1. Francesco PECCI University of Verona Maria SASSI University of Pavia
Is decoupling only an
agricultural matter
for each single EU-15 Region?
Francesco PECCI University of Verona
Maria SASSI
University of Pavia
Two authors have made this presentation one is me and the other is Francesco Pecci, the expert of the spatial approaches.
With our analysis we have tried to give an answer to a quite stimulating question:
GENEDEC – Verona - October 20-21, 2006

2. Importance of the analysis
Evolving interest in society understood
Impact on the
agricultural sector
The impact on economic, social and structural variables
as important to many governments
Why the topic is important.
The implications of decoupling are traditionally analysed with respect to the agricultural sector, particularly on its production and trade.
However the CAP reform process has empahasised the need to consider the evolving interest in society concerning the role of agricultre, interest understood as important to many government. This underlines the need to understand not only the agricultural impact of degoupling but also its impact on certain politically sensitive economic …
On the other side, the effects of decoupling are traditionally analysed considering the regions as isolated islands despite a wide theoretical body on the role of geographic proximity, territorial, policy spill overs. For this reason we have adopted a spatial analysis.
Geograqhic proximity
Regions as
“Isolated islands”
territorial, policy spill over
Spatial dimension

3. Object
Relationship between agricultural value added and agricultural, socioeconomic and structural variables
What “spiral effect” among the three groups of variables?
Classification of the EU- 15 regions according to an homogeneous profile
Spatial effect
In this context the aim of our analysis has been the classification of the EU-15 regions according to homogeneous characters in terms of the ….
Different typologies of subsidies payed within the CAP.
Relationship estimated taking into account the spatial effect (spill over effect) (regional interaction).
The comparison between the two classifications underlines the spiral effect among the
Relationship between agricultural value added and the CAP subsidies

4. Structure 1. Data set 2. Methodology 3. Results 4. Conclusions
We ha structured our analysis into four sections.
First, we have selected a set of data. Second, we have defined the methodology. Than results and conclusions.

5. (Montresor, Bertocchi -Braunschweig 21-22 October 2006)
Data set
(Montresor, Bertocchi -Braunschweig October 2006)
Population
Labour market
CAP Subsidies
FADN
REGIO
Land use
Agriculture
The variables selected have made reference to the data set collected within this project and presented by Montresor and Bertocchi on October this year in Germany. On the basis of the FADN and Regio data we have selected, for 164 EU-15 regions at Nuts2 and 1 level, a suitable set of variables in the area of
164 Regions:
NUTS 2 and
NUTS 1
Competitiveness

6. 2. Methodology Geographically Weighted Regression
Spatial models estimation
Relationship between AVA and:
Economic and social structure;
Agricultural structure;
Land use;
Livestocks;
Total subsidies;
Subsidies livestocks;
Compensatory payments.
Non stationary estimators
The first step in our analysis has consisted in the estimation, through the GWR approach, of 7 models. Four of which concerning the relationship … and the other three the relationship of AVA and Subsidies. The non stationary estimators achieved have been at the basis of the classification of the regions in sub groups. More precisely, one cluster made reference to the non stationary estimators of first four models and the other cluster tu the last three models.
Here you have the estimation of the spatial models that has also allowed us to understand the variables suitable for the analysis of the implications of decoupling, that are those resulted statistically significant. Due to time restriction I am not enter into the issue, but I’m coming back to this point in my conclusions.
Cluster based on theAVA/agricultural, socio-economic and structural variables;
Cluster based subsidies

7. 3. Cluster analysis AVA/agricultural, socioeconomic
and structural variables
AVA/CAP subsidies
This slide shows the sub-groups of regions based on the relationship between … on your left side and between .. On your right side.
For each cluster we have underlined the profile.

8. n. cluster agric., socio-ec. & struct. % regions cluster subsidies
AVA/agricultural, socioeconomic
and structural variables
n. cluster agric., socio-ec. & struct.
% regions cluster subsidies 1
90% cl. 1
10% cl. 5 2
64% cl. 2
14% cl. 3
14% cl. 7
8% cl. 5 3
82% cl. 5 12% cl. 7 6% cl. 2 4
37% cl. 5 33% cl. 3
30% cl. 7 5
100% cl. 4 6
49% cl. 6
43% cl. 5
8% cl. 3
n. cluster agric., socio-ec. & struct.
% regions cluster subsidies 7
69% cl. 8
31% cl. 5 8
64% cl. 3
36% cl. 9 9
100% cl. 10 10
50% cl. 5
40% cl. 6
10% cl. 8 11
50% cl. 7
44% cl. 9 6% cl. 3
AVA/CAP subsidies
AVA/subsidies

9. Cluster analysis "Country effect" AVA/agricultural, socioeconomic
and structural variables
AVA/CAP subsidies
"Country effect"
Coming back to the two clusters it clearly emerges a “country effect”.

10. 4. Conclusions GWR models Cluster analysis Suitable variables for
Per-capita income
Population density
Employment structure
GWR models
Suitable variables for
analysing decoupling
are not only agricultural
Total unemployment
Long-term unemployment
Female unemployment
Spatial dimension
Cluster analysis
The need for additional variables
Only partial superimposition
among sub-groups

11. Cluster senza GWR
Variabili socioeconomiche
e strutturali
Sussidi PAC

12. Avanzamenti
Variabili socioeconomiche
e strutturali
Sussidi PAC

13. I cluster basati sulle variabili socioeconomiche e strutturali
Cluster 1 – regioni mediamente ricche, competitive, con propensione al terziario e agricoltura dedita a allevamento e cereali
Cluster 2 – regioni mediamente ricche, tasso di occupazione agricola superiore a media e agricoltura specializzata in vite, ortofrutta, foraggi e allevamento
Cluster 3 – regioni ricchezza leggermente inferiore a media, significativa quota occupazione agricola e industriale.
Cluster 7 – regioni mediamente ricche con elevata concentrazione industria alimentare
Cluster 4 – regioni a forte specializzazione in colture industriali e con significativa disoccupazione di lunga durata
Cluster 6 – regioni povere con agricoltura settore importante
Cluster 5 – regioni mediamente ricche, competitive, propensione attività industriale e agricoltura ricca

14. Effetti elementari della GWR