Index insurance, probabilistic climate forecasts, and production.
Index insurance and probabilistic seasonal forecasts are becoming available in developing countries to help farmers manage climate risks in production. Although these tools are intimately related, work has not been done to formalize the connections between them. We investigate the relationship between the tools through a model of input choice under uncertainty, forecasts, and insurance. While it is possible for forecasts to undermine insurance, we find that when contracts are appropriately designed, there are important synergies between forecasts, insurance, and effective input use. Used together, these tools overcome barriers preventing the use of imperfect information in production decision making.
Climate-related risks have profound impacts on agricultural producers around the world. (1) These effects are particularly important in developing countries, where agriculture makes a significant contribution to gross domestic product (World Bank, 2001) and insurance markets are underdeveloped un·der·de·vel·oped
Not adequately or normally developed; immature. or nonexistent non·ex·is·tence
1. The condition of not existing.
2. Something that does not exist.
non . Recently, microinsurance and probabilistic seasonal forecasts have become available to help farmers manage climate risks in production. Although these tools are intimately related, work has not been done to formalize the fundamental connections between them. (2)
It is well known that climate risk can keep low-income households in poverty traps (see a review by Barnett, Barrett, and Skees, 2007). Lack of assets and risk exposure may lead households to forego activities with high returns, perpetuating their poverty. In response to this challenge, innovative insurance pilots to help farmers in developing countries have been gaining increasing interest (Hellmuth et al., 2009). (3) Recent studies analyze the potential of insurance in helping to escape poverty traps (Kovacevic and Pflug, Forthcoming).
Work in the agricultural economics Agricultural economics originally applied the principles of economics to the production of crops and livestock - a discipline known as agronomics. Agronomics was a branch of economics that specifically dealt with land usage. literature has examined the relationship between insurance and input usage for both farm-level insurance (e.g., Ramaswami, 1993; Babcock and Hennessy, 1996) and index insurance (Chambers and Quiggin, 2000; Mahul, 2001). Focusing on the relationship between insurance and input use, this literature does not address interactions between insurance, climate forecast, and input decisions. Trade-offs between basis risk and moral hazard Moral Hazard
The risk that a party to a transaction has not entered into the contract in good faith, has provided misleading information about its assets, liabilities or credit capacity, or has an incentive to take unusual risks in a desperate attempt to earn a profit before the afforded by index-based products have been analyzed from an insurance company standpoint (Doherty and Richter, 2002).
Seasonal climate is predictable in many regions of the world (Goddard et al., 2001), with the El Nino Southern Oscillation Oscillation
Any effect that varies in a back-and-forth or reciprocating manner. Examples of oscillation include the variations of pressure in a sound wave and the fluctuations in a mathematical function whose value repeatedly alternates above and below some (ENSO ENSO El Niño Southern Oscillation ) linked to variations of seasonal precipitation (Ropelewski and Halpert, 1987). Work in agricultural and climate science has modeled the impact of probabilistic climate forecasts on production decisions (e.g., Hansen, 2002) but has, with few exceptions (Mjelde, Thompson, and Nixon, 1996; Cabrera, Letson, and Podesta podesta
(Italian: “power”) In medieval Italian communes, the highest judicial and military magistrate. The office was instituted by Frederick I Barbarossa in an attempt to govern rebellious Lombard cities. , 2005), ignored the impact of insurance. (4)
Although yields and production practices are impacted by ENSO phases, this relationship is not built into existing insurance products. Insurance implementers acknowledge that climate forecasts may undermine the financial soundness of a product by providing opportunities for intertemporal adverse selection. The advocated strategies are to finalize fi·nal·ize
tr.v. fi·nal·ized, fi·nal·iz·ing, fi·nal·iz·es
To put into final form; complete or conclude: "They have jointly agreed ... insurance transactions contracts months ahead of time, before the forecast is informative (see, e.g., Hess and Syroka, 2005; World Bank, 2005), or allow insurance premiums to reflect forecast information (Skees, Hazell, and Miranda, 1999). Implementation of these strategies is undermined by the lack of a clear conceptual understanding about the interaction between seasonal forecast, insurance, and production decisions. Potential opportunities to exploit synergies may be being missed. Since the reinsurance The contract made between an insurance company and a third party to protect the insurance company from losses. The contract provides for the third party to pay for the loss sustained by the insurance company when the company makes a payment on the original contract. industry uses probabilistic seasonal climate forecasts in pricing (Hellmuth et al., 2009), (5) it is important to understand forecasts and pricing from the perspective of the insurance company that must develop a product that: (1) results in products that provide value to farmers, unlocking consumer demand; (2) reflects fluctuating reinsurance costs; and (3) cannot be undermined through strategic use of forecast information. As microinsurance projects advance, the use of ENSO in these products is being increasingly explored, increasing the urgency for formalizing the basic features connecting insurance, production decisions, and forecasts. Existing analyses of insurance and forecast focus on pricing while ignoring the production value of the interaction can lead to misguided decision making as these production benefits are critical in driving insurance demand.
