x

Please fill out this short user survey of only 3 questions in order to help us improve the site. We appreciate your feedback!
Measuring the effect of credit subsidy schemes and performance rewards (incentives) on technology take-Up and subsequent investment decisions
Last registered on April 07, 2020

Pre-Trial

Trial Information
General Information
Title
Measuring the effect of credit subsidy schemes and performance rewards (incentives) on technology take-Up and subsequent investment decisions
RCT ID
AEARCTR-0005643
Initial registration date
April 07, 2020
Last updated
April 07, 2020 4:38 PM EDT
Location(s)
Primary Investigator
Affiliation
International Center for Insect Physiology and Ecology
Other Primary Investigator(s)
PI Affiliation
international Centre for Insect physiology and Ecology
Additional Trial Information
Status
On going
Start date
2019-08-01
End date
2021-08-30
Secondary IDs
Abstract
Adoption of productivity-enhancing technologies, such as the Push-pull technology (PPT) remains limited despite the promotional efforts by icipe and partners necessitating sustainable interventions to address barriers to adoption and impact at scale. Liquidity constraints, uncertainties and lack of reliable and persuasive sources of information about new technologies, their benefits and relevance to local agronomic conditions and details on how to apply the technologies are some of the barriers to their adoption. Smallholder farmers often make technology adoption decisions under uncertainty about the costs or benefits of subsequent investments in the technology after the initial take-up. Subsequent investment in technologies are likely to be low when the technology is perennial in nature such as the PPT. Self-learning may play an important role in reducing uncertainties that influence adoption and investment. We use a field experiment to test whether relaxing liquidity and informational barriers through credit subsidy and performance rewards (to ensure self-learning) can increase adoption (and subsequent investment) of a new technology. The specific objectives include:
1) Test for effect of variations in credit subsidy and incentives (performance rewards) on decision to take up the technology in the presence of uncertainty about the costs and benefits PPT.
2) Test the effect of variations in incentives (performance rewards ) to subsequent investment in PPT after initial take up of the technology
External Link(s)
Registration Citation
Citation
Diiro, Gracious and Menale Kassie. 2020. "Measuring the effect of credit subsidy schemes and performance rewards (incentives) on technology take-Up and subsequent investment decisions." AEA RCT Registry. April 07. https://doi.org/10.1257/rct.5643-1.1.
Sponsors & Partners

There are documents in this trial unavailable to the public. Use the button below to request access to this information.

Request Information
Experimental Details
Interventions
Intervention(s)
Credit subsidy schemes
Performance rewards (materials rewards/reputation rewards)
Push-Pulll technology for controlling striga and insect pests (fall army worm and stemborer) in cereal crops production
Intervention Start Date
2020-01-13
Intervention End Date
2020-11-01
Primary Outcomes
Primary Outcomes (end points)
(i) Technology adoption and investment
(ii) yield and household income
(iii) milk productivity
Primary Outcomes (explanation)
credit subsidies (providing subsidized seed at flexible repayment) was expected to encourage technology take-up whereas self learning about the benefits and costs associated with the technology during the first two seasons is expected to influence investment in the technology;

yield and income benefits (through striga and pest control; fertilizer benefits), milk productivity (through quality fodder)
Secondary Outcomes
Secondary Outcomes (end points)
food and nutrition security, health and environmental benefits
Secondary Outcomes (explanation)
food and nutrition security (through increased cereals yields and milk productivity) and health and environmental benefits of the PPT (through reduced use of pesticides in cereal production);
Experimental Design
Experimental Design
The study design involves two stages of randomization of the two main treatments. Both stages of randomization were done during farmer training (organized at at group level) before the beginning of the main cropping season for 2020. The first stage of randomization involved four treatments of credit subsidy (cs) assigned at farmer group level. The cost of the PPT companion planting materials for the technology (i.e. perennial legume (Desmodium) and perennial grass (Napier or Brachiaria) that) formed the subsidy. The credit subsidy treatments include CS1=no subsidy, CS2 =25% subsidy, CS3=50% subsidy, CS4=75% subsidy. All farmers were given a take it or leave it offer of a crops planting material (enough to cover one acre) to be planted and managed by the farmer and his or her household. The second stage of randomization involved assigning performance rewards (R) at individual farmer level to three treatments (R1=material reward such as hand hoe or credit waiver, R2= reputational reward (recognitional certificate), R3=both). The program will offer the performance rewards to the farmer conditional on proper management of the PPT plot during the cropping season.
Experimental Design Details
Not available
Randomization Method
We randomized by computer to assign credit subsidies to farmers at group level
we then used coin flip to assign the performance rewards at individual level:
Randomization Unit
farmer group level and individual farmer level
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
104 farmer groups
Sample size: planned number of observations
629 maize farmers
Sample size (or number of clusters) by treatment arms
For first stage of randomization: 24 farmer groups -no subsidy, 28 farmer groups-25% subsidy; 22 farmer groups- 50% subsidy and 30 farmer groups 75% subsidy

For second stage of randomization: 204 farmers material reward; 205 reputation reward and 220 both material and reputation rewards



Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
IRB Approval Date
IRB Approval Number