Credit for Climate Change: Water Tanks as a Means of Resilience

Last registered on January 09, 2024


Trial Information

General Information

Credit for Climate Change: Water Tanks as a Means of Resilience
Initial registration date
July 12, 2022

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
July 12, 2022, 1:56 PM EDT

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

Last updated
January 09, 2024, 8:41 PM EST

Last updated is the most recent time when changes to the trial's registration were published.


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Primary Investigator

Precision Development

Other Primary Investigator(s)

PI Affiliation
Development Innovation Lab, University of Chicago
PI Affiliation
University of Chicago
PI Affiliation
University of Michigan

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Rainfall and temperature variability induced by climate change pose a substantial economic risk to smallholder dairy farmers in developing countries (Tadesse and Dereje 2018). Rainwater harvesting tanks may help farmers adapt to climate uncertainty. Previous work has found that Asset Collateralized Loans (ACLs) help farmers purchase water tanks in Kenya (Jack et al, 2023). This study aims to validate past work and evaluate the impact of ACLs for water tanks on economic and household outcomes among dairy farmers in Kenya, with a focus on climate resilience. The study will also test an alternative loan design in which required monthly payments are a function of milk income, which may be better adapted to farmers with seasonally variable income.
External Link(s)

Registration Citation

Carney, Kevin et al. 2024. "Credit for Climate Change: Water Tanks as a Means of Resilience." AEA RCT Registry. January 09.
Experimental Details


This study will evaluate the impacts of addressing credit constraints to improve smallholder dairy productivity and climate resilience. The study has a control group and two treatment groups: (1) a standard declining-balance asset collatearalized loan, and (2) an income-sharing loan, with payments defined as a percentage of monthly milk income capped by a minimum and maximum payment. Preferences for both loan contracts among treated farmers will be elicited by a Becker-DeGroot-Marschak procedure.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Loan take-up and repayment
Milk production and sales
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Health indicators such as incidence of diarrhea
Time use
Water availability and allocation
Subjective well-being
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This experiment will evaluate the impacts of addressing an important constraint that limits agricultural incomes among smallholder dairy farmers: credit. We have partnered with two dairy cooperatives in Kenya and their corresponding financial institutions (SACCOs) to implement a randomized control trial among dairy farmers in the Rift Valley of Kenya. We will offer interested, eligible participants an asset-collateralized loan for a water storage tank.

Partner cooperatives will communicate information to their members about a new lending program for water tank loans. Following this communications campaign, we will call members to conduct a pre-screening for interest in taking an asset-collateralized loan for a water tank. Our primary experimental sample will consist of the first 1,500 eligible, interested members identified by the cooperatives.

Among this sample, we will conduct an in-person baseline survey. We will then randomize this sample at the household level into a treatment group which will be invited to apply for an asset-collateralized loan for a water tank (n = 750) and a control group which will receive no additional loan access (n = 750). Farmers in the treatment group will be randomly assigned to receive either an offer of a standard declining balance loan or an income-sharing loan with required monthly payments determined as a function of milk income. We will stratify the randomization on SACCO, agroecological zone, and characteristics in the baseline survey and/or administrative data that strongly predict milk production and sales.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer
Randomization Unit
Household level
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
1500 farmers
Sample size (or number of clusters) by treatment arms
750 farmers (50:50), with 325 farmers offered each type of loan
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Using administrative data on monthly milk sales with 12 pre-intervention and 18 post-intervention measurements with an expected across-measurement correlation of 0.75 (based on baseline administrative data), we expect to be able to detect an intent-to-treat effect on milk sales of 6 percentage points at 80% power with 617 farmers in each arm. To account for margin of error, we aim to conduct the baseline survey with 1500 households and assign 750 farmers to the treatment group.

Institutional Review Boards (IRBs)

IRB Name
Health Media Lab (HML)
IRB Approval Date
IRB Approval Number
IRB Name
Maseno University Scientific and Ethics Review Board (MUSERC)
IRB Approval Date
IRB Approval Number