‘Dog Days’ Full Employment without Depreciation: Can It Be Done?
Garnaut, in his book Dog Days: Australia after the Boom, explores the outlook for the Australian economy as the China resources boom goes into retreat (Garnaut 2013). Modelling of this scenario was presented to the July meeting of the Melbourne Economic Forum (Dixon et al. 2014a, 2014b). We identified a smooth adjustment path to changed circumstances, which minimised the increase in unemployment and reduction in living standards, and also an alternative path of disorderly adjustment (DA), characterised by economic volatility and unemployment. The original DA scenario reported in Dixon et al. (2014a, 2014b) was predicated on inflexibility in wages and consumer behaviour. This article explores an alternative DA scenario in which the exchange rate is slow to adjust.
Quantifying “Dog Days”
In his recent book "Dog Days", Garnaut (2013) argues that the Australian economy faces significant economic challenges, with a risk that just as the investment phase of the mining boom ends, Australia will be entering an economic environment characterised by declining terms of trade, a rising dependency ratio and rising world interest rates. In this paper, we evaluate the consequences of these challenges for key macroeconomic variables and per-capita measures of economic wellbeing using an economy-wide model. With plausible assumptions on key inputs to our model, we produce a scenario that is broadly consistent with the pessimistic picture for the immediate future of the Australian economy as portrayed in Dog Days. We forecast real per-capita incomes to fall at a rate of 0.3% p.a., so that by 2019/20 they have returned to the level of 2010/11. To maintain an unchanged unemployment rate over the forecast period, the average real wage must fall by 0.89% p.a., returning to its 2007/08 level by 2019/20. Fiscal consolidation sees damped growth in consumption. With real aggregate per-capita consumption falling at -079% p.a., it returns to its 2008/09 level by 2019/20. These are our forecast outcomes under an orderly adjustment scenario. However as Garnaut notes, there is a risk of mismanagement of the policy adjustments that will be required in the face of Australia's new economic realities. We simulate this through a scenario in which public and private consumption per capita, and real wages, initially remain at their 2013/14 levels, despite a decline in real per capita national income. The macroeconomic adjustments that this scenario entails ultimately generate significant economic volatility, with the unemployment rate rising to 6.6 per cent by 2015/16, before a 2.8% real wage fall in 2016/17 commences the process of returning the unemployment rate to its current level by 2019/20. Maintenance of our recent experience of rapid growth in per-capita income will require a substantial increase in multifactor productivity growth. In our third simulation, we model the size of the increase in multifactor productivity necessary to offset the forecast declining terms of trade, rising dependency ratio, and other forecast economic challenges. We find that the required rate of productivity growth, at 1.96% p.a., is of a level not seen in Australia since the last program of major structural reform in the 1990s. It seems unlikely that we can count on the tailwinds of any recent programs of micro reform to generate the levels of multifactor productivity growth that will be needed to maintain recent growth rates in real income. In our fourth simulation, we set a more modest goal, uncovering the productivity growth required to maintain, not increase, real per capita incomes. At 0.49% p.a., this rate is higher than the average productivity performance of the last decade, suggesting Australia faces a significant reform challenge in maintaining, let alone growing, real per capita incomes in the post-boom environment.
Complexity, Network-Decisions And Alternative Risk Premia: Some New Theories Of Structural Change And Portfolio Decisions.
Structural Change is the basis for many decisions such as government fiscal and monetary policies, government interventions, corporate-expenditure decisions, corporate strategic decisions, portfolio management decisions in finance; household allocation decisions, etc.. Structural change in industries and national economies is increasingly more difficult to detect partly because of the growth and dominance of Complex Systems and “Networks” in modern industries and economies – such Networks and Complex Systems include GVCs (global value chains), retail store networks; franchising networks; internet networks; services networks; credit-chains in industries; online social networks; etc.. The relationship between Complexity and Structural Change is well established in the physics, applied math and economic dynamics literatures. Traditional definitions and modeling of structural change have erroneously focused on changes in labor, technological progress, market share, and or capital – which are grossly inadequate in the new economy and post- global financial crisis (as confirmed by changes in the global retailing and housing industries and other industries). Also the modeling of structural change seems to be constrained by the availability of data, and often omits the relationship between structural change on one hand, and globalization, financial stability and international financial contagion. This article: i) introduces the Operations Strategy Model of structural change, explains how it differs from the Lewis Model of Structural Change; and explains the often omitted relationship between operations strategy (of companies and government agencies) and structural change; ii) introduces other new Complexity-based models/theories of Structural Change in industries; iii) reviews the relationships among Operations Research, Complexity and Structural Change; iv) explains the symbiotic relationship between structural change on one hand, and economic growth, financial stability, financial contagion and globalization; v) discusses the significant gap between academic research and practice and explains some issues and factors that should be considered in the development and testing of structural change models; vi) explains how alternative risk premia can be generated from new models of Structural Change.