for Agricultural and Food Economics, K.U.Leuven for Agricultural and Food Economics, K.U.Leuven GMO’s in Food: Economic Impact on Various Stakeholders in the EU and in the World This presentation can be downloaded at http://www.biw.kuleuven.be/aee/clo/euwab.htm Email: [email protected]Koen Dillen Erik Mathijs Eric Tollens Course ‘Social and Ethical Aspects of Biotechnology’, VUB, Brussels, 29 November 2007.
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Centre for Agricultural and Food Economics , K.U. Leuven
GMO’s in Food: Economic Impact on Various Stakeholders in the EU and in the World This presentation can be downloaded at http://www.biw.kuleuven.be/aee/clo/euwab.htm Email: [email protected]. Course ‘Social and Ethical Aspects of Biotechnology’, VUB, Brussels, 29 November 2007. - PowerPoint PPT Presentation
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Centre for Agricultural and Food Economics, K.U.LeuvenCentre for Agricultural and Food Economics, K.U.Leuven
GMO’s in Food: Economic Impact on Various Stakeholders in the EU and in
IntroductionIntroduction Most of the recent agbiotech innovations
have been developed by private sector (upstream), mostly because of very stringent regulations and as such high costs for legislation
Therefore, the central focus of societal interest is not on the ROR of R&D, but on distribution of benefits among stakeholders in the technology diffusion chain
But what are the « benefits » and « costs » arising from GM crops?
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
ResultsResults
DiscussionDiscussion
IntroductionIntroduction4 Quadrants of Research in B/C Analyses:4 Quadrants of Research in B/C Analyses:
Global Case StudiesGlobal Case Studies Farmers capture sizeable gains Size and distribution of welfare effects of the
first generation of GE crops are function of:1. Adoption rate2. Crop3. Biotech trait4. Geographical region5. Year6. National policies and IPR protection7. Assumptions and underlying dataset
On average, domestic farmers and consumers extract 2/3 of the benefits while 1/3 is captured by the seed industry
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
ResultsResults
DiscussionDiscussion
Table 1: Global welfare distribution of the first generation of transgenic crops Country Crop Year Adoption Welfare Welfare distribution
Global Case StudiesGlobal Case Studies This 2:1 rule of thumb seems to be
valid for both industrial and developing countries
Typical for large exporting countries: international trade of both the innovation (multinationals) and the commodity international spillover effects possibility of immiserising growth (Bhagwati, 1958)
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
ResultsResults
DiscussionDiscussion
EU Case StudiesEU Case Studies De facto moratorium on GM crops: October 1998
– May 2004 (Syngenta Bt 11 maize) 1998-2002: Adoption stagnated at 25,000 ha Bt
maize in Spain, doubled afterwards 2007: 6 Bt maize growing EU Member States:
Spain, Portugal, France, Czech Republic, Germany, Slovakia (but still only MON810)
De facto moratorium and the postponement nowadays implies a cost to society = deadweight cost or benefits foregone of GM crops
But we need a representative EU case study to show this!
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
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DiscussionDiscussion
GM crops in EUGM crops in EUHectares 2005 2006 2007Spain 53,225 53,667 75,148
France 492 5000 21,174
Czech Republic
150 1,290 5,000
Portugal 750 1,250 4,500
Germany 400 950 2,685
Slovakia 30 900
Romania 110,000 90,000 350
Poland 100 320
TOTAL 62,187 110,077
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
ResultsResults
DiscussionDiscussion
EU Case StudiesEU Case StudiesPreferable conditions of a good EU case study:1. Crop representative for EU agriculture 2. Crop problem representative for EU agriculture3. Important EU export commodity (spillover)4. Acceptance of GM variety realistic5. GM variety near commercialization6. Some impact data available, e.g. field trials
Sugar beet fullfills most criteriaAnd we have ex post impact evidence from Spain
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
ResultsResults
DiscussionDiscussion
EU Case StudiesEU Case Studies
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
ResultsResults
DiscussionDiscussion
Table 1: Accordance of selected EU case studies on the impact of GE crops with criteria Crop
Criterion HT sugar beet Bt maize
1. Representativeness of the crop +++ grown in all EU regions
+ grain maize more important in southerly
regions 2. Representativeness of the pest +++
weed control is crucial to profitability
+ corn borers more important in southerly
regions 3. Representativeness of trade +++
EU provides 20% of global trade
– EU-15 and EU-25 are net importers of
maize, only internal EU trade 4. Availability of genetic resources +++
presence of wild relatives, e.g. sea beet
– no wild relatives in Europe,
primary centre of origin is Mexico 5. Realistic acceptance –
main impediments are manufacturers
+++ widely accepted in Spain, entirely used for animal feed, no labelling required
6. Realistic commercialisation ++ registrations are
pending
+++ already commercialised in Spain, France, Germany, Portugal and the Czech Republic
7. Availability of impact data + research capacity has declined since 2001
- assume standard HT replacement programs - compare costs with observed programs-model the heterogeneity among farmers-some farmers rationaly decide to adopt, others not choose not to-calculate the optimal technology fee
BtBt Maize in Hungary Maize in HungaryEuropean Corn Borer (European Corn Borer (Ostrinia nubilalisOstrinia nubilalis Hübner) Hübner)
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
ResultsResults
DiscussionDiscussion
BtBt Maize in Hungary Maize in HungaryWestern Corn Rootworm (Western Corn Rootworm (Diabrotica virgifera virgiferaDiabrotica virgifera virgifera
