• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.849-855
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• 2021

The purpose of this study is to analyze and compare Islamic commercial banks and Islamic banking units with the stochastic frontier analysis (SFA) method during 2014-2018. The data in research using Islamic commercial banks and Islamic banking units. There are 10 Islamic commercial banks and 5 Islamic banking units that meet the criteria of purposive sampling. The calculation of efficiency level using the SFA method with the function of production shows that Islamic commercial banks and Islamic banking units always experience an increase in efficiency every year with the average level of efficiency of Islamic commercial banks being 0.43994, while the average rate of efficiency of Islamic banking units is slightly higher at 0.47654. This shows that Islamic banking units are slightly more optimal in generating total financing in the period 2010-2014. The test results using Independent Sample T-Test can be concluded that there is no difference in the efficiency value between Islamic commercial banks and Islamic banking units. Operating costs are not significant and have a positive effect on the total financing; total assets have a significant effect and a positive impact on total financing; labor costs are not significant and have a negative effect on total financing.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.5
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• pp.1559-1569
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• 2014

The purpose of this study is to estimate the degree of cost inefficiency for the urban railway lines of a metropolitan city operated by public institutions in Korea and identify the causes of this inefficiency. To this end, we assume that the urban railway lines produce the output of train-km by putting three production factors of labor, electric power and maintenance and set the variable cost function model with the translog function to make a stochastic cost frontier analysis. Based on estimated result, we conclude that the cost savings for 6 years of all lines are about 6,672 hundred million won and top five lines with high inefficiency are Busan Line1, Daegu Line1, Daejeon Line1, Gwangju Line1, and Daegu Line2. The causes of inefficiency are attributable to labor and maintenance factors. The results of this study can be useful in case of finding the priorities of measures and specific plans for reducing labor and maintenance costs in the urban railway operation.

• Journal of Korean Society of Rural Planning
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• v.1 no.1
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• pp.99-110
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• 1995

이 연구는 한국 쌀생산의 효율성을 경영규모별로 파악하고, 영농규모 확대를 통한 쌀생산의 효율성 중대 가 가능하다는 가설을 검정해 보고자 하였다. 이 분석에 필요한 기술적 선도농가들의 생산함수인 프런티어(frontier) 생산함수를 구하기 위해서는 교 란항의 정보를 이용할 수 있는 확률(stochastic) 모형아 바람직하고, 아울러 경영규모별로 규모의 효율성을 파악하기 위해서는 레이 동조(ray-homothetic) 함수가 적절하다. 따라서 여기에서는 농림수산부의 1992년도 쌀생산비 자료에서 임의로 추출한 1,203호의 표본 자료를 이용해 앞에서 언급한 두가지 요소를 동시에 감안 할 수 있는 $\ulcorner$레이 동조 확률 생산함수(ray-homothetic stochastic production function)$\lrcorner$를 최우추정법 (Maximum likelilood estimation method)으로 추정하였으며, 이를 토대로 쌀생산의 경영규모별 비효율성 을 순수 기술적 비효율성과 규모의 비효율성으로 나누어 계측하였다. 게측결과에 의하면 쌀생산의 비효율성은 굉균 35.loyo에 이르고 있다. 이 가운데 순수 기술적 비효율성은 12.0%이고, 규모의 비효율성은 24.l%에 달했다. 기술적 비효율성과 규모의 비효율성 모두 경지규모 확대와 더불어 감소하는 것으로 나타나, 경영규모 확대와 더불어 미곡생산의 효율성이 증대될 수 있다는 가설은 기 각되지 않았다. 그러나 대농의 경우에도 규모의 비효율성이 여전히 높은 것으로 나타나 영농규모 확대를 저 해하는 제도적 장벽이 아직도 높다는 것을 알 수 있다. 아울러 대농과 소농과의 효율성 격차가 현저하지는 않은 것으로 나타나 단순히 경지를 중심으로 한 경영규모 확대만으로는 효율성 제고에 한계가 있음을 보여 주고 있다. 이 연구의 결과는 다음과 같은 정책적 함의를 가지고 있다. 첫째, 한국 미곡생산의 효율성 중대 잠재력이 결코 과소 평가되어서는 안된다. 둘째, 영농규모 확대가 쌀생산의 효율성 증대를 위해 필요한 것은 사실이지 만 단순한 경지규모의 확대에 치중하는 것보다 영농규모 확대를 저해하는 제도적 기술적 장애요인을 제거해 나가는 것이 더욱 중요하다. 마지막으로, 새로운 영농기술의 개발은 물론이고 현행 선진영농기술의 보급도 쌀생산의 효율성 중대에 상당한 역할을 할 수 있다는 사실이 간과되어서는 안된다.

