Brian R. Parmenter

An applied general equilibrium analysis of the economic effects of tourism in a quite small, quite open economy

A computable general equilibrium model is used to project the effects of tourism on the industrial and regional structures of the Australian economy. The most striking conclusion is that Queensland, usually thought to be the most tourism-oriented of the Australian states, would be a net loser from an economy-wide expansion of tourism. As well as having a relatively large share of its GSP accounted for by tourist-oriented activities, Queensland is also relatively heavily dependent on agriculture and mining, traditional export sectors which are crowded out by the expansion of international tourism.

Computable general equilibrium modelling for policy analysis and forecasting

Publisher Summary This chapter describes computable general equilibrium (CGE) modeling and the history of its development. The chapter illustrates the computation of solutions for CGE models and reviews its achievements, failures, and potential. The model illustrated in the chapter can be used in two ways: as a single-period model suitable for comparative–static analyses; and as a model for multi-period forecasting. Before CGE models, there were input–output models that emphasized input–output linkages among industries. CGE models go beyond input–output models by linking industries via economy-wide constraints including constraints on the size of government budget deficits; constraints on deficits in the balance of trade; constraints on the availability of labor, capital, and land; and constraints arising from environmental considerations, such as air and water quality. Much of CGE modeling has been concerned with the welfare implications of proposed policy changes—for example, changes in protection, changes in taxes, and changes in environmental regulations. Many interesting welfare results have been obtained, especially in the analysis of tax changes.

Forecasts for the Australian economy using the MONASH model

This paper describes annual forecasts for the period 1990–1991 to 1996–1997 made with a new CGE model of the Australian economy called MONASH. Using MONASH, we project the implications for the structure of the economy of macroeconomic forecasts made by conventional, less formal methods. MONASH has enough dynamics to enable it to track, at the micro level, business-cycle phenomena which are assumed in the macro forecasts. The CGE model is very detailed, distinguishing 112 industries, 6 regions and up to 283 labour-force occupations. Apart from the level of detail, the strength of our MONASH forecasting system is that it produces forecasts which can be interpreted fully in terms of the models theory, data and the assumptions underlying the exogenous input.

Computable General Equilibrium Modeling of Environmental Issues in Australia: Economic Impacts of an Emissions Trading Scheme

A key distinguishing characteristic of computable general equilibrium (CGE) modeling in Australia is its orientation to providing inputs to the policy-formation process. Policy makers require detail. They want to be able to identify convincingly which industries, which occupations, which regions and which households would benefit or lose from policy changes, and when the benefits or losses might be expected to flow. In this chapter, we explain how the necessary level of detail can be provided, using as an example analysis that was undertaken by Centre of Policy Studies (CoPS) and Frontier Economics of the potential economic impacts of a carbon price on the Australian economy. The Australian carbon price framework is assumed to be part of a global emissions trading scheme (ETS). Over time, the global ETS becomes the dominant greenhouse abatement policy for all countries including Australia. It sets the price for carbon permits and allocates the number of permits available to each country. A number of key findings emerge from the CGE simulations of the effects of the ETS policy. (i) Domestic abatement falls well short of targeted abatement, requiring significant amounts of permits to be imported. (ii) Despite the requirement for deep cuts in emissions, the ETS reduces Australia’s GDP by only about 1.1% relative to the base case in 2030. The negative impact on real household consumption (the preferred measure of national welfare) is somewhat greater, reflecting the need to import permits. (iii) The national macroeconomic impacts of the ETS might be described as modest in the context of the policy task. However, this does not carry through to the industry and state/territory levels where some industries and regions prove particularly vulnerable in terms of potential lost employment. The need for detail is highlighted throughout the analysis. For example, a suitably detailed treatment of electricity supply is provided by linking CoPS’ CGE model with Frontier’s detailed bottom-up model of the stationary energy sector. Similarly, necessary detail on the effects of the global ETS on Australia’s international trading conditions is provided by linking with a multicountry model.

Long-run Effects on China of APEC Trade Liberalization

Plans for APEC trade liberalisation include the elimination of all tariffs between member states. In this paper we use two computable general equilibrium models to examine the effects of these plans, focussing on China. Our modelling shows that liberalisation increases Chinas capital stock and real GDP. The implication for Chinese industries depend on the extent to which liberalisation exposes them to additional import competition. Industries strongly stimulated include Textiles and Communications Equipment. Transport Equipment is the most adversely affected. Chinese regional results follow from the industrial compositions of the regions, with Zhejiang the most favourably affected and Jilin the least.

