This paper was prepared to complement a presentation made by Jerry Skees at a microfinance conference in Santa Cruz, Bolivia on May 11, 2004. Some sections of this paper also appear in a more detailed paper by Skees presented at the USAID conference entitled “Paving the Way Forward”, June 2003. The ideas are copyrighted by Jerry Skees. Please email your comments to mailto:jskees@globalagrisk.com Anne Goes and Celeste Sullivan provided valuable assistance in drafting and editing this paper.
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This paper reviews an important innovation that provides unique opportunities for microfinance entities (MFEs) to manage correlated risk and expand their ability to assist rural households. The use of index insurance contracts to shift correlated natural disaster risk into global markets offers some promise for MFEs. Index-based insurance involves making insurance payments based upon an independent measure that is highly correlated with losses. When insurance payments are made based upon an independent measure of losses, the need for monitoring individual behavior is significantly reduced as compared to traditional approaches to insurance. Linking index insurance to microfinance activities could facilitate improved risk taking behavior and expansion of services for MFEs.
Rural financial markets in emerging and developing economies face numerous challenges. Managing and coping with risks are among the most significant challenges. Effective financial markets should include both banking and insurance markets. Banking allows for ex post borrowing to smooth disruptions in consumption that result from unexpected shocks (risk) that beset a rural household. Insurance allows for ex ante indemnity payments for well-specified risk events that also disrupt consumption.
Financial markets are largely about pooling risk. In banking, users have the opportunity to save and borrow. Pooling savings allows banks to loan to individuals who need funds most urgently. When a household needs to borrow funds they must pay interest. With insurance, rather than having a group of investors, a firm collects premiums from many individuals so that unfortunate individuals in the group can be paid when bad luck besets them. In either case, if everyone has bad luck and needs funds at the same time, the insurance solution cannot work. Thus, to the extent that rural financial markets are capable of pooling risk, the risks that are pooled must be independent (i.e., the groups participating cannot have bad luck at the same time). A major advantage of microfinance entities and other forms of collective action has been the ability to pool risk. However, correlated risk can not be pooled. Small and geographically concentrated MFEs are simply not capable of pooling and managing correlated risk on their own.
Agriculture remains a dominant activity in many rural economies of the poorest nations in the world. A large majority of the poorest households in the world are directly linked to agriculture in some fashion. Risks in agriculture are most certainly not independent in nature. When one household suffers bad fortune it is likely that many are suffering. These common risks are referred to as correlated risk. When agricultural commodity prices decline everyone faces a lower price. When there is a natural disaster that destroys either crops or livestock, many suffer. Insurance markets are sorely lacking in most developing and emerging economies, and rarely do local insurance markets emerge to address correlated risk problems.
If a group of individuals working within a MFE purchase either price insurance (via put options) or yield insurance (via index insurance with indemnity payments based upon extreme weather events), there are opportunities to mitigate the basis risk. The collective group could form a mutual insurance company or they could be involved in formal or informal lending to members of the group. As will be developed below, the use of innovations presented in this paper could clear the way for blending mutual banking and financial innovations at a local level (Mahul, 2002). The focus of the conceptual model presented here will be on localized rural finance (both formal and informal). Still, the risk management instruments that are introduced have a wider application: They can be used by larger MFEs as well as by individual households.[1]
Linking the use of risk-shifting innovations that are being tried around the world directly to rural finance has been largely missing. This paper builds a set of recommendations for using index insurance and, in some cases, futures markets in combination with rural finance. The intent is for the MFE to have the opportunity to purchase index insurance and put options to protect against the correlated risk of crop disaster, livestock deaths due to natural disasters, and commodity price declines. The indemnity payments can be used by the small local banking interest to 1) protect against credit defaults that follow a risk event; 2) facilitate a form of mutual insurance, and 3) offer lower interest rates after the risk event.
Index-based insurance products are an alternative form of insurance that make payments based not on measures of farm yields, but rather on either area yields or some objective weather event such as temperature or rainfall. Index insurance products are similar to several other innovations in global financial markets (e.g. use of CAT Bonds for natural disaster risk; emergence of weather markets; etc.). The major motivation for using index based insurance products rather than traditional agricultural insurance is well documented (Skees, Hazell, and Miranda).
