Efficiency Analysis of Global Steel Companies

Seok-Young Lee 인문사회과학기술융합학회 2016 예술인문사회융합멀티미디어논문지 Vol.6 No.1

This study analyzes efficiency of global steel companies for the period 1997-2008. I employ Data Envelopment Analysis (DEA) to estimate the relative efficiency of global steel companies in using their labor, materials and capital resources to generate sales. Based on a panel dataset for global steel companies for the period 1997-2008, I find that the means (medians) of aggregate, technical, and scale efficiency scores were 0.4954 (0.4619), 0.5803 (0.5420), and 0.8656 (0.9385) respectively. Average aggregate efficiency increased from 1998 until 2002 before trending down. Average technical efficiency increased from 1998 until 2003 before trending down. Therefore, it is apparent that the aggregate efficiency and technical efficiency move together. In contrast, average scale efficiency decreased from 1997 until 2008, which indicated a steadily downward trend from the first year in the sample period. Furthermore, average scale efficiency was greater than average technical efficiency for each year throughout the entire sample period, suggesting that the technical factor was a more important source of inefficiency than the scale factor in each year during the entire sample period. That is, there existed a higher level of technical inefficiency compared with scale inefficiency, indicating that there was more room for improvement in technical efficiency.

도시가스 업체의 효율성 측정과 영향 요인 분석

김용덕,강상목 한국기업경영학회 2014 기업경영연구 Vol.21 No.3

The purpose of this paper is to measure the efficiency and impact factors of city gas firms in S. Korea using DEA (Data Envelopment Analysis) and SFA (Stochastic Frontier Analysis). The analysis period is from 2009 to 2012 and the number of sample firms is thirty-two. Empirical result shows that; first, four years average efficiency of city gas firms in S. Korea is estimated low. Second, city gas firms in S. Korea have big difference between firms to have a minimum efficiency and maximum efficiency. Third, the gap of efficiency by region or listed company had decreased since 2009. The city gas companies in non-metropolitan area or unlisted gas companies had caught up metropolitan city gas companies or listed gas companies. Also, the ranking of companies’ efficiency is almost similar based on Spearman's rank correlation coefficient. Tobit model is used to examine impact factors of efficiency. The dependent variable is city gas firms’ efficiency in S. Korea. The independent variables are population density, proportion of industrial supply, labor costs per total number of employees, current ratio, ratio of gross profit, ratio of operating profit, turnover ratio of capital, gross profit per total number of employees, metropolitan dummy, and listed dummy. impact factors of efficiency are industry supply, liquidity ratio and whether the company is listed or not and so on. Efficiency is increased by the higher industry supply, liquidity ratio and metropolitan area. Based on the empirical results, we can try to draw following implications. First of all, this measurement results can be the opposite evidence of arguing that the City Gas Firms reached the critical point with efficient self-management. The average efficiency of city gas firms is estimated low. And based on analysis with DEA and SFA, the efficiency can be improved. Therefore, Non-metropolitan companies and listed companies are required to focus on improving the efficiency. Looking at the empirical results, metropolitan dummy and listed one have a negative influence on efficiency. Because the population density per pipe length in non-metropolitan areas is lower than metropolitan ones. So the government should try to improve the efficiency of these companies. In addition, In order to facilitate supply of city gas, the government's support is needed. And for supporting non-metropolitan areas, an element of the external force should be removed. Because these things can lead to inefficiencies. In the case of listed companies, CEOs are more likely to focus on company's appearance like focusing on gross profit and sales. Thus individual shareholders should lead them not only to focus on company appearance but also increase efficiency. In this study, we measured the efficiency with DEA and SFA simultaneously because measuring only with DEA has several disadvantages and those two ways are complementary. Accordingly if you use both methods at the same time, you can obtain more reliable information. 본 연구의 목적은 소매 도시가스 업체의 효율성을 계측하고 영향요인을 분석하고자함이다. 본 연구에서 자료포락분석(DEA)과 확률변경분석(SFA)을 이용하여 효율성을 추정하였다. 분석기간은 2009년부터 2012년까지의 4년간이었으며, 표본기업의 수는 32개 업체이다. 추정 결과, 첫째, 32개 소매도시가스 업체의 4개년 평균효율은 낮게 측정되었다. 둘째, 최소효율을 가지는 기업과 최대효율을 가지는 기업의 차이가 아주 크게 나타났다. 셋째, 2009년 이후로 지역별, 상장여부에 따른 효율성 차이가 점점 줄어드는 것으로 나타났다. 이는 지역적으로 비수도권에 위치하는 소매도시가스업체들 또는 비상장 소매도시가스업체들이 상대적으로 효율성이 높은 수도권 지역 소매도시가스업체들 또는 상장 소매도시가스업체들을 추격(catching-up)하고 있는 것으로 볼 수도 있다. 또한 두 측정결과의 일관성을 보기 위하여 Spearman의 순위상관계수를 확인한 결과, 두 측정결과는 효율 수준의 값에서 다소 차이를 보이나 소매도시가스 32개 업체의 두 효율 순위는 대체로 유사하게 나타났다. 이러한 효율성에 대한 영향요인을 살펴보기 위하여 Tobit 모형을 이용하여 분석하였다. 종속변수로는 자료포락분석(DEA)과 확률변경분석(SFA)을 이용하여 도출한 효율성을, 설명변수로는 인구밀집도, 산업용비중, 총직원수 당 인건비, 유동비율, 매출총이익률, 영업이익률, 총자본회전율, 총직원수 당 매출총이익, 수도권․비수도권 더미, 상장․비상장 더미를 사용하였다. 그 결과, 효율성 영향요인은 산업용 공급량이 높을수록, 유동성비율이 높을수록, 수도권 지역에 위치할수록 효율성이 증가하는 것으로 나타났다.

