  (72),  France  (70)  and  Italy  (66).  All  EU-27   countries  are 
represented though five of them show limited participations with less than 10 respondents each: 
Lithuania, Cyprus, Denmark, Luxembourg, Malta. A few participants from non-EU country also 
participated to the consultation (3 responses from the UK and 7 responses from other countries).  

The answers to the optional set of questions on the Common Agricultural Policy and territorial 
development  are summarised as follows (including the  most important  contribution(s) for each 
category of the identified stakeholders, in order to demonstrate their preferences): 

Limitations versus drivers of success 

According to the respondents, the first and foremost factor in the implementation of the current 
CAP instruments and measures that limits their contribution to balanced territorial development 
of  the  EU  rural  areas  is  ‘administrative  complexity  for  beneficiaries  to  apply  for  measures’ 
(selected  509  times).  This  is  followed  by  ‘disproportionate  administrative  burden  for 
administrations’  (352  replies),  ‘beneficiaries’  fear  of  excessive  controls’  (327  replies),  ‘too 
restrictive  eligibility  and  selection  criteria’  (316  replies),  and  ‘limited  availability  of  non-
agricultural  support’  (314  replies).    Looking  at  the  responses  by  stakeholder  types,  the  option 
‘Administrative complexity for beneficiaries to apply for measures’ is the most selected by Rural 
Development  Networks  (12.6%),  and  the  second  most  important  for  academic/research 
institutions (8.8% of total responses). Responses by academic/research institutions indicate ‘low 
ambition of the relevant measures’ (9.21% of responses) as the main limiting factor for the CAP 
instruments  and  measures.  Citizens  consider  the  ‘disproportionate  administrative  burden  for 
administrations’  as  the  second  most  important  factor  inhibiting  the  CAP’s  contribution  to 
balanced  territorial  development  (7.4%),  and  it  appears  as  the  third  most  important  factor  for 
public authorities (8.4%) and rural development networks (10.4%). The second most important 
limitation for public authorities (9.9%) and businesses and associations (8.6%) is ‘too restrictive 
eligibility and selection criteria’, while ‘limited availability of non-agricultural support’ is ranked 

79 

 
 
as the second most limiting factor by rural development networks and NGOs responses (11.8% 
and 8.7%, respectively). 

Regarding  the  drivers  of  success,  ‘measures  targeted  and  tailored  to  local  context/needs’  are 
identified as main driver (444 replies). This is followed by ‘the level of support under Pillar II – 
EAFRD’  (395  replies),  ‘the  involvement  of  regions  in  programming  measures  under  Pillar  II 
(332 replies), ‘advice and knowledge transfer’ (328 replies), ‘innovative approaches in delivering 
environmental  and  social  public  goods’  (325  replies)  and  ‘clear  and  targeted  objectives  of  the 
CAP’ (299 replies). Looking at the responses by types of stakeholder, three groupings respond in 
similar ways: 1) citizens and NGOs; 2) businesses and associations, rural development networks, 
public authorities; 3) academic/research institutions. 

The  driver  ‘measures  targeted  and  tailored  to  local  context/needs’  is  selected  as  the  most 
important  by  citizens (14% of  citizens’ responses) and NGOs (15% of NGO responses), while 
‘the level of support under Pillar II – EAFRD’ is the second most important (10.9% for citizens 
and 12.6% for NGOs); 

Businesses and associations, public authorities, and rural development networks select ‘the level 
of  support  under  Pillar  II  –  EAFRD’  as  the  most  important  driver  (13.8%,  14.1%,  and  16.2%, 
respectively). The driver ‘measures targeted and tailored to local context/needs’ is selected as the 
as the second most important driver by public authorities (13.2%) and as the third most important 
driver by rural development networks (14.7%) and businesses and associations (10%). 

Academic/research  institutions  select  ‘Advice  (quality,  independence)  and  knowledge  transfer’ 
as the most important driver (15.7%), the ‘measures targeted and tailored to local context/needs’ 
as the second most important (15%). The driver ‘the level of support under Pillar II – EAFRD’ 
ranks third (13.2%). 

Effectiveness 

According to participants, LEADER129 and Community-Led Local Development (CLLD) has the 
highest  contribution  to  balanced  territorial  development  (79%  of  responses  ‘To  a  very  large 
extent’, ‘To a large extent’ and ‘To some extent’) (Figure 15). This is followed by the measure to 
support organic farming (M11) and basic services and village renewal in rural areas (M07) with 
respectively 73% and 72% of favourable opinions, and payments to areas facing natural or other 
specific  constraints  (69%),  payment  for  young  farmers  and  measures  for  farm  and  business 
development (68% each), and cooperation measures (M16) (67%). In this case, the results do not 
show significant distributional differences across stakeholder groups, sector, age and gender of 
the respondents, or between those living in urban and rural areas. 

129   Liaison  Entre  Actions  de  Développement  de  l'Économie  Rurale,  meaning  'Links  between  the  rural  economy 

and development actions'. 

80 

 
 
                                                           
Figure  15.  Extent  to  which  the  relevant  CAP  instruments  and  measures  contribute  to 
balanced territorial development of EU rural 

Source: Consultation on CAP and balanced territorial development  

LEADER  is  considered  as  the  most  effective  CAP  measure  when  it  comes  to  supporting  local 
communities (almost 80% of positive replies). There is a consensus across information sources 
that  LEADER  and  Community-led  Local  Development  (CLLD)  are  the  most  effective  CAP 
measures  when  it  comes  to  supporting  local  communities.  Positive  social  contributions  from 
LEADER have been reported on multiple levels. 

81 

 
 
 
Efficiency 

For  beneficiaries,  the  main  aspects  of  administrative  burden  are  related  to  (i)  complexity  in 
submitting  an  aid  application  (610  replies),  (ii)  the  time  required  to  receive  the  payment  after 
submitting  the  aid  application  (368  replies)  and  (iii)  the  time  and  effort  required  for 
administrative controls (356 replies). For administration, the main aspects are (i) the complexity 
of management in  the  administrative system (368 replies) and the lack of human and financial 
resources (290 replies) and (iii) the frequency of policy changes (229 replies). Notably, there are 
no remarkable differences in terms of respondents’ groups, with comparable patterns of replies 
from  both  stakeholders  more  involved  as  beneficiaries,  and  those  representing  competent 
authorities. 

Coherence 

The majority of respondents consider that the various CAP instruments and measures deliver a 
coherent and complementary contribution to balanced territorial development of EU rural areas 
‘to some extent’ (49% of the 789 respondents, 385), with 26% replying ‘to a very large’ and ‘to a 
large extent’, compared to 17% ‘to very small extent’ and ‘not at all’. The results do not show 
significantly distributional differences between stakeholder groups, sectors, age or gender of the 
respondents, or between those living in urban and rural areas. 

Replies  on  the  coherence  of  the  CAP  measures  and  instruments  with  other  EU  funds  and/or 
national, regional or local policies provide a mixed picture, with is 42% of favourable opinion on 
coherence  with  the  European  Regional  Development  Fund  (ERDF).  Respondents  belonging  to 
the stakeholder group ‘rural development networks’ were those reporting the higher percentage 
of positive responses (37 out 0f the 61 respondents in the group consider ERDF as either fully or 
mostly coherent with CAP instruments) 

Results also show 28% of favourable opinion for the coherence with European Cohesion Fund, 
29% for the European Social Fund, 23% for European Maritime and Fisheries Fund, 29% with 
national  policy  and  34%  for  regional/local  policy,  with  no  major  difference  from  stakeholder 
groups..  

Relevance 

A  majority  of  respondents  (64%)  consider  that  CAP  instruments  and  measures  are  relevant  to 
actual  needs.  The  relevance  of  CAP  instruments  to  economic  needs  appears  high,  and  CAP 
instruments  and  measures  have  been  perceived  as  well  fitted  to  address  needs  related  to  the 
social, economic and governance dimensions. Respondents found as well the LEADER measure, 
under Pillar II, to be particularly relevant, and position papers of the consultation reflect so. 

82 

 
 
EU added value 

A majority of respondents consider that CAP instruments and measures create EU added value 
(37% to some extent, 43% to a very large or to a large extent). Furthermore, respondents found 
that  CAP  funding  provides  EU  added  value  especially  as  it  provides  an  EU-wide  framework 
while also enabling a more territorially specific approach. 

Position papers input  

LEADER  is  the  instrument  most  commonly  cited  in  the  position  papers  as  effective  in 
contributing  to  balanced  territorial  development.  The  position  papers  acknowledge  that 
community-led local development has been effective in strengthening and increasing the number 
of partnerships between public, private and civil society organisations, through the activities of 
Local  Action  Groups  (LAGs).  A  key  value  of  community-led  local  development  support, 
according to respondents, is the way in which is can ensure a greater degree of inclusiveness and 
cooperation across different stakeholders, at national, regional and local level. 

