Neoliberalismo como temática escolar para la formación política

Max Corden has made an extensive contribution to the study of Dutch disease internationally through his series of analyses of structural change in a small open economy (Corden 1981, 1984; Corden & Neary 1982). Corden and Neary (1982) provided a three-sector economic framework, the ‘core model’ of Dutch disease

18 economics, to analyse the impact of growth in the ‘booming sector’ (a resource sector) to the lagging sector (tradable manufacturing) and to the non-tradable sector (non- tradable manufacturing and services). Tradable goods are exposed to international competition, and hence their prices are determined in the world market, whereas non- tradable goods are not exposed to international competition and thus their prices are dependent upon the domestic supply of and demands for them.

As a result of a resources boom, there are two types of effects, according to the core model: the resource movement effect and the spending effect.

A boom is generated by a price rise, or a new resource discovery raises the marginal products of mobile factors in the booming sector, which, in turn, increases the factor prices. This draws resources from other sectors, causing structural changes. This is the resource movement effect.

A boom increases domestic income, resulting in extra spending for both tradable and non-tradable goods. As the prices of tradable goods are determined in the world market for a small country, extra spending does not induce increases in the prices of tradable goods. Extra spending, however, causes prices of non-tradable goods, which are determined in the domestic market, to increase, resulting in a real exchange rate appreciation (thatis, a rise in the relative price of non-tradable goods to tradable goods). As a result, the production of non-tradable goods becomes attractive, discouraging the production of tradable goods. This effect is called the spending effect. Both effects can have a negative impact on the tradable manufacturing sectors, leading to a de- industrialization effect.

According to Corden and Neary (1982), there are three possible reasons for the Dutch disease effects: (a) an improvement in the technology of the booming sector; (b) an increase in foreign capital flows; and (c) an increase in the price of the export commodity.

New Dutch disease literature 1.3.2

Gregory (2011) analyses the mining boom of 2000s in comparison with the 1970s boom, focusing on the important economic differences of the two booms: the recent one was generated by export price increases and the older one was generated by export volume increases. Further, Gregory (2012) measures the increase in Australian living standards relative to the United States resulting from the terms of trade changes –

19 through their direct trading gain effect and indirect real GDP effect as about 25 per cent and concluded that this increase probably placed Australian living standards well above those of the United States. Gregory and Sheehan (2013) view the recent mining boom as moving through three stages- the increase in the terms of trade, an induced mining investment response and a significant increase in mining exports, and explore the implications and policy issues arising as the mining boom passes through these three stages.

Corden (2012) defines a three-speed economy for Australia to explain the recent mining boom. He argues that the mining boom leads to a real appreciation that pressures lagging sectors such as manufacturing, tourism, education and agriculture, and he offers options to reduce Dutch disease: piecemeal protectionism, moderate exchange rate effects by running a fiscal surplus, combined with lowering the interest rate, and establishing a sovereign wealth fund.

Using the balance of payments model, Freebairn (2015) analyses the time path of pressures for exchange rate adjustment to different stages of a mining boom under different industry and economic circumstances. The time path effects of a mining boom is considered for four discrete time periods: initial demand driven boom (the terms of trade boom), investment period, production period and the end of boom. He finds that the relationship between the terms of trade and the exchange rate is not simply monotonic as it may be different in different phases or it may reverse from one phase to another. Finally, Freebairn concludes that a computable general equilibrium model which captures the different stages of a mining boom is desired in the next stage of the analysis.

Theoretically, the abundance of natural resources could improve the host countries economic performance due to ‘big push’ effects of higher investment in infrastructure and human capital development (Sachs & Warner 2001). In fact, some countries, such as Australia where the existence of a strong mining sector has led to the rapid expansion of the export of mining technology and services and the development of human capital associated with those new technologies, have avoided resource curse. Some research work such as Alexeev and Conrad (2009) find that natural wealth has positive effects on living standard, when controlling for a number of variables, particularly dummies for East Asia and Latin America in their cross-country analysis. Therefore, whether mineral resources are a blessing or a curse still remains a controversial question.

