4. Aportes a la enseñanza de las ciencias sociales
4.4 Educación popular dentro de la escuela „‟formal‟‟
4.4.4 Educación popular inmersa en la educación formal
The IIP measures the stock of Mongolia’s foreign financial liabilities and foreign financial assets at a point in time. The difference between foreign financial liabilities and foreign financial assets is referred to as Mongolia’s net international investment position or net foreign liability (NFL).
The NFL represents either a net claim on or net liability to the ROW. Aggregated accumulation accounts, such as the KA, FA and other changes in financial assets and liabilities accounts (OCA), show the accumulation of assets and liabilities, their financing, and other changes that affect them. Accordingly, they explain changes between the opening and closing assets and liabilities in the IIP.
Whereas the CA is concerned with resource flows oriented to the current period, the accumulation accounts deal with the provision and financing of assets and liabilities, which are items that will affect future periods. That is, net liabilities imply that interest must be paid to foreigners.
The FA shows the net acquisition of financial assets and net incurrence of liabilities during the specified period. In contrast, the OCA shows flows that do not result from BOP transactions. The OCA covers changes in volume, other than BAP transactions, revaluation due to exchange rates, and other revaluation.
Table 5.11 International Investment Position, 2012 (in millions USD)
Item 2012
Assets
DIA by Mongolians abroad 1,297.0
Foreign credit, total 5,183.0
Foreign credit, government 4126.1
Foreign credit, private 956.9
Total Foreign assets 6,380.0 Liabilities
FDI stock in Mongolia 13,458.24
Foreign debt, total 4,451.90
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Private debt 2,267.80
Total Foreign liabilities 20,599.50 Net Foreign Liabilities 14,219.40
GDP in current USD 12,292.6
Net foreign liabilities as percent of GDP (%) 115.7
Foreign debt as percent of GDP (%) 36.2
Source: The Central Bank of Mongolia
Concluding remarks 5.5
In this chapter, we have presented databases for ORANIMON and MONAGE and have described the procedures, methods and sources to create them. IO data are the main input data to CGE models, as they play two important roles: providing an initial solution and serving as data for the evaluation of numerous coefficients in ORANIMON and MONAGE equations. In addition to IO data, our databases contain several other types of data on capital stocks, investment, depreciation and rates of return, government accounts and accounts with the ROW. Enhancing the information content of CGE models is crucial for the development of CGE modelling as a mainstream contributor to policy dialogue and a practical aid to economic decision making. With two base years’ data in 2005 and 2012, the models can be used in different analysis: forecasting, policy, historical and decomposition.
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Part III. Application
Overview
CGE models are useful for analyzing a developing small economy such as Mongolia, which recently transitioned from a centrally planned to a market-oriented economy.
Today, a market mechanism plays a crucial role for resource allocation in Mongolia. The prices of goods and services are determined by supply and demand in their respective markets. During the communist period, the government, a central planner, set and fixed the prices of all goods and services and planned the production, consumption and other economic activities of all agents. The fixed price system ensured the stability and predictability of the planned economy yet it eventually led to the demise of the system (Chuluunbat 2012).
Second, there was a major change in economic structure due to the transition. After 70 years of socialist development, the sudden collapse of communism in 1990 resulted in a massive economic contraction and devastation in the Mongolian economic structure and its industrial base between 1989 and 1993. The contraction was almost double that experienced by the United States during the Great Depression of the 1930s in terms of the plunge in domestic absorption (Boone 1994).
Third, positive or negative external shocks that have occurred to Mongolia recently are unprecedented. The sizes of those shocks were extremely large for a small economy like Mongolia’s. For example, the value of Mongolian mineral exports increased by 125% in 2006 due to an unprecedented improvement in terms of trade. Hence, it is helpful to use CGE models for evaluating impacts and clarifying thinking relating to the likely consequences of shocks for which there is no equivalent historical example in the Mongolian economic context.
Metaphorically speaking, CGE models are like economic ‘operating theatres’ where modelers or users can be considered economic ‘surgeons’. Of course, economic ‘surgeons’ do not remove ‘an infected part’ of the economy. They do have to look at all parts and interconnections of the economy inside and out, and can identify the issues and may offer policy alternatives. The models are not, however, remedies to Mongolia’s economic problems or fortune tellers for the roller coaster economy. It is true that no single model could ever serve as a sole base for policy making on any significant issue.
167 There are some other aspects of the Mongolian economy, notably the lack in governance and institutional quality (particularly corruption), which the models do not capture directly. But we think that ORANIMON and MONAGE can serve as laboratories for analyzing important economic issues and simulating potential impacts of various shocks to help develop informed views on policy in Mongolia.
The application part of the thesis is concerned with the analysis of the mining boom during 2005-2012. We apply ORANIMON 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. We will then move to the MONAGE simulations in Chapters 7. The MONAGE simulations are concerned with the analysis of the structural changes in the Mongolian economy between 2005 and 2012. Chapter 7 describes historical simulation, which provides detailed estimation of changes in structural variables such as technologies, preferences and the movement in export demand and supply curves and discusses decomposition simulation, which analyses the contributions of the structural changes to the macro- and industry-level economic performance of the economy during the period.
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