Chapter 5 – An Introduction to Intertemporal Modeling

This chapter discusses the basic techniques used in intertemporal modeling and how they can be applied in building an intertemporal general equilibrium model. Intertemporal modeling uses a number of mathematical methods. The reading guide is intended to help you fill in gaps in knowledge of optimal control, differential equations, numerical methods, and linear algebra. It also includes references to the economic literature on intertemporal analysis. The problem set is in three parts. Part A helps set up and solve a simple investment problem. By performing qualitative analyses with the investment model in partial equilibrium, insight into how it works and learn some analytical techniques, which are useful in intertemporal modeling, is gained. Part B deals with methods of obtaining numerical results from such models. Part C first links the investment problem into a small, static general equilibrium model, thus producing an intertemporal general equilibrium model. Finally, the resulting model to analyze the effects of a number of different policies is available for use.

Forecasting versus policy analysis with the ORANI model

ORANI is a detailed general equilibrium model of the Australian economy. It has been applied many times by economists in universities, government departments and business in analyses of the effects on industries, occupational groups and regions of changes in policy variables (e.g., taxes and subsidies) and in other aspects of the economic environment (e.g., world commodity prices). These applications have been comparative static, i.e, they have been concerned with questions of how different the economy would be with and without the changes under investigation. They have not been concerned with forecasting the future state of the economy. More recently we have experimented with the model for forecasting. In a pilot exercise (Dixon, 1986), ORANI was used to forecast growth rates of industry outputs in Australia for the period 1985-1990. This revealed some problems inherent in the use of computable general equilibrium models for forecasting. In section 2, we describe the difference between comparative static analysis and forecasting, in general terms, and with specific reference to the ORANI model. We also discuss some computational difficulties which arise in applying ORANI to forecasting. in section 3 we present some numerical examples to supplement the theoretical material in section 2. Finally, section 4 contains some brief concluding comments on the strengths and limitations of ORANI as a forecasting device.

Input-Output Data and Input-Output Models

This chapter discusses the input–output data and input–output models. The prototype for modern applied general equilibrium models is Leontiefs input–output model. It emphasizes interdependencies between different industries and between industries and households that arise from their roles as each others customers: The purchase of material inputs by one industry from others or of labor and capital inputs from households, and the purchase of consumer goods by households from industries. Models that attempt to capture all these interdependencies require two types of data: input–output tables and behavioral parameters. Input–output tables record, for one period in time, the commodity flows, which take place among the components of the economy. Behavioral parameters summarize how agents respond to changes in activity variables and prices, for example, how producers adjust their demands for inputs in response to changes in their output levels and input prices or how households adjust the level and composition of their consumption in response to changes in their incomes and consumer prices. Input–output models, because they include only direct interdependencies among the components of the economy, require as data only input–output tables.

The Construction of A Model for Practical Policy Analysis

This chapter discusses the construction of a model for practical policy analysis. The DMR model is chosen for several reasons. First, it is an advanced contribution of practical importance. Variants exist for about a dozen countries and have been used in the regular reporting and advising functions of the World Bank. If one understands the DMR model in detail, then one is ready to participate actively in applied general equilibrium modeling. Second, it provides a convenient framework for extending the discussion of the previous chapter to include international trade and investment. Third, it has been directed towards macro issues of general interest. For example, special attention has been given to analyzing the effects of different approaches to coping with exchange shortages. Finally, the members of the DMR model team have generously made themselves available for answering questions. Coefficients are quantities that at any step in a Johansen-style computation are held constant, while the effects on the endogenous variables of shifts in exogenous variables are computed. The examples of coefficients include cost shares and sales shares. The term parameter is used in the usual way to refer to quantities that are treated as constants throughout the computation of any solution. The examples of parameters in the DMR model include substitution elasticities between capital and labor and substitution elasticities between imported and domestic products.

The Johansen Approach

This chapter discusses a class of general equilibrium models in which an equilibrium is a vector, V, of length n satisfying a system of equations F (V) = 0, where F is a vector function of length m. Linearization plays a key role. The chapter discusses the approach pioneered by Johansen. As system can be very large and involve a wide variety of nonlinear functional forms, from a computational point of view, it might be quite intractable. Johansens approach is to derive from a system of linear equations in which the variables are changes, percentage changes, or changes in the logarithms of the components of V. As system contains more variables than equations, exogenously given values to (n-m) variables are assigned and it is solved for the remaining m, the endogenous variables. In applications of Johansen models, many different allocations of the variables between the exogenous and endogenous categories can be made. The Johansen approach is satisfactory for computing the effects on the endogenous variables of small changes in the exogenous variables.