Alan A. Powell

The role of miniatures in computable general equilibrium modelling: Experience from ORANI

Abstract ORANI is a large general equilibrium model of the Australian economy used by policy analysts and university research workers. This paper describes the role of miniature models in the development and application of ORANI. Miniature versions of ORANI have been useful in (a) explaining results; (b) detecting errors; (c) conducting sensitivity analyses; (d) experimenting with new specifications; and (e) teaching. The present paper concentrates mainly on (a) and (b). A miniature model is developed to explain ORANI results for the effects of increases in protection, reductions in the real costs of labour, increases in oil prices and increases in real aggregate demand.

Trade liberalization and labor market disruption

Abstract In the depressed economic climates which have prevailed in recent years, the most common ground for opposing trade liberalization has been fear of the short-run adjustment costs, especially in the labor market. A computable general equilibrium model (ORANI) is used to explore the amount of occupational disruption (on a 72-occupation classification) likely to be generated in the Australian labor market by four alternative trade-liberalization packages, all of which reduce the average rate of protection by 25 percent. We conclude that in all cases the disruption involved would be slight relative to historically experienced occupational change.

Forecasting Small-Area Agricultural Incomes Using a CGE Model of the Australian Economy

In this paper the ORANI model, which is a large computable general equilibrium (hereafter, CGE) model of the Australian economy, is used to generate forecasts of agricultural incomes for the south-western region of Victoria. ORANI is first solved for the effects of an economic scenario on commodity output responses in a geographically defined agricultural industry of the model which encompasses the region. These responses are then used to generate output indexes in local government areas (LGAs) in the region according to their base-period commodity mixes. Finally, these projections are converted into real farm income forecasts. Our methodology breaks new ground in combining short- and long-term forecasts, and in disaggregating forecasts for a national agricultural industry to the LGA level.