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Study Overview

KhetScore: A cluster randomized trial on the impacts of using digital technologies to unlock credit and insurance for marginal farmers in Odisha
Study is 3ie funded:
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Reducing risk and promoting investments that increase agricultural productivity are important to reduce hunger and poverty, enhance incomes and food security, and strengthen resilience of the world’s poor. Smallholder farmers however lack access to formal financial services that can help them make these investments due to high transaction costs, asymmetric information, and a lack of documented land rights. This study will evaluate impacts of “KhetScore”, an innovation developed by Dvara E-Registry (DER) that uses digital technologies, including satellite remote sensing combined with ground reference data, to unlock credit and insurance for small and marginal farmers in Odisha, a state in eastern India.

We will implement a cluster randomised trial with 1,800 potential KhetScore clients from 58 villages in the district of Jajpur. Treatment farmers are offered a digital credit product that is bundled with insurance to protect farmers from the downside risk of crop failure. The product relies on satellite imagery and smartphone pictures for crop monitoring. We will analyse how recent advances in remote sensing and crop analytics can reduce transaction costs and overcome information asymmetries and documentation requirements in the provision of financial instruments for marginal farmers, and to what extent this, in turn, improves farmers’ access to credit and insurance, investments in agriculture, incomes, food security and wellbeing. We will pay particular attention to gender and other dimensions of social inclusion as women and other marginalized farmers are less likely to hold documented land rights, restricting their access to credit and insurance.

Registration Citation:
Agriculture and Rural Development
Information and Communications Technology
Additional Keywords:
Agricultural risk management
Secondary ID Number(s):

Principal Investigator(s)

Name of First PI:
Berber Kramer
International Food Policy Research Institute (IFPRI)
Name of Second PI:
Patrick Ward
Duke Kunshan University

Study Sponsor

International Initiative for Impact Evaluation (3ie)
Study Sponsor Location:
United States

Research Partner

Name of Partner Institution:
Dvara E-Registry
Type of Organization:
Private firm

Intervention Overview


KhetScore is an innovation that uses digital technologies to unlock digital credit and insurance product targeted to small and marginal farmers in Jajpur district in the eastern Indian state of Odisha. KhetScore has been designed by Dvara E-Registry (DER), a social enterprise operating in the intersection of agriculture and financial technology (‘fintech’) in India. KhetScore provides a means to (i) digitize information on a farmer’s land parcels in the absence of documented land rights, (ii) create plot-level agricultural credit scores by estimating past and current productivity from satellite imagery, with smartphone images used as a source of ground-truth information, and (iii) verify the extent of crop damage that a farmer may have experienced as a result of extreme weather or other natural hazards. The resulting plot-level reports inform financial institutions in the provision of their credit and insurance products, with automatic and instant disbursement of credit when investments are due, and crop insurance coverage to help farmers de-risk their investments in agriculture.

The project will be implemented in two blocks of Jajpur, a district in Odisha’s low-lying coastal plains. In this district, agricultural activities are one of the primary sources of income, with paddy being the dominant crop grown. Irrigation is available in Jajpur due to the vicinity of the Mahanadi, the largest river of the state. This creates opportunities for farmers to expand production during the Rabi season, but credit and risk are often cited as a primary reason for not investing in crop production during this season. Although farmers in Jajpur have relatively higher levels of education and literacy than farmers in other districts, sharecropping and marginalization of tenant famers is still commonplace

Theory of Change:

The central premise behind our theory of change is that risk and credit constraints cause marginal farmers to underinvest in commercial agricultural activities. Access to sound financial instruments is limited because of asymmetric information in credit and insurance markets, which become a problem especially for small farmers who lack documented land rights as collateral or means of verification for government-sponsored programs such as the national crop insurance scheme (PMFBY) to support crop farming. The main option for these farmers to finance their investments is to use informal credit or group loans from a microfinance institution (MFI). Such loans are uninsured, and farmers risk sanctions when unable to repay, which may discourage them from investing in profitable agricultural activities that could expose them to the risk of being unable to repay their loan.

This project will therefore introduce farmers to new agricultural credit and insurance products. DER will offer farmers the opportunity to apply for a product that uses georeferenced crop images in lieu of land records to verify access to land. If farmers are interested in the product, and absence of land records was indeed their main constraint, we expect targeted clients to apply for the product. DER will use remote sensing and crop images to monitor production, which will facilitate the automatic and instant disbursement of credit when investments are due, and claims settlement so that in cases where farmers incur significant losses due to natural causes, insurance will trigger payouts towards loan repayment, to reduce chances that farmers are unable to repay and become too far indebted.

