• Tuberculosis and Respiratory Diseases
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• v.50 no.1
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• pp.52-66
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• 2001

Background : NF-${\kappa}B$ is the most important transcriptional factor in IL-8 gene expression. Triptolide is a new compound that recently has been shown to inhibit NF-${\kappa}B$ activation. The purpose of this study is to investigate how triptolide inhibits NF-${\kappa}B$-dependent IL-8 gene transcription in lung epithelial cells and to pilot the potential for the clinical application of triptolide in inflammatory lung diseases. Methods : A549 cells were used and triptolide was provided from Pharmagenesis Company (Palo Alto, CA). In order to examine NF-${\kappa}B$-dependent IL-8 transcriptional activity, we established stable A549 IL-8-NF-${\kappa}B$-luc. cells and performed luciferase assays. IL-8 gene expression was measured by RT-PCR and ELISA. A Western blot was done for the study of $I{\kappa}B{\alpha}$ degradation and an electromobility shift assay was done to analyze NF-${\kappa}B$ DNA binding. p65 specific transactivation was analyzed by a cotransfection study using a Gal4-p65 fusion protein expression system. To investigate the involvement of transcriptional coactivators, we perfomed a transfection study with CBP and SRC-1 expression vectors. Results : We observed that triptolide significantly suppresses NF-${\kappa}B$-dependent IL-8 transcriptional activity induced by IL-$1{\beta}$ and PMA. RT-PCR showed that triptolide represses both IL-$1{\beta}$ and PMA-induced IL-8 mRNA expression and ELISA confirmed this triptolide-mediated IL-8 suppression at the protein level. However, triptolide did not affect $I{\kappa}B{\alpha}$ degradation and NF-$_{\kappa}B$ DNA binding. In a p65-specific transactivation study, triptolide significantly suppressed Gal4-p65T Al and Gal4-p65T A2 activity suggesting that triptolide inhibits NF-${\kappa}B$ activation by inhibiting p65 transactivation. However, this triptolide-mediated inhibition of p65 transactivation was not rescued by the overexpression of CBP or SRC-1, thereby excluding the role of transcriptional coactivators. Conclusions : Triptolide is a new compound that inhibits NF-${\kappa}B$-dependent IL-8 transcriptional activation by inhibiting p65 transactivation, but not by an $I{\kappa}B{\alpha}$-dependent mechanism. This suggests that triptolide may have a therapeutic potential for inflammatory lung diseases.

• Tuberculosis and Respiratory Diseases
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• v.45 no.5
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• pp.1012-1021
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• 1998

Background : It is known that airway inflammation is present in most patients with asthma, but the relationship between symptoms and the severity and nature of airway inflammation has not been established. Cough variant asthma is defined as an asthma in which the dominant symptom is cough, and the condition can be successfully treated with inhaled steroids. This study was performed to evaluate the time course of bronchial responsiveness according to an inhaled anti-inflammatory therapy and the factors which affect the resolution of bronchial responsiveness, and an efficacy of nedocromil to cough asthma. Method: A prospective study for the investigation of bronchial responsiveness according to an inhaled anti-inflammatory treatment in sixty-one cough asthmatics was performed. Twenty-three entered budesonide ($400{\mu}g{\times}2/day$), twenty-two entered nedocromil ($4mg{\times}2/day$) and sixteen patients entered combined group. The bronchial hyperresponsiveness (BHR) was estimated by methacholine challenge test using counted breath method. The symptom was estimated by 'symptom score'. Reevaluation of BHR and symptom was performed at 2 month after treatment, and if BHR was not resoluted at this time, regarded as a non-responder, and then follow-up of BHR and symptom was performed at 4- and/or 6 month after treatment. Results: The improvement of BHR and symptom was significant in 2 month (p<0.05), but there was no change of them during follow-up period of 4- and/or 6 month in non-responders. In comparison of allergic markers such as serum total IgE, peripheral eosinophil count and skin test reactivity between responders and non-responders, there was no difference in each other. However, in comparison of other factors such as cumulative pack-years, symptom duration, age, gender, and the initial degree of PC20, there was a significant difference in each other(p<0.05). The percent of patients with the resolution of BHR in 2 month was not different in each group(p=0.95). There was no significant difference in the degree of improvement of BHR and symptom in each group. Conclusion: Bronchial responsiveness and symptom was not significantly improved in non-responders during follow-up period of 4- and/or 6 month. The effect of inhaled nedocromil was equivalent to that of inhaled steroid in cough asthmatics, and the response to combined treatment is not superior to that achieved by either of these agents used alone.

