Presentation 1 falck zepeda introduction to socioeconomics

Program for Biosafety Systems – http://pbs.ifpri.info/

Introduction to socioeconomic considerations and biosafety

José Falck Zepeda

Senior Research Fellow

International Food Policy Research Institute –Program for Biosafety Systems (IFPRI - PBS)

Presented at Cornell Alliance for Science, Ithaca, NY October 26 2016.


Content

1. Methods and approaches

2. Practical case study on the potential adoption of Insect resistant cotton in Uganda

3. Practical considerations for SEC and decision making


Socioeconomic Assessments: A Definition

• Slight paraphrase of International Association of Impact Assessment (IAIA)“All the issues of interest to humans that may be affected by an intervention or natural phenomena following a line of causality, that is, from cause to effect. The characterization of socioeconomics considerations may include all the potential ways by which people and communities interact with their socio-cultural, economic and biophysical surroundings and the impact that such intervention or causal agent may have on them.”

• Simplified version (author to be revealed someday…)"Socioeconomic assessment can include examination of a variety of social

and economic factors with the objective to better understand the potential impacts of relevant interventions on people and communities."

SEC assessments can be done at different levels and for different cross-

cutting issues

• Gender and age

• Trade

• Labelling

• Cost of regulatory compliance

• Impact on innovation

• Policy impacts

National / Sub regional/

Global

Industry / sector

Community

Household

Farmer


Impact on Farmers / Household / Community: Potential issues for a socio-economic assessment

Value of biodiversity to indigenous communities

Value of biodiversity to individual farmers, households, and communities

Profits and benefit/cost ratios

Net income

Use of productive inputs (pesticides,…)

Production practices

Gender differentiated access and control, knowledge and/or attitudes

Health impacts

Safety first, downside risk, minimum production for survival

Irreversible costs and benefits

Freedom of choice and freedom to operate


Socio-economics and biosafety & biotechnology decision making

BEFORE RELEASE

An impact assessment during the regulatory evaluation stage to decide on the approval of a technology needs to be ex ante

AFTER RELEASE

For monitoring purposes or for standard technology evaluation purposes this is a conventional ex-postassessment


Ex ante – before release assessments

• Use secondary data from all available sources and/or limited primary data from existing producers to project potential gains from an intervention

• Multiple methods/approaches

– Cost/benefit

– Economic surplus

– Social impact assessment

– Value chain analysis

– Models and modelling


Ex post – after release assessment

• Use primary data collected from adopting and non-adopting producers– May use secondary data from existing surveys collected by other

researchers

• Multiple methods/approaches

• Adoption/impact studies issues– Statistical sampling and biases “the early adopting issues”

– Proper counterfactual and comparison to baseline approaches

– Multiple issues which may affect adoption unrelated to specific economic intervention


How does a producer benefit? Insect resistance traits

The case of Bt cotton

Producer Profit

Producer Surplus

Cost to Benefit

Additional

Cost of

Using the

Technology

Tech fee:

US$80/ha

0

+

-

Decrease

pesticide

application

cost

-Insecticide

-Machinery &

Equipment

Yield /

Reduction

in damage

-Timing

applications

-Reduced

damage bolls

Price change

due to increase

in supply

Additional

cost of

controlling

secondary

pests

Amenable to

IPM and/or

controlled

easily

Labor

Labor


How does a producer benefit?: Herbicide resistance traits

Herbicide resistant cotton

Additional

Cost of

Using the

Technology

Tech fee:

US$40/ha

0

+

-

(Small)

Increase

pesticide

application

cost

- Herbicide

-Machinery &

Equipment

Decrease

Yield

Small decrease,

no change or

a very

small increase

Price change

due to increase

in supply

The management

convenience factor

Allow use of

alternative production

technologies

- No-till low-till

Producer Profit

Producer Surplus

Cost to Benefit

Labor

Labor


2. Case study example:Genetically Modified Cotton

in Uganda: An Ex-ante Evaluation

Daniela Horna, Patricia Zambrano, Jose Falck-Zepeda, Theresa Sengooba,

Guillaume Gruere, Miriam Kyotalimye


Cotton in Uganda

• Importance of cotton:

– Cotton produced by smallholders (250,000 hhs)

– 3rd agricultural commodity exported but 2 -5% of total exports

– Traditional crop

• Problems:

