Support for Farmers' Cooperatives Clustering European Cooperatives: Towards A Useful Typology Of Cooperative Profiles Giel Ton
The 2011-2012 project „Support for Farmers‘ Cooperatives (SFC)“ has been commissioned and funded by the European Commission, DG Agriculture and Rural Development. Contract Number: 30-CE-0395921/00-42. The SFC project is managed by Wageningen UR’s Agricultural Economics Research Institute LEI and Wageningen University. Project managers: Krijn J. Poppe and Jos Bijman. Other members of the consortium are: Pellervo Economic Research PTT, Finland: Perttu Pyykkönen University of Helsinki, Finland: Petri Ollila Agricultural Economics Research Institute, Greece: Constantine Iliopoulos Justus Liebig University Giessen, Germany: Rainer Kühl Humboldt University Berlin, Germany: Konrad Hagedorn, Markus Hanisch and Renate Judis HIVA Katholieke Universiteit Leuven, Belgium: Caroline Gijselinckx Rotterdam School of Management, Erasmus University, The Netherlands: George Hendrikse and Tony Hak
How to cite this report: Ton, G. (2012). Support for Farmers’ Cooperatives; Clustering European Cooperatives: Towards A Useful Typology Of Cooperative Profiles. Wageningen: Wageningen UR.
Disclaimer: This study, financed by the European Commission, was carried out by a consortium under the management of LEI Wageningen UR. The conclusions and recommendations presented in this report are the sole responsibility of the research consortium and do not necessarily reflect the opinion of the Commission or anticipate its future policies.
Support for Farmers' Cooperatives Clustering European Cooperatives: Towards A Useful Typology Of Cooperative Profiles Giel Ton LEI Wageningen UR, The Netherlands
November 2012
Corresponding author: Giel Ton LEI Wageningen UR Hollandseweg 1 6707 JB Wageningen, The Netherlands E-mail:
[email protected]
Preface and Acknowledgements In order to foster the competitiveness of the food supply chain, the European Commission is committed to promote and facilitate the restructuring and consolidation of the agricultural sector by encouraging the creation of voluntary agricultural producer organisations. To support the policy making process DG Agriculture and Rural Development has launched a large study, “Support for Farmers’ Cooperatives (SFC)”, that will provide insights on successful cooperatives and producer organisations as well as on effective support measures for these organisations. These insights can be used by farmers themselves, in setting up and strengthening their collective organisation, and by the European Commission in its effort to encourage the creation of agricultural producer organisations in the EU. Within the framework of the SFC project, this report on clustering European agricultural cooperatives has been written. In addition to this report, the SFC project has delivered 34 case study reports, 27 country reports, 8 sector reports, 6 EU synthesis and comparative analysis reports, a report on cluster analysis, a report on the development of agricultural cooperatives in other OECD countries, and a final report.
Table of contents
1.
Introduction ...........................................................................................................................................................7
2.
Clustering methodology....................................................................................................................................8
3.
Results ................................................................................................................................................................... 10
4.
Further Exploration of the regional clusters ......................................................................................... 13
5.
Conclusions ......................................................................................................................................................... 19 Appendix 1: Dendrogram derived from the Hierarchical Cluster Analysis......................................... 20 Appendix 2. ANOVA results of comparing clusters ....................................................................................... 21 Appendix 3. Regional classification of the 27 EU Member States ........................................................... 22
1.
Introduction
This report presents the first results of the cluster analysis of the data on the more than 500 agricultural cooperatives in the EU on which we collected data. The elaboration of clusters is Theme 6 of the project ‘Support for Farmers’ Cooperatives’. This clustering is needed as input for decisions on the case studies that will be investigated in the second part of the project (Themes 7 and 8). Clustering is an interesting process as it requires both quantitative analysis of the data and qualitative iterations of reasoning about meaningful clusters that can be used for further research. At the same time it is a challenging process, where the researchers have to make many decisions on which variables to include and which to exclude. Based on the results of the EU Syntheses Reports and Sector Reports (Theme 4 and 5), as well as on our building blocks, choices have been made on regions, categories of cooperatives and particular organisational and strategy characteristics of the cooperatives in including/excluding variables in the cluster analysis. This report is structured as follows. In section 2 we briefly discuss our methodology and the steps that have been taken to generate the clusters. In section 3 the eight resulting clusters are presented. In section 4 we present some additional explorations into clustering the cooperatives in our database.
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2.
Clustering methodology
The cluster analyses presented in this report are based on data on more than 500 farmers’ cooperatives in the EU. This data has been collected in the first half of 2011 by the National Experts, using an extensive questionnaire with questions performance, position in the food chain, international governance, and other organisational characteristics. The cooperatives in our sample were strategically chosen as we wanted to cover the majority of the farmers in a sector. Thus, the largest cooperatives in each of the eight sectors in our project were surveyed. The clustering process followed is inspired by the principles of case-base analysis (Byrne and Ragin, 2009), i.e., respecting the integral complexities of each observation while looking for transcending patterns. We applied a combination of statistical clustering and identification of contrasting cases. We see the present analysis as a first and motivating step for more in-depth analysis of differences between (groups of) farmers’ cooperatives in the EU. The data-base on the cooperatives allowed the use of hierarchical cluster analysis as a means to explore sets of differences between groups as well as communalities within groups. The procedure is an iterative process that is heavily influenced by the variables used to compute clusters. As a first step to facilitate clustering, we recoded several continuous variables to ordinal/categorical values and explored the thresholds used to define the categories in each of these variables. Size of the membership was recoded to the categories ‘tiny’, ‘small’ ‘medium’ and ‘big’, with respective thresholds of 20, 100 and 1000. Economic service provision was estimated by the variable “turnover per member”, and recoded to the categories ‘extremely low’ (
