use of economic modelling in agricultural policy · 2013-05-02 · • quite detailed scenario...

Use of economic modelling in agricultural policy

Wolfgang Britz, Peter WitzkeInstitute for Food and Resource Economics, University Bonn

Context

Types of modelling applicationsTypes of modelling applicationsTypes of models usedInstitutional solutions to host modelsOverview on CAPRIExamples for applicationsD lt tt f li ?Do results matter for policy?Summary and conclusion

Britz/Witzke : Use of economic modelling in agricultural policy, 18.04.2013, Krško

Application types

Scoping: What are possible future developments?(e.g. study “SCENAR 2020” or IPCC Climate Change scenarios)E i f l d li hEx-ante impact assessment of planned policy changes,mandatory for EU Commission if larger budget impact(e.g. recent legal proposal for Common Agricultural Policy - CAP 2014-2020)Ex-post evaluation of past policies, in the meantime mandatory(e.g. regular tenders by DG-AGRI)


Types of models for policy analysis (1)

Focus here on Europe and Common Agricultural Policy (CAP)CAP changes typically provoke impacts(at least potentially) • on prices => equilibrium models [supply=demand=f(prices)]• on trade => global modelson trade > global models• across EU, but not uniformly => at least detail at country level

⇒ Market entry costs for new models are quite high(data and parameter requirements due to EU/global coverage, IT infrastructure)

⇒ Relative small set of models available, not much movement in market …



Equilibrium models:• Single market modelsSingle market models

– For prices, quantities, policy in single market (or a few)– e.g. Banana model (G Anania), Dairy model (V Requillart) ..

• Multi-Commodity ModelsMulti Commodity Models– Equations for many agricultural markets– e.g. “AGMEMOD”, “AGLINK”, “CAPRI”, “ESIM”, “GLOBIOM”

• Computable General Equilibrium (CGE) modelsComputable General Equilibrium (CGE) models– for all markets in whole economy plus budget constraints– e.g. “GTAP” variants like “MAGNET”, “MIRAGE”, “GLOBE”,

regional CGEs in CAPRIregional CGEs in CAPRI



Equilibrium models possible linked to• more dis-aggregated supply models

( b i b ilt i CAPRI) “A j” “RAUMIS” “FARMIS”(can be in-built as in CAPRI), “Aropaj”, “RAUMIS”, “FARMIS” …• bio-physical models/components

e.g. Dutch model “INTEGRATOR” for nitrogenous emissions, d l li k f CAPRI “DNDC“post model link of CAPRI to “DNDC“

• land use models:– CLUE-S (P Verbourg)– GLOBIOM (P Havlik)

• external studies used for baseline– CAPRI uses several sources (AGLINK, GLOBIOM...)( )– Large agencies often combine several inhouse models (IFPRI,

FAO, LEI, EU Commission)


Institutional solutions

Tool may be hosted in• in administration or government sponsored agency

– ESIM model used by DG-AGRI (in the past)– Models used by ERS (USA), JRC (Europe), INRA (France), TI (Germany)

• in independent institution, – long-term contracts or – Case by case tenders

Viewpoints:p• Development versus maintenance• Control over results

R ti / t ti t• Response time / transaction costs• Synergies / antagonism if several models


CAPRI overview

Developed since 1997, operational and maintained since 2000, financed to a larger extent by EU framework programsComparative-static (i.e. no description of path of adjustment),deterministicConsistent combination of different sub-models/components:Consistent combination of different sub models/components:• Global trade model• European supply models

R i l CGE• Regional CGEs• Post-model processing (environmental indicators, welfare analysis,

CAP budget, energy use indicator)• Spatial down-scaling to 1x1 km grid cells and link to bio-physical

models


CAPRI overview (2)

Price• Global: 40 trade

blocks and ca. 80 countries

• Spatial: “Armington” assumption ( > d diff by i i )

S l + F d d d

(=> goods differ by origin)

• Detailed trade policy

Supply + Feed demand• Non-linear programming models for

280 regions or 1.900 farm types • Profit maximization under constraints• Profit maximization under constraints• => detailed policy representation

(premiums, quotas…)

• and links to environment


• … and links to environment

CAPRI overview

Data from Eurostat, FAOstat, EU Commission (FADN) …Technical solution:• Data transformations and simulation in GAMS• User interface in Java (steering, tables/graphs/maps)User interface in Java (steering, tables/graphs/maps)• Server based software versioning system (SVN) for exchange of

data and computer code in networkInstit tional modelInstitutional model• Open source/access, …• Network of users/developers across Europe, including EU

