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Weather-based Crop Insurance in India: Towards a Sustainable Crop Insurance Regime?

The enormous dependency of crop production on weather highlights the pressing need for an effective mechanism to cope with weather-related production risks faced by farmers. This paper focuses on the recent developments in the weather insurance market and evaluates the performance of the Weather-based Crop Insurance Scheme in India. Through a micro-analysis of indemnity payouts under the traditional and the weather insurance schemes, the study reveals the much larger spread of benefits under the latter, thus significantly reducing a prominent drawback of the decades-old area yield scheme. While the product has tremendous potential to emerge as a sustainable crop insurance model that can meet the risk management needs of the rural poor, there are critical issues that merit action to achieve the desired results.

SPECIAL ARTICLE

Weather-based Crop Insurance in India: Towards a Sustainable Crop Insurance Regime?

Reshmy Nair

The enormous dependency of crop production on weather highlights the pressing need for an effective mechanism to cope with weather-related production risks faced by farmers. This paper focuses on the recent developments in the weather insurance market and evaluates the performance of the Weather-based Crop Insurance Scheme in India. Through a micro-analysis of indemnity payouts under the traditional and the weather insurance schemes, the study reveals the much larger spread of benefits under the latter, thus significantly reducing a prominent drawback of the decades-old area yield scheme. While the product has tremendous potential to emerge as a sustainable crop insurance model that can meet the risk management needs of the rural poor, there are critical issues that merit action to achieve the desired results.

Reshmy Nair (reshmy25@yahoo.com) teaches at the Centre for Poverty Studies and Rural Development, Administrative Staff College of India, Hyderabad.

I
n a country where floods ravage more than 40 million hectares of land mass and 68% of the total area is vulnerable to periodical droughts (Sarkar and Sarma 2006), the enormous dependency of crop production on rainfall hardly needs reiteration. According to the crop loss data for the period 1985-2002, more than 70% of the crop loss is a result of drought and about 20% owing to excess rainfall (Parchure 2002). While crop productivity can be improved with the usage of hybrid seeds and application of scientific techniques of production, these have little impact on negating the adverse consequences of weather on the crop yields. The sheer size of the population involved in agriculture and the fact that 60% of the crop production is under rainfed conditions highlight the need for income stabilisation programmes for the farmers. Reducing weather vulnerability may well be the most critical challenge facing agrarian development in the country and effective mechanisms for transferring risk can go a long way in catalysing investment and economic growth.

Weather insurance can play a vital role as an alternative e x ante risk coping instrument to enable poor farmers cope with weather-related production risk and reduce their overall vulnerability to climate variability and change. The initial euphoria with weather-based insurance in India had a lot to do with its r educed susceptibility to some of the problems intrinsic in traditional multi-peril crop insurance. For the insured, the most important advantage is the prospect of receiving timely indemnity payouts that goes a long way in protecting their overall income, improving their risk profile and thereby enhancing access to bank credit, as opposed to the traditional scheme where the d elayed claim settlement procedure negates the very objective of insurance. These products also provide incentive to the insured to put in additional efforts or cost to save the crop as the claim is payable irrespective of the yield. From the insurer’s point of view, these products are easier to administer and reduce costs by eliminating the need for yield estimation1/field visits. Moreover, they significantly reduce the problems of adverse selection and moral hazard2 that tremendously limit the scope of traditional insurance. They also have the possibility of getting reinsurance from the international markets, where the traditional yield insurance does not find any takers.3

Weather insurance seeks to provide farmers compensation in case of happening or non-happening of a specific weather event that is likely to have bearing on the crop yields. In other words, the weather index measures a specific weather variable (e g, rainfall, temperature, relative humidity, wind speed, etc) and pays indemnities based not on the actual losses experienced by the

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i nsured, but rather on the realisations of the index (that proxies the loss in yield owing to the adverse weather incidence). The product, designed after a critical study of the weather parameters affecting the crop growth in its various phases specifies a threshold and a limit that establish the range of values over which indemnity payments are made. The contract is based on the burn rate approach that establishes criteria for a specific event risk and uses historical data on an annual basis to determine the frequency and magnitude of the occurrence of a specific event. Weather insurance has clearly expanded the domain of crop insurance programme (popularly known as the National Agricultural Insurance Scheme or NAIS) in the country as insurance can now also be provided for crops with no historical yield data as also for horticultural crops where age group-wise yield estimates are not available.

Though weather insurance and traditional insurance are not by definition mutually exclusive and can ideally coexist, an a ttempt is still made to compare the performance of the two schemes as the former practically continues to be treated as an “alternative” to the NAIS.4 As more and more areas are increasingly adapting the scheme year after year, it becomes important to empirically examine if the product can indeed act as an effective risk mitigation tool. This would come from the possibility of affirmatively answering certain questions. First, would moving to a weather-based scheme improve the viability and the fiscal sustainability of the programme as opposed to the traditional forms of insurance? Second, would the index insurance prove to be an improvement over the traditional form of insurance in terms of better spread of benefits across crops and regions? Third, would there be a significant improvement in tackling problems associated with high basis risk, one of the major reasons for disillusionment with area yield insurance programme? Fourth, will the product enhance the economic efficiency in terms of suitably addressing the needs of poor households? This paper attempts to answer the above questions. The two schemes are compared on a relative plane with regard to the coverage, claims experience and distribution of indemnity. The study is conducted with the help of data from Agriculture Insurance Company of India (AIC), the public sector insurance company specialising in crop insurance. It continues to be the dominant insurer, while private insurers account only for a minuscule p roportion of the total weatherbased crop insurance business in the country.

