• Environmental and Resource Economics Review
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• v.23 no.3
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• pp.453-472
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• 2014

Analyzing the effects of carbon emissions trading, which is scheduled to be introduced in Korea in 2015, requires an accurate assessment of $CO_2$ abatement costs by both industries and firms. Firms faced with regulatory constraints are unlikely to minimize their production costs due to rising production costs caused by allocative inefficiency of inputs. The use of a distance function would results in underestimation of $CO_2$ abatement costs, because it fails to capture the allocative distortion costs. Recognizing the disadvantage of the previous approach, first, this paper tests for allocative efficiency of input for the Korean steel industry over the period 1990-2010, then derives the marginal $CO_2$ abatement costs by applying a cost function approach. The hypothesis of allocative efficiency in inputs is rejected and the steel industry pays an annual average cost of 92,000 won in removing an additional ton of $CO_2$ over the sample period.

• Environmental and Resource Economics Review
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• v.21 no.1
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• pp.3-25
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• 2012

Generally speaking, firms, faced with a regulatory environment, are likely to use more or less inputs than optimal level due to allocative inefficiency of inputs. This paper, first, tests allocative efficiency of fuel inputs and calculates the divergence between the actual and optimal levels of each fuel input conditional on the optimal level of capital stock in Korean thermal power industry. Then, given that each fuel is efficiently allocated. potential reduction of $CO_2$ is estimated over the period 1987~2008. The null hypothesis of allocative efficiency with respect to all fuels is rejected, indicating that thermal power plants fail to attain cost minimization subject do market prices. Allocative efficiency between each pair of fuels is also tested; efficient uses of fuels relative to each other are all rejected. Empirical results indicate that coal and gas are used more and oil is used less than optimal level. On average, more than 10 million tons of $CO_2$ per year could be reduced by achieving allocative efficiency of fuels.

• 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.