20 Background to Historical and Decomposition simulation studies

1.4

COPS style CGE models contain a large number of economic relationships linking observable features of the economy, such as macroeconomic aggregates, commodity prices and outputs, household consumption composition and commodities with the structural features of the economy, such as production technologies and household tastes. In the historical simulation, many of the variables which represent observable features of the economy are determined exogenously. This enables models to calculate the outcomes for typically unobservable variables describing the features of the economy’s structure. These variables include industry production technologies, household tastes and the positions in export demand and import supply curves. The original historical and decomposition analysis was by Dixon and McDonald (1993). They explained the structural changes in the Australian economy for the period 1986-87 to 1990-91. Dixon and Rimmer (2002) defined the analytical method for historical, decomposition, policy and forecasting simulations with an illustrative application of the Australian motor vehicle industry from 1987 to 2016. Dixon, Mennon and Rimmer (2000) explored the impacts of changes in technology and preferences on the rapid growth of trade for the period 1986/87-1992/93. Wittwer and Anderson (1999) used a regional CGE model to assess the Australia's grape and wine industries through historical, decomposition, policy and forecasting analyses for the period 1986 to 2003. Dixon and Rimmer (2003) quantified several aspects of technical change in US industries for the period 1992 to 1998 with the USAGE model. With the MONASH model, Giesecke (2004) carried out historical and decomposition simulations for the period 1996/97-2001/02. Tran (2007) carried out historical and decomposition simulations with the COPS style dynamic CGE model of the Vietnamese economy, MVN. Giesecke and Tran (2009) further refined the historical and decomposition analysis of the Vietnamese economy. Mai, Adams and Dixon (2009) carried out historical and forecasting simulations in the case of China with the MONASH CHINA Multi-Country model. They estimated China’s technological convergence with developed countries empirically. Dixon and Rimmer (2014), with USAGE, decomposed movements in U.S macro and industry variables from 1992 to 1998 into the contributions of North American Free Trade Agreement (NAFTA) factors and other factors.

21 Structure of the thesis

1.5

The thesis consists of four parts and eight chapters. The first part, Introduction and Background, includes this chapter and Chapter 2. This chapter provides a brief general background to the Mongolian economy. In addition, it presents a brief literature review on the Dutch disease literature and on the COPS style approach for analysing structural change: historical and decomposition simulations. Chapter 2, dedicated to the COPS- style modelling, defines that modelling, describes its history and offers a brief literature review on CGE modelling in general.

The methodology part contains the theoretical frameworks and database construction of two CGE models developed in three chapters. Chapter 3 presents the theoretical framework of ORANIMON, focusing on examining the underlying mechanisms inherited from ORANI. Chapter 4 describes the theoretical additions of MONAGE, focusing on dynamics, closures and additional technical innovations related to technology and tastes, welfare measures and the facilitation for different types of simulations. Chapter 5 provides descriptions of data, methods for building the database, estimations of parameters and the results from related validity analysis. In building a CGE model the crucial step is to set up a database that is formulated in a given year. This database creation requires painstaking interpretation of statistics and frequent interactions with statistical agencies (Dixon & Rimmer 2002). Fortunately, the National Statistical Office of Mongolia (NSO) provided various unpublished data and involved the author in its discussions and projects related to the compilation and dissemination of input output tables (IOTs), enabling the creation of twin databases for 2005 and 2012.

The application part of the thesis is concerned with the analysis of the mining boom during 2005-2012. The part comprises two chapters. The ORANIMON applications are for studying the impacts of early commodity price increases, started around 2005, and the associated sudden growth of investment in the Mongolian economy. The analysis and findings are presented in Chapter 6. The MONAGE simulations are concerned with the analysis of the structural changes in the Mongolian economy between 2005 and 2012. Chapter 7 discusses historical simulation, which provides detailed estimation of changes in structural variables such as technologies, preferences and the movement in export demand supply curves. In addition, the chapter presents the decomposition simulation, which analyses the contributions of the structural changes to the macro- and industry-level economic performance of the economy during the period. These

22 simulations are concerned with the implications of the mining boom for macroeconomic performance, employment, the balance of trade, the overall price level and the level of output in each industry. In addition, we can identify the winners and losers as a result of the mining boom. Further, these simulations enable us to investigate the Dutch disease effects in the Mongolian economic context.