In some situations, index insurance offers superior risk protection when compared to traditional multiple-peril crop insurance that pays indemnities based on individual farm yields. This happens when the provider of traditional insurance must impose large deductibles. A deductible basically means that the insurance policy will not pay until the loss is very serious. Deductibles and co-payments (or partial payment for losses) are commonly used to combat adverse selection and moral hazard problems. Since these problems are not present with index insurance, there is less need for deductibles and co-payments.
Index insurance provides an effective policy alternative as it seeks to protect the agricultural production sector from widespread, positively correlated, crop-yield losses (e.g., drought). When index insurance is used to shift the risk of widespread crop losses to financial and reinsurance markets, the residual idiosyncratic risk often has characteristics that make it more likely that rural banks can work to smooth consumption shortfalls with loans.
Two types of index insurance products are considered; those that are based on area yields where the area is some unit of geographical aggregation larger than the farm, and those that are based on weather events. An area-based yield contract has been offered in the United States since 1993. This policy was developed by the author and is named the Group Risk Plan (GRP). There are numerous ways to calculate payments on index contracts (Skees, 2000).
Expected county yields are estimated using up to 45 years of historical county yield data. For GRP, liability is calculated as where Expected County Revenue per Acre in the equation above is equal to the product of the official estimate of price and expected county yield per acre and Scale is chosen by the policyholder but is limited to between 90 and 150 percent.[2]
To be clear, an example of how the Group Risk Plan works is in order. Estimates of the county yield are made using forecasting procedures that account for trends in yields due to technology. If the corn yield forecast for the county yield is 100 bushels, the farmer can obtain a contract that will pay any time the actual estimate of the county yield is below 90 bushels (the trigger= 90 bushels). Assume that the expected price on corn is $2.00 per bushel. The farmer can purchase a liability that is equal to 150 percent of the product of the expected county yield and the expected price, times their acres planted. The calculations for a farmer with 100 acres follow:
Liability = 100 x $2 x 1.5 x 100; or $30,000
If the farmer has a yield average that is above the county they have incentives to purchase the maximum protection or liability by using the maximum scale factor of 1.5. For a farmer who purchases a 90 percent coverage level, indemnity payments will be calculated by multiplying the percent shortfall in county yields times the $30,000 of liability. Thus, if the realized estimate of county yields for the year is 60 bushel (which is 1/3 below the 90 bushel trigger) the indemnity payment calculation is
Indemnity = (90 -60) / 90 * $30,000; or $10,000
Premium payments are based upon premium rates. Thus, if the rate is 5 percent for the 90 percent coverage level policy, the calculations for the premium would be
Premium = .05 x $30,000; or $1,500.
Of course, one could easily adapt this contract design to any number of other indexes such as aggregate rainfall measured over a stated period at a specific weather station or the number of days with temperatures above or below a specified level. The contract design used in GRP is sometimes called a “proportional contract” because the loss is measured as a percentage of the trigger. Proportional contracts contain an interesting feature called a “disappearing deductible.” As the realized index approaches zero, the indemnity approaches 100 percent of liability, regardless of the coverage chosen.
The weather markets developed contracts that look very much like what Martin et al. (2001) proposed. They use unique language that is very similar to that used in futures markets. For example, rather than referring to the threshold where payments will begin as a ”trigger,” they refer to it as the ”strike.” In an attempt to make things more straightforward, they also pay in increments or what is referred to as ‘ticks’ in the exchange market world. Consider a situation where a contract is being written to protect against shortfall in rain. The writer of that contract may choose to make a fixed payment for every 1 mm of rainfall below the strike/trigger. If an individual or a MFE purchase a contract where the strike/trigger is 100 mm of rain and the limit is 50 mm, the amount of payment for each tick would be a function of how much liability was purchased. There are 50 ticks between the 100 mm and the limit of 50 mm. Thus, if $50,000 of insurance were purchased, the payment for each 1 mm below 100 mm would be equal to
$50,000/(100-50) or $1,000
Once the tick and the payment for each tick are known, the indemnity payments are easy to calculate. For example, if the rainfall is measured at 90 mm, there are 10 ticks of payment at $1,000 each; the indemnity payment will equal $10,000. Figure 1 maps the payout structure for a hypothetical $50,000 rainfall contract with a strike of 100 mm and a limit of 50 mm.