Weather Indexed Contracts in India

The pioneering work on agricultural insurance by J S Chakravarty titled Agricultural Insurance: A Practical Scheme Suited to Indian Conditions (1920), was in fact a proposal for a rainfall insurance scheme to protect farmers against drought. He felt that a direct system of crop insurance (yield insurance), though desirable, would not be feasible in India given the associated problems of moral hazard, difficulty of arriving at a consensus on the basis of insurance (value of crop or its quantity) besides the inherent characteristics of Indian agriculture, namely, illiterate cultivators, inadequate village statistics and the general level of backwardness. According to him, given the extreme dependence of Indian agriculture on rainfall, it was practicable to introduce an “indirect system” of insurance in the form of rainfall or drought insurance which would also eliminate the problem of moral hazard and issues relating to the estimation of the crop yield or value. Suggesting an area approach as the only feasible one for the implementation of the scheme (as rain gauges cannot be set up in every field), the proposed scheme envisaged the following terms of a rain-insurance contract:

If the aggregate rainfall from the beginning of the agricultural year as measured at the rain gauge at the taluka headquarters up to a certain date is less than a certain amount, then a certain sum of money will be paid in respect of the insured field as compensation.

Though Chakravarty did not envisage a scheme of compulsory insurance, the key elements of the contract suggested by him, namely, a specified risk fixing date (periods of preparing land, sowing, maturing and harvesting), a specified degree of deficiency in rainfall (affecting crop yield) and a prescribed amount of compensation (indemnity to stabilise net farm income) have become the bedrock for the present models of rainfall/weather insurance. Surprisingly, despite the very promising concept, the same was not given a practical shape till a few years ago with the introduction of rainfall insurance and weather-based schemes in the country.

In India, weather-based insurance was first introduced in 2003 by ICICI Lombard with technical assistance from the World Bank for groundnut and castor farmers of Mahbubnagar district in Andhra Pradesh, a region characterised by low and uncertain rainfall, low levels of irrigation, and shallow and infertile soils. About 154 groundnut farmers and 76 castor-bean farmers participated in the scheme in 2003 followed by 430 farmers in the following year for an average sum insured of Rs 6,000. About 305 farmers of the 657 insured farmers in the first two years received payouts, with the average payout of approximately Rs 1,475. The company in conjunction with the Government of Rajasthan, also launched a weather insurance programme in 2004, insuring 783 orange farmers and 1,036 coriander farmers which was scaled up to include more crops and farmers in 2005.

IFCCO-Tokio, a joint venture insurance company, launched a similar weather insurance contract, selling more than 3,000 policies in 2004 and more than 16,000 in 2005. This was followed by the pilot rainfall insurance scheme in 2004-05 in Andhra Pradesh, Karnataka and Gujarat.

The early pilot schemes offered by the private sector were followed by the entry of the public sector insurer AIC into this market with the introduction of rainfall insurance (Varsha Bima) targeting three risks: inadequate rainfall over the entire cropping cycle; inadequate rainfall during critical stages of crop development; and sowing failure due to inadequate rainfall at the start of the season. Under the seasonal rainfall option, coverage is against adverse deviations of 20% and beyond in “actual rainfall” from “normal rainfall” for the entire season. The payout structure, designed on the basis of yield output elasticity is on a graded scale, corresponding to different degrees of adverse deviation in rainfall, with the full sum insured for adverse deviation beyond 80%. For rainfall distribution index option, coverage is against deviations of 20% and beyond in “actual rainfall index” from “normal rainfall index” for the entire season. Lastly, for sowing

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failure option, the coverage is against adverse deviations of 40% and beyond in “actual rainfall” from “normal rainfall” between 15 June and 15 August. Varsha Bima was piloted in 20 rain gauge areas spread over 27 districts of four states of Andhra Pradesh, Karnataka, Rajasthan and Uttar Pradesh in 2004-05. In the following year, the scheme was extended to 142 districts in 10 states viz, Andhra Pradesh, Gujarat, Maharashtra, Madhya Pradesh, Karnataka, Orissa, Tamil Nadu, Uttar Pradesh, Chhattisgarh and Uttarakhand. The coverage expanded from 1,100 farmers in 2004 to 1.25 lakh farmers in 2005 to more than seven lakh farmers in 2008.

In 2005, with technical inputs from the Coffee Board and Central Coffee Research Institute, AIC developed a product for coffee farmers in Karnataka, which was later on extended to the states of Tamil Nadu and Kerala. The scheme compensates the insured against the likelihood of diminished output resulting from shortfall/excess in the actual rainfall for different coverage options within a specific geographical location and specified time period. Since 2007, the Coffee Board has been extending 50% subsidy to the small growers (landholdings below 10 hectares). In its three years of implementation, around 30,000 farmers have been insured generating a total premium of around Rs 6 crore. The product has however resulted in high losses for the insurer with the indemnity payouts exceeding three times the premium received.

Another product introduced in 2005 targeted wheat in parts of Haryana and Punjab states, and used the remotely sensed Normalised Difference Vegetation Index (NDVI) as a proxy for crop health. However this has encountered problems due to cloud cover during critical crop growth periods. A generic product was also developed by AIC for protection against adverse temperature deviations and unseasonal rainfall in 2006 and implemented in Madhya Pradesh, Uttar Pradesh, Rajasthan and Maharashtra. The crops covered were potato, mustard, chickpea, barley and wheat. Similar index products have also been developed for about 30 different crops, including perennial horticultural crops such as cashew nut, grapes, mango and apple.

However, the coverage remained limited as all the above initiatives on the part of insurers suffered from lack of government support, making the premium rates seemingly unaffordable. This continued till 2007-08, when the Government of India for the first time earmarked Rs 100 crore for the implementation of the Weather-Based Crop Insurance Scheme (WBCIS) on a pilot basis in a few states as an alternative to NAIS. Piloted by the AIC in Karnataka in kharif 2007, weather-based schemes are presently being offered in selected regions for different crops by both the public and private sector insurers.

The WBCIS too operates on the concept of “area approach” (as adopted under NAIS), whereby each reference unit area (RUA) is linked to a reference weather station (RWS) and all farmers in a given RUA are deemed to have suffered the same level of adverse weather incidence. To the extent possible, such RUAs are restricted to 25 km radius around the RWS. The risk period under weather insurance products extends from sowing to harvesting and thus varies from crop to crop. The payout structure that defines the scale of payout for a given strike and exit in different covers is specific to a particular crop in a notified RUA. The WBCIS is based on actuarial rates of premium (with a cap at the rate of 8%-10% for food crops

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and oilseeds and 12% for commercial crops) but to make it affordable, the premium actually charged from farmers has been fixed “at par” with the NAIS. The scheme operates with upfront premium subsidy contribution by the government (central and state), with the same level of financial support to the public and private insurers, while the entire claims are borne by the insurers.