Since the fundamental relationship between insurance, input use, and seasonal climate forecasts has not been addressed, we propose to fill this gap by explicitly modeling input use and index insurance demand given probabilistic seasonal climate forecasts. While seasonal climate forecasts, insurance contracts, and input use choices can each be used to mitigate uncertainty and risk, they each play a different role and have potential synergies or unanticipated impacts when used together.
We investigate the relationship just described through the simplest possible model of input choice under uncertainty, forecasts, and index insurance. Our goal is to formalize the fundamental features of the relationship between insurance, forecasts, and production in a highly stylized styl·ize
tr.v. styl·ized, styl·iz·ing, styl·iz·es
1. To restrict or make conform to a particular style.
2. To represent conventionally; conventionalize. model, in order to represent the key features in the most transparent way. Although it is not central to our problem, we include features in our model that allow for a basic discussion of the impacts of basis risk because it is of so much interest in the literature on new index-based insurance products (Golden, Wang, and Yang, 2007). In this line, the inclusion of the idiosyncratic id·i·o·syn·cra·sy
n. pl. id·i·o·syn·cra·sies
1. A structural or behavioral characteristic peculiar to an individual or group.
2. A physiological or temperamental peculiarity.
3. shock allows the modeling of basis risk. The way in which the shock is introduced, linearly in this case to follow the literature and simplify the analysis, will provide only a subset of the results possible. Work focusing more on complex basis risk modeling would provide deeper insights into these issues.
We find that if contracts are appropriately designed there are important synergies between forecasts and insurance and effective input use. Insurance allows the farmer to map a probabilistic forecast into a much more deterministic 1. (probability) deterministic - Describes a system whose time evolution can be predicted exactly.
2. (algorithm) deterministic - Describes an algorithm in which the correct next step depends only on the current state. payout, allowing the farmer to commit to production choices that take advantage of forecast information that is too noisy to utilize without risk protection. We also find that the presence of skillful skill·ful
1. Possessing or exercising skill; expert. See Synonyms at proficient.
2. Characterized by, exhibiting, or requiring skill. probabilistic forecasts may affect the demand for insurance as well as its effectiveness as a risk-reducing tool. In our stylized treatment, basis risk attenuates impacts but does not lead to findings that are fundamentally different.
We begin by presenting the base framework that will be used throughout the article. Probabilistic seasonal climate forecasts and index insurance with their well-known impacts on production decisions are then introduced individually in order to provide benchmarks for our findings. Next, we combine the instruments (forecasts and insurance) and analyze their joint interactions with production practices. The last section provides concluding remarks and proposes some avenues for future research.
PRELIMINARIES AND BASE MODEL
Consider first a competitive farmer with a single crop with yield (y) dependent on the level of a controllable input (N, may be thought of as nitrogen, an improved seed, or the level of technology used), a systemic weather shock (r, hereafter In the future.
The term hereafter is always used to indicate a future time—to the exclusion of both the past and present—in legal documents, statutes, and other similar papers. rainfall) affecting all farmers in the area, and an idiosyncratic production shock ([epsilon]) as follows:
y = f(N, r) + [epsilon]. (1)
A special case of this yield function was used by Mahul (2001). (6) For simplicity, let rainfall take only two values, [r.sub.g] and [r.sub.b] (denoting good and poor or bad growing condition, respectively). It is assumed that [f.sub.N](N,r) = [partial derivative]f(N,r)/[partial derivative]N > 0, and [f.sub.NN](N,r) = [[partial derivative].sup.2]f(N,r)/[partial derivative][N.sup.2] [less than or equal to] 0. We assume further that f(N, [r.sub.g]) > f(N, [r.sub.b]), and [f.sub.N](N, [r.sub.g]) > [f.sub.N](N, [r.sub.b]) for all N. (7)
The value of the random variables is learned after the input has been applied. In the sections to follow, the systemic shock A systemic shock is a shock to any system that perturbs a system enough to drive it out of equilibrium. Systemic shocks occur in a wide range of fields, ranging from medicine (see shock) to economics to engineering. r is the variable on which the forecast provides information and on which the index insurance is written. The farmer knows that the climatological cli·ma·tol·o·gy
The meteorological study of climates and their phenomena.
clima·to·log (historical) probability of observing r = [r.sub.g] is [[omega].sub.g]. The expected value Expected value
The weighted average of a probability distribution. Also known as the mean value. of the idiosyncratic shock, which by definition is independent of the systemic shock, is assumed to be zero, and its variance is given by [sigma]2[epsilon]. Both the price of the controllable input ([p.sub.N]) and the price of the output (p) are assumed to be nonrandom, and the latter price is normalized to I without loss of generality Without loss of generality (abbreviated to WLOG or WOLOG and less commonly stated as without any loss of generality) is a frequently used expression in mathematics. . (8) Conditional on the idiosyncratic shock, and defining [[pi].sup.0] (N, r) = f(N, r) - [p.sub.N]N, profits for the farmer are given by
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII ASCII or American Standard Code for Information Interchange, a set of codes used to represent letters, numbers, a few symbols, and control characters. Originally designed for teletype operations, it has found wide application in computers. ] (2)
The farmer is assumed risk averse Risk Averse
Describes an investor who, when faced with two investments with a similar expected return (but different risks), will prefer the one with the lower risk.
