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.
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