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

General Information

Title

RCT ID

AEARCTR-0005643

Initial registration date

April 07, 2020

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

April 07, 2020, 2:09 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Country

Uganda

Region

Kamuli district

Primary Investigator

Name

Gracious Diiro

Affiliation

International Center for Insect Physiology and Ecology

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Menale Kassie

PI Affiliation

international Centre for Insect physiology and Ecology

Contact Investigator

Additional Trial Information

Status

On going

Start date

2019-08-01

End date

2021-08-30

Keywords

Agriculture, Finance & Microfinance, Other, Welfare

Additional Keywords

Food security

JEL code(s)

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.5643-1.0

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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

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

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)

Supporting Documents and Materials

IRB