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Berkeley Hill Emeritus Professor of Policy Analysis University of London (Imperial College)

Data Sources and Quality Improvements for Statistics on Agricultural Household Income in 27 EU Countries. Berkeley Hill Emeritus Professor of Policy Analysis University of London (Imperial College). Introduction. Rising awareness of multiple incomes 1985 Commission ‘Green Paper’ + Annex

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Berkeley Hill Emeritus Professor of Policy Analysis University of London (Imperial College)

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  1. Data Sources and Quality Improvements for Statistics on Agricultural Household Income in 27 EU Countries Berkeley Hill Emeritus Professor of Policy Analysis University of London (Imperial College)

  2. Introduction • Rising awareness of multiple incomes • 1985 Commission ‘Green Paper’ + Annex • Eurostat IAHS statistics 1988-2002 • Based in system of national accounts • Harmonised methodology (Definitions) • Sector-level results • Declining EU and national priority • Some political and institutional hostility • Some MS used micro data as source of results

  3. IAHS hiatus of early 2000s • IAHS results increasingly out-of-date • Importance of distributional information • EU enlargement – new types of agricultural household and business • Court of Auditors 2003 review of IAHS • Met central objective of CAP but • Statistics of poor quality (NL evidence) • Recommended a feasibility study of a uniform micro-approach across all MS – endorsed by Council

  4. Interim research work • Gradual accumulation of information on data sources – Eurostat, OECD etc • ISTAT 2002 TAPAS Initiative reviewed income data sources and alternative calculations in Italy • Statistics Sweden 2006 study on feasibility of adding questions to the FADN farm form • No significant technical barriers

  5. The 2007 feasibility study - AgraCEAS • Template of uniform key definitions (income, household etc.) developed from • 2005 UNECE Handbook • Survey of users • Visits to MS and surveys to find data sources • Feasibility of using template assessed • Method of filling data gaps proposed and costed • Recommendation made to Eurostat

  6. Key definitions • Household – single budget unit • Household classification • ‘narrow’ – agriculture main income source of reference person • ‘broad’ – range of possibilities (any farm income, holding characteristics - FSS, SFP) • Net disposable income – as in Handbook • Detailed breakdown Imputed items shown separately

  7. Inventory of data sources (25 MS) • Farm accounts surveys • Household income not part of EU-FADN • Only some MS collect household data • EU-SILC • Generally few agricultural cases • Income data often of poor quality • Household budget surveys (as above) • Taxation records and registers • In many MS farmers not taxed on actual income • Tax income definitions pose problems / disclosure

  8. Example – Austria • Farm accounts - sample of 2,500 holdings which also covers household income • EU-SILC - 271 cases in 2005. • HBS - carried out once every 5 years. • Tax records - For a large proportion of farmers tax payment is not based on accounting income (farmers pay 'lump sum' taxes)

  9. Example – Poland • Farm accounts - data on five types of non-agricultural income collected from about 10,000 farmers in 2005. • EU-SILC - agricultural cases not known; they are combined with other self-employed. • HBS - Some 2,000 agricultural households in 2005; problems with income data quality. • Tax records - assessment (mainly) uses a standard rate based on land and forest area, land quality and distance to market.

  10. Example - Spain • Farm accounts survey – no household data  • EU-SILC - only 253 agricultural cases in 2004 (similar number in 2005). • HBS - only about 120 agricultural cases, but there are difficulties with incomes from self-employment. • Tax data - some farmers do not pay tax based on actual incomes, and incomes may be estimated.

  11. Example - Luxembourg • Farm accounts survey - questions covering household income were used for 1989 only. • EU-SILC - only 78 agricultural cases in 2004 . • HBS - few agricultural cases. • Tax records - most farmer incomes are not on an accounts basis. • Other - There is a poverty survey of households (CEPS).

  12. Feasibility testing of definitions • Each aspect of the template (household, agric household narrow and broad, income definition, comparison with others) was assessed as: • Currently in use • Not in use but technically possible • Requires development of existing data sources • Requires a new data source

  13. Example – ‘narrow’ agric household based on the reference person • Currently used – 3 MS • Technically possible – 16 MS • Required data source development – 4 MS • Requires new data source – 3 MS

  14. Example – Can comparisons be drawn with other socio-professional groups? • Currently made – 7 MS • Technically possible – 10 MS • Requires data source development – 4 MS • Requires new data source – 5 MS

  15. Example – use of Net Disposable Income? • With existing data source – 19 MS • New data source needed – 5 MS • UK – main data source does not collect tax paid • Germany – questionable reliability of existing sources • (others were Slovakia, Hungary and Luxembourg)

  16. Filling the data gaps MS fall into three broad groups • Special survey needed to cover both ‘narrow’ and ‘broad’ definitions of an agricultural household – hybrid of FADN and EU-SILC questions, collected by EU-SILC method • ‘Narrow’ covered, but special survey for ‘broad’, typically below FADN size threshold • No new data collection needed – only extraction

  17. Costing – MS costed individually • Transparent calculations allow alternative figures to be used • Survey costs based on existing national EU-SILC data costs, and commercial rates • Case numbers • ‘narrow’ – as for existing FADN samples • ‘broad’ – below FADN threshold same sample rate as above, and at a 1% rate

  18. Examples • Denmark – data extracted from existing registers (no additional survey) • Germany – additional special period survey for both the ‘narrow’ and ‘broad’ definitions • Poland – use of existing farm accounts survey for ‘narrow; additional survey below FADN threshold to cover the ‘broad’. Other work needed to faciliate comparisons with other spg.

  19. Results • Aggregate cost of collecting data to enable comparable and robust statistics (one-off surveys) • €11.5 million survey costs + €1 central costs for the ‘narrow’ definition of an agricultural household • Additional costs of extending coverage to the ‘broad definition’ €9.1 – 13.3 (totalling €22-26m) • In comparison EU-SILC costs c.€27m p.a. • If the cost led to a 1% efficiency gain in Pillar 1 spending, this would be 19 times greater

  20. In conclusion • Good quality data are essential to quality • A useful inventory of data sources on incomes of farm households is now to hand • A technical assessment of the feasibility of producing robust EU-wide IAHS statistics has been made and costed. • Eurostat has not taken up the proposed actions; some MS do not seem to be keen • The Court of Auditors has expressed interest in why progress has not been made

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