Eurobarometer 

The Special Eurobarometer 504130 carried out between 3 August and 15 September 2020 in the 
27  Member  States  of  the  Union  (i.e.  without  UK  following  its  withdrawal  from  the  EU  on 
1 February  2020),  involved  the  participation  of  27 237  EU  citizens.  More  than  nine  in  ten 
respondents (95%) think that agriculture and rural areas are important for the future in the 
EU, including 56% who considered they are very important. In every Member State, over 80% 
of EU respondents said that agriculture and rural areas are important for the future, and over half 
say they are very important. 

The  majority  of  Europeans  consider  that  the  infrastructure  and  services  are  good  in  rural 
areas  in  their  country,  with  proportions  varying  between  82%  for  the  environment  and 
landscape and 51% for access to high-speed internet connections; however, only a minority hold 
this view about job opportunities (37%). Compared with 10 years ago, a clear majority have said 
that  things  have  improved  in  terms  of  access  (55%)  and  transport  infrastructure  connecting  to 
cities (37%) conversely, a majority think that things have got worse for job opportunities (42%) 
and health services (36%). Opinion on state of services and infrastructure in rural areas now and 
ten years ago vary widely across the EU: for instance, 83% of respondents in Luxembourg say 
health services are good compared with 18% in Bulgaria. 

There  is  a  consensus  on  the  EU  value  added  of  the  CAP,  with  a  majority  of  respondents 
thinking that the CAP benefits all European citizens and not just farmers (76% in Eurobarometer 
504, up by 15 percentage points from Eurobarometer 473).   

Consultations done by other EU Institutions 

To feed the debate on the long-term Vision for the EU’s rural areas, the European Committee 
of the Region’s (CoR) also launched from 5 May to 15 June 2020 a consultation conducted by 

130   https://data.europa.eu/euodp/en/data/dataset/S2229_93_2_504_ENG. 

83 

 
 
                                                           
the network of regional hubs (RegHub)131. Over 200 stakeholders contributed via 24 members of 
the Network of Regional Hubs. The questionnaire aimed to put together the views of the hubs on 
the  impact  of  the  EU's  Common  Agricultural  Policy  (CAP)  implementation  on  territorial 
development of rural areas and changes that would be needed in order to overcome difficulties 
encountered by rural areas. The RegHub consultation covered the six main challenges faced by 
rural  areas  over  the  2014-2020  programming  period:  low  growth;  underemployment;  poor 
generational renewal; sub-optimal infrastructure  and services;  territorial  imbalances;  and social 
inclusion and poverty.  

The current measures in their entirety appear unsuited to address the socio-economic challenges 
rural areas are facing and to achieve the Treaty objective of territorial cohesion. This might be 
largely  due  to  the  fact  that  the  CAP  has  initially  not  been  designed  to  meet  socio-economic 
challenges  of  rural  areas,  but  rather  to  support  the  agricultural  sector  in  the  wake  of  mayor 
economic structural changes.  

As  a  consequence,  only  a  few  measures  entailed  by  the  examined  Regulations  are  explicitly 
dedicated  to  enhance  territorial  cohesion  and  many  of  the  measures  that  could  potentially 
mitigate  the  abovementioned  challenges,  are  characterised  by  a  lack  of  targeting,  insufficient 
support  volumes,  exhaustive  administrative  burden  and  thus  a  low  level  of  cost-effectiveness. 
Moreover,  important  'soft'  factors  such  as  a  culture  that  supports  cooperation  and  adequate 
training capacities for farmers and other rural actors, are often missing and prevent an effective 
implementation  of  the  said  measures.  However,  for  many  of  the  measures  one  or  more  hubs 
indicated a potential for improvement and positive impact.   

The  contribution  of  the  Direct  Payment  Regulation  (Regulation  EU  1307/2013)  to  overcome 
rural  areas'  challenges  was  considered  quite  weak  by  the  hubs.  In  those  cases,  where  hubs 
recognised the role of direct payments for supporting farmers' income, they highlighted that it is 
no longer effective enough to overcome market fluctuations and repeated agricultural crises, the 
latter of which they partially link to the EU open trade policy. While first-pillar instruments seem 
to  be  relatively  well-established,  they  often  lack  the  with  regard  to  different  farm  business 
models. Moreover, socio-economic as well as environmental objectives are often not accounted 
for sufficiently.  

  Payments  to  young  farmers  were  assessed  as  having  the  highest  effects  on  poor 

generational renewal. The effect of other measures is judged as being very limited;  

  None of the direct payments are considered by the hubs to have a significant effect on the 

challenges posed by sub-optimal infrastructures and services;   

  Two  measures  of  the  first  pillar  are  considered  to  have  a  significant  impact  on  low 

growth: the Payments to young farmers and the Basic income support. 

Two thirds of the hubs replied that the CMO Regulation largely addressed low growth and was 
the most effective to stabilise agricultural markets, but that it was less effective in addressing the 

131     https://cor.europa.eu/en/engage/Documents/RegHub/report-consultation-05-cap-territorial-development.pdf. 

84 

 
 
                                                           
 
than  rural  development  measures.  Regarding 

other  challenges  like  under  employment,  poor  generation  renewal,  among  others.  One  main 
factor of success appears to be the clear common framework, which is deemed more flexible and 
responsive 
implementation,  several  hubs 
highlighted  the  important  role  of  producer  organisations  (POs),  which  they  advise  to  further 
strengthen. It is interesting to note that generally, the hubs considered the CMO Regulation to be 
more efficient than the Rural Development Policy to overcome some challenges faced by rural 
areas,  such  as  the  economic  development  of  rural  areas  because  of  their  role  in  mitigating  the 
impact of price fluctuations on farmers´ income.  

According  to  the  hubs,  the  level  of  effectiveness  of  individual  measures  and  instruments  in 
addressing  a  balanced  territorial  development  varied  considerably:  a  high  level  for  LEADER 
programmes,  knowledge  transfer  and  information  actions  and  farm  and  business  development; 
and  at  a  particularly  low  level  for  Natura  2000  and  Water  Framework  Directive  payments, 
technical  assistance  and  risk  management.  The  majority  of  hubs  also  considered  that  a 
harmonisation  of  structural  funds'  operating  rules  would  facilitate  rural  development 
programming and management. The main proposals of the hubs for improving the CAP's impact 
on territorial development of rural areas were the following:  

  Generational renewal is key to the future of European agriculture. Therefore, it should 

be considered as an overarching objective in the next programming period.  

  Policymakers  should  strive  to  simplify  both  procedures  and  criteria.  For  almost  all 
measures dealt with in the RegHub consultation, hubs report administrative burden as a 
primary constraint to their implementation.  

  The  harmonisation  of  the  Structural  Funds'  operating  rules  via  the  Common  Strategic 
Framework  would  facilitate  rural  development  programming  and  management  by 
simplifying administrative procedure and control.  

  The  majority  of  the  hubs  underline  that  there  is  a  higher  potential  for  a  regional 
programming of Rural Development in targeting the territorial specificities of a region 
and  farmers’  needs,  but  also  in  terms  of  involvement  of  local  stakeholders  and  of 
communication/dissemination activities.  

  The CAP should be evaluated at the sub-regional level. An assessment at lower levels of 
government  is  deemed  necessary  mainly  because  of  the  existence  of  local  specificities 
and/or wide differences across individual sub-regions.  

  According to a majority of hubs, coherence and complementarity of CAP instruments 

and measures with other EU policies should be improved.  

  Hubs  deem  EU  trade  policy  to  be  not  coherent  enough  with  CAP  objectives  as 
international trade agreements are causing potential threats both to the competitiveness of 
local products and to food quality and safety.  

The  European  economic  and  social  committee  (EESC)  also  launched  a  consultation  on  the 
CAP's impact on territorial development of rural areas (Information report)132, that was open for 

132   https://www.eesc.europa.eu/it/our-work/opinions-information-reports/information-reports/evaluation-caps-

impact-territorial-development-rural-areas-information-report. 

85 

 
 
                                                           
contributions from January 2021 to the end of February on the same year. It was composed of a 
questionnaire, open to civil society organisations to get their opinion on how they assessed the 
CAP's  impact  on  territorial  development  of  rural  areas,  along  with  five  virtual  missions  with 
semi-structured interviews to local civil organisations and public authorities. 

The report on the consultation concluded that generally, positive effects of the CAP funding on 
rural  development  were  recognised  in  all  countries.  One  of  the  most  important  CAP 
contributions,  according to  the EESC report, is  the increase in  the range  of quality  agricultural 
products  affordable  for  all  in  the  EU,  in  line  with  the  CAP  primary  objective.  The  rest  of  the 
conclusions  from  the  questionnaire  go  very  much  in  line  with  the  Commission’s  public 
consultation  analysis  done  above.  Nevertheless,  some  conclusions  from  this  report  should  be 
highlighted in the scope of this evaluation: 

  The  CAP  measures  did  not  impact  employment  in  rural  areas  as  positively  as  most 
countries  needed,  even  though  it  was  admitted  by  all  that,  also  with  the  spread  of  new 
farming professions and technologies due to digitalisation, they had helped people stay in 
the  countryside  and  fight  depopulation.  The  social  inclusion  of  vulnerable  groups  into 
agricultural activities was also not sufficiently ensured, even if farming has always been 
able  to  offer  employment  to  vulnerable  people  who  experience  difficulties  in  finding 
work. 

