• Korean Journal of Agricultural Science
• /
• v.37 no.3
• /
• pp.509-514
• /
• 2010

The purpose of this study is to analyze the technical efficiency and its determinants for Korean Apiculture farming by using from door to door and e-mail inquiry data. The analysis was implemented through the Cobb-Douglas stochastic frontier production function (SFPF) model including the technical inefficiency effect model for cross-sectional data. To measure the SFPF model, honey production was used for a dependent variable, and for input variables labor cost, preventive cost, material cost, feeding cost, depreciation cost were used. Farmer's age, farmer's career, farming scale, full-time or half-time firm and movement or fixed firm variables were used to measure the inefficiency effect model. The average technical efficiency on apiculture farming in Korea is estimated to be 0.8112. It means that there were technical inefficiency of about 18.88% in Korea apiculture farming. In this study there are some suggestions which could increase the technical efficiency of Korean apiculture farming.

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

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

• The Journal of Society for e-Business Studies
• /
• v.16 no.2
• /
• pp.111-127
• /
• 2011

The aim of this study is to reveal the effects of government and public electronic procurement on the private sector's productivity and technical inefficiency. Three cases are analyzed. The first scenario is that the government just appears as a new consumer to enterprises. Simply participating as a new consumer to enterprises reveals that government does not contribute to the private sector's productivity or improve technical efficiency. The second case is one where the government publicly procures the private sector's service or goods using IT technologies. It is revealed that government contribute to improve the private sector's productivity and reduce inefficiency. The last case is where the government demands business to connect to governments using new IT technologies. The government demands to adopt the IT technology for connection results in improved productivity and efficiencies in the private sectors.

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

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

• Korea Journal of Hospital Management
• /
• v.15 no.1
• /
• pp.27-48
• /
• 2010

The purpose of this paper is to analyze the efficiency change and its determinants of the regional public hospitals. We utilize 34 regional public hospital's panel data for 6 years from 2003 to 2008. We use DEA(Data Envelopment Analysis)-CCR, BCC model, DEA/Window model, and DEA Profiling. The empirical results show the following findings. First, technical efficiency shows that approximately 3.6% of inefficiency exists on the regional public hospitals and it reveals that the cause for technical inefficiency is due to scale inefficiency. Second, DEA/Window results show that the stable dissimilarity by standard deviation, LDP of CCR. Third, the results of partial efficiency by DEA Profiling show that increase efficiency depends on the number of beds, doctors, and nurses.

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

• Journal of Korea Technology Innovation Society
• /
• v.14 no.4
• /
• pp.1000-1023
• /
• 2011

The government adopts mainly three venture-supporting systems such as Venture Certification, InnoBiz Certification, and MainBiz Certification. This study compares the policy effects of productive aspects and technical inefficiency aspects of Venture Certification and InnoBiz Certification using the data on 'The 2010 Actual Conditions of Venture Enterprise.' With the estimation of production functions, it is found that only 'financing and ease of investment' produces a statistically significant effects on productivity. On the other hands, the other effects of 'the venture certification' do not produce positive effects. However, it is revealed that 'the venture certification policy' primarily reduces technical inefficiencies. The InnoBiz certification policy shows primarily insignificant effects in both sides of the production aspects and technical inefficiency aspects. However, the 'marketing ability effect' produces a positive effect on production and reduces the technical inefficiencies as well.

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