estimating supply response in the eu carlos arnade, david kelch, and susan leetmaa ers economists
TRANSCRIPT
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 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
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; Germany- mixed
Adoption of new rapeseed variety and oilseed policy likely affected results
Price and Technology Comparison:Germany
-200.00
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Wheat Grain Barley Rape Oats Pulse O-root Vegetable Fruit
Price Tech
Price and Technology Comparison:France
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Wheat o-Grain Maiz Barely Rape Pulse Vegetable Fruit
Price Tech
Price and Technology Comparison:UK
-100.00
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Wheat Grain Barley Rape Pulse Root-Cr Veg Fruit
Price Tech