• 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.

• 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.

• Journal of Korea Port Economic Association
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• v.24 no.4
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• pp.47-70
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• 2008

The purpose of this study is to estimate total factor productivity(TFP) growth by stochastic frontier function and to grasp contributing factors of its growth rate by decomposing the total factor productivity into efficiency change, technical progress, scale change, and allocation change. Annual growth rate of total factor productivity for 1990-2003 is 0.019 (1.9%), higher than that of overall industry (0.010). The main component of TFP growth is not efficiency change but technical progress. Contributing factors of total factor productivity growth are change of allocation efficiency in port industry, technical progress in sea-transportation industry, and change of scale efficiency in transportation-equipment industry. The change of total factor productivity shows a decreasing trend since late in the 1990s. The annual technical efficiency of port-logistics industry is less than that of overall industry. Capital elasticity for output (0.391) is higher than labor elasticity (0.227), but scale economy of port-logistics industry is 0.618, which is far from optimal scale economy.

• 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.