Matty Demont Promoter: Prof. E. Tollens Jury President: Prof. G. Volckaert

Economic impact of agricultural biotechnology in the European Union:

Transgenic sugar beet and maizeMatty Demont

Promoter: Prof. E. TollensJury President: Prof. G.

VolckaertJury Members: Prof. E. Mathijs

Prof. J. SwinnenProf. J. Vanderleyden

Prof. J. Wesseler

Dissertationes de Agricultura, No. 713, Jozef Heuts-auditorium, Landbouwinstituut,Faculteit Bio-ingenieurswetenschappen,Katholieke Universiteit Leuven, 1 September 2006, 16:00pm

Introduction:Introduction:AgBiotech adoption in the worldAgBiotech adoption in the world

IntroductionIntroduction

MethodologyMethodology

DataData

ResultsResults

Model evaluationModel evaluation

ConclusionsConclusions

AcknowledgementsAcknowledgements

Most of the recent agbiotech innovations have been developed by private sector

Therefore, the central focus of societal interest is not on the ROR of R&D, but on distribution of gains among stakeholders in the technology diffusion chain



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GOVERNMENTGOVERNMENT

FARMERSFARMERS

MARKETING SYSTEMMARKETING SYSTEM

CONSUMERSCONSUMERS

Research Expenditures Regulatory ApprovalIPR Legislation

Labelling Policy Trade Regulation

ENVIRONMENTENVIRONMENT

ACTIVISTS, LOBBY GROUPS, MEDIA


INPUT SUPPLIERSINPUT SUPPLIERSbiotechnology seeds,

pesticides, ...technology fee, contract

GMO crops or GMO fed livestock productscontract

marketing GM products

environmental benefits and risks

GOVERNMENTGOVERNMENT

FARMERSFARMERS

MARKETING SYSTEMMARKETING SYSTEM

CONSUMERSCONSUMERS

Research Expenditures Regulatory ApprovalIPR Legislation

Labelling Policy Trade Regulation

ENVIRONMENTENVIRONMENT



INPUT SUPPLIERSINPUT SUPPLIERSbiotechnology seeds,

pesticides, ...technology fee, contract

GMO crops or GMO fed livestock productscontract

marketing GM products

environmental benefits and risks



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Downstream


Upstream



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p

y

S0(p ) S c(p )

B

A

C

F

w

x

c G

H

MR

X(w )

D(p )

(a) (b)

Sm(p )

D

E

w1 /

c /

Change in Marshallian surplusInnovated inputsuppliers’ rent

x0 x1

I

Upstream private sector is highly consolidated

Existence of market power and extraction of monopoly rents




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p

y

S0(p ) S c(p )

B

A

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w

x

c G

H

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X(w )

D(p )

(a) (b)

Sm(p )

D

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w1 /

c /

Change in Marshallian surplusInnovated inputsuppliers’ rent

x0 x1

I

Alston, Norton & Pardey (1995) (ANP)

Moschini & Lapan (1997) Widely used in agbiotech impact

literature


Table 1: Global welfare distribution of the first generation of transgenic crops Country Crop Year Adoption Welfare Welfare distribution

(%) (m$) Domestic farmers

Innovators Domestic consumers

Net ROW

USA Bt cotton 1996 14% 134 43% 47% 6% 4% USA Bt cotton 1996 14% 240 59% 26% 9% 6% USA Bt cotton 1997 20% 190 43% 44% 7% 6% USA Bt cotton 1998 27% 213 46% 43% 7% 4% USAa Bt cotton 1996-98 20% 151 22% 46% 14% 18% USAb Bt cotton 1997 20% 213 29% 35% 14% 22% USAc Bt cotton 1997 20% 301 39% 25% 17% 19% USA HT cotton 1997 11% 232 4% 6% 57% 33% USAd HT soyb. 1997 17% 1,062 76% 10% 4% 9% USAe HT soyb. 1997 17% 437 29% 25% 17% 28% USA HT soyb. 1999 56% 804 19% 45% 10% 26% USA HT soyb. 1997 17% 308 20% 68% 5% 6% Canadaf HT canola 2000 54% 209 19% 67% 14% . Argentina HT soyb. 2001 90% 1,230 25% 34% 0.3% 41% Argentina Bt cotton 2001 5% 0.4 21% 79% . . China Bt cotton 1999 11% 95 83% 17% 0%g . India Bt cotton 2002 7% 6.2 67% 33% 0%g . Mexico Bt cotton 1998 15% 2.8 84% 16% . . South Africah Bt cotton 2000 75% 0.1 58% 42% . . South Africai Bt cotton 2001 80% 1.2 67% 33% 0%g 0%