– Very low yields (~400 Kg/Ha), link

– Quality deteriorated despite 1 variety policy

http://spreadsheets.google.com/ccc?key=pa765ZrB0oGtolvAxYqABfw


Cotton Performance

0

1,000

2,000

3,000

4,000

5,000

6,000

0

50

100

150

200

250

300

19

61

19

64

19

67

19

70

19

73

19

76

19

79

19

82

19

85

19

88

19

91

19

94

19

97

20

00

20

03

20

06

Yie

lds

(Hg

/Ha)

Pro

du

ctio

n

(1,0

00

to

nn

es)

1 2 3 4 5

1Post-independence

2Idi Amin period

3Macro-economic reforms

4CDO

Liberali.

5Yield

increase

Source:

FAO

Stats


Organic Cotton

2000 / 01 1,642,458 54,996,904 3,066 102,200 3.0%

2001 / 02 1,734,187 63,898,025 3,406 126,148 2.7%

2002 / 03 1,203,753 57,563,429 2,407 114,619 2.1%

2003 / 04 2,030,465 84,344,870 3,626 160,000 2.3%

2004 / 05 2,979,969 130,854,714 5,321 254,000 2.1%

2005 / 06 1,499,030 51,847,138 2,677 102,000 2.6%

2006 / 07 7,377,333 68,681,469 13,174 134,000 9.8%

2007 / 08 13,766 66,500 20.7%

Source: CDO 2007 / 08


Cotton Production

3 4 12 274

694 768

1,536

2,190

53 149 489

813 1,285

2,082

7,403

41 80 204 563

1,528

3,058

4,235

1 6 21 624

840 1,216

77 96 114 345

6,786

3 3

702

1,358

10,087

-

2,000

4,000

6,000

8,000

10,000

12,000

Kay

un

ga

Jin

ja

May

uge

Bu

giri

Bu

sia

Kam

uli

Igan

ga

Toro

ro

Kab

eram

aido

Kat

akw

i

Soro

ti

Mb

ale

Siro

nko

Ku

mi

Pal

lisa

Gu

lu

Ko

tid

o

Pad

er

Kit

gum

Am

ora

tar

Lira

Ap

ac

Luw

ero

Kib

ale

Kib

oga

Nak

aso

ngo

la

Ho

ima

Mas

ind

i

Ad

jum

ani

Mo

yo

Yum

be

Aru

a

Neb

bi

Kan

ungu

Kye

njo

jo

Kam

wen

ge

Bu

shen

yi

Kas

ese

South Eastern North Eastern Northen Mid Western andCentral

West Nile Western

Seed

Co

tto

n P

rod

uct

ion

(1,0

00

Kg)


GM cotton

• Public interest to improve performance of cotton:

– Confined trials approved in 2008

• Goal: provide stakeholders and policy makers with tools to analyze and make decisions about the approval and adoption of GM cotton in Uganda


Farm

• Will cotton producers be better off with the adoption of GM seed?


Farm

• Source of Data:

– Household survey

– 151 producers, 35 in Lira and 116 in Kasese

– Expert opinions

• Method: Partial budget

– Real scenarios: traditional low input, higher use of inputs, organic production

– Simulated scenarios: conventional with GM


Farm

Cost components UnitsLow Input

(N=124)

Share

(%)High Input

(N=27)

Share

(%)

Seed cotton - Yield Kg/acre 361.7 458.4

Yield loss bollworm % 72% 78%

Total income Sh/acre 231,638 322,369

Land rent Sh/acre 52,333 26% 47,857 16%

Chemical fertilizer Sh/acre 16,998 6%

Organic fertilizer Sh/acre 14,614 5%

Herbicide use Sh/acre 11,877 4%

Chemical pesticide Sh/acre 17,300 9% 14,963 5%

Labor to apply pesticides Sh/acre 4,001 2% 5,088 2%

Labor to apply herbicides Sh/acre 4,500 1%

Labor for weeding Sh/acre 42,066 21% 66,221 22%

Labor for other activities Sh/acre 87,676 43% 120,718 40%

Total costs Sh/acre 203,376 302,836

Margin Sh/acre 28,262 19,533

Downside risk % 49 47

B/C 1.13 1.06


Farm

• Findings:

– Simulated scenarios using IR and HT varieties yield the highest returns, but profitability does not increase dramatically

– Yield variability is the main determinant of marginal benefits

– Need to pay attention to investment in fertilizers and good quality seed


Industry

• Research question– What are the benefits and risks associated with the adoption of

IR and HT cotton in Uganda?