Experience with Index Insurance
Various area-yield insurance products have been offered in Quebec, Canada, Sweden, India, and, since 1993, in the United States (Miranda, 1991; Mishra, 1997; Skees, Black, and Barnett, 1997). Ontario, Canada currently offers an index insurance instrument based on rainfall. The Canadians are also experimenting with other index insurance plans. Alberta corn growers can use a temperature-based index to insure against yield losses in corn. Alberta is also using an index, based on satellite imagery to insure against pasture losses. Mexico is the first non-developed country to enter into a reinsurance arrangement that was based on weather derivatives.
In the United States, participation in the area-yield based Group Risk Plan has been relatively low. Nonetheless, in 2003, over 18 million acres were insured under GRP or the GRIP (Group Revenue Insurance Program). Participation is strongest is some markets where sales agents have focused on GRP. The loss experience (indemnities divided by premiums) since the introduction of GRP has been good, around 90 percent.
The Ontario rainfall insurance product was fully subscribed in the first year that it was introduced (2000). However, this is a limited pilot test of only 150 farmers and the product was introduced following a major drought. By 2001, 235 farmers had purchased about $5.5 million in liability with payments of $1.9 million.[3] This policy was targeted toward alfalfa hay production. Alberta has also introduced a rainfall index insurance product for forage production. This contract has been available for two years. In 2002, over 4000 ranchers subscribed to the contract.
For many emerging economies or developing countries, weather index insurance merits consideration (Hazell, 1992; Skees, Hazell, and Miranda, 1999). While basis risk may generally be lower with area-yield index insurance, there are several reasons why weather index insurance may be preferable in a developing or emerging economy. First, the quality of historical weather data is generally much better than the quality of yield data in developing countries. Quality data are essential in pricing an insurance contract. Second, it may be less costly to set up a system to measure weather events for specific locations than to develop a reliable yield estimation procedure for small geographical areas. Finally, either insufficient or excess rainfall is a major source of risk for crop losses in many regions. Drought causes low yields and excess rainfall can cause either low yields or serious losses of yield and quality during harvest (Martin, Barnett, and Coble, 2001).
The World Bank Group is pursuing the feasibility of rainfall index insurance in a number of countries. The International Finance Corporation (IFC) of the World Bank is interested in supporting these innovations so that developing countries can participate in emerging weather markets. The feasibility of weather-based index insurance is being considered for a number of countries, including Nicaragua, Morocco, Ethiopia, Ukraine, Tunisia, Mexico, and Argentina. The most progress has been made in India as is developed below.
A major challenge in designing an index insurance product is minimizing basis risk. The phrase “basis risk” is most commonly heard in reference to commodity futures markets. In that context, “basis” is the difference between the futures market price for the commodity and the cash market price in a given location. Basis risk also occurs in insurance. It occurs when an insured has a loss and does not receive an insurance payment sufficient to cover the loss (minus any deductible). It also occurs when an insured has a loss and receives a payment that exceeds the amount of loss.
Since index insurance indemnities are triggered by area-yield shortfalls or weather events, an index insurance policyholder can experience a yield loss and not receive an indemnity. The policyholder may also not experience a farm-yield loss and yet, receive an indemnity. The effectiveness of index insurance as a risk management tool depends on how positively correlated farm-yield losses are with the underlying area yield or weather index. In general, the more homogeneous the area, the lower the basis risk and the more effective area-yield insurance will be as a farm-yield risk management tool. Similarly, the more a given weather index actually represents weather events on the farm, the more effective the index will be as farm-yield risk management tool.
