the importance of evidence in designing “last mile” solutions
TRANSCRIPT
The Importance of Evidence in Designing “Last Mile” Solutions
David J Spielman
International Food Policy Research Institute
Presentation at the 7th Global Forum for Rural Advisory Services (GFRAS) Annual Meeting: “The Role of Rural Advisory Services for Inclusive Agripreneurship, Limbé,
Cameroon, October 3-6
*Adapted from
Birner et al. (2009)
Adapted* Framework for Designing and Analyzing Extension and Advisory Services
Business Environments
Market Infrastructure
Property Rights
Outside manageable interests
Research Education
Other AIS Actors
Within manageable interests
Livelihood Strategies
Community Engagement
Frame Conditions Other agricultural innovation
system components
Systems-level Performance
Access
• Timeliness, Inclusion, Scale
Quality
• Feedback, Relevance
Sustainability
• Effectiveness, Efficiency
Political Economy
Political Systems
Development Strategies
Public Policies
Rules and Regulations
Collective Action
Civil Society
Community Engagement
Agroecology/agroclimate
Agronomic potential
Farming systems
Extension and Advisory Services
Characteristics
Governance Structures
Decision-making Processes
Partnerships, Collaborations
Linkages, Networks
Market Engagement
Advisory Methods
Farm Households• Δ knowledge
• Δ attitudes, behavior
• Δ uptake, adoption
• Δ decision-making
capacity
ImpactProductivity
Welfare & Equity
Empowerment
Environmental sustainability
Impact pathway
Influencing factors
Feedback line
Ability to exercise
voice
Ability to demand
accountability
Organization & Management
Innovative Capacities
Organizational Cultures
Outside manageable interests
Intermediate Outcomes → Primary Outcomes → Impact
Evaluation 1: Africare’s ISFM program in Volta
Region, Ghana
Extension training-of-trainers (ToT)• ToT on a variety of ISFM practices
• Conveniently located demo plots
ISFM
Expected project outcomes
• 70% increase in # farmers recording increased food security and incomes
• Yield increases: 213% for maize, 188% for cassava, and 400% for cowpea
• 17,000 farmers with access to production information and best practices
• 16,000 farmers educated and trained in use of ISFM technologies
• 15,000 farmers with access to and participation in input and output markets
• 15,000 farmers adopting ISFM technologies
• 6,000 hectares of farmland under ISFM
Research questions
Awareness
• Does ToTincrease smallholder awareness of purchased inputs and ISFM?
Adoption
• Does ToTchange farmer behavior to use purchased inputs and ISFM practices?
Productivity gains
• Does ToT result in increases in land and labor productivity for major crops?
Farmer welfare
• Does ToT result in an increase in the returns to farming and improvements in household welfare?
Short-term
Within the scope of this evaluation
Long-term
Beyond the scope of this evaluation
• Evaluate the impact and cost-effectiveness of the DG approach to agricultural extension
• By using modern impact evaluation methods• By generating robust quantitative measures of impact on “trialing”• By measuring the “cost per trialing” and other cost/benefit indicators• By exploring variations on the standard Digital Green approach
• Provide evidence on scale-up options• To Digital Green • To the Ministry of Agriculture • To the regional bureaus of agriculture• To other stakeholders
Evaluation 2: Digital Green’s ICT-enabled extension in Ethiopia
Research questions
How effective is the DG approach in increasing farmers’ willingness to “trial” a modern technology?
Does technology trialing increase when both spouses in a single hhparticipate in the DG approach?
• Does male + female spouse participation affect decision-making on the technology?
• Does male + female spouse participation affect how the technology is used?
Does technology trialing increase when participants in the DG approach know
about other farmers’ prior experiences in similar/nearby locales?
• Are farmers more willing to trial technologies if they know about other farmers’ experiences?
• Are farmers influenced by information about “trialing rates” in ecologically similar locales?
The complete design
Group Control DG approach only
DG approach + adoption rate
info
(no. of kebeles)
Participation of hh head only
150(C)
68 (T1)
68(T1 + T3)
Participation of both M&F spouses --
68 (T1 + T2)
68(T1 + T2 + T3)
Note: Parentheticals denote households receiving the following: (C) = standard FTC training; (T1) = normal DG approach; (T1 + T3) = normal DG approach plus adoption rate information; (T1 + T2) = normal DG approach with M&F spouses; (T1 + T2 + t3) = normal DG approach with M&F spouses plus adoption rate information. Sample size: 6 hh/kebele x 422 kebeles= 2543 hh
Random sample of kebeles
Standard extension approach
Random sample of development groups
from each kebele
Farmers who do notparticipate in DG
approach
DAs not using DG approach
CONTROL GROUP
Farmers who participate in DG approach alone
DG approach Random sample of development groups
from each kebele
DAs using DG
approachTREATMENT GROUP1
DAs using DG approach with M&F spouses
M&F spouses who participate in DG
approach
Random sample of development groups
from each kebele
DG approach with M&F spouses
T1 + T2
Farmers with DG approach and have adoption rate info
Random sample of development groups
from each kebele
DG approach with adoption rate info DAs using DG
approach with adoption rate
info
T1 + T3
M&F spouses with DG approach and adoption rate info
Random sample of development groups
from each kebele
DG approach with M&F spouses and adoption rate info DAs using DG
approach with adoption rate
info
T1 + T2 + T3
• Do gendered dimensions of information acquisition play a role in household decision-making on technology adoption?• Do women and men in the same household have different social networks?
• If so, how these do these differences affect learning and adoption?
Evaluation 3: Gendered dimensions in the promotion of laser land leveling in India
• Eastern Uttar Pradesh (EUP): poorest part of UP
• Highly agrarian; intensive rice-wheat farming system
• Sample site• 3 districts in EUP• 8 (randomly selected) villages per district• 20 (randomly selected) farmers per village
Study design
1.Info session on LLL
2.LLL auction and lottery: Divides sample into 3 groups
3.Lottery-winning farmers paid for and received LLL
4.One-year later: Follow-up auction with no lottery
Random sample from village v
Auction(self-selection)
Auction winners
Auction losers
Lottery(random
selection)
Lottery losersLottery winners
Uniform
Perfect
District
Landholdings
BPL
first hour discount
100
150
200
250
300
350
100 150 200 250
Sub
sid
y co
st p
er
wat
er
save
d
(Rs/
m3
'00
0)
Subsidy cost per farmer leveling (Rs/farmer)
Net gain for provider
Net loss for provider
Efficiency tradeoffs exist when subsidizing LLLs in India
Male ties
Female ties
With household relationship
RelationshipsNodes
Baspar Village, Maharajganj District
• Male 15 is a big source of ag info• Male 22 is not
Wife of male farmer
Male farmer
Female household head
Social networks differ between men and women, with marginal influences on LLL adoption
Baspar Village, Maharajganj District
• Female 22 is a big source of ag info• Male 15 is not (totally isolated)
Social networks differ between men and women, with marginal influences on LLL adoption
Male ties
Female ties
With household relationship
RelationshipsNodes
Wife of male farmer
Male farmer
Female household head
Key findings
• Women and men in same households have very little overlap in their agricultural
information networks
• Women’s agricultural networks are as large as men’s and, in the case of poor
households, substantially larger
• Poor men tend to talk to wealthier ones about agriculture, whereas poor women tend
to talk to other poor women
• Poorer women’s networks might be sources of less information, despite large
networks
• Having adopters in networks help women learn about technology
Female social networks are likely more relevant to technology promotion and extension
efforts in many “male-dominated” cereal systems than previously believed