• Journal of Korea Technology Innovation Society
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• v.2 no.3
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• pp.48-60
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• 1999

과학기술지표는 전통적으로 R&D 투입 데이터와 특허자료가 사용되었지만 본 연구에서는 이들의 단점을 극복할 대안으로 기업의 혁신활동 지표를 제시하고자 한다 먼저 한국의 CIS 데이터가 소개되고 계량분석을 이용한 방법론이 소개된다. 한편 이를 이용하여 산업별 혁신 능력지수를 제시한다

• Proceedings of the Korea Technology Innovation Society Conference
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• 2015.11a
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• pp.27-33
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• 2015

• 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.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.91-95
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• 2000

본 연구는 우리나라 산업구조에 있어서 지식과 기술의 영향을 파악하기 위하여 기술지표(R&D, IT집약도, 특허)와 인적자원지표(R&D인력, 대졸자수, 과학기술인적자원)를 기초로 117개 산업에 대한 지식기반도 측정을 시도한 것이다. 지식기반도 측정을 위한 분석모형으로 LISREL 모형이 적용되었다. 지식기반산업에 대한 기존의 연구로서는 OECD(1996)에 의한 R&D 직접투자와 간접투자를 이용한 분석이 있으나 기술과 인적자본 지표를 모두 활용하여 계량적으로 분석한 것은 새로운 시도이다.

• KDI Journal of Economic Policy
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• v.9 no.1
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• pp.43-67
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• 1987

산업구조(産業構造)의 고도화과정(高度化過程)에서는 고용흡수력(雇傭吸收力)이 감소(減少)하는 양적측면(量的側面)의 과제(課題)와 기술인력(技術人力)의 수요(需要)가 상대적으로 증가(增加)하는 질적측면(質的側面)의 인력정책과제(人力政策課題)가 동시에 야기(惹起)된다. 본(本) 논문(論文)에서는 이러한 여건변화(與件變化)에 대응(對應)하는 정책과제(政策課題)를 도출(導出)하기 위하여 인력수요(人力需要)의 결정요인(決定要因)을 실증분석(實證分析)하였다. 거시분석(巨視分析)에서는 1970~84년(年) 기간(期間)의 연간(年間) 시계열자료(時系列資料)를 이용(利用)하여 노동수요함수(勞動需要函數)를 추정(推定)하여 산업별(産業別) 결정요인(決定要因)을 비교분석(比較分析)하였으며, 미시분석(微視分析)에서는 섬유(纖維) 자동차(自動車) 전자산업(電子産業)에서 임의추출(任意抽出)한 200개(個) 표본기업(標本企業)에 대한 횡단면자료(橫斷面資料)를 이용(利用)하여 기업(企業)의 인력수요행태(人力需要行態)를 분석(分析)하였다. 산업별(産業別) 노동수요(勞動需要) 분석결과(分析結果)를 보면, 자본비용(資本費用)에 대한 노동비용(勞動費用)의 상대적상승(相對的上昇)이 노동수요(勞動需要)를 감소(減少)시켜 왔으나, 고용(雇傭)은 기본적으로 자본투자(資本投資)로부터 더 큰 영향을 받는 것으로 나타났으며, 또한 자본(資本)의 고용창출효과가(雇傭創出效果), 특히 제조업부문(製造業部門)에서, 시간(時間)의 경과(經過)에 따라 감소(減少)하는 것으로 분석(分析)되었다. 미시분석결과(微視分析結果)를 보면, 전문대졸(專門大卒) 및 고졸학력(高卒學力) 근로자(勤勞者)에 대한 수요(需要)는 중소기업(中小企業)에서, 대졸(大卒) 및 중졸이하학력(中卒以下學歷) 근로자(勤勞者)는 대기업(大企業)에서 흡수(吸收)하려는 것으로 나타났으며, 생산요소중(生産要素中) 노동(勞動)에 대한 투자우선순위(投資優先順位)는 자동차산업(自動車産業)에서 높게 나타났으며, 숙련기능인격(熟練技能人格)에 대한 수요(需要)는 세 산업(産業) 모두 높게 나타났다.

