The research reported in this chapter refers to five European Union (EU) selected regions (Emilia Romagna, Italy; East Wales, United Kingdom; Anatoliki Makedonia and Thraki, Greece; Östergötland, Sweden; and Kassel, Germany) to identify and measure the Common Agricultural Policy’s (CAP) effects on employment throughout regional economies. It accounts for agricultural and non-agricultural effects, and covers the diversity of EU rural regions. A framework of three different approaches was developed and then applied to these five regions in order to trace out the current and anticipated employment effects of Pillars 1 and 2 changes. The focus of this work is to consolidate the conclusions derived from the different models applied in order to deduce valuable policy generalizations and to derive conclusions which may guide policymakers in making decisions related to regional and rural development. The results demonstrate that CAP funding, particularly for Pillar 2, contributes to the maintenance of employment in the farming sector but also in the non-farming sector, thus serving as a permanent regional “stimulus” package.

In the current situation of limited empirical evidence on the impact of modulation, a combination of modelling and non-modelling approaches is used in a study for Directorate General Agriculture and Rural Development in order to provide a comprehensive analysis of consequences on distribution of funds and budgets, farm structure, socio-economic conditions (competitiveness, farm income, employment, quality of life) and environmental quality. This chapter analyses a variety of economic outcomes achieved through a range of modelling methods.

This chapter analyses the regional dynamic and spatial distribution of agricultural production in France. The analysis is based on data obtained at two spatial levels: region and département, and the data cover the period from 1990 to 2006. Different methods are applied to analyse the French production structure: maps and regional specialization are combined with regional concentration, the calculation of spatial autocorrelation and a local indicator of spatial association. These methods are applied to ten agricultural sectors. Results indicate that the activities which are regionally concentrated are not inevitably spatially autocorrelated, especially for production activities which are supported by the Common Agricultural Policy (CAP). A more specific analysis was conducted to determine the factors influencing the spatial dynamics using as an application the dairy sector (which is revealed as the most spatially autocorrelated). This approach was applied using spatial econometric models for dairy production in 1995 and 2005. It shows that market signals are more important in determining the dairy farm location in 2005 than they were in 1995. Environmental regulations also become more relevant in 2005 than in 1995, and seem to decelerate the rate of concentration amongst dairy farms