A Comparative Analysis on the Infrastructure Efficiency of the Korea's Large Sized Trade Seaports

나호수,홍기진,이재승 한국해양비즈니스학회 2014 해양비즈니스 Vol.- No.29

This paper explores the infrastructure efficiency of the Korean large-sized trade seaports using DEA(data envelopment analysis and then finds out the characteristics of these trade seaports during the period 2001-2012. We try to think the implications of the infrastructure investment to the trade seaports. Main finding facts are as follows: 1)There have been decreasing tendencies of efficiency in terms of relative efficiency levels during the period 2001-2012. 2)Pohang trade seaport has shown the highest efficiency levels around 1, and Kwangyang trade seaport has shown the lowest efficiency levels 3)Ulsan trade seaport has maintained the 3rd ranking during the period 2001-2012 and shown DRS patterns and the very similar patterns to the average efficiency of 6 large-sized trade seaports. 4)Pyeongtaek trade seaport has shown high speeds of decreasing efficiency from roughly 1 to 0.6. 5)The tendency of DRS is stronger in Koreaʼs trade seaports during the past several years except Busan trade seaport. In particular, Korean large trade seaports have strong tendency toward DRS over time. 6)In terms of scale efficiency, Kwangyang and Ulsan trade seaport show the relatively higher scale efficiency during the period 2001-2012. Pyongtaek trade seaportʼs scale efficiency has the lowest scale efficiency. From these results, in Asian highly growing areas. we need consider serious overinvestment problems about the infrastructure of airports and seaports through various efficiency analyses.