  The  main  limiting  factors  or  obstacles  for  balanced  territorial  development,  including 
factors  beyond  those  linked  to  the  CAP,  are  considered  in  relation  to  deficits  in 
infrastructures  (such  as  transport  facilities  or  digital  services  such  as  broadband). 
Insufficient  investments  in  human  capital  development  (such  as  vocational  training  or 
investment  in  applied  technological  modernisation)  was  also  mentioned.  Insufficient 
access to finance is also considered a major barrier. 

  When assessing the effectiveness of specific CAP measures and instruments, support for 
LEADER and Community-led local development (CLLD) (EAFRD, M19) was found to 
be potentially the most effective of all measures, diversifying the economy of rural areas, 
creating  new  governance  mechanisms,  preserving  historical  and  cultural  heritage  and 
supporting entrepreneurship. However, it needs to be resourced to a much greater extent. 

6. 

ANNEX 3: METHODOLOGY 

The  methodological  approach  to  the  evaluation  combined  quantitative  and  qualitative  analysis, 
and included literature review, statistical analysis and interviews-case studies, primarily as part 
of  the  external  and  independent  evaluation  support  study  carried  out  by  ADE  SA,  and 
complemented by  additional analysis by DG Agriculture and Rural  Development  and the Joint 
Research  Centre.  The  consultation  methods  and  activities  carried  out  for  this  evaluation  are 
described in Annex 2. 

Methodological process  

Figure 16 illustrates the sequence of methods, their relations and contribution to answering the 
evaluation questions, in the framework of the support study.  

86 

 
 
Figure 16. Methods’ interlinkages 

Note: The support study covered 16 evaluation questions (ESQ)  
Source: Evaluation support study 

Quantitative analysis 

The  quantitative  analysis  follows  a  multi-step  approach,  in  which  methodological  tools  are 
applied in sequence to investigate impacts associated with CAP funding. With the help of cluster 
analyses, the correlation133 and regression analyses134 incorporate a territorial dimension, as to 
account  for  the  heterogeneous  state  of  economic  development  across  Europe.  Both  regression 
and correlation analyses were conducted to investigate the CAP influence on changes in socio-

133   Two cluster correlation analyses were carried out, one was conducted per cluster to investigate developments 
associated between CAP funding and socio-economic indicators, another was conducted across all (rural and 
intermediate) NUTS2/3 EU-28 regions. 

134   CAP  influence  on  changes  in  socio-economic  indicators  (see  Table  3  ‘overview  of  data  sources’  in  annex  I) 
between  2014  and  2017.  Pillar  I  direct  payments  and  CMO  are  aggregated  and  compared  to  aggregated 
ERDF/ESF/CF  funding.  This  is  necessary  because  there  is  no  direct  focus  area  correspondence  between 
individual Pillar I instruments and individual thematic objectives as there is between Pillar II measures and TO 
via the common focus area. 

87 

Clustering of NUTS-3 regionsCluster correlation analysis on CAP funding and context indicatorsReduction of data-set dimensionalityCreation of typology based on characteristics relevant to study topicRegression analyses on funding effectiveness per clusterPCA of context indicators and funding data per clusterReduction of data-set dimensionality per regional cluster. ESQ 2 –5: effectivenessInput-output analysisCase study analysiscomparative correlations per cluster based on indicators of relevanceEU interviewsLiterature analysisESQ 6 -10: efficiencyEvidenceanalysisESQ 11 -12: coherenceESQ 13 -15: relevanceESQ 16: EU added valueAggregate correlation analysis on CAP funding and context indicatorsPCA with socio-economic context indicators for rural and intermediate regionsESQ 1: Causal analysisTheory-based impact assessmentTerritorial distribution analysisSynthesisIntervention logics 
 
 
                                                           
economic indicators between 2014 and 2018. In addition, sectoral impacts of CAP funding (i.e. 
whether CAP funding exerts spill-overs into downstream sectors) are investigated via an input-
output  analysis.  A  territorial  distribution  analysis  was  also  performed  was  performed  by 
identifying the paid out Pillar I funding (2015-2018) and planned Pillar II funding (2014-2020) 
on a NUTS3 level, to enable a targeted evaluation of impacts and discussion of causal links.  

In  the  cluster  analysis,  by  differentiating  between  types  of  rural  regions,  analytical  results 
stemming from the qualitative and quantitative methods (case studies, regression, and correlation 
analyses)  allow  for  more  differentiated  insights  into  the  effectiveness  of  implementation  in 
relation  to  the  territorial  specificities  of  the  regions.  The  cluster  analysis  relies  on  two  core 
principles: intra-cluster homogeneity (e.g. territories within the same cluster show similarities 
regarding  their  territorial,  socio-economic,  demographic  and/or  other  thematic  profile)  and 
extra-cluster heterogeneity (e.g. territories from two distinct clusters show different territorial, 
socio-economic, demographic and/or other thematic profiles).135 The results of the clusters can be 
seen clearly in Map 2 

135   The  cluster analysis based on an extension of the  prevalent k-means clustering  method,  which can deal  with 

missing data, has been conducted in R.  

88 

 
 
                                                           
Map 2. Types of rural and intermediate regions (clusters) 

Source: Evaluation support study  

The first cluster is characterised by diversified rural and intermediate regions featuring ageing 
societies  within  structurally  well-developed  regions.  Educational  attainment  is  high,  as  is  the 
trust  in  the local  government. Social cohesion  is  also  high, with  inhabitants  placing significant 
trust in their social networks. These regions are more often associated with high labour costs and 
strong inter-sectoral competition. 

The second cluster contains the peripheral rural and intermediate regions which feature very 
low  degrees  of  accessibility.  Rural  peripheries  typically  have  inhabitants  who  migrate  away, 
while  the  remaining  population  is  characterised  by  low  educational  attainment.  Trust  in  local 
governments  and  social  networks  is  generally  low.  These  regions  feature  smaller  population 
density and a lower degree of farm diversification and technological intensification, with a high 
share  of  NATURA  2 000  surface  area.  The  agricultural  sector  is  important  in  these  regions. 
These regions are lagging in terms of productivity and standards of living. Population outflows 
to wealthier regions negatively impact human capital endowments. 

The  third  cluster  consists  of  dynamic  rural  and  intermediate  regions  which  are  generally 
situated in closer proximity to urbanised regions. These regions are clustered around urban areas 
with good accessibility, younger, well-educated populations, and high in-migration. Trust in the 

89 

 
 
 
quality of governance and social networks is very high in these regions. Dynamic rural regions 
have stronger development patterns, however, farming in these regions faces pressure from the 
neighbouring urban centres by means of land value for uses for purposes other than agriculture.  

The fourth  cluster is  traditional  rural  and intermediate regions. These  regions  are  generally 
younger  (lower  dependency  ratio)  and  feature  high  employment  growth.  The  regions  retain  a 
strong rural character, with large NATURA 2000 areas, a large share of the population in rural 
areas.  Trust  in  social  networks  and  local  governance  is  low,  as  is  regional  accessibility.  These 
regions retain a strong and viable agricultural sector.  

With regards  to  the  input-output analysis, a number of datasets were used to  arrive to  strong 
and significant conclusions. These databases were: 

  Starting point is the input-output table for 29 countries (EU-28 plus United States) and 64 
industries  (NACE  Rev.  2),  as  provided  in  the  FIGARO136  project.  The  present  dataset 
refers to the year 2010.137 

  Average labour compensation costs per sector is based on data provided by Organisation 
for  Economic  Co-operation  and  Development  (OECD)138  and  Farm  Accountancy  Data 
Network (FADN)139. The OECD also provides a dataset on unit labour compensation per 
employee  in  US  dollars,  which  have  been  converted  to  EUR  for  the  purpose  of  this 
analysis.  FADN  agricultural  data  was  used  when  OECD  data  was  not  available,  to 
establish unit labour costs in the agricultural sector per country. Additionally, FADN data 
was used in the case study analysis to assess specificities of the agricultural sector in the 
case study regions. 

  Funding data sources (see below). 

The  efficiency  ratios  were  constructed  by  dividing  the  value  of  the  output  indicator  by  the 
volume of associated funding. The project team calculated these ratios at programme level, due 
to  the  funding  and  output  indicator  data  availability  being  restricted  to  this  level.  The  CMEF 
output indicators chosen140 were: 

  O9  –  Number  of  holdings  participating  in  supported  schemes  (relevant  for  M09  – 
producer groups and organisations, M16 – cooperation and M17 – risk management).  

