Evidence-based policy-making: The role of impact assessment studies and their implications for agricultural biotechnologies presentation by David Spielman, International Food Policy Research Institute, Washington D.C., United States of America

1. Evidence-based policymaking
The role of impact assessment studies and
their implications for agricultural
biotechnologies
David J. Spielman , Patricia Zambrano, and Jose Falck-Zepeda
International Food Policy Research Institute
Rome, February 15-17, 2016

2. Agricultural biotechnologies
• Broad range of tools and products already in wide use
• Persistent challenges, contested narratives
• Considerable uncertainty around new technologies
• Extreme country-level variation in
– National policy
– Public, private investment
– Scientific capacity
– Engagement in dialogue
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3. Evidence-based decision-making
• Look back: what is the impact of biotechnologies
on productivity, sustainability, and welfare?
• Look forward: What are plausible scenarios for the
future impact of biotechnologies on productivity,
sustainability, and welfare?
• Look deep: Is the structure, conduct, and
performance of biotechnology’s global innovation
system conducive to progress?
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4. What do we know?
Ex post impact assessment
• There is rich literature assessing social and
economic impacts of biotechnologies on
– Farm-level productivity (yields, margins, input use)
– Consumer choices and preferences
– Human and environmental health, biodiversity
– Innovation, competition, and industry
– Trade and investment
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5. Consistent findings from meta-level studies
• Meta-level studies present similar findings
– Insect-resistant and herbicide-tolerant traits contribute to
yield increases, higher returns to farming, and reductions in
insecticide use
Meta-analysis authors (year) Sample size
(no. of papers reviewed)
Smale et al. (2009)* 321
Areal et al. (2012) 72
Finger et al (2012) 63
Klumper and Qaim (2014) 147
Fischer et al. (2015) 99
* Reviews studies from developing countries only; all others are global reviews.
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6. What more can we do to expand the
empirical evidence base?
• Expand the focus of attention
– New biotechnologies, crops, traits, countries
• Kabunga et al (2012) on adoption of TC banana in Kenya
• Improve the rigor of inquiry
– Better tools and methods
• Experimental designs  stronger counterfactuals
– Longer data panels
• Qaim et al. on Bt cotton in India
• Expand into more complex questions
– Psychological and behavioral dimensions
– Market structure and industry conduct
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7. Foresight analysis: Potential impact of
biotechnologies on global food security
Potential yield impact
of three
biotechnologies
assessed in the study,
aggregated globally,
compared to no
adoption in 2050
Percent change in world
price of maize in 2050,
comparing with and
without technology
adoption; heat and
drought tolerance
technologies rank
considerably high
Source: Rosegrant et al. 2014
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8. Potential impact of biotechnology on global
food security
Change (%) in population at
risk of hunger in developing
countries in 2050,
comparing with and without
adopting the technologies
-60.0
-50.0
-40.0
-30.0
-20.0
-10.0
0.0
Maize Rice Wheat
No-Till Drought tolerance
Heat Tolerance Nitrogen Use Efficiency
Integrated Soil Fertility Mgt Precision Agriculture
Water Harvesting Irrigation - sprinkler
Irrigation - Drip Crop Protection
Price effects of assumed
adoption of multiple,
combined technologies in
2050, compared to the
baseline of no-adoption
Source: Rosegrant et al. 2014 8

9. Philippines: Estimated net benefits of GM crop
adoption and increased regulatory costs
Source: Bayer, Norton and Falck Zepeda (2008). Note: Baseline values for each technology expressed in millions US$ using a discount rate for the
estimation of net present value = 5%; change in Net benefits defined as the total benefits estimated using the economic surplus minus total
regulatory costs. 9

10. Philippines: Estimated net benefits of GM
crop adoption and regulatory time lags
Source: Bayer, Norton and Falck Zepeda (2008),; Baseline values for each technology expressed in millions US$ using a discount rate for the
estimation of Net Present Value = 5%; change in net benefits defined as the total benefits estimated using the economic surplus minus total
regulatory costs. 10

