Understanding the potential of crop insurance in India: a study of the Prime Minister’s Crop Insurance Scheme

3ie evidence programme:  Agricultural Insurance Evidence Programme
Author(s): Padmaja Pancharatnam, Shreekanth Mahendiran,  Madhusudhan B.V. Rao,  Sridhar R Prasad,  Bhavani Seetharaman,  Jyotsna Jha,  Sowmya,  Thyagarajan R
Institutional affiliation(s): Centre for Budget and Policy Studies
Grant-holding institution: Centre for Budget and Policy Studies
Main implementing agency: Centre for Budget and Policy Studies
Sex disaggregation: Yes
Gender analysis: Yes
Equity focus: Yes
Study type: Process evaluation

Context

In India, half of the population depends on agriculture and about 67 per cent of all cultivators are small or marginal farmers who own less than one hectare of land. Weather variations cause considerable crop loss and uncertainty over decisions around agriculture. Farmers are highly dependent on rainfall, and states with a significant proportion of dry zones, as Karnataka the second driest state in the country, have invested to make irrigation available. The state is well-known as reform-oriented and focuses on introducing interventions to benefit the farmers.

This study explores the feasibility and acceptability of using crop insurance by various stakeholders utilising their experiences and roles during the implementation process of the scheme.

Intervention design

Pradhan Mantri Fasal Bima Yojana (PMFBY) is a crop insurance scheme introduced in Kharif 2016. The PMFBY is operational in 22 out of the 30 Indian states.

The PMFBY consisted of insurance coverage for about 40 crops, primarily food crops, and some horticultural. The entire implementation of the scheme has three stages: pre-notification and notification, enrolment, and claims. During the first stage, farmers in a particular district receive notification about enrollment availability, the cut-off dates and the premium payable. All through the second stage, farmers usually enroll through banks and the bank has to enter all the information of the farmer enrolled with PMFBY on the crop insurance portal. In the third stage, claims and processes of assessing crop damage will depend on the risk type that the PMFBY covers. PMFBY employs a mixture of an area approach basis and individual approach for the assessment of crop damage.

The intervention hypothesised that a faster and greater accuracy of the estimation of yields should hasten the disbursal of funds, this in turn should stabilise farmers’ income.

This hypothesis was based on these assumptions:

1. Technology is being employed for faster and better estimation of yields.
2. Online enrollment integrates data on farmers enrolled and land records, and provide greater accessibility.

Evaluation design and methodology

This mixed-method study was conducted in Karnataka, a southern Indian state. Quantitative methods comprised a primary survey,  Agriculture Census (2011), and the  Status of Agricultural Farmer surveys from the National Sample Survey Organization (NSSO).  810 farmers were enrolled in the study, of which 781 respondents completed the baseline survey.  Qualitative methods included participant observation, semi-structured interviews with key stakeholders, and focus group discussions (FGDs).

Primary evaluation questions

This study answers the following questions:

a.    What are the vulnerabilities faced by farmers and the need and rationale for crop insurance?
b.    How does the PMFBY function? What are the operational processes? what are the design and operational needs of this scheme in particular?
c.    What is the socio-demographic profile of enrolled farmers vis-à-vis non-enrolled farmers?
d.    What are the farmers’ expectations from and experience of PMFBY and other crop insurance schemes?
e.    What is the budget allocation made towards this scheme? What does it reveal in terms of the budgetary priorities of the state?
f.    In what ways might the design and operational barriers be addressed to enhance the uptake of the scheme by the most vulnerable? Has the scheme helped in enhancing the security and reducing vulnerabilities associated with crop failure?
g.    What is the size of public expenditure for this scheme and how different it is from earlier schemes? How does it relate to the total public expenditure on agriculture and how has it impacted the budget for agriculture in Karnataka?

Primary findings

There is a need for greater awareness of the scheme and its features among marginal and small farmers and local government functionaries. Farmers expressed a weak understanding of the enrollment process, features of the area approach, their implications for eligibility for claims and other related aspects.

The authors reported that the claims were not paid before the next season, as expected. The reasons are that insurance companies rely on  crop cutting experiments (CCEs) for providing yield estimates and estimating the insurance payout accordingly, and enrollment errors.

Findings also suggest that only about 7 per cent of enrolled farmers consider crop insurance as the top priority measure of relief from the impact of crop loss and about 12 per cent reported crop insurance as their first response to mitigate crop loss. These findings indicate that farmers rely upon informal measures at the household or community levels as measures to mitigate agricultural risks.

Implications

Authors recommend an improvement in the CCE exercise would benefit not only the PMFBY but also other schemes in operation. The Department of Agriculture (DoA) should invest in operationalising smart sampling for easy identification of areas and more accurate randomisation of plots, thereby making the CCE process more efficient.

Even though operational guidelines mention the need for ‘special efforts to promote female participation’, it is recommended that the insurance company and its intermediaries take specific measures to step up improving female access to insurance schemes.

The incorporation of smart sampling will estimate yield accurately, and the team suggested that the government invests in weather stations and move towards the weather-based index to adopt satellite imagery.