- The document discusses barriers to technology adoption in the Caribbean region and Peru, focusing on standard economic explanations like access to credit and inputs as well as behavioral factors like risk attitudes. - A survey of over 600 farming households in Guyana, St. Lucia, St. Kitts, and Trinidad found low rates of technology adoption and that standard factors did not fully explain patterns of adoption. Behavioral factors like risk aversion positively influenced adoption in some cases. - Experiments in Guyana and Peru further explored how social learning and uncertainty about technologies influenced adoption decisions. The research aims to better understand behavioral determinants of technology adoption in these regions.

1. Barriers to Technology Adoption in
the CARICOM and Peru
International Food Security Dialogue 2014
Edmonton May 2, 2014
Enhancing Food Production, Gender Equality and Nutritional Security in a Changing World
Engle-Warnicka,b, J., Escobalc, J., Laszloa,b,c, S. and Raeburna, K.
a: Department of Economics, McGill; b:CIRANO; c:GRADE, Peru
Presented by Sonia Laszlo
Associate Director, ISID, McGill University
Associate Professor, Economics, McGill University

2. Motivation
§ Low rate of technology adoption in developing countries
§ Standard & conventional explanations (Feder et al., 1985)
• Market (inputs, outputs) & liquidity (credit) constraints
• Inappropriate technologies
• Lack of know-how…
§ Non-standard & behavioural explanations
• Attitudes towards risk and uncertainty (Engle-Warnick et al., 2011)
• Inconsistent time preferences & impatience (Duflo et al., 2011)
• Social networks & learning (Conley & Udry, 2010; Bandiera & Rasul,
2006)

3. CARICOM Food Security Project
§ CIFSRF1 project, joint venture McGill & UWI and number of Third Party
Partners (Guyana, St Kitts, St Lucia, Trinidad & Peru)
§ Main goals:
• Improve health & nutrition in CARICOM population (high rates of overweight
and obesity)
• Develop food production systems based on agricultural diversification, water
conservation and efficient use of land
§ “Farm to Fork” approach:
• Interventions in farming practices (GUY, SKN, SLA, TTO)
• Nutritional interventions in schools (SKN & TTO)
§ Great opportunity to study these issues in a context where technology
adoption in agriculture matters
1 Canadian International Food Security Research Fund, IDRC & DFATD

4. Technology adoption – research questions
§ What are the existing barriers to technology
adoption in GUY, SKN, SLA & TTO?
• What is the degree of adoption?
• Do standard explanations go far in explaining these patterns?
§ What are the behavioural determinants of
technology adoption in these countries?
• Do risk and uncertainty aversion matter?
• What can we learn about beliefs about the technology and peer
effects?

5. Overview of multi-method approach
Producer Household Surveys
(PHS)
§ Multi-purpose modular design
• Demogs & farm practices & tech
adopt
• Risk and uncertainty attitudes
• …
§ Directly comparable questionnaire
§ Data collection: Oct 2011- Mar 2012
§ Partnership with local partners
Economics Experiments (EXP)
N hhlds Local partner
GUY 304 NAREI
SKN 91 MARM & ind. cons.
STL 118 MALFF
TTO 93 UWI & ind. cons.
§ Technology adoption decision-
making under uncertainty
§ Peru (Feb 2012, GRADE)
• Does social learning play a role in
forming beliefs about relative riskiness
and ambiguity of different
technologies?
• Known v unknown probability
distributions
• 305 farmers in 3 regions participated
§ Guyana (Nov 2012, NAREI)
• Tech adoption modeled as a public good
• Experimentation with a technology provides
information about unknown distributions
• 136 farmers from 1 region participated

6. PHS Results
Table 1: Socio-demographic characteristics of household heads in the samples of
farming households from Guyana, St. Lucia, St. Kitts and Trinidad
Characteristics
GUY
SLA
St. Kitts
Trinidad
Pooled
Age (Mean)+
44.4 (12.5)
51.8 (10.8)
49.2(9.6)
49.0(11.3)
47.2 (12.1)
Female
14.1%
23.7%
20.1%
10.0%
16%
Married
84.9%
55.1%
50.5%
72.0%
72%
Education
Primary
38.6%
66.7%
12.7%
26.5%
38%
Secondary
54.1%
18.9%
67.6%
45.8%
48%
College/Univ.
4.3%
7.8%
9.9%
21.7%
9%
Total N
304
118
91
93
606
Source: PHS Baseline 2012. Reproduced from Laszlo et al. (2013), Table 2.
+ Standard deviation in parenthesis.

