The Potential for Scale and Sustainability in Weather Index Insurance

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THE POTENTIAL FOR SCALE AND SUSTAINABILITY IN WEATHER INDEX INSURANCE FOR AGRICULTURE AND RURAL LIVELIHOODS Table 13: Performance of AIC’s index insurance products (2004-2008) Lessons learned Real-time weather data AIC found that the lack of real-time weather data presents one of the most significant challenges to settling claims accurately and on time. It frequently takes 30-75 days for insurers to receive data from public weather stations, delaying the timely settlement of farmers’ payouts and discouraging the participation of reinsurers in the market. In addition, current weather data are not adequately captured. In many regions, the provision of data for every day of the season (which is required for accurate estimation of a crop’s water uptake) is not guaranteed, since most stations are manually operated. To address this issue, both the public and private sectors have started to automate manual weather stations. Though private companies have already installed a network of approximately 500 stations, access to private weather data is expensive, ranging from ANNEX No. of farmers Area Total sum Premium Claims Product insured insured (ha) insured (Rs a ) (Rs a ) (Rs a ) 2004-05 Varsha Bima 1 050 2 200 2 620 406 611 656 562 639 2005-06 Varsha Bima 125 453 97 690 558 582 520 31 704 876 1 996 106 Coffee Rainfall Insurance 58 514 16 943 000 366 039 192 500 Sookha Suraksha Kavach 327 295 844 595 83 752 55 454 Wheat Weather Insurance 121 248 1 712 000 84 072 54 550 Mango Weather Insurance 16 - 655 440 35 292 83 039 Total 125 975 98 747 578 737 555 32 274 031 2 381 648 2006-07 Varsha Bima 12 328 15 873 109 230 588 6 443 885 3 699 995 Wheat Weather Insurance 2 502 11 291 39 091 200 2 186 408 1 046 953 Mango Weather Insurance 126 225 5 280 370 295 692 421 342 Rainfall Insurance 10 885 10 256 71 432 483 4 170 195 2 462 596 Rabi Weather Insurance 5 612 19 398 125 462 457 5 951 298 6 405 764 Total 31 453 57 044 350 497 098 19 047 478 14 036 650 2007-08 Varsha Bima 8 125 18 120 102 945 362 5 941 415 5 758 651 Coffee Rainfall Insurance 16 355 30 488 1 914 003 988 29 737 668 86 431 100 Wheat Weather Insurance 1 821 23 411 79 506 000 4 548 098 946 500 Mango Weather Insurance 60 90 3 706 570 183 958 56 540 Rainfall Insurance 6 703 15 626 55 332 785 3 728 344 8 553 490 Rabi Weather Insurance 5 585 11 703 111 965 380 5 808 291 5 314 613 Apple Weather Insurance 1 406 1 120 62 695 725 1 567 394 1 567 394 WBCIS – Kharif 43 790 50 075 530 118 846 70 307 563 52 411 718 WBCIS – Rabi 627 167 984 553 17 049 511 084 1 384 512 875 1 006 981 789 Total 711 012 1 135 186 19 909 785 740 1 506 335 606 1 168 021 795 a In March 2009, US$1 = Rs 50.56. 117
118 CASE STUDY 5 PRIVATE AND PUBLIC INDEX INSURANCE IN INDIA US$40-100 per month per station, which may prohibit the public sector from using this infrastructure. There is also some controversy over data from private stations, since many are set up close to residential areas (due to security reasons and the required telephone line) and thus record slightly higher temperatures than the public stations. The Government has suggested that insurers use public weather stations where they are available. Public providers, IMD and the Indian Space Research Organization (ISRO) planned to install 1,550 automatic weather stations and 1,350 automatic rain gauges by the end of 2009. In addition, provincial governments have recently shown interest in installing more automatic stations. The State of Karnataka has already set up about 650 telemetric rain gauges and the State of Tamil Nadu is in the process of establishing about 225 automatic weather stations. Until this additional infrastructure is installed, data gaps can be filled using WMO-prescribed methodologies under the supervision of a qualified national meteorologist. Moreover, it is important to incorporate accurate local historical weather data in the pricing models, as weather parameters tend to vary spatially. Better data information systems can make the product more affordable when product prices are based on a region’s specific weather risk. Stakeholder trust and understanding AIC recognized that weather insurance should present the right balance between technology and simplicity in order to have a product that is easily accessed by the various stakeholders involved. It found that even after four years of marketing its products, understanding was still low among farmers, provincial governments and programme managers. In some cases, farmers expected to receive a payout every time they experienced a small loss, and they were reluctant to repurchase coverage for the coming seasons in the absence of a payout. A consumer court recently upheld the plea of a complainant who asked for a payout on the basis of a government-owned weather station, disregarding the fact that it was not the reference weather station, which incidentally did not record a condition requiring payout. Additionally, provincial governments wrongly accused insurers of pocketing federal subsidies when payouts were not triggered, as any government subsidies for index insurance are paid ex ante, instead of ex post as with NAIS. Thus some provincial governments have not supported the premium subsidy for index insurance due to their belief that the subsidy would actually benefit insurers. Subsidies and government financial support During the pilot year of AIC’s WBCIS in 2007/08, 200 locations were served to insure 670,957 farmers. This positive response could be attributed to the subsidized premium, which averaged 65 per cent depending on the type of crop. At the same time, there is a concern that these subsidies might undermine farmers’ willingness to pay the full cost of coverage in the future.
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The Potential for Scale and Sustainability in Weather Index Insurance

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