A risk averse person dislikes risk. with a Bernoulli utility function given by u([pi]), with u''([pi]) < 0 < u'([pi]). If the farmer's choice is on the level of the input to apply, the farmer's problem is given by
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII](3)
where as in Mahul the indirect utility function In economics, a consumer's indirect utility function gives the consumer's maximal utility when faced with a price level [??](*) is [??]([[pi].sup.0]) = Eu([[pi].sup.0] + [epsilon]) for all [[pi].sup.0]. Kihlstrom, Romer
- This page is about the cartographic mechanism called a "Romer" or "Roamer"; for people named Romer see Romer (surname)
A Romer or Roamer is a simple device for accurately plotting a grid reference on a map. , and Williams (1981) show the indirect utility function is increasing and concave Concave
Property that a curve is below a straight line connecting two end points. If the curve falls above the straight line, it is called convex. in [[pi].sup.0]. It is well known that producers will self-insure in this context by selecting a level of inputs that reduce the magnitude of a loss when one occurs. The level of inputs selected will be lower than that of a risk-neutral producer. For future
reference notice that, in the absence of insurance or seasonal forecasts, the expected level, and variability of profits are given by
E([pi]) = E(E([pi]|[epsilon])) = [[omega].sub.g][[pi].sup.0]([N.sup.*],[r.sub.g]) + (1 - [[omega].sub.g])[[pi].sup.0]([N.sup.*],[r.sub.b]) (4a)
Var([pi]) = E(Var([pi]|[epsilon])) + Var(E([pi]|[epsilon])) = [[omega].sub.g](1 - [[omega].sub.g])([[pi].sup.0]([N.sup.*],[r.sub.g]) - [[pi].sup.0][([N.sup.*],[r.sub.b])).sup.2] + [[sigma].sup.2.sub.[epsilon]]. (4b)
FORECASTS WITHOUT INSURANCE
Suppose now that a skillful probabilistic seasonal climate forecast is available before decisions over the input are made. The forecast indicates the future state of the world (high or low rainfall) for the coming season and an associated uncertainty given by the probability that the forecast is incorrect. If high levels of rainfall are forecasted, there is probability cog I g that rainfall is actually high. Thus, forecast error is climate risk updated by the information in the forecast. A forecast is skillful if 1 > [[omega].sub.g|g] > [[omega].sub.g], and 1 > [[omega].sub.b|b] > [[omega].sub.b]. (9) Assume that the forecast is unbiased; that is, the frequency with which a high rainfall forecast is issued ([m.sub.g]) equals that of high rainfall years ([[omega].sub.g]).
In this situation, the decision of the farmer will depend on the forecast received. If a good year is forecasted, the decision of the farmer is
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (5)
The [N.sup.*g], the optimal amount of input use when a good year is forecasted, will depend on the skill of the forecast. Again, the farmer self-insures against the uncertainty in the forecast.
INSURANCE WITHOUT A FORECAST
Suppose now that instead of a forecast, insurance (I) is available to farmers, and they must decide how much of it to buy at a price r per unit. To include a simple representation of basis, we assume that the insurance is available for the systemic shock (r) but not for the idiosyncratic shock ([epsilon]). In this case, the objective function and first-order conditions (at an interior solution) are
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (6)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (7)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (8)
If the insurance is actuarially fair (r = 1 - [[omega].sub.g]), one obtains the standard result that the risk-averse farmer insures fully against the systematic risk; that is, [I.sup.*] = [[pi].sup.0] ([N.sup.*], [r.sub.g]) [[pi].sup.0]([N.sup.*],[r.sub.b]) = f([N.sup.*],[r.sub.g])- f([N.sup.*],[r.sub.b]), (10) and a higher level of inputs (the risk-neutral level) is utilized. The farmer's expected level and variability of profits when actuarially fair insurance is available are given by
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (9a)
Var([[pi].sub.I]) = E(Var([[pi].sub.I]|[epsilon])) + Var(E([[pi].sub.I]|[epsilon])) = [[sigma].sup.2][epsilon]. (9b)
Although profit variability is reduced, Equation (9b) indicates that some basis risk remains for farmers even when they are fully insured against the systemic shock.
COMBINING THE FORECAST WITH INSURANCE The impacts of the interaction between forecasts and insurance depend critically on the timing of the forecast who has little flexibility, who must commit to exogenously determined production practices prior to the forecast information and insurance decision. We then model the case in which a farmer simultaneously makes insurance and production decisions after the forecast is available.