• Korean Journal of Plant Resources
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• v.15 no.3
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• pp.177-187
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• 2002

In our experiment, selected mutants were used which showed not only the phenotype of a specific unpolished rice but also phenotypes of EM 40, LO 1050, and TAL 214. Reciprocal crosses between the mutants were conducted to select strains which would have more quantity of lipids than before. The constitution of fatty acid was also tested to figure out nutritional aspects of the mutants. In the crossing between EM 40 mutants and mutants (LO 1050) having a thick aleurone layer, the expression of EM 40 mutants has no relation with the thickness of the aleurone layer. And the lipid content of new F$_2$ strains through the crossing is 4.15 ％. The lipid content is larger than those of the parents including Kinmaze and in other crossings of this experiment. This is attributed to the fact that the new F$_2$ strains are the products of the crossing between genes responsible for the size of buds, where lipid is accumulated, and genes accountable for the thickness of the aleurone layer. In the crossing between EM 40 mutants and TAL 214 mutants, lipid content of the new F$_2$ strains is 3.8 ％, higher than 2.92 ％ of TAL 214 mutants. But the degree of lipid increase is smaller than in two other crossings. This is probably because genes expressing the phenotypes of TAL 214 affect the size of EM 40, which gets smaller. The aleurone layer of the new F$_2$ strains is 12 $\mu\textrm{m}$ thicker than the layer of TAL 214 mutants, but 6 $\mu\textrm{m}$ thinner than that of parents (LO 1050) having a thick aleurone layer. This seems to be affected by the size of a microscope. The phenotype of the new F$_2$ strains appears to be similar to that of TAL 214. The lipid content of the new F$_2$ strains is 3.85 ％, larger than 2.92 ％ of TAL 214 and 3.01 % of LO 1050. The increase may be due to the aleurone layer of LO 1050. And the size of the bud of the unpolished rice, though it is not big enough like that of LO 1050, seems to be affected by the accumulation of genes in the thick aleurone layer. The accumulation may contribute to the increase in the content of lipid. When it comes to the constitution of fatty acid, there is little difference between parents like Kinmaze and the new F$_2$ strains. But oleic acid increases while linoleic acid decreases. And the decrease in the linolenic acid seems to contribute to the increase in lipid content. This fact also raises the possibility that genes accountable for specific phenotypes could change the quality of rice if the genes are accumulated. Now, experiments on strains which have large lipid content in EM 40 type 1(ge-1, 3.68 ％), EM type 2(ge-2, 2.91 ％), thick aleurone layer(4.63 ％), and starch layer(3.44 ％) are under way to figure out the effects of gene accumulation. These experiments are likely to present the ways for increasing the lipid content.

• Tuberculosis and Respiratory Diseases
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• v.54 no.5
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• pp.551-562
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• 2003

