Demand for and impacts of mobile phone-based index insurance in agriculture: Experimental evidence from Kenya.

Last registered on March 04, 2020
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Pre-Trial

Trial Information

General Information

Title
Demand for and impacts of mobile phone-based index insurance in agriculture: Experimental evidence from Kenya.
RCT ID
AEARCTR-0005170
Initial registration date
March 04, 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
March 04, 2020, 11:55 AM EST

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

Locations

Country
Kenya
Region
Siaya County

Primary Investigator

Name
William Jack
Affiliation
Georgetown University
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Other Primary Investigator(s)

PI Name
Jacobus Cilliers
PI Affiliation
Georgetown University
Contact Investigator
PI Name
Andrew Zeitlin
PI Affiliation
Georgetown University
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Additional Trial Information

Status
Completed
Start date
2018-03-01
End date
2019-12-20
Keywords
Agriculture
Additional Keywords
Weather index insurance, mobile payments, development economics
JEL code(s)
Secondary IDs
Abstract
We evaluate the impact an index-based insurance product aimed at protecting smallholder farmers against weather hazards in rural Kenya. The product aims to alleviate credit constraints by allows farmers to buy insurance with ‘bite-sized’ premium payments over a period of time. In order to test for this mechanism, we experimentally vary the time when farmers are allowed to buy the insurance: in 90 randomly selected villages farmers can buy the product up four to months prior to the planting season, in another 90 villages the farmers can only buy the product a few weeks before the planting season. Moreover, we also test for the elasticity of demand by providing subsidized insurance in 90 villages. Another 90 villages are assigned to the control. We track the same group of 2,160 farmers over a period of a year, and sell them insurance for two different agricultural seasons. Household-level data is collected at three different points in time: once prior to the start of the program, once after the completion of the first season, and again at the end of the second season. We estimate the impact of insurance on investment in agriculture, the distribution of profits, asset ownership, and subsequent demand.
External Link(s)

Registration Citation

Citation
Cilliers, Jacobus, William Jack and Andrew Zeitlin. 2020. "Demand for and impacts of mobile phone-based index insurance in agriculture: Experimental evidence from Kenya. ." AEA RCT Registry. March 04. https://doi.org/10.1257/rct.5170-1.0
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Experimental Details

Interventions

Intervention(s)
Bima Pima (BP) is an index-based insurance product aimed at protecting smallholder farmers against weather hazards. Pay-outs are based on rainfall during the planting season. The insurance can be bought in the form of scratch cards that cost 50 KSh each. Farmers will also have the option to buy the insurance directly through a mobile platform. Those who have already purchased a scratch card may buy additional insurance through this mobile platform to “top up” their insurance for any desired amount. The location of the insurance cover is tied to the initial BP purchase, which is calculated by cell tower triangulation. This information is collected when the scratch card’s code is registered via SMS, that is if the initial bit of insurance is obtained through a scratch card. For those purchasing their initial bit of coverage from a mobile phone, location data is collected at the time of mobile registration.


The trial consists of four evaluation arms:
1. Pure control
2. Late access, no subsidy: farmers are sold the product a few weeks before the beginning of the planting season.
3. Early access, no subsidy: farmers are sold the product 4 months before beginning of planting season. This grants farmers more time to purchase “top ups”.
4. Early access with subsidy: farmers receive 50% subsidy.

At baseline all treatment groups were visited twice, early (i.e., four months prior to the beginning of the planting season) and late (i.e., a few weeks prior to the beginning of the planting season), during which we promote the product. However, at the time of the first visit, the late-access farmers are told the product will not be available to them until closer to the planting season. For the second season, we sold the insurance at the same time in all the treatment arms. There was therefore no difference between the Early and Late treatment arms.
Intervention Start Date
2018-03-01
Intervention End Date
2019-11-30

Primary Outcomes

Primary Outcomes (end points)
1. Demand for insurance.
2. Investment in agriculture
3. Agricultural profits
4. Household assets
Primary Outcomes (explanation)
1. Demand for insurance:
a. Take-up rate (binary variable equal to one if the farmer purchased insurance).
b. Total value of insurance purchased
2. Investment in Maize. Defined as the sum of the dollor value of all labor and non-labor inputs, as well as total acreage of land allocated to maize.
3. Profits. Total revenue from all farming activities minus total costs. Costs include: seeds, labor and non-labor inputs. Agricultural yield that is not sold but used for re-planting will be evaluated at the market price.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The trial consists of four evaluation arms:
0. Pure control (90 villages)
1. Late access, no subsidy: farmers are sold the product a few weeks before the beginning of the planting season (90 villages).
2. Early access, no subsidy: farmers are sold the product 4 months before beginning of planting season. This grants farmers more time to purchase “top ups” (90 villages).
3. Early access with subsidy: farmers receive 50% subsidy (90 villages).

During the second agricultural there was no distinction between early and late access. The farmers in the first and second treatment arms were therefore exposed to the same treatment in the second season.
Experimental Design Details
Randomization Method
In office by a computer.
Randomization Unit
Village level.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
360 villages.
Sample size: planned number of observations
2,160 households. (6 per village)
Sample size (or number of clusters) by treatment arms
90 villages in the control and 90 villages in each treatment arm.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Georgetown-MedStar
IRB Approval Date
Details not available
IRB Approval Number
N/A
Analysis Plan

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Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials