Upload
tyrone-garrett
View
213
Download
0
Tags:
Embed Size (px)
Citation preview
1
Applications to Agriculture and Other Sectors: The Role of Weather Index Insurance
Dr. Jerry SkeesH.B. Price Professor, University of Kentucky, andPresident, GlobalAgRisk, Inc.
US CLIVAR/NCAR ASP Researcher ColloquiumStatistical Assessment of Extreme Weather Phenomena under
Climate ChangeNCAR Foothills Lab, Boulder, Colorado, USA
June 13-17, 2011
Special Thanks to GlobalAgRisk Team!
2
GlobalAgRisk, Inc.
Activities Research and development tied
to University of Kentucky research program
Technical capacity building Educational outreach
Supported by Multinational donors Governments Nongovernment organizations
MissionImprove access to financial services and the value chain for the rural poor through innovative approaches for transferring weather risk Select Country Work
Peru – El Niño/Flood Mongolia – Livestock Vietnam –
Flood/Drought Indonesia –
Earthquakes Mali – Drought Morocco – Drought Mexico – Drought Romania – Drought Ethiopia – Drought
Current Support for GlobalAgRisk, Inc.
Bill and Melinda Gates Foundation (Peru)
Ford Foundation (Vietnam and Indonesia)
Gov’t of Mongolia via Swiss Trust Fund
UNDP (Peru)GiZ (Peru)Risk Management Agency of USDA
Actuary and Underwriting Reviews3
State of Knowledge Reports from GlobalAgRisk
Supported by Bill and Melinda Gates Foundation Innovation in Catastrophic Weather Insurance to Improve the Livelihoods of Rural Households
March 2010 “Data Requirements for the Design of Weather
Index Insurance.” March 2011
“Market Development for Weather Index Insurance Key Considerations for Sustainability and Scale Up.”
Under Revision: “Legal Considerations for Index Insurance”
Forthcoming a book
4
Somebody Always Pays for Catastrophic Risk Who? How?
The poor pay through direct losses and long-term economic impacts
Financial institutions restrict services as they learn that the correlated losses of many of their borrowers and savers create significant banking problems
Governments — Disaster relief and recovery expenses, infrastructure investments, subsidized agricultural insurance
Donors forgive debt and divert funds for recovery
Society needs to understand the cost of natural disaster risk
Someone always pays
Need incentives for proper risk management and mitigation5
Designing Sustainable and Scalable Weather Index Insurance Programs Is Challenging
Products must be developed in contextCostly (technical support, capacity building, R&D)Not easily replicable
Basis riskTradeoff between transaction costs and basis risk
Limited or no data to develop productsHigh delivery costsSmall transactions/Small market volumeNascent legal and regulatory systems
6
Lessons LearnedRisk Assessment
Understanding vulnerability necessitates examining a specific context:The risk, geography, demographics, risk
management strategies, government policies, etc.
Risk assessment requires a systems approach
Proper risk assessment benefits decision makers and can guide policy decisions
Cognitive failure – decision makers often underestimate extreme risk of natural disasters
7
AdaptationMultilateral Organizations Call for Insurance
Decision makers suggest insurance can play an important role in adaptation to climate changeArticle 4.8 of the United Nations Framework
Convention on Climate Change (UNFCCC) Article 3.14 of the Kyoto ProtocolThe Hyogo Framework
Agricultural insurance, in particular, gains increased attention because agriculture is the primary livelihood of households, especially the poor, in developing countries
8
Insurance and AdaptationInsurers assess specific risksInsurers price the risk – a critical aspect of risk assessmentInsurance can encourage adaptation through
pricing – if insurance costs that much, maybe I should do something different!
Access and price of insurance can be directly linked to adaptation strategiesFire insurance: extinguishers in the house? Proximity to a fire hydrant Improved building codes
Insurance reduces variability in wealth/revenues
9
Pricing the Risk“Fairness” and Perverse Incentives
If insurance is not priced to reflect differences in
relative risk (or if more subsidies are paid to higher
risk areas), perverse incentives are likely to follow
– if you pay people to take risk, they will take more
risk
The result will be more risk exposure rather than
lessHigher risk activitiesHigher risk areasLess investment in risk reduction
10
In the U.S. Subsidies Have Been Used to Lower Loss Ratios
Coverage Level
1980 Act 1994 Act2000
ARPA
55 30.0 46.1 64.0
65 30.0 41.7 59.0
75 16.9 23.5 55.0
85 --- 13.0 38.0
11
Increased Risk-Taking
As a result of purchasing insurance, farmers tend to engage in riskier behavior (plant higher risk crops, monoculture, less irrigation, etc.)