Net ROW




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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 (Ch.1) and IPR protection7. Assumptions and underlying dataset (Ch.4)




Net ROW


UpstreamAverage

= 37%



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However, benefit sharing seems to follow a general rule of thumb:1/3 upstream vs. 2/3 downstream

This rule of thumb seems to be valid for both industrial and developing countries



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Introduction:Introduction:AgBiotech adoption in the EUAgBiotech adoption in the EU

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

2006: 5 Bt maize growing EU Member States: Spain, Portugal, France, Czech Republic, Germany

De facto moratorium implies a cost to society = deadweight cost or benefits foregone of GM crops



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Introduction:Introduction:AgBiotech adoption in the EUAgBiotech adoption in the EU

0

10

20

30

40

50

60

70

80

90

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Maize Sugar beet

Figure 1: Evolution of the number of field trials of maize and sugar beet in the EU-25 Source: SNIF database (European Commission, 2006a)

We need to know this cost in ex post, but also for future decisions in ex ante



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Introduction:Introduction:HypothesesHypotheses

1. The first generation of agbiotech innovations could and can significantly contribute to productivity and welfare in EU agriculture

2. The largest share of total welfare creation is captured downstream (farmers, processors, manufacturers, distributors and consumers)

3. Conventional benefit-cost analysis cannot capture uncertainty and potential irreversibility regarding environmental effects. It can be extended by a real option approach to assess maximum tolerable levels of irreversible environmental costs that justify a release of these innovations in the EU



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Introduction:Introduction:HypothesesHypotheses

4. Some of the variability of welfare estimates reported in literature can be explained by the modeling of supply shift in conventional equilibrium displacement models



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Introduction:Introduction:Case studiesCase studies

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

+ very little data publicly available



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pending






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pending






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pending






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Introduction:Introduction:Herbicide tolerant (HT) sugar beet in EU-15Herbicide tolerant (HT) sugar beet in EU-15



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Introduction:Introduction:Bt [Bt [Bacillus thuringiensisBacillus thuringiensis] maize in Spain] maize in Spain



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Methodology:Methodology:Herbicide tolerant (HT) sugar beet in EU-15Herbicide tolerant (HT) sugar beet in EU-15

1. Farm level analysis: - assume standard HT replacement programs - compare costs with observed programs- assume technology pricing (see data)



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2. Aggregation to the global level through standard methodologies- Alston, Norton & Pardey (1995) (ANP)- 3 regions: EU, ROW beet, ROW cane- Dynamic world price behaviour- Moschini, Lapan & Sobolevsky (2000) (MLS)- Former EU’s CMO sugar- Technology spillovers included- Non-spatial: no intra-EU trade flows- Disaggregated supply: 16 prod. blocks- Aggregate EU demand



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3. Real option approach (Wesseler & Weichert, 1999): decision to release GM crops in EU is one under flexibility, irreversibility, and uncertainty

- Neo-classical decision criterion: benefits ≥ costs

- Include an additional ‘safety factor’ to take into account uncertainty & irreversibility

- Decision criterion: benefits > costs by a factor, the so-called ‘hurdle rate’ (estimated through historical gross margin series)

- Calculate break-even points maximum tolerable costs



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Methodology:Methodology:Bt [Bt [Bacillus thuringiensisBacillus thuringiensis] maize in Spain] maize in Spain

1. Farm level analysis: - standard damage abatement function- damage = stochastic (lognormal)- calibrated on real corn borer damage data

2. Aggregation to national level- Alston, Norton & Pardey (1995) (ANP)- small, open economy- Oehmke & Crawford (2002) & Qaim (2003) (OCQ)