LeConte)LeConte)
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
ResultsResults
DiscussionDiscussion
BtBt Maize in Hungary Maize in HungaryWestern Corn Rootworm (Western Corn Rootworm (Diabrotica virgifera virgiferaDiabrotica virgifera virgifera
LeConte)LeConte)
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
ResultsResults
DiscussionDiscussion
BtBt Maize in Hungary Maize in HungaryWestern Corn Rootworm (Western Corn Rootworm (Diabrotica virgifera virgiferaDiabrotica virgifera virgifera
LeConte)LeConte)
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
ResultsResults
DiscussionDiscussion
BtBt Maize in Hungary Maize in HungaryWestern Corn Rootworm (Western Corn Rootworm (Diabrotica virgifera virgiferaDiabrotica virgifera virgifera
LeConte)LeConte)
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
ResultsResults
DiscussionDiscussion
BtBt Maize in Hungary Maize in HungaryWestern Corn Rootworm (Western Corn Rootworm (Diabrotica virgifera virgiferaDiabrotica virgifera virgifera
LeConte)LeConte)
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
ResultsResults
DiscussionDiscussion
BtBt Maize in Hungary Maize in HungaryWestern Corn Rootworm (Western Corn Rootworm (Diabrotica virgifera virgiferaDiabrotica virgifera virgifera
LeConte)LeConte)
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
ResultsResults
DiscussionDiscussion
BtBt Maize in Hungary Maize in HungaryWestern Corn Rootworm (Western Corn Rootworm (Diabrotica virgifera virgiferaDiabrotica virgifera virgifera
LeConte)LeConte)
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
ResultsResults
DiscussionDiscussion
BtBt Maize in Hungary Maize in HungaryWestern Corn Rootworm (Western Corn Rootworm (Diabrotica virgifera virgiferaDiabrotica virgifera virgifera
LeConte)LeConte)
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
ResultsResults
DiscussionDiscussion
BtBt Maize in Hungary Maize in HungaryWestern Corn Rootworm (Western Corn Rootworm (Diabrotica virgifera virgiferaDiabrotica virgifera virgifera
LeConte)LeConte)
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
ResultsResults
DiscussionDiscussion
BtBt Maize in Hungary Maize in HungaryWestern Corn Rootworm (Western Corn Rootworm (Diabrotica virgifera virgiferaDiabrotica virgifera virgifera
LeConte)LeConte)
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
ResultsResults
DiscussionDiscussion
BtBt Maize in Hungary Maize in HungaryWestern Corn Rootworm (Western Corn Rootworm (Diabrotica virgifera virgiferaDiabrotica virgifera virgifera
LeConte)LeConte)
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
ResultsResults
DiscussionDiscussion
BtBt Maize in Hungary Maize in HungaryWestern Corn Rootworm (Western Corn Rootworm (Diabrotica virgifera virgiferaDiabrotica virgifera virgifera
LeConte)LeConte)
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
ResultsResults
DiscussionDiscussion
WCR in Czech RepublicWCR in Czech Republic
MethodologyMethodology
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
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Micro-economic level: Develop bio-economic pest damage abatement models Calibrate on real field data (surveys, expert opinions,
literature) Model heterogeneity Pre-coexistence Incorporate uncertaintyMacro-economic level: Model GM crop adoption through partial equilibrium
displacement model (EDM) Incorporate market structure and response Incorporate trade policies Incorporate uncertainty
DataData
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
ResultsResults
DiscussionDiscussion
Ex ante: no adoption data available Data mining, combine different data sources:
– National and international statistics– National and regional farmer surveys– Field trials– Expert opinions– Literature– Assumptions– Economic theory
Importance of modelling data uncertainty and conducting sensitivity and scenario analyses
ResultsResults
DiscussionDiscussion
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
ResultsResults
DiscussionDiscussion
Total benefits per hectare are fairly robust measure of value or “size” of the innovation
This value is distributed among input industry and farmers (who share it with consumers)
Market power of input industry is constrained by 5 factors:1. Farmer heterogeneity (e.g. Bt maize)2. Uncertainty and irreversibility3. Competition from chemical industry4. Competition within biotechnology industry5. Coexistence regulation (EU)
Immiserising growth unlikely due to:1. Smaller scale & heterogeneous innovation pattern2. Common Agricultural Policy (CAP) protecting farmers against
eroding world prices
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IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
ResultsResults
DiscussionDiscussion
Non-Pecuniary Benefits of HT Crops:
Management Flexibility and Convenience
DiscussionDiscussion
DiscussionDiscussion
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
ResultsResults
DiscussionDiscussion
Coexistence, the last hurdle to GM crops?European Commission (2003):
“Coexistence refers to the ability of farmers to make a practical choice between conventional, organic and GM [genetically modified] crop production, in compliance with the legal obligations for labelling and/or purity standards. The adventitious presence of GMOs [genetically modified organisms] above the tolerance threshold set out in Community legislation triggers the need for a crop that was intended to be a non-GMO crop, to be labelled as containing GMOs. This could cause a loss of income, due to a lower market price of the crop or difficulties in selling it. Moreover, additional costs might incur to farmers if they have to adopt monitoring systems and measures to minimise the admixture of GM and non-GM crops. Coexistence is, therefore, concerned with the potential economic impact of the admixture of GM and non-GM crops, the identification of workable management measures to minimise admixture and the cost of these measures.”