While most of the academic literature has focused on basis risk for index type insurance products, it is important to recognize that farm-level multiple-peril crop insurance has basis risk as well. To begin, a very small sample size is used to develop estimates of the central tendency in yields. Given simple statistics about the error of the estimates with small samples, it can be easily demonstrated that large mistakes are made on estimating central tendency. This makes it possible for farmers to receive insurance payments when yield losses have not occurred. It is also possible for farmers to not receive payments when payable losses have occurred. Thus, basis risk occurs not only in index insurance but also in farm-level yield insurance.
Another type of basis risk results from the estimate of realized yield. Even with careful farm-level loss adjustment procedures, it is impossible to avoid errors in estimating the true realized yield. These errors can also result in under- and over-payments. Between the two sources of error, measuring expected yields and measuring realized yields, farm-level crop insurance programs also have significant basis risk.
Longer series of data are generally available for area yields or weather events than for farm yields. The standard deviation of area yields is also lower than that of farm yields. Since the number of observations (n) is higher and s (the standard deviation) is lower, the square root of n rule suggests that there will be less measurement error for area-yield insurance than for farm-yield insurance in estimating both the central tendency and the realization. In most developing countries, long series of weather data are available.
Summary of Relative Advantages and Disadvantages of Index Insurance
Index contracts offer numerous advantages over more traditional forms of farm-level multiple-peril crop insurance. These advantages include
1. No moral hazard: Moral hazard arises with traditional insurance when insured parties can alter their behavior so as to increase the potential likelihood or magnitude of a loss. This is not possible with index insurance because the indemnity does not depend on the individual producer’s realized yield.
2. No adverse selection: Adverse selection is a misclassification problem caused by asymmetric information. If the potential insured has better information than the insurer about the potential likelihood or magnitude of a loss, the potential insured can use that information to self-select whether or not to purchase insurance. Index insurance on the other hand is based on widely available information, so there are no informational asymmetries to be exploited.
3. Low administrative costs: Unlike farm-level multiple-peril crop insurance policies, index insurance products do not require underwriting and inspections of individual farms. Indemnities are paid solely on the realized value of the underlying index as measured by government agencies or other third parties.
4. Standardized and transparent structure: Index insurance policies can be sold in various denominations as simple certificates with a structure that is uniform across underlying indexes. The terms of the contracts would therefore be relatively easy for purchasers to understand.
5. Availability and negotiability: Since they are standardized and transparent, index insurance policies can easily be traded in secondary markets. Such markets would create liquidity and allow policies to flow where they are most highly valued. Individuals could buy or sell policies as the realization of the underlying index begins to unfold. Moreover, the contracts could be made available to a wide variety of parties, including farmers, agricultural lenders, traders, processors, input suppliers, shopkeepers, consumers, and agricultural workers.
6. Reinsurance function: Index insurance can be used to transfer the risk of widespread correlated agricultural production losses. Thus, it can be used as a mechanism to reinsure insurance company portfolios of farm-level insurance policies. Index insurance instruments allow farm-level insurers to transfer their exposure to undiversifiable correlated loss risk while retaining the residual risk that is idiosyncratic and diversifiable (Black, Barnett, and Hu, 1999).
There are also challenges that must be addressed if index insurance markets are to be successful.
1. Basis Risk: The occurrence of basis risk depends on the extent to which the insured’s losses are positively correlated with the index. Without sufficient correlation, “basis risk” becomes too severe, and index insurance is not an effective risk management tool. Careful design of index insurance policy parameters (coverage period, trigger, measurement site, etc.) can help reduce basis risk. Selling the index insurance to microfinance or other collective groups can also pass the issue of basis risk to a local group that can develop mutual insurance at some level. Such a group is in the best position to know their neighbors and determine how to allocate index insurance payments within the group.
2. Security and dissemination of measurements: The viability of index insurance depends critically on the underlying index being objectively and accurately measured. The index measurements must then be made widely available in a timely manner. Whether provided by governments or other third party sources, index measurements must be widely disseminated and secure from tampering.
3. Precise actuarial modeling: Insurers will not sell index insurance products unless they can understand the statistical properties of the underlying index. This requires both sufficient historical data on the index and actuarial models that use these data to predict the likelihood of various index measures.