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DataData Ex ante (HT sugar beet in the EU-15)

- No adoption of the new technology- No farm level impact data, only field trials- Assumptions: 1. Yield impact 2. Technology pricing- Sources: expert opinions, literature, economic theory, national surveys, Eurostat- Stochastic simulation

Ex post (Bt maize in Spain)- Scarce data sources- Data mining (e.g. corn borer damage)- Sources: literature, national surveys- Stochastic simulation



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Table 1: Price and welfare effects (in million euros) of the adoption of herbicide tolerant sugar beet in the EU and the rest of the world

Year Price effects

1996/97 Benchm.

1996/97 1997/98 1998/99 1999/00 2000/01 2001/02 Aggr.

Average LSR

World price (%) 100% 99.87% 99.87% 99.79% 99.73% 99.69% . . A beet price (%) 100% 99.99% 99.99% 99.99% 99.99% 99.99% . . B beet price (%) 100% 99.94% 99.95% 99.94% 99.95% 99.85% . . Welfare effects Belgium 0 1.3 1.8 2.8 3.5 4.7 18.0 -0.34% Denmark 0 0.7 1.0 1.3 1.7 2.3 9.0 -0.34% Germany 0 5.6 8.1 10.5 14.0 19.5 74.2 0.07% Greece 0 0.6 0.9 0.9 1.4 2.1 7.6 -0.35% Spain 0 2.0 2.9 3.9 5.2 7.0 26.8 -0.33% France 0 3.7 5.6 6.5 8.5 12.7 47.7 0.09% Ireland 0 0.3 0.5 0.7 1.0 1.3 4.8 -0.34% Italy 0 2.6 3.7 5.2 6.9 9.0 35.2 -0.34% The Netherlands 0 1.0 1.5 2.2 2.8 3.7 14.5 -0.34% Austria 0 0.7 1.0 1.3 1.7 2.3 9.2 0.17% Portugal 0 0.0 0.2 0.3 0.4 0.5 1.9 -0.39% Finland 0 0.6 0.7 1.2 1.4 1.9 7.4 -0.35% Sweden 0 0.6 0.8 1.1 1.4 2.0 7.6 -0.34% United Kingdom 0 1.3 1.9 2.3 3.0 4.4 16.7 0.10% EU-15 producers 0 21.0 30.5 40.3 53.1 73.5 280.5 -0.10% EU-15 consumers 0 0.0 0.0 0.0 0.0 0.0 0.0 . ROW cane 0 -32.5 -30.9 -36.2 -41.2 -71.4 -277.6 -0.06% ROW beet 0 38.0 52.4 68.0 91.2 116.7 472.8 -0.33% Net ROW producers 0 5.5 21.5 31.8 50.0 45.3 195.2 -0.11% ROW consumers 0 41.5 39.4 44.7 50.8 88.2 346.9 . Net ROW 0 47.0 60.9 76.5 100.8 133.5 542.2 . Input suppliers 0 20.3 27.2 37.2 50.3 61.3 253.3 . Total 0 88.3 118.6 154.0 204.3 268.2 1,076.0 -0.11% Welfare distribution EU-15 producers (%) . 23.8% 25.8% 26.2% 26.0% 27.4% 26.1% . EU-15 consumers (%) . 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% . Net ROW (%) . 53.2% 51.3% 49.6% 49.3% 49.7% 50.3% . Input suppliers (%) . 23.0% 23.0% 24.2% 24.7% 22.9% 23.6% . Total (%) . 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% . LSR: land supply response



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Year Price effects

1996/97 Benchm.

1996/97 1997/98 1998/99 1999/00 2000/01 2001/02 Aggr.

Average LSR


99.50%99.55%99.60%99.65%99.70%99.75%99.80%99.85%99.90%99.95%

100.00%

Benchmark 1996/97 1997/98 1998/99 1999/00 2000/01

World sugar price (%) A sugar price (%) B sugar price (%)



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Year Price effects

1996/97 Benchm.

1996/97 1997/98 1998/99 1999/00 2000/01 2001/02 Aggr.

Average LSR




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Year Price effects

1996/97 Benchm.

