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Murphy, 1988) and, in transportation, the decision on how much
to invest in snow removal equipment (Howe and Cochrane, 1976).
The two crop choice studies cited above (Tice and Clouser, 1982;
Adams et al., 1995) are illustrative of the issues concerning
rational expectations raised by Johnson and Holt (1997). Tice and
Clouser (1982) examined the use of a seasonal climate forecast by a
farmer to determine relative area planted to corn versus soybeans.
The forecast was assumed to be perfectly accurate. Two allocations
of crops are computed by using historical climatic averages and by
using forecast information. The simple arithmetic difference
between average net revenues per hectare using climatic averages
and that dictated by the forecast was computed. Use of the forecast
to allocate areas planted in corn and soybeans was shown to
increase revenues by $3.65 per hectare per year beyond revenues
using historical climate.
Adams et al. (1995) investigated the use of climate forecasts to
determine allocations of areas planted to cotton, corn, sorghum and
soybeans in the southeastern United States. The chief difference
between their study and that of Tice and Clouser (1982) was that
they computed forecast value in terms of total net social welfare
(combined producer and consumer surplus) for the nation rather than
revenues for the individual farmer. Using a general equilibrium
economic model, they computed welfare using forecast-assisted crop
allocations under an assumption that all southeastern farmers would
plant accordingly. Furthermore, they explicitly considered the case
in which forecast accuracy is imperfect. They found that the use of
a perfect forecast increased social welfare by $145 to $265 million
per year. The use of an imperfect (though still skillful) forecast
increased welfare by $96 to $130 million per year.
Several research problems remain unsolved for Bayesian decision
theory applications to climate forecasts. These applications do not
address how forecast information available in an invariant, and
possibly irrelevant, format is made relevant and incorporated into
individual decision makers' information requirements, which differ
considerably from one decision maker to the next. They do not
adequately explore the possibility that decision makers' utility
functions are nonlinear. Most applications do not estimate the
distributional effects of the use of forecasts (i.e., winners
versus losers). Finally, the lack of data and empirical techniques
for clearly valuing forecasts precludes the testing of Bayesian
models against the real world.
Challenges in Estimating the Value of
There remain some significant challenges in applying the general
concept of the value of forecasts. One is in addressing the