CGE modelling of the resources boom in Indonesia and Australia using TERM



trade liberalization under GATT and WTO, greatly increased the import competition
faced by Australian manufactures. In the period from 1982 to 2004, manufacturing’s
share of GDP in Australia shrank from 19 percent to 12.5 percent. The sector of greatest
decline was textiles, clothing and footwear (TCFs), shrinking from 1.4 percent to 0.4
percent in this time (ABS, 2006a).

Part of the relative decline of agriculture and manufactures reflects the emergence of
services, both in domestic usage in a modern, increasingly knowledge-based economy,
and the rise of international exports of tourism and education in Australia. Yet the trend
towards globalization has resulted not only in increased exports of services. Now, in
combination with rising energy scarcity due to burgeoning demand in east Asia and
disruptions to supply and growing fears of resource diminution in the Middle East,
demand for Australia’s mineral resources has jumped since 2003-04.

The potential lasting structural changes to the Australia economy are not confined to
resource exports. Although Australia is a significant exporter of crude oil, it also imports
significant quantities of both crude oil and petroleum products (Table 2). The changes in
the price of internationally traded goods between 2003-04 and March 2006 provide
material for a short-run simulation using TERM (outlined in the next section) to examine
the sectoral and regional impacts of movements in international demand and supply. In
Table 3, we see how the composition of the economy varies between regions.3 For
example, in Mineral_QLD, Mineral_SA and Mineral_WA, mining’s share of gross
regional product (GRP) exceeds 30 percent. We would expect these regions to benefit in
the short-run from the boom in minerals demand. The predominantly urban regions
(designated by Urban_NSW, etc.) each earn more than 90 percent of total income from
non-resource sectors, with one exception. That exception is Urban_WA.4 Since the
changes in trade prices shown in Table 2 have opposing effects on different sectors and
regions, we have placed the impacts on Australia into four groups for the national results
presented in Table 4. First, we isolate the impact of mineral export price shocks. Second,
we present the remaining export price shocks, to various farm and food sectors. Next, the
imported fuel shocks are isolated, and finally, the fourth group includes the impact
mainly of the rising supply of manufactures from China.

Preparing the TERM database

In order to run TERM, we first had to prepare a national database and then gather and
process regional data for use in the model. The TERM data process starts from the 2001-
2 Australian input-output tables, distinguishing 107 sectors. Our first step was to convert
these tables to the file format of ORANI-G, a standard single-country CGE model. Next,
working at the national level, we expanded the 107 sectors to 169. The selection of new
sectors is based on our judgment as to which sectors are likely to be important in future
modelling research. For example, we split the single health and single education sectors

3 The column for agriculture includes fishing, forestry and food processing.

4 UrbanWA’s inhabitants rely to an unusual extent on mining income, due to fly-in, fly-out mining
operations which result in employees spending non-working days in the urban region. This results in a
significant exception to urban outcomes from our modeling, as shown in Table 6.



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