• Korea Trade Review
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• v.42 no.5
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• pp.137-159
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• 2017

This study divides manufacturing in 18 countries including Korea, China, Japan and OECD countries into 11 areas and estimates and compares the technological efficiency of each industry. The traditional view of productivity is to increase production capacity through technological innovation or process innovation, but it is also influenced by the technological efficiency of production process. A Stochastic Frontier Production Model (SFM) is a representative method for estimating the technical efficiency of such production. First, as a result of estimating the production function by setting the output variable as total output or value-added, in both cases, the output increased significantly in all manufacturing sectors as inputs of labor, capital, and intermediate increased. On the other hand, R&D investment has a large impact on output in chemical, electronics, and machinery industries. Next, as a result of estimating the technological efficiency through the production function, when the total output is set as the output variable, the overall average of each sector is 0.8 or more, showing mostly high efficiency. However, when value-added was set, Japan had the highest level in most manufacturing sectors, while other countries were lower than the efficiency of the total output. Comparing the three countries of Korea, China and Japan, Japan showed the highest efficiency in most manufacturing sectors, and Korea was about half or one third of Japan and China was lower than Korea. However, in the food and electronics sectors, China is higher than Korea, indicating that China's production efficiency has greatly improved. As such, Korea is not able to narrow its gap with Japan relatively faster than China's rapid growth. Therefore, various policy supports are needed to promote technology development. In addition, in order to improve manufacturing productivity, it is necessary to shift to an economic structure that can raise technological efficiency as well as technology development.

• International Area Studies Review
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• v.22 no.4
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• pp.19-35
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• 2018

This paper is to estimate the total factor productivity(TFP) in the Korean manufacturing and service industries during the period 1975:1-2016:4 using the stochastic frontier analysis model. In order to analyze the determinants for the total factor productivity the paper estimates the industry-specific determinant elasticities of TFP using the autoregressive distributed model. The industry-specific determinants, which reflect the industrial structure and properties include markup, the ratio of capital to labor(KL), and the ratio of foreign intermediate goods (FIG) to industrial output. The average value for total factor productivity growth was estimated to be 0.0199 in manufacturing and 0.0063 in the service industry. The markup and KL elasticities of TFP were estimated to be 2.481 and 0.651 in manufacturing respectively and -1.403 and 0.042 in the service industry respectively. The empirical results suggest that the industrial markup and the ratio of capital to labor have had decisive effects on the changes in the total factor productivity in the Korean manufacturing and service industries during the period 1975:1-2016:4.

• Proceedings of the Technology Innovation Conference
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• 2003.06a
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• pp.41-56
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• 2003