우리나라 지방국세청의 동태적 효율성 및 생산성변화

박정은,유상열 한국세무학회 2012 세무학 연구 Vol.29 No.3

국세행정의 성과는 국세청이 징수활동을 얼마나 효율적으로 수행했는지에 따라 결정되며, 궁극적으로는 지방국세청의 징세 효율성 및 생산성에 의해 영향을 받는다. 본 연구는 2000년부터 2010년까지 우리나라 6개 지방국세청의 연도별 산출요소 및 투입요소에 대해 산출기준 자료포락분석(DEA)을 이용하여 효율성을 평가하였다. 산출요소는 직접세징수액, 간접세징수액과 기타세징수액이며, 투입요소는 직접납세자 수, 간접납세자 수, 직원 수와 관할세무서 수이다. 기간별 효율성의 동태적 변화를 분석하기 위해 DEA 윈도우 분석을 실시하였으며, Malmquist 생산성지수를 측정하였다. 연구결과를 요약하면 다음과 같다. 첫째, 기술적 효율성을 측정한 결과 RTO_A가 가장 높았고, 다음으로 RTO_F, RTO_D, RTO_C의 순으로 높았으며, RTO_B와 RTO_E는 낮았다. 2008년 이후에는 효율성이 높은 집단(RTO_A, RTO_D와 RTO_F)과 낮은 집단(RTO_B, RTO_C와 RTO_E)이 구분되는 양상을 보였다. 둘째, 순수효율성을 측정한 결과 RTO_B와 RTO_F의 효율성 점수는 1이었고, RTO_A, RTO_D와 RTO_C는 0.9이상이었으며, RTO_E가 0.889로 가장 낮았다. RTO_B와 RTO_E는 기술적 효율성이 낮으나 순수효율성은 높은 수준으로 평가되어 규모비효율이 존재하는 것으로 분석되었다. 셋째, 규모효율성은 RTO_A, RTO_F, RTO_D, RTO_C의 순으로 높고, RTO_B와 RTO_E는 낮았다. RTO_A, RTO_D와 RTO_F의 규모효율성은 1에 가깝게 나타났으며, 최적 규모에 근접해서 운영되고 있음을 알 수 있었다. 또한 우리나라 지방국세청의 기술적 효율성은 순수효율성보다는 규모효율성에 의해 결정됨을 확인하였다. 넷째, 산출기준 Malmquist 생산성지수를 측정한 결과 기술적 효율성이 높게 평가된 RTO_A, RTO_F와 RTO_D의 생산성변화는 모두 기술변화에 의한 것으로 분석되었다. 기술적 효율성이 낮은 집단인 RTO_C, RTO_B와 RTO_E는 효율성변화가 모두 1보다 컸으며, 이들은 분석기간 중에 생산프런티어에 가까워지는 추격효과가 있었음을 알 수 있었다. 본 연구는 지방국세청의 효율성을 동태적으로 평가하고 기술적 효율성을 순수효율성과 규모효율성을 분리하였으며, 생산성변화의 원인을 효율성변화와 기술변화로 세분화한 최초의 시도로서 의미가 있다. 또한 지방국세청이 자신의 경제적 효율을 높이기 위해서는 어떤 노력을 해야 하는지에 대한 지침을 제공해 줄 수 있다는 공헌점이 있다. The performance of tax administration depends on whether the tax collection activity of regional tax offices (RTOs) is efficient. We measure the tax efficiency of 6 RTOs (RTO_A, RTO_B,RTO_C, RTO_D, RTO_E, and RTO_F) in Korea for the period of 2000-2010 using data envelopment analysis (DEA). Direct tax amounts, indirect tax amounts and other tax amounts are used as output variables. Direct tax payers, indirect tax payers, employees and the district offices of regional tax jurisdiction are used as input variables. We conduct window analysis and calculate Malmquist productivity index in order to investigate the dynamic changes of the efficiency. The main results of our paper are summarized as follows. First, we found that technical efficiency (TE) is the highest in RTO_A, and high in order of RTO_F, RTO_D, and RTO_E. RTO_B and RTO_E show relatively small TE. The rankings of RTOs by tax efficiency for the various years do not converge. Since 2008, RTOs are divided into two groups which are high efficiency group (RTO_A, RTO_D, and RTO_F) and low efficiency group (RTO_B, RTO_C, and RTO_E). Second,pure technical efficiencies (PTEs) of RTO_B and RTO_F are the same as 1.00, PTEs of RTO_A and RTO_D are over 0.9, and RTO_E shows the lowest PTE (0.889). The low TEs and relatively high PTEs in RTO_B and RTO_E represent scale inefficiencies. We found that RTO_B and RTO_E represent scale inefficiency because they have low TEs and relatively high PTEs. Third,scale efficiency (SE) shows that SE is high in order of RTO_A, RTO_F, RTO_D, and RTO_C. SEs of RTO_B and RTO_E are relatively low. SEs of RTO_A, RTO_D, and RTO_F are approximately 1.00, therefore these RTOs are operated near the optimal size. Furthermore, we identify that TEs of RTOs are more affected by SEs than PTEs. Fourth, according to the Malmquist productivity index, we can know that the productivity changes (PCs) of high efficiency group are caused by technological changes (TCs). There would be catch-up effect in low efficiency group during the period because the efficiency change (EC) of that group is larger than 1.00. The main contribution of this study is that we attempt to measure the dynamic changes of tax efficiency for RTOs in Korea for the first time. The insights gleaned from this paper can be helpful when RTOs establish strategies for increase in efficiency and productivity.

