Australia has one of the most variable rainfall climates in the world. Drought can have devastating impacts on agriculture, including dramatic plunges in crop production, loss of livestock and other farm capital and deterioration in the natural resource base. Australia has traditionally afforded its farmers a great deal of drought support. From the early 1980s, however, this free-flowing support came under attack as unnecessary and distorting (see for example, Freebairn 1983). Drought was removed from the natural disasters list and from 1992 has been covered instead by the National Drought Policy. The National Drought Policy has three aims as follows: to encourage farmers to manage their own risks; to look after the natural resource base; and to encourage a quick recovery (O’Meagher et al. 1998).
The aim of this paper is to analyse the regional economic impacts of a prolonged period of recurrent droughts using TERM-H2O, a dynamic computable general equilibrium (CGE) successor to the bottom up, comparative static TERM (The Enormous Regional Model). We concentrate on the regions of the southern Murray-Darling basin (SMDB). One issue concerns the dynamics of drought and drought recovery. In particular, we focus on the implications of drought for regional investment and capital. Another issue is the broader regional economic and employment implications of drought. TERM-H2O models the interaction between irrigation and dry-land agriculture in times of drought, allowing re-allocation of resources across these two activities. Finally, modelling of the impacts of drought provides a benchmark for analysing the impacts of the Australian Government’s water ‘buyback’ policy.
Some analysts and lobbyists have asserted that planned reductions in water used by irrigators in the Murray-Darling basin are similar to the effects of drought (Rizza 2010). Regional impacts generated by various models including TERM-H2O (Dixon et al. 2010) and an ABARE model (ABARE–BRS 2010) have been dismissed as understating the probable employment impacts of reducing allocations, most notably by Murray-Darling Basin Authority board members (Akerman 2010). It would appear that water buybacks, which started during drought, were blamed for job losses that actually arose from drought. Therefore, there is some value in modelling the impacts of drought and estimated impacts on basin employment.
Drought is hard to model, as it entails substantial inward supply shifts for farm sectors. Large change simulations are a challenge for modellers. Linear programming models are likely to reach unrealistic corner solutions with relatively modest supply shifts. Computable general equilibrium models that include CES functional forms will perform better, but most still struggle in large change cases. Consequently, studies on CGE modelling of drought are rare: the only previous studies of which we are aware are Sherony et al. (1991), Horridge et al. (2005) using a version of TERM without water accounts and Pauw et al. (2010). To depict the impacts of a drought as severe as that in southern Australia from 2006–07 to 2008–09 is an extreme test of a multi-regional CGE model. This paper outlines various theoretical modifications undertaken to improve the modelling of drought in a CGE framework and then applies the model to the period from 2005–06 to 2017–18. In particular, we apply a theory of excess capacity to downstream processing sectors.
Results are explained by starting with naïve calculations and outlining how the theory of the model moves simulated results away from these calculations. In addition, the approach provides some estimate as to the impact of prolonged drought on structural change in predominantly rural regions of south-eastern Australia.
1.1. The prolonged drought of 2006–07 to 2008–09
South-eastern Australia endured recurrent droughts after that of 2002–03. From 2003–04 to 2005–06, there was a partial recovery to near-average rainfall in SMDB. Then, the alpine regions of Victoria and New South Wales, which are the source of the Murray River, suffered record rainfall deficits in the period from 2006–07 to 2008–09.1 This resulted in recurrent reductions in water allocations throughout the SMDB. The Goulburn–Murray water authority’s allocations illustrate the severity of the first decade of the new millennium: it formerly aimed at providing 100 per cent allocations in 97 years of 100 for the Goulburn system (although the authority removed this aim from its website early in 2010), but has failed to do so in five of eight irrigation seasons starting with 2002–03.
The CGE approach enables us to keep in context the contribution of agriculture to ostensibly rural economies. As agriculture’s contribution to the national economy has shrunk, so too has its contribution to regional Australia’s economies. For example, our estimates of regional GDP shares indicate that the SMDB’s contribution from agriculture in 2005–06 was less than 13 per cent (Table 2, row (5)), little more than the national share in 1962–63 when Australia’s population was half of its present total (Maddock and McLean 1987). It follows that although drought still depresses regional economies, the potential impacts are not as large as they might have been had the pattern of drought in the first decade of the new millennium occurred several decades ago. That is, rural economies have also diversified over time, with an increasing share of income being accounted for by service sectors.
|Water allocations and productivity levels (100 = average)|
|(1) Dry-land productivity*||42||42||42||42||42||36||36||69||69||69||69||69||36||51|
|Contributions to GDP in 2005–06 base (%)|
|(3) Dry land||8.3||8.4||6.4||2.3||8.0||13.6||14.4||3.9||1.4||6.9||7.4||3.8||8.0||6.8|
|Naïve estimates of contributions to GDP (%)|
|(6) Dry land||−4.8||−4.9||−3.7||−1.3||−4.6||−8.7||−9.2||−1.2||−0.4||−2.1||−2.3||−1.2||−5.1||−3.3|
|Modelled contributions by broad sector|
|(9) Dry land||−4.4||−2.6||−3.1||0.1||−3.0||−9.0||−9.2||−0.7||−0.3||−0.9||−1.4||−0.8||−5.6||−2.7|
|(13) Net Water||0.4||3.8||−0.2||−4.9||−1.5||−0.7||−1.6||0.1||0.0||0.6||0.3||−0.2||−0.7||0.0|
|(15) Net water sold (GL)||83||456||−38||−194||−33||−29||−86||5||−4||−104||2||−20||−39||0|