Commission services


Examples of CAPRI applications

Research project applications:• Selection mainly to give a good illustration of model development• Relevant example may raise awareness of potential users• But no influence on scenario design: leave or take option for policyBut no influence on scenario design: leave or take option for policy

Tendered applications:• First one for CAPRI 6 years after development started and 3 years

ft fi t ti l iafter first operational version• CAP Mid Term Review, sugar market reform, some contributions to

ex post evaluations ..• Recent years:

– Ongoing CAP reforms (premium, market organizations)– Free-trade agreements– Agri-Environmental interactions– Study on ban of GMOs (genetically modified organisms) by LEI– Climate related studies


Example: SCENAR 2020 scoping study

Scenario study => no immediate policy under investigation, only broad options for EU policy:options for EU policy:• “Baseline”, “Regionalisation”, “Liberalisation”• Research environment better than administration for scenario

approachModelling suite• LEITAP-IMAGE (global CGE) ESIM (ag baseline biofuels) CAPRILEITAP IMAGE (global CGE), ESIM (ag baseline, biofuels), CAPRI

(CAP, NUTS2), CLUE-S (complete land use model)Plus statistical analysis, cluster analysis, SWOT analysis for regionsR lt f d lli h i t ti l l l b t l blResults from modelling chain at national level but rural problems (beyond agriculture) need further analysis• Picked up in project CAPRI-RD with regional CGEsp p j g• Other methodology also picked up in CAPRI trunk

– Land supply curve – Implementation of Rural Development policiesp p p


Example: OECD 2011 expost study

Tendered by OECD secretariat to contribute to study MORREDU ET.AL. Evaluation of Agricultural Policy Reforms in the European Union.OECD, Paris, 2011, ,CAPRI selected as it offered• rich detail in describing the CAP pillar I instruments

(premiums and market price support)(premiums and market price support)• full EU coverage• farm type and regional dis-aggregation(not available with OECD in-house tools)

What has been the effect of decoupling?• Overall limited effects on allocation• Overall limited effects on allocation• But transfer efficiency increased


Example: DEFRA study on drastic reform

Background:• UK Department for Environment, Food and Rural Affairs (DEFRA)

in favor of drastic reform for CAP 2013-2020in favor of drastic reform for CAP 2013 2020• But what about land abandonment in marginal areas?

Combination of CAPRI and Land Use Cover Change Model (CLUE-S) d CAPRI d d t i l t h i ki d i fused, CAPRI expanded to simulate shrinking and expansion of

agricultural areaMain findings:g• At country level reduction in land use between 6 (UK) and 15%

(Greece), larger effects for marginal areas• Price increases for outputs partially offset loss of subsidiesPrice increases for outputs partially offset loss of subsidies• Environmental impacts overall reduced, but higher intensity on

remaining agricultural land


Example: JRC study on Mercosur issue

Recent study by EU Joint Research Centre on Free Trade Agreement with Mercosur

C bi d CGE (GLOBE) d lti dit d l (CAPRI)• Combined CGE (GLOBE) and multi-commodity model (CAPRI) application

• Explicit representation of trade policies in CAPRI permits realistic l ianalysis

• Quite detailed scenario definition (tariffs and tariff rate quotas, sensitive products…)

• Equally interest in regional effects, e.g. will freer trade in beef with South-America lead to heavy income losses in mountainous regions specialized in suckler cows?


Example: MIRAGE-BIOF analysis of ILUC

Background and motivation:g• Searchinger et al 2008, Science: Pay back period of ethanol from

US maize is 167 years, because indirect land use change (ILUC) effects release huge quantities of carboneffects release huge quantities of carbon

• Stimulated criticism and numerous studies• But also marks turning point in biofuel enthusiasm

MIRAGE d f DG T d (2009 11) di ILUC ff f EUMIRAGE study for DG Trade (2009-11) studies ILUC effects of EU Renewable Energy Directive (“RED“, 10% target for transport fuel)• ILUC effects had to be investigated according to REDg g• With several delays finally presented Nov 2011• State of the art CGE model MIRAGE:

– Endogenous yields by-products disaggregated oilseedsEndogenous yields, by products, disaggregated oilseeds, substuitution of cropland, pasture, forest, income elasticities updated on baseline, Monte Carlo for parameters...

