PowerPoint-pr

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EU dairy policy analysis Exploring the
importance of quota rent estimates
By Research Fellow Chantal Pohl Nielsen
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Why dairy ? Why quota rents ?

• Dairy remains one of the most protected sectors
  in the EU
• Price support, intervention purchases,
  production quotas, import tariffs,
• TRQs, domestic consumption and export
  subsidies
• The EU is a dominant player on world dairy
  markets
• 36 of production
• 27 of exports
• 14 of imports
• 2003 CAP reform fine-tunes the dairy policy
• Fast-tracking certain price cuts, limiting
  intervention purchases,
• decoupling compensatory payments
• Production quotas prolonged until 2014/15

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Objective

• To discuss the level of milk quota rents in the
  EU
• What are the correct estimates for the
  individual EU member countries?
• What are the underlying assumptions re. e.g.
  farmers expectations?
• To illustrate the impact of using different
  initial quota rents
• when analysing EU dairy policy reform using
  CGE models and to stress the
• importance of recognising EU heterogeneity
• To emphasise the need for further data work

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Binding production quotas generate rents

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Binding production quotas generate rents but
how large?
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Problem of unobservable marginal cost curves

• Determining the size of quota rents amounts to
  determining whether
• a country is capable of competing
  (unsupported) on world markets
• Analytical, were trying to determine the
  position of a countrys supply
• curve in an unregulated market, i.e.
  unobservable.
• Approaches taken in the literature
• Direct Estimates of marginal costs using
  farm accounts
• Indirect Quota prices (rent or lease)
  supplemented by assumptions
• about the annual value of quota
• Which real interest rate ? Length of
  depreciation time ?
• Farmers expectations re. future compensation
  ?
• ? All important assumptions with implications
  for the evaluation of the effects of dairy policy
  reform

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The Danish case
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The Danish quota value, of producer price
Value of Danish Quota, of producer price
Real interest rate, pct.
Depreciation time, years
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The Danish quota value, of producer price
Guyomard (2002)
Jansson (2002)
Jensen og Frandsen (2003)
Value of Danish Quota, of producer price
Real interest rate, pct.
Depreciation time, years
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Different estimates

• 1. Guyomard et al. (2002)
• Use EC Farm Accountancy Data Network (FADN) to
  estimate marg.
• costs but not entirely clear how. Quota rents
  15-49. DK 42
• 2. Jansson (2002) Depreciation time 8 years,
  real interest rate 6
• gt quota rent 22 of milk price
• 3. Jensen Frandsen (2003) Depreciation
  infinite, real interest 4
• gt quota rent 5 of milk price
• Depreciation time reflects farmers
  expectations
• High quota rents No compensation for policy
  changes
• Low quota rents Compensation for future policy
  change

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Substantial EU-15 differences ...
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Quota rents, EU-15, 2001
of producer price
Source Jensen Frandsen (2003)
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Illustrative simulations

• To illustrate the impact of using different
  initial quota rents
• when analysing EU dairy policy reform using
  CGE models
• To stress the importance of recognising EU
  heterogeneity
• Starting point is standard GTAP model and
  database (v 6.2)
• Four different adjusted databases
• Small (Jensen and Frandsen) and large
  (Guyomard) quota rents
• EU-15 as a single region and EU-15 disaggregated
• Simulation eliminates EU dairy export subsidies

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Effects on third countries

• Exports increase substantially
• USA 20-24
• Canada 27-19
• Australia 22-25
• New Zealand 11-12
• Using large or small initial quota rents -gt
  differences in results between 0.5 and 1
  -points

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Preliminary findings

• Initial quota rent estimates/assumptions
  influence results
• Magnitude of change in production, exports and
  welfare
• Both at EU aggregate level and for individual
  countries
• Influence on 3rd country results is less
  significant
• Ranking of initial quota rents is important for
  results
• Important to discuss relative competitiveness
  among EU-15

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Future work

• National quota rent estimates (reliable marginal
  cost estimates and/or milk quota prices)
• Relative competitiveness of EU15 (primary and
  processing)
• gt Survey initiated among agri-economic
  researchers in the EU
• Disaggregate GTAP dairy sector into categories
  such as butter, cheese, SMP, WMP, and other dairy
  products
• gt Possibilities of collaboration with Trinity
  College, Dublin
• Integration of full set of dairy policy
  instruments in models
• Include rest of CAP in model, incl. EU budget
  and interregional transfers, accession of new
  member states, etc.
• Baseline / update databases (dairy sector
  competitiveness depends on changes in factor
  markets, technology, policy, etc.)