• Journal of Information Technology Applications and Management
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• v.21 no.4_spc
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• pp.381-401
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• 2014

This study proposes the accurate economic effect (employment inducement coefficient, hiring inducement coefficient, index of the sensitivity of dispersion, index of the power of dispersion, and ratio of value added) of Korea software industry by analyzing the inter-industry relation using the modified inter-industry table. Some previous studies related to the inter-industry analysis were reviewed and the key problems were identified. First, in the current inter-industry table publishedby the Bank of Korea, the output of software industry includes not only the output of pure software industry (package software and IT services) but also the output of non-software industry due to the misclassification of the industry. This causes the output to become bigger than the actual output of the software industry. Second, during rewriting the inter-industry table, the output is changing. The inter-industry table is the table in the form of rows and columns, which records the transactions of goods and services among industries which are required to continue the activities of each industry. Accordingly, if only an output of a specific industry is changed, the reliability of the table would be degraded because the table is prepared based on the relations with other industries. This possibly causes the economic effect coefficient to degrade reliability, over or under estimated. This study tries to correct these problems to get the more accurate economic effect of the software industry. First, to get the output of the pure software section only, the data from the Korea Electronics Association(KEA) was used in the inter-industry table. Second, to prevent the difference in the outputs during rewriting the inter-industry table, the difference between the output in the current inter-industry table and the output from KEA data was identified and then it was defined as the non-software section output for the analysis. The following results were obtained: The pure software section's economic effect coefficient was lower than the coefficient of non-software section. It comes from differenceof data to Bank of Korea and KEA. This study hasa signification from accurate economic effect of Korea software industry.

• Journal of Korea Technology Innovation Society
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• v.21 no.4
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• pp.1296-1312
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• 2018

The wave of the Fourth Industrial Revolution was spreaded in Moon Jae-in Government. The efficiency of the R&D budget of government began to pay attention, the role of the National Science & Technology Information Service (NTIS) which provides all national R&D information in real time World's first R&D information portal has been emphasized. NTIS provides information on national R&D projects with their participating researchers, outcomes, and facilities & equipment through of information from each ministry and institution. NTIS supports to build science and technology policies, to enable transparent and efficient management of national R&D projects and to promote joint utilization of the R&D data among researchers and industrial utilization of the R&D data. NTIS provides since 2005. In this paper, I have analyzed the economic ripple effect of NTIS using the Input-Output model technique based on the input-output tables published by the Bank of Korea. When I set the NTIS R&D budget (about 120M$ for the last 13 years) as input coefficients, the effect on production inducement, value added inducement and employment inducement was analyzed by 211M$, 100M$ and 1,882 respectively.

• Land and Housing Review
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• v.11 no.4
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• pp.15-26
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• 2020

This study analyzes the Domestic Economic Ripple Effect (DERE) of the Donghae-Bukpuseon Railway (DBR). Input-Output Analysis and Scenario Analysis are employed. First, the future demand is approximately 6.86 billion people, 1.4 billion tons of logistics, and future forecast production is 1.2 trillion won for passengers, and 0.15 trillion won for logistics. Second, the production inducement (PI) coefficient of the railway industry is 2.080, the value-added inducement (VAI) coefficient is 0.680, the import inducement (II) coefficient is 0.32 and the employment inducement (EI) coefficient is 6.45. Third, for the DERE, PI is 2.846 trillion won, VAI is 0.939 trillion won, II is 0.446 trillion won, and EI is 8,737 people/1 billion won. Fourth, PI is approximately 2.8 trillion won, and the payback period is 35 years. Scenario 1 (a 50% increase in the demand for tourism) takes approximately 27 years, Scenario 2 (an 100% increase), 20 years, and Scenario3 (an 150% increase), 16 years. The successful way of the DBR is to enlarge the linkage effect of trans-railways for which international cooperation and agreements are needed. Also, even if the DBR is isolated due to worsening inter-Korea relations, the development of tourism resources is important for public investment feasibility.

• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.945-956
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• 2013

The character industry is a high value-added industry, and is one of the strategic industries to be fostered. However, the character industry is struggling due to the lack of national consensus on the importance and value of the character industry. Therefore, in order to resolve this issue, the study used the character Input-Output Table of year 2009 of korea to analyze how much the character industry(Toys and games, Models and decorations) contributes to the national economy by measuring economic spreading effects of character industry on national economy. The results shows that character industry shows that production inducement coefficient is column 1.602, row 1.007, index of the sensitivity of dispersion is 0.543, Index of the power of dispersion is 0.864, value-added coefficient is 0.620, income inducement coefficient is 0.334, tax inducement coefficient is 0.066, employment inducement coefficient is 0.008.