7. PHS Results
Table 2: Use and Adoption of New Technology by Farmers Surveyed in Guyana,
St. Lucia, St. Kitts and Trinidad
Country
Crop
Tool or
Equipment
Irrigation
Technique
Fertilizer
Pesticide
Record-
keeping
Technique
N
GUY
2.7%
1.0%
0.7%
28.2%
47.6%
0.3%
298
SLA
13.0%
0.9%
3.4%
0.9%
14.7%
0.0%
116
SKN
17.5%
14.8%
1.6%
3.2%
3.2%
1.9%
63
TTO
10.8%
10.5%
3.9%
19.2%
11.4%
3.9%
78
Total
7.62%
3.68%
1.81%
18.38%
30.36%
0.92%
555
N
551
543
554
555
550
542
Source: PHS Baseline 2012. Table reproduced from Laszlo et al. (2013), Table 12

8. PHS Results
Table 3: Probit estimates for predicting the use of new technology (last 12 months)
is an adopter
new crop
new fertilizer
new pesticide
Age
-0.0028
-0.0004
-0.0022
-0.0028
[0.003]
[0.001]
[0.002]
[0.003]
Female
0.2108***
-0.0022
0.1103*
0.1874**
[0.076]
[0.005]
[0.063]
[0.080]
Educ – primary
0.1409
0.5570**
0.9792***
0.2601
[0.235]
[0.243]
[0.033]
[0.301]
Educ - secondary
0.3917*
0.5251**
0.9858***
0.4934**
[0.200]
[0.252]
[0.022]
[0.241]
Educ – post-secondary
0.0742
0.9875***
0.9361***
-0.1636
[0.257]
[0.030]
[0.022]
[0.342]
Unmet Basic Needs
0.0324
-0.0018
-0.0234
0.1099**
[0.053]
[0.004]
[0.030]
[0.054]
Risk Aversion
0.0652*
0.0001
0.0378*
0.0744*
[0.033]
[0.002]
[0.022]
[0.042]
Ambiguity Aversion
-0.0160
-0.0005
-0.0146
-0.0119
[0.027]
[0.002]
[0.018]
[0.031]
Distance to daily mkt
-0.0011***
-0.0002*
-0.0007*
-0.0009**
[0.000]
[0.000]
[0.000]
[0.000]
Observations
328
286
317
300
Table reproduced from Laszlo et al. (2013), Table 16. Controls for ethnicity, Household size, marital status, country dummies,
Robust standard errors in brackets*** p<0.01, ** p<0.05, * p<0.1

9. PHS Results
Table 3: Probit estimates for predicting the use of new technology (last 12 months)
is an adopter
new crop
new fertilizer
new pesticide
Age
-0.0028
-0.0004
-0.0022
-0.0028
[0.003]
[0.001]
[0.002]
[0.003]
Female
0.2108***
-0.0022
0.1103*
0.1874**
[0.076]
[0.005]
[0.063]
[0.080]
Educ – primary
0.1409
0.5570**
0.9792***
0.2601
[0.235]
[0.243]
[0.033]
[0.301]
Educ - secondary
0.3917*
0.5251**
0.9858***
0.4934**
[0.200]
[0.252]
[0.022]
[0.241]
Educ – post-secondary
0.0742
0.9875***
0.9361***
-0.1636
[0.257]
[0.030]
[0.022]
[0.342]
Unmet Basic Needs
0.0324
-0.0018
-0.0234
0.1099**
[0.053]
[0.004]
[0.030]
[0.054]
Risk Aversion
0.0652*
0.0001
0.0378*
0.0744*
[0.033]
[0.002]
[0.022]
[0.042]
Ambiguity Aversion
-0.0160
-0.0005
-0.0146
-0.0119
[0.027]
[0.002]
[0.018]
[0.031]
Distance to daily mkt
-0.0011***
-0.0002*
-0.0007*
-0.0009**
[0.000]
[0.000]
[0.000]
[0.000]
Observations
328
286
317
300
Table reproduced from Laszlo et al. (2013), Table 16. Controls for ethnicity, Household size, marital status, country dummies,
Robust standard errors in brackets*** p<0.01, ** p<0.05, * p<0.1

