Technology adoption in rural Ghana under
different index insurance schemes
Twente, 13 April 2012
Jan Jozwik
Oxford University
Context
Yield-improving technologies can solve rural poverty traps
 Low adoption of new technologies in LDCs (often around 40%)
Potential reasons:
Learning (Foster & Rozenzweig, 1995)
Risk preferences (Zilberman, 1983)
Imperfect credit/insurance markets (Hoff, Braverman, Stiglitz, 1993)
Behavioral: time-inconsistency (Duflo, Kremer & Robinson, 2009)
 Low levels of continued adoption of new technologies
i.e. over 30% Kenyan maize farmers switch across seasons (Suri, 2012)
 Uninsured risk of very low consumption
Dercon & Christiaensen (2011) argue that Ethiopian farmers prefer not to
adopt high-mean technology giving very low returns in bad years
 Adding insurance to technology package provided on credit may lower
demand if farmer borrow with implicit insurance (Gine & Yang, 2007)
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Framed Field Experiment
Insurance could raise aggregate adoption
High-return adopters can pay premium
Low-return adopters get insurance and are able to adopt next season
 Benefits of experiment
Controlling for other factors constraining adoption and insurance take-up
Technology with insurance understudied in framed field experiment:
2 exceptions give mixed evidence :
• Carter (2008): ‘60% of farmers purchased the insurance and chose the high
return activity’
• Hill & Viceisza (forthcoming): ‘some evidence that insurance has a positive
impact on insurance’
• these studies differ substantially in price and basis risk levels of insurance
Adoption decision may depend on insurance product
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Cocoa farming in Ghana
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World leading exporter but mostly traditional technology
Abrabopa: technology inputs on credit since 2007, 19000 members today
Key farming concern: tree destruction via Swollen Shoot Virus disease
(Stutley,2010)
Cocoa Marketing Board offers limited compensation schemes
Sample-based index insurance (historical data)?
Objectives of the Experiment
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1. Elicitation of Risk Preferences
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2. Technology adoption decision in multiple-period setting under different
insurance schemes
Can insurance encourage higher adoption of technologies?
Can insurance encourage sustained adoption of technologies?
Will the impact vary across different insurance products?
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Framed Field Experiment
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Subjects: cocoa farmers from Abrabopa
Framing: choosing between high-risk/low-risk technology affecting yields
and income
4 rounds
Fixed endowment – income evolves over rounds
Control: no insurance
Treatments: different types of index insurance which is offered only with
high-risk technology
20 subjects randomly allocated to a session (Control - 2 sessions, each
Treatment - 2 sessions)
25%: probability of bad weather
Randomisation Structure
Two-stage procedure (similar to Clarke, 2011)
 Stage 1: wheel spun (weather)
75% yellow bag (no virus in this district)
25% red bag (virus in this distrct)
 Stage 2: token drawn (yield)
yellow token: high yield, red token: low yield
3 tokens in each bag
Yellow bag: 2 yellow tokens + 1 red token
Red bag: 1 yellow token + 2 red tokens
 Basis risk = 75% * 33% = 25%
This happens if index shows good weather but subject has low yields
(yellow bag+red token)
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Control
Dummy variable for adoption decision Ait
 Ait =0
low-risk, low-variance traditional technology
 Ait =1
high-risk high-variance modern technology
 No insurance option for modern technology
 Yield payoffs unknown yet – will be carefully calibrated from survey data
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Treatments
Choosing low-risk technology – no insurance
 Choosing high-risk technology – actuarially fair insurance
 T1
partial coverage: insurance covers 50% losses but premium is low
 T2
full coverage: insurance covers 100% losses but premium is high
 T3
T1 + no basis risk
 T4
T2 + no basis risk (insurance in Hill & Viceisza)
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Hence in yellow bag (good weather) there are:
- 2yellow+1red tokens (25% basis risk) under T1 & T2
- 3yellow tokens (0% basis risk) under T3 & T4
Additional treatment options
Framing
- Positive frame
(insurance pays 1 out of 4 years+benefits of insuring)
- Negative frame
(insurance does not pay 3 out of 4 years+problems of not insuring)
 Probability of bad weather
- Virus occurs with 25% probability
- Virus occurs with 10% probability
 Insurance loading
- Actuarially fair loading
- 30% above actuarially fair
- free
 Basis risk
- Basis risk is 25%
- Basis risk is 10%
- Basis risk is 0%
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Key Empirical Specifications
Probit model for adoption decision Ait :
Ait = β0 + β 1 T1it + β 2T2it + β 3T3it + β 4T4it + γXit + εit
(Ait is dummy for adoption choice, Xit is vector of additional controls)
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Probit model for continued adoption Yit :
Yit = β0 + β 1 T1it + β 2T2it + β 3T3it + β 4T4it + γXit + εit
(Y is dummy for continuing adoption from t onwards once Ait-1 =1
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