• Journal of Korea Water Resources Association
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• v.41 no.6
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• pp.597-603
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• 2008

This paper estimates technical efficiency of industrial water in order to afford some information for improving the efficiency of government water management policy. To estimate technical efficiency, this paper uses data envelopment analysis. The result shows that the average pure technical efficiency of industrial water is 0.407. This estimate is less than the estimates when all inputs are considered as variables in the previous researches. This result means that the managers may have not tried to improve the efficiency of industrial water usage since the cost for industrial water is trivial compared to other inputs. In addition, this result shows that the previous researches which assume that all inputs are used in efficient way may give a biased results.

• Journal of Navigation and Port Research
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• v.29 no.3 s.99
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• pp.215-220
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• 2005

This paper aim, to measure and evaluates the technical efficiency, pure technical efficiency and scale efficiency with two inputs and four outputs with the use of DEA(Data Envelopment Analysis) in Korean RCC(Rescue Co-ordination Center)/RSC(Rescue Sub-Center). Several conclusion emerge. first the average efficiency of overall technical efficiency measure about $91.03{\%}$ and pure technical efficiency $96.80{\%}$ is much large then scale efficiency $93.83{\%}$. It means that inefficiency has much more to do whit the inefficient utilization of resources rather then the scale of production. second, DRS(decreasing return to scale)is Tongyeong and IRS(increasing return to scale) is Incheon, Taean, Gunsan, Yeosu, Ulsan, Donghae in RCC/RSC finally, inefficiency RCC/RSC have to benchmarking with reference sets.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.08a
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• pp.88-88
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• 2012

High efficiency thin film photovoltaic device technology is reviewed. At present market situation, the industrial players of thin film technologies have to confront the great recession and need to change their market strategies and find technical alternatives again. Most recent technology trends and technical or industrial progress for Silicon thin film and CIGS are introduced and common interests for high efficiency and reliability are discussed.

• The Journal of Fisheries Business Administration
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• v.49 no.1
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• pp.37-50
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• 2018

The purpoose of this paper is to analyze the relative effciency of dreid laver processing companies in Korea and provide the development direction and improvement plan for the dried laver industry. Data on 76 dried laver processing companies were selected as the subjects for Dea. As a result of Dea, the average efficiency rate is shown that the technical efficiency is 84.90%, the pure technical efficiency is 93.83%, and the scale efficiency is 86.65%. and based on BCC results 38 companies are relatively efficient. comparing pure technical efficiency and scale efficiency, it showed that inefficiencies caused by scale of the company was greater than inefficiencies caused by the scale of technical matter. It implies that expanding the size is essential for achieving high-efficiency of dried laver processing company. In the inefficiency factor analysis, the result reveals that unstable supply of raw materials, quality management, capital flexibility and distribution ability influence the efficiency of laver processing company.

• KDI Journal of Economic Policy
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• v.12 no.4
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• pp.21-46
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• 1990

This paper, a sequel to Yoo and Lee (1990), attempts to investigate the interindustry determinants of technical efficiency in Korea's manufacturing industries, and also to conduct an exploratory analysis on the stability of technical efficiency over time. The hypotheses set forth in this paper are most found in the existing literature on technical efficiency. They are, however, revised and shed a new light upon, whenever possible, to accommodate any Korea-specific conditions. The set of regressors used in the cross-sectional analysis are chosen and the hypotheses are posed in such a way that our result can be made comparable to those of similar studies conducted for the U.S. and Japan by Caves and Barton (1990) and Uekusa and Torii (1987), respectively. It is interesting to observe a certain degree of similarity as well as differentiation between the cross-section evidence on Korea's manufacturing industries and that on the U.S. and Japanese industries. As for the similarities, we can find positive and significant effects on technical efficiency of relative size of production and the extent of specialization in production, and negative and significant effect of the variations in capital-labor ratio within industries. The curvature influence of concentration ratio on technical efficiency is also confirmed in the Korean case. There are differences, too. We cannot find any significant effects of capital vintage, R&D and foreign competition on technical efficiency, all of which were shown to be robust determinants of technical efficiency in the U.S. case. We note, however, that the variables measuring capital vintage effect, R&D and the degree of foreign competition in Korean markets are suspected to suffer from serious measurement errors incurred in data collection and/or conversion of industrial classification system into the KSIC (Korea Standard Industrial Classification) system. Thus, we are reluctant to accept the findings on the effects of these variables as definitive conclusions on Korea's industrial organization. Another finding that interests us is that the cross-industry evidence becomes consistently strong when we use the efficiency estimates based on gross output instead of value added, which provides us with an ex post empirical criterion to choose an output measure between the two in estimating the production frontier. We also conduct exploratory analyses on the stability of the estimates of technical efficiency in Korea's manufacturing industries. Though the method of testing stability employed in this paper is never a complete one, we cannot find strong evidence that our efficiency estimates are stable over time. The outcome is both surprising and disappointing. We can also show that the instability of technical efficiency over time is partly explained by the way we constructed our measures of technical efficiency. To the extent that our efficiency estimates depend on the shape of the empirical distribution of plants in the input-output space, any movements of the production frontier over time are not reflected in the estimates, and possibilities exist of associating a higher level of technical efficiency with a downward movement of the production frontier over time, and so on. Thus, we find that efficiency measures that take into account not only the distributional changes, but also the shifts of the production frontier over time, increase the extent of stability, and are more appropriate for use in a dynamic context. The remaining portion of the instability of technical efficiency over time is not explained satisfactorily in this paper, and future research should address this question.