Effects of a Skillful Forecast on Insurance Purchases With Fixed N
Consider a farmer who is constrained to commit to production decisions before insurance becomes available but after a skillful forecast has been issued. If skillful seasonal forecasts are released before the closing date for the insurance purchase, the premium rates must be modified to reflect the climate information available for the insurance to be financially sustainable, and the problem is state contingent. When a good year is forecasted, the actuarially fair rate becomes lower (from [tau] = [[omega].sub.b] to [[tau].sub.1] = [[omega].sub.b|g]) and the farmer's problem and first-order condition (for an interior solution) are
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (10)
[??]'([[pi].sup.0](N,[r.sub.g]) - [[tau].sub.1]I)/[??]'([[pi].sup.0](N,[r.sub.b]) - (1 - [[tau].sub.1])I = [[omega].sub.b|g]/[[omega].sub.g|g] (1 - [[tau].sub.1])/[[tau].sub.1]. (11)
Since the insurance is actuarially fair, the farmer will insure fully against the systemic
risk setting [I.sup.*] = [[pi].sup.0] (N,[r.sub.g]) - [[pi].sup.0] (N,[r.sub.b]) = [[pi].sup.0.sub.g] - [[pi].sup.0.sub.b]. Furthermore, if inputs cannot be changed, the insurance purchase depends neither on whether the forecast is for a good or bad year nor on its skill. Hence, if a bad year is forecasted, and the premium rates reflect it (defining [[tau].sub.2] = [[omega].sub.b|b] > [[omega].sub.b] > [[tau].sub.1]), an analogous problem can be solved and the farmer will insure fully against the systemic risk Systemic Risk
Risk common to a particular sector or country. Often refers to a risk resulting from a particular "system" that is in place, such as the regulator framework for monitoring of financial_institutions. .
When a forecast for a good year is issued, profits equal [[pi].sup.*.sub.g]|[epsilon]. = [[pi].sup.0.sub.g] - [[tau].sub.1]([[pi].sup.0.sub.g] - [[pi].sup.0.sub.b]) + [epsilon] across realizations of the insured variable and thus E([[pi].sup.*.sub.g]) = [[pi].sup.0.sub.g] - [[tau].sub.1]([[pi].sup.0.sub.g] - [[pi].sup.0.sub.b] and Var([[pi].sup.*.sub.g]) = [[sigma].sup.2.sub.[epsilon]]. If the forecast is for a poor year, profits equal [[pi].sup.*.sub.b]|[epsilon]. = [[pi].sup.0.sub.g] - [[tau].sub.2]([[pi].sup.0.sub.g] - [[pi].sup.0.sub.b]) + [epsilon] across realization of r, expected profits are E([[pi].sup.*.sub.g]) = [[pi].sup.0.sub.g] - [[tau].sub.2]([[pi].sup.0.sub.g] - [[pi].sup.0.sub.b]), and Var([[pi].sup.*.sub.b]) = [[sigma].sup.2.sub.[epsilon]]. Since the forecast is unbiased and the insurance is actuarially fair, we have E([[pi].sup.*]|[epsilon])= [[omega].sub.g][[pi].sup.0.sub.g] + (1 - [[omega].sub.g])[[pi].sup.0.sub.b] + [epsilon, and E([[pi].sup.*]) = [[omega].sub.g][[pi].sup.0.sub.g] + (1 - [[omega].sub.g])[[pi].sup.0.sub.b].
Notice that expected profits change across realizations of the forecast. The difference is given by E([[pi].sup.*.sub.g] - E([[pi].sup.*.sub.b] = ([[tau].sub.2]) - ([[tau].sub.1]) ([[tau].sup.0.sub.g] - [[tau].sup.0.sub.b]]). Since the insurance is actuarially fair, [[tau].sub.2] = [[omega].sub.b|b] and [[tau].sub.1] = [[omega].sub.b|g], indicating that as the skill of the forecast increases, so does the difference in expected profits across forecasts. The resulting profit variability is
Var([pi]*) + E(Var([[pi].sup.*]|[epsilon])) + (Var(E([pi].sup.*]|[epsilon])) = [[omega].sub.g](1 - [[omega].sub.g])[([[tau].sub.2] - [[tau].sub.1])[([[pi].sup.0.sub.g] - [[pi].sup.0.sub.b])].sup.2] + [[sigma].sup.2.sub.[epsilon]]. (12)
Equation (12) indicates that the existence of a skillful forecast that is issued before purchases of the insurance are made increases the variability of profits when compared to the no-forecast situation. In the absence of the forecast (or when the forecast has no skill), we have [tau] = [[tau].sub.1] = [[tau].sub.2] and thus the farmer will only face the idiosyncratic risk Idiosyncratic Risk
Risk that affects a very small number of assets, and can be almost eliminated with diversification. Similar to unsystematic risk.
This is news that is specific to a small number of stocks. One example is a sudden strike by employees. (compare with Equation (9b)). As a skillful forecast is introduced, the difference [[tau].sub.2] - [[tau].sub.1] increases, undermining the effectiveness of the insurance to provide protection against the insurable risk An insurable risk is a risk that meets the ideal criteria for efficient insurance. The concept of insurable risk underlies nearly all insurance decisions.
For a risk to be insurable, several things need to be true:
In summary, since the forecast is assumed to be unbiased and available to both parties, the ex ante expected profit in this and the variability of that profit increases when the forecast is available. Hence, the presence of a forecast undermines the effectiveness of the insurance as a risk-mitigation mechanism in this situation and reduces welfare.
