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Bringing Regional Detail to a CGE Model using Census Data

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  • Glyn Wittwer
  • Mark Horridge

Abstract

Abstract The number of regions and sectors in most regional CGE models is small, due to data and computing limitations. The uses of such models will broaden if they have larger CGE databases. The TERM model combines a massive database with a variable aggregation facility and techniques to economize on computing capacity. This paper goes further, by outlining the use of small-region census data to devise a CGE database with an unprecedented number of regions. Already, the original TERM methodology has been used to devise multi-regional models for a number of countries. Census detail could enhance the detail in these models. Here we group small regions of Australia to develop the first bottom-up regional CGE model which distinguishes all 150 Federal single-seat electoral districts. Introduire des détails à l’échelle régionale dans un modèle CGE en utilisant les données de recensements Rèsumè Le nombre de régions et de secteurs dans la plupart des modèles CGE régionaux est limité, en raison des limitations des données et du calcul. Il est possible de généraliser les applications de ces modèles en les dotant de bases de données de CGE plus importantes. Le modèle TERM allie une base de données considérable à une fonction et des techniques d'agrégation variables permettant d’économiser sur la capacité de calcul. Cette communication va plus loin, et décrit l'application de données découlant de recensements pour des petites régions pour concevoir une base de données CGE avec un nombre de régions sans précédent. La méthodologie initiale TERM a déjà été utilisée pour concevoir des modèles multirégionaux pour un certain nombre de pays. Le détail de ce recensement pourrait renforcer le détail de ces modèles. Nous groupons ici de petites régions de l'Australie pour créer le premier modèle CGE régional du bas en haut qui distingue toutes les circonscriptions électorales fédérales à siège unique. Agregando detalle regional a un modelo CGE mediante datos de censo Extracto El número de regiones y sectores en la mayoría de los modelos CGE regionales es pequeño, debido a limitaciones de datos e informáticas. Los usos de dichos modelos se ampliarán si cuentan con bases de datos CGE mayores. El modelo TERM combina una base de datos masiva con una facilidad y técnicas de agregación variable para economizar en capacidad informática. Este estudio se extiende aún más perfilando el uso de datos de censo de regiones pequeñas para crear una base de datos CGE con un número de regiones sin precedentes. La metodología TERM original ya se ha empleado para crear modelos de regiones múltiples aplicables a varios países. Los datos de censo podrían mejorar el detalle en dichos modelos. Aquí, agrupamos regiones pequeñas de Australia para desarrollar el primer modelo CGE regional ascendente que distingue los 150 distritos electorales federales de un solo escaño.

Suggested Citation

  • Glyn Wittwer & Mark Horridge, 2010. "Bringing Regional Detail to a CGE Model using Census Data," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(2), pages 229-255.
  • Handle: RePEc:taf:specan:v:5:y:2010:i:2:p:229-255
    DOI: 10.1080/17421771003730695
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    Cited by:

    1. Gabriele Standardi & Yiyong Cai & Sonia Yeh, 2016. "Sensitivity of Modeling Results to Technological and Regional Details: The Case of Italy’s Carbon Mitigation Policy," EcoMod2016 9557, EcoMod.
    2. repec:eee:jpolmo:v:40:y:2018:i:1:p:37-59 is not listed on IDEAS
    3. Giesecke, James A. & Madden, John R., 2013. "Regional Computable General Equilibrium Modeling," Handbook of Computable General Equilibrium Modeling, Elsevier.
    4. Karyn Morrissey, 2016. "A location quotient approach to producing regional production multipliers for the Irish economy," Papers in Regional Science, Wiley Blackwell, vol. 95(3), pages 491-506, August.
    5. Jouko Kinnunen & Heikki Metsäranta & Hannu Törmä & Seppo Laakso & Urszula Zimoch, 2015. "Evaluation tool for wider economic effects of railway investments ? making CGE results accessible," ERSA conference papers ersa15p16, European Regional Science Association.
    6. Katarzyna Zawalińska & Nhi Tran & Adam Płoszaj, 2016. "Planning R&D in a Post Centrally-Planned Economy: Assessing the Macroeconomic Effects in Poland," Centre of Policy Studies/IMPACT Centre Working Papers g-268, Victoria University, Centre of Policy Studies/IMPACT Centre.
    7. Törmä, Hannu & Kujala, Susanna & Kinnunen, Jouko, 2015. "The employment and population impacts of the boom and bust of Talvivaara mine in the context of severe environmental accidents – A CGE evaluation," Resources Policy, Elsevier, vol. 46(P2), pages 127-138.
    8. Mardones D., Cristián, 2012. "Chile: building a computable general equilibrium model with an application to the Bío Bío region," Revista CEPAL, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), April.
    9. Glyn Wittwer & Janine Dixon, 2012. "Upgrading irrigation infrastructure in the Murray Darling Basin: is it worth it?," Centre of Policy Studies/IMPACT Centre Working Papers g-228, Victoria University, Centre of Policy Studies/IMPACT Centre.
    10. John R. Madden, 2017. "University location and city development: the effects of Victoria University on the Western Melbourne economy," Centre of Policy Studies/IMPACT Centre Working Papers g-272, Victoria University, Centre of Policy Studies/IMPACT Centre.
    11. Anping Chen & Nicolaas Groenewold, 2011. "Regional Equality and National Development in China: Is There a Trade‐Off?," Growth and Change, Wiley Blackwell, vol. 42(4), pages 628-669, December.
    12. Ha, Soo Jung & Hewings, Geoffrey & McGregor, Peter G & Swales, J Kim & Turner, Karen, 2010. "Econometric estimation of Armington import elasticities and their system-wide impact in a regional CGE model of the Illinois economy," Stirling Economics Discussion Papers 2010-19, University of Stirling, Division of Economics.
    13. Esmedekh Lkhanaajav, 2016. "CoPS-style CGE modelling and analysis," Centre of Policy Studies/IMPACT Centre Working Papers g-264, Victoria University, Centre of Policy Studies/IMPACT Centre.
    14. repec:bla:sajeco:v:84:y:2016:i:4:p:574-593 is not listed on IDEAS
    15. Glyn Wittwer, 2017. "The relevance of inter-regional trade data produced by the 2012 Commodity Flow Survey for multi-regional CGE modelling," Centre of Policy Studies/IMPACT Centre Working Papers g-275, Victoria University, Centre of Policy Studies/IMPACT Centre.

    More about this item

    Keywords

    Regional modelling; CGE modelling; database development; C68; R13; R15;

    JEL classification:

    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models
    • R13 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General Equilibrium and Welfare Economic Analysis of Regional Economies
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

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