• Method– Economic surplus, estimate the changes in benefits due to the

adoption of GM

– Scenarios: public and private delivery, without and with reduced technology fee, for IR only or staked gene technology

• Data sources– Household survey

– Experts’ opinion

– Secondary information


Model and Assumptions

• Small open economy model augmented to include stochastic parameters

• Use approach described by Alston, Norton and Pardey augmented to introduce risk considerations using simulation software

• Elements of best practice to gauge quality of evidence on existing studies


Assumptions – Distributions to consider uncertainty

Parameter Unit Value Distribution Description Source/Citation

Yield Seed cotton Kg / Ha 627

Normal distribution with mean 627 kg/ha and standard deviation of 375 kg/ha.

Mean and standard deviations estimated from IFPRI survey

Producer Price US$ / Kg

0.188

Triangular distribution with values maximum=0.38, most likely= 0.26, minimum=0.06

The maximum value for the triangular distribution is the average of all observations. The most likely is the

Elasticity supply (Es)

0.91

Triangular distribution with minimum= 0.3, most likely=1 and maximum =1.5.

Minot and Daniels 2005; Dercon 1993, Delgado and Minot 2000, Alston, Norton and Pardey, 1995

Cost reduction/change due to GM (pesticides - spraying)

% 13%

Triangular distribution with minimum =-6, most likely=3, maximum=16

Mean and maximum values are estimated from IFPRI survey. Minimum value is an estimate average control cost for lepidoptera relative to total costs in the IFPRI survey.

Percent yield difference (%)

% 23%

Logistic distribution with α= 0.0939, β= 0.168, truncated at -10% and 74%

Estimated using @Risk application from IFPRI survey estimates for the costs control of lepidopterans. Truncation is the maximum yield estimate from IFPRI survey

Change(reduction) in glyphosate applications

#

Uniform distribution with values between 1 and 2 applications

Change in cost of herbicide relative to total cost

%

Multiply the change(reduction) in glyphosate applications times the average cost

Change in weeding costs from the use of herbicide

%

Assume that 1 application of herbicide glyphosate substitutes for 3 manual weeding

Technology fee GM (US$/ha)

Triangular distribution with minimum = 15, most likely=32, maximum=56

Falck-Zepeda et al., 2000; Huang et al., 2003; Bennett et al., 2004; Huang et al., 2004

Reduced technology fee (US$/ha)

Triangular distribution with minimum =5, most likely =11.44, maximum = 18.66

Falck-Zepeda et al., 2000; Huang et al., 2003; Bennett et al., 2004; Huang et al., 2004


Assumption source

Variables Unit Value Source

Weeding costs for hired labor (US$/ha) 16.49 Average for IFPRI survey respondents

Base year - 2008

Interest rate % 18% Bank of Uganda

Exchange Rate (2006) Ug. Schgs/ US$ 1,710 Bank of Uganda

Elasticity of demand (Ed) Unit 0.50 Literature

R&D and Regulatory Years 5

Experts consulted

Adoption (time to max.) Years 5

Time at maximum Years 10

Years to dis-adoption Years 5

Total years profile Years 25

Maximum adoption level % 40%

Total R&D and Regulatory costs GM

US$ 300,000 Pray et al. 2005, Quemada 2003 and Falck Zepeda and Cohen, 2006


Economic Surplus Estimates

Scenarios Description NPV Total benefits IRR

Scenario I Public sector release 6,804,135 64,997,970 81%

Scenario II Include tech fee 1,533,175 15,989,645 54%

Scenario III Include tech fee (reduced to 1/3 global levels) 4,121,746 39,961,923 68%

Scenario IV Include herbicide management and tech fee 1,821,044 18,626,829 56%

Scenario V Include herbicide management and reduced tech fee 7,473,349 71,299,556 83%

Scenario VI Double area and maximum adoption rate 34,104,934 319,700,453 129%

Higher seed prices Higher areas cultivated

Herbicide management Higher adoption rates


Examining risk and return

Scenario I Scenario II Scenario III Scenario IV Scenario V Scenario VI

Prob(Unfavorable) 0.13 0.62 0.26 0.57 0.17 0.23

Prob(Cautionary) 0.47 0.32 0.55 0.35 0.38 0.11

Prob(Favorable) 0.40 0.06 0.18 0.08 0.44 0.65

StopLight Chart for Probabilities Less Than 1,500,000.000 and Greater Than 7,000,000.000

