| With anticipated increased demand for economic impact analysis to support public policy and investment decisions at all levels, steps to improve the efficiency of the economic impact analysis process must be examined. Presently, the most common method of impact analysis involves the use of ready-made interactive, input-output models. These models along with the necessary data can be very expensive and require substantial skills to use. The primary purpose of this study is to investigate the feasibility of providing more readily available, situation-specific, economic multipliers, to assist in the economic impact analysis process. The study focuses on the wood products industries and also examines the response of multipliers to changes in the size of economic impact areas.; Regression analysis techniques are used to determine the relationship between several types of multipliers and reasonably available explanatory variables including human population, population density, number of economic sectors, total industry output, personal income, and the physical size of the impact area.; Results of the study demonstrate that regression models, based on readily available information, might not be the best approach to predict economic multipliers for the wood products industries. These models vary greatly in their ability to predict multipliers. In some specific situations the resulting models might be useful but at the same time risky. Additional study would be needed to analyze the relative merits of alternative prediction methods including costs and the extent and quality of results (e.g. statistical confidence) needed to support decisions. Study results also highlight the effect of impact area size on multipliers—as economic impact areas become relatively large, the growth of multipliers rapidly culminates. Therefore, analysts should use caution when increasing the size of impact areas to capture additional economic effects, especially in heavily populated areas with large economies. |