1996/97 1997/98 1998/99 1999/00 2000/01 2001/02 Aggr.

Average LSR




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Year Price effects

1996/97 Benchm.

1996/97 1997/98 1998/99 1999/00 2000/01 2001/02 Aggr.

Average LSR




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Year Price effects

1996/97 Benchm.

1996/97 1997/98 1998/99 1999/00 2000/01 2001/02 Aggr.

Average LSR




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Year Price effects

1996/97 Benchm.

1996/97 1997/98 1998/99 1999/00 2000/01 2001/02 Aggr.

Average LSR




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Year Price effects

1996/97 Benchm.

1996/97 1997/98 1998/99 1999/00 2000/01 2001/02 Aggr.

Average LSR




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Year Price effects

1996/97 Benchm.

1996/97 1997/98 1998/99 1999/00 2000/01 2001/02 Aggr.

Average LSR




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Year Price effects

1996/97 Benchm.

1996/97 1997/98 1998/99 1999/00 2000/01 2001/02 Aggr.

Average LSR




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Table 1: Parameter estimates generated by EUWABSIM, hurdle rates, annual social reversible net-benefits (Wa), social irreversible benefits (Ra) and maximum tolerable social irreversible costs (I*a) per hectare of transgenic sugar beet, per household and per sugar beet growing farmer Member State

aW SD bW SD Wmax (€/ha)

Wa (€/ha)

Ra (€/ha)

Hurdle rate

I*a (€/ha)

I*a (m€)

I*a (€/HH)

I*a (€/F)

Belgium –2.85 0.10 0.41 0.03 209 197 2.09 1.26 158 6.7 1.59 436 Denmark –2.78 0.05 0.42 0.02 154 151 2.06 1.73 89 2.4 1.05 306 Germany –2.80 0.08 0.41 0.03 169 164 1.57 1.36 122 25.3 0.68 478 Greece –2.79 0.12 0.37 0.04 213 193 7.97c 3.12 70 1.3 0.36 61 Spain –2.88 0.03 0.44 0.01 210 206 0.53 2.10 99 6.0 0.39 213 France –2.80 0.14 0.40 0.04 139 131 1.05 1.25 106 18.1 0.79 533 Ireland –2.77 0.01 0.42 0.00 155 154 –0.96c 2.29 66 0.9 0.81 215 Italy –2.83 0.09 0.40 0.03 156 146 2.32 1.82 82 9.3 0.42 148 Netherl. –2.80 0.08 0.41 0.02 144 137 0.83 1.31 106 5.1 0.80 265 Austria –2.78 0.06 0.41 0.02 213 207 3.36 2.88 75 1.5 0.46 129 Portugal –2.98 0.17 0.44 0.05 313 294 –0.65c 1.67d 175 0.5 0.13 599 Finland –2.86 0.15 0.39 0.05 270 246 0.74 3.69 67 1.0 0.46 247 Sweden –2.75 0.06 0.40 0.02 143 139 0.18 3.01 46 1.1 0.29 214 UK –2.79 0.12 0.39 0.04 121 113 1.78 1.76 66 4.5 0.21 406 EUa –2.78 0.07 0.41 0.02 158 154 1.59 1.04 149 122.8 0.82 442 EUb –2.78 0.07 0.41 0.02 158 154 1.59 1.67 94 77.1 0.52 277 SD: standard deviation; HH: household; F: farmer

Results:Results:Herbicide tolerant (HT) sugar beet in the EU-Herbicide tolerant (HT) sugar beet in the EU-

1515



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Table 1: Parameter estimates generated by EUWABSIM, hurdle rates, annual social reversible net-benefits (Wa), social irreversible benefits (Ra) and maximum tolerable social irreversible costs (I*a) per hectare of transgenic sugar beet, per household and per sugar beet growing farmer Member State

aW SD bW SD Wmax (€/ha)

Wa (€/ha)

Ra (€/ha)

Hurdle rate

I*a (€/ha)

I*a (m€)

I*a (€/HH)

I*a (€/F)