• Journal of Digital Convergence
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• v.6 no.2
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• pp.97-105
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• 2008

This paper tries to measure the spill-over effect of the production and investment of telecommunication service industry (hereafter telecommunication industry), using the most recent data of 2003 input-output tables. The results are summarized as follows. First, the industries which have the biggest spill-over effect from the production of telecommunication industry is miscellaneous business service (including the sale commission of telecommunication service), other engineering services (including royalty), and business consumption. Second, the production of telecommunication industry induces more value-added, and less production, less import, and less employment than related industries such as radio and television equipment, communications and broadcasting equipment, and computer and peripheral equipment. Third, while the investment of telecommunication service amounts to 15% of its production, the effect of the investment on production, value-added, consumption, and employment reaches 70% of that of its production. The policy implication of this paper is that the telecommunication industry contributes to overall economy mainly through its investment.

• Spatial Information Research
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• v.21 no.1
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• pp.37-44
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• 2013

This study sets the boundary of U-City industry based on expert surveys. Based on this U-City industry boundary, an appropriate inter-industry analysis is performed. The result shows production inducement coefficients, value added inducement coefficients, employment & enter employment inducement coefficients, influence coefficients and induction coefficients. Based on these coefficients, overall characteristics and spillover effects of U-City industry are examined. The result of this study shows that U-City industry has bigger value added-induced effects and employee-induced effects than other industry. The result also shows that U-City industry also has a great forward linkage effect. This study has a meaning that could be used to make political decisions as a basic data.

• Informatization Policy
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• v.17 no.2
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• pp.86-103
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• 2010

This paper computes the economic impact of handset subsidies using the recent Input-Output Tables and compares the results with other alternatives which telecommunications companies can choose. The first scenario is that telecommunications companies give handset subsidies to consumers and sales agents. The second is that the companies do not give the subsidies to them, but instead spend the same amount of subsidy on facility investment. The third is that the companies lower the prices of their mobile communications services and consumers spend the saved expenses on other goods and services. The result is that the production, value added, import, job, employment inducement coefficients of the first scenario is larger than those of the second and third scenarios. The reason is as follows. The handset subsidy results in the incentive to consumers for handset purchase or the incentive for sales agents to sell the telecommunication services of the companies. The former has larger production and import inducement effect, and the latter also has larger value-added, job, and employment inducement effect than those of other scenarios.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.7
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• pp.375-383
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• 2018

This study was carried out to grasp the economic effects of the social welfare foundation by establishing and operating it. For this purpose, the effects of the social welfare law of Daegu Metropolitan City on the regional economy were analyzed using the input-output analysis model. As a result, the effects of GDP was 43,445 billion won, the total value-added effect was 1,940 billion won, and the total employment inducement effect was 37,411. Based on these results, the future direction of the social welfare corporation is suggested as follows. First, it is necessary to shift the perception of consumer-oriented welfare toward welfare that contributes to the activation of the local economy. Second, efforts should be made to continuously expand employment linked to social welfare services, to create an environment where jobs can be created from a long-term perspective, and to establish a separate support system. Third, the value-added created by the social welfare foundation should be newly recognized and sought to be expanded in various fields. Fourth, efforts should be made to secure the legitimacy of social service provision and ensure accountability by appropriately promoting the economic ripple effects of social welfare foundation to the local community.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.8B
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• pp.1241-1250
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• 2010

With the expanding concept of Smart Grid, it is widely used by mixing with various industries, and therefore the potential and value of Smart Grid should be verified and evaluated. To this end, this study is conducted to look at industrial fields that can be expanded by mixing with Smart Grid, and based on it, draw an economic ripple effect of the Smart Grid industry. To grasp the spread direction of the Smart Grid industry, Our study focused on Smart Grid participants and new businesses that can be derived. Through this, energy, construction, home appliances, and automobile industries are selected as convergence businesses. Our study estimated an economic effect by drawing generation rates from input-output tables that applies the selected industry fields and Korean projections. According to the result, effect on total production inducement will be about 77 trillion won, effect on total value added inducement will be about 24 trillion won, and effect on total employment inducement will be about 31 trillion won. Through this, various functions of Smart Grid and the ripple effects on national economy could be expected.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.363-374
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• 2021

The purpose of this study is to analyze how much the mining industry contributes to the Mongolian national economy using the 2019 input-output table released by Asian development bank/ERCD in 2021 to understand the characteristics of the Mongolian economy and to use it as a reference. For this study, the Mongolian economy was classified into 35 industries and the contribution of the national economy was analyzed. As a result of the analysis, the total production inducement amount of the Mongolian mining industry was $38,418 million, the total production inducement coefficient was 1.473, the index of sensitivity of dispersion was 1.696, the value added inducement coefficient was 0.707, and the production inducement coefficient was 1.473. It can be seen that the Mongolian mining industry has a higher production inducement effect than other industries, and has great potential for development as a strategic industry leading other industries.