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Uncovering the Factors behind Comparative Regional Economic Performance: A Dynamic CGE Approach

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  • James A Giesecke
  • John R Madden

Abstract

Recently a new method has emerged for uncovering the factors driving regional disparities in growth performance. The method involves historical analysis with a multiregional computable general equilibrium model. This paper has three main aims. The first is to demonstrate the capacity of the CGE historical technique to decompose the causes of regional divergence into clearly-specified economic factors. The second is to provide a generic miniature model that can be used as a template for adapting any multiregional CGE model to give it the capacity for undertaking historical analysis. The third is to demonstrate that this same miniature model can be used to explain the regional results in terms of the major model mechanisms behind them.

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Bibliographic Info

Paper provided by Victoria University, Centre of Policy Studies/IMPACT Centre in its series Centre of Policy Studies/IMPACT Centre Working Papers with number g-165.

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Date of creation: Dec 2006
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Publication status: Published in Regional Studies, Vol. 44(10), December 2010, pp. 1329-1349.
Handle: RePEc:cop:wpaper:g-165

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Keywords: Computable general equilibrium; Regional growth; Regional divergence; Multi-regional historical analysis;

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  1. Maria Abreu Henri L. F. de Groot & Raymond J. G. M. Florax, 2005. "A Meta-Analysis of β-Convergence: the Legendary 2%," Journal of Economic Surveys, Wiley Blackwell, vol. 19(3), pages 389-420, 07.
  2. Harrison, W Jill & Pearson, K R, 1996. "Computing Solutions for Large General Equilibrium Models Using GEMPACK," Computational Economics, Society for Computational Economics, vol. 9(2), pages 83-127, May.
  3. Johan Lundberg, 2006. "Using spatial econometrics to analyse local growth in Sweden," Regional Studies, Taylor & Francis Journals, vol. 40(3), pages 303-316.
  4. W. Jill Harrison & J. Mark Horridge & K.R. Pearson, 2000. "Decomposing Simulation Results with Respect to Exogenous Shocks," Computational Economics, Society for Computational Economics, vol. 15(3), pages 227-249, June.
  5. Adams, Philip D. & Dixon, Peter B. & McDonald, Daina & Meagher, G. A. & Parmenter, Brian R., 1994. "Forecasts for the Australian economy using the MONASH model," International Journal of Forecasting, Elsevier, vol. 10(4), pages 557-571, December.
  6. James Giesecke, 2002. "Explaining regional economic performance: An historical application of a dynamic multi-regional CGE model," Papers in Regional Science, Springer, vol. 81(2), pages 247-278.
  7. Suahasil Nazara & Geoffrey J.D. Hewings, 2004. "Spatial Structure and Taxonomy of Decomposition in Shift-Share Analysis," Growth and Change, Gatton College of Business and Economics, University of Kentucky, vol. 35(4), pages 476-490.
  8. Dixon, Peter B. & Parmenter, B. R. & Powell, Alan A., 1984. "The role of miniatures in computable general equilibrium modelling : Experience from ORANI," Economic Modelling, Elsevier, vol. 1(4), pages 421-428, October.
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Cited by:
  1. Philip Adams & Janine Dixon & James Giesecke & Mark Horridge, 2010. "MMRF: Monash Multi-Regional Forecasting Model: A Dynamic Multi-Regional Model of the Australian Economy," Centre of Policy Studies/IMPACT Centre Working Papers g-223, Victoria University, Centre of Policy Studies/IMPACT Centre.

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