다양한 제품들이 존재하는 시장에는 타 제품에 비하여 품질대비 가격이 낮은 혁신적인 경쟁력있는 제품과 그렇지 못한 제품들이 혼재하고 있다. 그러나 정보의 부족(limited information), 제한적 합리성(bounded rationality) 등 여러 가지 원인으로 인하여 혁신적인 제품들만이 소비자들에게 선택되어 소비되는 것은 아니다. 본 연구에서는 이러한 현상을 설명하기 위하여 소비효율성(consumption efficiency)라는 개념을 도입, 제시하고자 한다. 만약 소비효율성이 극도로 낮다면 혁신적인 제품을 내어놓는다 하더라도 소비자들에게 선택되어 이윤이 발생될 확률이 낮기 때문에 생산자 입장에서는 혁신의 유인(innovation incentive)이 낮아질 수밖에 없게 된다. 이처럼 소비효율성의 문제는 혁신의 유인과 결과를 이해하는데 중요한 단초를 제공할 수 있게 된다. 이에 반하여 혁신을 이해하기 위한 기존의 분석틀은 생산경제이론(production economics)에 기반하고 있고, 효율성의 개념도 생산효율성(production efficiency) 혹은 기술적 효율성(technical efficiency)의 범주에서 다루어져 왔다. 본 연구에서 제시하는 소비효율성의 개념은 효용이론에 근거하고 있다는 점에서 기존 연구와 차별화된다. 본 연구는 효용함수 극대화이론에서 출발하여 경계헤도닉함수(frontier hedomic function)을 도출하는 이론적 유도과정을 제시한다. 실증분석을 위해서는 SFA(Stochastic Frontier Analysis)의 방법론 체계를 적용하였다. 제시된 분석틀은 국내 PC산업의 데이터에 적용되었다. 분석의 결과 몇 가지 가정하에 국내 PC산업이 약 13%정도의 비효율성을 안고 있는 것으로 판단할 수 있으며, 초기혁신구매자(early adopter)들은 일정 정도의 비효율성을 기꺼이 감수할 것으로 분석되었다. 궤적 분석에서는 각 산업별 기술의 특성을 분석하는 것으로, 특정 기술 지식의 활용 기간을 통해 기술 주기를 도출하고, 산업 내 평균 권리 청구 항목 수를 이용하여 각 산업의 기술 범위를 비교하였다. 각각의 동적 분석을 통해 시간에 따른 변화 양상이 관찰하였고, ANOVA 분석을 이용하여 통계적 유의성을 검증하였다. 본 연구는 현재의 기술 패러다임 내에서 Pavitt이 제시한 산업 분류의 근거를 보충 설명하였고 특허 정보를 이용하여 기술혁신의 산업별 유형에 대한 폭넓은 분석방법을 제시하였다.별 시간대별 효과분석을 통하여 정책의 시행여부가 결정되어야 할 것이다. 한편, 화물전용차선의 설치로 인한 물류비용의 절감을 보다 효과적으로 달성하기 위해서는 종합류류 전산망의 시급한 구축과 함께 화물차의 적재율을 높이고 공차율을 낮출 수 있는 운송체계의 수립이 필요한 것으로 판단된다. 그라나 이러한 화물전용차선의 효과는 단기적인 치유책일 수밖에 없기 때문에 물류유통 시설의 확충을 위한 사회간접자본의 구축을 서둘러 시행하여야 할 것이다.으로 처리한 Machine oil, Phenthoate EC 및 Trichlorfon WP는 비교적 약효가 낮았다.>$^{\circ}$E/$\leq$30$^{\circ}$NW 단열군이 연구지역 내에서 지하수 유동성이 가장 높은 단열군으로 추정된다. 이러한 사실은 3개 시추공을 대상으로 실시한 시추공 내 물리검층과 정압주입시험에서도 확인된다.. It was resulted from increase of weight of single cocoon. "Manta"2.5ppm produced 22.2kg of cocoon. It is equal to 9% increase in index, as compared to that of control. In case of R-20458, the increasing

• Journal of Korea Port Economic Association
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• v.26 no.4
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• pp.63-87
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• 2010

The purpose of this paper is to measure the efficiency of Korean container terminals by using SFA(Stochastic Frontier Analysis). Inputs[Number of Employee, Quay Length, Container Terminal Area, Number of Gantry Crane], and output[TEU] are used for 3 years(2002,2003, and 2004) for 8 Korean container terminals by applying both SFA and DEA models. Empirical main results are as follows: First, Null hypothesis that technical inefficiency is not existed is rejected and in the trasnslog model, the estimate is significant. Second, time-series models show the significant results. Third, average technical efficiency of Korean container terminals are 73.49% in Cobb-Douglas model, and 79.04% in translog model. Fourth, to enhance the technical efficiency, Korean container terminals should increase the handling amount of TEUs. Fifth, both SFA and DEA models have the high Spearman ranking of correlation coefficients(84.45%). The main policy implication based on the findings of this study is that the manager of port investment and management of Ministry of Land, Transport and Maritime Affairs in Korea should introduce the SFA with DEA models for measuring the efficiency of Korean ports and terminals.

