• Journal of Technology Innovation
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• v.7 no.1
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• pp.14-35
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• 1999

This paper presents the relationship between the pace of structural change and the magnitude of employment growth in the manufacturing sector in OECD countries. To measure the pace of structural change, the compositional change index in value-added in manufacturing sector is introduced. For mid to long-term there seems to be a positive relationship between the pace of structural change and the magnitude of employment growth. In those countries with higher value of the compositional index, the employment growth in manufacturing sector was generally higher. To analyse the characteristics of structural change in manufacturing sector, this paper classifies manufacturing industries into groups: one based on technology, one on orientation, one on wages and one on skills. The international comparison of manufacturing sector's employment patterns based on above four classifications are presented. International comparison suggests that Korean manufacturing sector move into jobs with more skills and knowledge The structural change of SMEs and large firms are compared based on above four classification methods. It is shown that SMEs' employment in low value sectors, that is low-technology, labor-intensive, tow-wage, and unskilled sectors, have risen faster than SMEs' employment in high-technology, science-based, high-wage and skilled sectors. Large firms' employment have been mainly increased in high value sectors. However, the employment growth of both large and small firms have been concentrated on production worker-intensively-using sectors, i.e. unskilled sectors. This widened the wage differential of production workers by firm sizes and concurrently led to severe shortage of production workers for SMEs, which has little ability to pay high wage to production workers because they usually belong to low-wage sectors. Korea need to push SMEs forward to high value sectors. The premise of that is, however, to pull large firms out of production worker-intensively-using sectors.

• Safety and Health at Work
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• v.5 no.4
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• pp.198-202
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• 2014

Background: The economy is being lifted by the new concept of robotics, but we cannot be sure of all the possible benefits. At this early stage, it therefore becomes important to find out the possible benefits/limitations associated with robotics, so that the positives can be capitalized, established, and developed further for the employment and motivation of employees in the health care sector, for overall economic development. The negatives should also be further studied and mitigated. Methods: This study is an exploratory research, based on secondary data, such as books on topics related to robotics, websites, public websites of concerned departments for data and statistics, journals, newspapers and magazines, websites of health care providers, and different printed materials (brochures, etc). Results: The impact of robotics has both positive and negative impacts on the employment and motivation of employees in the retail sector. So far, there has been no substantial research done into robotics, especially in the health care sector. Conclusion: Replacing employees with robots is an inevitable choice for organizations in the service sector, more so in the health care sector because of the challenging and sometimes unhealthy working environments, but, at the same time, the researchers propose that it should be done in a manner that helps in improving the employment and motivation of employees in this sector.

• Journal of Distribution Science
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• v.18 no.2
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• pp.59-67
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• 2020

Purpose: This study examines the relationship between formation of employment subcenters and regional industrial structures in Incheon. Research design, data, and methodology: We used the five-year panel data from 2012 to 2016 in 146 basic municipal units of Incheon to analyze panel regression models. Gross employment density and employment to population ratio were used as indicators of employment subcenters formation. The entropy index and Hachman index were used for analyzing the diversity and heterogeneity of industrial structures. Result: The analyses of two panel regression models showed that for the formation of employment subcenters, both the Entropy and Hachman index were significantly negative in most models. But tertiary industry was shown to have a significant positive relationship in all models. In the wholesale and retail sector, it was found that the average number of employees in the employment subcenters is significantly higher than that in the non-employment subcenters. Conclusions: The specialization of the industrial structure rather than the diversification contributes to the formation of the employment subcenters in Incheon. In particular, it can be considered that the wholesale and retail sector plays a very important role in forming the employment subcenters in many areas of Incheon.

• Journal of Korea Technology Innovation Society
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• v.14 no.2
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• pp.223-245
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• 2011

We have analysed the impact of innovation on the employment in the Korean Service sector using KIS 2006 data. Our study is based on Bogliacino and Pianta (2010)'s model, which involves two main innovation strategies, technological competitiveness and cost competitiveness, as the main explanatory innovation variables for the employment dynamics. Empirical results show that an increase in demand was the strongest driver for an increase in employment in all four service sectors, including Science-based, specialized supplier, size and information intensive, and supplier dominated. Innovation strategy appeared to have a significant effect on employment at the firm level; Technological competitiveness showed a positive effect on employment in science based sector while cost competitiveness appeared to give a negative effect in specialized supplier sector. The firms in size and information intensive sector showed that increase in wages gives negative impact on their employment. However, cost competitiveness did not give any significant effect on employment dynamics of firms in size and information intensive sector and supplier dominated sector. This finding is different from the research of Bogliacino and Pianta (2010) on the European countries. One possible explanation would be that innovation strategy does not affect employment of firms in size and information intensive sector and supplier dominated sector since the level of innovativeness of these sectors in Korea fall behind that of European countries.

• Journal of Convergence for Information Technology
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• v.11 no.1
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• pp.175-182
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• 2021

The purpose of this study is to establish the direction of industrial policy by comparing the employment inducement effect on the related industries of the digital bio-healthcare industry. The analysis data used the three-year input-output table measured by the Bank of Korea. First, the research method was rewritten into 7 major industries to compare statistical data by period. Second, the Bank of Korea's industry-related analysis methodology was utilized. Third, the weight was reflected and compared by employment, production, and investment sectors of the digital bio-healthcare industry. As a result of the analysis, first, the employment sector had a higher effect than the average of the entire industry, second, the production sector was low, and third, the investment sector required investment in the service sector. The conclusions drawn from the analysis showed that direct investment and continuous investment are required in the employment sector, the development of professional manpower is urgent, and direct investment and long-term investment are effective in the production sector.