Background : NF-${\kappa}B$ is a characteristic transcriptional factor which has been shown to regulate production of acute inflammatory mediators and to be involved in the pathogenesis of many inflammatory lung diseases. There has been some evidence that PI3K/Akt pathway could activate NF-${\kappa}B$ in human cell lines. However, the effect of PI3K/Akt pathway on the activation of NF-${\kappa}B$ varied depending on the cell lines used in the experiments. In this study we evaluated the effect of PI3K/Akt pathway on the activation of NF-${\kappa}B$ in human respiratory epithelial cell lines. Methods : BEAS-2B, A549 and NCI-H157 cell lines were used in this experiment. To evaluate the activation of Akt activation and I${\kappa}B$ degradation, cells were analysed by western blot assay using phospho-specific Akt Ab and $I{\kappa}B$ Ab. To block PI3K/Akt pathway, cells were pretreated with wortmannin or LY294002 and transfected with dominant negative Akt (DN-Akt). For IKK activity, immune complex kinase assay was performed. To evaluate the DNA binding affinity and transcriptional activity of NF-${\kappa}B$, electrophoretic mobility shift assay (EMSA) and luciferase assay were performed, respectively. Results : In BEAS-2B, A549 and NCI-H157 cell lines, Akt was activated by TNF-$\alpha$ and insulin. Activation of Akt by insulin did not induce $I{\kappa}B{\alpha}$ degradation. Blocking of PI3K/Akt pathway via wortmannin/LY294002 or DN-Akt did not inhibit TNF-$\alpha$-induced $I{\kappa}B{\alpha}$ degradation or IKK activation. Inhibition of PI3K/Akt did not affect TNF-$\alpha$-induced NF-${\kappa}B$ activation. Overexpression of DN-Akt did not block TNF-$\alpha$-induced transcriptional activation of NF-${\kappa}B$, but wortmannin enhanced TNF-$\alpha$-induced in NF-${\kappa}B$ transcriptional activity. Conclusion : PI3K/Akt was not involved in TNF-$\alpha$-induced $I{\kappa}B{\alpha}$ degradation or transcriptional activity of NF-${\kappa}B$ in human respiratory epithelial cell lines.

• KDI Journal of Economic Policy
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• v.34 no.3
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• pp.95-131
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• 2012

This study examines whether the executive turnover and the executive resignation during the term of office affect the performance of Korean state-owned enterprises. The executive turnover in the paper means the comprehensive change of the executives which includes the change after the term of office, the change after consecutive terms and the change during the term of office. The 'resignation' was named for the executive change during the term of office to distinguish from the executive turnover. The study scope of the paper is restrained to the comprehensive executive change itself irrespective of the term of office and the resignation during the term of office. Therefore the natural change of the executive after the term of office or the change after consecutive terms is not included in the study. Spontaneous resignation and forced resignation are not distinguished in the paper as the distinction between the two is not easy. The paper uses both the margin of return on asset and the margin of return on asset adjusted by industry as proxies of the performance of state-owned enterprises. The business nature of state-owned enterprise is considered in the study, the public nature not in it. The paper uses the five year (2004 to 2008) samples of 24 firms designated as public enterprises by Korean government. The analysis results are as follows. First, 45.1% of CEOs were changed a year during the sample period on the average. The average tenure period of CEOs was 2 years and 3 months and 49.9% among the changed CEOs resigned during the term of office. 41.6% of internal auditors were changed a year on the average. The average tenure period of internal auditors was 2 years and 2 months and 51.0% among the changed internal auditors resigned during the term of office. In case of outside directors, on average, 38.2% were changed a year. The average tenure period was 2 years and 7 months and 25.4% among the changed internal directors resigned during the term of office. These statistics show that numerous CEOs resigned before the finish of the three year term in office. Also, considering the tenure of an internal auditor and an outside director which diminished from 3 years to 2 years by an Act on the Management of Public Institutions (applied to the executives appointed since April 2007), it seems most internal auditors resigned during the term of office but most outside directors resigned after the end of the term. Secondly, There was no evidence that the executives were changed during the term of office because of the bad performance of prior year. On the other hand, contrary to the normal expectation, the performance of prior year of the state-owned enterprise where an outside director resigned during the term of office was significantly higher than that of other state-owned enterprises. It means that the clauses in related laws on the executive dismissal on grounds of bad performance did not work normally. Instead it can be said that the executive change was made by non-economic reasons such as a political motivation. Thirdly, the results from a fixed effect model show there were evidences that performance turned negatively when CEOs or outside directors resigned during the term of office. CEO's resignation during the term of office gave a significantly negative effect on the margin of return on asset. Outside director's resignation during the term of office lowered significantly the margin of return on asset adjusted by industry. These results suggest that the executive's change in Korean state-owned enterprises was not made by objective or economic standards such as management performance assessment and the negative effect on performance of the enterprises was had by the unfaithful obeyance of the legal executive term.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.19-43
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• 2014

