• The Journal of Fisheries Business Administration
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• v.47 no.2
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• pp.1-14
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• 2016

This study applied the Stochastic Frontier Analysis to estimate which independent variable affects to efficiency of aquaculture household. This study used wage and facility scale as input variables, sales volume as an output variable to estimate efficiency. Also, the study used region, species, water quality to estimate technical inefficiency factors of the model. The data used for this study were obtained by the operating costs survey using 1:1 interview method. The study selected translog production model with technical inefficiency term estimated as half-normal distribution. In addition, the study used pearson and spearman correlation coefficient among efficiency estimating models. Also, the study analysed differences among estimated efficiencies through t-test, and showed us 0.1793 in species, 0.4677 between Geojae and Masan.

• Environmental and Resource Economics Review
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• v.14 no.3
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• pp.595-626
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• 2005

The purpose of this paper is to estimate technical efficiency, scale efficiency, and environmental efficiency by income level of Korean farms, and analyze the factors to decide three efficiencies. Depending on the non-parametric methods, we estimate technical using inputs and outputs of total farms without assuming of goods or behavior of optimization. The average technical efficiency of total firms under constant return to scale and strong disposability is 0.437. The technical inefficiency was caused by 47.7% in pure technical inefficiency, 11.3% in scale failure, and 3.2% in environmental inefficiency. The number of firms under increasing return to scale occupied almost 70% and 27% of total firms respectively. Higher are income class, middle debt & long debt per asset, and N effluents per cultural land, higher technical efficiency. The increases of BOD discharges per cultural land and machines per cultural land deteriorate environmental efficiency.

• Journal of Korean Society of Transportation
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• v.26 no.5
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• pp.41-49
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• 2008

According to the demand for services. a single point contract between a user and provider spreads over the industry, and the relationship between them is a main issue. The user wants to make a deal with the contributor which can provide the effective services to the user. This study is to estimate the efficiency of global logistics industry with Data Envelopment Analysis, by nations from 2001 to 2005. Furthermore, it tries to estimate the inefficiency affected by macro factors, and proves the association between them using Tobit model. Global logistics industry has made growth both externally and internally more than doubled for the last 5years. Technical inefficiency of global logistics industry is more influenced by pure technical inefficiency than inefficiency of scale. Therefore technical efficiency can be increased by decreasing pure technical inefficiency. Through this study, it found that the logistics industry got influenced to its efficiency by high price of oil, and courier and transportation service market is formed stably. Furthermore, it advocates policy planners to consider effectiveness and clearness of policy which influence to inefficiency of logistics industry. Also, it found that labor and financial support can give critical effect.

• Journal of Information Management
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• v.41 no.2
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• pp.111-131
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• 2010

This paper examines the levels and influence factors on efficiency of public libraries in metropolitan cities. For this purpose, In the first stage, the efficiency score analysis of 129 sample public libraries has been undertaken. In the second stage, the efficiency scores obtained from the first stage are regressed on environmental factors. The result of this study shows that : (1) main source of technical inefficiency is pure technical inefficiency rather than scale inefficiency; (2) it is positive effect environmental factors on the efficiency that the population, the dummy variable of capital area.

• The Journal of the Korea Contents Association
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• v.15 no.5
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• pp.475-487
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• 2015

This study measures the managerial efficiency of Korea's 14 public enterprises using bootstrap DEA in 2013. In addition, it examines the factors that affect on the bootstrap bias-corrected efficiency using truncated regression analysis. The results and implications of this study are as follows. First, using bootstrap DEA model analysis, the results showed that the mean technical efficiency was 0.3182, the mean pure technical efficiency was 0.4994 and the mean scale efficiency was 0.6585. The main cause of technical inefficiency was due to pure technical inefficiency. Second, rank test between technical efficiency of general DEA model and bootstrap DEA model was no significant difference under CRS and VRS assumption. Third, the main cause of the inefficiency in 11 DMUs among 14 DMUs were mainly due to the pure technology and three DMUs were because of the scale efficiency. Finally, in the truncated regression analysis, cost of labor, profit, sales, return of equity, and the number of employees appeared as factors affecting the scale efficiency at the 10% significance level.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.3
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• pp.79-98
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• 2004

