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

This study will evaluate the impacts of addressing credit constraints which has the potential to unlock smallholder dairy productivity and income of the farmers. In our interventions, we will (1) conduct sensitization on the asset-collateralized loan (ACL) and register interest with the dairy farmers from two dairy cooperatives in the Rift Valley of Kenya, (2) offer an asset-collateralized loan (ACL) for a water tank to a random sample of the interested and eligible treatment participants, (3) pilot the use of low-cost SMS surveys to gather additional insights and measure time use and milk production immediately during the implementation phase.

2022-08-01

2025-06-30

Milk production and sales
Health indicators such as incidence of vomiting, diarrhea, coughing and fever
Time use: times spent on fetching water, grazing/watering livestock, non-farming jobs, schooling and leisure
Water availability and allocation
Subjective and Mental Well-Being: Self-reported overall satisfaction with life, Index of mental well-being and access to water.

This experiment will evaluate the impacts of addressing an important constraint that limits agricultural incomes among smallholder dairy farmers: credit. We will partner 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.

Staff of our 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). 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.

Not available

Randomization done in office by a computer

Household level

No

NA

1500 farmers

750 farmers (50:50)

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 of on milk sales of 6 percentage points at 80% power with 617 farmers in each arm. For our milk production and child health outcomes, we will conduct monthly SMS surveys. For production, we assume autocorrelation of X, and for child health we assume autocorrelation of X. With Y (less than 12, presumably) pre-intervention measures and Z post-intervention measures, we expect to be able to detect intent-to-treat effects of X and Y, respectively, at 80% power with X 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.