Estimating Supply
Response in the
EU
Carlos Arnade, David Kelch,
and Susan Leetmaa
ERS Economists
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
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
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
The Method




Price and technology elasticities estimated from a twostep 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
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)
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
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 f&v and roots &
tubers; price dominates output of all grain except “other
grains”

Some crops are area substitutes but yield
complements
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
Summary



Technology most important in UK;
prices in France and Germany
France- area elasticities important;
UK- yield elasticities; Germanymixed
Adoption of new rapeseed variety
and oilseed policy likely affected
results
Price and Technology Comparison:Germany
1000.00
800.00
600.00
400.00
200.00
0.00
-200.00
Wheat
Grain
Barley
Rape
Oats
Price
Pulse
Tech
O-root
Vegetable
Fruit
Price and Technology Comparison:France
2500.0
2000.0
1500.0
1000.0
500.0
0.0
-500.0
Wheat
o-Grain
Maiz
Barely
Rape
Price Tech
Pulse
Vegetable
Fruit
Price and Technology Comparison:UK
400.00
350.00
300.00
250.00
200.00
150.00
100.00
50.00
0.00
-50.00
-100.00
Wheat
Grain
Barley
Rape
Pulse
Price Tech
Root-Cr
Veg
Fruit