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.INTRODUCTION

2.BACKGROUND OF THE INTERVENTION

2.1Description of the intervention and its objectives

2.2Baseline and points of comparison

3.IMPLEMENTATION/STATE OF PLAY

4.METHODOLOGY

5.ANALYSIS

5.1Effectiveness

5.2Efficiency

5.3Coherence

5.4 Relevance

5.5 EU added value

6.CONCLUSION

4.ANNEX 1: PROCEDURAL INFORMATION

5.ANNEX 2: STAKEHOLDER CONSULTATION

6.ANNEX 3: METHODOLOGY

7.ANNEX 4: TABLES AND GRAPHS SUPPORTING THE STUDY

Glossary

BPS    Basic Payment Scheme

CAP    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    Ecological focus area

EIP    European Innovation Partnership

ESIF    European structural and investment funds

EU    European Union

EUR    Euro, Eurozone currency

FADN    Farm Accountancy Data Network

FAO    Food and Agriculture Organization of the United Nations

GDP    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.

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 Union 1 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 areas 2 . 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 poverty 3 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 payments 4 , rural development 5 , the common market organisation 6 and the Horizontal Regulation. These all target the aforementioned objectives. 

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 study 7 , 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 areas 8 .

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 2014-2020 9 . Priority 6 of the latter on social inclusion, poverty reduction and economic development is particularly relevant to balanced territorial development.

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

·maintain market stability on both supplier and demand sides;

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

·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  

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:

oPillar 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

oPillar 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

Source: DG Agriculture and Rural Development

* The CAP 2014-2020 did not have as objective to address the needs of all vulnerable groups.

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 regulation 10 , 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.

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 level 11 ;

·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 II 12 .

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 development 13

Pillar I measures include income support 14 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 decisions 15 .

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 2020 16 .

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 payment 17 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%. 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

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 markets 18 . 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 structured along product types (i.e. products eligible for intervention 19 ), 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.

The most relevant EAFRD measures contributing to balanced territorial development 20 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 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 expenditure 21 . 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)

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 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).

Table 3. – Progress towards social targets by 2019

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 development 22 , 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;

·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 2011-2018 24 . 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 Areas 25 , 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 published 26 . Other European Institutions, the Committee of 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.

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 respondents 27  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 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

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 convergence 29 . 18 Member States apply the Basic payment scheme (BPS) and 10 Member States 30 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 2015 31 at national or regional level;

(2)full convergence of the per hectare value by 2019 32 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 payments 34 , the difference in the level of direct payments received by EU farmers reduced significantly 35 : 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 model 36 . 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.

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.

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 farms 38

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 areas 39 . 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 farms. This opinion is largely shared by respondents 40 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 payment 41 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 average 42 . 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 Romania 43 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%).

Figure 6. Income and direct payments per hectare by economic farm size in 2016-2018 44

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 productivity 45 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 and remote rural areas 46 . As concluded by The challenge of land abandonment after 2020 and options for mitigating measures of rural areas 47 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 subsidies 48 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.

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 1990s 49 . 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 significantly 50 .

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.

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

Source: DG Agriculture and Rural Development on the basis of Eurostat data 51

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 2 52 ) and dynamic regions (cluster 3 53 ), 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 considered as beneficial to modernisation and farm-based productivity in several case studies 54 . 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 CAP 55 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 renewal 57 , 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 developed areas with many small farmers. The World Bank report 58 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 1 59 ) 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 work. The risk of exploitation and underpayment of seasonal workers, and 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.

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 EU 61 . 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 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 population 62 .

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 2019 63 , 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. Austria 64 ) 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).

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

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 504 66 , 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%).

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

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 services 70 . Over 50% of respondents to the public consultation of the long-term Vision for the EU’s rural areas 71 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.

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 areas 72 ) 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 4 74 ) 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 infrastructure 76 ). 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 renewal 77 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 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 study 78 .

This is further complemented by the Annual Implementation Report 2019 79 , 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-migration 80 . 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 commitments 81 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 areas 82 (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 measures 83 ’ 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 might total 5 million ha by 2030. As demonstrated by the Scenar 2030 study 84 , 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 resilience 85 . The ROBUST project 86 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 LEADER and M07 – Basic services 88 . 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 report 89  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 areas 91 , 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.

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

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 populations 94 . 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).

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 EU 95 , 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 communities 96 (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 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 areas 97 , 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 strategies 98 . 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 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-3 100 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

Source: Evaluation support study

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 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 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 that the territories with lower scores for these indicators will have lower LEADER impact 101 , 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 costs 102 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 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 ESIF 104 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);

·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, Puglia 106 , 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 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 support 108 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, Ireland 109 , Saxony-Anhalt 110 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 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 workload 111 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

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 CAP 113 , 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 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 costs 114 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.

5.3Coherence

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 farms 115 . 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).