4. Education: Index insurance policies are typically much simpler than traditional farm-level insurance policies. However, since the policies are significantly different than traditional insurance policies, some education is generally required to help potential users assess whether or not index insurance instruments can provide them with effective risk management. Insurers and/or government agencies can help by providing training strategies and materials not only for farmers, but also for other potential users such as bankers and agribusinesses.
5. Marketing: A marketing plan must be developed that addresses how, when, and where index insurance policies are to be sold. Also, the government and other involved institutions must consider whether to allow secondary markets in index insurance instruments and, if so, how to facilitate and regulate those markets.
6. Reinsurance: In most transition economies, insurance companies do not have the financial resources to offer index insurance without adequate and affordable reinsurance. Effective arrangements must therefore be forged between local insurers, international reinsurers, national governments, and possibly international development organizations.
Index insurance is a different approach to insuring crop yields. Unlike most insurance where independent risk is a precondition, the precondition for index insurance to work best for the individual farmer is correlated risk. It is possible to offer index contracts to anyone at risk when there is an area-wide (correlated) crop failure. Furthermore, unlike traditional insurance, there is no reason to place the same limits on the amount of liability an individual purchases.
As long as the individual farmer cannot influence the outcome that results in payments, then placing limits on liability is not necessary as it is with individual insurance contracts. Finally, the true advantage of blending index insurance into banking is that the banking entity can use such contracts to manage correlated risk. In turn, the bank should be able to work with the individual to help them manage the residual risk or basis risk. In simple terms, if the individual has an independent loss when the index insurance does not pay, they should be able to borrow from the bank to smooth that shock. This could effectively remove the primary concern associated with index insurance contracts ¾ that someone can have a loss and not be paid.
As more sophisticated systems are developed to measure events that cause widespread problems (such as satellite imagery) it is possible that indexing major events will be more straightforward and accepted by international capital markets. Under these conditions, it may become possible to offer insurance to countries where traditional reinsurers and primary providers would previously have never considered. Insurance is about trust. If the system to index a major event is reliable and trustworthy, there are truly new opportunities in the world to offer a wide array of index insurance products.
What is Needed to Make the Innovation Work?
There are market makers who are keenly interested in offering rainfall index insurance in developing countries. For example, PartnerRE New Solutions from Connecticut presented the following list of items that are needed to get them interested in offering such contracts[4]
ü Historic Weather Data
ü Prefer 30+ years of data, especially to cover extreme risk
ü Limited Missing Values and Out of Range Values
ü Prefer Less than 1% Missing
ü Data Integrity
ü Availability of a nearby station for a “buddy check”
ü Consistency of Observation Techniques: Manual vs. Automated
ü Limited changes of Instrumentation / Orientation / Configuration
ü Reliable settlement Mechanism
ü Integrity of recording procedure
ü Little potential for measurement tampering
The Role of Technology in Providing Needed Information
In recent years, state-of-the-art methods to forecast food shortages created by bad weather have significantly improved. For example, the East African Livestock Early Warning System (LEWS) is now able to provide reliable estimates of the deviation below normal up to 90 days prior to serious problems. These systems use a variety of information: 1) satellite images; 2) weather data from traditional ground instruments; 3) weather data from new systems, and 4) sampling from grasslands to determine nutrient content. More importantly, these systems allow problems to be forecast at a local level using geographic information systems. Since many of the early warning systems have now been in place for as long as twenty years, it is now possible to model the risk and begin pricing insurance contracts that match the risk profile.
Reinsurance and Weather Markets
Much can be said about the international reinsurance community and their resistance to entering new and untested markets. The use of the capital markets for sharing “in-between” risks remains in the infant stages, leaving the issue of capacity and efficiency in doubt. This raises questions about the role of government in sharing such risk. For the United States, Lewis and Murdock (1996) recommend government catastrophic options that are auctioned to reinsurers. Part of the thinking is that the government has adequate capital to back stop such options and may be less likely to load these options as much as the reinsurance market. Skees and Barnett (1999) have also written about a role for government in offering insurance options for catastrophes as a means of getting affordable capital into the market. However, the demand for catastrophic insurance will be limited where free disaster assistance is available.
