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efforts to model the effects of improved climate forecasts. For summaries of the literature on induced innovation, see Thirtle and Ruttan (1987) and Ruttan (1997).

Several of the challenges that have been mentioned in connection with estimating the effects of climate variability are equally relevant to estimating the value of improved forecasts. One of these is estimating the effects on outcome variables that are hard to quantify. For example, decreasing the amount of uncertainty about next month's or next season's weather may facilitate vacation planning for some people. It may relieve anxiety about possible extreme events—or, depending on the content of the forecast—it may produce anxiety. Improved forecasts will, at least at first, cause people engaged in weather-sensitive activities to rethink their usual methods of coping—a rethinking that may bring long-term benefits but that has short-term costs, at least in time and effort. It may be difficult even to identify all the important nonfinancial effects, and it is always difficult to weigh them against each other and against monetary outcomes.

It is also important but difficult for models to disaggregate the estimates of net value and to consider the distributional effects of improved forecasts. Models should address the likelihood that some groups may benefit from improved forecasts at the expense of others. We have already noted some of the possibilities, such as that commodities speculators, farmers, and consumers are to some extent competitors in how they use forecasts. There is also the possibility revealed by the experience of the Green Revolution—that to the extent that there are fixed costs of interpreting forecast information, larger operators will benefit more by spreading those costs over a larger output, leaving smaller and less economically successful operators at a relative disadvantage. It is important to estimate the value of climate forecasts both throughout entire economies and disaggregated by sector, region, and type of actor.

Addressing many of the challenges alluded to above is made difficult by a glaring lack of appropriate data sets. Long-time-scale, comprehensive data sets archived at appropriate geographic scales (household/firm, local, regional, national) are nonexistent or not readily accessible to the broader research community. Data on particular attributes are often of dubious quality and not comparable over space and time. Moreover, there is no general agreement about which data are most important to collect for the purposes of estimating the effects of climate variations or the value of forecasts.

Because the quality of the relevant data is probably far short of what is needed for good analysis, it is important to set priorities for improving the data base. In doing this, it makes sense to consider at least these factors:



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