Abstract
Index insurance is a relatively new approach for providing climate risk protection to low-income farmers in developing countries. Because this insurance is implemented in data-poor environments, information constraints and uncertainty substantially affect the products. Since insurance is a tool that can be used to exchange uncertainty in the market, the level of information available directly alters prices, with insurance protection for climate risk and insurance protection for information uncertainties about climate risks both being components of the final price. Using data, methodologies, and contracts for index insurance applications in Africa, the chapter presents this concrete component of the value of information by quantifying the value of improved data in lowering insurance prices. It provides a brief overview of index insurance in developing countries and discusses the value of remote sensing in informing the index and the role of climate trends.
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- 1.
For the Malawi premium calculation, the interest rate on the money held to be able to pay for the 99th percentile event was increased slightly to reflect administrative and delivery costs.
- 2.
For example, if a $10 premium is paid for a policy with a maximum liability of $100, the percentage price is 10 % for a contract that provides (full) payouts 10 % of the time (an expected payout of $10, or zero loading) as well as for a contract that provides (full) payouts 1 % of the time (an expected payout of $1, or a loading of 900 %).
- 3.
Reinsurance is purchased by insurance companies from global reinsurance companies, which handle very large events that would overwhelm an individual insurance company. Reinsurance companies address these risks through a global portfolio of varied insurance company clients and other investments.
- 4.
Following the pricing process, partners decided to use the largest payout year that would have occurred using the approximately 50 years of rainfall data to estimate the 98th percentile. This choice was made to make the explanation of the premium simpler and more transparent for early stages of the project; it did not meaningfully change the cash premiums paid by farmers.
- 5.
In addition to the importance of addressing purely statistical pricing issues, it is worthwhile to address physically based processes, such as climate change. This is discussed in Sect. 1.3.
- 6.
In 2006, 145 Kwacha was worth about $1, and typical maximum liabilities were approximately 4,000 Kwacha, depending on the specific input package insured.
- 7.
The simulation is set to generate the number of realizations that would most closely sum to 1,000 years of total years generated. This size was selected for feasibility of computation on a web server in a classroom environment.
- 8.
This is a nontrivial challenge, and the development of formal models is currently still in process.
- 9.
See the technical annex to Hellmuth et al. (2009) at http://iri.columbia.edu/publications/id=1008
- 10.
More modern satellites use additional information, but their coverage is limited and does not extend very far back in time.
- 11.
- 12.
There was one additional feature to these simple contracts. In order to assure that rainfall must be relatively uniform over the contract period, each ten day period had a cap, above which additional rainfall was not included in the total. In this way, a two month period of drought can still trigger the index payment, in spite of a single large rainfall event at the end of the contract period.
- 13.
Some of the 2009 data from the newly installed rain gauge were lost because of equipment failure.
- 14.
The views expressed here are the author’s alone and should not be attributed to the World Bank Group or its member countries.
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1. Commentary: Informational and Institutional Challenges to Providing Index Insurance for Farmers
1. Commentary: Informational and Institutional Challenges to Providing Index Insurance for Farmers
Footnote 14Managing weather-related risk has been a long-standing challenge in Africa. Poor farmers are especially vulnerable to unexpected weather-induced crop damages or failures because agricultural output plays such a large role in family consumption, and alternative income generation opportunities are limited. For crop insurance to be effective and affordable, the pool of insured farmers needs to be large and dispersed enough that weather conditions across participating farmers are not highly correlated. Because insurance is not a familiar product, however, initial reluctance to purchase it needs to be overcome, in particular by providing credible guarantees that payouts actually will occur once premiums are paid.
Adding to those challenges are the difficulties that are the focus of this chapter. Because decisions by individual insured farmers on protecting their crop yields are difficult to observe, any insurance contract based on measures of farmer-specific loss would be prone to misrepresentation, moral hazard (farmers would reduce their own protective measures), and adverse selection (those less capable of protecting themselves, and thus more costly to cover, would be more likely to buy the insurance). The chapter highlights how insurance coverage based on movement of a general index of weather conditions correlated with individual farm yields can provide reasonably effective coverage without these problems. The analysis is informed by several innovative, controlled field experiments in two African countries. The discussion of this method of analysis is itself an important contribution of the chapter.