Choice of Both Insurance and Input Purchases in the Presence of a Skillful Forecast
To analyze the full interaction between the risk management tools and production decisions, we now allow farmers to choose both the level of the controllable input and the insurance purchase after observing the skillful forecast. Finally, the systemic and idiosyncratic shocks are observed. Since the forecast is released before farmers make their decisions, we have a state-contingent problem. The objective and first-order conditions when a good year is forecasted are
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (13)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (14)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (15)
Since the insurance is actuarially fair, we know that [I.sup.*] = [[pi].sup.0]([N.sup.*g],[r.sub.g]) - [[pi].sup.0]([N.sup.*g],[r.sub.b]) = f([N.sup.*g], [r.sub.g]) - f([N.sup.*g], [r.sub.b]). Using this result, the first-order conditions evaluated at the optimum are written
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (16)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (17)
Equation (16) indicates that in the presence of a state-dependent, actuarially fair insurance, the risk-neutral solution is replicated. Although the existence of the idiosyncratic risk imposes utility penalties, the presence of market inputs applied will maximize expected profits.
To investigate how the farmer's decisions are affected by the skill of the forecast, we need to sign [[partial derivative]I.sup.*g]/[[partial derivative][[omega].sub.g|g], and [[partial derivative]N.sup.*g]/ [partial derivative][[omega].sub.g|g]. Comparative statics Comparative statics is the comparison of two different equilibrium states, before and after a change in some underlying exogenous parameter. As a study of statics it compares two different unchanging points, after they have changed. on the system given by (16) and (17) indicate that
[[partial derivative]I.sup.*g]/[[partial derivative][[omega].sub.g|g] = ([f.sub.N]([N.sup.*g],[r.sub.g]) - [f.sub.N]([N.sup.*g],[r.sub.b]))[[partial derivative]N.sup.*g]/[[partial derivative][omega].sub.g|g]. (18)
Since we assumed that the marginal productivity of N is higher in good years, the partial derivatives in Equation (18) have the same sign. The effect of the skill of the forecast on the optimal nitrogen application is given by
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (19)
where H is the determinant determinant, a polynomial expression that is inherent in the entries of a square matrix. The size n of the square matrix, as determined from the number of entries in any row or column, is called the order of the determinant. of the Hessian of the problem (positive by second-order sufficient conditions (SOSC SOSC Southern Oregon State College
SOSC Smithsonian Oceanographic Sorting Center
SOSC special operations support command (theater army) (US DoD)
SOSC System Operational and Support Capability ) for a maximum). E ([??]"([[pi].sup.*]|g)) is negative by SOSC. The second term in the numerator numerator
the upper part of a fraction.
see additive genetic relationship.
numerator Epidemiology The upper part of a fraction is positive by technology assumptions, and thus [[partial derivative]N.sup.*g]/[[partial derivative][omega].sub.g|g] > 0 and [[partial derivative]I.sup.*g]/[[partial derivative][[omega].sub.g|g]> 0. Analogous analysis and previous results indicate that the farmer will purchase less insurance and use less inputs when the forecast is for a poor year.
The previous comparative statics exercise reveals that, counter to intuition, when the skillful forecast indicates a good (poor) year is likely, the farmer will purchase more (less) of an insurance of actuarially fair price. The expected change in overall insurance purchases brought about by a forecast of increasing skill depends on the relative adjustment induced by each kind of forecast (good versus poor growing conditions) and the natural frequency of each event, protecting the farmer from climate risk, it protects the farmer from forecast error.
The farmer is able to remove uncertainty from forecast error and improve utility by operating at the expected profit-maximizing input level instead of self-insuring with less aggressive changes in input. When a good year is forecast, the farmer can intensify to the expected profit-maximizing level, and when a bad year is forecast the farmer can prevent losses through the efficient level of input reduction while still maintaining inputs at a level that maximizes expected profits by taking into account the chance that a good year may still occur. Ex ante expected profits when both insurance and forecast are allowed to interact with the farmer's input decisions are given by
E([[pi].sup.*]) = [[omega].sub.g]([[pi].sup.0]([N.sup.*g],[r.sub.g]) - [[tau].sub.1][I.sup.*g]) + (1 - [[omega].sub.g])([pi]0([N.sup.*b],[r.sub.g]) - [[tau].sub.2][I.sup.*b]) (20a)
Var([[pi].sup.*]) = [[omega].sub.g](1 - [[omega].sub.g])[(E([[pi].sup.*g]) - E([[pi].sup.*b])).sup.2] + [[sigma].sup.2.sub.[epsilon]] (20b)
where we used the assumptions that the insurance is actuarially fair and that the forecast is unbiased ([m.sub.g] = [[omega].sub.g]). E ([[pi].sup.*.sub.i]) denotes expected profits for an i = g, b forecast. The actuarially fair insurance will lead farmers to maximize expected profits, and the skill of the forecast allows farmers to make better-informed decisions. Thus, expected profits increase when both the insurance and a skillful forecast are available. However, the introduction of a forecast comes at the cost of increasing profit variability. If the forecast has no skill, the farmer will not adjust input usage, and thus expected profits are invariant (programming) invariant - A rule, such as the ordering of an ordered list or heap, that applies throughout the life of a data structure or procedure. Each change to the data structure must maintain the correctness of the invariant. to the information released. In this situation, the insurance is able to remove the systemic risk (first term in Equation (20b)). If the skill of the forecast creates a wedge between expected profits obtained under different forecasts, the ex ante variance of profits increases and the effectiveness of the insurance to manage variability is reduced. Counter to intuition, the variability of profits when both risk management tools are available can be higher than when none is available. This can be seen by comparing Equations (20b) and (4b). Whenever expected profits under different forecasts differ more than the profit difference in the base case, variability will be increased.
