Introduction to Input-Output Framework for Analysis of Tourism as an Industry
Input-Output (I-O) is an economic framework which was awarded two Nobel Prizes in Economics, one in 1973 to Dr. Wassily Leontief and the other for its extended Social Accounting Matrix (SAM) to Sir Richard Stone in 1984. The I-O/SAM framework remains as a core component for Computable General Equilibrium (CGE), which cannot function without the data of I-O/SAM. Applications of I-O family have been published not only in the broader social science fields including but not limited to economics, regional science, international development, but also in the tourism field. I-O framework is the foundation of System of National Accounts (SNA), which has been used by all the national governments in the world to measure important data such as gross domestic product (GDP), capital formation etc. Need for studying I-O has been increased significantly for tourism researchers because of a simple fact that a global common recording system to measure economic activities of tourism, officially endorsed by United Nations, World Bank, International Monetary Fund, Organization for Economic Cooperation and Development, to name a few, is based on I-O.
Economic Impacts from Development of the Coastal Town in Queensland on Tourism and Regional Economy
This paper discusses the impacts of proposed development projects at Emu Park—a coastal town in Queensland, Australia—on regional economy. The input–output (IO) analysis is used to assess the economic benefits to the region. The economic impacts from the construction stage are identified. The ongoing benefits from the projects to the regional economy due to a potential increase in tourists’ visits are investigated. The regional economy will grow more rapidly if the connections among the tourism related sectors and key industries are encouraged. Local procurement strategies can assist in enhancing the benefits from increased tourism to the region.
Rapid Spatially Coupled AVO Inversion
The Bayesian methodology yields a natural framework for many geophysical inverse problem, see e.g. (Tarantola and Valette, 1982; Tarantola, 1987; Duijndam, 1988a; Duijndam, 1988b). The solution of a Bayesian inverse problem is represented by the posterior distribution. From the posterior distribution the best estimate and the corresponding uncertainty can be extracted and a set of plausible solutions can be drawn.