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Modelling the EU agriculture and policy: Departing from the first best world Alexandre Gohin [email protected] 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 1 2 2 EC W0 PY .Y PCI .I R.T DP . . Y . PY Y , I ,T 2 W0 E ~ s.t. E ~ PY .Y PCI .I R.T DP max s.t. Y 0 . 1 .I s.t. DP dp.TH 1 1 1 .T 1 . Our modelling contribution : R dp .TP W0 WNF R dp 1 Illustration on US corn Standard specification Direct payments Market price support Production Final wealth -0.067 -7.98 -3.58 -0.51 -1.18 -8.31 -39.79 -14.94 Our specification Direct payments Market price support 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 Standard GE PE Distorted GE “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 income EV -13.4 +8.9 +29.7 -40.8 -19.1 “Total Welfare” +8.9 +12.8 -19.1 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) 4 2 0 -2 -4 -6 -8 -10 -12 -14 -16 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 0,0% 1 2 3 4 5 6 7 -0,2% -0,4% -0,6% -0,8% -1,0% -1,2% -1,4% -1,6% Sans erreurs 8 9 10 11 Trade reform with nerlovian expectations 40% 30% 20% 10% 0% 1 2 3 4 5 6 7 8 -10% -20% -30% -40% -50% Sans erreurs Avec erreurs 9 10 11 Trade reforms with nerlovian expectations and investment 40% 30% 20% 10% 0% 1 2 3 4 5 6 7 8 -10% -20% -30% -40% -50% Avec erreurs Investissements 9 10 11 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 25 20 15 10 5 0 2011 2016 2021 2026 2031 2036 2041 2046 2051 2056 -5 -10 Rational-Brutal Rational-gradual Imperfect-Brutal Imperfect-gradual The EU welfare following CAP reform 1500 1000 500 0 2011 2016 2021 2026 2031 2036 2041 2046 2051 2056 -500 -1000 -1500 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 Total risk aversion 27% 11.4% 3rd order Total risk aversion 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