Jose Falck Zepeda Side Event What Do We Know Socio-economics Sept 2010

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Socio-Economic Impact Assessments and Biotechnology: The Experience to Date José Falck Zepeda Research Fellow Leader Policy Team Program for Biosaf ety Systems (PBS) IFPRI From left to rigth: a) Damage by Asia corn borer and b) Bt maize plot in Barangay Conel, Mindanao, The Philippines, c) Transgenic Garden, UP-LB Los Banos, Luzon, Philippines

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“Socio-Economic Impact Assessments and Biotechnology: TheExperience to Date”

José Falck ZepedaResearch Fellow

Leader Policy Team Program for Biosafety Systems (PBS)

IFPRI

From left to rigth: a) Damage by Asia corn borer and b) Bt maize plot in Barangay Conel, Mindanao, The Philippines, c) Transgenic Garden,UP-LB Los Banos, Luzon, Philippines

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Current status 2009 90 million hectares

Six countries cultivated 96% of this area

USA 50.0

Argentina 17.1

Canada 5.8

China 3.3

Paraguay 1.8

India 1.3

Mainly four crops and two technologies

Private sector dominates product development

Public sector in developing countries developed

multiple technologies, very few have reached farmers

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In developing countries

Argentina, Brazil, China and India represent closeto 90% adoption in developing countries

Only two countries planting locally-developed

public sector technologies: India and China

Three countries in Africa planting GM crops: SouthAfrica, Burkina Faso and Egypt

Confined Field Trials in Kenya, Uganda, Nigeria,Philippines, India, China, Brazil, Guatemala,Honduras, Mexico, Argentina, others...

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What do we know from the economic impactassessment literature to date? –

A review of 187 peerreviewed studies

Different impact domains

Farmers, household andcommunity

Industry and markets

Consumers

Trade

Citation: Smale, Melinda; Zambrano, Patricia; Gruère, Guillaume; Falck-Zepeda, José; Matuschke, Ira; Horna, Daniela; Nagarajan, Latha;Yerramareddy, Indira; Jones, Hannah. 2009. Measuring the economic impacts of transgenic crops in developing agriculture during the first

decade: Approaches, findings, and future directions. (Food policy review 10) Washington, D.C.: International Food Policy Research Institute(IFPRI) 107 pages

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On average, profitable—but averages maskvariability by agro-climate, host cultivar, trait, farmer

Some Ex ante results from studies conducted byIFPRI and country partners

Country Crop/Trait Representative findings

Uganda Fungal

resistantbananas

• If approval delayed, forego potential annual (social)

benefits of +/- US$200 million

West Africa Bt cotton • Countries are worse off by not adopting• Smaller net benefits and returns than other studies• Negotiating downward the technology fee is key

Uganda Bt cotton • Positive yield impacts on net benefits• Smaller IRR probably explained due to low initial yields –

need to improve overall cotton productivity• Probability of a negative return can be as high as 38%with a full technology fee charge – negotiate the fee…

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On average, profitable—but averagesmask variability by agro-climate, hostcultivar, trait, farmer (2) Some Ex post studies conducted by IFPRI and

country partners

Country Crop/Trait Representative findings

Honduras Bt maize • Excellent control of target insects

• Yield advantage 893-1136 Kg/ha yield (24-33% higher yield Bt)• Bt maize preferred even by risk averse producers based on yield only• 100% higher seed cost than conventional hybrid

Philippines Bt maize • Adopters tend to be larger, use hired labor and are more educated.• Growing Bt maize significantly increases profits and yields• Significant insecticide use reductions

Colombia Bt cotton • Evidence of yield enhancement rather than pesticide reductions• Bt farmers benefited from the technology for one of the two regions inthe study, where the target pest is economically important• Sources of bias important: farmers who adopt are those that arebetter off• Institutional context crucial

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Bt cotton production parametersParameter India China South

AfricaArgentina,

Mexico

Yield Advantage

(n)

40 11 41 9

Min (%) -0.17 -0.06 -0.36 -0.03Median (%) 0.42 0.06 0.56 0.32

Max (%) 0.92 0.55 1.29 0.65

Reduction in

insecticide

Applications (n)

29 7 29 8

Min (%) -0.83 -0.82 -0.95 -0.81

Median (%) -0.3 -0.66 -0.53 -0.51

Max (%) 0.83 -0.56 0.68 -0.02

Profit (n) 16

Min (%) -0.65

Median (%) 0.47

Max (%) 1.36

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Estimates potential GM cropadoption

Source: Qaim 2009

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Too few traits, too few cases/authors—

generalizations should not be drawn

yet...need more time to describe adoption

Focus on existing crops/traits

Insect protected/ herbicide tolerance

Four crops: corn, cotton, canola, soybeans

Concern over potential estimates bias

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Next decade: Cross cutting issues forfurther study and the need for improvedmethods

Issues

Gender, health and generational

Institutional

Information and knowledge flows Impacts on poverty and inequality

Externalities

Need for improved methods

Household modeling Risk and uncertainty

Address selection bias and endogeneity