Abstract

1 This paper summarizes recent efforts to refine tools for estimating the economic impacts of tourist spending on local regions. Although tourism's impacts are most clearly felt within local tourism destination regions, economic impact models, data, and methods have been more widely available for national and state regions, e.g., The Travel Industry Associations' TEIM mo del (TIA, 2001). Convention and Visitor Bureaus (CVB's) and other local tourism organizations frequently lack the resources and skills to conduct or contract for economic studies, although many have conducted small scale studies of the economic impacts of individual facilities or special events (Crompton 1999). These studies vary widely in quality and generally require considerable local data gathering. The most widely used tourism economic impact models are variations on the simple visits * spending * multiplier equation, where total spending by visitors is estimated by multiplying measures of tourist volume for a region times an average spending per unit of visitation. Tourist spending estimates can then be applied to economic ratios, multipliers or an input-output model to convert spending to the associated income and jobs and, if desired, to estimate secondary effects. The Travel Industry Association's economic impact model With the assistance of Travel Michigan and the Greater Lansing Convention and Visitor Bureau, I've been able to triangulate across a number of data sources and develop reasonably consistent estimates of tourism's overall economic contribution to local areas. The approach combines the MITEIM model with TSA approaches, making use of IMPLAN's county economic data and local area multipliers, as well as what local spending and visit data exists. Perhaps the greatest advantage for local tourism organizations of the approach is that spending and economic impact estimates can be derived at minimal cost. Methods for estimating tourism's economic significance There are three basic approaches to estimating tourism's local economic impact. Each has its own strengths and weaknesses. My approach here is to triangulate by drawing on multiple methods and data sources to provide as defensible and reliable estimates as the topic permits. A variety of ad hoc methods have been used to make what I would call "quick and dirty" estimates of tourism's economic importance. These rely on the mo st convenient secondary data sources, even though they may provide an incomplete picture or require fairly heroic or unrealistic assumptions. For example,