• Korean Management Science Review
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• v.34 no.1
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• pp.15-25
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• 2017

We evaluated industry growth and employment effects of every possible pairs of 22 manufacturing sectors and 16 regions (i.e, 352 region-sectors). We used annual data of manufacturing sectors from 2008 to 2014 for the evaluation. The evaluation comprises of two steps; We first find several region-sectors that outperform others with respect to the effects of industry growth and employment, which are measured by location quotient analysis, shift share method, employment to GDP ratio and employment elasticity. In addition, cross-efficiency analysis follows to classify region-sector pairs into two sub-categories : efficient region-sectors that deserve to hold the current level of investments and inefficient region-sectors where we should consider efficiency improvements. To examine the efficiency, R&D investment, employment size, and capital investment were used as input factors and production volume, added value, changes in employment size, changes in annual salary per capita were used as output factors. For region-sector pairs that have outstanding growth and employment effects but are inefficient, we employed a CCR DEA model and analyzed how much to adjust the values of input and output factors to improve the efficiency scores. The analysis results showed that inefficiency is mainly due to several factors such as R&D investment, changes in employment size and changes in annual salary per capita.

• Journal of Industrial Convergence
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• v.2 no.1
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• pp.187-216
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• 2004

After Asian financial crisis hit the Korea, Korean government and Korean firms need to restructure their system. Specially financial sector has to renew its system. One way renew its system was adjust company's labor system. The forms of labor adjustment are external numerical flexibility, externalization, internal numerical flexibility, functional flexibility and wage flexibility. These five forms based on two big categories which are wage flexibility and employment flexibility. This study only focus on th effect of employment flexibility in financial sector in Korea. Employment flexibilities can be practice as employment adjustment. Also we concentrate on separation and re-accessors to other financial institute after the separation. The result shows that Korean financial sector are in the range of 10.78% and the job destruction rates are about 11.26%. During the period from year 1998 to year 2002, the numbers of accession has down about 30%. The logit statistical analysis for separation shows that demographical variables and the reasons of separation affected separation and reemployment.

• STI Policy Review
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• v.7 no.2
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• pp.85-105
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• 2016

This study compares the economic impacts among ICT sectors and derives strategic considerations for development of the ICT industry in Korea. Prior to analysis, this study classified ICT industry into four sectors: ICT equipment, components, service, and SW/system. This study conducted Input-Output Analysis on the four ICT sectors. An Input-Output Analysis is a quantitative economic technique that represents the direct and indirect interdependencies between different industries of a national economy. Features of each ICT sector were observed in the results. Within the ICT equipment sector, production is decreasing, import dependence is increasing, and employment size is very low, relative to the overall ICT industry. The component sector accounts for the over half of the output and value added of the ICT industry, but domestic production has recently declined. The subsector experienced decreasing production and increasing imports relative to the other ICT subsectors. In the service sector, output is small but its production and employment impact is very high. The fourth sector, ICT SW/system, has very low impact on production but high impact on employment. These features suggest two strategies to develop the ICT industry in Korea. First, the ICT component and service sectors should be promoted to stimulate growth of the national economy. Second, to encourage employment growth, policies should promote the ICT SW/system and service sectors.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.185-195
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• 2018

This study aims to explore the development of FinTech industry in Korea by comparing and analyzing the effect of job creation in the FinTech industry in Korea and the US. The industry Input-Output table used in the analysis used WIOD, which is composed of the same industrial classification and monetary unit in both Korea and the US. For the analysis, the FinTech industry is composed of ICT sector and financial sector as one industry. This study also compares the employment creation effect in the ICT sector and the financial sector, in addition to the FinTech industry, in order to distinguish the FinTech industry characteristics of the two countries. As a result of the analysis, when the investment or production of 1 million US dollars was made in the FinTech industry in Korea, the employment inducement effect was 11.33 and the employment inducement effect was 9.47, indicating a total employment creation effect of 20.8 persons. In the United States, the Direct employment effect was 8.07 and the indirect employment effect was 7.72, indicating that the employment creation effect was 15.79. However, as a result of classification by the average employment creation effect of the national economy, Korea 's FinTech industry is classified as indirect employment advantage with a high indirect employment inducement effect and the United States is classified as an employment creation advantage with high both direct and indirect employment inducement effect.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1031-1039
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• 2015

This study examines the economic ripple effect on the domestic information security manufacturing and service sectors through input-output analysis. The production inducement coefficient of the manufacturing sector is bigger than the average of whole industry, but that of the service sector is smaller than the average. On the other hand, the service sector is superior to the manufacturing sector in the value added and employment inducement coefficients. Forward and backward linkage effects of manufacturing and service sectors are generally lower than those of the average of whole industry. The information security industry has insignificant contribution to national economy and employment growth overall. In particular, the manufacturing sector records minus contribution to employment growth, which means that a lot of effort for increasing employment must be given further on in the sector.