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FARMS MULTIFUNCTIONALITY AND HOUSEHOLDS INCOMES IN SUSTAINABLE RURAL DEVELOPMENT

Session 4: Income and Employment of the Rural Household. FARMS MULTIFUNCTIONALITY AND HOUSEHOLDS INCOMES IN SUSTAINABLE RURAL DEVELOPMENT. By Marco Ballin Andrea Mancini Edoardo Pizzoli. ISTAT, Italy. General purpose. Evaluating the capacity of statistical analysis:

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FARMS MULTIFUNCTIONALITY AND HOUSEHOLDS INCOMES IN SUSTAINABLE RURAL DEVELOPMENT

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  1. Session 4: Income and Employment of the Rural Household FARMS MULTIFUNCTIONALITY AND HOUSEHOLDS INCOMES IN SUSTAINABLE RURAL DEVELOPMENT By Marco Ballin Andrea Mancini Edoardo Pizzoli ISTAT, Italy

  2. General purpose • Evaluating the capacity of statistical analysis: • in respect to the agriculture sector; • disclosed by the integration of data collected with different surveys on farms belonging to the same population.

  3. Main contents In our paper we discuss Farms multifunctionality Income structure of agriculture households Economic issues Statistical informations for economic analysis and policy making An overall design of statistical surveys on farms Statistical issues Data integration techniques in relation to data collection methods (sample, census or administrative sources)

  4. The multifunctionality of farms Primary economic functions of farms: to produce foods and raw materials MARKET ACTIVITIES Secondary economic functions of farms Tourist activities Processing of agriculture products aquaculture Landscape maintenance MARKET ACTIVITIES Social functions of farms: Environmental benefits (e.g. soil conservation, biodiversity) Food safety Socio economic viability in rural areas NON MARKET ACTIVITIES

  5. Farms, households and incomes Statistical unit Typologie FARM Direct management farming Definition A farm conducted by an holder with some degree of his work and of his family components. Household income structure From agricultural activities Off - farms incomes (holder and his family components) Mixed incomes Interest and profits From connected multifunctional activities Wages Pensions

  6. The surveys system by Census each 10 years European farm structure survey (FSS) bysample surveyeach2years sample sizeof55,000farms by sample survey each year Business survey on agriculture sample size of 17,000 farms since 2002 incorporates the European Farm Accounting Data Network (FADN) Data used for our exercise come from Census 2000 and Business Survey 2000

  7. Ex post Data integration (reference year 2000) The 2000 edition of Business Survey was based on a random sample selected from a list of units not selected from the Census archives. The Business Survey questionnaire didn’t include any information on physical and production structure of farm. For these reasons data integration has been carried out in 2 steps: Record linkage through the farms statistical identification code Step 1 About 90% of Business Survey farms linked with the Census Distributions of main structural variables estimated on the linked units distributions resulting from the Census Step 2 Macrolevel integration – the sample weights of Business Survey have been calibrated with respect to the main results of the Census

  8. Ex ante Data integration(starting year 2002) Only the second level of integration between FSS and Business Survey is necessary Because The Main structural variables are observed on each sample unit of Business Survey

  9. The Structure of Italian agriculture Table1 – Distribution of structural variables (inputs) by classes of AAU 57.2% <5ha 80.3% 20.3% 79.7% 87.5% 77.9% 42.8% ≥5ha 19.7%  Source: Istat – Agricultural Census 2000

  10. Main economic results of Italian farms Table2 – Distribution of structural variables (outputs) by classes of AAU <5ha 35.2% Sources: Istat –Business Survey on Farms, Agricultural Census 2000 (*)About 20% of the farms in the first row are specialised in animal breading 

  11. Italian multifunctional farms and households Table 3 - Households and multifunctional farms distributions (%) 50ha >91% Sources: Istat –Business Survey on Farms, Agricultural Census 2000 

  12. Italian multifunctional farms and households Table 4 - Ratios between median values of multifunctional farms over monofunctional ones, belonging to households >  Sources: Istat –Business Survey on Farms, Agricultural Census 2000

  13. Italian multifunctional farms and households Table 5 - Off-farm incomes of the holder and his enlarged family components by classes of AAU Sources: Istat –Business Survey on Farms, Agricultural Census 2000

  14. Italian multifunctional farms and households Table 6 - Percentage composition of households incomes holding a mono- or a multi-functional farm by classes of AAU 74.6% 67.5% 78.6% 31%  > Sources: Istat –Business Survey on Farms, Agricultural Census 2000

  15. Conclusions 1 From an economic point of view: 1 multifunctional activities: low diffusion but increasing with AAU class; multifunctional farms managed by households have a better performance because the relative increase of revenues related to the secondary activities is higher than the increase of intermediate and labour costs; 2 in households managing farms up to 15 hectares AAU, the off-farm incomes are relevant and spread; 3 multifunctional activities allows integration of agriculture income and substitution of off-farm sources; 4 • the relative effect of the two phenomena is different in classes of AAU: • Integration effect higher for households with smallest farms; • substitution effect higher for households with farms in the intermediate classes of AAU. 5

  16. Conclusions 2 From a statistical point of view: Definitions and measurement methods of social costs and benefits connected to agriculture activities are needed; 1 surveys should include small farms important for rural development; 2 integration between a Business Survey and a Structural Survey is a suitable tool 3 IF surveys are coherent with respect to definitions of statistical units and common structural variables; a in the case of non over lapping samples, business survey should collect a minimum set of structural variables useful to calibration and microdata analysis. b

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