A firm offering weather index based crop insurance still faces the challenges of assessing the risks to which its portfolio of policies is exposed, and pricing the insurance coverage accordingly so as to reduce to a minimal level the probability that large contemporaneous claims could exceed its financial reserve. It is in this context that the chapter explores how strategies to improve information about index insurance risks can have value for both the insurance company and its customers. Important findings of Osgood and Shirley include these:
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High uncertainties about payout probabilities can significantly increase index insurance cost. Such uncertainties are common in the context of drought risks, for example, given limited information and modeling available for predicting their occurrence. This presents a challenge for establishing financially sustainable premiums—low enough to be affordable yet actuarially sound.
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Simulation models for assessing risks are an important complement to limited observed data on droughts for assessing payout probabilities. It turns out that assessments based on past patterns alone can be very inaccurate and are not very sensitive to changes in information, since new information can only marginally alter the patterns implied by a historical data set. A modeling approach can be useful for exploring how future risks might be altered by climate change. Satellite-based information also can be very useful to improve confidence in probability estimates.
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Index insurance is not a substitute for reducing vulnerability. Drought index insurance addresses only one component of risk, so in the absence of effective insurance for other risks, such as storm or flood damage, complementary farm-level measures still will be needed for reducing vulnerability. Individual actions to reduce vulnerability to drought risks is a cost-effective complement to insurance coverage. An example is output diversification, including crops and livestock, so that planned allocations of land to different products can be modified based on predicted conditions. Here again, earth observation systems that provide better forecasts for an upcoming growing season, and more timely information about emerging threats, can be very valuable.
Osgood and Shirley are careful to note that even though improved information for assessing risks can make index insurance a better value and thus more easily marketed, it is not a sufficient condition for successful introduction of crop insurance. In light of the persistent difficulties encountered in establishing financially sustainable markets for this insurance, it may be useful to highlight some other important considerations that could even preclude the successful introduction of insurance in some circumstances.
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Constraints on liquidity limit the ability of farmers to purchase the insurance, even with modest premiums. This is an especially important consideration if farmers also have used microloans to help finance their current cultivation activity, in which case premiums to cover both farmer and lender may be considerably higher.
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Risk aversion toward using a novel product can decrease demand for insurance, even if improved probability assessments lower the cost. On the other hand, since index insurance is inherently only partial coverage, it is important that potential customers appreciate this. As illustrated by the field experiments underlying the analysis in the chapter, potential customers may require considerable information and education to evaluate the potential advantages of insurance.
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The prospect of climate change inherently reintroduces “noisy priors” for how crop risks may evolve over time, given the degree of quantitative uncertainty about climate change impacts. If crop loss insurance comes into greater use to reduce impacts of short-term climate variability, what adaptive measures by farmers are needed to reduce vulnerability to effects of climate change over the longer term?
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Public policies can weaken the development of an effective insurance market in several ways. For example, to what extent would expectations that the government will continue to provide disaster assistance reinject moral hazard into the insurance system? Prospective purchasers also will be concerned about the strength of policies to ensure the creditworthiness of the insurers, a common concern in the financial sector of many developing countries. Ultimately, policymakers need to consider what portfolios of risk mitigation policies can have the greatest impact for a given resource cost. In addition to improved information about risks, such measures could include reducing institutional barriers to accessing insurance, and supporting measures by farmers to reduce their own vulnerability—which will also provide collective benefit by lowering economy-wide risks.
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Osgood, D., Shirley, K.E. (2012). The Value of Information in Index Insurance for Farmers in Africa. In: Laxminarayan, R., Macauley, M. (eds) The Value of Information. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4839-2_1
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