  O11- Number of training days given (relevant for M01 – knowledge transfer). 
  O15  –  Population  benefitting  from  improved  services/infrastructure  (IT  or  other) 

(relevant for M07 – basic services). 

136   https://ec.europa.eu/eurostat/web/experimental-statistics/figaro see Remond-Tiedrez, I. and Rueda-Cantuche J. 

(ED.) (2019). 

137   An update including data from 2011 to 2015 is announced will and be available in the course of the year 2020 

and can thus not be used for the project. 

138   OECD, Unit Labour Costs – Annual Indicators: Labour Compensation per Employee/Hour ($US PPP adjusted) 

1995-2012. https://stats.oecd.org/index.aspx?queryname=345&querytype=view.  

139   FADN, Farm Net Value Added/AWU – mean. 
140   These  output  indicators  were  deemed  to  be  the  closest  illustration  of  the  general  spill-overs  into  the  wider 

socio-economic environment of rural areas. 

90 

 
 
                                                           
  O16  –  Number  of  EIP  grouped  supported,  number  of  EIP  operations  supported  and 

number and type of partners in EIP groups (relevant for M16 – cooperation).  

  O18 – Population covered by LAG (relevant for M19 – LEADER). 
  O20 – Number of LEADER projects supported (relevant for M19 – LEADER). 

The  underlying  data  for  Pillar  II  is  funding  and  output  data  up  until  2018,  the  most  recent 
available.  Many  output  indicators  only  provide  very  limited  information  due  to  their 
concentration on farm-level outputs.  

Funding  data  sources  were  used  across  all  steps  of  the  quantitative  analysis  (input-output, 
correlation, regression and JRC analyses) and case study selection. This includes: 

  Pillar I realised expenditure by scheme on NUTS3 level based on actual expenditure 

2015-2018. 

  Pillar II committed expenditure by measure and by focus area on rural development 
programme  level  based  on  amounts  2014-2018  from  the  annual  implementation 
reports (AIR). 

  The  territorial  distribution  analysis  makes  use  of  planned  expenditure  2014-2020  to 

account for delays in the implementation of measures. 

  For the JRC analysis, data on realised expenditure for Pillar I and II at Nuts3 level, 
covering  the  previous  programming  period  (financial  years  2011-2015)  and  the 
current programming period up to 2018 financial year. 

In order to ensure that the evaluation incorporates all relevant findings and analysis of available 
studies and the most up-to-date data on the implementation of CAP measures and instruments, as 
well as market developments, the evaluation draws on a broad range of additional sources, 
which have been referenced throughout the document.  

In regards of the regression performed, Annex 4 shows the statistically significant findings from 
one  cluster  correlation  analyses  along  the  funding  volumes  of  the  selected  instruments  and 
measures  and  series  of  context  indicators.  This  was  performed  to  investigate  developments 
associated  between  CAP  funding  and  socio-economic  indicators.  Correlation  analyses  can 
provide  insights  into  the  relationship  between  two  variables.  Stronger  correlations  indicate 
stronger  relationships.  A  negative  coefficient  denotes  a  negative  relationship  between  the  two 
variables  (i.e.  that  a  positive  change  in  one  variable  is  related  with  a  negative  change  in  the 
other) and vice versa (Table 4).  

Qualitative analysis 

All  the  qualitative  data  collection  activities  complement  and  contextualise  the  findings  of  the 
quantitative  analysis  steps.  Furthermore,  these  insights  allowed  to  thoroughly  triangulate  the 
findings  to  address  the  evaluation  criteria:  effectiveness,  efficiency,  relevance,  coherence,  and 
EU added value. Nevertheless, as it was explained in Chapter 4, the lack of appropriate data was 
an issue, especially regarding the indicators on social inclusion, which were difficult to collect at 

91 

 
 
NUTS3  level.  To  mitigate  this,  a  larger  scope  of  questions  regarding  social  inclusion  were 
included in the case study.  

Qualitative  evidence  was  gathered  primarily  via  regional  case  studies,  interviews  with 
stakeholders and EU officials and a literature review. The project team selected thirteen cases 
study regions at NUTS2 level, with a responsible author for each of them.  For the selection of 
the case study regions, funding concentration of the 2014-2020 period was analysed among the 
CAP measures and instruments most relevant to balanced territorial development, as derived in 
the  causal  analysis.  The  NUTS2  regions  obtaining  an  above  average  amount  of  funding  were 
shortlisted.  From  the  shortlist,  the  selection  of  case  study  regions  was  based  on  regional  and 
implementation  specificities,  and  geographical  and  territorial  characteristics.  Then,  each  case 
study author was asked to select, within the NUTS2 region, two study areas at NUTS3 level, and 
if  possible,  at  LAU  level.  The  selection  of  appropriate  NUTS3  or  LAU  level  study  areas  is 
undertaken by reference to a series of criteria141. It also distinguishes between rural, intermediate, 
and urban areas142. 

The case study regions selected were then assessed in terms of the cluster typologies developed 
to identify whether a balanced and logical distribution had been achieved (Table 5).  

Table 5. Cluster typologies of case study regions 

CS Region – NUTS2 

NUTS3 – study areas 

NUTS3 code  

Cluster 

Germany 
(DEE0) 

–  Sachsen  Anhalt 

Börde 

Stendal  

Greece –Peloponnese (EL65) 

Argolida-Arcadia  

Lakonia-Messinia 

Bulgaria  –  Southern  Central 
(BG42) 

Plovdiv 

Estonia – (EE00) 

Pazardzik  

southern Estonia  

Central Estonia  

Spain  –  Castilla-La  Mancha 
(ES42) 

Ciudad Real  

Cuenca  

DEE07 

DEE0D 

EL651 

EL653 

BG421 

BG423 

EE008 

EE006 

ES422 

ES423 

1 

1 

2 

2 

4 

2 

2 

2 

4 

2 

141   e.g.  presence  and  proximity  of  urban  centres;  the  degree  to  which  funding  is  concentrated  on  one  area,  if 

applicable; accessibility of the area and geographical influences on the agricultural sector 

142   This study is thus carried out at a NUTS3 geographical resolution to identify and depict the territorial effects of 
the  CAP  in  the  socio-economic  environment.  This  means  that  NUTS3  will  be  the  default  level  of  territorial 
analysis with potential deviations (NUTS2, NUTS0) if data availability calls for it. The analysis will then use 
the NUTS3 classification ‘rural’, ’intermediate’ and ‘urban’ to differentiate the effects of the CAP territorially. 
LAU units will be considered as part of the case studies. 

92 

 
 
  
  
  
  
  
                                                           
CS Region – NUTS2 

NUTS3 – study areas 

NUTS3 code  

Cluster 

Italy – Puglia (ITF4)143  

Lecce  

Brindisi 

France – Auvergne (FR72) 

Cantal 

Haute-Loire  

ITF45 

ITF44 

FR722 

FR723 

Czechia  –  Jihozápad  (Southwest, 
CZ03) 

Plzeňský kraj (Pilsen region) 

CZ031 

Jihočeský  kraj  (South  Bohemian 
region)  

CZ032 

Poland – Świętokrzyskie (PL33) 

Sandomiersko-jędrzejowski  

PL332 

Netherlands – Zeeland (NL34) 

Zeelandic-Flanders (NL341)  

NL341 

Kielecki  

PL331 

Central and Northern Zeeland 

NL342 

Austria – Tirol (AT33) 

East Tyrol 

Tyrolean Unterland 

Italy – Emilia-Romagna (ITH5) 

Parma 

Reggio nell’Emilia 

Modena 

Ireland 
(IE02)144 

– 

Southern  Region 

South-East Region 

AT333 

AT335 

ITH52 

ITH53 

ITH54 

IE024 

Source: Consortium, 2020 

South-West Region 

IE025 

2 

2 

1 

1 

4 

4 

2 

4 

1 

1 

1 

3 

4 

4 

3 

1 

1 

Interestingly,  the  frequencies  of  the  regional  typologies  represented,  are  not  only  relatively 
balanced  in  distribution,  but  also  quite  representative  of  areas  which  are  expected  to  be  of 
particular interest. Namely, while the diversified rural and intermediate regions (cluster 1), peripheral 
rural  and  intermediate  regions  (cluster  2),  and  traditional  rural  and  intermediate  region  (cluster  4) 
clusters are represented in almost complete balance, dynamic rural and intermediate regions (cluster 
3) are less frequently selected.  

