Deborah RobertsArkleton Centre for Rural Development Research University of

Aberdeen, Scotland

Partners:•Federal Institute for Less-Favoured and Mountainous Areas, Austria•Institute of Spatial Planning, University of Dortmund, Germany•National Institute for Regional and Spatial Analysis, Ireland

Territorial Impacts of the CAPESPON Project 2.1.3

Aim of Project: To provide new knowledge, concepts and indicators of the territorial impact of agricultural and rural development policy

(across EU27 at NUTS3)

Background• CAP is a key sectoral policy

• Gradual CAP reform (from Pillar 1 to Pillar 2)

• Assessed against higher level EU objectives

• Networking with other TPGs and Common Platform

MethodsTerritorial Impact Assessment (TIA) method

• Development of hypotheses• Statistical analysis of incidence of support• Initial statistical analysis of impact• Literature search• Apportionment and analysis of output from CAPRI

model of MTR proposals• Case studies plus….

Stage 1:

Stage 2:

Data sources and coverage• EU sources• National sources for apportionment data• Policy data from OECD, FADN, RDP budgets

CAP and cohesion (Pillar 1)

GDP perinhabitant

Unemployrates

Pop. change95-99

Pillar 1 per AWU - -** +**

Pillar 1 per ha +** -** +**

Note: ** means significant at the 5% level

•Pillar 1 support works strongly against cohesion

•Distribution of direct income payments more consistent with cohesion objectives (esp. crops)

•Level of Pillar 1 support favours core as against periphery (EU level)

Single variable regression analysis:

Total Pillar 1 Support per AWU

CAP and cohesion (Pillar 2)

•At EU level, pillar 2 support does not seem to be consistent with cohesion objectives

•Distribution of Pillar 2 support positively associated with peripherality (EU level)

Single variable regression analysis:GDP perinhabitant

Unemp.rate

Pop. change95-99

Based on FADN data:Pillar 2 per AWU +** -** -Pillar 2 per ha +** -** -Based on RDF data:Pillar 2 per AWU (RD) - -** +**Pillar 2 per ha (RD) -** -** +

Note: ** means significant at the 5% level

• Very uneven allocation of RDR funds

• Difficulties of co-financing in poorer regions

• Richer regions use Pillar 2 to promote environmental land management, while poorer regions seek to modernise agriculture.

Differences in territorial application of Pillar 2

Dwyer et al analysed use of Pillar 2 measures across EU15 and SAPARD in CEECs.

LFA support per AWU

Agri-environmental subsidies per AWU

Percentage change in Farm Incomes resulting from MTR Proposals

Policy implications • Increase switch from Pillar 1 to Pillar 2 and broaden focus of RD policies.

•Allocate RDF according to criteria of relative needs for rural development and environmental management.

• Need for a coherent framework for horizontal and vertical integration of policies.

•Polycentricity: the RDF could be used to offset centralising forces at regional level, targeting rural hinterlands.

•Database should be improved so as to enable comparable European wide analysis.

Main challenges for next phase

Development of TIA method

• Further statistical analysis of Nuts 3 database

– CAP and Polycentricity

– CAP and environmental sustainability

– Panel data analysis

• Micro-scale analysis based on FADN

• Case studies in farm household adaptation and good practice in territorial rural development

– Cluster analysis to help inform choice of case studies.