MALAWI’S EXPERIENCE ON CLIMATE CHANGE INSURANCE SCHEME By Gray Munthali Meteorological Services P.O. Box 1808 Blantyre Tel: +265 1 822014 Fax: +265 1 822215 Email:[email protected] Website:www.metmalawi.com African Ministerial Conference on the Environment.
Download ReportTranscript MALAWI’S EXPERIENCE ON CLIMATE CHANGE INSURANCE SCHEME By Gray Munthali Meteorological Services P.O. Box 1808 Blantyre Tel: +265 1 822014 Fax: +265 1 822215 Email:[email protected] Website:www.metmalawi.com African Ministerial Conference on the Environment.
MALAWI’S EXPERIENCE ON CLIMATE CHANGE INSURANCE SCHEME By Gray Munthali Meteorological Services P.O. Box 1808 Blantyre Tel: +265 1 822014 Fax: +265 1 822215 Email:[email protected] Website:www.metmalawi.com African Ministerial Conference on the Environment (AMCEN), Nairobi, Kenya 25 – 29 May 2009 Purpose of the Insurance Scheme The purpose of this Insurance scheme is to use an index-based weather derivative contract to transfer the financial risk of severe and catastrophic national drought that adversely impacts the Government’s budget to the international risk markets. It will also provide micro weather insurance for farmers and agribusinesses in Malawi WEATHER AND CLIMATE CHANGE RISK SECTORS INCLUDE: Agriculture Water Resources Health Energy Infrastructure Social Protection Livestock Tourism Construction Industry Gender Weather Risk Management What is it? • Financial protection against adverse weather conditions that result in losses or additional costs • Contracts can be structured as insurance or derivatives • Based on the performance of a specified weather index • Payouts are made if the index crosses a specified threshold at the end of the contract period Objective and timely Malawi context at the national level: • GoM is concerned about the impact of rainfall on maize production • Can provide payouts in the event of contractually specified shortfalls in rainfall during growing season • Essentially “budget insurance” for GoM • Timely access to cash in times of crisis, reducing reliance on international appeals Prerequisites for a Risk Transfer Program An index that captures national drought risk in Malawi • Government’s Maize Yield Assessment Model • Rainfall-based FAO model used since 1992 to forecast maize production High quality historical weather data and reliable real-time communication is key for risk transfer • Malawi Met Office data reliable: 23 stations with over 40 years, few gaps • Can provide real-time data required by market Premium: • DFID supported initial premium cost in piloting phase starting 2008 International market wants to diversify and grow their portfolios, wants new risks Why Climate Change Insurance Scheme? In 2004, the National Smallholder Farmers Association of Malawi (NASFAM) wanted to expand its operations and grow the Malawi groundnut market domestically and for export • Greater output potential for farmers, profit; reliable yields; lower risk of disease; will receive training by NASFAM; access to high quality seed; export potential • Farmers needed financing to purchase quality seed from NASFAM Perceived high risks from drought and high loan default rates deterring financing institutions from providing loans • 2004/2005 drought led to recovery rates for lenders in the range 50-70% • Major government and donor lending program was discontinued • Two microfinance institutions stopped lending to agriculture, many reduced activities Pilot Details, 2005-2006 Loans to cover seed, insurance premium and interest: • Opportunity International Bank of Malawi • Malawi Rural Finance Corporation Program linked to larger NASFAM sensitization program on groundnut Policies: • Insurance Association of Malawi (seven companies pooled the risk) • Premium: 6-7%, Max Payout per farmer: Loan Size given by bank Seed & Product Distributor: • NASFAM: Groundnut in 2005, Groundnut & Hybrid Maize in 2006 Participants: • Farmers all members of NASFAM clubs • 2005: 900 farmers, 4 weather stations, sum insured $35,000 • 2006: 1710 farmers, 5 weather stations, sum insured $110,000 Insurance Payout Payment details: • Payout: channeled from insurance company directly to the bank; • No Payout: farmers benefit from selling the higher value production Pilot Results Major Achievements: • Unlocking credit facilities for smallholder farmers • Access to high yielding seeds and fertilizers • Generated high-level of interest from banking sector But… • program discontinued in 2007 – Groundnuts market prone to side-selling, leading to nonweather related defaults – Emerging agricultural supply chain with many problems greater than weather – Banks stopped lending to groundnuts in 2007, so no need for insurance Other lessons learned: – Stand alone product had no takers – Premiums will always have to be pre-financed through loans – Distributor channel operational capacity critical …Malawi: 2007 Onwards Focus on established agricultural supply chains, e.g. tobacco • 70% of current loan portfolios, has a maize loan component • Economies of scale and critical diversification for insurers • Tie-in with emerging contract farming relationships in Malawi Since 2007: • Working with 3 banks and 2 contract farming companies, more