Difficult for insurer to monitor these changes in behavior
Increased risk-taking increases loss ratiosHigh loss ratios lead to higher premium ratesHigher premium rates reduce insurance
purchasingHigher premium subsidies are then needed to
maintain or increase insurance purchasing
12
U.S. Agricultural Policy Has Also Favored More Risk Areas
If you pay people to take on more risk; they will take on more risk!
Program yields for commodity payments are based on planted acre yields (not harvested acre yields); favored areas with large abandoned acres
Ad hoc disaster payments (long history)
Highly subsidized crop insurance
Premium subsidizes are a percent of premium: Once again favoring high risk areas
13
Premium Subsidy per Dollar of Liability2002
14
Loss Ratios Vary Across the Country1981–2002
Change in Crop Share
Legend
Highest Loss of Crop Share
Moderate Loss of Srop Share
Small Loss or Gain of Crop Share
Moderate Gain of Crop Share
Highest Gain of Crop Share
Missing Data 16
Farmer Knowledge and Decision Processes
Literature shows Farmers optimizeFarmers adaptFarmers are good Bayesians Farmers know central tendency on yieldsCognitive failure sets in for catastrophic
eventsChallenge for adaptation
Communicating information about climate change in a fashion that is useful for decision makers
17
Lessons LearnedPricing the Risk
Pricing risk is an important aspect of insuranceInsurance can encourage households to
reduce risksGovernment policies can motivate households
to take more risk at government’s expenseAgricultural insurance is extremely complex
Risk classification is difficultRisk varies by commodityRisk varies by regionRisk varies by producer
Difficult to monitor farmer behaviorAdministrative cost is very high
18
19
Weather Index Insurance: Gaining Popularity in Developing Countries
Payouts are based on a measurement of an event correlated with losses Weather event (e.g., rainfall, temperature, or river levels
associated with yields)
Insurance pays when index crosses a certain threshold
Payouts do not require inspection of client lossesEliminates most information asymmetry problemsTransfers extreme risk to reinsurers
Example of an Excess Rainfall Insurance Product
Extreme rainfall in India – payments would occur anytime rainfall exceeds 2000 mm
Household might buy US$100 in liability
20
500 1000 2000 3000 40000
Payout Structure for Example Excess Rainfall Contract
$1 for every 10 mm excess of 2000 mm$100 limit at 3000 mm
21
Pricing Index Insurance
Price of Index Insurance =
22
Index Insurance Creates Flexibility for Different Users
HouseholdsIndia: Drought coverage to groundnut farmers Malawi: Cooperative links insurance to farmer
loans for high-yield seed varietiesMongolia: Index-based livestock insurance
Firms (e.g., Banks/input suppliers)Increase access to credit and other servicesVietnam and Peru: Possible use of index
insurance for protecting credit riskGovernments/Donors
Mexico: State governments provide quick drought relief to farmers in need using a drought index
23
Sahel: Shifts in the Central Tendency
24
SahelSemi-arid region below the Sahara
Sahel data 1900 – 2007
• Dynamic climate largely due to oceanic oscillations
• Unlike the Sahel, climate change may lead to more permanent changes
Sahel: Hypothetical Insurance ProductInsurer entry point at 1962, 1990, and 2007 to develop a drought insurance
Insurer would re-center data arounda current forecast of the central tendency
25
Insurance contract for wheat farmersIndemnities when rain is below 425 mm
Central Tendency: 510 mm Payout Threshold: 425 mm Pure Risk: 2%
Sahel: Rainfall Distribution Using Data from 1900 to 1961 (Reference Point – 1962)
Pure risk will likely result in insurance affordable to
households
26
Central tendency is below payout thresholdInsurance is inappropriate in this setting
Sahel: Rainfall Distribution Using Data from 1962 to 1989 (Reference Point – 1990)
Pure risk would make insurance unaffordable for
households
Central Tendency: 328 mm Payout Threshold: 425 mm Pure Risk: 44%
27
Increases in rainfall reduced the weather riskInsurance would be affordable depending on other costs
(Delivery, ambiguity loading, etc.)
