Proposals for the financing and development of Greek farms based on a clustering method for categorical data

Menexes, George/ Aggelopoulos, Stamatis/ Αγγελόπουλος, Σταμάτης/ Μενεξές, Γεώργιος

Institution and School/Department of submitter: ΤΕΙ Θεσσαλονίκης
Keywords: Greece;Investments;Farms;Design and development;Financing
Issue Date: 2008
Publisher: Emerald
Citation: Aggelopoulos, S., Menexes, G. (2008). Proposals for the financing and development of Greek farms based on a clustering method for categorical data.EuroMed Journal of Business 3, (3). Διαθέσιμο σε: (Ανακτήθηκε 8 Ιουλίου 2015).
Journal: EuroMed Journal of Business, vol. 3, no. 3, 2008
Abstract: The aim of the study is to propose certain agricultural policy measures for the financing and development of Greek farms, established by young farmers, based on the results of a clustering method suitable for handling socio‐economic categorical data. Design/methodology/approach – The clustering method was applied to categorical data collected from 110 randomly selected investment plans of Greek agricultural farms. The investment plans were submitted to the “Region of Central Macedonia” administrative office, in the framework of the Operational Programme “Agricultural Development – Reform of the Countryside 2000‐2006” and refer to agricultural investments by “Young Farmers”, according to the terms and conditions of Priority Axis III: “Improvement of the Age Composition of the Agricultural Population”. The input variables for the analyses were the farmers' gender, age class, education level and permanent place of residence, the farms' agricultural activity, Human Labour Units (HLU) and farms' viability level. All these variables were measured on nominal or ordinal scales. The available data were analyzed by means of a hierarchical cluster analysis method applied on the rows of an appropriate matrix of a complete disjunctive form with a dummy coding 0 or 1. The similarities were measured through the Benzécri'sχ2distance (metric), while the Ward's method was used as a criterion for cluster formation. Findings – Five clusters of farms emerged, with statistically significant diverse socio‐economic profiles. The most important impact on the formation of the groups of farms was found to be related to the number of HLU, the farmers' level of education and gender. This derived typology allows for the determination of a flexible development and funding policy for the agricultural farms, based on the socio‐economic profile of the formulated clusters. Research limitations/implications – One of the limitations of the current study derives from the fact that the clustering method used is suitable only for categorical, non‐metric data. Another limitation comes from the fact that a relative small number of investment plans were used in the analysis. A larger sample covering and other geographical regions is needed in order to confirm the current results and make nation‐wide comparisons and “tailor‐made” proposals for financing and development. Finally, it is interesting to contact longitudinal surveys in order to evaluate the effectiveness of the funding policy of the corresponding programme. Originality/value – The study's results could be useful to practitioners and academics because certain agricultural policy measures for the financing and development of Greek farms established by young farmers are proposed. Additionally, the data analysis method used in this study offers an alternative way for clustering categorical data.
Description: Δημοσιεύσεις μελών--ΣΤΕΓ--Τμήμα Αγροτικής Ανάπτυξης & Διοίκησης Αγροτικών Επιχειρήσεων,2008
ISSN: 1450-2194
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Appears in Collections:Δημοσιεύσεις σε Περιοδικά

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