• KDI Journal of Economic Policy
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• v.12 no.2
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• pp.51-79
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• 1990

Research on technical efficiency, an important dimension of market performance, had received little attention until recently by most industrial organization empiricists, the reason being that traditional microeconomic theory simply assumed away any form of inefficiency in production. Recently, however, an increasing number of research efforts have been conducted to answer questions such as: To what extent do technical ineffciencies exist in the production activities of firms and plants? What are the factors accounting for the level of inefficiency found and those explaining the interindustry difference in technical inefficiency? Are there any significant international differences in the levels of technical efficiency and, if so, how can we reconcile these results with the observed pattern of international trade, etc? As the first in a series of studies on the technical efficiency of Korea's manufacturing industries, this paper attempts to answer some of these questions. Since the estimation of technical efficiency requires the use of plant-level data for each of the five-digit KSIC industries available from the Census of Manufactures, one may consture the findings of this paper as empirical evidence of technical efficiency in Korea's manufacturing industries at the most disaggregated level. We start by clarifying the relationship among the various concepts of efficiency-allocative effciency, factor-price efficiency, technical efficiency, Leibenstein's X-efficiency, and scale efficiency. It then becomes clear that unless certain ceteris paribus assumptions are satisfied, our estimates of technical inefficiency are in fact related to factor price inefficiency as well. The empirical model employed is, what is called, a stochastic frontier production function which divides the stochastic term into two different components-one with a symmetric distribution for pure white noise and the other for technical inefficiency with an asymmetric distribution. A translog production function is assumed for the functional relationship between inputs and output, and was estimated by the corrected ordinary least squares method. The second and third sample moments of the regression residuals are then used to yield estimates of four different types of measures for technical (in) efficiency. The entire range of manufacturing industries can be divided into two groups, depending on whether or not the distribution of estimated regression residuals allows a successful estimation of technical efficiency. The regression equation employing value added as the dependent variable gives a greater number of "successful" industries than the one using gross output. The correlation among estimates of the different measures of efficiency appears to be high, while the estimates of efficiency based on different regression equations seem almost uncorrelated. Thus, in the subsequent analysis of the determinants of interindustry variations in technical efficiency, the choice of the regression equation in the previous stage will affect the outcome significantly.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.2 no.4
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• pp.71-102
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• 2007

There were many comprehensive analyses conducted within the existing research activities wherein factors affecting technology progress including investment in R&D vis-${\Box}$-vis their influences act as the determinants of TFP. Note, however, that there were few comprehensive analysis in the industrial research performed regarding the impact of the economy of scale as it affects TFP; most of these research studies dealt with the analysis of the non -parametric Malmquist productivity index or used the stochastic frontier production function models. No comprehensive analysis on the impacts of individual independent variables affecting TFP was performed. Therefore, this study obtained the TFP increase rate of each industry by analyzing the factors of the existing growth accounting equation and comprehensively analyzed the TFP determinants by constructing a comprehensive analysis model considering the investment in R&D and economy of scale (smallness by industry) as the influencers of TFP by industry. First, for the TFP increase rate of the 15 industries as a whole, the annual average increase rate for 1993${\sim}$ 1997 was approximately 3.8% only; during 1999${\sim}$ 2000 following the foreign exchange crisis, however, the annual increase rate rose to approximately 7.8%. By industry, the annual average increase rate of TFP between 1993 and 2000 stood at 11.6%, the highest in the electrical and electronic equipment manufacturing business and IT manufacturing sector. In contrast, a -0.4% increase rate was recorded in the furniture and other product manufacturing sectors. In the case of the service industry, the TFP increase rate was 7.3% in the transportation, warehousing, and communication sectors. This is much higher than the 2.9% posted in the electricity, water, and gas sectors and -3.7% recorded in the wholesale, food, and hotel businesses. The results of the comprehensive analysis conducted on the determinants of TFP showed that the correlations between R&D and TFP in general were positive (+) correlations whose significance has yet to be validated; in the model where the self-employed and unpaid family workers were used as proxy variables indicating the smallness of industry out of the total number of workers, however, significant negative (-) correlations were noted. On the other hand, the estimation factors of variables surrogating the smallness of scale in each industry showed that a consistently high "smallness of scale" in an industry means a decrease in the increase rate of TFP in the same industry.