Knowledge that Transforms

INVENTORY CYCLE analysis has so far assumed sales forecasts by firms-projecting a constant multiple of either the latest level, or the latest rate of change, of sales into the future. This structure is adequate to show the way in which expectations and the level of business activity interact, but it has a certain ring of implausibility. Other ways of forecasting sales change the behavior of the model, so we may ask if stability is particularly sensitive to the technique assumed, and if more realistic forecasting techniques will enhance or diminish cyclical stability. Here I propose one forecasting technique that pretends to added realism (while remaining sufficiently simple to be incorporated in a formal inventory cycle model) and partly evaluate its effect on cyclical stability. If we are to shift our attention to forecasting models very far removed from naive extrapolation, we must endow the hypothetical seer with information beyond his own past sales levels. A wide choice of alternative assumptions confronts us; casual inspection of forecasting texts will show that some firms find it useful to look at trends in national income or similar aggregates, while others find more specific indicators useful: automobile tire manufacturers use estimates of the stock of cars on the road, for example. But many of these alternatives are specific to a particular industry; here we seek a model that could plausibly be used by most industries, one which will take account of industry differences while using one functional form (for mathematical simplicity). We shall assume that the forecaster knows which sectors of the economy buy the output of his industry, and has some idea of both the trends in

This report utilizes decision theory to investigate the possible benefits of better weather information to California raisin growers. A supply curve is fitted to the raisin industry and used to evaluate the importance of various factors: weather is of overwhelming importance. Subsequent analysis focuses on the most vulnerable aspect of raisin production: its dependence on early forecasts of rain in September and October when the grapes are being dried and in danger of being damaged by rain. Section 1, in the tradition of micro-economic, partial equilibrium analysis, is concerned with the value of weather information to a single grower. Only the grower focused upon is assumed to receive the better information. This assumption implies there is high value for forecasting rain three weeks in advance. A figure of 90.95 per acre is the value of perfect three week forecasts. Were this value to be realized for each bearing acre in 1960, the total value of better weather information to the raisin industry would be 20,300,000. But merely summing across firms to get industry totals may lead to a fallacy of composition: the analysis must be extended to a wider partial equilibrium framework. Section 2 examines the value of better weather information to the raisin industry as a whole. Since costs are not greatly affected by improved weather information, profit differences are closely approximated by changes in revenue. The elasticity of demand for raisins is calculated from a demand curve fitted to industry data. The inelasticity of demand causes profit to fall under the impact of better information, at least in the short run. The scope of the analysis is then extended to other industries. Using this wider viewpoint, the Conclusion examines the possibility of a simple tax that would reallocate land and labor. The presence of released resources seems to imply a clear gain for better information. However, the basis of such a gain is positive value for these resources in other uses. But the short run inelasticity of other possible products makes it clear that, at least in the short run, better weather information results in net loss. However, some relief is offered through the possibility of regulation. An Appendix examines the question of the value of inaccurate forecasts.