143   Although the regions Lecce and Brindisi, according to data, would be classified as urban at the NUTS3 level 
due  to  the  presence  of  the  urban  centre,  the  case  study  author  reporting  on  Italy,  looked  into  Lecce  and 
Brindisi,  at  a  more  granulated  level.  Taking  into  account  areas  surrounding  the  city  centres  which  are 
predominantly  agricultural,  and  carry  relevance  for  the  study  themes.  Lecce  and  Brindisi  were  therefore 
categorised as rural peripheries, and as part of the second cluster, due to a high rate of outmigration (2014-2017 
-1,3/-2,4 against positive rates in Emilia-Romagna), high unemployment (10-12% against 2-2.5% province of 
Emilia-Romagna), and multimodality (between 56 and 80 against 95-103 in provinces of Emilia-Romagna). 
144   From the NUTS2013 to NUTS2016 version there have been changes implemented in Ireland. The borders for 
region  IE024  have  been  slightly  shifted  and  IE025  has  been  recoded.  The  NUTS2  region  IE02  has  been 
recoded to IE05 and Dublin (IE021) and Mid-East (IE022) have been assigned to another NUTS2 region. 

93 

 
 
  
  
  
  
  
  
  
  
  
                                                           
Literature review 

The impacts  of the CAP on socioeconomic development have been studied at  the EU level,  as 
well  as  regional  and  country  levels.  Several  studies  measured  the  effect  of  the  CAP  on  rural 
employment145,  the  impact  of  the  CAP  on  poverty  reduction,  income,  and  within-region 
inequality146, and the impact of the CAP on gender mainstreaming147. However, only a limited 
number  of  recent  studies  provide  comparative  or  cross-national/regional  analysis  of 
socioeconomic  disparities  and  convergence  trends  in  rural  areas  of  the  EU,  or  cover  the 
particular impact of the CAP on balanced socio-economic development.  

Research  has  highlighted  the  potential  positive  role  of  CAP  measures  and  instruments  – 
particularly  those  in  Pillar  II  –  to  address  social  and  economic  needs  in  rural  areas  including 
tackling social exclusion, and promoting social capital and enhanced quality of life (EC, 2008; 
ENRD factsheets; Copus and De Lima, 2015; EDORA, PEGASUS and SEFARI H2020 projects, 
TiPSE ESPON project).  

JRC counterfactual analysis 

This section describes the methodology applied to estimate the causal impacts of the CAP made 
by JRC. It is based on a three dimensional characterisation of the CAP implementation involving 
time, the spatial distribution and the policy mix. 

The time dimension 

During  the  period  of  analysis  (financial  years  2011-2018)  the  CAP  has  gone  through  several 
reforms,  changing  the  funds  utilisation  under  income  support,  market  measures  and  rural 
development. As a result, the composition of both Pillar I and Pillar II payments in the regions 
has  changed.  This  suggests  a  Reform  Based  Approach  to  define  two  sub-periods  of  the  CAP 
implementation in the interval 2011-2018: 

1. The period between 2011-2015: The CAP payments in the period post-Health Check reform 
provide  evidence  on  the  Decoupling  of  direct  payments  and  on  the  increase  of  Pillar  2 
expenditure. 

2. The period 2016-2018: This is mainly characterised by the Greening reform promoted by the 
2013  CAP  reform.  This  period  is  associated  with  many  features  of  this  reform,  including, 
external  convergence.  Paradoxically,  while  the  CAP  historically  has  been  promoting  the 
decoupling of direct payments, in this period, the weight of Coupled payments increased in all 
Member States. 

The Spatial Dimension 

As was already mentioned in METHODOLOGY there is not a policy-wide precise definition of 
‘rurality’ and no consensus among researchers about how to measure it. The existing measures 

145   See for example; Křístková and Ratinger, 2012; Kaditi, 2013; Olper et al., 2014; Dupraz and Latruffe, 2015; 

World Bank, 2017; Angioloni et al., 2019; Garrone et al., 2019; Schuh et al. 2019; Vigani et al., 2019. 

146   See, for example, Severini and Tantari, 2013; World Bank, 2017. 
147   Shortall, 2015; Franić and Kovačićek, 2019. 

94 

 
 
                                                           
developed  by  the  European  Commission  (Eurostat,  2010148,  2017149)  and  the  OECD  (1994150; 
1996151; 2006152) are based on distinctions between predominantly urban (PU), intermediate (IR) 
and predominantly rural (PR) regions using only a single indicator, i.e., demographic density.  

In this work, the spatial dimension of the CAP was characterised using a cluster analysis based 
on a Principal Components Analysis. This allowed the JRC to develop measures of rurality that 
capture the relevant dimensions of NUTS 3 regions. Two complementary characterizations were 
provided: 

a. A multidimensional approach, leading to eight clusters: Table 6 and Table 7 show the clusters 
in 2010 and 2015, respectively, using a ‘heat colour’ representation of the relative importance of 
each  variable  across  clusters.  The  degree  and  type  of  rurality  of  the  NUTS  3  regions  are 
classified  according  to  the  chosen  indicators  for  the  following  six  regional  dimensions: 
agricultural sector, economy, demographics, innovation, land use and remoteness.  

Table 6. Multidimensional rurality clusters: 2010 

Source: JRC analysis 

148  Eurostat (2010). A revised urban-rural typology. Eurostat regional yearbook 

2010. Luxembourg: Publications Office of the European Union. 

149  Regulation  (EU)  2017/2391  of  the  European  Parliament  and  of  the  Council  of  12  December  2017 amending 

Regulation (EC) No 1059/2003 as regards the territorial typologies (Tercet), OJ L350, 29.12.2017. 

150 OECD (1994). Creating Rural Indicators for Shaping Territorial Policy. Paris: OECD. 

151 OECD (1996). Territorial Indicators of Employment. Focusing on Rural Development. Paris: OECD. 

152 OECD (2006), The New Rural Paradigm. Policies and Governance. Paris: OECD. 

95 

 
 
 
                                                           
Table 7. Multidimensional rurality clusters: 2015 

Source: JRC analysis 

b. An Agri-sector based approach to rurality, leading to six clusters: Table 8 and Table 9 as how 
the clusters in 2010 and 2015. The degree of Agri-sector specialization was classified according 
to: labour productivitydu (Gross Valued Added in agriculture based), employment in agriculture 
by agricultural area, and share of Gross Value Added in agriculture and land use.  

96 

 
 
 
Table 8. Agri-sector based rurality clusters: 2010 

Source: JRC analysis 

Table 9. Agri-sector based rurality clusters: 2015 

Source: JRC analysis 

The CAP policy mix 

The  CAP  is  implemented  through  several  Pillar  1  and  Pillar  2  instruments  and  measures.  This 
multiplicity  of  interventions  suggests  that  the  portfolio  of  policies,  which  MS,  regions  and 
farmers adopt, can be grouped and used to provide a spatial description (at NUTS3 level) of the 
CAP implementation choices across the periods. 

This  required  aggregation  of  both  Pillar  1  and  Pillar  2  measures  and  instruments  in  groups 
comparable across MS, grouping measures with the same characteristics, where quantities were 
specified relatively to GVA in the agricultural sector and total GVA across time. 

97 

 
 
 
 
 
 
The payments in Pillar 1 were grouped in the three categories: Market Measures; Coupled Direct 
Payments  and;  Decoupled  Direct  Payments.153  The  Pillar  2  aggregation  aims  at  reducing  the 
number of instruments by considering five categories: Competitiveness (Productive investment, 
New Businesses, Knowledge & Innovation and Risk management), Public Investment, Climate 
& Environmental and LEADER. 

The  groups  of  Pillar  1  and  Pillar  2  measures  were  used  as  input  variables  in  a  Principal 
Component  Analysis  to  define  CAP  mix  clusters.  In  each  of  the  two  periods  considered  the 
spatial distribution of the CAP was characterized by four clusters according to the table below 

Table 10. CAP policy mix clusters 

Source: JRC analysis 

The  table  colours’  show  the  relative  importance  of  each  variable  within  each  period  while  the 
index  measures their relative position across the three periods, therefore allowing to trace their 
intensity across time.154  

The characterisation provided by the CAP clusters generate a categorical treatment variable with  
which one can assess the effectiveness of the CAP as a mix of policies across periods. 

The causal impact 

The  aim  of  ex-post  policy  evaluation  is  to  estimate  the  impact  the  policy  has  caused  on  the 
outcomes of interest. In the context of the CAP as mix of policies, a relevant evaluation question 
is: ‘what is the effect that different combinations of CAP funds have caused on the economic and 
agricultural sector growth of European regions’? 

In order to achieve a causal result, one needs to acknowledge that the choice of the Policy Mix a 
given region  adopts depends of the economic, and agri-sector  characteristics of the  region; the 

153   This classification ignores the specificity of some of these payments, regarding the nature of the beneficiaries 
(small farmers, young farmers, LFA etc.) and the eligibility and  implementation rules (area related, historical 
payments, new rules as definition of entitlements etc.) and focus on the nature of the payments. 

154   The full analysis considered the period 2007-2018. 

98 

 
 
 
                                                           
same  regional  characteristics  may  also  determine  the  outcome.155  To  achieve  causal  result, 
appropriate Counterfactual Impact Evaluation methods need to be applied.  

