The economic impacts of a construction project, using SinoTERM, a multi-regional CGE model of China

[thumbnail of g-164.pdf]
Preview
g-164.pdf - Published Version (245kB) | Preview
Available under license: Creative Commons Attribution

Horridge, Mark ORCID: 0000-0002-1070-5763 and Wittwer, Glyn (2007) The economic impacts of a construction project, using SinoTERM, a multi-regional CGE model of China. Working Paper. Centre of Policy Studies (CoPS).

Abstract

The paper outlines the theory and database preparation of SinoTERM, a "bottom-up" computable general equilibrium model of the Chinese economy. The methodology by which we construct the multi-regional model allows us to present the economy of China in an unprecedented amount of detail. SinoTERM covers all 31 provinces and municipalities. The database of the model extends the published national input-output table for 2002 to 137 sectors. The single crops sector in the published national input-output table is split into 11 and the single livestock sector into 3. The multi-regional CGE model provides a framework that we could modify to apply to many different policy applications. We can use SinoTERM to analyse the regional economic impacts of region-specific shocks. Such shocks could major construction projects or investments in health and education sectors, in an effort to accelerate economic growth in the lagging inland provinces. We use a 63 sector, 10 region aggregation of the SinoTERM master database to model the regional economic impacts of the proposed Chongqing-Lichuan rail link construction project.

Additional Information

CoPS/IMPACT Working Paper Number G-164

Item type Monograph (Working Paper)
URI https://vuir.vu.edu.au/id/eprint/29306
Official URL http://www.copsmodels.com/elecpapr/g-164.htm
ISBN 0732615712
Subjects Historical > FOR Classification > 1403 Econometrics
Current > Division/Research > Centre of Policy Studies (CoPS)
Keywords C68; R13; L74; CGE modelling; regional modelling; construction projects
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login