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Tasmania and the Northern Territory, like Queensland, have strong
representations in their economies of commodity exports and of tourism.
As a result these regions will obtain growth with greater use of
e-commerce similar to that of Queensland.
The industrial composition of the New South Wales economy is broadly
in line with that of Australia at large. Thus the forecast deviation path for
GSP in New South Wales is close to that for Australia’s GDP.
Victoria and South Australia have above average prospects with greater
use of e-commerce. Neither of these regional economies has a heavy
reliance on commodity exporting.
The Australian Capital Territory is the State/Territory with least reliance
on commodity exports. It therefore obtains most of the benefits without
significant costs. This gives it the top ranking in Exhibit 4.6.
Statistical divisions
The MONASH model also permits detailed disaggregation of simulation
results into more detailed local impacts based on the Statistical Divisions
used by the ABS. These divide Australia into 57 areas selected to reflect
‘identifiable social and economic links between inhabitants and between
the economic units within the region, under the unifying influence of one
or more major towns or cities’. 18 (See Appendix B about technical aspects
of the MONASH model).
The long run deviations from the base case forecasts for Statistical
Divisions are shown in Table 4.2 below. This reveals that almost every
region in Australia is better off as a result of the changes brought by
greater use of e-commerce. Over 50 regions can expect a long run
increase in the Gross Regional Product (GRP) of between one and four
per cent. This is despite the fact that output in some sectors is expected to
be lower as a result of the expected changes, particularly in retail, mining
and agriculture.
The biggest employment effect is an increase of 1.4 per cent and the
smallest is a loss of 0.2 per cent.
The main cause of variations in the results for Statistical Divisions is
differences in dependence on tourism and dependence on commodity
exporting. The top three Statistical Divisions shown in Table 4.2, Far
North (QLD), Morton (QLD) and Kimberley (WA), all have heavy
reliance on tourism. At the other end, we find Mackay (QLD), South East
(SA), Barwon (VIC), Peel (WA), Far West (NSW), South West (WA),
Fitzroy (QLD), North West (QLD) and Goldfields-Esperance (WA). All of
these areas of Australia rely heavily on either export-oriented agriculture
or export-oriented mining, activities that (in this simulation) are likely to
obtain only modest additional direct gains from e-commerce. Further
work to identify and analyse more of the direct gains that could be
realised in mining and agriculture may find that the outcomes for these
regions would be a net boost.
Only one Statistical Division sees a decline in GRP and employment.
This decline is small and little different from a zero change (i.e. the result
is not larger than the rounding error involved in the shocks and
model calculations).
18 ABS, Australian Standard Geographical Classification, Cat no. 1216.0, AGPS, Canberra, 1995, p. 18.
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