• KDI Journal of Economic Policy
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• v.31 no.1
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• pp.169-206
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• 2009

This study stems from a question, "How should we understand the pattern of the Korean economy after the 1990s?" Among various analytic methods applicable, this study chooses a Structural Vector Autoregression (SVAR) with long-run restrictions, identifies diverse impacts that gave rise to the current status of the Korean economy, and differentiates relative contributions of those impacts. To that end, SVAR is applied to four economic models; Blanchard and Quah (1989)'s 2-variable model, its 3-variable extensions, and the two other New Keynesian type linear models modified from Stock and Watson (2002). Especially, the latter two models are devised to reflect the recent transitions in the determination of foreign exchange rate (from a fixed rate regime to a flexible rate one) as well as the monetary policy rule (from aggregate targeting to inflation targeting). When organizing the assumed results in the form of impulse response and forecasting error variance decomposition, two common denominators are found as follows. First, changes in the rate of economic growth are mainly attributable to the impact on productivity, and such trend has grown strong since the 2000s, which indicates that Korea's economic growth since the 2000s has been closely associated with its potential growth rate. Second, the magnitude or consistency of impact responses tends to have subsided since the 2000s. Given Korea's high dependence on trade, it is possible that low interest rates, low inflation, steady growth, and the economic emergence of China as a world player have helped secure capital and demand for export and import, which therefore might reduced the impact of each sector on overall economic status. Despite the fact that a diverse mixture of models and impacts has been used for analysis, always two common findings are observed in the result. Therefore, it can be concluded that the decreased rate of economic growth of Korea since 2000 appears to be on the same track as the decrease in Korea's potential growth rate. The contents of this paper are constructed as follows: The second section observes the recent trend of the economic development of Korea and related Korean articles, which might help in clearly defining the scope and analytic methodology of this study. The third section provides an analysis model to be used in this study, which is Structural VAR as mentioned above. Variables used, estimation equations, and identification conditions of impacts are explained. The fourth section reports estimation results derived by the previously introduced model, and the fifth section concludes.

• KDI Journal of Economic Policy
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• v.12 no.4
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• pp.3-20
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• 1990

The multisector model is designed to analyze and forecast structural change in industrial output, employment, capital and relative price as well as macroeconomic change in aggregate income, interest rate, etc. This model has 25 industrial sectors, containing about 1,300 equations. Therefore, this model is characterized by detailed structural disaggregation at the sectoral level. Individual industries are based on many of the economic relationships in the model. This is what distinguishes a multisector model from a macroeconomic model. Each industry is a behavioral agent in the model for industrial investment, employment, prices, wages, and intermediate demand. The strength of the model lies in the simulating the interactions between different industries. The result of its simulation will be introduced in the next paper. In this paper, we only introduce the structure of the multisector model and the coefficients of the equations. The multisector model is a dynamic model-that is, it solves year by year into the future using its own solutions for earlier years. The development of a dynamic, year-by-year solution allows us to combine the change in structure with a consideration of the dynamic adjustment required. These dynamics have obvious advantages in the use of the multisector model for industrial planning. The multisector model is a medium-term and long-term model. Whereas a short-term model can taken the labor supply and capital stock as given, a long-term model must acknowledge that these are determined endogenously. Changes in the medium-term can be analyzed in the context of long-term structural changes. The structure of this model can be summarized as follow. The difference in domestic and world prices affects industrial structure and the pattern of international trade; domestic output and factor price affect factor demand; factor demand and factor price affect industrial income; industrial income and relative price affect industrial consumption. Technical progress, as measured in terms of total factor productivity and relative price affect input-output coefficients; input-output coefficients and relative price determine the industrial input cost; input cost and import price determine domestic price. The differences in productivity and wage growth among different industries affect the relative price.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.6
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• pp.1230-1236
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• 2012

To verify that the electrokinetic remediation is effective for decreasing salinity of fields of the plastic-film house, field tests for physical property, chemical property, and crop productivity of soils have been conducted. The abridged result of those tests is as follows. In the EK treatment, the electrokinetic remediation has been treated at the constant voltage (about 0.8 V $cm^{-1}$) for fields of the farm household. At this time, an alternating current (AC) 220 V of the farm household was transformed a direct current. The HSCI (High Silicon Cast Iron) that the length of the stick for a cation is 20cm, and the Fe Plate for an anion have been spread out on the ground. As the PVC pipe that is 10 cm in diameter was laid in the bottom of soils, cations descend on the cathode were discharged together. For soil physical properties according to the EK treatment, the destruction effect of soil aggregate was large, and the infiltration rate of water was increased. However, variations of bulk density and porosity were not considerable. Meanwhile, in chemical properties of soils, principal ions of such as EC, $NO_3{^-}$-N, $K^+$, and $Na^+$ were better rapidly reduced in the EK treated control plot than in the untreated control plot. And properties such as pH, $P_2O_5$ and $Ca^{2+}$ had a small impact on the EK. For cropping season of crop cultivation according to the EK treatment, decreasing rates of chemical properties of soils were as follows; $NO_3{^-}$-N 78.3% > $K^+$ 72.3% > EC 71.6% $$\geq_-$$ $Na^+$ 71.5% > $Mg^{2+}$ 36.8%. As results of comparing the experimental plot that EK was treated before crop cultivation with it that EK was treated during crop cultivation, the decreasing effect of chemical properties was higher in the case that EK was treated during crop cultivation. After the EK treatment, treatment effects were distinct for $NO_3{^-}$-N and EC that a decrease of nutrients is clear. However, because the lasting effect of decreasing salinity were not distinct for the single EK treatment, fertilization for soil testing was desirable carrying on testing for chemical properties of soils after EK treatments more than two times. In the growth of cabbages according to the EK treatment, the rate of yield increase was 225.5% for the primary treatment, 181.0% for the secondary treatment, and 124.2% for third treatment compared with the untreated control plot. The yield was increased by a factor of 130.0% for the hot pepper at the primary treatment (Apr. 2011), 248.1% for the lettuce at the secondary treatment (Nov.2011), and 125.4% for the young radish at the third treatment (Jul. 2012). In conclusion, the effect of yield increase was accepted officially for all announced crops.