한국 물류산업의 효율성과 생산성: 비모수적 기법과 모수적 기법의 적용

김창범 ( Chang Beom Kim ) 한국해운물류학회(구 한국해운학회) 2015 해운물류연구 Vol.31 No.3

본고는 우리나라 물류산업에 대해 DEA기법을 적용하여 효율성과 생산성을 분석하고, 패널 SFA기법과 패널 Tobit모형을 적용하여 규모의 수익 패턴과 효율성의 결정요인을 분석하였다. 첫째, CCR기준 초효율성과 SBM기준 초효율성 분석 결과 육상운송업이 가장 비효율적이며, 수상운송업이 가장 효율적인 것으로 나타났다. 또한 업종별로는 외항화물운송업이 가장 효율적이며, 도시철도운송업이 가장 비효율적인 것으로 나타났다. 또한 SFA기법을 적용한 결과 생산의 노동탄력성과 자본탄력성이 1에 근접하여 규모의 수익불변 특징을 보여주었다. 둘째, 패널 Tobit분석 결과 1인당 생산성 향상은 효율성에 긍정적인 영향을 미쳤으며, 1인당 자본량과 평균비용 증가는 효율성에 부정적인 영향을 미쳤다. 셋째, DEA/Window 기법으로 효율성의 동태적 안정성 여부를 살펴본 결과, 도선업이 가장 불안정적이며 도시철도운송업이 가장 안정적이었다. 넷째, Malmquist 생산성 분석을 통해 생산성 증가와 감소의 가장 큰 요인은 각각 기술진보와 기술퇴보로 분석되었다. The main purpose of this paper is to investigate the efficiency and productivity of 40 logistics industries in Korea using a variety of method: super CCR, super SBM, DEA window analysis, and the Malmquist productivity index. The super efficiency model is a method for selecting the most efficient of all efficient decision making units (DMUs). The super SBM (slack-based measure of super-efficiency) model was introduced as an alternative method to calculating the super efficiency score without considering the slacks of input variables and output variables. Using these models, the most efficient DMU has a value greater than one. The Malmquist productivity index has many attractive features. For one thing, it decomposes the result into a technical efficiency change index and frontier change index, allowing the productivity change to be attributed to either changes in technical efficiency or changes in technology(i.e., technological progress in the industry), or both. The total factor productivity change is the product of technical efficiency change and technological change. Technical efficiency change can be decomposed into pure technical efficiency and scale efficiency change. Window analysis is one of methods used to verify productivity change over time and works on the principle of moving averages. DEA window analysis is used to measure efficiency in cross-sectional and time variant data. Thus, it is useful for detecting performance trends for a DMU over time. Each DMU is treated as a different industry in a different period, which allows for increasing the number of data points. In other words, each DMU in a different period is treated as if it were an independent DMU but remains comparable in the same window. The advantage of DEA window analysis is that the performance of an industry in a given period can be compared to itself and to other industries over time. The empirical results are as follows: First, by both the super CCR efficiency and super SBM efficiency methods, inefficiency of land transport and water transport was indicated as 67-74% and 30-44% respectively. These results indicate that land is the most inefficient form of transport, whereas water transport is the most efficient. The annual efficiency values revealed that the degree of inefficiency has increased since 2011. Also, detailed results for sectors show that ocean shipping is the most efficient way to transport cargo, whereas urban rail transport is the most inefficient. Second, tobit panel analysis showed that labor productivity has positive impact on efficiency, whereas average cost has negative impact on efficiency. Third, the DEA window analysis results calculated using LDP values and standard deviations show that ferry transport is the most stable, whereas urban railway transport is the most unstable. Fourth, Malmquist productivity index results show that productivity improved 1.6% on average, with technological progress being the major factor decreasing productivity. These results indicate that it is necessary to expand the logistics market to increase the efficiency of the industry. To do so, many efforts must be made to global leadership in logistics enterprises, invest on global logistics infrastructure, increase research and development, and expand the future use of new technologies.

