The Analysis Of The Link Between Capital Flows And Macroeconomic-Financial-Spatial Income Distribution Indicators: Combining Financial Cge And Perception Models

This dissertation analyzes the intricate yet critical link between macroeconomic, financial, and social variables, including spatial income distribution, and how the income inequalities are affected by certain policies and external shocks. The first chapter shows the importance of including the financial sector in today's economic and policy analyses by demonstrating the difference between the computable general equilibrium (CGE) model and its extended version that incorporates financial sector, the financial computable general equilibrium (FCGE) model. The updated FCGE model in the second chapter is then employed to analyze the increased foreign capital inflows intermediated through the banking sector, reflecting the current phenomenon in Asian emerging countries. Based on the results of simulations, some policies are proposed. The upsides and the downsides of each are analyzed in great detail in Chapter 3 by using the analytic hierarchy process (AHP) and analytic network process (ANP). Chapter 1 analyzes the difference between CGE and FCGE models, from which we conclude that serious erroneous implications and inaccuracies arise from CGE's neglect of the financial sector's role. By simulating both models in scenarios of increased government spending, depending on whether government spending is financed through taxes or government bonds, the results clearly show how the negative impacts on the social indicators generated by the CGE model can be underestimated. Examples of this underestimation are the macroeconomic impact of increased government spending and the social impact of financing the spending through taxes. I also found that the CGE results underestimated these negative impacts of increased capital flows in the same fashion. The analysis in Chapter 2 highlights the negative impact of risky financial investment behaviors of the banking sector resulting from the increased capital inflow on the economy. This chapter, in particular, stresses that one must consider not only its macroeconomic impact but also its negative repercussions on spatial income distribution and poverty conditions. Chapter 2 also shows that the risks of a boom and bust cycle where the bank-led flows are reversed from inflows to outflows and the impact of the change in banks' behavior from risk-taking to risk-averse. While riskaverse behavior can produce more favorable macroeconomic and social outcomes, there is no reason to expect that such behavior will be maintained by banks when capital inflows increase. It is therefore suggested that some measures should be taken to limit the size of bank-led flows. Chapter 3 focuses on the policy analysis based on the results of model simulations in Chapter 2. From three alternative policies - i.e., aggressive monetary policy, assigning a levy on non-core liabilities, and encouraging capital outflows - it is suggested that policymakers seriously consider imposing some sort of levy on bank- led flows. Such a conclusion is derived after taking into account the benefits, opportunities, costs, and risks of bank-led inflow based on the priority ranking of the policies, the components (criteria) and strategic goals that include macroeconomic, financial and social considerations. A series of sensitivity analyses confirm that the results are robust. In the context of the present situation in many countries, the suggested policy is part of what is known as macroprudential policy. Finally, directions for future research are suggested. The analysis in Chapter 2 could be extended upon by incorporating more financial instruments and other social indicators, or by improving the accuracy of the parameters involved in the model. For example, rather than calibrating all of the parameters, one could estimate some of the parameters by utilizing econometric equations with time series data. Furthermore, for Chapter 3, one could conduct the analysis by using direct interviewing with the same approach and model. Respondents could include experts or policymakers who would express their perceptions regarding the relations among variables in the model. In this way, the resulting priority ranking from the model simulation can be compared with, or tested against, policymakers' perceptions, from which new insights may emerge