In this case, with a perfect forecast, there is no role for insurance, while insurance is completely relied upon when the forecast has no skill. The difference is that the forecast directly allows improved input application that leads to increased yields and increased profits, while the insurance does not directly increase profits but allows the farmer to behave less conservatively. Thus, with insurance and a forecast, the farmer can have increased variability because of the potential to produce more in good years. However, to the extent that bad years are perfectly forecast, the farmer must face the full brunt brunt
1. The main impact or force, as of an attack.
2. The main burden: bore the brunt of the household chores. of the drought, albeit with full information for optimal input use.
Since insurance plays different roles when priced using climatology climatology
Branch of atmospheric science concerned with describing climate and analyzing the causes and practical consequences of climatic differences and changes. Climatology treats the same atmospheric processes as meteorology, but it also seeks to identify slower-acting or the forecast, it may be worthwhile to offer both pre- and postforecast policies: preforecast to protect against climatology and postforecast to protect against forecast error. The relative value of the pre- versus postforecast depends on the skill of the forecast and the farmer's flexibility in making changes in order to use effectively the forecast information in production to increase profits in good years and reduce damages in bad years. Future work should analyze the potential value of this risk management strategy.
Risk-driven barriers to development and innovations in financial markets have fed a renewed interest in the search that allows farmers to intensify their operations and invest in higher returns but in riskier activities. This is touted as key in helping farmers in developing countries escape poverty traps.
A substantial effort has been devoted to the study of the interaction between insurance and input decisions. Work has also explored the relationship between climate forecasts and input usage. Since previous literature has said little about the interaction between insurance, in particular index insurance, and climate forecasts, we have formalized for·mal·ize
tr.v. for·mal·ized, for·mal·iz·ing, for·mal·iz·es
1. To give a definite form or shape to.
a. To make formal.
b. and studied the basic relationship between forecasts, insurance, and production decisions through a theoretical model.
Climate scientists have made remarkable progress at forecasting rainfall and temperature deviations from long situations with the capacity to threaten the effectiveness and survival of existing index insurance mechanisms to alleviate poverty.
We find fundamental interactions between insurance and probabilistic climate forecasts. Insurance (in the absence of moral hazard effects) will induce farmers to use more of a risk-increasing input. The presence of a skillful probabilistic find that if an actuarially fair insurance is available, and the farmer's profits are not sufficiently responsive to the input mix, the introduction of a climate forecast harms the farmer if the premiums reflect the forecast (even if they are actuarially fair). Hence, a necessary condition for farmers to prefer a state-contingent, commercially viable insurance product is that farmers can increase their profits by taking the forecast information into account. Perhaps surprisingly, we find that forecast information may induce farmers to buy more insurance even as it reduces risk. The intuition is that the forecast may widen the wedge between optimized profits among states of the world. Although basis risk is an important issue in the treatment of index insurance, instead of having fundamental implications to the basic relationship between the forecast and insurance, we find that our straightforward representations of basis risk simply attenuate To reduce the force or severity; to lessen a relationship or connection between two objects.
In Criminal Procedure, the relationship between an illegal search and a confession may be sufficiently attenuated as to remove the confession from the protection afforded by the our findings. Nevertheless, it is likely that future work on this topic would be of value.
Since insurance priced using climatological probabilities protects against the climate and insurance priced on forecast probabilities protects against forecast error, farmer preferences for climatological- versus forecast-based insurance mirror the value of the forecast information in production. It is likely that both products could be useful, particularly when farms are heterogeneous, especially in the rates at which they are willing to trade expected levels by variability in profits Insurance prices may communicate forecast information when farmers do not have direct access to the forecast. Studies exploring the potential of insurance prices as aggregators of forecast information would be valuable.
Implementation of forecast-contingent insurance policies will require nontrivial nontrivial - Requiring real thought or significant computing power. Often used as an understated way of saying that a problem is quite difficult or impractical, or even entirely unsolvable ("Proving P=NP is nontrivial"). The preferred emphatic form is "decidedly nontrivial". innovation, as current insurance regulations and financing methodologies are not necessarily well suited to quickly fluctuating premiums, value at risk, and market size. Because an insurance policy typically does not include the option for resale at a market price, the pricing of information cannot directly rely on market movements. For insurance, it is likely that information pricing will be explicitly engineered into the products offered. Future work addressing these issues may be worthwhile.
