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

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Wheat Grain Barley Rape Oats Pulse O-root Vegetable Fruit

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Price and Technology Comparison:France

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Wheat o-Grain Maiz Barely Rape Pulse Vegetable Fruit

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Price and Technology Comparison:UK

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Wheat Grain Barley Rape Pulse Root-Cr Veg Fruit

Price Tech