The Pareto principle, also known as the 80-20 rule, states that roughly 80% of the effects come from 20% of the causes for many events including natural phenomena. It has been recognized as a golden rule in business with a wide application of such discovery like 20 percent of customers resulting in 80 percent of total sales. On the other hand, the Long Tail theory, pointing out that "the trivial many" produces more value than "the vital few," has gained popularity in recent times with a tremendous reduction of distribution and inventory costs through the development of ICT(Information and Communication Technology). This study started with a view to illuminating how these two primary business paradigms-Pareto principle and Long Tail theory-relates to the success of virtual knowledge collaboration. The importance of virtual knowledge collaboration is soaring in this era of globalization and virtualization transcending geographical and temporal constraints. Many previous studies on knowledge sharing have focused on the factors to affect knowledge sharing, seeking to boost individual knowledge sharing and resolve the social dilemma caused from the fact that rational individuals are likely to rather consume than contribute knowledge. Knowledge collaboration can be defined as the creation of knowledge by not only sharing knowledge, but also by transforming and integrating such knowledge. In this perspective of knowledge collaboration, the relative distribution of knowledge sharing among participants can count as much as the absolute amounts of individual knowledge sharing. In particular, whether the more contribution of the upper 20 percent of participants in knowledge sharing will enhance the efficiency of overall knowledge collaboration is an issue of interest. This study deals with the effect of this sort of knowledge sharing distribution on the efficiency of knowledge collaboration and is extended to reflect the work characteristics. All analyses were conducted based on actual data instead of self-reported questionnaire surveys. More specifically, we analyzed the collaborative behaviors of editors of 2,978 English Wikipedia featured articles, which are the best quality grade of articles in English Wikipedia. We adopted Pareto ratio, the ratio of the number of knowledge contribution of the upper 20 percent of participants to the total number of knowledge contribution made by the total participants of an article group, to examine the effect of Pareto principle. In addition, Gini coefficient, which represents the inequality of income among a group of people, was applied to reveal the effect of inequality of knowledge contribution. Hypotheses were set up based on the assumption that the higher ratio of knowledge contribution by more highly motivated participants will lead to the higher collaboration efficiency, but if the ratio gets too high, the collaboration efficiency will be exacerbated because overall informational diversity is threatened and knowledge contribution of less motivated participants is intimidated. Cox regression models were formulated for each of the focal variables-Pareto ratio and Gini coefficient-with seven control variables such as the number of editors involved in an article, the average time length between successive edits of an article, the number of sections a featured article has, etc. The dependent variable of the Cox models is the time spent from article initiation to promotion to the featured article level, indicating the efficiency of knowledge collaboration. To examine whether the effects of the focal variables vary depending on the characteristics of a group task, we classified 2,978 featured articles into two categories: Academic and Non-academic. Academic articles refer to at least one paper published at an SCI, SSCI, A&HCI, or SCIE journal. We assumed that academic articles are more complex, entail more information processing and problem solving, and thus require more skill variety and expertise. The analysis results indicate the followings; First, Pareto ratio and inequality of knowledge sharing relates in a curvilinear fashion to the collaboration efficiency in an online community, promoting it to an optimal point and undermining it thereafter. Second, the curvilinear effect of Pareto ratio and inequality of knowledge sharing on the collaboration efficiency is more sensitive with a more academic task in an online community.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.25-44
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• 2013