• => ILUC factors for policy package and by crop (gCO2/MJ)• => ILUC factors for policy package and by crop (gCO2/MJ)


Do results matter? (1)

Weak empirical evidence (no systematic with and without observations, interviews likely to provoke strategic answers …)Need to look at historical examples #1: CAP reform process (from priceNeed to look at historical examples, #1: CAP reform process (from price to coupled to decoupled support) as of 1992• Standard welfare analysis did back up Commission position

B t thi t l d d f d d b J li– But this argument was already made for decades by Josling 1973, Koester/Tangermann 1977

• Perhaps more relevant: stabiliser policy unable to meet income lgoals

• Inviting to use economic model results as a justification for reform proposals

• But sometimes apparently studies are effective: Choice of Ireland for complete decoupling in view of farm income benefits



Example #2 Sugar market reform 2006• Several preceeding studies have demonstrated the inefficiency of

the Sugar Common Market Organisation and the need for reformthe Sugar Common Market Organisation and the need for reform• Why 2006 after decades of stability?

– Everything But Arms agreementWTO l d i i 2005 ( C t )– WTO panel decision 2005 (no C-sugar exports),

• But unclear whether Commission has defended its sugar policy as good as possible in panel

h l d i d b d f f ?– perhaps already convinced by need for reform?• But choice of reduced intervention price clearly informed by earlier

studies • Could be a general phenomenon: quantitative analysis for choice of

details in implementation



Example #3: Increasing weight for environmental issues in CAP :• Several criticisms of the CAP from academia and non-Several criticisms of the CAP from academia and non

governmental organisations• Therefore cross-compliance and decoupling? • Could also be political vehicle to rescue CAP support• Could also be political vehicle to rescue CAP support• Some econ arguments are being picked up

– Details of crop diversity requirementsExample #4: Any effect of ILUC studies on policy?• Yes for Searchinger et al 2007!• Unclear for MIRAGE study in view of Commission ImpactUnclear for MIRAGE study in view of Commission Impact

Assessment (Oct 2012):– Option D (ILUC Factors) discarded because

Devalution of existing investments in bidiesel industryg yRED target of 10% in danger (and a past EU decision cannot be wrong)

– Preferred option E: Cap for conventional biofuels in meeting RED t t t 50% f d f t i t tRED target at 50% => safeguard for past investmnts (and past policies)Britz/Witzke : Use of economic modelling in agricultural policy, 18.04.2013, Krško

Conclusions (1)

Overall: direct influence of economic modelling is open or unclearg p• Probably highest for fine-tuning of policies

But indirect effect through training of economic thinking for staff d ith li d iconcerned with policy design

Modelling may serve to structure the debate on policy issues• Example: Impacts of rural development supportp p p pp• To quantify regional CGE layer of CAPRI (for example)• Forces to interpret each measure in terms of the rigorous model

frameworkframework


References for examples

NOWICKI, P., H. VAN MEIJL, A. KNIERIM, M. BANSE, J. HELMING, O. MARGRAF, B. MATZDORF. R. MNATSAKANIAN, M. REUTTER, I. TERLUIN, K. OVERMARS, D. V OOG C W G H W S O S 2020 S i t dVERHOOG, C. WEEGER, H. WESTHOEK: Scenar 2020 - Scenario study on agriculture and the rural world, Contract No. 30 - CE - 0040087/00-08. European Commission, Directorate-General Agriculture and Rural Development, Brussels.p ,RENWICK, A., JANSSON, T., VERBURG, P. H., REVOREDO-GIHA, C., BRITZ, W., GOCHT, A. AND MCCRACKEN, D.,: Policy reform and agricultural landabandonment in the EU, Land Use Policy, 30 (2013) 446 - 457, doi:10 1016/j landusepol 2012 04 005doi:10.1016/j.landusepol.2012.04.005MORREDU, C.(MAIN AUTHOR AND EDITOR), WITH CONTRIBUTIONS BY MARTINI, R., KIMURA S., BRITZ, W., GOCHT, A., PEREZ, I., HART, K. AND BALDOCK, D.: Evaluation of Agricultural Policy Reforms in the European Union.OECD, a uat o o g cu tu a o cy e o s t e u opea U o O C ,Paris, 2011A. BURRELL, E. FERRARI, A. GONZÁLEZ MELLADO, M. HIMICS, J. MICHALEK, S. SHRESTHA AND B. VAN DOORSLAER (AUTHORS), A. BURRELL (EDITOR): Potential EU Mercos r Free Trade Agreement Impact Assessment JRCPotential EU-Mercosur Free Trade Agreement: Impact Assessment. JRC Reference Reports, EUR 25011 EN, Luxembourg, 2011 LABORDE D (2011), Assessing the Land Use Change Consequences of European Biofuel Policies. ATLASS Consortium, 111 p.,October 2011.European Biofuel Policies. ATLASS Consortium, 111 p.,October 2011.

Britz: IMAP meeting march 2011 - long term projections with CAPRI

use of economic modelling in agricultural policy · 2013-05-02 · • quite detailed scenario...

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