11. What more can we do to expand the
forward-looking evidence base?
• Expand the focus of attention
– New biotechnologies, crops, traits, countries
• Birol et al. (2009) on GM maize in Mexico
• Ward et al. (2014) on DT rice hybrids in India
• Improve the rigor of inquiry
– Better tools and methods
• Update assumptions with data from ex post analysis
• Integrate new scenarios on regulatory costs/delays,
agbiotech industry competiveness, etc.
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12. Policy and policy change
• Simplistic theories of policy change
• Complexity and complex policy systems
Identify
problem
Research
solutions
Implement
solutions
Monitor and
evaluate
solutions
Revise and
adapt
solutions
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13. Research &
Analyze
Design &
Recommend
Advise
Strategically
MediateDemocratize
Clarify
Values &
Arguments
Rational style
What is good knowledge?
Interactive Style
What is good for mutual
understanding?
2. Policy analysis styles linked to activities 3. Underlying values and criteria of policy
analysis
Political effectiveness
Workability
Pro-activeness
Personal goal achievement
Quality of debate and
Arguments
Consistency
Richness, openness
Idealistic and generic values & criteria Pragmatic & particular values and criteria
Facilitator; mediator;
process manager
Democratic
Advocate
Narrator,
Logician,
ethic
Independent Scientist
Objective researcher
Independent expert
Impartial advisor
Client counselor
4. Conceptual model of policy analysis
Source: Mayer et al. 2004
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14. Thank you

15. References
Areal, F. J., Riesgo, L., & Rodriguez-Cerezo, E. (2013). Economic and agronomic impact of commercialized GM crops: a meta-analysis. The
Journal of Agricultural Science, 151(01), 7-33.
Bayer, J.C., G.W. Norton, and J.B. Falck-Zepeda. 2010. Cost of compliance with biotechnology regulation in the Philippines: Implications for
developing countries. AgBioForum 13:53–56.
Birol, E., Villalba, E. R., & Smale, M. (2009). Farmer preferences for milpa diversity and genetically modified maize in Mexico: a latent class
approach.Environment and Development Economics, 14(04), 521-540.
Finger, R. et al. "A meta analysis on farm-level costs and benefits of GM crops." Sustainability 3.5 (2011): 743-762.
Fischer, K., Ekener-Petersen, E., Rydhmer, L., & Björnberg, K. E. (2015). Social Impacts of GM Crops in Agriculture: A Systematic Literature
Review.Sustainability, 7(7), 8598-8620.
Fuglie, K., Heisey, P., King, J. L., Day-Rubenstein, K., Schimmelpfennig, D., Wang, S. L., & Karmarkar-Deshmukh, R. (2011). Research
investments and market structure in the food processing, agricultural input, and biofuel industries worldwide. USDA-ERS Economic Research
Report, (130).
Gruère, G., & Sengupta, D. (2011). Bt cotton and farmer suicides in India: An evidence-based assessment. The journal of development
studies, 47(2), 316-337.
Herring, R. J. (Ed.). (2015). The Oxford Handbook of Food, Politics, and Society. Oxford University Press, USA.
Kabunga, N. S., Dubois, T., & Qaim, M. (2012). Heterogeneous information exposure and technology adoption: The case of tissue culture
bananas in Kenya. Agricultural Economics, 43(5), 473-486.
Kikulwe, E. M., Birol, E., Wesseler, J., & Falck‐Zepeda, J. (2011). A latent class approach to investigating demand for genetically modified
banana in Uganda. Agricultural Economics, 42(5), 547-560.
Klümper, W., & Qaim, M. (2014). A meta-analysis of the impacts of genetically modified crops. PLoS One, 9(11), e111629.
Mayer, I. S., van Daalen, C. E., & Bots, P. W. (2004). Perspectives on policy analyses: a framework for understanding and design. International
Journal of Technology, Policy and Management, 4(2), 169-191.
Rosegrant, M. W., Koo, J., Cenacchi, N., Ringler, C., Robertson, R. D., Fisher, M., & Sabbagh, P. (2014). Food security in a world of natural
resource scarcity: The role of agricultural technologies. Intl Food Policy Res Inst.
Smale, M., Zambrano, P., Gruère, G., Falck-Zepeda, J., Matuschke, I., Horna, D., & Jones, H. (2009). Measuring the economic impacts of
transgenic crops in developing agriculture during the first decade: Approaches, findings, and future directions (Vol. 10). Intl Food Policy Res
Inst.
Spielman, D. J., Kolady, D. E., Cavalieri, A., & Rao, N. C. (2014). The seed and agricultural biotechnology industries in India: An analysis of
industry structure, competition, and policy options. Food Policy, 45, 88-100.
Ward, P. S., Ortega, D. L., Spielman, D. J., & Singh, V. (2013). Farmer preferences for drought tolerance in hybrid versus inbred rice: Evidence
from Bihar, India.
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