National Agricultural Insurance Scheme in India

Making Insurance Markets Work for Farmers

National Agricultural

Insurance Scheme in India

Highlights

 With 25 million farmers insured, the National

Agricultural Insurance Scheme (NAIS) in India is the

largest crop insurance scheme in the world.

 The Indian Government moved from a social crop

insurance scheme to a market-based crop insurance

program with actuarially sound premium rates, up-

front subsidies, and participation of private insurers.

 The World Bank provided technical assistance to

support modification of NAIS based on international

best practice and in-country experience.

Background

With two-thirds of the population dependent on

agriculture for a livelihood, crop insurance is an

important element of agricultural risk management in

India. The Government of India (GoI) has historically

focused on crop insurance as a planned mechanism to

mitigate the risks of natural perils on farm production. In

1999, GoI established the National Agricultural Insurance

Scheme (NAIS) to reduce farmers' vulnerability to natural

disasters. The NAIS offers insurance for food crops,

oilseeds, and selected commercial crops through state-

owned insurer, Agriculture Insurance Company of India

(AICI). With about 25 million farmers insured, it is the

largest crop insurance program in the world.

NAIS is based on an indexed approach known as the area

yield-based approach, where the index used is the crop

yield of a defined area called an insurance unit (IU, e.g.,

an administrative block). The actual yield of the insured

crop, measured by crop-cutting experiments in the IU, is

compared to historical yields. If the former is lower than

the latter, all insured farmers in the IU are eligible for the

same rate of indemnity payout. Individual crop insurance

would have been virtually impossible given the large

number of very small landholdings. Using the area yield-

based approach also has other merits. Most importantly,

it mitigates moral hazard and adverse selection.

NAIS is funded by post-disaster government

contributions, entailing an open-ended and highly

variable fiscal exposure for GoI. Farmers’ premiums are

subsidized; the annual claim/farmers’ premium ratio is

higher than 100 percent. At the end of the crop season,

aggregate claims exceeding the farmers’ premium are

funded 50-50 by the state and central governments.

India’s post-disaster funding arrangement was

necessitated by the lack of an actuarially sound premium

rating methodology, which means that estimating

payouts is not feasible. This system is not optimal for

GoI’s budget management and delays claims settlement,

leading to distress of farmers and exposing farmers to a

vicious debt cycle.

To address these challenges, in 2005 the Government

formed a joint task-force with AICI and requested the

World Bank to provide non-lending technical assistance

(NLTA) in modifying the crop insurance program and in

improving insurance coverage.

Objectives

The objectives of the NLTA were to:

 Review current underwriting methodology;

 Develop an actuarially sound pricing methodology

based on international best practice;

 Develop product design and pricing methodology for

new weather index insurance products;

 Suggest cost-effective catastrophe risk financing

solutions for the public crop insurance company AICI.

Outcomes

Key outputs of the NLTA include:

 Development of a best practice, standard, actuarially-

sound pricing procedure using an experience-based

approach for area-yield insurance;

 Detailed inputs into the design of the modified NAIS

(mNAIS), which was launched for the 2010-11

growing season in 12 districts, covering 332,628

farmers, with an expected claims ratio within 50

percent; AICI is targeting 400,000 farmers for the next

growing season;

 Development of commercial weather-based crop

insurance products;

 Building of AICI’s capacity to transition NAIS to a

market-based approach;

 Policy dialogue with various line ministries about the

fiscal impact of the modified NAIS as well as the

welfare implications of the modified scheme;

 Designing prototype actuarial software; pricing over

200 insurance products; advising on use of mobile

technology for improving crop cutting data quality

and timeliness.

 Actuarial regime. The mNAIS scheme operates on an “actuarial regime” in which the government’s financial liability is

predominantly in the form of premium subsidies given to AICI and funded ex-ante, thereby reducing the contingent and

uncertain ex-post fiscal exposure currently faced by the government under NAIS and reducing delays in claims settlement.

 Up-front premium subsidies. AICI receives premiums (farmer collections plus premium subsidies from the government) and

is responsible for managing the liability of the mNAIS through risk transfer to private reinsurance markets and risk retention

through its reserves. It is financially able to operate on a sustainable basis.

 On-account partial payment. The mNAIS product continues to be based on an area yield-based approach, with a provision

for an early part payment to farmers (in season) based on weather indices.

 Small Insurance Units. Crop-cutting experiments to assess crop yield estimates are lowered from Block level to Village level

to reduce basis risk (i.e., the mismatch between the actual, individual crop yield losses and the insurance indemnity).

 Cutoff dates. Adverse selection is reduced through the enforcement of early purchase deadlines ahead of the crop season.

 Additional benefits. Additional benefits are offered for prevention of sowing, replanting, post harvest losses, and localized

risk, such as hail losses or landslides.

Main Features of the mNAIS

Updated March 2012 www.worldbank.org/fpd/drfip www.gfdrr.org

Lessons Learned

. S

tate-of-the-art tools should be developed in close

collaboration with the client, and second-best technical

solutions should be deployed when necessary to reflect

on-the-ground realities and political and economic

considerations. Drawing on international best practice

and in-country experience, the actuarially sound pricing

methodology helps attract international reinsurance

capacity. Such pricing methodology ensures the financial

sustainability of the program and its relevance to the

country context. An open approach helped close

collaboration with the client, leading to drawing on their

country and domain knowledge to a significant extent, a

process which also enabled the Bank team to learn from

the client’s experience and knowledge.

2. Technical tools can pave the way for policy dialogue.

The actuarial tools by themselves were the defined

output sought by the client. These actuarial tools were

used as the basis for a shift from ex-post to ex-ante

funding. They were also used to demonstrate efficiency

and the political and economic gains possible through

faster claims settlements. The tools therefore helped

translate technical work into a policy dialogue.

3. Extensive institutional capacity building and technical

inputs for both the implementing agency and

policymakers is critical. Agricultural insurance is a highly

specialized line of business that requires intensive

institutional capacity building. Major efforts were

undertaken to ensure that the proposed technical

recommendations would be fully understood and

implemented. Intensive training was provided to AICI

technical staff through technical documents, monthly

teleconferences, and quarterly on-site visits.

4. Combine traditional and innovative crop insurance.

Although much development literature and debate on

traditional versus new generation (weather-based)

insurance, technical grounds were used to demonstrate

the benefits of combining the two approaches, based on

their respective comparative advantages. Weather-

based indices are used for on-account partial payment of

claims in case of adverse mid-season conditions, while

area yield indices are used for final payment of claims.

Glossary

Area-yield based insurance: Insurance scheme under

which insurance payments are based on an area-yield

estimate determined by harvest production

measurements taken at a series of randomly chosen

Crop Cutting Experiments locations.

Crop cutting experiment: Sampling process by which

crop yields are statistically estimated in each insurance

unit.