0.13

0.62

0.26

0.57

0.17 0.23

0.47

0.32

0.55

0.35

0.380.11

0.40

0.060.18

0.08

0.44

0.65

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Scenario I Scenario II Scenario III Scenario IV Scenario V Scenario VI

StopLight Chart for Probabilities Less Than 1,500,000.000 and Greater Than 7,000,000.000

Notes:1) StopLight Analysis is © 2008 by SIMETAR, 2) Lower Cut-Off Value= 1,500,000, Upper Cut-Off

Value=7,400,000, are the mean NPV for lower and highest scenario outcome.


Industry

• Findings

– Uganda can gain from the introduction of GM cotton but rates of return estimated are not as high as other results for agricultural research

– Low yields due to low input use have a critical role in explaining benefits generated by GM adoption

– The probability of a negative rate of return could be as high as 38% in the case of paying full technology fee (same as the rest of the world.• Uganda has to negotiate the technology fee!!!


Impacts on Trade• Research questions

– Would GM cotton affect trade?

– Are there possibilities for co-existence of organic and GM cotton?

• Methods– Examination of trade flows

– Qualitative examination of value chains and marketing channels

• Data– FAOSTAT

– National databases


Impacts on Trade - Findings

• GM cotton is not a risk to exports of conventional cotton but potentially to organic exports

• Coexistence of both systems is possible if seed mixing is avoided

• Discuss seed marketing strategies beforeintroduction of GM cotton


Institutions

• Research question– How has and will the current

institutional setting affect the approval and adoption of GM cotton?

• Method:– Based on consultation with

experts– Used of social networks tool

(NetMap)

• Examined three stages of the process: – approval of confined trials– approval of the Biosafety Bill– adoption of GM seed by farmers


Institutions - Findings

• Despite some bottlenecks, the regulatory process is leading to the commercial approval of GM cotton

• Need to look beyond the regulatory process and focus on how to facilitate adoption

– technology delivery, multiplication

– extension

– credit


Conclusions

• GM cotton: – Can be a risk management tool to reduce downside risk for

farmers

– has the potential to improve cotton productivity

• But, there is a need to facilitate farmers’ access to complementary inputs (such as fertilizer, herbicides) and to seed

• Policy makers need to discuss:– Technology delivery

– Technology fee

– Seed marketing strategies


What determines whether farmers gain or lose from Bt/HT cotton?

• Price of the technology and of output (fiber and seed)

• Pest infestation and pest dynamics (including secondary pests)

• Use of appropriate germplasm and the ability to have the most appropriate germplasm over time and location – gene technology without the appropriate germplasm is

useless

• Information about use of the technology


What determines whether farmers gain or lose from Bt/HT cotton?

• Type of input and output economic markets (monopoly Vs. competitive)

• Institutional issues– contractual agreements and obligations

– vertical integration and the value chain

• Input use and management

• Existing production and financial risk profile and situation

• Credit and other financial characteristics of the producer unit


Elements of best practice – a checklist

• Assumptions and their sources listed

• Sensitivity analysis on assumptions conducted

• Sampling and analysis methods described

– Number of samples used

– Proper counterfactual /baseline described

– Sampling method used

• Consideration of statistical and sampling biases and issue of outliers


3. Practical considerations and implications for implementation

Photos: KARI –Kenya glasshouse, KARI-Thika confined field trial of Bt cotton, IRRI conventional rice trials Los Baños, Philippines


• Drivers

– International agreements

– Regional considerations

– National laws and regulations

• National Biosafety Frameworks

• Implementing regulations, directives, administrative acts

– Stakeholder interests

• Desire of a number of countries to understand the impacts of regulated technologies

• An alternative to contribute to the protection of producers and consumers ... but, is this the best option?

What drives socioeconomics inclusion in decision making?


