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The Importance of Evidence in
Designing “Last Mile” Solutions
David J Spielman
International Food Policy Research Institut...
*Adapted from
Birner et al. (2009)
Adapted* Framework for Designing and Analyzing
Extension and Advisory Services
Business...
Evaluation 1:
Africare’s ISFM program in Volta
Region, Ghana
Extension training-of-trainers (ToT)
• ToT on a variety of IS...
Expected project outcomes
• 70% increase in # farmers recording increased food
security and incomes
• Yield increases: 213...
Research questions
Awareness
• Does ToT
increase
smallholder
awareness of
purchased
inputs and
ISFM?
Adoption
• Does ToT
c...
• Evaluate the impact and cost-effectiveness of the DG approach to
agricultural extension
• By using modern impact evaluat...
Research questions
How effective is the DG approach in increasing farmers’ willingness to “trial” a
modern technology?
Doe...
The complete design
Group Control DG approach
only
DG approach +
adoption rate
info
(no. of kebeles)
Participation of
hh h...
Random
sample of
kebeles
Standard extension
approach
Random sample of
development groups
from each kebele
Farmers who do n...
• Do gendered dimensions of information acquisition play a role in household
decision-making on technology adoption?
• Do ...
Study design
1.Info session on LLL
2.LLL auction and lottery: Divides sample into 3 groups
3.Lottery-winning farmers paid ...
Uniform
Perfect
District
Landholdings
BPL
first hour discount
100
150
200
250
300
350
100 150 200 250
Subsidycostperwaters...
Male ties
Female ties
With household relationship
RelationshipsNodes
Baspar Village, Maharajganj District
• Male 15 is a b...
Baspar Village, Maharajganj District
• Female 22 is a big source of ag info
• Male 15 is not (totally isolated)
Social net...
Key findings
• Women and men in same households have very little overlap in their agricultural
information networks
• Wome...
The Importance of Evidence in Designing “Last Mile” Solutions
The Importance of Evidence in Designing “Last Mile” Solutions
The Importance of Evidence in Designing “Last Mile” Solutions
The Importance of Evidence in Designing “Last Mile” Solutions
The Importance of Evidence in Designing “Last Mile” Solutions
The Importance of Evidence in Designing “Last Mile” Solutions
The Importance of Evidence in Designing “Last Mile” Solutions
The Importance of Evidence in Designing “Last Mile” Solutions
The Importance of Evidence in Designing “Last Mile” Solutions
The Importance of Evidence in Designing “Last Mile” Solutions
The Importance of Evidence in Designing “Last Mile” Solutions
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The Importance of Evidence in Designing “Last Mile” Solutions

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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 made during the side event on “Last Mile Delivery,” convened by PIM, IFPRI, TEAGASC, and Irish Aid.

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The Importance of Evidence in Designing “Last Mile” Solutions

  1. 1. 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
  2. 2. *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 Impact Productivity 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
  3. 3. 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
  4. 4. 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
  5. 5. Research questions Awareness • Does ToT increase smallholder awareness of purchased inputs and ISFM? Adoption • Does ToT change 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
  6. 6. • 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
  7. 7. 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 hh participate 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?
  8. 8. 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
  9. 9. Random sample of kebeles Standard extension approach Random sample of development groups from each kebele Farmers who do not participate 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
  10. 10. • 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
  11. 11. 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
  12. 12. Uniform Perfect District Landholdings BPL first hour discount 100 150 200 250 300 350 100 150 200 250 Subsidycostperwatersaved (Rs/m3'000) Subsidy cost per farmer leveling (Rs/farmer) Net gain for provider Net loss for provider Efficiency tradeoffs exist when subsidizing LLLs in India
  13. 13. 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
  14. 14. 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
  15. 15. 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

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