Estimating Supply Response in the EU

1
Estimating Supply Response in the EU

• Carlos Arnade, David Kelch, and Susan Leetmaa
• ERS Economists

2
Introduction

• While models cannot represent all the
  complexities of the CAP, economic theory and
  statistical methods can determine the relative
  importance of prices and technology on
  agricultural production. We chose to model
  France, Germany, and the UK

3
Objectives of the Study

• Estimate price elasticities of area and yields
  consistent with standard assumptions of economic
  theory
• Estimate technical change elasticities using the
  technological component of a Malmquist index as
  an explanatory variable
• Compare the short-run effects of technology and
  prices on crop production
• Determine if EU output growth in EU agriculture
  would continue despite lower prices

4
The Results Include

• Price and technology elasticities of supply and
  input demand
• Price and technology contribution to supply
  changes
• Components of crop supply elasticities- yield and
  acreage

5
The Method

• Price and technology elasticities estimated from
  a two-step profit function (Chambers and Just)
  with first step profit function conditional on
  acreage allocation
• Technology represented by the technology
  component of a lagged Malmquist index
• Multicollinearity- estimated four separate profit
  functions (grains, roots and tubers and sugar,
  fruits and vegetables, livestock index) with
  additional corrections for grains
• Estimated each component of uncompensated
  elasticity shadow price equations for allocated
  area

6
Countries, Crops, and Data

• Grains- all countries wheat, barley, rapeseed,
  other grains. France has corn and Germany has
  oats
• Other crops- fruits and vegetables, livestock,
  roots, tubers and sugarbeets(a fixed output)
• Variable input demand for fertilizer, labor, and
  pesticides
• Quasi fixed factors exogenous- technology, animal
  stocks, and tractors
• DATA from 1973-1997(SPEL)

7
Model Estimation

• Naïve relative price expectations lagged
  normalized output prices
• Crop categories for the four profit functions- 1)
  grains and oilseeds 2) roots, tubers, and
  sugarbeets, 3) fruits and vegetables, 4)
  livestock
• 1) Reverse two-step correction procedure- term
  from first model accounts for short term
  disequilibrium
• 2) Imposes symmetry conditions among prices and
  area allocations, and between area allocations
  and the relevant variables in the shadow price
  equations
• 3) Imposed parameter restrictions to insure
  symmetry of area price response

8
Model Results

• Compared technology and price effects on output.
  Took average annual change in price and
  technology and multiplied by appropriate
  coefficient.
• UK- technology the dominant factor
• France- mixed results with technology reducing
  output of other grains, rapeseed, and pulses
• Germany- technology dominant in fv and roots
  tubers price dominates output of all grain
  except other grains
• Some crops are area substitutes but yield
  complements

9
Other Salient Features of Results

• Two results consistent across all three countries
• Technology has a significant positive impact on
  wheat production France .71, Germany 1.22, UK
  1.03
• Technology has a positive impact on pesticide
  use France 1.89, Germany .05, UK 4.42
• DK hypothesis- UK became a member in 1973 so high
  guaranteed CAP prices available to relatively
  large farms allowed new technology to be adopted
  over this period

10
Summary

• Technology most important in UK prices in France
  and Germany
• France- area elasticities important UK- yield
  elasticities Germany- mixed
• Adoption of new rapeseed variety and oilseed
  policy likely affected results

11
(No Transcript)
12
(No Transcript)
13
(No Transcript)