Macro-economy - POLES: Difference between revisions
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* sectoral value added depend on the level of development of the country / region, given by the GDP per capita; | * sectoral value added depend on the level of development of the country / region, given by the GDP per capita; | ||
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'''Figure 1'''. Share of value-added as a function of GDP per capita | '''Figure 1'''. Share of value-added as a function of GDP per capita | ||
Revision as of 12:53, 24 August 2016
Corresponding documentation | |
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Previous versions | |
Model information | |
Model link | |
Institution | JRC - Joint Research Centre - European Commission (EC-JRC), Belgium, http://ec.europa.eu/jrc/en/. |
Solution concept | Partial equilibrium (price elastic demand) |
Solution method | SimulationRecursive simulation |
Anticipation | Myopic |
In POLES key macro-economic assumptions are exogenous: population and growth if GDP per capita. Information comes from external sources: population usually from the UN Population Prospects (latest: the 2012 revision), GDP information from international sources (IMF, MIT, CEPII, national and regional estimates, ..). The consistency between population and income assumptions is checked.
An on-going work will allow connecting POLES to the macro-econometric model MAGE (CEPII) through the use of an "energy factor" in the MAGE production function, allowing to use a more dynamic relation between GDP and energy.
The key marco-economic assumptions are then derived into sectoral economic activity variables:
- sectoral value added depend on the level of development of the country / region, given by the GDP per capita;
Figure 1. Share of value-added as a function of GDP per capita
- industrial physical production depends on demand, which is itself depending on the level of development;
- mobility (for passengers and for goods) depends on the cost of transport compared to income, and is declined in equipment rates, degree of utilisation of this equipment, etc.. ;
- building surfaces depend on households size (cohabitation) and surface per dwelling, both depending on personal income.
All variables also depend on historical data, that capture local specificities.