Analysis across Seasons, Regions and Crops

The coverage of the scheme has been increasing with every season with the expansion of the scheme to more number of notified areas and crops. Table 1 shows the season-wise progress of the scheme in the country. The scheme has, so far, insured about 21.5 lakh farmers covering approximately 29,000 hectares of land for an insurance value of around Rs 5,000 crore. The quantum jump in the expansion coverage has come in the latest kharif season which saw about 11.35 lakh farmers being insured, a jump of a lmost 600% from the previous year.

In terms of implementing states, Rajasthan has remained the dominant state, with 42% of the total farmers insured and

Table 1: Season-wise Coverage and Indemnity under WBCIS

No of Farmers Area Total FP TP TC FB
Implementing Insured (‘000 ha) Liability (‘000)
States (‘000) Amount in Rs lakh
Kharif 2007 1 44 50 5,301 142 703 524 35
Kharif 2008 10 165 179 31,313 831 3,168 1,440 104
Kharif 2009 13 1,135 1,460 1,98,602 5,784 19,873 CNF
Total 1,344 1,689 2,35,216 6,757 23,744
Rabi 2007-08 4 627 985 1,70,495 4,300 13,845 10,072 188
Rabi 2008-09 10 169 190 42,623 874 3,590 2,610 112
Total 796 1,175 2,13,118 5,174 17,435 12,682 300

FP = Farmers Premium, TP = Total Premium, TC=Total Claims, FB = Farmers Benefited. CNF: Claims not finalised. Source: Agriculture Insurance Company of India.

Table 2: State-wise Coverage under WBCIS (2007-09)

State FI (‘000s) Area (‘000 Ha) SI FP TP
(in Rs lakh)
Rajasthan 894 (42.0) 1,549(54.2) 2,04,845(45.9) 5,464(46.1) 17,416(42.5)
Bihar 631 (29.6) 711(24.9) 1,46,167(32.7) 3,451(29.1) 13,993(34.1)
Karnataka 171 (8.0) 203(7.1) 24,917(5.6) 802(6.8) 2,735(6.7)
Gujarat 141 (6.6) 56(2.0) 6,340(1.4) 159(1.3) 634(1.5)
Orissa 95(4.5) 135(4.7) 18,048(4.0) 451(3.8) 1,805(4.4)
Maharashtra 53(2.5) 63,232(2.2) 9,485(2.1) 180(1.5) 1,138(2.8)
Madhya Pradesh 39(1.8) 47,887(1.7) 8,582(1.9) 325(2.7) 864(2.1)
Jharkhand 39(1.8) 21,882(0.8) 2,569(0.6) 65(0.5) 256 (0.6)
Tamil Nadu 30(1.4) 26,318(0.9) 6,674(1.5) 191(1.6) 644(1.6)
Andhra Pradesh 17(0.8) 11,425(0.4) 14,078(3.2) 676(5.7) 1,140(2.8)
Chhattisgarh 15(0.7) 27,263(1.0) 4,102(0.9) 83(0.7) 301(0.7)
West Bengal 7.0(0.3) 3,128(0.1) 7,21(0.2) 17(0.1) 66(0.2)
Total 2,131 28,56,952 4,46,526 11,864 40,991

SI = Sum Insured, FP = Farmers Premium, TP = Total Premium. Source: Same as Table 1. Figures in parenthesis denote percentages to total.

accounting for almost half of the total area, liability and premium generated (Table 2). In fact, the state government promoted the scheme in a major way in the initial years and the scheme had covered about 5.8 lakh farmers contributing about Rs 129 crore of total premium (inclusive of subsidy) in rabi 2007-08. Bihar is the second most important state, covering about 30% of the total farmers insured. The other emerging states are Gujarat, Orissa and Maharashtra.

The crops notified under weather-based schemes are generally the ones growing under rain-fed conditions. Table 3 shows that wheat is the most important crop accounting for one-third of the total farmers insured, acreage and premium generated. Paddy and gram amongst the food crops and cumin amongst the annual commercial and horticulture crops (ACH) are the other important crops in terms of coverage. Surprisingly, though the ACH crops offer a distinct advantage in terms of applicable premium rates,

Table 3: Crop-wise Coverage under WBCIS (Kharif 2007 to Rabi 2008-09 Season)

insures all the loans that are disbursed by scheduled commercial banks for insured crops/areas within the stipulated cut-off dates, WBCIS requires that those farmers who have sanctioned credit limits within the said dates are to be compulsorily covered.

Second, under the WBCIS, the sum insured limits, broadly reflecting the per unit input cost are identical for both borrowing and non-borrowing farmers. In case of NAIS, the sum insured for loanee farmers is the loan availed, with no upper limits. Thus,