• Environmental and Resource Economics Review
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• v.20 no.2
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• pp.255-289
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• 2011

This study made linked Energy IO Table 00-05-08 of 76 sectors in intermediate sectors and analyzed structural decomposition analysis in energy consumption change in industrial sector with both by aggregate data and micro data. Structural decomposition analysis focused value added level change, value added share change of each industry, output structural change of each industry and energy intensity change of each industry as factors. Supply side model based on Ghosh inverse matrix was applied as empirical model because Korea has export driven industrial structure. Empirical results with aggregate data showed that value added change increased energy consumption and output structural change of each industry decreased energy consumption in both 2000~2005 and 2005~2008. However value added share change and energy intensity change caused opposite direction in energy consumption change with time. Policy based on aggregate data can not evaluate effort of each industry in energy efficiency and make effective results because aggregate data delete character of each industry.

• Journal of Korea Technology Innovation Society
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• v.11 no.2
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• pp.264-286
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• 2008

In 2006, the share of energy in Korea amounted to 28% from the total import, 97% from overseas dependency, and 83% for the national Greenhouse Gas (GHG) emission in 2004. Thus, from the aspects of economical and environmental policies, an energy analysis is very important, for the industry to cope with the imminent pressure for climate change. However, the estimation of GHG gas emissions due to an energy use is still done in a primitive way, whereby each industry's usage is multiplied by coefficients recommended from international organizations in Korea. At this level, it is impossible to formulate the prevailing logic and policies in face of a new paradigm that seeks to force participation of developing countries through so called post-Kyoto Protocol. In this study, a hybrid energy input-output (E-IO) analysis is conducted on the basis of the input-output(IO) table of 2000 issued by the Bank of Korea in 2003. Furthermore, according to economic sectors, emission of the GHG relative to an energy use is characterized. The analysis is accomplished from four points of view as follows: 1) estimating the GHG emission intensity by 96 sectors, 2) measuring the contribution ratio to GHG emissions by 14 energy sources, 3) calculating the emission factor of 3 GHG compounds, and 4) estimating the total amount of national GHG emission. The total amount estimated in this study is compared with a national official statistical number. The approach could be an appropriate model for the recently spreading concept of a Life Cycle Analysis as it analyzes not only a direct GHG emission from a direct energy use but also an associated emission from an indirect use. We expect this model can provide a form for the basis of a future GHG reduction policy making.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.257-267
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• 2020

In this study, we predicted the employment ratio by the export industry using various machine learning methods and verified whether the prediction performance is improved by applying the constrained forecast combination method to these predicted values. In particular, the constrained forecast combination method is known to improve the prediction accuracy and stability by imposing the sum of predicted values' weights up to one. In addition, this study considered various variables affecting the employment ratio of each industry, and so we adopted recursive feature elimination method that allows efficient use of machine learning methods. As a result, the constrained forecast combination showed more accurate prediction performance than the predicted values of the machine learning methods, and in particular, the stability of the prediction performance of the constrained forecast combination was higher than that of other machine learning methods.

• Journal of Labour Economics
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• v.29 no.2
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• pp.117-143
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• 2006

This paper illuminates the patterns of growth and declines in sizes of union membership in metal, chemical, financial, and auto transport sectors in three distinct periods during the last four decades from 1963 to 2003. This paper also calculates union densities in auto assembly, auto supply, and shipbuilding industries of the metal sector, cement, petroleum refining, and pharmaceutical industries of the chemical sector, private banking industry of the financial sector, and city bus industry of the auto transport sector. Such diversities in both sizes of union membership and union densities among sectors and industries turned out to be associated with attitudes and choices of employers and unions in interaction with sector- and industry-specific economic (growth stage and path), institutional (degrees of government intervention), and social (demographic features of employees and prevailing sizes of firms) environment. Such finding shows that theoretical reasonings on sizes of union membership and union densities across sectors and industries in advanced nations are also relevantly useful to analyze the Korean case.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.271-283
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• 2012

In this paper, we predicted the rise or the fall in eleven major industrial stock price indices unlike existing studies dealing with the prediction of KOSPI that combines all industries. We used as input variables not only domestic economic indices but also foreign economic indices including the U.S.A, Japan, China and Europe that have affected korean stock market. Numerical analysis through SAS E-miner showed above or below about 60% accuracy using the logistic regression and neural network model.