Modelling the EU agriculture and policy: Departing from the first best world

Alexandre Gohin

Alexandre.Gohin@rennes.inra.fr

122 EAAE Seminar

February 17-18 2011

Ancona (Italy)

Operational market models

• PE models– AGLINK COSIMO– CAPRI– ESIM– AGMEMOD– FAPRI– PEATSIM

– IMPACT– ATPSM

• CGE models– GTAP Agri– MIRAGE– LEITAP(MAGNET)– LINKAGE(ENVISAGE)– GLOBE– GTAPPEM

– « ID3-Momagri »

Messages of the presentation• PE models should be used with CGE thinking

– Impact of energy prices on agriculture– Wealth effects of direct payments

• CGE models should be used in second best world– Labor market rigidities– Imperfect price transmission

• More modelling efforts should be devoted to dynamic, stochastic and financial issues– The issue of expectations and the costs of information– Downside risk aversion

1.a. Impacts of energy prices on agricultural prices

• Biofuels +– Quid of the contribution of market forces / policy

instruments

• Production costs +

• Transport/processing costs –

• Macro-economic effects ? – Mostly ignored in PE analysis– CGE results : macro-economic closure matters

Our methodological approach

• Starting point : GTAP standard model (GTAP 6 database)

• Introduction of GTAP-E and GTAP-Agr specifications– Latent separability here

• Three macro-economic closures– Da = f(Pa) : No budget constraint– Da = f (Pa, Pe, Income=Income0) Fixed income– Da = f(Pa,Pe,Income) CGE

• 20% decrease of oil reserve

Impact on EU price

Wheat Beef Dairy

No budget 3.6 1.3 0.8

Fixed income

2.6 0 -0.5

CGE 1.8 -1.5 -2.1

1.b. Wealth effects of direct payments

• Large literature on the coupling effects of lump sum payments

• No longer production neutral with market failures (fixed costs, credit constrained, …)

• Wealth effects of risk averse farmers (with DARA)

• Overall limited effects

• What is wealth ?

Standard specification

H

CIP

PCIPTIY

TdpDPts

TIYts

DPTRIPYEts

YEW

DPTRIPYWEC

Y

YY

...

.1....

...~..

..~.2

1...max

1.

1

1

1

10

22

00

,,

PdpR

TdpR

WNFW .0

Our modelling contribution :

Illustration on US cornProduction Final wealth

Standard specificationDirect paymentsMarket price support

-0.067-7.98

-3.58-0.51

Our specification

Direct paymentsMarket price support

-1.18-8.31

-39.79-14.94

2. CGE results in second best

• Welfare computed by CGE models can be decomposed in initial distortions and endowments effects :

• EV = sum(i, tmi*Mi) + sum(f, wf*Qf)

• By definition all policies should be removed. A policy can be welfare improving only due to the presence of other policies.

• Where are the market imperfections ? Public goods, externalities, imperfect competition, informational failures?

2.a. First illustration

• Starting with the standard GTAP framework :

• A PE version where prices and productions of other goods, regional incomes and wages are fixed

• A « Distorted » GE model with wage rigidity and unemployment (like Harrison et al (1993) or Mercenier (1995)).

• Simulation of a complete removal of the CAP.

Welfare impacts

“Producer surplus” (cap+land) Crop Animal Services

 -24.0-41.8+32.8

 -24.8-42.2

-

 -24.4-42.0+2.5

Taxpayer “surplus”Values of preceding taxes/subsidies

 +51.0

 +50.2

 +49.7

“Consumer surplus”Disposable incomeEV

 -13.4+8.9

 -

+29.7

 -40.8-19.1

“Total Welfare” +8.9 +12.8 -19.1

  Standard GE PE Distorted GE

2.b. « Real » figures

• Using the own made CGE model on EU

• Removing the CAP– Without imperfections– With imperfect price transmission– With unemployment

Welfare impacts (billion euros)

-16

-14

-12

-10

-8

-6

-4

-2

0

2

4

First best Transmission Chomage

3. Dynamic, stochastic analyses

• Most available models are not truly dynamic, nor stochastic (no risk aversion)

• Dynamics involve expectations

• Two main theories in the past : rational expectations (forward looking) and nerlovian expectations (backward looking)

• The information is not costless. What is the structure of information used by economic agents in our models, in real life ?

3.a. Dynamic effects : trade reforms

• First version : Gtap agri static

• Second version : consistent dynamic CGE model with rational expectations (more difficult to solve)

• Third version : Temporary GE with succession of static CGE models where dynamic decisions are made with imperfect knowledge of the future

• Simulation of trade liberalisation by the EU and US

Trade reform with rational expectattions

Prix du blé européen

-1,6%

-1,4%

-1,2%

-1,0%

-0,8%

-0,6%

-0,4%

-0,2%

0,0%

1 2 3 4 5 6 7 8 9 10 11

Sans erreurs

Trade reform with nerlovian expectations

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

1 2 3 4 5 6 7 8 9 10 11

Sans erreurs Avec erreurs

Trade reforms with nerlovian expectations and investment

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

1 2 3 4 5 6 7 8 9 10 11

Avec erreurs Investissements

3.b. Policy implications

• When designing policy reforms, trade off between economic and political economy pressures

• Because people need to learn, there may be an optimal way of implementing policy reforms

• How long should be the implementation period of CAP reforms ?

The EU wheat price following CAP reform

-10

-5

0

5

10

15

20

25

2011 2016 2021 2026 2031 2036 2041 2046 2051 2056

Rational-Brutal Rational-gradual Imperfect-Brutal Imperfect-gradual

The EU welfare following CAP reform

-1500

-1000

-500

0

500

1000

1500

2011 2016 2021 2026 2031 2036 2041 2046 2051 2056

Rational-Brutal Rational-gradual Imperfect-Brutal Imperfect-gradual

3.c. Risk analyses to third order

• Use of the mean variance approach does not recognize that price series may be skewed (due to storage issues in particular)

• Downside risk aversion not really taken into account

• Analysis of the interaction between biofuel and food markets with focus on volatility

Effects of the US biofuel policy on corn

Price Production

Without risk Total 26%

11.6%

2nd order risk aversion

Total 27% 11.4%

3rd order risk aversion

Total 30% 5.6%

Concluding comments

• Coupling models is interesting

• But efforts should also be spend on dynamic and stochastic issues

• Our direction : understand future markets and interaction with real economy

• More generally analyse one fondamental issue justifying agricultural policy: risk in agriculture