Since insurance providers must typically reinsure re·in·sure
tr.v. re·in·sured, re·in·sur·ing, re·in·sures
To insure again, especially by transferring all or part of the risk in a contract to a new contract with another insurance company. their risks, the forecast-dependent price fluctuations of global weather derivative Weather Derivative
An instrument used by companies to hedge against the risk of weather-related losses. The investor who sells a weather derivative agrees to bear this risk for a premium. If nothing happens, the investor makes a profit. markets will lead to variations in reinsurance costs that must somehow be managed. Retail products that adjust based on the forecast could be one alternative that insurers have to address this problem. Future work will need to address both the technical issues of appropriately translating forecast information into an unbiased insurance and the financial and implementation issues In the Business world, companies frequently set-up a connection between which they transfer data. When the connection is being set-up, it is referred to as implementation. When issues occur during this phase, they are known as implementation issues. of how to build a product that can be marketed and financed by an insurance company, that meets the demands of clients, and that falls within the allowable legal framework of insurance. One ENSO-based strategy might be to charge a nonvarying premium for a base liability calculated for an unfavorable ENSO phase and to increase the liability covered at no cost when the forecast is favorable. These changes might be financed by the insurer through purchases of ENSO derivatives or related products.
Babcock, B. A., and D. A. Hennessy, 1996, Input Demand Under Yield and Revenue Insurance, American Journal of Agricultural Economics, 78: 416-427.
Barnett, B. J., C. B. Barrett, and J. R. Skees, 2007, Poverty Traps and Index-Based Risk Transfer Products, University of Georgia Organization
The President of the University of Georgia (as of 2007, Michael F. Adams) is the head administrator and is appointed and overseen by the Georgia Board of Regents. , Department of Agricultural and Applied Economics, Working paper.
Cabrera, V. E., D. Letson, and G. Podesta, 2005, The Value of Climate Information When Farm Programs Matter, Southeast Climate Consortium Technical Report Series SECC-05-004, Gainesville, FL.
Chambers, R. G., and J. Quiggin, 2000, Optimal Producer Behavior in the Presence of Area-Yield Crop Insurance, American Journal of Agricultural Economics, 84: 320-334.
Chang, C. C., S. K. Lin, and M. T. Yu, 2011, Valuation of Catastrophe Equity Puts With Markov-Modulated Poisson Processes, Journal of Risk and Insurance, 78: 447-473.
Doherty, N. A., and A. Richter, 2002, Moral Hazard, Basis Risk, and Gap Insurance, Journal of Risk and Insurance, 69: 9-24.
Goddard, L., S. J. Mason, S. E. Zebiak, C. F. Ropelewski, R. Basher, and M. A. Cane, 2001, Current Approaches to Seasonal-to-Interannual Climate Predictions, International Journal of Climatology, 21: 1111-1152.
Golden, L. L., M. Wang, and C. Yang, 2007, Handling Weather Related Risks Through the Financial Markets: Considerations of Credit Risk, Basis Risk, and Hedging, Journal of Risk and Insurance, 74:319-346
Hansen, J. W., 2002, Applying Seasonal Climate Prediction Climate prediction refers to :
- Global warming
Hellmuth, M. E., D. E. Osgood, U. Hess, A. Moorhead, and H. Bhojwani (eds.), 2009, Index Insurance and Climate Risk: Prospects for Development and Disaster Management (New York New York, state, United States
New York, Middle Atlantic state of the United States. It is bordered by Vermont, Massachusetts, Connecticut, and the Atlantic Ocean (E), New Jersey and Pennsylvania (S), Lakes Erie and Ontario and the Canadian province of : Climate and Society No. 2. International Research Institute for Climate and Society (IRI Iri (ē`rē`), former city, North Jeolla (Cholla) prov., SW South Korea. An agricultural center and transportation hub, it was absorbed into Iksan. ) Columbia University Columbia University, mainly in New York City; founded 1754 as King's College by grant of King George II; first college in New York City, fifth oldest in the United States; one of the eight Ivy League institutions. ).
Hess, U., and J. Syroka, 2005, Weather-Based Insurance in Southern Africa: The Case of Malawi, The World Bank Agricultural and Rural Development Discussion paper No. 13.
Just, R. E., and R. D. Pope, 2002, A Comprehensive Analysis of the Role of Risk in U.S. Agriculture (Boston: Kluwer Academic Publishers).
Kihlstrom, R., D. Romer, and S. Williams, 1981, Risk Aversion risk aversion
The tendency of investors to avoid risky investments. Thus, if two investments offer the same expected yield but have different risk characteristics, investors will choose the one with the lowest variability in returns. With Random Initial Wealth, Econometrica, 49 (July): 911-920.
Kovacevic, R. M., and G. Ch. Pflug, Forthcoming, Does Insurance Help Escape the Poverty Trap poverty trap
the situation of being unable to raise one's living standard because any extra income would result in state benefits being reduced or withdrawn
Noun 1. ? A Ruin Theoretic Approach, Journal of Risk and Insurance. DOI (Digital Object Identifier) A method of applying a persistent name to documents, publications and other resources on the Internet rather than using a URL, which can change over time. : 10.1111/j.1539-6975.2010.01396.x.
Mahul, O., 2001, Optimal Insurance Against Climatic Experience, American Journal of Agricultural Economics, 83: 593-604.
Mjelde, J. W., T. N. Thompson, and C. J. Nixon, 1996, Government Institutional Effects on the Value of Seasonal Climate Forecast. American Journal of Agricultural Economics, 78:175-188
Moschini, G., and D. A. Hennessy, 2001, Uncertainty, Risk Aversion, and Risk Management for Agricultural Producers, in: B. Gardner and G. Rausser, eds., Handbook of Agricultural Economics, Volume 1 (Amsterdam: Elsevier), pp. 87-153.