10. PHS Results
Table 3: Probit estimates for predicting the use of new technology (last 12 months)
is an adopter
new crop
new fertilizer
new pesticide
Age
-0.0028
-0.0004
-0.0022
-0.0028
[0.003]
[0.001]
[0.002]
[0.003]
Female
0.2108***
-0.0022
0.1103*
0.1874**
[0.076]
[0.005]
[0.063]
[0.080]
Educ – primary
0.1409
0.5570**
0.9792***
0.2601
[0.235]
[0.243]
[0.033]
[0.301]
Educ - secondary
0.3917*
0.5251**
0.9858***
0.4934**
[0.200]
[0.252]
[0.022]
[0.241]
Educ – post-secondary
0.0742
0.9875***
0.9361***
-0.1636
[0.257]
[0.030]
[0.022]
[0.342]
Unmet Basic Needs
0.0324
-0.0018
-0.0234
0.1099**
[0.053]
[0.004]
[0.030]
[0.054]
Risk Aversion
0.0652*
0.0001
0.0378*
0.0744*
[0.033]
[0.002]
[0.022]
[0.042]
Ambiguity Aversion
-0.0160
-0.0005
-0.0146
-0.0119
[0.027]
[0.002]
[0.018]
[0.031]
Distance to daily mkt
-0.0011***
-0.0002*
-0.0007*
-0.0009**
[0.000]
[0.000]
[0.000]
[0.000]
Observations
328
286
317
300
Table reproduced from Laszlo et al. (2013), Table 16. Controls for ethnicity, Household size, marital status, country dummies,
Robust standard errors in brackets*** p<0.01, ** p<0.05, * p<0.1

11. PHS Results
Table 3: Probit estimates for predicting the use of new technology (last 12 months)
is an adopter
new crop
new fertilizer
new pesticide
Age
-0.0028
-0.0004
-0.0022
-0.0028
[0.003]
[0.001]
[0.002]
[0.003]
Female
0.2108***
-0.0022
0.1103*
0.1874**
[0.076]
[0.005]
[0.063]
[0.080]
Educ – primary
0.1409
0.5570**
0.9792***
0.2601
[0.235]
[0.243]
[0.033]
[0.301]
Educ - secondary
0.3917*
0.5251**
0.9858***
0.4934**
[0.200]
[0.252]
[0.022]
[0.241]
Educ – post-secondary
0.0742
0.9875***
0.9361***
-0.1636
[0.257]
[0.030]
[0.022]
[0.342]
Unmet Basic Needs
0.0324
-0.0018
-0.0234
0.1099**
[0.053]
[0.004]
[0.030]
[0.054]
Risk Aversion
0.0652*
0.0001
0.0378*
0.0744*
[0.033]
[0.002]
[0.022]
[0.042]
Ambiguity Aversion
-0.0160
-0.0005
-0.0146
-0.0119
[0.027]
[0.002]
[0.018]
[0.031]
Distance to daily mkt
-0.0011***
-0.0002*
-0.0007*
-0.0009**
[0.000]
[0.000]
[0.000]
[0.000]
Observations
328
286
317
300
Table reproduced from Laszlo et al. (2013), Table 16. Controls for ethnicity, Household size, marital status, country dummies,
Robust standard errors in brackets*** p<0.01, ** p<0.05, * p<0.1

12. PHS Results
Table 3: Probit estimates for predicting the use of new technology (last 12 months)
is an adopter
new crop
new fertilizer
new pesticide
Age
-0.0028
-0.0004
-0.0022
-0.0028
[0.003]
[0.001]
[0.002]
[0.003]
Female
0.2108***
-0.0022
0.1103*
0.1874**
[0.076]
[0.005]
[0.063]
[0.080]
Educ – primary
0.1409
0.5570**
0.9792***
0.2601
[0.235]
[0.243]
[0.033]
[0.301]
Educ - secondary
0.3917*
0.5251**
0.9858***
0.4934**
[0.200]
[0.252]
[0.022]
[0.241]
Educ – post-secondary
0.0742
0.9875***
0.9361***
-0.1636
[0.257]
[0.030]
[0.022]
[0.342]
Unmet Basic Needs
0.0324
-0.0018
-0.0234
0.1099**
[0.053]
[0.004]
[0.030]
[0.054]
Risk Aversion
0.0652*
0.0001
0.0378*
0.0744*
[0.033]
[0.002]
[0.022]
[0.042]
Ambiguity Aversion
-0.0160
-0.0005
-0.0146
-0.0119
[0.027]
[0.002]
[0.018]
[0.031]
Distance to daily mkt
-0.0011***
-0.0002*
-0.0007*
-0.0009**
[0.000]
[0.000]
[0.000]
[0.000]
Observations
328
286
317
300
Table reproduced from Laszlo et al. (2013), Table 16. Controls for ethnicity, Household size, marital status, country dummies,
Robust standard errors in brackets*** p<0.01, ** p<0.05, * p<0.1