Assuming that risk and credit constraints are the primary barriers to agricultural investments for tenant and landless farmers, we expect high demand for the products, and the loans could increase investments in agriculture.

Multiple Treatment Arms Evaluated?

Implementing Agency

Name of Organization:
Dvara E-Registry
Type of Organization:
Private for profit organization

Program Funder

Name of Organization:
Dvara Trust
Type of Organization:

Intervention Timing

Intervention or Program Started at time of Registration?
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Evaluation Method

Evaluation Method Overview

Primary (or First) Evaluation Method:
Randomized control trial
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Method Details

Details of Evaluation Approach:

We are implementing a cluster randomized trial in 58 villages from 2 blocks in Jajpur (Jajpur and Dashrathpur), Odisha. We selected these blocks because of their high concentration of small and marginalized farmers, and because of DER’s existing operations in the two blocks. DER had been working in 25 villages, and expanded operations to include an additional 33 villages to achieve the desired study sample. DER included all villages within these blocks, except for those where lending operations were considered unviable due to the region’s extreme flood exposure, which would drive up insurance premiums and reduce farmers’ ability to repay their loans.

Of all 58 study villages, we randomly selected 50 percent, or 29 villages, to our treatment. These villages will be exposed to the intervention for the duration of the project period. Randomization was done in private, by the researchers, prior to the product trainings implemented by DER. To ensure balancing of characteristics between treatment and control, we stratified villages by block, whether the village had an above-median number of recruited DER clients, and whether the village had an above-median percentage of farmers that had taken loans in the past. For the duration of the impact evaluation, the remaining 29 villages will serve as the control group.

In both treatment and control, farmers can access government crop loans and insurance (including under PMFBY), if they meet the requirements around documented land rights, and with the caveat that the insurance products under PMFBY are considered to have high levels of basis risk.

Outcomes (Endpoints):

Intermediate outcomes: The extent to which KhetScore is more predictive of yields, profits, and repayment behaviour than current metrics used by financial service providers to sanction loans; and by financial service providers’ future willingness to pay for KhetScore reports; and farmers’ access to credit and insurance, as measured by the percentage of beneficiaries with (sustainable levels of) agricultural credit (and at reasonable interest rates), the percentage of beneficiaries with insurance, as well as credit repayment rates and insurance payouts during years with and without production shocks.

Primary outcomes: Whether the intervention improves (in the short run) sustained insurance take-up, awareness, perception of quality and adequacy, insurance income; (in the medium run) agricultural investments, particularly during the winter (Rabi) season, agricultural production, productivity, and profitability.

Secondary outcomes: Household income, consumption, resilience, well-being and outcomes related to gender parity.

We will also track potentially unintended negative consequences of the intervention for targeted women, which could include an increase in workload and/or increasing inequalities between women when gender norms prevent a subgroup of women to take out the KhetScore loans and insurance products.

Unit of Analysis:
Household (in some cases individuals)

H1: The digital risk-contingent credit product that we are introducing will increase farmers' investments in agricultural inputs, especially in the Rabi season.
H2: The digital risk-contingent credit product that we are introducing will increase farmers' agricultural production.
H3: The digital risk-contingent credit product that we are introducing will increase farmers' agricultural productivity.
H4: The digital risk-contingent credit product that we are introducing will increase farmers' agricultural profits.

Unit of Intervention or Assignment:
Unit of randomization: Village.
Number of Clusters in Sample:
58 villages
Number of Individuals in Sample:
Size of Treatment, Control, or Comparison Subsamples:
900 treatment, 900 control

Supplementary Files

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Outcomes Data

After 3 production seasons in which participants have been exposed to the intervention, we will estimate impacts and cost-effectiveness using quantitative in-person surveys and – where applicable – administrative data, following our pre-analysis plan, which was developed after analysing the phone-based baseline survey data. We will also use administrative data, including KhetScore ratings and data derived from remote sensing and smartphone images, for monitoring of intermediate outcomes.
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Treatment Assignment Data

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Data Analysis

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Study Materials

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Registration Category

Registration Category:
Prospective, Category 1: Data for measuring impacts have not been collected

Completion Overview

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Preliminary Report:
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Study Stopped