• 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.

• Journal of Agricultural Extension & Community Development
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• v.23 no.2
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• pp.145-156
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• 2016

The purpose of this study is to analyze the farms growing domestic kiwi fruit 'Gold' on their management performance and efficiency in order to reduce the risks involved with introducing new kind of crops for growing, and suggest improvements. First, the result of analysis showed that domestic kiwi fruit 'Gold' growers' income were higher than the average growers due to the fruit's high unit price and productivity. Second, the analysis of management efficiency resulted in scale efficiency having greater impact on inefficiency rather than pure technical efficiency. As for the analysis of technical efficiency, the depreciation costs of agricultural facilities had the greatest influence on its inefficiency. Third, inefficient farms put in excessive inputs across the board, while labor costs(self labor cost + hired labor cost) were the largest factor of optimal inputs according to the models of technical efficiency and pure technical efficiency. Fourth, because of greater reliance on mechanical tools from rising labor costs, there's a need for individual farms to avoid buying farming equipments and instead share the equipments of nearby farms and agricultural cooperatives, or start renting agricultural machines from companies.

• Korean Journal of Organic Agriculture
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• v.29 no.1
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• pp.41-50
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• 2021

This paper analyzes the efficiency of social enterprises by analyzing bootstrapping data envelopment analysis. Unlike the definitive DEA model, we analyze the confidence intervals of efficiency estimates through the DEA model, which takes into account stochastic factors. Major analysis results are summarized as follows: First, the results of the bootstrapping DEA analysis of social enterprises estimated that the technical efficiency was 0.459 and the 95% confidence interval was 0.389 to 0.601. Second, the number of inefficient social enterprises with efficiency values of less than 0.5 was found to be 15 (55.56%) in technical efficiency, 5 (18.52%) in pure technical efficiency, and 8 (29.63%) in scale efficiency. It can be seen that a significant number of social enterprises are operating in an inefficient state. Third, looking at the returns of scale of social enterprises, 25 (67.57%) are currently in the increasing returns of scale, 10 (27.02%) are in the constant returns of scale, and 2 (5.41%) are in decreasing returns of scale. In other words, it can be seen that social enterprises are under-invested in terms of input factors.

• Korea Journal of Hospital Management
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• v.4 no.2
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• pp.219-241
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• 1999

This study used the Data Envelopment Analysis, a mathematical linear programming method, to evaluate cost efficiency of hospitals in Korea. DEA method was applied to 244 hospitals: 31 bankrupt hospitals and 213 survived hospitals. Among the 213 sound hospitals, 11 hospitals showed efficiency score 100, but more than 40 hospitals recorded efficiency scores lower than 60. This result implies that more hospitals can be bankrupt in the restructuring process of the industry within 1-2 years. Among the 31 bankrupt hospitals, the highest technical efficiency score was 0.821 and 11 hospitals showed technical efficiency lower than 0.6. This implies that selective financial support based on cost efficiency by the government will be valuable to prevent bankruptcy of these hospitals. The logistic analysis showed statistically significant relationship between bankruptcy and efficiency of hospitals in Korea.

• Journal of Korean Society for Quality Management
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• v.43 no.4
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• pp.533-544
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• 2015

Purpose: The purpose of this study is to analyze the cost efficiency of food industry in JeollaNamdo. And this study is focused on the correlation between the economic efficiency of food industry and its cost efficiency, based on the analysis of 372 food companies' data in JeollaNamdo in 2012. Methods: DEA cost minimization is the measurement of the cost efficiency of JeollaNamdo food industry in 2012. In this study, the CCR and BBC models have been employed to analyze the decomposing cost efficiency-technical efficiency, allocative efficiency, and scale efficiency respectively. And the Spearman rank correlation and Wilcoxon signed rank test also have been employed to check the correlation and difference between the ranking orders based on the efficiency scores respectively. Results: For the CCR model, mean cost efficiency was found to be 0.084(0.54 for allocative efficiency and 0.19 for technical efficiency). For the BCC model, mean cost efficiency was found to be 0.252(0.453 for allocative efficiency and 0.564 for technical efficiency). Average scale efficiency was found to be 0.38. In analyzing the results, this study argues that the optimal way to improve cost efficiency is by reducing inputs proportionally and changing their combination. Conclusion: The efficiency scores of the two models show high correlation, whereas, the differences between them are also found to be significant. Hence, it should be cautious to select a suitable model when we do the research.