Agricultural credit, insurance, and over-indebtedness among smallholder farmers

Last registered on June 28, 2023


Trial Information

General Information

Agricultural credit, insurance, and over-indebtedness among smallholder farmers
Initial registration date
June 26, 2023

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
June 28, 2023, 4:55 PM EDT

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


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Primary Investigator

International Food Policy Research Institute

Other Primary Investigator(s)

PI Affiliation
University of Florida

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
The digital credit revolution, expanding access to agricultural credit, may have the perverse effect of increasing farmers’ debt to unsustainable levels, particularly when crop damage from extreme weather makes it difficult to repay loans. This study analyzes whether credit disbursed based on a novel credit-scoring model, bundled with crop insurance, expands rural borrowing and investments in agricultural technologies, yet simultaneously protects farmers from default and over-indebtedness. We will do so by implementing a cluster randomized trial during the winter (Rabi) season of 2022, the monsoon (Kharif) season of 2023, and the Rabi season of 2023, targeting 2,280 households from 120 villages in the states of Maharashtra and Odisha, India. Villages will be randomly assigned to a control group; a treatment group in which farmers are offered digital agricultural credit; or a group in which digital agricultural credit is bundled with picture-based crop insurance. We hypothesize that our implementing partners’ novel credit-scoring model is less discriminatory towards women and landless households compared to standard methods of issuing credit for smallholder farmers, and that our experimental treatments will hence increase credit utilization. We additionally hypothesize that farmers with bundled credit-insurance products experience lower levels of default and indebtedness. Findings will have immediate relevance for our implementing partner and policymakers.
External Link(s)

Registration Citation

Kramer, Berber and Patrick Ward. 2023. "Agricultural credit, insurance, and over-indebtedness among smallholder farmers." AEA RCT Registry. June 28.
Sponsors & Partners

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Experimental Details


We are analyzing the impacts of KhetScore loans. KhetScore loans are agricultural loans provided to smallholder farmers. Credit scoring is done based on the KhetScore algorithm, which predicts farmers' ability to repay the loan based on past productivity estimates and other indicators measured through remote sensing. Unlike other agricultural loans, farmers do not need to provide land titles or other proof of land ownership in order to access these loans. The loans, in one treatment arm, are bundled with a picture-based insurance product that settles claims based on smartphone pictures of visible damage in insured crops.

Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
1. Whether the respondent or their household borrowed money/took any, formal and informal credit in the past two agricultural seasons
2. Whether the respondent or their household faced difficulties repaying these loans
3. Self-reported stress among beneficiaries and their household members
Primary Outcomes (explanation)
Stress is measured from a four-item mental health questionnaire.

Secondary Outcomes

Secondary Outcomes (end points)
1. Agricultural investments, productivity, and profitability
2. Women's empowerment, measured from an adapted version of the project-level Women's Empowerment in Agriculture Index
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The evaluation is implemented by means of a cluster randomized trial. We have selected 120 villages to participate in the study; 60 villages located in Maharashtra, and 60 villages in Odisha. These selected villages are randomly assigned into three groups: a control group consisting of 40 villages where DER is not offering any loans or agricultural insurance, a first treatment group with 40 villages where DER is offering KhetScore loans but no agricultural insurance, and a second treatment group comprising 40 villages where DER is providing KhetScore loans along with agricultural insurance. The products will be offered during the Rabi season in 2022-23 (the dry season), the subsequent Kharif season in 2023 and the subsequent Rabi season in 2023-24.
Experimental Design Details
Not available
Randomization Method
The project follows village level randomization done using STATA.
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
40 villages control
40 villages treatment 1 - Only credit
40 villages treatment 2 - Credit bundled with insurance
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Our calculations aimed at obtaining minimum detectable effect sizes (MDES) of 0.25 standard deviations, with a power (1-β) of 0.8 and a significance level (α) of 0.05, in the following variables: - An indicator for whether a household has access to credit - A proxy for indebtedness (what proportion of the most recent loan has been repaid) - Difference between the desired loan amounts and the actual loan granted as a proxy for unmet credit demand. Variable Mean Std. dev. Intra-cluster correlation Access to credit 0.72 0.45 0.11 Loan repayment (indebtedness) 0.74 0.19 0.08 Difference in loan requested vs granted 21,033 17,410 0.11

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number