Climate Risk Insurance Models from India

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Climate Risk Insurance Models from India Regional Dialogue on Climate Resilient Growth & Development Dhyanesh Bhatt 21 st Feb 2018

Agenda Crop insurance in India Guwahati city & Risk financing A case study What more can be done? 2

Agenda Crop insurance in India Guwahati city & Risk financing A case study What more can be done? 3

In India, agriculture remains a major economic activity & it is climate dependent Census data across years 107.5 144.3 74.6 31.5 55.5 27.3 69.9 99.6 92.5 110.7 127.6 118.6 FY 1951 FY 1961 FY 1981 FY 1991 FY 2001 FY 2011 Cultivators ( mn) Landless laborers (mn) ~ 263 mn people employed directly with agriculture ~ 600 mn including allied activities in agriculture directly linked with core agricultural activities * Source: Indian Census, 2001; HT article, TOI article Key challenges faced by Indian agriculture Large part of the Indian rural population directly connected with agriculture activities Most of these are small and marginal farmers Dependent on weather & challenge of irrigation Increasing trend of adverse & unpredictable weather Need of a strong & mainstream agriculture insurance programme 4

5 Pradhan Mantri Fasal Bima Yojana

Evolution of crop insurance in India has led to Pradhan Mantri Fasal Bima Yojana (PMFBY) National Agriculture Insurance Scheme (1999-00 - 2015-16) National Crop Insurance Programme (2013-14 -2015-16) Pradhan Mantri Fasal Bima Yojana (2016-17 onwards) Yield based product Claims beyond 95% to be borne by Govt AIC sole implementing agency MNAIS* (yield based) and WBCIS* (index based) All claims to be borne by insurer 11 players in the market Majorly yield based (PMFBY) and RWBCIS* (index based) All claims to be borne by insurer 18 players including 5 PSUs *MNAIS: Modified National Agriculture Insurance Scheme *WBCIS: Weather based Crop Insurance Scheme *RWBCIS: Restructured Weather based Crop Insurance Scheme more responsive to the impact of climate change on Indian agriculture 6

Key features of PMFBY Full Sum Insured Prevented sowing coverage Mid season adversity coverage Localized risk coverage Fixed insurance premium rates for farmers Use of technology envisaged Based on cost of cultivation If >75% of area is not sown, immediate relief If > 50% of sown area impacted, on account payment For individual losses reported to insurers ( hail ) Difference between actuarial rate & farmer share borne by government Increased usage of Remote Sensing Technology & digitization of various aspects In addition to an all risk cover for of yield drop have increased the scope of coverage under the scheme 7

PMFBY implementation mainstreams crop insurance in Indian context Crop Insurance Market Size of India (In ` Cr) 313% 22900 USA, 24% 4610 36% -11% 6247 5550 Others, 52% China, 17% India, 8% FY14 FY15 FY16 FY17 FY18* Global Market Share (FY17) Crop insurance market grew to USD 4 Bn ; 3 rd largest market after USA (USD 11.5 bn ) & China ( USD 8 bn ) 18 insurers participated in crop insurance in FY 17 Focused approach by government & insurers - ~ 57 mn farmers insured 8

ICICI Lombard PMFBY implementation in FY 17 HP HA MP FY 16-17 Geographical spread UP JH OD WB MG Presence: 10 states Farmers covered: ~ 4 mn Major crops covered: Paddy, Bajra, Soyabean, Groundnut, Cotton, Gram, Wheat, Mustard, etc AP TN 9

10 Parametric insurance models implemented by ICICI Lombard

ICICI Lombard Seed germination failure Concept A brand of Direct Sowing Rice (DSR) requires minimum 200 mm rainfall within 21 days of sowing to ensure germination Perils covered Protection against the risk of rainfall being 200 mm rainfall in 21 days post sowing If <200 mm, cost of seed is paid to farmer Key feature Floating start date in line with minimum 50 mm rainfall being recorded at weather station in the block ( minimizes basis risk ) Scale Covered ~ 10000 farmers across 6 districts in the state of Chhattisgarh 11

ICICI Lombard Insurance against Disease conducive weather Concept Perils covered Potato crop in West Bengal is prone to Late Blight if the atmospheric conditions constitute of low temperature and high Relative Humidity Coverage was provided against the occurrence of low temperature and high RH Key features Use of technology to onboard farmers since field enrollment had to be done Scale ~ 2000 acres of sown area covered under insurance 12

Agenda Crop insurance in India Guwahati city & Risk financing A case study What more can be done? 13

Guwahati City: Key parameters 27 districts Population of ~ 31 mn ( 2011 census) Area of ~ 78,500 sq kms Population density of ~ 400 / km sq ( 2011 census) Three physiographic domains Brahmaputra Valley Central Assam Hills Barak Valley Major rivers Brahmaputra Barak Key Statistics Disaster proneness Prone to multiple hazards EQ: Falls under seismic zone V, moderate to high intensity EQ High vulnerability due to population explosion and ill-maintained infrastructure Floods: High risk due to high rainfall in short time and overflow of river Bhramaputra and tributaries Soil erosion: in Azara and Chandarpur revenue circle Floods & EQ: major perils that need to be addressed 14

4 areas identified as potential risk financing / insurance focus areas Insurance for Homeowners Insurance for local authorities Risk Financing / Insurance focus areas Insurance for proposed infrastructure projects Insurance for current critical infrastructure of the city 15

Structuring Disaster Risk Financing Solutions through insurance - Key discussion points Insurability of disaster risk High frequency & High severity events need risk mitigation; risk transfer can be for residual risk High Severity, Low Frequency events insurance ideal Asymmetry information in Overall impact is available; granular data - a challenge Detailed information typically available with various departments & needs to be collated Clarity on use of insurance Can insurance be used as a disaster risk financing tool & are there any precedence for the same International examples; precedence in India? Sum insured estimation Challenges of determining the exposure currently Loss limit based insurance covers may be structured 16

Structuring Disaster Risk Financing Solutions through insurance - Key discussion points Pricing & Extent of coverage Customization is possible as required Pilot projects could be the way forward to test sustainability Continuity coverage of Multi year covers can be structured in conjunction with the requirements of the programme Speed of Claim settlement Can be as low as two weeks for parametric insurance covers Mode of claims payment Who can insurers settle the claim to? Can be settled to the government for onward distribution 17

Agenda Crop insurance in India Guwahati city & Risk financing A case study What more can be done? 18

Sovereign risk covers may be explored by the government for macro covers Trigger Earthquake > X in the defined area denoted by the box Trigger Wind speed > Y in the area defined by the box Trigger Rainfall volume > Z in pre-defined area eg. District Customization of peril as well as the triggers can be carried out 19

Meso / Micro cover can be provided for borrowers of financial institutions (FIs) Concept Perils covered Most FIs working with urban and rural poor provide income generation loans Paying capacity reduced in case of natural catastrophe Coverage can be provided against deficit rainfall (drought), excessive rainfall ( floods) and high wind speeds ( cyclone/tsunami) Key feature Parametric cover faster claims settlement Can be contributory / non-contributory Scale Coverage should be provided at state / district level depending upon the exposure of FIs 20

21 Thank you