The final part, Chapter 8, summarizes major findings, highlights the contributions and limitations of the study, and proposes avenues for further research.

23

COPS Style CGE Modelling and Analysis

Chapter 2.

Economics is the study of how economic agents – producers, investors, households, foreigners and governments – make choices under conditions of scarcity, and of the results and efficiency of those choices. In any economic system, scarce resources have to be allocated among competing uses. These resources are allocated by the combined choices and interactions of economic agents in an economy. Inevitably, the choices of economic agents come down to the relative importance of competing uses, thereby creating trade-offs. Economic theories postulate the optimisation behaviours of economic agents under given resource and technology constraints, with signalling from market prices. Households maximise their utility subject to their budget constraints, and producers maximise their profits subject to their production technology constraints. Solutions to these optimisation problems yield the demands and supplies of commodities and services respectively. Prices, determined by market equilibria, play a crucial role in resource allocation. Hence, the optimising behaviours of economic agents are the means of introducing market or price mechanism in the model (Dixon & Rimmer 2002).

Interactions of agents and repercussions of episodes in an economy are capable of being captured in an economy-wide general equilibrium framework. The theory of general equilibrium analysis was pioneered by Walras (1877) and Edgeworth (1881). Leon Walras provided the first general equilibrium description of a complex economic system with the interactions of independent economic agents. Francis Edgeworth introduced the well-known tool of general equilibrium analysis of exchange that is named after him – the Edgeworth box. Major theoretical contributions related to the existence, uniqueness, stability and optimality of general equilibria were made also by Kenneth Arrow, Gerard Debreu, Hiroshi Atsumi, Hirofumi Uzawa and Michio Morishima from 1950 to the 1970s.

CGE modelling is an empirical approach of general equilibrium analysis. Since 1960, CGE modelling has gradually replaced other economy-wide approaches such as input- output modelling and economy-wide econometric modelling. It also became a dominant economy-wide framework for policy analysis in 1990s, with a vast amount of literature concerning various aspects and applications of CGE modelling (Dixon 2006; Dixon &

24 Jorgenson 2013). Dixon et al. (1992) described CGE modelling as an integration of a general equilibrium theoretical structure, data about the economy of interest, and solution methods to solve the models numerically. Dervis and Robinson (1982) identified CGE models as those that ‘postulate neo-classical production functions and price-responsive demand functions, linked around an input-output matrix in a Walrasian general equilibrium model that endogenously determines quantities and prices’. Shoven and Whalley (1992) defined CGE modelling as a conversion of the Walrasian general equilibrium structure into realistic models of actual economies by specifying production and demand parameters, and incorporating data reflective of real economies. Dixon and Parmenter (1996) described the distinguishing characteristics of CGE models as follows:

(i) CGE models are general since they include explicit specifications of the behaviour of several economic agents/actors;

(ii) CGE models employ market equilibrium assumptions as they describe how demand and supply decisions made by different economic agents determine the prices of at least some commodities and factors that in turn ensure market equilibria; and

(iii) CGE models are computable and produce numerical results.

CGE modelling can therefore be characterised by its applied nature and quantitative approach in general equilibrium analysis. Applied general equilibrium (AGE) modelling is an alternative term used to describe CGE modelling.

CGE models belong to the economy-wide class of models. Hence, they provide industry disaggregation and the behaviours of economic agents in a quantitative description of the whole economy. According to Dixon and Rimmer (2010), the original empirical economy-wide model was Leontief’s input-output system (Leontief 1936). Leontief’s input-output system portrays ‘both an entire economy and its fine structure by plotting the production of each industry against its consumption from every other’ (Leontief 1951, p. 15). He provided a tabular representation of the economy – the input-output tables. These tables show a detailed disaggregation of the supply and use of inputs and outputs in the economy.

