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							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
   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
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   IAHS results increasingly out-of-date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
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
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
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
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
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)
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.
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.
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).
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
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
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
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)
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
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
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.
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
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