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 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 complementarity 116 . 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.

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 other enterprises in rural areas served by improved transport infrastructure. Similar 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 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 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 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-overs 117 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 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 infrastructure 118 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, networks), social innovation 119  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 tapped 120 . Pisani et al. also evidenced positive impact of LEADER on social capital in several Italian LAGs 121 .

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 women’s needs 122 and a significant gender imbalance among farm managers (only 30% of farms are managed by women across the EU 123 ) 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 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 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 exchange 124 . 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.

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.

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 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 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.

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 Areas 125 . 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 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 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.

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.

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.

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 contractor of this evaluation 126 , 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 areas 127 . 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.

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 website 128 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.

- 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 interested parties took place from 7 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.

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 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, LEADER 129 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.

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.

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.

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 504 130 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 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 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 than rural development measures. Regarding 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 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.

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 correlation 133 and regression analyses 134 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-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

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 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 FIGARO 136 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 chosen 140 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).

·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 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 criteria 141 . It also distinguishes between rural, intermediate, and urban areas 142 .

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

Source: Consortium, 2020

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.

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 employment 145 , the impact of the CAP on poverty reduction, income, and within-region inequality 146 , and the impact of the CAP on gender mainstreaming 147 . 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 developed by the European Commission (Eurostat, 2010 148 , 2017 149 ) and the OECD (1994 150 ; 1996 151 ; 2006 152 ) 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

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.

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.

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 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.

7.ANNEX 4: TABLES AND GRAPHS SUPPORTING THE STUDY

Regression analysis: model set-up and results

Table 11. Regression model set-up

Source: Evaluation support study

Table 12. Legend regression outputs

Source: Evaluation support study

Table 13. Impact of CAP measures by cluster

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%).

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 2016 157 .

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%.

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.

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

‘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 transfer 158 . Map 5 displays the relevant results.

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

‘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.

Map 6. Output efficiency of output indicator O15 Population benefitting from improved services/infrastructure (IT or other – 2014-2018)

‘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.

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)

‘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.

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)

‘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.

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 innovation 160 .

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.

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

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

Source: European Commission, DG Agriculture and Rural Development

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

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

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

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

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

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

Figure 27.  Income and direct payments per hectare by physical farm size in 2016-2018 161

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

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

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

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

Figure 30. Income and direct payments per hectare by income class in 2016-2018 162

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

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

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

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

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

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

(3)      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.

(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.

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

(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.

(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.

(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 .

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

(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. 

(21)      Including national co-financing.

(22)       Evaluation support study on the impact of the CAP on territorial development of rural areas - Publications Office of the EU (europa.eu) .

(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) .

(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 .

(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.

(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.

(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 compared to EUR 260/ha on average. Higher coupled payments explain a large share of the difference.

(43)      In 2019, direct payments per hectare averaged EUR 200 in Romania, well below the EU average of EUR 290/ha.

(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.

(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.

(49)      EU regular economic report 4, Thinking CAP, Supporting agricultural jobs and incomes in the EU, World Bank (2018).

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

(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.

(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 .

(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.

(60)      Jobs in agriculture in 2016 according to ESTAT Farm Structure survey.

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

(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.

(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 .

(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 36

(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 .

(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 36.

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

(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.

(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) .  

(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.

(94)      Integro Association (2019) Community-led local development for Roma Inclusion.

(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).

(97)      See footnote 36.

(98)      Summary Report – Synthesis of the evaluation components of the enhanced Annual Implementation Report 2019: Chapter 7.

(99)      See footnote 2.

(100)      NUTS - Nomenclature of territorial units for statistics.

(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.

(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.

(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.

(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.

(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.

(113) ECORYS (2018) Analysis of administrative burden arising from the CAP. https://op.europa.eu/en/publication-detail/-/publication/dabd45ab-9baf-11e9-9d01-01aa75ed71a1 .

(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.

(116)      See footnote 2.

(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.

(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.

(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.

(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.

(124)      Evaluation support study on the CAP's impact on knowledge exchange and advisory activities, ADE, CCRI & OIR (2020).

(125) See footnote 36.

(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.

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

(129)      Liaison Entre Actions de Développement de l'Économie Rurale, meaning 'Links between the rural economy and development actions'.

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

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

(132)       https://www.eesc.europa.eu/it/our-work/opinions-information-reports/information-reports/evaluation-caps-impact-territorial-development-rural-areas-information-report .

(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 2 ‘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.

(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.

(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.

(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.

(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.

(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.

(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.

(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.

(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.

(156)      Only available up until 2014.

(157)      2016 represents the most complete data point for primary sector GVA data. The years 2017 and 2018 feature relatively larger geographical data gaps.

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

(159)      ENRD (2017) LEADER/CLLD.

(160)  ENRD (2017) LEADER Cooperation.

(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).

(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.