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werewere used to assess how farmers' welfare would have
been improved and stocks of bullocks increased if farmers had had
weather insurance or increased capabilities to borrow.
Such models can also incorporate learning. A standard method for
doing this is to incorporate Bayesian learning. Model estimation
then reveals, along with preference parameters and standard
technology parameters, how fast learning takes place and how it is
affected by the underlying uncertainty of the economy. Such
estimable dynamic models have shed new light on behavior and
reveal, among other results, how important it is to achieve an
understanding of the consequences of technological change to
understand the constraints facing decision makers. Because the
techniques involve iterative estimation and model solution,
obtaining estimates of the structure underlying dynamic decisions
requires a great deal of computing power. To obtain estimates in
realistic time frames, the number of parameters characterizing the
structure is kept to a minimum, so that a common criticism of such
models is that they are too simple. Absent substantial innovations
in dynamic solution techniques or computing power in the near
future, hybrid estimable models that take estimates of biophysical
processes from other studies and fix them for purposes of
estimation may be a promising technique in coming years.
Input-output models have been used to trace flows of costs and
revenues among linked sectors of regional and national economies.
Such models (e.g., Bowes and Crosson, 1993) fully replicate
interindustry exchanges of capital and labor costs embodied in
producer and consumer goods and show how such exchanges are
affected by changes in final demand for goods and services. They
enable climate-induced changes in supplies of basic materials
(e.g., agricultural production, fish harvests) to ramify throughout
the connected industries in an affected economy. In the MINK study
mentioned above, an input-output model was used to compute the
overall effect of a recurrence of the Dust Bowl droughts of the
1930s on the MINK region's economy. Absent adjustments to on-farm
production, the droughts prompted a 9.7 percent ($29.9 billion)
decrease in total regional production.
The main strength of input-output models is their ability to
track interindustry exchanges in great detail. Intersectoral
linkages are realisticthat is, they are based on observation.
The main disadvantage of input-output models is their static
nature. The coefficients used to represent interindustry exchanges
are constants, with the result that the models are unable to
represent the reinvestment of underused resources induced by
climatic events (e.g., unemployed agricultural labor) in other
sectors of the economy. Consequently, input-output models tend to
overstate the negative impacts of climatic events.