13. Peru EXP: Beliefs about potato late blight loss
probability depending on technology/strategy?
§ Design:
• Stage 1: Elicit beliefs (MCQ) about
probability of crop loss depending on
different technologies & strategies
• Stage 2: Group discussion (random
assignment to participate/observe)
• Stage 3: Coordination game on
beliefs (same MCQ as in Stage 1)
§ Coordination game incentivized
(earn for each matched answer)

14. Peru EXP: Beliefs about potato late blight loss
probability depending on technology/strategy?
§ More educated farmers:
• Stronger beliefs about probability distributions of technologies
• Less likely to change beliefs after participating in discussion
• Important peer effects: less educated farmers may benefit
disproportionately from group discussions about technologies (may
be enhanced by grouping them with educated farmers)
§ Women:
• Less likely to use modern technologies to protect against blight
• Less likely to change their belief about probability distributions after a
chat
• Gender composition of peer groups matter

15. Guyana EXP: Social learning & technology
adoption as public good
§ Basic idea:
• Experimentation allows realization of ambiguous outcome
• The more farmers who experiment, the more outcomes are realized.
§ Design:
• Farmers choose between relatively risky and relatively ambiguous
gambles (simulating technologies).
• Step 1: Elicit their preference between the two gambles.
• Step 2: Revise their decision knowing that other participants in the
group would observe the realization of their choice
• Step 3: Allow them to revisit their decision after having uncovered
partial information about the ambiguous probability distribution.

16. Guyana EXP: Social learning & technology
adoption as public good
§ Women are less likely to change their
decisions following discussion.
§ They were also more likely to choose
the ambiguous gamble once they
receive more information about the
probability distribution.
§ Participants who tend to observe other
farmers’ farming practices in the real
world were also more likely to provide
the public good in the experiment.
§ Risk averse participants are less likely
to provide the public good, consistent
with Public Economics theory.

17. Conclusions – Policy Considerations
1. Policies which foster access to markets should
improve technology adoption.
• Given the geographical constraints, one possible avenue for policy
intervention is extending the road network to improve transportation
between producing areas and markets.
2. CARICOM farmers face binding credit and
financing constraints. Policy makers should
consider options to facilitate access to financing
(credit and insurance).

18. Conclusions – Policy Considerations
3. Increase information exchange about the yield
probability distribution functions of different:
• Technical assistance through agricultural extension services.
• Peer groups and social networks can be instrumental in facilitating
this sort of information, and at relatively low cost.
4. Women farmers have a larger propensity to adopt
new technologies than men:
• Most responsive to receiving new info & more likely to choose
ambiguous options with new info on probability distributions.
• Technical assistance particularly effective if targeted to women, and
allowing the diffusion process within their social networks.

20. References
§ Conley, T. and C. Udry. 2010. “Learning about a New Technology: Pineapple in Ghana” American
Economic Review 100 (1): 35-69.
§ Dulfo, E., M. Kremer and J. Robinson. 2011. “Nudging farmers to use fertilizer: Theory and
Experimental Evidence from Kenya.” American Economic Review 101 (6): 2350-90.
§ Engle-Warnick, J., J. Escobal and S. Laszlo. 2011. “Ambiguity Aversion and Portfolio Choice in Small-
Scale Peruvian Farming” B.E. Journal of Economic Analysis & Policy 11(1):1-56.
§ Engle-Warnick, J., J. Escobal and S. Laszlo. 2014. Technical report: Technology adoption in Peruvian
potato farming: Evidence from a Coordination Game experiment on farmer beliefs about strategies to
combat Late Blight. Technical report for CARICOM Food Security Project, McGill University, Quebec,
Canada.
§ Engle-Warnick, J., S. Laszlo and K. Raeburn. 2014. Technical report: Technology adoption as a public
good: evidence from an economics experiment in Guyana. Technical report for CARICOM Food
Security Project, McGill University, Quebec, Canada.
§ Laszlo, S., T. Thompson-Colon and L. Sjolander. 2013. Producer Household Survey: Report on
General Baseline Findings for Guyana, St Lucia, Trinidad-Tobago and St Kitts-Nevis. CARICOM Food
Security Project, McGill University, Quebec, Canada.
§ Thompson-Colón, T. and S. Laszlo. 2013. Producer Household Survey Methodology Report for the
Baseline Survey Data Collection in Guyana, St. Lucia, Trinidad-Tobago, and St. Kitts-Nevis.
CARICOM Food Security Project, McGill University, Quebec, Canada.