Several CGE models with a regional dimension have been developed at the Centre of Policy Studies (CoPS). Each model has its own advantages. Two different approaches are used to add a regional dimension: top-down and bottom-up.
Under the top-down approach, national results for variables such as output, employment, and final demands are disaggregated into 8 states or into more, sub-state, regions. An input-output methodology is used, which recognizes regional variation in quantities but not in prices. Local multiplier effects are recognized, so that, for example, a construction project in Queensland would generate employment there, which would in turn stimulate consumer spending on non-tradeable goods produced in Queensland. The 'top-down' approach is economical both of computer resources and data -- which allows very detailed models(say 120 sectors, 60 regions) to be implemented and solved quite easily. On the other hand, region-specific supply behaviour is not easily modelled, and propinquity effects (when growth in one region benefits its neighbours) are neglected.
The alternative bottom-up approach consists in linking a series of independent CGE models [one for each region] which interact through trade and primary factor flows. In these multiregional CGE models both prices and quantities may vary independently by region. This type of model makes fewer theoretical compromises -- but imposes high computing and data demands. Consequently a more aggregated model must be used, sacrificing some sectoral or regional detail. In practice, the number of regions plus the number of sectors must not exceed 100.
Hybrid models combine top-down and bottom-up approaches. For example, MMRF-GREEN is a bottom-up model of the 8 Australian states. Top-down methods are used to break down results for each state into results for 10 or more sub-state regions.
Regional detail in the MONASH model is achieved by top-down methods. MMRF and its successor, MMRF-GREEN are 8-region bottom-up models. TERM (constructed more recently) is another bottom-up model distinguishing up to 57 separate regions. The differences are summarized in the table below.
|
Model |
||||
|
Type of regional modelling |
Top-down |
Bottom-up |
Bottom-up |
Bottom-up |
|
Region-specific prices |
no |
yes |
yes |
yes |
|
Region-specific quantities |
yes |
yes |
yes |
yes |
|
Typical no. of sectors |
113 |
around 40 |
around 40 |
around 40 |
|
Typical no. of regions |
8 states or 57 statistical divisions |
8 states |
8 states
|
57 statistical divisions |
|
Forecasting (year-to-year dynamics) |
yes |
no |
yes |
sometimes |
|
Region-specific demand-side shocks |
yes |
yes |
yes |
yes |
|
Region-specific supply-side shocks |
no |
yes |
yes |
yes |
|
State Government Accounts module |
no |
yes |
yes |
sometimes |
|
Additional "top-down" regional detail |
Top-down breakdown to 8 states or 57 statistical divisions |
|
Within-state top-down breakdown to 57 statistical divisions |
Within-statistical-division top-down breakdown to 1379 statistical local areas |
|
Other features |
In recent versions, inclusion of full bilateral matrices of inter-regional trade allows for growth in one region to spill over into neighbouring regions. |
|
Detailed modelling of CO2 emissions |
Exports and imports distinguished by port of exit/entry. Sectoral and regional aggregation tailored to particular simulations |
Go to MMRF page
Go to TERM page
Go to MONASH model page