DiscussionDiscussion
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
ResultsResults
DiscussionDiscussion
What is coexistence? A cost or an incentive? Ex-ante measure The right to choose (farmers & consumers) Gene flow, pollen drift, contamination, commingling Coexistence is only relevant
– if there is a significant long-term domestic or international (export) consumer demand for non-GM crops (e.g. not cotton)
– if this demand translates into market signals (e.g. price premiums for non-GM crops)
– if there is a significant farmer demand for cost-reducing transgenic crops (e.g. not ECB-resistant Bt maize in Belgium)
– Costs proportional to economic incentives
DiscussionDiscussion
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
ResultsResults
DiscussionDiscussion
adoption
co-e
xist
ence
co
sts rupture
point
Coexistence costs borne by 2 incentives:1. Farmer profits of GM crops (“GM rent”)2. Price premium of identity preserved (IP)
crops (“IP rent”)
Phase I Phase II Phase III
clustering, reallocation of land
IP rentseekingGM rents
Coexistence Coexistence measuresmeasures
IntroductionIntroduction
Global Case StudiesGlobal Case Studies
EU Case StudiesEU Case Studies
MethodologyMethodology
DataData
ResultsResults
DiscussionDiscussion
Definitions: Isolation distances = rigid minimum
distance rules between GM and non-GM crop fields of the same species and imposed on GM crop producers
Buffer zones = flexible segregation measures by using field surroundings (which serve as cross-pollination zones) with non-GM crops of the same species, planted on (negotiable between farmers):
Represents most stringent scenario of coexistence in a single season
ResultsResults
IntroductionIntroduction
ArcView ModellingArcView Modelling
AssumptionsAssumptions
Economic IncentivesEconomic Incentives
ResultsResults
ConclusionConclusion
The domino-effect caused by rigid coexistence regulations
ResultsResults
IntroductionIntroduction
ArcView ModellingArcView Modelling
AssumptionsAssumptions
Economic IncentivesEconomic Incentives
ResultsResults
ConclusionConclusion
The domino-effect caused by rigid coexistence regulations
ResultsResults
IntroductionIntroduction
ArcView ModellingArcView Modelling
AssumptionsAssumptions
Economic IncentivesEconomic Incentives
ResultsResults
ConclusionConclusion
The domino-effect caused by rigid coexistence regulations
Domino-effectDomino-effectTable 2 Domino-effect on costs (€/ha) of rigid coexistence regulations in oilseed rape cultivation in Central France Phase OSR
Notes: OSR = oilseed rape. Areas and costs are averages, based on 10 random allocations of GM and non-GM OSR fields. Standard deviations are shown between brackets. The domino-effect expresses the relative difference in per cent between the cumulative value and the value in Phase 1. Source: Authors’ calculations based on GIS dataset of the sample square (Pessel et al., 2001).
Conclusion (coexistence)Conclusion (coexistence) Rigid regulations may impose severe burden on GM
crop production in Europe Even under low demand for IP crops, and hence,
low demand for coexistence Costly, not proportional to incentives and hence not
consistent with EC’s objectives Flexible measures are preferable as they are less
costly and proportional to incentives Should be negotiable between adopters and non-
adopters as both farmer segments have economic incentives to ensure coexistence in long-run
Conclusion (coexistence)Conclusion (coexistence) Trade-off between GM and IP rent depends on market
signals from consumers IP incentive only sustainable if consumers
• Have strong & sustainable preferences for non-GM• Are willing to pay significant IP price premiums
Otherwise no coexistence issue strictu sensu and cost = pure regulatory burden
EU policy makers: under absence of clear market signals for IP, we recommend to shift regulatory rigidity from ex ante ex post
To avoid jeopardizing economic incentives for coexistence of GM/non-GM in Europe
ConclusionConclusion
System approach needed Case by case Producers capture an important part of
the benefits of transgenic crops: most often between 2/3 and 3/4
Government’s trade policy can influence the impact of biotechnology (e.g. sugar sector)
Coexistence only relevant when 2 incentives are both present at the same time: GM rent & IP rent