• Knowledge Management Research
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• v.10 no.3
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• pp.115-132
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• 2009

This paper makes an analysis of economic spillover effects of credit guarantee by linking the remainder of guarantee according to industries from 2003 to 2006 in this study. Specifically, total remainder of guarantee in 2006 is approximately 3922 billion and 600 million won on the basis of unchangeable price in 2003, and each funds becomes the input of the last demand in 27 industries over the cow1try and, thereby, induces production, added value, and the effect of employment. The last demand according to industries shows that a lot of funds have been supported to the manufacturing industry for about 1200 billion won, the wholesale and retail for about 1299 billion and 500 million won, food and lodging industry for about 144 billion and 100 million won, education and health care industry for about 132 billion and 600 million won, and social and other service industry for about 339 billion and 300 million won. The spillover effect about the remainder of guarantee in 2006 classified by industries over the country on the basis of production shows the high effects on the manufacturing industry for 37.8%, 2625 billion and 90 million won, the wholesale and retail for 20.7%, 1439 billion and 290 million won, food and lodging industry for 9.4%, 654 billion and 570 million won, real estate and business service industry for 9.2%, 637 billion and 310 million won, social and other service industry for 5.3%, 369 billion and 90 million won, and education and health care industry for 2.9%, 199 billion and 300 million won of the effect causing production over the country, 6945 billion won in order. The effect causing added value shows high spillover effect on the wholesale and retail for 36.7%, 1186 billion and 830 million won, the manufacturing industry for 25.8%, 831 billion and 500 million won, food and lodging industry for 14.9%, 480 billion and 980 million won, social and other service industry for 9.3%. 300 billion and 160 million won, and real estate and business service industry for 4.2%, 135 billion and 36 million won of the effect causing added value over the country in order. Finally, the effect causing employment shows a lot of employment have occurred in the wholesale and retail for 37.4%, 23,060 people, the manufacturing industry for 18.9%, 11,637 people, food and lodging industry for 13.7%, 8,429 people, social and other service industry for 7.9%, 4,866 people, and real estate and business service industry for 5.6%, 3,429 people of 61,617 people in order.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.6
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• pp.47-64
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• 2014

Considering the network externality and spill-over effects, this paper constructs the theoretical model for analyzing the financial policies focusing on the credit guarantee system for Creative Economy, which has been set as the new policy paradigm for the Korean Economy. The analytical results show that it is as much as important to improve the efficiency of the financial markets and construct the infrastructure for reducing the information asymmetry problem which would be more serious in the creative finance. Although it is important to fill the gap due to the market failure it is also crucial to construct the appropriate financial schemes for the various stages of the innovative firms growth. Without these, the impacts of current public funding policies may crowd out the amount of private market funds for the innovative firms or reduce the possibility of commercialization of new technologies in these firms. Based on the evaluation of current related public policies from the perspectives of creative finance, we imply that the current financial policies appear to be quantity oriented not the quality based. Although the policy goals would be appropriately set for vitalizing the Creative Economy in Korea, they appear to be still unsuccessful to address the information asymmetry issue which is the major concern in vitalizing the creative economy. Thus we emphasize the market friendly policies, risk-sharing between the various market participants, revitalizing the relationship banking and efficient management of credit guarantee system in Korea based on the analytical model as well as the evaluation of related policies regarding the creative economy.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.9 no.3
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• pp.191-198
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• 2011

In general, conventional criticality analyses for spent fuel transport/dry storage systems have been performed based on assumption of fresh fuel concerning the potential uncertainties from number density calculation of Transuranic and Fission Products in spent fuel. However, because of economic loss due to the excessive criticality margin, recently the design of transport/dry storage systems with Burnup Credit(BUC) application has been actively developed. The uncertainties in criticality analyses on transport/storage systems with BUC technique show strong dependance upon initial enrichment and burnup rate, whereas those in the conventional criticality evaluation based on fresh fuel assumption do not show such a dependance. In this study, regulatory-required uncertainties of the criticality analyses for BK 26 Cask, which is conceptually designed spent fuel transport cask with BUC corresponding to the limiting circumstances on nuclear power plants in Korea, are evaluated as a function of initial enrichment and burnup rate. Results of this study will be used as basic data for spent fuel loading curve of BK 26 Cask.

