• 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.

• Journal of Korean Society of Forest Science
• /
• v.110 no.4
• /
• pp.648-657
• /
• 2021

This study was conducted to estimate the volume growth by forest type and major species using the national forest resource inventory and to predict the final age of maturity by deriving the mean annual increment (MAI) and the current annual increment (CAI). We estimated the volume growth using the Chapman-Richards model. In the volume estimation equations by forest type, coniferous forests exhibited the highest growth. According to the estimation formula for each major species, Larix kaempferi will grow the highest among coniferous tree species and Quercus mongolica among broad-leaved tree species. And these estimation formulas showed that the fitness index was generally low, such as 0.32 for L. kaempferi and 0.21 for Quercus variabilis. In the analysis of residual amount, which indicates the applicability of the volume estimation formula, the estimates of the estimation formula tended to be underestimated in about 30 years or more, but most of the residuals were evenly distributed around zero. Therefore, these estimation formulas have no difficulty estimating the volume of actual forest species in Korea. The maximum age attained by calculating MAI was 34 years for P. densiflora, 35 years for L. kaempferi, and 31 years for P. rigida among coniferous tree species. In broad-leaved tree species, we discovered that the maximum age was 32 years for Q. variabilis, 30 years for Q. acutissima, and 29 years for Q. mongolica. We calculated MAI and CAI to detect the point at which these two curves intersected. This point was defined by the maximum volume harvesting age. These results revealed no significant difference between the current standard cutting age in public and private forests recommended by the Korea Forest Service, supporting the reliability of forestry policy data.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.22 no.3
• /
• pp.21-36
• /
• 2019

Dead fuel moisture content is a key variable in fire danger rating as it affects fire ignition and behavior. This study evaluates simple regression models estimating the moisture content of standardized 10-h fuel stick (10-h FMC) at three sites with different characteristics(urban and outside/inside the forest). Equilibrium moisture content (EMC) was used as an independent variable, and in-situ measured 10-h FMC was used as a dependent variable and validation data. 10-h FMC spatial distribution maps were created for dates with the most frequent fire occurrence during 2013-2018. Also, 10-h FMC values of the dates were analyzed to investigate under which 10-h FMC condition forest fire is likely to occur. As the results, fitted equations could explain considerable part of the variance in 10-h FMC (62~78%). Compared to the validation data, the models performed well with R2 ranged from 0.53 to 0.68, root mean squared error (RMSE) ranged from 2.52% to 3.43%, and bias ranged from -0.41% to 1.10%. When the 10-h FMC model fitted for one site was applied to the other sites, $R^2$ was maintained as the same while RMSE and bias increased up to 5.13% and 3.68%, respectively. The major deficiency of the 10-h FMC model was that it poorly caught the difference in the drying process after rainfall between 10-h FMC and EMC. From the analysis of 10-h FMC during the dates fire occurred, more than 70% of the fires occurred under a 10-h FMC condition of less than 10.5%. Overall, the present study suggested a simple model estimating 10-h FMC with acceptable performance. Applying the 10-h FMC model to the automatic mountain weather observation system was successfully tested to produce a national-scale 10-h FMC spatial distribution map. This data will be fundamental information for forest fire research, and will support the policy maker.

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.2 no.2
• /
• pp.147-154
• /
• 1969

The larvae of the ark shell Anadare broughtoni(Schrenck) were grown at room temporature (approximately $20.4^{\circ}C$), and fed laboratory-cultured Cyclotella nana. The egg of the ark shell produced in the laboratory measured about $54.9\mu$ in diameter. The embryos gradually developed into larvae up to $110.8\mu$ shell length, $83.9\mu$ shell height and with shell breadth of $58.2\mu$ even in the absence of the algal food. Beyond this sire, however, the growth of the larvae was considerably retarded. The larvae showed better growth rate when they were fed the algal food two days after spawning, i. e., early straight-hinge stage. Daily rate of food consumption varies according to the larval sizes. But the rate increases considerably when the larvae begin to form umbos. In general the rate Is indicated by the following formula: $Y=0.0025161\;X^{2.76459}$. The growth experiments of the larvae indicate that the efficiency of food conversion was higher when fed centrifuged food. Regarding to the difference in the slopes of growth curve, centrifuged food showed better growth rate as compared to those grown with the non-centrifuged food. The smaller the larval size, the greater will be the difference in growth. The larvae began settling when they reathed 261.7 to $289.6\;{\mu}$ in shell length, 199.2 to $221.7\mu$ in shell height and 147.6 to $170.8\mu$ in shell breadth. The time which elapsed from spawning to the larval settlement was about 28 days. The mean growth of the larvae is indicated with regression line and exponential curve equations as follows. Regression line shell length. 94.3 to $133.9\mu$ : Y==85.22857+3.35000X 141.6 to $269.3\mu$: Y=10.83036X-36.05357 296.8 to $373.2\mu$ : Y=19.10000X-279.30000 shell height: 72.7 to $89.7\mu$ : Y=67.11429+2.15714X 108.4 to $206.4\mu$ : Y=8.31607X-27.45357 228.6 to $282.1\mu$: Y=173.46700+13.37500X shell breadth: 45.3 to $77.8\mu$ : Y=38.08510X+2.73570X 87.4 to $157.7\mu$: Y=5.77320X-5.99640 175.4 to $214.0\mu$: Y=19.65000X-114.13300 Exponential curve shell length. 94.3 to $373.2\mu$: Y=72.45 $e^{0.04697x}$ shell height: 72.7 to $282.1\mu$: Y=54,96 $e^{0.04720x}$ shell breadth: 45.3 to $214.0\mu$ : Y=39.82 $e^{0.04927x}$ The relationships between the shell length and shell height and between the shell length and shell breadth are indicated as follows- shell height: 72.7 to $98.7\mu$ : Y=12.87780+0.63817X 108.4 to $206.4\mu$ : Y=0.90220+0.76456X 228.6 to $282.1\mu$ : Y=25.02630+0.69156X shell breadth: 45.3 to $77.8\mu$:Y=0.81373Xx-31.18914 87.4 to $157.7\mu$ : Y=13.37549+0.53230X 175.4 to $214.0\mu$: Y=30.24328+0.49545X