Sahel: Rainfall Distribution Using Data from 1990 to 2006 (Reference Point – 2007)
Pure risk may result in affordable insurance for
households
Central Tendency: 456 mm Payout Threshold: 425 mm Pure Risk: 6%
28
Climate Change Increases Ambiguity and Catastrophe Loads
Misestimating the central tendency is very costlyGreen distribution Insurer forecast in 1962Blue distribution Forecasted loss experience based on 1990 Reference
29
Adaptation, Policy Interventions, and Climate Change
Households must adapt or experience increasing farm losses1.Change farming practices2. Invest in infrastructure3.Transition out of farming
Insurance, by itself, is not a means of adaptationPolicy interventions should assess the opportunity
costs when supporting insurance programs – other adaptation investments may be more important
Insurance can be used to facilitate adaptation (e.g., linking insurance, credit, and improved seed varieties
in Malawi)
30
Premium Subsidies Will likely Slow Adaptation
Significant caution is needed for insurance premium subsidies
If climate change shifts the central tendency, premium subsidies could slow adaptation
Households account for subsidies when comparing profits from farming and other activities
These subsidies can encourage farmers to take more risk and delay adapting to climate change
31
How to Improve Access to Catastrophic Weather Insurance? Our Experience Suggests . . .Index insurance is best suited for catastrophic and
consequential lossesIndex insurance that addresses weather risk of
firms that serve the poor (risk aggregators) presents a feasible avenue for market growth; build a sustainable market first and then move to micro products
Household products must find innovative delivery mechanisms to improve product affordability and offer value to clients (insurance-linked products)
Solutions that involve public-private partnerships must clearly delineate the role for markets and the role for governmentUnderstanding cognitive failure for extreme risk
can helpRisk layering – Putting catastrophic insurance into
a broader conceptual framework33
Risk Aggregator Products Are Less Costly to Develop and Implement than Household Products
Risk aggregator products face lower basis riskRisk aggregators effectively diversify much of the
idiosyncratic risks born by their clients
Data constraints are less binding for risk aggregator products
It is more cost effective for the insurer to establish a partnership with a risk aggregator than to market and distribute products to small holders
Risk aggregators are more likely to understand hedging and basis risk
34
El Niño Insurance for FloodInnovation in Northern Peru
35
Piura and other areas in the NorthSeverely affected by 1998 El Niño
Extreme rains (Jan – Apr 1998) 40x normal rainfall
Severe floods 41x normal river volume
Widespread losses Many disrupted markets Agricultural production, ↓ 1/3 Public infrastructure losses Cash-flow, debt repayment
problems Health problems
Total losses in Piura estimated at USD 200 million
Contract is Written Using NOAA Data
Nino estimates are derived from satellite data, observations of buoys and readings of the temperature on the surface and at deeper levels.
The data are publicly available monthly from NOAA (The U.S. National Oceanic and Atmospheric Administration)
http://www.cdc.noaa.gov/Correlation/nina1.data
38
Strong El Niño in 1982-83 and 1997-98
21
22
23
24
25
26
27
19
79
19
80
19
81
19
82
19
83
19
84
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
ENSO 1.2 Nov and Dec
0
200
400
600
800
1000
1200
1400
1600
1800
2000
19
79
19
80
19
81
19
82
19
83
19
84
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
Precipitaciones Aeropuerta Piura
2 extreme events in the last 32 years
37
31
Correlation Matrix for top 10 percentile El Niños
40
Correlation MatrixSouth North ENSO1.2ENSO3
South 100% 90% 92% 89%North 100% 90% 90%ENSO1.2 100% 100%ENSO3 100%
Example of a Payout from the 1997 Event- Nino 1.2 (Nov-Dec) temperature = 26.28°C
The insured selects the sum insured
Minimum payment = 5%
Sum insured = 10,000,000 Soles
1998 payment = 76% x 10,000,000 = 760,000 Soles
Primary Goal:Improve Access to and Terms of LoansCapacity building with
Financial institutionsPeruvian banking regulatorPeruvian credit rating agenciesSources of social capital flows into Peruvian Institutions
Case to be made1)Strengthen the resiliency of the financial
institution 2)Financial institution can be ready to lend when
the community needs capital the most – post disaster
42
Risk Aggregator StrategyNatural Disaster Effects on BankingLoan portfolio — Systemic repayment problems
for borrowers, problems can remain for yearsDeposits — Depositors withdraw fundsCosts increase — Costs of funds (e.g., Interbank
loans), administrative costsResulting problems
LiquidityProfitabilityCapital Adequacy
Lending institutions have many ways of managing these risks (e.g., Provisions, restructuring loans, etc.)
