Date of Award

1987

Degree Type

Dissertation

Degree Name

Doctor of Philosophy in Economics

Department

Economics

First Advisor

Timothy Tyrrell

Abstract

The tourism industry is the fastest growing industry and the third largest employer in the State of Rhode Island. In terms of industrial output, tourism is currently estimated to generate about five percent of the State's gross domestic product.

Rhode Island attracts an estimated 5 to 8 million visitors annually. Among these are visitors to Rhode Island's short-term events. The sales impact that is generated in the business sector, the personal income and employment that is generated for individuals and households and the government revenue provided through taxes qualify it as a major economic force for the future.

The measurement of such impacts is a challenge to researchers because of the diverse nature of the spending groups present and the fact that the services they demand are also demanded by the residents of the State.

Traditionally, economic impact studies have focused on estimating sales impacts of short-term tourism events. Predicting these impacts has not yet been attempted. This study attempts to make some headway in this direction by developing a framework for forecasting the impacts of short-term tourism events.

The economic impact of a short-term event can be described as the product of an average participant's expenditures and total participation in visitor-days. The forecasting framework developed in this study is composed of these two parts: a participation forecast and an expenditure forecast.

The expenditure forecast is based on a model which relates expenditure per person-day to numbers of persons per group and days per visitor at the event. This model is used to predict total expenditures. An allocation model is used to estimate expenditures on particular categories of event goods and services.

The participation forecast is a synthesis of empirical and expert judgmental prediction estimates. The empirical forecast is based on a model relating numbers of visitors per event to prices, weather and consumer perception. The empirical and judgment estimates of participation are weighted according to the proportions of total variance and added together. This synthesis is related to the Bayesian procedure of revising belief in the light of new information.

Some of the questions which remain unanswered by this research include: How to devise methods of measuring the reliability of subjective estimates by experts; How to construct a detailed typology of events for impact assessment; and How to incorporate event and site reputation into a dynamic forecasting model. Specific answers to these questions could have a strong influence on future data collection and updating procedures. General answers will enable transference of the forecasting procedure to other states.

The preliminary model derived in this thesis should serve as a useful guide to researchers and potential sponsors of future Rhode Island events. It is hoped that the State of Rhode Island Department of Economic Development might use the results for logistic and planning purposes.

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