SEC and regulatory design issues

Issues Options

Type of inclusion? • No inclusion vs. Mandatory vs. Voluntary

What? • Issues for review

Who? • Developer vs. dedicated government unit vs. third party experts

Scope? • Narrow interpretation article 26.1 • Narrow set of socio-economic issues • Broader set of assessments (SIA or SL)

Approach? • Concurrent but separate vs. Sequential vs. Embedded• Implementation entity

Assessment trigger? • Each submission vs. Event-by-event vs. class of events

When? • Laboratory/greenhouse vs. CFTs vs. Commercialization• For post release monitoring

How? • Will the assessment require a de novo study? • Choice of methods limited• Decision making rules and standards• Method integration, standards, tolerance to errors


Implementation options

– Inclusion only for commercialization

– Statement of no significant impact can undergo expedited process

– For other purposes than commercialization focus on broad impacts of biotechnology and technology in society rather than on an event basis

– Also consider doing by class of events (i.e. insect resistance or herbicide tolerance) while focusing on specific differences a event may have with respect to other events of the same class


Potential implications from SEC inclusion into decision making

• Gain more and/or better information about technology impacts for decision making - may support valuable technologies

• Balance gains in information, additional costs & effort, and impacts on innovation

• Potential for introducing uncertainty that can lead to an unworkable system if rules and standards are not clear


Potential implications from SEC inclusion into decision making

• Cost of compliance costs will increase

• Potential regulatory delays

– Reduction in the number of technologies especially those released by the public sector and crops/traits of a public good nature

– Some public sector institutions may not be able to deploy technologies due to fixed costs necessary to enter market


Contrasting baseline net benefit levels from GE crop adoption with higher costs in the Philippines

Notes: 1) Source: Bayer, Norton and Falck Zepeda (2008), 2) Baseline values for each technology expressed in millions US$ using a discount rate for

the estimation of Net Present Value = 5%, 3) Change in Net benefits defined as the total benefits estimated using the economic surplus minus total

regulatory costs.


Contrasting benefit levels from GE crop adoption with larger regulatory lags in the Philippines

Notes: 1) Source: Bayer, Norton and Falck Zepeda (2008), 2) Baseline values for each technology expressed in millions US$ using a discount rate for

the estimation of Net Present Value = 5%, 3) Change in Net benefits defined as the total benefits estimated using the economic surplus minus total

regulatory costs.


Biotechnology and Biosafety Rapid Assessment and Policy Platform (BioRAPP)

• A new project at IFPRI-PBS funded by the Gates Foundation

• Why BioRAPP?

– Policy and decision makers are likely to be confronted with economic and environmental questions that require a local evidence-based answer

– International evaluations may exist to draw some answers, but there is typically scant national evidence and capacity to efficiently and timely respond to these questions


The need for local “before release” economic assessments

• Addressing specific GM crops in the pipeline

• Developed and implemented by local experts

• Answering specific questions posed by policy and decision makers

• Timely manner

• Backed by IFPRI/PBS expertise

• In support of ongoing policy reform efforts

IFPRI Milo Mitchel


BioRAPPTwo distinct components working to achieve a goal


BioRAPP – Proposed crop/trait targets for national discussion and final

selection

• Cotton/ Insect resistant

• Enset/ Bacterial blight resistant• Cowpea/ Insect resistant

• Rice/ Nitrogen-use efficiency,

salt tolerant, water efficiency

• Cassava/ Nutrition enhancement

• Cassava/ Brownstreak virus resistant

• Maize/ Insect resistant or herbicide

tolerant and drought tolerant• Cassava/ Brownstreak virus resistant

• Maize/ Drought tolerant

• Maize/ Insect resistant

• Banana/ Brownstreak virus resistant

• Cassava/ Bacterial wilt resistant

• Maize/ Drought tolerant

• Maize/ Drought tolerant and insect

resistant

Ghana

Nigeria

Ethiopia

Uganda

Tanzania

José Benjamin Falck-Zepeda, Ph.D.Senior Research Fellow

IFPRI 2033 K Street NW

Washington, DC 20006-1002USA

[email protected] bio/pubs:

http://www.ifpri.org/staffprofile/jose-falck-zepeda

Blog: http://socioeconomicbiosafety.wordpress.com/

Follow me on Twitter: @josefalck

mailto:[email protected]

http://www.ifpri.org/staffprofile/jose-falck-zepeda

http://socioeconomicbiosafety.wordpress.com/

Presentation 1 falck zepeda introduction to socioeconomics

Technology

Transcript of Presentation 1 falck zepeda introduction to socioeconomics