The  methodology  proposed  -  the  Generalised  Propensity  Score  -  acknowledges  the  differences 
between  the  regions,  as  given  by  the  spatial  characterization  proposed  above  and  isolates  their 
effect from the effect of the policy. 

This  procedure  estimates  the  Expected  Potential  Outcome  (EPO)  for  each  CAP  mix  type, 
controlling  from  the  characteristics  that  determine  both  the  implementation  choice  and  the 
outcome.  It  is  the expected value of the outcome that regions  with  a  certain  policy  mix would 
have  had  if  they  were  similar  in  the  observed  characteristics  to  regions  with  different  policy 
mixes.  The  average  in  EPO  is  computed  with  respect  to  the  distribution  of  the  regions’ 
characteristics present in the observed population of regions. 

Comparison of the EPO across different CAP mix types, provides the Average Treatment Effect 
of a given CAP mix with respect to another, under the assumption that the relevant differences 
across the regions’ characteristics that affect both the CAP implementation and the outcome of 
interest are taken into account. 

155   If  all  regions  were  similar,  comparing  the  outcomes  of  different  CAP  mixes  would  provide  a  causal  result. 
Alternatively, if the CAP mix choices were independent of the region characteristics (as if randomly assigned) 
again causality would be achieved in the same fashion} It is the effect of this characteristics that needs to be 
isolated from the effect of the policy. 

99 

 
 
 
 
                                                           
7. 

ANNEX 4: TABLES AND GRAPHS SUPPORTING THE STUDY 

Regression analysis: model set-up and results 

Table 11. Regression model set-up 

Variable 

Dependent variable 

Overview 

Remarks 

Rate of change in each socio-economic 
indicator (from 2014 to 2017) 

Explanatory variable of interest 

Funding dummy (ESIF/CAP) for relevant 
funding group 

Additional explanatory variables: regional 
controls 

GVA per employee 

NATURA 2000 area share 

Degree of rurality 

Multimodal accessibility156 

Perception of social network  

Perception of governance  

EU 15 membership 

Per  cluster  and  funding  group  the  shortlisted 
indicator. This results in three indicators of interest 
per  cluster  and  funding  group,  or  60  indicators  in 
total. 

A dummy denoting whether the region obtains high 
(1)  or  low  funding  (0).  A  region  obtains  high 
funding  if  the  funding  for  a  measure/instrument 
exceeds the national median. This is the variable of 
interest.  A  significant  coefficient  denotes  a 
statistical  difference  in  the  dependent  variable 
between  regions  obtaining  high  funding  and  low 
funding in the cluster 

Regional  controls  use  2016  data  as  a  baseline  and 
are static. 

The  dummy  signifying  whether  the  region  belongs 
to EU 15 (‘1’ if belongs to that group) was included 
for clusters 2 and 4. 

The  perception  of  social  network  quality  was  not 
included for cluster 2 due to low observation count. 

Source: Evaluation support study  

Table 12. Legend regression outputs  

Symbol 

+++/--- 

++/-- 

+/- 

n/a 

Definition 

Significant at less than 1%, coefficient positive/negative 

Significant at less than 5%, coefficient positive/negative 

Significant at less than 10%, coefficient positive/negative 

No statistically significant coefficient 

Source: Evaluation support study  

Table 13. Impact of CAP measures by cluster  

Cluster 1: diversified rural 
and intermediate regions 

Cluster 2: peripheral rural 
and intermediate regions 

Cluster 3: dynamic rural 
and intermediate regions 

Cluster 4: traditional rural 
regions and intermediate 

Pillar II: Basic 
services (M07) and 
LEADER (M19) 

++ 
Change in tourism 
attractiveness 

- 
Change in active 
employment rate  

+++ 
Change in active 
employment rate  

-- 
Change in GVA (primary 
sector) 

Pillar II: Knowledge 
transfer and 
innovation (M01, 

++ 
Change in training rate 

+++ 
Change in GVA (primary 
sector) 

++ 
Change in education 
attainment (tertiary) 

156   Only available up until 2014. 

100 

++ 
Medical doctors 

+++ 
Change in GVA 

 
 
 
                                                           
Cluster 1: diversified rural 
and intermediate regions 

Cluster 2: peripheral rural 
and intermediate regions 

Cluster 3: dynamic rural 
and intermediate regions 

Cluster 4: traditional rural 
regions and intermediate 

M02 and M16) 

Pillar II: Enhancing 
farm viability and 
competitiveness 
(M04 and M06) 

++ 
Change in GVA 

+++ 
Change in employment 
rate (primary sector) 

n/a 

- 
Change in tourism 
attractiveness 

+++ 
Medical doctors  

++ 
Change in employment 
rate 

+ 
Change in GVA (primary 
sector) 

--- 
Change in active 
employment rate (f) 

+++ 

n/a 

n/a 

Change in education 
attainment (secondary) 

- 
Change in GVA (primary 
sector) 

+ 
Change in active 
employment rate (f) 

+++ 
Change in GVA (primary 
sector) 

Pillar I (direct 
payments and CMO) 

+++ 
Change in employment 
(primary sector) 

-- 
Change in tourism 
attractiveness 

--- 
Change in tourism 
attractiveness 

+++ 
Change in training rate 

Pillar II: Agri-
environment-climate 
commitments (M10, 
M11, M12) and ANC 
support (M13) 

Source: Evaluation support study  

Input-output analysis: results 

Figure  17  illustrates  an  overview  of  the  relative  importance  of  other  economic  sectors  to  the 
agricultural sector. Almost one third (27%) of the inputs to the agricultural sector stem from the 
agricultural sector itself, i.e. crop and animal production. Further, the manufacturing of food and 
fodder  plays  an  important  role  to  the  agricultural  sector  as  14%  of  all  inputs  come  from  this 
industry. Other important input sectors are wholesale trade (8%), the manufacturing of chemicals 
(fertilisers,  pesticides,  etc.,  8%),  the  manufacturing  of  refined  petroleum  products  (fuel,  5%), 
retail trade (4%) and financial services (3%). 

101 

 
 
 
 
 
Figure 17. Importance of the input of different industries to the agricultural sector (A01) – Share of 
input of the respective industry to the agricultural sector in %  

Source: Evaluation support study 

Quantitative analysis for efficiency: results 

Pillar I direct payments expenditure –insights into the efficiency  

Complementary to the outputs of the case study efficiency assessment, the efficiency of Pillar I 
was assessed by dividing the total  Pillar  I direct payments expenditure in 2016 by the primary 
sector gross value-added in 2016157. 

Quantitative analysis for efficiency: results 

Map  3  displays  the  Pillar  I  direct  payments  results.  A  lower  percentage  rate  corresponds  to  a 
higher efficiency of funding, as the funding is relatively smaller than the funded sector. Funding 
volume to agricultural sector ratios of below 20% are generally only observed in rural areas with 
significant agricultural industries. Discounting these regions, clusters of similar funding volumes 
in comparison to the size of the sector can be observed across Europe at around 30-40%.  

157   2016 represents the most complete data point for primary sector GVA data. The years 2017 and 2018 feature 

relatively larger geographical data gaps. 

102 

27%14%5%7%1%0%1%2%1%1%8%4%2%3%2%1%1%18%0%5%10%15%20%25%30%Crop and animal production, hunting and related service activitiesManufacture of food products; beverages and tobacco productsManufacture of coke and refined petroleum productsManufacture of chemicals and chemical productsManufacture of machinery and equipment n.e.c.Manufacture of motor vehicles, trailers and semi-trailersRepair and installation of machinery and equipmentElectricity, gas, steam and air conditioning supplyConstructionWholesale and retail trade and repair of motor vehicles and…Wholesale trade, except of motor vehicles and motorcyclesRetail trade, except of motor vehicles and motorcyclesLand transport and transport via pipelinesFinancial service activities, except insurance and pension fundingInsurance, reinsurance and pension funding, except compulsory…Rental and leasing activitiesEmployment activitiesOther industriesA01C10T12C19C20C28C29C33D35FG45G46G47H49K64K65N77N78.Source: Eurostat & JRC, FIGARO, IOT by industries 
 
 
 
                                                           
Map 3. Efficiency of direct payments 

Source: Evaluation support study 

Pillar II expenditure –insights into the efficiency  

For  this  assessment,  efficiency  ratios  were  constructed.  These  are  the  ratio  between  output 
indicator value and associated funding volume. The  assessment was undertaken on level of the 
individual rural development programmes for data between 2014 and 2018. Information on the 
data sources is presented in section. 

