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were—were 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 realistic—that 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.