43
1997–1998 El Niño Spike and Recovery
10% Spike 3.5-Year
Recovery
With this event every 1 in 15 years, 300 basis points must be added
PROBLEM
LOANS
44
45 45
Default Risk Significantly Affects Interest Rates!
π – Expected profitsp – Exogenous probability of non-
defaulti – Interest rater – Lender’s opportunity costsL – Amount of funds loaned
LrLip 11 11 p
ri
Example (No default risk)
r = 10%p = 100%
Example (10% default risk)
r = 10%p = 90%
El Nino may add 300 basis points to interest rates
Historical Pattern of Agricultural Lending in Piura1994–2006
0%
2%
4%
6%
8%
10%
12%
14%
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Year
Per
cen
t o
f L
oan
s to
Ag
ricu
ltu
re
El Niño
Lenders say they have“fixed the problem” by not making
loans when they see El Niño coming
46
El Niño Reduces Capital Adequacy and the Ability to Leverage and Make Profits
47
Comparison of Sum Insureds using Monte Carlo
48
% is the sum insured corresponding to a percent of credit portfolioe.g., Sum insured = 5% of credit portfolio
Risk Assessment Includes Evaluating Current Risk Management Strategies
Potential strategies for managing these risks and their costs
Liquidity Hold higher portion of assets in cashEffect — Reduces investment in productive assets
Profitability Avoid exposed regions and sectorsEffect — Limits growth opportunities, especially for
untapped marketsCapital adequacy Leverage a lower amount of
equity to provide a “cushion” for the riskEffect — Limits growth
49
50 50
Mongolia — Massive Deaths of Animals
Mongolia: 45 million animals at the start of 2010Sheep, goats, cattle and yak, horses, camelValue of animals = US $2 BillionSome 11 million animals were lost in 2001–2002
due to dzud (harsh winter weather).. 9.7 million were lost in early 2010!
Animal husbandry in Mongolia is 20+% of the GDP and over 85% of all agriculture
Census is done every year — Mortality data are available by soum (county) from 1970 onwards
51
Index-based Livestock Insurance — Risk Layering A New Model for Public-Private Partnerships
GCC — Social Insurance
A layer of very infrequent risk where decision makers may have a cognitive failure problem
LRI — Commercial Insurance
Offered by private companies with reinsurance from government and now a global reinsurer
Government Catastrophe Cover (GCC)
Paid by governmentusing a World Bank
contingent loan
Livestock RiskInsurance (LRI)
Retained by Herders
6% mortality
30% mortality
100% mortality
If the government can’t continue to pay for extreme losses, the
commercial layer can continue
52
Climate Drivers Matter: Arctic Oscillation
-5
-4
-3
-2
-1
0
1
2
3
1899 1909 1919 1929 1939 1949 1959 1969 1979 1989 1999 2009
1944-45 33 percent
2009-201022 percent mortality - Feb value = -4.2
Underwriting MattersIndex insurance can indeed address many
of the adverse selection and moral hazard problems associated with traditional forms of insurance
However, sitting sales closing dates still maters and the insured can adversely select using weather forecast information!
54
Scale MattersNone of our efforts will succeed unless we
design products that capture the attention of the regulator and the market from the outset
Start with biggest risk targeted to risk aggregators Rural lendersValue ChainFarmer Associations
Carefully move to micro products with concept of livelihoods insurance for consequential losses suffered by small households; challenges will remain for demand and delivery
55
Visit our Website for Papers, Projects and More
www.globalagrisk.com
56