25

𝑋=𝐴𝑋+𝑌 (2.1)

where X is a vector of outputs; Y is a vector of final demands; and A is the input-output coefficient matrix. In input-output modelling, the production of each commodity (the vector X) satisfies the intermediate (the matrix AX) and final (the vector Y) demands with given technology specified by the input-output coefficient matrix (A). Each input- output or technical coefficient (𝑎_𝑗𝑗) in matrix A defines the value of intermediate inputs that are required by industry i from industry j to produce a unit of output in industry i

(𝑎_𝑗𝑗 =𝑍_𝑗𝑗/𝑋_𝑗 where 𝑍_𝑗𝑗 is the intermediate input sales from industry j to industry i). Input-output analysis, as Leontief described (1951, p. 21), is ‘a method of analysis that takes advantage of the flow of goods and services among the elements of the economy to bring a much detailed statistical picture of the system into the range of manipulation by economic theory’. Input-output modelling is still popular in applied economic research.

The next stage of economy-wide modelling was the programming model pioneered by Sandee (1960). In his demonstration of the planning model for India, Sandee used a linear programming method to maximise a welfare (material consumption) function, subject to Leontief’s technology specification. Notable contributions to the programming models were made by Manne (1963) and Evans (1972). H. D. Evans’ internationally acclaimed study of protection in Australia was an important methodological contribution to the analysis of protection and to the applied general equilibrium framework (Dixon & Butlin 1977).

Input-output and programming models could not provide underlying market mechanism of interactions in the economy and lacked clear descriptions of the behaviour of individual agents (Dixon & Rimmer 2010a). In these models, the economy is visualised as a single agent (Dixon & Jorgenson 2013).

Lief Johansen (1960) advanced economy-wide modelling through the explicit identification of behaviour by economic agents in his model of Norway’s economy. The publication of Johansen’s book, ‘A Multi-sectoral Study of Economic Growth’, marked the birth of CGE modelling (Dixon & Jorgenson 2013). In Johansen’s 22-sector model, households maximise utility subject to their income constraints; industries choose primary and intermediate inputs to minimise their costs of producing any given level of output, subject to their production frontiers and the need to satisfy demands for their

26 outputs; and investors allocate the economy’s capital stock between industries to maximise their returns. The overall outcome for the economy is determined by the actions of individual agents driven by the price adjustment mechanism (invisible hand) that equalises demand and supply in various markets.

Dixon (2010a) reminds us that there was no single starting point for CGE modelling even though Johansen was ‘the first one to plant a seed in what has now become the CGE forest’ (p. 5).

Herbert Scarf’s work (1967; Scarf & Hansen 1973) brought greater attention and enthusiasm to CGE modelling. His students, John Whalley and John Shoven, further contributed to the development of Scarf’s approach, which is also known as a combinatorial approach. Scarf’s method, however, has been largely abandoned in favour of much simpler methods used by other approaches (Dixon 2006).

Dale Jorgenson and his associates solved their CGE model by iterative methods independently. They continue to make path-breaking contribution to CGE modelling through theoretical and econometric innovations (Dixon & Jorgenson 2013; Dixon & Rimmer 2010a). Irma Adelman, Sherman Robinson and their associates at the World Bank developed another widely used CGE approach. The models developed using their framework belong to the tradition of World Bank CGE modelling (Bandara 1991a). The Generalized Algebraic Modelling System (GAMS) software (Brooke, Kendrick & Meeraus 1996) is used in their Social Accounting Matrix based models.

Peter Dixon and his associates developed the Centre of Policy Studies (COPS) approach, adopting and extending Johansen strategies for computing, and for organising and understanding results. In this sense, COPS style modelling is directly descended from Johansen. The influence of Johansen, combined with the institutional arrangements under which COPS style models have been developed, has given this form of modelling some distinctive technical characteristics (Dixon, Koopman & Rimmer 2013). COPS style models are solved with the General Equilibrium Modelling Package (GEMPACK) software (Harrison & Pearson 1996). The progress of CGE modelling software and the differences among the main systems such as GEMPACK and the GAMS are detailed in Horridge et al. (2012). COPS style modelling is also known as ‘Australian style’ CGE modelling (Hertel 2013).

27 Finally, Global Trade Analysis Project (GTAP), an exceptional venture and a collaborative network of organisations and individuals, has emerged as a united force in CGE modelling, bringing an explosion of interests in global environmental, trade, energy, land-use and many other economic issues. Thomas Hertel and associates of the GTAP network have shaped the development of CGE modelling into a new era since its inception in 1991. GTAP is now recognised as a global brand of CGE modelling. Alan Powell, one of the founders of COPS style modelling, concludes ‘in the discipline of