Eco-efficiency Analysis of Organic Agriculture in Korea

Kim, Chang-Gil,Hak-Kyun Jeong 한국유기농업학회 2011 韓國有機農業學會誌 Vol.19 No.S

Eco-efficiency which is calculated by dividing economic productivity by the environmental load was made by synthesizing eco and efficiency from ecology and economy, proposed by World Business Council for Sustainable Development in 2000. Eco-efficiency by connection of resource efficiency with resource intensity is used as an indicator for evaluating green growth for minimizing the impact on the environment and achieving economic development as well by means of efficient use of resources. This research analyzes eco-efficiency with the case of organic agriculture promoted as a key green growth p이icy. Thirty questionnaires for farmers producing organic rice in Hongseong-gun, Choongcheongnam-do were used for the analysis. Eco-efficiency was measured by means of the amount of used nitrogen with respect to the amount of income, and was represented that organic a9끼culture was 32.0 higher than conventional agriculture. The analytical result of technical efficiency, using the (Data Envelopment Analysis (DEA) model showed that it is 0.765 which has a possibility of 21% in management improvement, and higher eco-efficiency was with higher technical efficiency. The analytical results showed that an organic agriculture cont꺼butes to green growth more than conventional agriculture. In addition, higher technical efficiency groups exhibited higher eco-efficiency indices.

DEA 모형을 이용한 TV홈쇼핑기업의 상대적 효율성 연구

김순홍,안영효,오승철 한국유통과학회 2014 유통과학연구 Vol.12 No.8

Purpose - The method of TV home shopping is a kind of retail method that provides the viewer with information about products and, further, sells the products to consumers through the media of television. The domestic home-shopping industry has been expanding since 1995, and there are six companies in this arena as of 2012. In this study, we evaluate the management efficiency of TV home-shopping companies and provide suggestions for improving efficiency, using the DEA (data envelopment analysis) model. Hence, we expect to contribute to the progress of the companies’ efficiency and the development of the TV home-shopping industry, where deepening competition is inevitable because it is experiencing the maturing market stage in its life cycle. Research design, data, and methodology - Efficiency is the ratio of the quantity of input to the quantity of output of a product or service. It is necessary to estimate aggregate inputs and aggregate outputs, which are calculated by applying a weighting to a number of input and output factors, to measure the efficiency. The DEA model is divided into the CCR model and the BCC model. The CCR model is a basic model that assumed constant returns to scale (CRS), and the BCC model extends the CCR model to accommodate technologies exhibiting variable returns to scale (VRS), and concerns only the technical efficiency without considering the efficiency of returns to scale. In this study, we consider six companies each year from 2008 to 2012 as a DMU (Decision Making Unit) and analyze the differences in efficiency for each company in each year. Furthermore, we evaluate the operating characteristics of TV home-shopping companies, using three models, in accordance with the overall performance, profitability, and marketability of the business. Results - The result of the analysis, using DEA models, shows that Hyundai Home Shopping (2009, 2010, 2011), GS Home Shopping (2011), NS Home Shopping (2011) and CJ O Shopping (2012) possess MPSS (most productive scale size), with a score 1.0 in CCR, BCC, and scale efficiency. Particularly, Hyundai Home Shopping is shown to be the most efficient in terms of overall business performance, marketability, and profitability. The overall efficiency of the home shopping industry has displayed an increasing trend since 2008, even though it decreased marginally in 2012; further, we can observe that home shopping companies operate with increasing efficiency with the passage of time. Conclusions - Home shopping companies have focused on market expansion rather than profits, as they displayed better efficiency in marketability than increase in profitability during the period 2008-2012. In addition, the main reason for the increased efficiency in the home shopping industry is the market expansion through the revenue increase of each home shopping company. This study can be used as a reference when home shopping companies attempt to devise future strategies, as it suggests efficiency benchmarks and development levels for each home shopping company.