Sr Crops FI (‘000s) Area (000 Ha) SI FP TP TC FB (in ‘000) Per Hec Per Farmer FB as % of FI LC (%) Farmers Total CR (%)
No (Rs Lakh) SI (Rs) Claims (Rs) CR (%) CR (%)
1 Wheat 321(33) 439 (37) 89,995 (23) 1,349(33) 6,844(36) 5,168( 33 ) 145 20,500 3,564 45 6 383 76
2 Mustard 145(15) 212(16) 31,873(13) 637(11) 2,272(11) 1592(11) 31 15,000 5,135 22 5 250 70
3 Gram 164(17) 340(25) 49,493(20) 1,003(17) 3,864(19) 4,825(34) 106 15,000 4,552 65 10 481 125
4 Paddy 106(11) 121(9) 24,454(10) 597(10) 2,420(12) 1035(7) 61 20,252 1,697 58 4 173 43
6 Tur 22(2.3) 33(2.4) 4,008(1.7) 100(1.7) 503(2.4) 508(3.6) 19 12,000 2,674 86 13 507 101
7 Greengram 15(1.5) 15(1.1) 1,176(0.5) 29(0.5) 137(0.7) 42(0.3) 7 8,000 600 49 4 144 31
8 Barley 13(1.3) 9(0.7) 1,312(0.5) 28(0.5) 95(0.5) 27(0.2) 2 15,200 1,350 12 2 95 28
9 Groundnut 8(0.8) 12(0.9) 2,151(0.9) 61(1.0) 229(1.1) 116(0.8) 7 18,000 1,657 79 5 189 51
10 Maize 8(0.8) 11(0.8) 1,474(0.6) 36(0.6) 154(0.7) 49(0.3) 5 13,500 980 62 3 135 32
Total 802(82) 1,192(88) 2,05,936 (85) 3842(65) 16,518(80) 13,362(94) 384(91) 17,300 3,480 48 6 348 81
1 Cumin 90(9.2) 80(5.9) 19,680(8.1) 1,094(18.6) 2,089(10.2) 134(0.9) 22 25,000 609 24 1 12 6
2 Isabgol 52(5.3) 60(4.4) 9,031(3.7) 540(9.2) 1,080(5.3) 45(0.3) 0.004 15,000 1 1 0 8 4
3 Potato 17(1.7) 11(0.8) 5,438(2.2) 294(5) 623(3) 590(4.2) 14 46,500 4,214 85 11 201 95
4 Cotton 15(1.5) 12(0.9) 2,114(0.9) 102(1.7) 254(1.2) 39(0.3) 3 17,300 1,300 21 2 38 15
Total 173(17.7) 163(12.0) 36,263(15.0) 2,029(34.6) 4,045(19.7) 807(5.7) 39 (9.3) 22,300 2,069 23 2 40 20
Grand total 975(100) 1,355.5(100) 2,42,199(100) 5,870(100) 20,563(100) 14,169(100) 423 (100) 17,900 3,350 44 6 241 69

FI= Farmers Insured, SI = Sum Insured, FP = Farmers Premium, TP = Total Premium, TC=Total Claims, FB = Farmers Benefited, LC=Loss Cost (Claims/Sum Insured *100), CR = Claim Ratio (Claims/

Premium * 100) Figures in parentheses the note percentages to total. Source: Same as Table 1.

the coverage has not been very encouraging, though a few states like Maharashtra have lately been notifying crops (like cotton) showing a declining trend under the NAIS. Overall, food crops and oilseeds (FCOS) account for more than 80% of the coverage, insurance value and total premium generated as also more than 90% of the total indemnity payouts.

The crop-wise performance shows the aggregate of all the implementing states for the past four seasons. Potato and cumin have the highest per unit sum insured followed by food crops like wheat and paddy. The crop-wise coverage and indemnity reveals that certain crops like gram, tur and potato not only have high payouts but these seem to be benefitting an extremely high proportion of the insured farmers. For these crops, almost 85%-95% of the farmers insured have benefited from the payouts, with the claims even exceeding the total premium generated. On a general note, the crop-wise analysis reveals that the indemnity payouts are comparatively much more proportionate to coverage

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under WBCIS than under the NAIS.

Sustainability and Equity

Though both the area and weather-based schemes are based on area approach, are compulsory for the borrowing farmers and flat premium rates are applicable for FCOS, there are important differences in the coverage parameters (besides the technical differences) that are often missed out in the general comparison of the two schemes.

First, while the NAIS covers the loan disbursed, the latter covers the loan sanctioned. In other words, while NAIS compulsorily the entire loan amount, irrespective of the quantum is insured. For the non-loanee farmers, there are two limits; one till the value of guaranteed yield, on which flat premium rates are applicable and an upper limit which is the value of 150% of the value of average yield, with the actuarial rates becoming applicable on the liability amount exceeding the first limit. Thus, unlike the NAIS, WBCIS offers no flexibility to farmers; for seeking higher or lower coverage. This becomes difficult for farmers borrowing a loan amount less than the sum insured limits, as the scheme provides for charging of premium on the entire pre-fixed sum insured. On the other hand, farmers who borrow a higher quantum receive only partial insurance.

Third, while the premium rates are fixed at par for FCOS, the insured farmers have to pay an additional component of applicable service tax under the WBCIS. Also, the premium payable for the ACH crops under the WBCIS is capped at a maximum of 6%. Thus, while the NAIS, being exempted from service tax makes it advantageous for FCOS; the absence of any upper limit on premium rates under ACH crops makes it distinctly disadvantageous in comparison to WBCIS.

Fourth, as per the applicable franchise limit under the WBCIS, any loss exceeding 5% of the sum insured is compensated. Under the NAIS, the present levels of indemnity are 60%, 80% or 90% corresponding to high, medium and low risk crops.6 Needless to say, with guaranteed yield at 60% (of the average yield of 3-5 years), the small and medium intensity adversities fail to c ompensate the farmers. The farmers also do not have the flexibility of choosing a higher indemnity even by paying a higher

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premium. The guaranteed yield being the moving average (multiplied by the indemnity level) also fails to provide protection to the farmers in areas where there has been a consecutive adverse season. While weather insurance scores heavily over the traditional scheme in adequately reflecting yield losses in such cases, it fails to adequately compensate the catastrophic losses (given that the total sum insured is divided into various covers and only an adverse deviation in all the parameters would result in the insured receiving a substantial proportion of the total liability).

Weather Insurance: A Sustainable Insurance Programme?

