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and market adjustments, is the theoretical aggregate value of a
perfect forecast of drought.
The approach used in this study has been criticized by Antle
(1996) and must be viewed in light of fundamental criticisms. It
uses reduced-form relationships between climate and aggregate
decision making and thus does not make the structural elements of
decision making explicit. The approach requires the assumption that
the underlying conditions embedded in the reduced-form model, such
as agricultural policy, must be assumed to be constant between the
period of the data used to generate the model and the period being
simulated. It also requires invariance in the model structure over
time and space (Schneider, 1997). Moreover, the farmers in each
region use coping mechanisms (e.g., hedging against risk, using
seeds that are resilient to climatic fluctuations) based on the
lack of skillful forecasts; thus, unless they are completely
insured, they have lower profits on average than they would if
skillful forecasts were available. This last consideration calls
into question the validity of the assumption that the baseline
condition equates with having a perfect forecast because
technologies and other coping mechanisms will be different with
better forecasts. For instance, farmers with good forecasts will
use seeds that are more sensitive to weather (such as
water-dependent varieties if the forecast is for lots of rain).
Despite the criticisms, Easterling and Mendelsohn (in press)
illustrate some of the defensible approaches to estimating the
value of climate forecasts using the general concept of differences
in outcomes. One value of the concept is that it makes possible a
distinction between the potential value of a forecast and its
actual value: for example, actors who do nothing with forecast
information receive no value from it. The concept also allows for
the possibility that a skillful forecast can have negative value.
This may occur in at least two ways. Actors may do things with the
expectation that the forecast average will be realized, but,
because of residual error in the forecast, their outcomes might
have been better if they had followed normal routines. Or some
actors may take advantage of forecast information in ways that
benefit them at great cost to others, so that the aggregate value
of the forecast is negative.
Simulations of Climate Forecast
Johnson and Holt (1997) state that the theoretical basis for
valuing forecast information lies in Bayesian decision theory.
Bayesian theory treats information as a factor in the decision
process to be used by agents to reduce uncertainty. According to
Bayesian theory the following assumptions hold: (1) prior to having
a forecast available, economic agents have subjective "prior"
probability estimates of a set of possible future