tte production strategy selected becomes an important means of mitigating the risk of crop failure. Traditional cropping systems in many places rely on crop and plot diversification. Crop diversification and intercropping systems signify common strategies of reducing the risk of crop failure due to adverse weather events, crop pests, or insect attacks. Morduch (1995) presents evidence that households whose consumption levels are close to subsistence (and which are therefore highly vulnerable to income shocks) devote a larger share of land to safer, traditional varieties such as rice and castor than to riskier, high-yielding varieties. Morduch also finds that near-subsistence households diversify their plots spatially to reduce the impact of weather shocks that vary by location. Apart from altering agricultural production strategies, households also smooth income by diversifying income sources, thus minimizing the effect of a negative shock to any one of them. According to Walker and Ryan (1990), most rural households in villages of semi-arid India surveyed by the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) generate income from at least two different sources; typically, crop income is accompanied by some livestock or dairy income. Off-farm seasonal labour, trade, and sale of handicrafts are also common income sources, tte importance to risk management of income source diversification is emphasized by Rosenzweig and Stark (1989), who find that households with high farm-profit volatility are more likely to have a household member engaged in steady wage employment. Accumulating a buffer stock of crops or liquid assets at the expense of credit expenditure presents obvious means by which households can smooth consumption. Lim and Townsend (1998) show that currency and crop inventories function as buffers or precautionary savings. Crop-sharing arrangements in renting land and hiring labour can also provide an effective means of sharing risk among individuals, thus reducing producer risk exposure (Hazell 1992). Other risk sharing mechanisms, such as community-level risk pooling, occur in specific communities or extended households where group members transfer resources among themselves to rebalance marginal utilities (World Bank 2001). ttese arrangements, however, while effective for counterbalancing the consequences of events affecting onlysome members of the community, do not work well in the cases of covariate income shocks (Hazell 1992). ttat is, in the event of widespread shock where most members of the same community are affected, as is often the case with natural disasters, small-scale risk pooling offers little in the way of absorbing the impact of shock.
Typical ex-post informal income-smoothing mechanisms include the sale of assets, such as land or livestock (Rosenzweig and Wolpin 1993), or the reallocation of labour resources to off-farm labour activities. Gadgil et al. (2002) argue that southern Indian farmers who expect poor monsoon rains can quickly shift from 100 percent on-farm labour activities to predominantly off-farm activities. Fafchamps (1993), in his analysis of rain-fed agriculture among West African farmers, emphasizes the importance of building labour flexibility into the production strategy. In contrast, Rosenzweig and Binswanger (1993), Morduch (1995), and Kurosaki and Fafchamps (2002) all find considerable efficiency losses associated with risk mitigation, typically due to lack of specialization and inability to reach economies of scale. In effect, farmers stabilize income flows at the detriment of maximizing profits; this tendency to smooth consumption not only against idiosyncratic shocks but also against correlated shocks comes at a serious cost in terms of production efficiency and reduced profits, thus lowering overall levels of household consumption and prospects for asset accumulation.
A major consideration for innovation would be to shift correlated risk away from rural households (Skees 2003). One obvious solution would be for rural households to share risk with households or institutions from areas largely uncorrelated with the local risk conditions. Examples of such extra-regional risk sharing systems are found in the literature, including, credit and transfers between distant relatives (Rosenzweig 1988; Miller and Paulson 2000); migration and marriages (Rosenzweig and Stark 1989); or ethnic networks (Deaton and Grimard 1992). Although the examples above convey some degree of risk sharing and thus of informal insurance measures against weather, such systems cannot be scaled up to offer wider coverage nor do come even close to providing a fully efficient insurance mechanism. tte world's most vulnerable households are therefore left largely unprotected against correlated risks, the main source of which is weather.
Formal risk managementmechanisms
Formal risk management mechanisms can be classified as publicly provided or market-based. Government action plays an important role in agricultural risk management, both ex-ante and ex-post. Ex-ante education and services provided by agricultural extension help familiarize producers with the consequences of risk and help them adopt related coping strategies. Governments also help to reduce the impact of risk by developing relevant infrastructure and by adopting social schemes and cash transfers for relief after shocks have occurred. As mentioned under the explanation of informal mechanisms, production and market risks probably inflict the largest impact on agricultural producers. Various market-based, risk management solutions have been developed to address these sources of risk. Insurance is another formal mechanism used in many countries to share production risks. However, insurance does not as efficiently manage production risk as efficiently as do derivative merkets. Price risk is highly spatially correlated, and futures and options are tailor-made derivative instruments appropriate for dealing with spatially correlated risks. In contrast, insurance is most appropriate for managing independent risks that are spatially uncorrelated.
