1. 1 UOP - ITALY Filippo Arfini (University of Parma. Italy). Michele Donati (University of Parma. Italy) 1 The Impact of CAP reform on the Employment Levels in Rural Area CARERA WP3 Assessing the Structural Impact of CAP Reform in the Farming Sector Creta 18 / 03/ 2006

2. 1 Objectives and main issues of WP3 (1) 2 The objective of this WP is to analyse and assess the Common Agricultural Policy impacts on land use and economic variables across some specific EU regions in five countries: Greece, Italy, UK, Germany, Sweden. This phase of the project should examine the specificity and the characteristics of agricultural holdings at regional level. The assess of CAP is based on the methodology of Positive Mathematical Programming (PMP) in its most recent development.

3. 1 Objective of WP3 (2) 3 In detail: Analysis of the impact of different mix of CAP measures on: the economic performance of agricultural holdings the potential developments in incomes and changes in land use. We should identify policy-induced changes in some key variables at farm level. We should describe and assess to what extent the characteristics of each region change as a consequence of the implementation of different CAP policy scenario. We should provide information and data useful for other statistical methodologies and further analysis.

4. 1 Objective of WP3 (3) 4 In terms of data, the main input for the regional models will be the EU- FADN data. These data pertain to crop land use, yields, total variable costs, output prices and subsidies and are structured per farm typology in each EU region. FADN database is enough for the purpose of CARERA ?

5. 1 Objective of WP3 (4) 5 The PMP regional models will allow to assess the main relevant effects of each of the different scenarios envisaged by the implementation of different policy scenario both on: the supply side (land allocation and total output per crop) the economic variables (gross saleable production, total subsidies, total variable costs, gross margins and marginal land values)

6. 1 Description of the work 6 farm models Will be taken into account farm models who describe the agricultural sector of each rural area. The models will be then improved to deal with labour effects at farm holding level This Work Package contains three (3) different research tasks as following: Task 3.1: Organisation of the FADN Database (all partners) Task 3.2: Calibration of PMP Models (UOP) Task 3.3: Prediction Phase of PMP Models (UOP)

7. 1 Description of the work 7 Task 3.1: Organisation of the FADN Database This task is linked to Work Package 2 and its scope is to organise the FADN data in the form required by PMP. The information, collected from FADN in some relevant case study-areas, by interviews and focus groups, will lead to identify specific case studies that will represent farms’ types on which the reaction to the new scheme of the agricultural policy will be evaluated. Assessing the supply and demand allocation and income effects under “static” conditions, identifying structural changes and other secondary items will be other goals of this task. Impacts on rental values of land and quota and income distribution between producer and factor owners will be analyzed.

8. 1 Description of the work 8 Task 3.1: Organisation of the FADN Database This task is linked to Work Package 2 and its scope is to organise the FADN data in the form required by PMP. The information collected from FADN in some relevant case study-areas, by interviews and focus groups, will lead to identify specific case studies that will represent farms’ types on which the reaction to the new scheme of the agricultural policy will be evaluated. Assessing the supply and demand allocation and income effects under “static” conditions, identifying structural changes and other secondary items will be other goals of this task. Impacts on rental values of land and quota and income distribution between producer and factor owners will be analyzed.

9. 1 Description of the work 9 Task 3.2: Calibration of PMP Models Individual PMP Models will be calibrated and tested under consistent and comparable scenarios using common databases and assumptions. The models also will provide other partners with information on land use organisation, shadow prices of land, farm income, and use of specific inputs (labour, land, quota, rights). Some problems for detect the amount of work per crop and Ha

10. 1 Description of the work 10 Task 3.3: Prediction Phase of PMP Models Once the models are calibrated and describe the character of the agricultural sector of each region, policy scenario will be applied. The information generated will be used by other activities in the project, that is for (a) quantitative assessment of economic impacts by regional input-output models; (b) exploration of alternative options and Pillar-2 compliance; (c) evaluation of the sociological and structural impacts of different policies.

11. 1 Description of the work 11 Deliverables The present Work Package will produce two kinds of scientific results: (1) Modeling of an Experimental approach for the Assessment of the Structural Changes in Farming Sector (D6) (2) A Report on the Effects of CAP Reform on the Farming Activities and Employment Levels (D9) Milestones and Expected Results: (1) M3.1 Analysis of FADN database (2) M3.2 Model calibration (3) M3.3 Prediction of changes in the economic structure of farming activities

12. 1 Description of the work 12 Wp3 Timing

13.  Data for the models;  Model organisation;  Estimation of variable costs;  Integrated approach with PMP and MSA? Open issues:

14. 1 Open issues: data for the models 14 Some comments about EU FADN evaluating the income of agricultural holdings  FADN is an instrument for evaluating the income of agricultural holdings and the impacts of the Common Agricultural Policy. not cover all the agricultural holdings  The survey does not cover all the agricultural holdings in the Union but only those which due to their size could be considered commercial. Which FT we should consider? Problem of representativity. crops variable cost  FADN doesn’t provide information about crops variable cost  From a theoretical point of view  From a theoretical point of view. FADN is “the ideal” instrument for all researchers. It contains all the necessary information for the construction of an agricultural policy analysis model.  From a practical point of view  From a practical point of view. FADN in fact presents great limits that influence its use … the lack of representativity of land allocation especially at sub regional level