interested 2600 farmers insured in 2008, portfolio size of $3 million Currently limited expansion due to lack of local weather stations Access to reinsurance market since 2007 • Working at farmer and risk-aggregator (bank) level • Developing off-the-shelf products for other to support emerging supply chains and contract farming relationships Cotton, tea, soybeans, paprika, other upcoming crops in need of finance Banks and agri-businesses see this as a product for both themselves and farmers Who is the client and why? 1. FARMERS Gives farmers the ability to mitigate drought risk • Secure access to finance and inputs for improved production • Possibly improve long-term production and revenues 2. AGRIBUSINESSES Weather risk management can also serve to enhance the efficiency of agricultural supply chains • Increased use of ag technology and inputs • New product offerings and services 3. BANKS Protects both producer and loan provider from weather-related production risks • Allowing banks to expand their lending portfolios in a managed way • Managing weather risk can influence outreach, quantity, and cost of lending Helps financial institutions in determining their exposure to weather risk– adding value even without insurance 4. INSURERS Gives insurers the opportunity to re-enter rural markets • Agricultural markets are relatively untapped STATION NETWORK PLANNING LIST NO STATION ADD STATUS OTHER AUTOGAUGE STATIONS REMARKS 1 CHITIPA Karonga Full Automatic Misuku Nthalire 2 KARONGA (AIRPORT) Karonga Full Automatic Vinthukutu Lupembe Baka Baka is under Agricultural Research (AR) 3 RUMPHI Mzuzu Full Automatic Bolero Bwengu Livingstonia Nchenachena Nchenachena and Bolero are under Agricultural Research 4 MZIMBA Mzuzu Full Automatic Zombwe Chikangawa Mbalachanda Emfeni Euthini Embangweni Mbawa Mbawa is under Agricultural Research 5 MZUZU (AIRPORT) Mzuzu Full Automatic Kavuzi Ekwendeni 6 LUNYANGWA Mzuzu Full Automatic 7 NKHATA BAY Mzuzu Full Automatic Under Agricultural Research Chintheche, Mkondezi Mkondezi is under AR NO STATION ADD STATUS 8 LIKOMA ISLAND Mzuzu Full Automatic 9 KASUNGU Kasungu Full Automatic Mwimba KFCTA 10 DOWA Kasungu Full Automatic Madisi 11 OTHER AUTO-GAUGE STATIONS REMARKS Mponela 12 NTCHISI Kasungu Full Automatic Malomo 13 MCHINJI (TEMBWE) Kasungu Full Automatic Mchinji Boma 14 NKHOTAKOTA Salima Full Automatic Dwangwa 15 SALIMA Salima Full Automatic Chitala Lifuwu 16 LILONGWE (KIA) Lilongwe Full Automatic Kasiya, Nathenje KIA already automatic Kamuzu Dam Dzalanyama, Capital Hill Tembwe fully operational and was supported by World Bank Both auto-gauge stations are under Agricultural Research NO STATION ADD STATUS OTHER AUTOGAUGE STATIONS REMARKS 17 CHITEDZE Lilongwe Full Automatic 18 DEDZA Lilongwe Full Automatic Mtakataka Thiwi 19 NTCHEU Lilongwe Full Automatic Bwanje Mlangeni 20 BALAKA Machinga Full Automatic Toleza Phalula 21 MACHINGA Machinga Full Automatic Chikweo Ntaja Liwonde 22 MANGOCHI Machinga Full Automatic Monkey Bay Namiasi Namwera 23 ZOMBA Machinga Full Automatic Makoka Chingale Domasi Makoka under Agricultural Research 24 NENO Blantyre Full Automatic Tsangano Tsangano is under Agricultural Research 25 MWANZA Blantyre Full Automatic 26 BLANTYRE (CHICHIRI) Blantyre Full Automatic Under Agricultural Research Mpemba Balaka fully operational and was supported by World Bank NO STATION ADD STATUS OTHER AUTOGAUGE STATIONS 27 BLANTYRE (CHILEKA AIRPORT) Blantyre Full Automatic 28 CHIRADZULU Blantyre Full Automatic Mombezi 29 THYOLO Blantyre Full Automatic Luchenza 30 THYOLO (BVUMBWE) 31 PHALOMBE Blantyre Full Automatic Naminjiwa Migowi 32 MULANJE Blantyre Full Automatic Thuchila Mimosa Masambanjati 33 CHIKWAWA Shire Valley Full Automatic Ngabu Kasinthula 34 NSANJE Shire Valley Full Automatic Makhanga REMARKS Already automatic Full Automatic Under Agricultural Research Both auto-gauge stations under Agricultural Research COMMUNICATION CHALLENGES Communication link at national and regional levels not satisfactory Idea is to migrate from old costly communication technology to relatively low cost technologies Observed that number of stations reporting on regular basis has dropped In 2006/07 for example no data was received from Misuku, Mbalachanda, Embangweni in the North; Dzalanyama, Kasiya, Sinyala, Thiwi in the Centre; Masambanjati, Mwanza, Chikwawa, Phalula and Luchenza in the South. Global Satellite Mobile (GSM) communication and internet are promising options. PERCENTILE MAPS FOR 1948/49 SEASON -9 -9 -9 -10 -10 -10 -9 -9 -10 -10 -11 -11 -12 -12 -13 -13 PERCENTILES -11 -11 Above 30 -11 20 - 30 -12 -12 10 - 20 -12 05 - 10 -13 -13 -13 -14 -14 -14 -14 -14 -15 -15 -15 -15 -15 -16 -16 -16 -16 -16 NOVEMBER 1948 -17 33 34 DECEMBER 1948 -17 35 36 33 34 Below 5 JANUARY 1949 -17 35 36 33 34 -17 35 36 MARCH 1949 FEBRUARY 1949 33 34 -17 35 36 33 34 35 36 PERCENTILE MAPS FOR 1991/92 SEASON -9 -9 -9 -9 -9 -10 -10 -10 -10 -10 -11 -11 -12 -12 -13 -13 -14 -14 -14 -15 -15 -16 -16 PERCENTILES -11 -11 -11 -12 -12 -12 -13 -13 -13 -14 -14 -15 -15 -16 -16 Above 30 20 -30 10 - 30 05 - 10 NOVEMBER 1991 DECEMBER 1991 -17 -17 -17 33 34 35 36 Below 5 JANUARY 1992 -17 33 33 34 35 36 34 35 36 -15 -16 FEBRUARY 1992 MARCH 1992 -17 33 34 35 36 33 34 35 36