Worldwide, the public sector has had a bleak history of providing crop insurance. Hazell, Pomereda and Valdes (1986) cite numerous problems that have plagued public crop insurance programmes and point out that these programmes cannot be sustained without continual subsidies. It is also contended that the associated costs of providing crop insurance schemes have historically outweighed the gains from risk spreading and that crop insurance is uninsurable and unsustainable in the long run as the

Table 4: Season-wise Comparison of Financial Performance of NAIS and WBCIS

NAIS

WBCIS Average Average I/P LC

Average Average I/P I/FP LC Premium (P) Indemnity (I) Premium (P) Indemnity (I)

Rs % Rs % Kharif Kharif 2007 391 681 1.74 10 1,606 1,197 0.74 3.70 10 Kharif 2008 394 1,827 4.63 15 1,919 872 0.45 1.73 5 Rabi Rabi 2007-08 314 1,605 5.10 11 2,208 1,606 0.72 2.34 6 Rabi 2008-09 470 2,311 4.92 11 2,126 1,546 0.72 2.99 6 Grand total 395 1,397 3.54 10 2,120 1,457 0.68 2.38 6 Loss cost percentage (LC) is the total indemnity payouts as a percentage of the total liability. I/P is the average indemnity divided by average premium. I/FP is the average indemnity divided by the average premium (premium paid by the farmers). Source: Same as Table 1.

transfer of losses from affected groups to the community at large is not feasible at an affordable premium rate (Skees et al 1999). Given the recurring nature of natural calamities and their heavy influence on agriculture, these insurance schemes fail to earn enough premiums to cover the indemnity payouts, leave aside the administrative costs.

In what follows, we put forth the results of viability tests of both the weather-based and yield-based crop insurance schemes presently being implemented in the country. The condition for a viable and sustainable insurance contract (Hazell 1992) is of the following form:

(A + I)/P < 1

where A = Average administrative costs per insurance contract

I = Average Indemnities Paid

P = Average Premiums Paid

This suggests that for an insurance programme to be financially sustainable, premiums collected by the insurer must exceed indemnities paid plus administrative cost. In other words, the insurance programme must be profitable. However, in most cases the loss ratio exceeds one.

Despite experimenting with different schemes over three decades, public crop insurance policy in India has clearly failed in terms of viability and financial sustainability. Using historical sum insured and claims over the past 10 kharif and rabi seasons, the average loss costs for the area yield scheme worked out to be

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a staggering 10.7% and 9.64%, respectively. However, to make a comparative analysis, the season-wise claims and indemnity are considered for only those years when both the schemes were implemented (Table 4). Financially, the traditional scheme has been incurring continuous losses, with the state consistently bearing the indemnity payments which exceed the premium generated.

Yet, to be dismissive of the programme as another failed crop insurance initiative would not be completely fair as any scheme will have to be based on actuarial solidity to be sustainable. Theoretically, the premium paid by farmers should be at least equal to average loss cost to the insurance agency due to crop loss for a period of years, the premium rate varying directly with the degree of risk in the insured crop yield. Based on an examination of the historical loss cost, the subsidised premium rates that apply to all basic crops (including foodgrains, pulses and oilseeds) under NAIS is not commensurate with risk and remain much lower than the actuarially fair premium, making the scheme dependent on substantial fiscal support from the national and state governments even in non-calamity years.

Assuming zero administrative costs and other implicit costs (particularly related to crop-cutting experiments, the experience of NAIS and WBCIS in terms of viability and sustainability is summarised in Table 4. The average payout for every rupee premium income under both the schemes is depicted in Figure 1 and a comparison of season-wise loss cost under both the schemes is shown in Figure 2. Under the NAIS, the premium received is on an average less than one-third of the average payout, with the average loss cost at 10%. The product is clearly not financially viable and no insurer would be willing to implement the scheme without open ended government support. Unless premium calculations

Figure 1: Actuarial Performance of NAIS and WBCIS: Season-wise Comparison of Claim Ratio

6

5

4

3

NAIS I/P 2

WBCIS I/P 1

0 Kharif 2007 Kharif 2008 Rabi 2007-08 Rabi 2008-09

Figure 2: Actuarial Performance of NAIS and WBCIS: Season-wise Comparison of Loss Cost

16

14

NAIS Loss Cost % 12

WBCIS Loss Cost %

10

8

6

4

2

0 Kharif 2007 Kharif 2008 Rabi 2007-08 Rabi 2008-09

are based on actuarial determination of risks and gross premiums are set at levels to cover expected claims, improvement in its financial performance is unlikely.

In comparison to the traditional scheme, weather insurance shows a much better financial performance in all the seasons, with the overall claim ratio less than one and the applicable premium rates being broadly commensurate with the average loss cost ratio implying the financial viability of the scheme.

Since the weather-based scheme works on actuarial principles, with upfront premium subsidy from the government, it has proven advantageous for both the insurer and the farmer. For the farmer, the premium being charged “at par” with the NAIS makes it affordable and the aggregate indemnity payouts are still higher than the premium payments. On the other hand, the premium (inclusive of the subsidy component) makes it profitable to the insurer under WBCIS. The product proves a sustainable alternative since the indemnity payouts are within the premium generated by the product. While the indemnity for weather insurance is 2.4 times the premium received from the farmer, it is only about 70% of the total premium.

WBCIS: More Equitable Distribution of Benefits?

An argument often made in favour of the yield insurance scheme is its extremely high claim ratio which in a few seasons has exceeded five times the premium paid by the farmers. In terms of

Table 5: Season-wise Proportion of Farmers Benefited and Covered under NAIS and WBCIS

NAIS WBCIS
Farmers Farmers FB as % Per Farmer Farmers Farmers FB as % Per Farmer
Covered Benefited of FI Claims Covered Benefited of FI Claims
‘ 000s (Rs) ‘ 000s (Rs)
Kharif
2007 13,398 1,588 12 5,746 44 35 81 1,486
2008 12,984 4,206 32 4,794 165 104 63 1,378
Total 26,382 5,794 22 5,025 209 140 67 1,405
Rabi
2007-08 5,044 1,577 31 5,134 627 188 30 5,363
2008-09 6,170 1,803 29 7,913 169 112 66 2,331
Total 11,214 3,380 30 6,616 796 300 38 4,230
Grand total 37,596 9,174 24 5,568 1,005 440 44 3,332

Source: Same as Table 1.

claims ratio, the traditional product is seen faring better than the weather-based schemes in a majority of the seasons (Table 5). The high claim ratio is often taken as an indicator of the higher