Challenges for traditional crop insurance
Agricultural production risks typically lack sufficient spatial correlation to be effectively hedged using only exchange-traded futures or options. At the same time, agricultural production risks are generally not perfectly spatially independent; therefore, insurance markets do not work at their best. Skees and Barnett (1999) refer to these risks as "in-between" risks. According to Ahsan et al. (1982), "good or bad weather may have similar effects on all farmers in adjoining areas," and, consequently, "the law of large numbers, on which premium and indemnity calculations are based, breaks down." In fact, positive spatial correlation in losses limits the risk reduction capacity obtained by pooling risks from different geographical areas, thus increasing the variance in indemnities paid by insurers. In general, the greater the positive correlation in losses the less efficient traditional insurance is as a risk-transfer mechanism.
tte lack of statistical independence is not the only problem with providing insurance in agriculture. Another set of problems relates to asymmetric information, the situation in which the insured has more knowledge about his or her risk profile than does the insurer. Asymmetric information causes two problems: adverse selection and moral hazard. In the case of adverse selection, farmers have better knowledge than do the insurers about the probability distribution of losses, tte farmers thus occupythe privileged situation ofknowing whether or not the insurance premium accurately reflects the risk they face. Consequently, only farmers bearing greater risks will purchase the coverage, generating an imbalance between indemnities paid and premiums collected. Moral hazard similarly affects the incentive structure of the relationship between insurer and insured. After entering the contract, the farmer's incentive to take proper care of the crop diminishes, while the insurer has limited effective means to monitor what may prove hazardous behaviour by the farmer. Insurers may thus incur greater than anticipated losses. Agricultural insurance is often characterized by high administrative costs, due, in part, to the risk classification and monitoring systems that insurers must put in place to forestall asymmetric information problems. Other costs include acquiring the data needed to establish accurate premium rates and conducting claims adjustments. As a percentage of the premium, the smaller the policy, typically, the larger the administrative costs. Spatially correlated risk, moral hazard, adverse selection, and high administrative costs are all important reasons why agricultural insurance markets may fail.
Cognitive failure among potential insurance purchasers and ambiguity loading on the part of insurance suppliers are other possible causes of agricultural insurance market failure. If consumers fail to recognize and plan for low-frequency, high-consequence events, the likelihood that an insurance market will emerge diminishes. When considering an insurance purchase, the consumer may have difficulty determining the value of the contract or, more specifically, the probability and magnitude of loss relative to the premium (Kunreuther and Pauly 2001). Many decision makers tend to underestimate their exposure to low-frequency, high-consequence losses and thus are unwilling to pay the full costs of an insurance product that protects them against these losses. Low-frequency events, even when severe, are frequently discounted or ignored altogether by producers trying to determine the value of an insurance contract, ttis happens because the evaluation of probability assessments regarding future events is complex and often entails high search costs. Many people resort to various simplifying heuristics, but probability estimates based on these heuristics may differ greatly from the true probability distribution (Schade et al. 2002; Morgan and Henrion 1990).
Evidence indicates that agricultural producers forget extreme low-yield events, tte general finding regarding subjective crop-yield distributions is that agricultural producers tend to overestimate the mean yield and underestimate the variance (Buzby et al. 1994; Pease et al. 1993; Dismukes et al. 1989). On the other side, insurers will typically load premium rates heavily for low-frequency, high-consequence events where considerable ambiguity surrounds the actual likelihood of the event (Schade et al. 2002; Kunreuther et al. 1995). Ambiguity is especially serious when considering highly skewed probability distributions with long tails, as is typical of crop yields. Uncertainty is further compounded when the historical data used to estimate probability distributions are incomplete or of poor quality, a very common problem in developing countries.
Small sample size creates large measurement error, especially when the underlying probability distribution is heavily skewed. Kunreuther et al. (1993) demonstrate via experimental economics that when risk estimates are ambiguous, loads on insurance premiums can be 1.8 times higher than when insuring events with well specified probability and loss estimates. Together, these effects create a wedge between the prices that farmers are willing to pay for catastrophic agricultural insurance and the prices that insurers are willing to accept, ttus, functioning private-sector markets may fail to materialize or, if they do materialize, they may cover only a small portion of the overall risk exposure (Pomareda 1986).
To better understand agricultural risk management markets and government policies to facilitate access to risk management instruments, it is worthwhile to analyze critically the experiences of some developed countries, tte experiences of the United States, Canada, and Spain are thus described for reference, but it is important to consider that these systems may not be replicable in or suitable for most developing countries. In addition, many developed countries have involved market support and income transfer programs that extend well beyond crop insurance. To the extent they are based on farm income, these programs involve levels of protection against severe crop failures, tte European community has extensive policies focusing on income protection.
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