Belgium –2.85 0.10 0.41 0.03 209 197 2.09 1.26 158 6.7 1.59 436 Denmark –2.78 0.05 0.42 0.02 154 151 2.06 1.73 89 2.4 1.05 306 Germany –2.80 0.08 0.41 0.03 169 164 1.57 1.36 122 25.3 0.68 478 Greece –2.79 0.12 0.37 0.04 213 193 7.97c 3.12 70 1.3 0.36 61 Spain –2.88 0.03 0.44 0.01 210 206 0.53 2.10 99 6.0 0.39 213 France –2.80 0.14 0.40 0.04 139 131 1.05 1.25 106 18.1 0.79 533 Ireland –2.77 0.01 0.42 0.00 155 154 –0.96c 2.29 66 0.9 0.81 215 Italy –2.83 0.09 0.40 0.03 156 146 2.32 1.82 82 9.3 0.42 148 Netherl. –2.80 0.08 0.41 0.02 144 137 0.83 1.31 106 5.1 0.80 265 Austria –2.78 0.06 0.41 0.02 213 207 3.36 2.88 75 1.5 0.46 129 Portugal –2.98 0.17 0.44 0.05 313 294 –0.65c 1.67d 175 0.5 0.13 599 Finland –2.86 0.15 0.39 0.05 270 246 0.74 3.69 67 1.0 0.46 247 Sweden –2.75 0.06 0.40 0.02 143 139 0.18 3.01 46 1.1 0.29 214 UK –2.79 0.12 0.39 0.04 121 113 1.78 1.76 66 4.5 0.21 406 EUa –2.78 0.07 0.41 0.02 158 154 1.59 1.04 149 122.8 0.82 442 EUb –2.78 0.07 0.41 0.02 158 154 1.59 1.67 94 77.1 0.52 277 SD: standard deviation; HH: household; F: farmer

Results:Results:Herbicide tolerant (HT) sugar beet in the EU-Herbicide tolerant (HT) sugar beet in the EU-

1515



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Results:Results:Bt [Bt [Bacillus thuringiensisBacillus thuringiensis] maize in Spain] maize in Spain

Table 1: Economic impact of Bt maize on Spanish agriculture and the seed industry, 1998-2003 Year

1998

1999

2000

2001

2002

2003 Average

1998-2003 Aggregated value 2004

Adoption (%) 4.8% 7.6% 4.6% 5.0% 5.4% 6.8% 5.7% 5.7% Bt maize adopters (€/ha) 50.5 50.6 47.9 46.8 45.1 45.7 47.8 415.5 Agriculture (m€) 1.1 1.5 1.0 1.2 1.1 1.5 1.2 10.5 Seed industry (m€) 0.5 0.7 0.5 0.6 0.6 0.8 0.6 5.2 Total impact (m€) 1.6 2.2 1.4 1.8 1.7 2.2 1.8 15.8 Agriculture share (%) 67.9% 67.9% 66.7% 66.2% 65.3% 65.6% 66.6% 66.8% Seed industry share (%) 32.1% 32.1% 33.3% 33.8% 34.7% 34.4% 33.4% 33.2%



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1998

1999

2000

2001

2002

2003 Average





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1998

1999

2000

2001

2002

2003 Average



5 methods of supply shift calculation:1. CIR = Change in Revenue2. ANP = Alston, Norton & Pardey

(1995)3. ANP1 = ANP with supply elasticity =

14. OCQ = Oehmke & Crawford (2002)

& Qaim (2003)5. MLS = Moschini, Lapan &

Sobolevsky (2000)



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Model evaluationModel evaluationR

L

R(L)

C

pY

L1

R

L

R(L)

R(p,)

L1

R(p,0)

L0

p

qq1

p

qq1

)1(

p

p/

q1(p)q0(p)

1p

q0

q1(p)q0(p)

a. Change in Revenu Method (CIR) b. Moschini, Lapan & Sobolevsky (2000) (MLS)

c. Alston, Norton & Pardey (1995) (ANP) d. Oehmke & Crawford (2002) and Qaim (2003) (OCQ)

q1q0



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Model evaluationModel evaluationR

L

R(L)

C

pY

L1

R

L

R(L)

R(p,)

L1

R(p,0)