O9 – Number of holdings participating in support schemes  

Output  indicator  09,  number  of  holdings  participating  in  supported  schemes,  accounts  for 
participants and groups reached via three measures: M09 aims at setting up producer groups and 
organisations,  M16  focuses  on  co-operation,  and  M17  is  the  risk  management  measure.  This 
output  indicator  provides  the  number  of  holdings  that  the  programmes  under  these  measures 
have reached. The efficiency ratio is calculated by dividing the output indicator value with the 
associated funding in the related measures. Map 4 displays the relevant results. 

103 

 
 
 
Map  4.  Output  efficiency  of  output  indicator  O9  –  Number  of  holdings  participating  in  support 
schemes (2014-2018) 

per combined 
funding M9, 16 
and 17 

‘Not relevant regions’ are regions in which the Rural Development Programmes does not make use of this output indicator  
Source: Evaluation support study 

O11 – Number of training days given 

The number of training days given,  output indicator 11, is linked to the training days provided 
under  M01,  knowledge  transfer  and  information  actions.  M01  aims  to  provide  training  and 
information for improving the social and environmental, as well as overall performance, of rural 
businesses.  Those  businesses  working  in  agriculture,  food,  forestry,  and  rural  SMEs  are  the 
principle target. 

Since  the  primary  recipients  of  funding,  under  M01  –  knowledge  transfer,  are  the  service 
providers (as opposed to beneficiaries of the service), number of training days provided by the 
service providers is an integral indicator in understanding the relationship between funding and 
benefits obtained.  

The  efficiency  ratio  is  calculated  by  dividing  the  output  indicator  value  with  the  associated 
funding in M01 – knowledge transfer158. Map 5 displays the relevant results. 

158   Of  note,  other  indicators  included  in  M01  –  knowledge  transfer  include  O3  number  of  actions/operations 
supported  and  O12  number  of  participants  in  training.  These  indicators  are  not  described  above.  The  map 

104 

 
 
 
                                                           
 
Map 5. Output efficiency of output indicator O11 Number of training days given (2014-2018) 

per funding 
input M01 

‘Not relevant regions’ are regions in which the Rural Development Programmes does not make use of this output indicator  
Source: Evaluation support study 

Many  of  the  regions  applying  output  indicator  11  have  a  relatively  high  ratio  of  number  of 
training  days  provided.  Two  exceptions,  like  those  above,  include  Calabria  and  Lombardy.  In 
contrast  to  the  findings  above,  Lazio  likewise  has  a  relatively  small  ratio.  Others  include 
Denmark, some areas in Germany, the Netherlands, Czechia, Hungary, Cyprus, and Estonia. 

O15 – Population benefitting from improved services/infrastructure (IT or other) 

Knowledge  economies  are  recognised  as  an  undisputable  aim  for  improving  the  living  and 
economic conditions of rural areas, enabling rural residents to diversify their livelihoods, and to 
increase  efficiency  in  their  current  ventures.  Thus,  output  indicator  15,  population  benefiting 
from improved services/infrastructure (IT or others), has been a topic of great importance in the 
pursuit of balanced territorial development, socioeconomics, and social inclusion.  

M07, basic services and basic services in rural areas, aims to improve services and infrastructure. 
The  efficiency  ratio  is  calculated  by  dividing  the  output  indicator  value  with  the  associated 
funding in M07. Map 6 displays the relevant results.  

therefore  represents  only  O11,  number  of  training  days,  as  opposed  to  a  comprehensive  picture  of  all  of  the 
outputs of M01 – knowledge transfer. 

105 

 
 
 
                                                                                                                                                                                           
 
Map  6.  Output  efficiency  of  output 
services/infrastructure (IT or other – 2014-2018) 

indicator  O15  Population  benefitting  from 

improved 

per 
funding 
input M07 

‘Not relevant regions’ are regions in which the Rural Development Programmes does not make use of this output indicator  
Source: Evaluation support study 

What can be identified, is that the efficiency of M07 to reach large proportions of the population 
varies  significantly  across  Member  States,  and  between  programming  areas  in  one  Member 
State. 

O16  –  Number  of  EIP  grouped  supported,  number  of  EIP  operations  supported  and 
number and type of partners in EIP groups.  

The number of European innovation partnership (EIP) operations supported and the number and 
type of partners in EIP groups, O16, is a relevant evaluation aspect of Measure 16, cooperation. 
EIP  Operational  Groups  aim  to  support  innovation  among  farmers  and  the  rural  population. 
These  groups  work  together  on  innovation  projects,  collecting  partners  with  synergistic 
knowledge  in  one  place.  Groups  can  include  advisors,  researchers,  farmers,  business,  or  other 
relevant participants. Overall, 27 Member States planned to provide support for EIP groups. By 
2021 funding for around 2 000 operational groups has been provided under this measure, in the 
current programming period.  

106 

 
 
 
 
The  efficiency  ratio  is  calculated  by  dividing  the  output  indicator  value  with  the  associated 
funding in M16. Map 7 displays the relevant results. 

Map 7. Output efficiency of output indicator O16:  Number of EIP grouped supported, number of 
EIP operations supported and number and type of partners in EIP groups (2014-2018) 

per funding 
input M16 

‘Not relevant regions’ are regions in which the Rural Development Programmes does not make use of this output indicator or 
which make use of the indicator but have not reported data  
Source: Evaluation support study 

The efficiency of the provision of support for number of EIP operations, as well as the number 
and  type  of  partners,  appears  to  be  varied  among  the  participating  Member  States  and 
programming  regions.  Austria,  the  Netherlands,  the  UK  apart  from  Scotland,  Germany, 
Languedoc-Roussillon, Liguria, and Marche, have among the lowest efficiency ratios.  

O18 – Population covered by LAG 

LEADER  and  community-led  local  development  are  seen  as  integral  to  balanced  territorial 
development, socioeconomic aspects and social inclusion, as they encourage the participation of 
the most principle local unit, to address unique place-based needs. Local action groups (LAGs) 
are  the  recipients  of  funding,  and  are  those  which  distribute  funding  to  LEADER  and  CLLD 
projects. Thus, assessing the number of inhabitants covered by LAG (indicator O18) in relation 
to the money spent is showing one aspect of the efficiency of LEADER. 

107 

 
 
 
 
Output indicator 18, populations covered by LAGs, is linked to M19 support for LEADER and 
CLLD. M19 is a local development measure aimed at engaging actors at the local level to create 
locally important initiatives meeting local needs and creating local solutions. LEADER projects 
are funded through EARFD alone, while CLLD projects can be funded in addition through the 
EMFF, ERDF, and ESF.  

Approximately  2 600  LAGs  operate  in  the  EU  Member  States,  covering  above  54%  of  rural 
inhabitants.159  

The  efficiency  ratio  is  calculated  by  dividing  the  output  indicator  value  with  the  associated 
funding in M19. 

Map 8. Output efficiency of output indicator O18 Population covered by LAG (2014-2018) 

per funding 
input M19 

‘Not relevant regions’ are regions in which the Rural Development Programmes does not make use of this output indicator  
Source: Evaluation support study 

According  to  the  map  above,  the  efficiency  ratio  of  the  population  covered  by  LAGs  is  quite 
high. This is particularly true in France, which is to be expected as France has placed a particular 
emphasis  on  LEADER  and  M19  as  a  whole.  Other  high  efficiency  ratios  can  be  observed  in 
Czechia, in Slovakia and in Hungary. 

159   ENRD (2017) LEADER/CLLD. 

108 

 
 
 
                                                           
 
O20 – Number of LEADER projects supported 

Another approach to measure the performance of LEADER is to analyse the number of Leader 
projects  funded  as  monitored  by  the  output  indicator  20,  which  addresses  outputs  achieved 
through M19. Inter-territorial cooperation as supported by LEADER is increasingly important in 
rural areas, with respect to balanced territorial development and socioeconomic aspects including 
social inclusion. However, when a certain budget is given, the number of projects related to the 
money spent may not only show the efficiency. It could also be a hint that in some regions larger 
and therefore fewer projects were supported by LEADER. 

LEADER  programme  evaluations  have  demonstrated  the  effectiveness  of  cooperation  in 
improving balanced territorial development in rural areas through the collaborative development 
of solutions and the exchange of ideas and innovation160. 

The  efficiency  ratio  is  calculated  by  dividing  the  output  indicator  value  with  the  associated 
funding in M19. 

Many  regions  in  Italy  appear  to  have  a  relatively  low  efficiency  with  respect  to  LEADER 
projects support. This includes Toscana, Umbria, Trento, Friuli Venezia Giulia, Lazio, Abruzzo, 
Molise, Campania, Calabria, and Sicilia. In Spain, Andalusia, Extremadura, and Madrid likewise 
had a low efficiency ratio. As do the French regions Auvergne and Rhone-Alpes, Wales in the 
UK, and Lithuania. 