15. 1 Open issues: data for the models 15 FADN is good for provide data for sector models (by FT), but… Sector model are good for represent agricultural sector at regional (rural) level ? How we can built a good regional model in term of representativity of farm holdings considering also others sectors present in the rural area? Should we consider also Local Work Systems? Our proposal is to merge EU FADN with others statistical source relevant at NUTS II level (ie REGIO)

16. Information for PMP models FADN FADN Databank: Crop yields Prices Subsidies Total Costs Labour REGIO REGIO Databank: Land harvested for different crops and AWU New Database New Database to be used with PMP models PMP models Estimation of variables (costs) and adjustments to produce information adapted to the quantitative tool The two database will be merged with respect to the Fadn stratification criteria (Nuts2, Altitude, Class of Size). Some problems... YEAR 2002

17. Information collected  Land use: hectars cultivated for each crop  Production: quantity producted for each crop  Price  Subsidy  Total cost: totale specific cost per farm FADN Archive – year 2002  Land harvested for different crops  Total production for each crop  Yields (tons/ha)  Labour: family labour and external labour REGIO Databank – year 2002

18. Information lacks and merging problems FADN The information collected by the FADN archive provide very few information about the amount of subsidies per crop. REGIO, Considering the information from REGIO, we encounter more difficulties in order to merging the two databank:  We have not a reference on the Altitude level nor for the Classes of Size;  The land use is not completely mapped (A2crop); i.e. there are not information about forage crops and the possible infromation conintained in the table A2efarm provide information only upto year 2000.

19. Organisation of the model NUTS2 Altitud e 1 Altitud e 2 Altitud e 3 Size 1 Size 2 Size 3 Size 4 Size 5 Size 6 Size 7 NUTS2 Regions One Q matrix for each NUTS2 Region The model provides simulations for each class of size, for each altitude area and for each NUTS2 region.

20. 1 20 Organisation of the model Gross Margin (1) Gross Margin (2) Gross Margin (3) Gross Margin (n) Resource (1) Resource (2) Resource (3) Resource (n) Milk Produc. (1) Milk Produc. (2) Milk Produc. (3) Milk Produc. (n) Nuts2 region 1 Nuts2 region 2 Nuts2 region 3 Nuts2 region n Objective Function Structural costraints (LandUse) Quota Costraint Regional Gross Margin (PROFT) Sub-regional structure capacity(b n ) Regional Milk Quota

21. Information scheme Regio tables Fadn data GAMS-GDX Routines PMP Dataset Database Processor Tables for PMP models Spreadsheet automatic generator Integrated Database

22. 1 22  For lack of representativity became necessary to find new data source.  Integration of FADN with REGIO can provide unique regional data and help modelers for policy propose.  The integration of the two databases with PMP allows the later aggregation of the models, moving the policy analysis from sub-regional, to regional and to national level.  This approach is characterized by high level of flexibility, which allows to join together sub-regions with similar administrative and climatic characteristics. Organisation of the model

23. Estimation of specific variable costs The lack in specific variable cost information at crop level doesn’t permit to derive completely the cost function parameters to use in the prediction phase. So, we have to implement an approach to derive this kind of cost different respect to the traditional PMP. In particular we can proceed substituting the first phase of PMP model in the follow alternative ways: 1)Derive the parameters for the cost function by an estimation using the Maximun Entropy approach (Leon et alt., 2000); 2)Derive the marginal cost associated to each crop imposing directly the first order optimality conditions in the first phase of PMP (Heckelei, 2003).

24. The integrated model for CARERA Analysis

25. GroupIndicators Main social and demographic indicators and territorial morphology % change in population 1990-2001; female activity ratio, dependency ratio, ageing index, population density, % hilly areas, % mountain areas. Economic structureEmployees per km 2 : agriculture, industry, services; ratio of industry employees; ratio of services employees; per capita GDP; unemployment ratio. Agricultural structureAvg. UAA per farm; % UAA of farms under 2 ha; % UAA of farms above 50 ha; tractors per 100 ha UAA; AWU per 100 ha UAA; % UAA on total agricultural surface; % change in number of farms; % change in UAA. Agricultural activities and land use Crops as UAA % Animal production: heads/UAA Soft wheat; durum wheat; barley; maize; rice; dried leguminous vegetables; potatoes; beet; sunflower; soya beans; horticulture; grapes; oilseeds; apples; pears; peaches; kiwi; chestnuts; feeding crops; meat; milk; woods; pastures. Productivity of agriculture Standard gross margin per ha of UAA; marginal price of land; gross saleable production per ha (vegetables), GSP per ha (animal production); variable costs for saleable production per ha; variable costs for re-used production per ha; variable costs for animal production per ha. Integration with food industry % food firms on total manufacturing firms; employees per food firm; employees in the food sector per km 2.

26. We propose all the EU NUTSII Regions(D6). 1.Use intrgrated PMP/MSA approach for all the EU NUTSII Regions (D6). 2.Develop a specific software able to merge data and integrate PMP/MSA for the purpose of regional analysis. 3.Soft analysis for all the 15 (or more) EU countries (D9). 4.Deep analysis for specific sub-regions in five Country where take in consideration the effect of Rural Development Plan (D9).

27. Open questions What need to others WP in terms of data? Which regions we have to consider? How to consider Rural Development Plan? Which policies we have to take in consideration ?