L0

p

qq1

p

qq1

)1(

p

p/

q1(p)q0(p)

1p

q0

q1(p)q0(p)

a. Change in Revenu Method (CIR) b. Moschini, Lapan & Sobolevsky (2000) (MLS)

c. Alston, Norton & Pardey (1995) (ANP) d. Oehmke & Crawford (2002) and Qaim (2003) (OCQ)

q1q0

But if = 0 ANP = ANP1 = OCQ



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Table 1: Summary statistics for distributions of farmers’ surplus Central Tendencies Dispersion Meana Modea Kurtosis Skewness SDa CVb (%) 95% PIc CIR 1.38 1.16 43.69 3.73 0.81 58% (0.40; 3.35) ANP 1.25 1.17 1,099.72 17.95 0.84 68% (0.31; 2.73) ANP1 1.48 1.28 33.38 3.20 0.80 54% (0.43; 3.39) OCQ 1.46 1.32 17.55 2.34 0.75 51% (0.42; 3.26) MLS 1.32 1.14 35.65 3.44 0.75 57% (0.38; 3.14) n = 100,000 a Values are in million euros. b CV represents the coefficient of variation which is the ratio of standard deviation to mean. c The 95 percent probability intervals are ranges of benefits with 95% probability. Lower limits are rounded up while upper limits are rounded down.



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1. ANP method seems not robust at first sight



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Table 1: Summary statistics for distributions of farmers’ surplus differences Griffiths and Zhao (2000) Davis and Espinoza (1998, 2000) Meana SDa 95% PIb P(d0)c Chebychev 95% PIb P(d0)d

CIR-ANP 0.14 1.17 (-1.67; 2.31) 0.446 (-5.10; 5.37) 1 CIR-ANP1 -0.10 1.14 (-2.26; 2.13) 0.447 (-5.21; 5.00) 1 CIR-OCQ -0.08 1.10 (-2.15; 2.13) 0.450 (-5.02; 4.85) 1 CIR-MLS 0.06 1.00 (-1.86; 2.11) 0.467 (-4.41; 4.52) 1 ANP-ANP1 -0.24 0.65 (-1.31; 0.45) 0.103 (-3.15; 2.67) 1 ANP-OCQ -0.22 0.63 (-1.19; 0.47) 0.106 (-3.05; 2.61) 1 ANP-MLS -0.08 0.82 (-1.43; 1.17) 0.448 (-3.75; 3.60) 1 ANP1-OCQ 0.02 0.08 (0.01; 0.13) ***0.000 (-0.34; 0.37) 1 ANP1-MLS 0.16 0.51 (-0.83; 1.17) 0.383 (-2.14; 2.46) 1 OCQ-MLS 0.14 0.52 (-0.85; 1.15) 0.397 (-2.17; 2.45) 1 n = 100,000 a Values are in million euros. b The 95 percent probability intervals are ranges of benefits with 95% probability. Lower limits are rounded up while upper limits are rounded down. c The number of asterisks indicates the significance level (*0.10, **0.05, ***0.01). d The maximum p-values reported as equal to 1 have been truncated to 1 because it is irrelevant to have higher p-values based on the procedure used here.

Let’s have a look at the differences between the

5 methods:





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Model evaluationModel evaluation2. No systematic differences

between the models when fed with stochastic market data, except between ANP1 and OCQ





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Model evaluationModel evaluationThus, if we are only interested in the

mean value, given stochastic market data,

model choice does not matter





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Model evaluationModel evaluationIn other words:Data uncertainty > model uncertainty



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Model evaluationModel evaluation3. Not surprisingly, supply elasticity

plays a major role in ANP sensitivity

Table 1: Normalised regression coefficients of the parameters Farmers’ surplus CIR ANP ANP1 OCQ MLS Yield boost Y and 0.908 0.526 0.871 0.847 0.899 Per-hectare cost reduction C and 0.410 0.467 0.489 0.525 0.439 Average production costs AC n.a. n.a. n.a. n.a. 0.000 Supply elasticity n.a. -0.280 -0.002 -0.002 -0.002 Yield elasticity n.a. -0.012 0.000 0.000 -0.015 R2 1.000 0.573 1.000 0.996 0.999 Supply response