Accessing movie contents has become easier and increased with the advent of smart TV, IPTV and web services that are able to be used to search and watch movies. In this situation, there are increasing search for preference movie contents of users. However, since the amount of provided movie contents is too large, the user needs more effort and time for searching the movie contents. Hence, there are a lot of researches for recommendations of personalized item through analysis and clustering of the user preferences and user profiles. In this study, we propose recommendation system which uses ontology based knowledge base. Our ontology can represent not only relations between metadata of movies but also relations between metadata and profile of user. The relation of each metadata can show similarity between movies. In order to build, the knowledge base our ontology model is considered two aspects which are the movie metadata model and the user model. On the part of build the movie metadata model based on ontology, we decide main metadata that are genre, actor/actress, keywords and synopsis. Those affect that users choose the interested movie. And there are demographic information of user and relation between user and movie metadata in user model. In our model, movie ontology model consists of seven concepts (Movie, Genre, Keywords, Synopsis Keywords, Character, and Person), eight attributes (title, rating, limit, description, character name, character description, person job, person name) and ten relations between concepts. For our knowledge base, we input individual data of 14,374 movies for each concept in contents ontology model. This movie metadata knowledge base is used to search the movie that is related to interesting metadata of user. And it can search the similar movie through relations between concepts. We also propose the architecture for movie recommendation. The proposed architecture consists of four components. The first component search candidate movies based the demographic information of the user. In this component, we decide the group of users according to demographic information to recommend the movie for each group and define the rule to decide the group of users. We generate the query that be used to search the candidate movie for recommendation in this component. The second component search candidate movies based user preference. When users choose the movie, users consider metadata such as genre, actor/actress, synopsis, keywords. Users input their preference and then in this component, system search the movie based on users preferences. The proposed system can search the similar movie through relation between concepts, unlike existing movie recommendation systems. Each metadata of recommended candidate movies have weight that will be used for deciding recommendation order. The third component the merges results of first component and second component. In this step, we calculate the weight of movies using the weight value of metadata for each movie. Then we sort movies order by the weight value. The fourth component analyzes result of third component, and then it decides level of the contribution of metadata. And we apply contribution weight to metadata. Finally, we use the result of this step as recommendation for users. We test the usability of the proposed scheme by using web application. We implement that web application for experimental process by using JSP, Java Script and prot$\acute{e}$g$\acute{e}$ API. In our experiment, we collect results of 20 men and woman, ranging in age from 20 to 29. And we use 7,418 movies with rating that is not fewer than 7.0. In order to experiment, we provide Top-5, Top-10 and Top-20 recommended movies to user, and then users choose interested movies. The result of experiment is that average number of to choose interested movie are 2.1 in Top-5, 3.35 in Top-10, 6.35 in Top-20. It is better than results that are yielded by for each metadata.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.29-44
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• 2017