Despite its contribution to the Korean medical service industry the question of how efficiently the Korean public hospitals have operated has been unresolved. This study gauges and analyzes the overall efficiency and the Malmquist productivity index in the industry over the period 1992 through 2001. In addition to cost efficiency. we also measure technical, allocative, pure technical and scale efficiencies. Furthermore. the Malmquist index is decomposed into efficiency and frontier changes. We identify several important factors which seem to have strong relationship with various inefficiency estimates. The results indicate that on average the public hospitals has wasted a significant amount of resources and costs over the period. Unlike many other industries, the low level of cost efficiency of the public hospital industry is mainly due to allocative inefficiency rather than technical inefficiency. The Maimquist productivity indices seem both due to the frontier change and efficiency change, but with more effect by the former. The results also indicate that the turnover of hospital beds has played an important role in determining efficiency and productivity of this important industry.

• 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 Digital Convergence
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• v.19 no.5
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• pp.353-363
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• 2021

This study analyzed to provide information for business improvement by analyzing the management efficiency of start-up investment companies so that startup investment companies can operate efficiently and by presenting information on inefficient factors. From 2014 to 2018, 83 start-up investment companies were analyzed using the DEA model. Input variables were he number of employees, capital, and output variables were selected for start-up investment assets, operating income, and net profit. As a result of the analysis, technical efficiency and pure technical efficiency showed a pattern with an increase in average, but scale efficiency repeatedly increased and decreased. It is believed that the decline in technology efficiency was due to the decrease in pure technology efficiency, and the inefficiency of start-up investment companies seems to have influenced the inefficiency of start-up investment companies rather than the inefficiency of scale. In addition, the size revenue shows that the DRS value is gradually decreasing, and the IRS value is generally increasing. It is believed that efficiency can be improved if operational inefficiency is improved based on the results and efficiency measures are established through scale expansion.

• Korean Journal of Organic Agriculture
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• v.22 no.1
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• pp.1-23
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• 2014

The purpose of this paper was to measure technical efficiencies and scale efficiencies of Korean seed industry by DEA method, and to identify the factors affecting the efficiencies by Tobit regression model. Survey data of 50 seed companies nationwide were applied for the analysis. The average score of overall technical efficiency for the surveyed companies in 2012 was 0.44, which is decomposed into pure technical efficiency 0.68 and scale efficiency 0.63. A majority of the seed companies exhibited at least one form of inefficiency except a few companies in optimal scale. It was also shown the most companies were operating in the stage of increasing returns to scale, which implies Korean seed companies are mainly in smaller scale than optimal. Additional results suggest that the Center for Private Seed Companies, which will be established at Gimje in 2015, plays an important role to make domestic seed companies improve their scale efficiency as well as pure technical efficiency by way of enlarging their size and co-using the high technology in the Center.

• Journal of Digital Convergence
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• v.17 no.5
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• pp.117-125
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• 2019

This study measured inefficiencies of Korean banks with weighted Russell directional distance function, WRDDM, for the years of 2004-2013. Checking contributions of inputs and outputs to these inefficiencies, we found that non-performing loan as undesirable output was the most influential factor. The annual average of inefficiencies of Korean banks was 0.3912, and it consisted of non-performing loan 0.1883, output factors 0.098 except non-performing loan, input factors 0.098. The annual average inefficiency went sharply up from 0.2995 to 0.4829 mainly due to the sharp increase of inefficiency of non-performing loan from 0.1088 to 0.2678 before and after 2007-2008 Global financial crisis. We empirically showed the non-performing loan needed to be considered since it was the most important factor among the influential factors of technical inefficiency such as manpower, total deposit, securities, and non-performing loan. This study had some limitation since we did not control financial environment factor in WRDDM.