Table 6 shows the state-wise coverage and indemnity under both the schemes. The per-farmer sum insured is noticeably higher under the WBCIS than under NAIS across all states, Tamil Nadu being the only exception. The reason has to do with the inflexible sum insured limits applicable for both the borrowing and nonborrowing farmers. The proportion of farmers benefited vis-à-vis insured is much higher under the WBCIS than under NAIS across

Figure 3: Distribution of Benefits – Season-wise Comparison of NAIS and WBCIS

Proportion of Farmers Benefited: NAIS and WBCIS

90

80

60

40

20

0

states. Nearly all the insured farmers received payouts under W BCIS in the states of Orissa, Maharashtra and West Bengal. Thus, under the NAIS, while less than one-fourth of the farmers insured receive claims, the claims under the WBCIS are distributed to more than 40% of the insured (in aggregate), and much higher in majority of the states. Second, with the exception of Rajasthan, the per-farmer claims is notably higher under the NAIS. As regards the average loss cost and claim ratios, other than Gujarat (implemented WBCIS only during kharif 2009), the applicable premium rates appear reasonable when compared to the average loss cost.

The following conclusions emerge from the above analysis. First, the WBCIS provides for a more viable claims experience with the average loss ratio being ideally reflective of the premium rate. Second, though the quantum of claims distributed is higher under the NAIS (with favourable claim ratios under both the schemes), the benefits are concentrated and received by a much lower proportion of those insured.

Indemnity Payouts in Karnataka

It is to be understood that despite the several advantages that the product offers to the insured, the state governments understandably remain sceptical whether a product covering just parametric weather events can be comparable (let alone superior) to the multi-peril yield insurance. The apprehensions also relate to the

WBCIS % Farmers Benefited NAIS % Farmers Benefited

Kharif 2007 Kharif 2008 Rabi 2007-08 Rabi 2008-09 Total Season

benefits accrued to the farmers. The season-wise Table 6: Indicators of State-wise Coverage and Indemnity under NAIS and WBCIS
farmers covered and benefited under both the schemes State NAIS WBCIS
also reveal the comparatively higher per-farmer claims Per Farmer Per Farmer FB as % LC (%) of FI CR (%) Per Farmer Per Farmer FB as % LC (%) of FI Farmers CR (%) Total CR (%)
under the NAIS. Yet the difference lies in the proportion of farmers Rajasthan Claims SI (Rs) (Rs) 4,192 13,584 32.5 10.06 Claims SI(Rs) (Rs) 370 5,678 27,787 29 6 238 73
benefited from the schemes. During kharif seasons, while Bihar 11,095 22,315 25 13 594 1,944 22,576 62 5 254 61

the WBCIS compensated 67% of the farmers insured, Karnataka 4,254 13,429 22 7 219 1,261 13,412 79 9 327 80

only 23% were claim beneficiaries under the NAIS. The Gujarat 548 4,500 86 10 418 105 Orissa 5,426 13,756 9 3.6 139 3,121 33,531 100 9 372 93

trend has been similar in all the seasons with the excep-

Maharashtra 2,735 4,745 50 29 631 1,198 20,364 100 6 311 49

tion of rabi 2007-08. Overall, while 44% of the farmers

Madhya Pradesh 5,572 16,226 25 9 317 3,079 25,206 31 4 101 38

insured received indemnity payouts under the WBCIS,

Jharkhand 2,271 3,417 19 13 515 229 6,464 92 3 132 33

only 25% of them benefited from the NAIS (Figure 3).

Tamil Nadu 12,023 25,760 70 32 1404 1,150 19,951 50 3 118 31

Thus the claims under the NAIS are given to a smaller

Chhattisgarh 561 11,870 3.8 0.18 10 2,538 28,134 26 2 117 32

proportion of beneficiaries and the higher per-farmer

West Bengal 7,489 15,280 80 39 500 372 9,923 97 4 182 46 claims only imply highly skewed distribution of benefits. Comparison is only made for the years in which both the schemes were implemented in the respective states.

78 August 21, 2010 vol xlv no 34

fact that basis risk and poor design of the weather index may result in “no claims” despite crop losses at the individual farmers’ level. These factors have contributed to the inadequate geographical spread of the weather products.

The comparative analysis in the above section is, however, based only on the aggregate experience; and the evaluation of the two schemes would require a disaggregated area-wise analysis. Here we present the analytical results of coverage and claims experience under both the schemes in Karnataka, where the pilot WBCIS was launched in 2007. The analysis includes all the three seasons during which WBCIS was implemented covering only those crop hoblis (circles) where both the schemes operated7 in the respective seasons. To facilitate the comparative analysis, per hectare indemnity payouts under both the schemes are calculated. While the payouts under WBCIS are as per the claims structures and the associated trigger levels, the per hectare claims under NAIS are arrived at by applying the shortfall percentage to the WBCIS sum insured limits. In other words, the per hectare NAIS claims are the product of percentage of shortfall in yield and the per hectare sum insured limits applicable under the WBCIS.

Table 7 shows that about 96%, 88% and 97% of the areas during kharif 2007, kharif 2008 and rabi 2008-09 did not produce claims under the NAIS. On the other hand 44%, 62% and 51% of Table 7: Comparison of Claims Experience under NAIS and WBCIS

Kharif 2007 Kharif 2008 Rabi 2008-09

WBCIS NAIS WBCIS NAIS WBCIS NAIS

No of Notified Crop Hoblis 140 148 57

No of claim producing areas 62 6 83 29 29 2

% claim producing areas 44 4 62 22 51 3.5

No of zero claim producing areas 77 134 59 112 55 29

% zero claim producing areas 56 96 38 88 49 96.5

Average claims (per ha) 641 85 916 916 834 204

Claim settlement period 30/1/08 2/7/09 & 20/01/09 5/08/2009 01/10/09 19/01/ 23/7/09 2010

Source: Same as Table 1.

all the notified areas received payouts under the WBCIS. The a reas with no payouts were thus significantly lower under the WBCIS. The WBCIS also seems to be faring better than the yieldbased schemes as far the quantum and the average claims are concerned. Thus even the micro experience in Karnataka reveals the highly concentrated claims under NAIS and the reasonably even spread under WBCIS.