In Internet commerce, consumers are heavily influenced by product reviews written by other users who have already purchased the product. However, as the product reviews accumulate, it takes a lot of time and effort for consumers to individually check the massive number of product reviews. Moreover, product reviews that are written carelessly actually inconvenience consumers. Thus many online vendors provide mechanisms to identify reviews that customers perceive as most helpful (Cao et al. 2011; Mudambi and Schuff 2010). For example, some online retailers, such as Amazon.com and TripAdvisor, allow users to rate the helpfulness of each review, and use this feedback information to rank and re-order them. However, many reviews have only a few feedbacks or no feedback at all, thus making it hard to identify their helpfulness. Also, it takes time to accumulate feedbacks, thus the newly authored reviews do not have enough ones. For example, only 20% of the reviews in Amazon Review Dataset (Mcauley and Leskovec, 2013) have more than 5 reviews (Yan et al, 2014). The purpose of this study is to analyze the factors affecting the usefulness of online product reviews and to derive a forecasting model that selectively provides product reviews that can be helpful to consumers. In order to do this, we extracted the various linguistic, psychological, and perceptual elements included in product reviews by using text-mining techniques and identifying the determinants among these elements that affect the usability of product reviews. In particular, considering that the characteristics of the product reviews and determinants of usability for apparel products (which are experiential products) and electronic products (which are search goods) can differ, the characteristics of the product reviews were compared within each product group and the determinants were established for each. This study used 7,498 apparel product reviews and 106,962 electronic product reviews from Amazon.com. In order to understand a review text, we first extract linguistic and psychological characteristics from review texts such as a word count, the level of emotional tone and analytical thinking embedded in review text using widely adopted text analysis software LIWC (Linguistic Inquiry and Word Count). After then, we explore the descriptive statistics of review text for each category and statistically compare their differences using t-test. Lastly, we regression analysis using the data mining software RapidMiner to find out determinant factors. As a result of comparing and analyzing product review characteristics of electronic products and apparel products, it was found that reviewers used more words as well as longer sentences when writing product reviews for electronic products. As for the content characteristics of the product reviews, it was found that these reviews included many analytic words, carried more clout, and related to the cognitive processes (CogProc) more so than the apparel product reviews, in addition to including many words expressing negative emotions (NegEmo). On the other hand, the apparel product reviews included more personal, authentic, positive emotions (PosEmo) and perceptual processes (Percept) compared to the electronic product reviews. Next, we analyzed the determinants toward the usefulness of the product reviews between the two product groups. As a result, it was found that product reviews with high product ratings from reviewers in both product groups that were perceived as being useful contained a larger number of total words, many expressions involving perceptual processes, and fewer negative emotions. In addition, apparel product reviews with a large number of comparative expressions, a low expertise index, and concise content with fewer words in each sentence were perceived to be useful. In the case of electronic product reviews, those that were analytical with a high expertise index, along with containing many authentic expressions, cognitive processes, and positive emotions (PosEmo) were perceived to be useful. These findings are expected to help consumers effectively identify useful product reviews in the future.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.1-27
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• 2017

It is known that the economic sentiment index and macroeconomic indicators are closely related because economic agent's judgment and forecast of the business conditions affect economic fluctuations. For this reason, consumer sentiment or confidence provides steady fodder for business and is treated as an important piece of economic information. In Korea, private consumption accounts and consumer sentiment index highly relevant for both, which is a very important economic indicator for evaluating and forecasting the domestic economic situation. However, despite offering relevant insights into private consumption and GDP, the traditional approach to measuring the consumer confidence based on the survey has several limits. One possible weakness is that it takes considerable time to research, collect, and aggregate the data. If certain urgent issues arise, timely information will not be announced until the end of each month. In addition, the survey only contains information derived from questionnaire items, which means it can be difficult to catch up to the direct effects of newly arising issues. The survey also faces potential declines in response rates and erroneous responses. Therefore, it is necessary to find a way to complement it. For this purpose, we construct and assess an index designed to measure consumer economic sentiment index using sentiment analysis. Unlike the survey-based measures, our index relies on textual analysis to extract sentiment from economic and financial news articles. In particular, text data such as news articles and SNS are timely and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. There exist two main approaches to the automatic extraction of sentiment from a text, we apply the lexicon-based approach, using sentiment lexicon dictionaries of words annotated with the semantic orientations. In creating the sentiment lexicon dictionaries, we enter the semantic orientation of individual words manually, though we do not attempt a full linguistic analysis (one that involves analysis of word senses or argument structure); this is the limitation of our research and further work in that direction remains possible. In this study, we generate a time series index of economic sentiment in the news. The construction of the index consists of three broad steps: (1) Collecting a large corpus of economic news articles on the web, (2) Applying lexicon-based methods for sentiment analysis of each article to score the article in terms of sentiment orientation (positive, negative and neutral), and (3) Constructing an economic sentiment index of consumers by aggregating monthly time series for each sentiment word. In line with existing scholarly assessments of the relationship between the consumer confidence index and macroeconomic indicators, any new index should be assessed for its usefulness. We examine the new index's usefulness by comparing other economic indicators to the CSI. To check the usefulness of the newly index based on sentiment analysis, trend and cross - correlation analysis are carried out to analyze the relations and lagged structure. Finally, we analyze the forecasting power using the one step ahead of out of sample prediction. As a result, the news sentiment index correlates strongly with related contemporaneous key indicators in almost all experiments. We also find that news sentiment shocks predict future economic activity in most cases. In almost all experiments, the news sentiment index strongly correlates with related contemporaneous key indicators. Furthermore, in most cases, news sentiment shocks predict future economic activity; in head-to-head comparisons, the news sentiment measures outperform survey-based sentiment index as CSI. Policy makers want to understand consumer or public opinions about existing or proposed policies. Such opinions enable relevant government decision-makers to respond quickly to monitor various web media, SNS, or news articles. Textual data, such as news articles and social networks (Twitter, Facebook and blogs) are generated at high-speeds and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. Although research using unstructured data in economic analysis is in its early stages, but the utilization of data is expected to greatly increase once its usefulness is confirmed.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.141-156
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• 2013