Ramaswami, B., 1993, Supply Response to Agricultural Insurance: Risk Reduction and Moral Hazard Effects, American Journal of Agricultural Economics, 75: 914-925.
Ropelewski, C., and M. Halpert, 1987, Global and Regional Scale Precipitation Patterns Associated With the E1 Nino/Southern Oscillation, Monthly Weather Review Monthly Weather Review is a publication of the American Meteorological Society. Topics covered by the journal include research related to analysis and prediction of observed and modeled circulations of the atmosphere, including technique development, data assimilation, model , 115(8): 1606-1626.
Skees, J., P. Hazell, and M. Miranda, 1999, New Approaches to Crop Yield Insurance in Developing Countries, International Food and Policy Research Institute, Environment and Production Technology Division Discussion paper No. 55.
World Bank, 2001, World Development Report 2000/2001: Attacking Poverty (New York: Oxford University Press).
World Bank, 2005, Managing Agricultural Production Risk: Innovations in Developing Countries, Agricultural and Rural Development Department Report No. 32727GLB (Gramm-Leach-Bliley Act) Enacted in 1999 and effective in mid 2001, the GLB stipulates that every financial institution shall protect the security and confidentiality of its customers' confidential personal information. .
(1) Since there is a large body of literature on the role of risk in agriculture (see, e.g., Just and Pope, 2002; Moschini and Hennessy, 2001), it is worthwhile to note that there are several sources of risk that are relevant from the farmer's perspective, including production, price, technological, and policy uncertainties. Since our focus is on climate risks, a case of production uncertainty, we will assume that prices are nonrandom.
(2) One type of insurance that is becoming available is index insurance, which is similar to a weather derivative because payouts are calculated based on a weather-based index. It is important to understand the relationship between insurance and forecasts in this context because insurance does not include an option common to weather derivatives Weather derivatives are financial instruments that can be used by organizations or individuals as part of a risk management strategy to reduce risk associated with adverse or unexpected weather conditions. , the option to perform repeatedly marginal transactions in a dynamic market. Therefore, instead of relying
on market-based updates for optimal use of information, mechanisms to incorporate the information must be built directly into the retail contracts.
(3) One example is the index insurance for groundnut groundnut, common name for several different genera of twining herbaceous, leguminous plants with geocarpie (underground fruits), chiefly the peanut. Groundnuts are classified in the division Magnoliophyta, class Magnoliopsida, order Rosales, family Leguminosae. and maize maize: see corn. farmers in Malawi (Hess and Syroka, 2005). In this case, the insurance provides the risk protection required for lenders to be willing to provide the credit farmers need to be able to adopt yield- and quality-increasing seeds.
(4) The two exceptions just mentioned analyze the impact of several government programs (including traditional yield insurance) on the value of seasonal forecast information. Based on numerical simulations of specific situations, these studies motivate the need for work that derives the fundamental relationships explaining their results.
(5) Longer-term climate forecasts have been recently investigated for the pricing of catastrophe equity puts (Chang, Lin, and Yu, 2011).
(6) The function used by Mahul (2001) is y = g(N)r + h(N) + [epsilon].
(7) The latter inequality reflects the assumption that the marginal response to an input is higher when other factors (e.g., rain) are not limiting. The assumption could be relaxed and the direction of the inequality reversed. In this case, the signs of the comparative statics presented in the following sections should also be reversed.
(8) In essence, we are assuming that the production region is small, in the sense that production shortfalls in this area do not "upset" world (and local) markets. Results will likely be attenuated Attenuated
Alive but weakened; an attenuated microorganism can no longer produce disease.
Mentioned in: Tuberculin Skin Test
having undergone a process of attenuation. if correlations between yields and prices are present. Malawi provides examples for both, export-oriented crops such as groundnuts and tobacco for which the assumption holds and local crops such as maize in which output affects prices (Hellmuth et al., 2009).
(9) Improved climate models can only provide a conditional distribution of yields. An inherent level of uncertainty is maintained as long as [[omega].sub.i|i] < 1 holds. Only in the case of a perfect forecast ([[omega].sub.i|i] = 1, for i = b,g), will all the systemic uncertainty be removed. Idiosyncratic variability still remains in this model.
(10) This result is analogous to Proposition 2 in Mahul (2001) with independent risks, where the trigger for the insurance is the maximum value of the weather variable, and the slope of the indemnity function with respect to the index equals its marginal productivity (given an input decision).
Miguel A. Carriquiry is at the Center for Agricultural and Rural Development, Iowa State University Academics
ISU is best known for its degree programs in science, engineering, and agriculture. ISU is also home of the world's first electronic digital computing device, the Atanasoff–Berry Computer. . Daniel E. Osgood is at the International Research Institute for Climate and Society, Columbia University. Osgood can be contacted via e-mail: email@example.com.
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|Author:||Carriquiry, Miguel A.; Osgood, Daniel E.|
|Publication:||Journal of Risk and Insurance|
|Date:||Mar 1, 2012|
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