Given that the payout triggers under the WBCIS are ideally fixed to reflect the yield losses, the per hectare claims under both the schemes should ideally be comparable to each other. Surprisingly, the analysis proves otherwise. The per hectare claims u nder both the schemes not only show a negative correlation, but in several instances, where NAIS claims are seen to be exceptionally high, there is nil/negligible payouts under the WBCIS (Appendix I, p 81). A major drawback of the NAIS as revealed by earlier studies (James and Nair 2009; GoI 2004; World Bank 2007) is the highly biased settlement of claims for certain areas and crops. Given that the spread of benefits under the weather-based scheme appears to be more even, the tinkering of yield data cannot be ruled out. Yet, concrete conclusions cannot be drawn in this regard u nless the exact cause of crop loss is known.

Economic & Political Weekly

EPW
August 21, 2010 vol xlv no 34

Though the data gaps and timely non-receipt of weather data do result in significant delays in the settlement of claims, these products still fare much better than the traditional scheme. In any case, the farmers receive indemnity payouts before the advent of the next season and on an average six to seven months earlier than the NAIS. On the other hand, the extremely delayed payouts under the yield insurance scheme provide limited insurance against consumption fluctuations.

Basis Risk: How Does the WBCIS Fare?

Because farm-level yields are not perfectly correlated with the area average yield, the insured farmers are exposed to basis risk. Thus, an insured may experience production losses on his/her farm and yet not receive an indemnity or may receive an indemnity triggered by a shortfall in the unit area average yield despite not experiencing individual losses. High basis risk is one of the greatest drawbacks of the NAIS, as most of the implementing states continue to notify larger insurance units.

The WBCIS was expected to bring about a significant improvement in this regard. While the basis risk associated with weather could be high for rainfall, it was expected to be moderate for many of the other weather parameters. Yet, the potential for a mismatch between parametric insurance claims settlement and the actual losses exists since these payouts too are not based on individual loss adjustments, but determined according to the measurement of the index. Experience with the implementation of the WBCIS has shown that there are various potential sources of basis risk under WBCIS too – the weather station being used for the contract may be too far from the insured; losses may be caused by factors other than the weather variable on which the index is based on, the management by the individual farm operator can be significantly different than the conditions that are imposed in a crop growth model, the poor design of the product resulting in its failure to establish accurate correlation between productivity levels and weather variations, etc. Given all the above, it is possible for the policyholder to experience a loss and yet receive no index insurance indemnity or receive indemnity despite no perceptible loss of yield, diametrically revealed by quadrants II and III, respectively in Figure 4. Alternatively, a perfectly correlated index along with low basis risk would result in a situation shown by quadrants I and IV.

Figure 4: Basis Risk in WBCIS

Quantum of payouts Nature of Season and Crop Yields
Favourable Weather Adverse Weather
Low Claims Low/nil claims during good season, i e, high crop yield (I) Low/nil claims during season of low crop yield (II)
High Claims High claims during good season, i e, high crop yield (III) High claims during season of low crop yield (IV)

Presently, weather data is often recorded at the taluka/ district level though rain gauge stations (RGSs) provided rainfall data do exist for lower insurance units. Given the high spatial variation in India, if RWS is located more than five to seven kms away from the farm location, the quantum and time of the weather parameter at the farm location may significantly vary from that of a RWS. Also, the capping of premium rates under the weather-based crop insurance scheme does not augur well for the usefulness of the product from the farmers’ point of view since it is most likely to come at the expense of payouts.

Thus, a critical element for decreasing the basis risk associated with weather insurance would be to make necessary investments in weather stations so as to ensure accurate and tamper-proof measurements. Also, a high correlation between the index and the individual’s risk is important for reducing basis risk. A continuous research on the climatic variations and suitable modifications in the product structure to adequately reflect the weatheryield relationship is also critical to reduce basis risk associated with this promising product.

Conclusions

The growing popularity of weather-based schemes reflected in the large and growing number of insured in such a short span of time is indeed impressive. In contrast to the traditional yield insurance scheme, weather insurance is market-based and financially sustainable with the potential to transform the lives of poor agricultural households by addressing output risk – one of the most important obstacles for agrarian households. The benefits enumerated in the analysis, viz, the even spread of benefits, the more advantageous premium regime for commercial and horticulture crops, and most importantly the swift payouts makes the product more advantageous to the vulnerable sections of the farming community as it prevents them from falling into a debt trap or having to pay high interest rates on moneylender loans. A full-fledged analysis of the scheme after a couple of years after it is implemented on a wider scale would show more concrete results.

Yet, there are major issues and constraints associated with weather index products that need to be successfully addressed. Foremost among the risks faced by this promising innovation is the likelihood of insurers continuing to implement the product with high basis risk, so long as it is profitable. There is therefore an urgent need to bring down basis risk arising from insufficient network and spread of weather stations. A hitherto active role of the government, in expanding the network of tamperproof weather stations appears inevitable considering the huge start-up costs. Also, government-owned weather stations would instil greater confidence amongst the insured regarding data transparency and security. Second, for WBCIS to emerge as an effective risk mitigation tool, the product should be suitably designed to cover catastrophic losses. Third, the successful introduction of weather index insurance would also require a significant educational effort. Presently, the claim structures are highly technical and complicated and even the insured farmers seldom have any knowledge of the various covers as also the extent of weather deviation that would result in claims. Adding

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August 21, 2010 vol xlv no 34

to the confusion is the fact that similar products (with identical Finally, there should be an in-depth research (on a continuous premium rates but different trigger values) are also being basis) of the associated weather risks for various crops grown in offered by the private insurers, often in the same area/crop, the country, preferably funded by the government so as to prowith the insured having little understanding that the payouts vide the insurers with the requisite technical inputs in developmay widely vary for the same crop in the same area. Lack of ing appropriate risk covers. The need of the hour is therefore to sufficient awareness and understanding of the product address the above challenges that would enable this promising specificities could prevent farmers from buying an otherwise product to emerge as an effective risk mitigation tool for farmers attractive product. in diverse parts of the country.