Reinsurers have now acquired many of the professionals who were trading weather. SwissRe acquired professionals from Enron and PartnerRe and ACE acquired professionals from Aquila. Reinsurers are now in a position to offer reinsurance using weather-based indexes. This type of reinsurance should be more affordable since it is not subject to the same adverse selection and moral hazard problems as traditional insurance.
Country Case Examples for Using Index Insurance
Mexico: Use of Weather Index Insurance for Mutual Insurance, Reinsurance, and to Facilitate Water Markets
Mexico has experience with using weather indexes to reinsure their crop insurance. Developments within the weather markets prompted new thinking about sharing catastrophic risk in agriculture. In 2001, the Mexican agricultural insurance program (Agroasemex) used the weather markets to reinsure part of their multiple crop insurance programs. By using weather indexes that were based on temperature and rainfall in the major production regions, a weather index was created that was highly correlated with the Mexican crop insurance loss experience. This method of reinsurance proved to be more efficient than traditional reinsurance.
The Mexican contract is an important development for many of the ideas presented in this paper. But beyond the use of weather indexes for reinsurance, Agroasemex also has begun working with Fondos, mutual insurance funds whose members are commercially oriented small farmers, to implement programs whereby they would purchase weather index insurance and then decide what type of mutual insurance to provide their members. These efforts remain in the early development stages.
Agroasemex researchers are also pursing the idea of using index insurance as a means of providing important linkages to the emerging water markets in Mexico. Under such a plan, the water irrigation authority would offer a certain amount of water or indemnity payments in years when water availability restricted how much irrigation water could be delivered. In principle, such an offering should improve the efficiency of water markets and provide improved incentives to irrigation authorities to manage water in such a fashion that they are making commitments to users (Skees and Zeuli, 1999).
Mongolia ¾ Using Livestock Mortality Rates as Index Insurance to Cover Deaths of Large Numbers of Animals in Mongolia
Herders in Mongolia have suffered tremendous losses in recent dzud (major event, ex. winter disasters) with mortality rates of over 50 percent of the livestock in some locales. Recent work by the World Bank has focused on the feasibility of offering insurance to compensate for animal deaths. Such an undertaking is challenging in any country. Mongolia offers even more challenges given the vast territory in which herders tend over 30 million animals. Traditional insurance approaches that insure individual animals are simply not workable. The ability to understand even the simplest issue of who owns specific livestock would require very high transaction costs. The opportunities for fraud and abuse are significant. Monitoring costs required to mitigate this behavior would be very high.
Work is moving ahead for using the livestock mortality rate at a local level (e.g. the sum or rural district) as the basis for indemnifying herders. Plans are to launch a pilot test of this program in the summer of 2005. No country has so far implemented such insurance for livestock deaths. But few countries have such frequent and high rates of localized animal deaths as does Mongolia, and Mongolia is one of the few countries to perform an animal census every year. This concept may therefore be precisely what is needed to start a social livestock insurance program.
India ¾ Linking Index Insurance to Microfinance (BASIX)
India began a privately supported pilot program on rainfall insurance in 2003 (Hess). ICICI Lombard General Insurance Company began a pilot insurance program that will pay farmers when there are shortfalls in rainfall in one area and pay others in case of excess rain. ICICI Lombard offers the drought cover policies via a small microfinance bank in southern India (BASIX) and the excess rain covers through the ICICI Bank. Such contracts offer the distinct advantage of solving the delayed payment problem that exist with India’s current area yield insurance program.
BASIX launched its first weather insurance program in July 2003 through its local area bank KBS in Mahbubnagar. Local area banks are limited to operations in three adjacent districts and therefore face limited natural portfolio diversification, which helped to convince KBS that weather insurance contracts for its borrowers could mitigate the natural default risk inherent in lending in drought prone areas such as Mahbubnagar, at the extreme eastern end of Andhra Pradesh, bordering Karnataka.
ICICI Lombard also offered excess rain policies to around 5,000 wheat farmers in Uttar Pradesh (in conjunction with ICICI Bank) and 150 soya farmers in Madhya Pradesh in 2003/2004 (in conjunction with BASIX).