Eco-efficiency Analysis of Organic Agriculture in Korea

Kim, Chang-Gil,Jeong, Hak-Kyun Korean Association of Organic Agriculture 2011 韓國有機農業學會誌 Vol.19 No.S

Eco-efficiency which is calculated by dividing economic productivity by the environmental load was made by synthesizing eco and efficiency from ecology and economy, proposed by World Business Council for Sustainable Development in 2000. Eco-efficiency by connection of resource efficiency with resource intensity is used as an indicator for evaluating green growth for minimizing the impact on the environment and achieving economic development as well by means of efficient use of resources. This research analyzes eco-efficiency with the case of organic agriculture promoted as a key green growth policy. Thirty questionnaires for farmers producing organic rice in Hongseong-gun, Choongcheongnam-do were used for the analysis. Eco-efficiency was measured by means of the amount of used nitrogen with respect to the amount of income, and was represented that organic agriculture was 32.0 higher than conventional agriculture. The analytical result of technical efficiency, using the (Data Envelopment Analysis (DEA) model showed that it is 0.765 which has a possibility of 21% in management improvement, and higher eco-efficiency was with higher technical efficiency. The analytical results showed that an organic agriculture contributes to green growth more than conventional agriculture. In addition, higher technical efficiency groups exhibited higher eco-efficiency indices.

식품기업의 경영효율성 제고방안에 관한 연구

오영삼 ( Youngsam Oh ) 한국유통경영학회 2021 유통경영학회지 Vol.24 No.5

Purpose: This study would look for measures for the promotion of efficiency by drawing static efficiency and dynamic efficiency, considering the needs for the promotion of interests and effort for developmental diversification of food companies characterized by their small size and problems that the food companies face and draw improvements for sustainable development by analyzing many large food companies’ plans for management efficiency through efficiency analysis. Research design, data, and methodology: The DEA model was used to analyze the static efficiency of food businesses with top actual yields, and the DEA Window model was used to assess their dynamic efficiency. The CCR and BCC models were used to calculate the value of static efficiency, and the DEA Window model was used to analyze the trend of change in efficiency and stability, using three input variables and one output variable. The efficiency from 2016 through 2020 was measured. Capital, debt, selling, and administrative expenses were chosen as input variables, with sales as the output variable. Results: The CCR analysis of the DEA model revealed that seven out of 18 food companies were efficient, whereas the BCC analysis revealed that 11 were efficient, in terms of pure technological efficiency. As a result of the dynamic efficiency analysis, similar estimates were drawn in 2018 and 2019; however, the value decreased in 2020, and it was assumed that it was affected by social environments. In 2020, sales decreased in numerous industries due to COVID-19, and it was an estimate that showed that food companies were also affected like other industry sectors. Implications: Estimates of large food companies’ efficiency were drawn, using the DEA and DEA Window models. Small food companies can improve their efficiency by digitizing and diagnosing their management efficiency status, benchmarking estimates in this study and utilize them as the baseline data to find their management efficiency. In addition, improving their input excesses and output shortfalls through an analysis of inefficient DMU and utilizing that as a measure for the promotion of their efficiency will turn out to be large ones’ continuous growth and small ones’ growth.

Efficiency Analysis of Global Steel Companies

이석영 사단법인 인문사회과학기술융합학회 2016 예술인문사회융합멀티미디어논문지 Vol.6 No.1

This study analyzes efficiency of global steel companies for the period 1997-2008. I employ Data Envelopment Analysis (DEA) to estimate the relative efficiency of global steel companies in using their labor, materials and capital resources to generate sales. Based on a panel dataset for global steel companies for the period 1997-2008, I find that the means (medians) of aggregate, technical, and scale efficiency scores were 0.4954 (0.4619), 0.5803 (0.5420), and 0.8656 (0.9385) respectively. Average aggregate efficiency increased from 1998 until 2002 before trending down. Average technical efficiency increased from 1998 until 2003 before trending down. Therefore, it is apparent that the aggregate efficiency and technical efficiency move together. In contrast, average scale efficiency decreased from 1997 until 2008, which indicated a steadily downward trend from the first year in the sample period. Furthermore, average scale efficiency was greater than average technical efficiency for each year throughout the entire sample period, suggesting that the technical factor was a more important source of inefficiency than the scale factor in each year during the entire sample period. That is, there existed a higher level of technical inefficiency compared with scale inefficiency, indicating that there was more room for improvement in technical efficiency.