Notes Appendix I: Season-wise State-wise Crops Insured under WBCIS

1 In India, yield estimation expenses are borne by the respective state governments as these form part of the General Crop Estimation Survey. State Kharif 2007 Crop
2 The insured is not likely to have better information than the insurer about the underlying index nor would he be in a position to influence the realisation of the index. KarnatakaRabi 2007-08 Bihar Blackgram, Greengram, Groundnut, Jowar, Maize, Ragi, Soyabean, Tur Gram, Wheat
3 4 Instead, the state and central governments act as de facto reinsurers, since claims in excess of premiums are shared equally. In RUAs where WBCIS is implemented, loanee Chhattisgarh MP Rajasthan Gram, Linseed, Rape and Mustard, Potato Rape and Mustard, Wheat, Gram, Potato Barley, Gram, Coriander, Cumin, Methi, Isabgul, Mustard, Wheat
farmers are to be compulsorily covered under the scheme and these farmers do not have option of NAIS. Given the low level of insurance awareness, Kharif 2008 Bihar Paddy, Maize
compulsory coverage is still considered a necessity and the borrowing farmers comprise more than 80% of the total farmers insured under crop HaryanaJharkhand Onion Blackgram
insurance schemes. Punjab Paddy
5 An analysis of crop-wise claims under NAIS reveals that while groundnut has about one-tenth of the total coverage, it accounts for more than one-third of indemnity. Karnataka MP Bengal-Gram, Chilly, Cotton, Groundnut, Greengram, Jowar, Maize, Onion, Ragi, Soyabean, Sunflower, Tur Cotton, Paddy, Soyabean
6 During kharif 2007 season, 53% and 34% of all crops were in the 60% and 80% indemnity zones respectively. MaharashtraOrissa Cotton Paddy
7 In those crops/areas, where both the schemes are notified, WBCIS would be compulsory for the loanee farmers while the non-loanee farmers would have Rajasthan Tamil Nadu Bajra, Blackgram, Cotton, Groundnut, Jowar, Maize, Sesamum, Soyabean Paddy
the option of choosing either of the two schemes. Rabi 2008-09
Bihar Wheat, Potato, Gram, Lentil
References Chhattisgarh Gram, Mustard
Chakravarty, J S (1920): Agricultural Insurance: A HP Tomato

Practical Scheme Suited to Indian Conditions

(Bangalore: Government Press).

Government of India (2004): “Report of Joint Group on Review of Crop Insurance”, Ministry of Agriculture, New Delhi.

– (2007): “Report of the Working Group on Risk Management in Agriculture for the Eleventh Five-Year Plan (2007-12)”, Planning Commission, New Delhi.

Hazell, P B R (1992): “The Appropriate Role of Agricultural Insurance in Developing Countries”, Journal of International Development, 4: 567-81.

Hazell, P B R, C Pomareda and A Valdes (1986): Crop Insurance for Agricultural Development: Issues and Experience (Baltimore: Johns Hopkins University

Press).

James, P C and Reshmy Nair (2009): “A Study of Yield Based Crop Insurance in India”, IRDA Journal, Vol VII, No 6, June and Vol VII, No 7, July.

Parchure, R (2002): “Varsha Bonds and Options: Capital Market Solutions for Crop Insurance Problems”, National Insurance Academy Working Paper Balewadi, available on http://www.utiicm.com/

rajaskparchure.html.

Skees, J R, P B R Hazell and M J Miranda (1999): “New Approaches to Crop-Yield Insurance in Developing Countries”, EPTD Discussion Paper 55, IFPRI, Washington DC.

Sarkar, Subrahdipta and Archana Sarma (2006): “Disaster Management Act 2005 – A Disaster in Waiting?”, Economic Political Weekly, Vol XLI, No 35, 2-28 September.

World Bank (2007): “India – National Agricultural Insurance Scheme: Market Based Solutions for

Haryana Barley, Tomato, Potato

Jharkhand Paddy

Karnataka Jowar (I UI), Bengalgram (I UI), Potato, Grape, Mango

Kerala Paddy, Mango, Cashewnut

Rajasthan Barley, Bengalgram, Coriander, Cumin, Fenugreek, Isabgul, Mustard, Wheat

Tamil Nadu Maize, Paddy, Groundnut, Sesamum, Sunflower, Tomato, Onion, Chilly, Cotton, Mango

West Bengal Paddy, Mustard, Wheat

Source: AIC

Appendix II: Areas with High NAIS Payouts and Nil WBCIS Payouts

NAIS-WBCIS Claims Comparison – Kharif 2008

Season District Hobli Crop Per Hec Claims WBCIS Per Hec Claims NAIS (Rs) (Rs)

Kharif 2007 Haveri Hanagal Maize (Rainfed) 0 2,283

Kharif 2007 Haveri Shiggaon Jowar (Rainfed) 0 6,136

Kharif 2008 Gulbarga Aland Blackgram (Rainfed) 0 5,285

Kharif 2008 Bijapur Sindagi Greengram (Rainfed) 0 5,612

Kharif 2008 Gadag Ron Greengram (Rainfed) 0 5,667

Kharif 2008 Haveri Ranebennur Greengram (Rainfed) 0 3,325

Kharif 2008 Gulbarga Aland Groundnut (Rainfed) 0 3,134

Kharif 2008 Tumkur Pavagada Groundnut (Rainfed) 0 4,695

Kharif 2008 Chitradurga Chitradurga Maize (Rainfed) 0 4,418

Kharif 2008 Haveri Shiggaon Maize (Rainfed) 0 6,417

Kharif 2008 Haveri Shiggaon Ragi (Rainfed) 0 6,326

Rabi 2008-09 Chitradurga Chitradurga Jowar (Rainfed) 0 10,000

Better Risk Sharing”, Report No 39353. Source: AIC Economic Political Weekly

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August 21, 2010 vol xlv no 34

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