Researchers in welfare services of local government in Korea have rather been on isolated issues as disables, childcare, aging phenomenon, etc. (Kang, 2004; Jung et al., 2009). Lately, local officials, yet, realize that they need more comprehensive welfare services for all residents, not just for above-mentioned focused groups. Still cases dealt with focused group approach have been a main research stream due to various reason(Jung et al., 2009; Lee, 2009; Jang, 2011). Social Security Information System is an information system that comprehensively manages 292 welfare benefits provided by 17 ministries and 40 thousand welfare services provided by 230 local authorities in Korea. The purpose of the system is to improve efficiency of social welfare delivery process. The study of local government expenditure has been on the rise over the last few decades after the restarting the local autonomy, but these studies have limitations on data collection. Measurement of a local government's welfare efforts(spending) has been primarily on expenditures or budget for an individual, set aside for welfare. This practice of using monetary value for an individual as a "proxy value" for welfare effort(spending) is based on the assumption that expenditure is directly linked to welfare efforts(Lee et al., 2007). This expenditure/budget approach commonly uses total welfare amount or percentage figure as dependent variables (Wildavsky, 1985; Lee et al., 2007; Kang, 2000). However, current practice of using actual amount being used or percentage figure as a dependent variable may have some limitation; since budget or expenditure is greatly influenced by the total budget of a local government, relying on such monetary value may create inflate or deflate the true "welfare effort" (Jang, 2012). In addition, government budget usually contain a large amount of administrative cost, i.e., salary, for local officials, which is highly unrelated to the actual welfare expenditure (Jang, 2011). This paper used local government welfare service data from the detailed data sets linked to the Social Security Information System. The purpose of this paper is to analyze the factors that affect social welfare spending of 230 local authorities in 2012. The paper applied multiple regression based model to analyze the pooled financial data from the system. Based on the regression analysis, the following factors affecting self-funded welfare spending were identified. In our research model, we use the welfare budget/total budget(%) of a local government as a true measurement for a local government's welfare effort(spending). Doing so, we exclude central government subsidies or support being used for local welfare service. It is because central government welfare support does not truly reflect the welfare efforts(spending) of a local. The dependent variable of this paper is the volume of the welfare spending and the independent variables of the model are comprised of three categories, in terms of socio-demographic perspectives, the local economy and the financial capacity of local government. This paper categorized local authorities into 3 groups, districts, and cities and suburb areas. The model used a dummy variable as the control variable (local political factor). This paper demonstrated that the volume of the welfare spending for the welfare services is commonly influenced by the ratio of welfare budget to total local budget, the population of infants, self-reliance ratio and the level of unemployment factor. Interestingly, the influential factors are different by the size of local government. Analysis of determinants of local government self-welfare spending, we found a significant effect of local Gov. Finance characteristic in degree of the local government's financial independence, financial independence rate, rate of social welfare budget, and regional economic in opening-to-application ratio, and sociology of population in rate of infants. The result means that local authorities should have differentiated welfare strategies according to their conditions and circumstances. There is a meaning that this paper has successfully proven the significant factors influencing welfare spending of local government in Korea.