160 ENRD (2017) LEADER Cooperation. 

109 

 
 
                                                           
Map 9. Output efficiency of output indicator O20 Number of LEADER projects supported (2014-
2018) 

per funding 
input M 19 

Note: ‘Not relevant regions’ are regions in which the Rural Development Programmes does not make use of this output indicator 
Source: Evaluation support study 

Table 14. Declared expenditure (2014-2019) for relevant measures, in % of financing plan 

M01  M02  M04  M06  M07  M09  M10  M11  M12  M13  M15  M16  M17  M19 

33.6 

23.8 

44.1 

48.6 

25.4 

62.3 

80.0 

16.7 

31.9 

16.9 

36.2 

31.9 

36.7 

31.9 

0.0 

18.4 

61.7 

37.3 

21.1 

37.7 

32.5 

67.6 

62.5 

63.5 

82.7 

26.8 

17.2 

37.5 

17.7 

27.7 

20.1 

24.5 

66.8 

91.0 

0.0 

78.8 

51.3 

11.2 

Tota
l 

44.5 

40.9 

69.6 

36.3 

34.1 

5.2 

24.6 

32.4 

40.1 

80.5 

27.3 

29.9 

34.5 

P2 

P3 

P4 

P5 

P6 

Tot 

44.8 
Source: European Commission, DG Agriculture and Rural Development 

32.0 

67.5 

62.8 

34.7 

21.3 

39.6 

38.6 

63.0 

82.6 

27.0 

18.5 

61.7 

29.9 

54.4 

110 

 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
Qualitative analysis 

Figure 18. Issues in Rural Regions – Socio-Economic Aspects 

Source: Evaluation support study; n=57; respondents are from the categories public authority and rural development expert 

Figure 19. Needs in Rural Regions – addressed by CAP  

Source: Evaluation support study, 2020; n=57; respondents are from the categories public authority and rural development 
expert 

111 

 
 
 
 
 
 
Figure 20. The services most limited in case study regions 

Source: Evaluation support study; n=104; respondents are from the categories public authority, rural development expert, 
farmer, processor, producer organisation, NGO, civil group and rural resident 

Figure 21. Impact of the CAP on the provision of services   

Source: Evaluation support study; n=57; respondents are from the categories public authority and rural development expert  

112 

 
 
 
 
 
 
 
Figure 22. Reasons for lack of effectiveness of Pillar I instruments 

Source:  Evaluation  support  study,  2020;  n=57;  respondents  are  from  the  categories  public  authority  and  rural  development 
expert 

Figure 23. Reasons for lack of effectiveness of Pillar II Measures 

Source: Evaluation support study, 2020; n=57; respondents are from the categories public authority and rural development 
expert 

Table 15. Role of EU regulations (Pillar I and II) according to public official and rural experts 
interviewed   

Countries 

Positive 

Positive, but burden 
influenced by 
national transposition 

Positive/ 
Negative 

Negative because 
constraining 

Not answered 

Total 

AT 

BG 

CZ 

DE 

EE 

EL 

ES 

FR 

IE 

IT 

5 

1 

1 

1 

2 

2 

2 

1 

2 

113 

1 

2 

1 

3 

5 

5 

1 

5 

5 

1 

2 

3 

2 

5 

8 

5 

9 

1 

1 

2 

4 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Countries 

Positive 

Positive, but burden 
influenced by 
national transposition 

Positive/ 
Negative 

Negative because 
constraining 

Not answered 

Total 

NL 

PL 

Total cases 

1 

11 

% out of total 

21.6 

1 

7 

13.7 

2 

3.9 

8 

15.7 

3 

1 

23 

3 

3 

51 

45.1 

100.0 

Source: Evaluation support study, 2020, questionnaire for public officials, rural development experts 

Figure 24. Income GAP between farming and the overall economy, 2010-2012 average 

65.000

60.000

55.000

50.000

45.000

40.000

35.000

30.000

25.000

20.000

15.000

10.000

5.000

0

Operating subsidies per worker
(including support covering
possible negative market income)

Market income (without support)
per worker

Gross wages and salaries in the
total economy (in current prices)

U
L

K
D

E
B

L
N

E
I

E
S

I
F

R
F

E
D

T
A

…
K E
U

T
I

S
E

I
S

Y
C

L
E

T
M

T
P

R
H

Z
C

K
S

U
H

E
E

L
P

V
L

T
L

O
R

G
B

Source: DG Agriculture and Rural Development on the basis of Eurostat 

114 

 
 
 
 
 
 
 
 
 
 
 
Figure 25.  Income support (direct payments and ANC) per hectare in 2015 

Source: CATS audit database 

Figure 26.  Income support (direct payments and ANC) per hectare in 2019 

Source: CATS audit database 

115 

 
 
 
 
 
 
Figure 27.   Income and direct payments per hectare by physical farm size in 2016-2018161 

Income in 
EUR per worker

 60.000

 50.000

 40.000

 30.000

 20.000

 10.000

 -

Income per worker
Direct payments/ha
EU average direct payments/ha

Direct payments in 
EUR/ha

260

 450
 400
 350
 300
 250
 200
 150
 100
 50
 -

Source: DG Agriculture and Rural Development, based on FADN data 

Figure 28. Income and direct payments per worker by physical farm size in 2016-2018 and share of 
direct payments in income 

EUR/worker

 60.000,00

 50.000,00

 40.000,00

 30.000,00

 20.000,00

 10.000,00

 -

Income other than Direct payments per worker
Direct payments per worker
EU average income per worker
Share of Direct payments in income

45%
40%
35%
30%
25%
20%
15%
10%
5%
0%

Source: DG Agriculture and Rural Development, based on FADN data 

161   The figure presents the 2016-2018 average to smooth market effects on income. The income indicator used is 
the farm net value added per agricultural work unit which is the amount available to remunerate all factors of 
production (land, labour and capital, both external and own factors). 

116 

 
 
 
 
 
                                                           
Figure 29. Income and direct payments per hectare by economic farm size in 2016-2018 

Income in
EUR/worker

Income per worker

Direct payments/ha

EU average Direct payments/ha

Direct payments 
in EUR/ha

260

50.000

45.000

40.000

35.000

30.000

25.000

20.000

15.000

10.000

5.000

0

400

350

300

250

200

150

100

50

0

(1) 2 000 - < 8
000 EUR

(2) 8 000 - < 25
000 EUR

(3) 25 000 - <
50 000 EUR

(4) 50 000 - <
100 000 EUR

(5) 100 000 - <
500 000 EUR

(6) >= 500 000
EUR

Source: DG Agriculture and Rural Development, based on FADN data 

Figure 30. Income and direct payments per hectare by income class in 2016-2018162 

Income in 
EUR/worker

Income per worker

Direct payments/ha

EU average Direct payments/ha

Direct payments in
EUR/ha

260

80.000

70.000

60.000

50.000

40.000

30.000

20.000

10.000

0

Q_1

Q_2

Q_3

Q_4

Q_5

Q_6

-10.000
Source: DG Agriculture and Rural Development, based on FADN data 

312

262

212

162

112

62

12

-38

Q_7

Q_8

Q_9

Q_10

162  The  indicator  of  income  per  worker  is  the  amount  available  to  remunerate  all  factors  of  production  (land, 
labour  and  capital,  both  external  and  own  factors).  Income  and  subsidies  by  quantile  of  intensity:  farms  are 
ranked  according  to  increasing  intensity  and  separated  into  10  groups  of  similar  size.  Intensity  has  been 
measured  as  the  ratio  between  the  intermediate  consumption  (specific  costs  like  fertilisers,  pesticides  and 
farming overheads e.g. water, electricity…) and the utilised agricultural area. 

117 

 
 
  
 
 
                                                           
Figure 31.  Income and direct payments per worker by income class in 2016-2018 

Income other than Direct payments/worker

Direct payments/worker

EU average income per worker

Share of Direct payments in income

EUR/worker

80.000

70.000

60.000

50.000

40.000

30.000

20.000

10.000

0

-10.000

-20.000

200%

150%

100%

50%

0%

-50%

21.507

Q_1

Q_2

Q_3

Q_4

Q_5

Q_6

Q_7

Q_8

Q_9

Q_10

Source: DG Agriculture and Rural Development, based on FADN data 

Figure 32.  Share of operating subsidies in FNVA by ANC class in 2011-2013 

EUR/worker

Direct payments/worker

Other RD*/worker

total operating subsidies/ha

ANC/worker

Income per worker

EUR/ha

25.000

20.000

15.000

10.000

5.000

0

500

450

400

350

300

250

200

150

100

50

0

Income

Operating
subsidies

not in ANC area

Income

Operating
subsidies

in ANC not mountain
area

Income

Operating
subsidies

in ANC mountain area

Note:  ANC  =  support  to  farms in areas  facing natural or other specific  constraints;  FNVA  =  farm  net  value  added per 
annual work unit = amount per worker working full time available to remunerate all factors of production (land, labour 
and capital, both external and own factors); other RD: rural development measures other than ANC (including national 
top-ups, but excluding investment support). 
Source: DG Agriculture and Rural Development, based on FADN data 

118