Table 1: Pairwise Levene test for equality of variances Levene (1960) Brown and

Forsythe (1974) F P(i j)a F P(i j)a CIR-ANP 74 ***0.000 54 ***0.000 CIR-ANP1 0.06 0.808 0.56 0.455 CIR-OCQ 2.64 0.104 0.69 0.408 CIR-MLS 27 ***0.000 22 ***0.000 ANP-ANP1 0.06 0.810 0.56 0.454 ANP-OCQ 59 ***0.000 52 ***0.000 ANP-MLS 14 ***0.000 8.36 ***0.004 ANP1-OCQ 3.90 **0.048 2.88 *0.090 ANP1-MLS 32 ***0.000 33 ***0.000 OCQ-MLS 15 ***0.000 18 ***0.000 n = 8,000 a The number of asterisks indicates the significance level (*0.10, **0.05, ***0.01).



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Model evaluationModel evaluationLet’s have a look at the variance

comparisons:



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Remember:





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Model evaluationModel evaluation4. ANP is significantly less robust than

CIR, OCQ and MLS, but not ANP1 the ANP vs. ANP1 discussion on supply elasticity is irrelevant, given stochastic data





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Model evaluationModel evaluation4. ANP is significantly less robust than

CIR, OCQ and MLS, but not ANP15. OCQ and MLS significantly more

robust and hence preferred methods



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Table 1: Comparison of the five equilibrium displacement models CIR ANP ANP1 OCQ MLS Model implementation Data requirement low medium medium high medium-high Ease high medium medium medium medium-low Transparency high medium medium medium medium-low




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ConclusionsConclusions1. The first generation of agbiotech

innovations could and can significantly contribute to productivity and welfare in EU agriculture

2. The largest share of total welfare creation is captured downstream (farmers, processors, manufacturers, distributors and consumers)

3. Conventional benefit-cost analysis cannot capture uncertainty and potential irreversibility regarding environmental effects. It can be extended by a real option approach to assess maximum tolerable levels of irreversible environmental costs that justify a release of these innovations in the EU



DataData

ResultsResults




ConclusionsConclusions4. Some of the variability of welfare

estimates reported in literature can be explained by the modeling of supply shift in conventional equilibrium displacement models

Recommend simplified and transparent model in combination with stochastic data mining

The real question is whether we want to produce information or whether we want to produce a model



DataData

ResultsResults





“The crippling flaw in much environmental and natural resource economics is that most practitioners believe that the models we build […] offer a clear and plausibly reliable mapping into propositions about the world of facts they presume to depict. All models are wrong, but some are more wrong than others”

(Bromley, 2005, p. 29).



DataData

ResultsResults




AcknowledgementsAcknowledgements Parents Siska De Borger Prof. E. Tollens, promotor Prof. G. Volckaert, jury president Jury Members: Prof. E. Mathijs, Prof. J.

Vanderleyden, Prof. J. Swinnen & Prof. J. Wesseler

Josée Verlaenen, Godelieve Vanzavelberg, Odette Moria

Collegues Centre Agr. & Food Economics VIB, K.U.Leuven, European Commission,

Monsanto Experts (zie p. ii, iii) & co-authors Audience

d

c

apb,1

a

b

p p

q q

(a) EU Quantity (b) Traded Quantity (c) ROW (’) Quantity

p

pa

Qa

pb,0

Qb

Qd = Qa + Qb – C

S0

Qc,0

S1

Q’e,0

S’0

D’

p1

ES1

Qc,1 Qc,1Q’e,1

S’1

Qc,0

ES0

p2

ED’1

pi

D

C

e f

g

PSROW = g – e 0

CSROW = e + f 0

ED’0

p0

PSEU = b – a + d – c 0

CSEU = 0




DataData

ResultsResults




Methodology:Methodology:Bt [Bt [Bacillus thuringiensisBacillus thuringiensis] maize in Spain] maize in Spain

p

qq1

q1(p)q0(p)

q1q0

pw

K

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Matty Demont Promoter: Prof. E. Tollens Jury President: Prof. G. Volckaert

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