D Sattaiah of BASIX presented updates on the progress[5] of the index insurance recently. He reports that they plan to improve the product and expand to target over 1,500 farmers in Andhra Pradesh in 2004. Further, BASIX itself is now interested in purchasing the index to protect their portfolio risk for three unit office operating locations. Sattaiah goes on to point to a list of improvements that are needed in the program offerings:
ü Simplification of the pay-out structure.
ü Reference to local rainfall stations.
ü Add excess rainfall as another risk to be covered.
ü Introduced phased payouts so that farmers don’t have to wait until the end of the season.
ü Improve client awareness on the product offerings.
ü More frequent meeting with farmers to clarify doubts.
ü Use of opinion leaders for information dissemination.
ü Build credibility of external systems.
ü Install village rain gauges.
ü Provide comparison with the reference station.
In addition to the introduction of rainfall index insurance by BASIX, Mosley also describes an alternative form of insurance that has been offered by BASIX. The BASIX program operates similarly to a cooperative and relies on peer monitoring to reduce incidences of moral hazard and adverse selection. Village committees perform individual loss adjustments. Because payments are based on individual losses, premium rates are higher than the Ugandan CERUDEB program, at 20 percent. Half of the premium is deposited into the village fund, a quarter goes to BASIX, and the remainder goes towards the inter-village fund that provides indemnity payments.
Recommendations for Blending Index Insurance and Rural Finance
Progress has been made in designing and offering index insurance contracts for a variety of correlated risk in developing countries. The motivation for using index insurance contracts rather than individual indemnity has been developed. Index insurance can shift correlated risk out of small countries into the global market. To the extent that the index is based upon a secure and objective measure of risk, this approach provides an important risk shifting innovation for developing countries where the legal structure for more sophisticated insurance products is commonly woefully inadequate. Index insurance contracts involve significantly lower transaction costs and can be offered directly to end users from companies that operate in a global market, particularly if the end user is positioned to aggregate large amounts of risk (e.g., MFEs).
It is possible that offering index insurance directly to the MFE can circumvent bad government, poor macroeconomic policies, and inadequate legal frameworks. To the extent that the writer of the index insurance is a reputable global partner, the MFE could pay premiums in dollars and be paid indemnities in dollars as well. This would mitigate inflation risk within the country. The legal framework that is needed to allow MFEs to purchase these contracts from a global writer should be much more straightforward than the legal framework needed to offer traditional insurance. The major challenge within the developing country will be in knowing that the global partner has the reputation and the resources to pay indemnities. Should the International Finance Corporation of the World Bank Group become more involved in partnering on writing index insurance contracts for price, yield, weather, and livestock, many of these concerns could be eased.
The issue of basis risk has been of some concern if one is selling index insurance contracts to individuals. However, if these contracts are sold to MFEs, the MFE should be in a position to mitigate basis risk in a number of creative ways. It is useful to illustrate some potential arrangements that could emerge between global sellers of index insurance contracts and rural finance entities. Consider a microfinance group or a small rural finance entity (MFE) with members having household activities in the same neighborhood. While this group of individuals may use many informal mechanisms to pool risk and assist individuals when bad fortune visits one of their members, they are unable to cope with a major event such as drought that adversely impacts all members at the same time.
If the group could purchase an index insurance contract that would simply make payments based upon the level of rainfall (an excellent proxy for drought), the group would be in a much better position to cope when everyone suffers a loss at the same time. The MFE would need to develop ex ante rules regarding how indemnity payments from index insurance would be used. Three examples of how those ex ante rules may be developed are presented for illustration.
Indemnity Payments Could be Used to Forgive Debt
Since making loans is a major activity of most MFEs, the ability to repay the loans will likely be in jeopardy when there is an event that adversely impacts everyone. Having loan defaults from a large number of borrowers at the same time is likely to put the MFE at some risk. Thus, indemnity payments from index insurance can be used to offset defaults that occur due to natural disaster. Effectively, indemnity payments become a form of credit default insurance. The MFE would still need to implement rules regarding debt forgiveness for individuals.