• Journal of the Economic Geographical Society of Korea
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• v.16 no.4
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• pp.631-646
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• 2013

This paper empirically explores the pattern of causality between bank lending and housing prices in Korea over a period of the early 1990s to the end of 2000s by employing a long term cointegration and short-term time series regression analysis. Although the contemporaneous correlation between bank lending and housing prices is large, the analysis shows that the intense interaction between credit growth and bank lending to household arises from a growth in banking lending responding to an increase in housing prices. In addition, the regulatory change such as the introduction of financial constraints on bank loans such as LTV and DTI in the early and mid-2000s has played a significant role in stabilizing financial and real estate markets.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Nuclear Engineering and Technology
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• v.43 no.2
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• pp.141-148
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• 2011

This paper presents the quantitative analysis results of research on the burnup effect on the nuclear fuel cycle cost of BeO-$UO_2$ fuel. As a result of this analysis, if the burnup is 60 MWD/kg, which is the limit under South Korean regulations, the nuclear fuel cycle cost is 4.47 mills/kWh at 4.8wt% of Be content for the BeO-$UO_2$ fuel. It is, however, reduced to 3.70 mills/kWh at 5.4wt% of Be content if the burnup is 75MWD/kg. Therefore, it seems very advantageous, in terms of the economic aspect, to develop BeO-$UO_2$ fuel, which does not have any technical problem with its safety and is a high burnup & long life cycle nuclear fuel.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.243-255
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• 2016

The developed composite index has limits to estimate and predict economic status due to economic pattern change and the response change of explanatory variables. A higher precedence individual indicators should be selected to predict the future accurately. In this study, effectiveness of Jeju Island precedence indicators consists of constituents in the area, the consumer price index, services production index, mining and manufacturing production index. The average temperature of Seogwipo and credit card purchase amount is reviewed as an economic turning point consideration and time lag correlation analysis with real data. In addition, we suggest the proper reference cycle in Jeju composite precedence index and evaluate the configuration in leading indicators for Jeju by comparing national economic indicators. Based on the derived results, the current problems of Jeju Island precedence indicators will be illustrated and the improvement methods to estimate a regional composite index will be suggested.

• Journal of the Korean Wood Science and Technology
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• v.41 no.6
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• pp.594-605
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• 2013

This study was performed to analyze GHG (Greenhouse gas) reduction effectiveness and economic feasibility in the wood pellet fuel switching project using JCDM (Japan Clean Development Mechanism) and KVER (Korea Voluntary Emission Reduction)data. The major data for the analyses consist of investment costs, annual GHG reductions, fuel prices and GHG credit prices. The wood pellet fuel switching projects are the $CO_2$-zero projects. Therefore, these projects are essential to accomplish the GHG mitigation target, especially in Korea. In order to raise the economic feasibility of the wood pellet fuel switching project, the results of this study suggest that the Korean government should reduce the price of wood pellet through the supply on a large scale and raise the KCER price of wood pellet fuel switching project.

• The Korean Journal of Applied Statistics
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• v.37 no.4
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• pp.509-523
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• 2024

This study estimates connectedness index among the US, China, Europe, Japan, and South Korea using monthly economic policy uncertainty (EPU) data from January 2000 to December 2023. The connectedness index allows us to analyze the effect of global economic uncertainty on domestic economic uncertainty. The EPU is used as a proxy for economic uncertainty. Inter-country connectedness index is computed from variance decomposition. The findings from forecast error variance decomposition show that three-fourths of total uncertainty comes from economic uncertainty in the own country and one-fourth of total uncertainty comes from economic uncertainty in the others. The analysis on net pairwise connectedness reveals that, even though the extent of the effect of economic uncertainty in one country from economic uncertainty in another country varies over time, economic uncertainty in South Korea, a small-open economy, is mainly affected by economic uncertainty in the others. The reverse situation rarely happens except in the specific occurrence such as the collapse of the credit bubble in 2003 and the subsequent years, the inter-Korean summit and North Korea-the US summit in 2018, and the period from the first outbreak of COVID-19 on the implementation of the government's severe regulation against COVID-19.

• Korean Journal of Rural Living Science
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• v.10 no.2
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• pp.1-14
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• 1999

The purposes of this study are to identify the factors affecting asset and debt choice behavior of rural households as well as to examine the differences in asset and debt choice behavior between rural and urban households. The data are taken from Korean Household Panel Study conducted in 1996 by Daewoo Economic Research Institute. Among 2,833, a final sample of 2,625 -537 rural and 2,088 urban sample- is used in this study. The results show that, the asset and debt choice behavior of rural households is totally different from that of urban households. The ratio of rural households holding all types of financial assets and sales credit is less than that of urban households while the ratio of holding real asset and loan is greater in rural than urban households. The most influential variable on the ownership of asset and debt is the age of household head. And there exist interrelationships between ownership of different assets and debts.