Indemnity Payments Could be Used to Facilitate a Form of Mutual Insurance
The indemnity payment from index insurance could be directly distributed to members of the MFE via insurance-like rules that are determined by the members. Given that only actual indemnity payments received would be distributed, a common problem among mutual insurance providers in developing countries would be avoided ¾ inadequate cash to pay for indemnities that are specified in insurance contracts (McCord, 2003). To the extent that the MFE is relatively small and members know one another, the asymmetric information problems discussed earlier would be avoided. This, of course, is the advantage of mutual insurance.
Indemnity Payments Could be Used to Facilitate Better Terms of Credit
Since lending is an excellent means of smoothing consumption when there are unexpected cash flow problems, the MFE could tie the index insurance directly into the loan arrangements. Loans that are made immediately following a good season where no indemnity payments are made could be higher than normal to collect premiums that would pay for the index insurance. Interest rates could be lowered using indemnity payments directly, immediately after a major event. Interest rate reductions could be tied directly to the severity of the event. (Parchure, 2002).
While there are many challenges to making some of the ideas presented here work, possibly the most significant among them involves paying for insurance. This is especially true if one expects the rural poor to pay. Premiums for some natural disaster risk could be quite expensive. Goes and Skees (2003) have been working with the concept of persuading those who give to victims of natural disasters ex post, that ex ante giving might be more effective. In fact, there are potentially some financial advantages to individuals to provide ex ante donations. NGOs and charities of all types have been quick to respond when a natural disaster such as a major drought or the Mongolia dzud victimizes the rural poor. Dumping in supplies or even large sums of money after the event is highly inefficient and many questions can be raised about who obtains the benefits.
To the extent that a credible risk consortium could be developed to write index-based insurance contracts for a wide array of disaster risk, NGOs and charities may be better served by purchasing these contracts. This would give them the needed resources for quick response. Further, they would have more influence in working with local groups regarding ex ante rules about how to spend the money.
Progress has been made on the ideas presented in this paper. While reliable historic data are important, integrity of data is still among the most significant requirements for gaining confidence from global markets that might be willing to take on natural hazard risk. Anyone interested in making these ideas work must pay close attention to the advice of global market makers. BASIX in India is in the best position to make many of the ideas for linking MFEs and index insurance work. In addition, COPEME of Peru has now contracted with GlobalAgRisk to pursue some of these ideas[6]. Among the most promising is the use of index insurance to protect the portfolio of MFEs. In the coming years, one can expect to see these innovations adopted and modified to the local settings. Linking index insurance to MFEs can help in layering risk and using appropriate institutions to manage different types of risk.
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[1] See Skees et al. (2002) for development of the same infrastructure on weather disasters in several settings: 1) as a replacement for traditional crop insurance; 2) as a means to insure groups of farmers and facilitate mutual insurance; 3) as a means of providing more affordable reinsurance for traditional crop insurance, and 4) as a mechanism to trigger objective disaster payments.
[2] The limitations on both Coverage and Scale were politically dictated. In principle, there is no reason that these parameters would need to be limited with index contracts. Still it is common to set some limits on how much index insurance a farmer can purchase. Some estimates of value-at-risk may be used for this purpose. For the GRP program, the farmer must certify the planted acreage used to calculate liability.
[3] Personal email communication with Mr. Paul Cudmore of Agricorp, Canada, October 23, 2001.
[4] Brian Tobben presented these items at the Annual Meeting of the International Task Force on Commodity Risk Management, Jointly Sponsored by the FAO and the World Bank at the FAO, Rome, 5 and 6 May, 2004.
[5] D. Sattaiah presented these items at the Annual Meeting of the International Task Force on Commodity Risk Management, Jointly Sponsored by the FAO and the World Bank at the FAO, Rome, 5 and 6 May, 2004.
[6] This contract is funded by USAID and GTZ. COPEME is an association that represents a number of microfinance entities operating in Peru. In early meetings with management of the MFEs, they understood the potential value of such contracts that would allow them to shift some of the most catastrophic risk to capital markets.