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.11 no.4
• /
• pp.1775-1782
• /
• 1969

The results of the study on the consumptine use of irrigated water in paddy fields during the growing season of rice plants are summarized as follows. 1. Transpiration and evaporation from water surface. 1) Amount of transpiration of rice plant increases gradually after transplantation and suddenly increases in the head swelling period and reaches the peak between the end of the head swelling poriod and early period of heading and flowering. (the sixth period for early maturing variety, the seventh period for medium or late maturing varieties), then it decreases gradually after that, for early, medium and late maturing varieties. 2) In the transpiration of rice plants there is hardly any difference among varieties up to the fifth period, but the early maturing variety is the most vigorous in the sixth period, and the late maturing variety is more vigorous than others continuously after the seventh period. 3) The amount of transpiration of the sixth period for early maturing variety of the seventh period for medium and late maturing variety in which transpiration is the most vigorous, is 15% or 16% of the total amount of transpiration through all periods. 4) Transpiration of rice plants must be determined by using transpiration intensity as the standard coefficient of computation of amount of transpiration, because it originates in the physiological action.(Table 7) 5) Transpiration ratio of rice plants is approximately 450 to 480 6) Equations which are able to compute amount of transpiration of each variety up th the heading-flowering peried, in which the amount of transpiration of rice plants is the maximum in this study are as follows: Early maturing variety ; Y=0.658+1.088X Medium maturing variety ; Y=0.780+1.050X Late maturing variety ; Y=0.646+1.091X Y=amount of transpiration ; X=number of period. 7) As we know from figure 1 and 2, correlation between the amount evaporation from water surface in paddy fields and amount of transpiration shows high negative. 8) It is possible to calculate the amount of evaporation from the water surface in the paddy field for varieties used in this study on the base of ratio of it to amount of evaporation by atmometer(Table 11) and Table 10. Also the amount of evaporation from the water surface in the paddy field is to be computed by the following equations until the period in which it is the minimum quantity the sixth period for early maturing variety and the seventh period for medium or late maturing varieties. Early maturing variety ; Y=4.67-0.58X Medium maturing variety ; Y=4.70-0.59X Late maturing variety ; Y=4.71-0.59X Y=amount of evaporation from water surface in the paddy field X=number of period. 9) Changes in the amount of evapo-transpiration of each growing period have the same tendency as transpiration, and the maximum quantity of early maturing variety is in the sixth period and medium or late maturing varieties are in the seventh period. 10) The amount of evapo-transpiration can be calculated on the base of the evapo-transpiration intensity (Table 14) and Tablet 12, for varieties used in this study. Also, it is possible to compute it according to the following equations with in the period of maximum quantity. Early maturing variety ; Y=5.36+0.503X Medium maturing variety ; Y=5.41+0.456X Late maturing variety ; Y=5.80+0.494X Y=amount of evapo-transpiration. X=number of period. 11) Ratios of the total amount of evapo-transpiration to the total amount of evaporation by atmometer through all growing periods, are 1.23 for early maturing variety, 1.25 for medium maturing variety, 1.27 for late maturing variety, respectively. 12) Only air temperature shows high correlation in relation between amount of evapo-transpiration and climatic conditions from the viewpoint of Korean climatic conditions through all growing periods of rice plants. 2. Amount of percolation 1) The amount of percolation for computation of planning water requirment ought to depend on water holding dates. 3. Available rainfall 1) The available rainfall and its coefficient of each period during the growing season of paddy fields are shown in Table 8. 2) The ratio (available coefficient) of available rainfall to the amount of rainfall during the growing season of paddy fields seems to be from 65% to 75% as the standard in Korea. 3) Available rainfall during the growing season of paddy fields in the common year is estimated to be about 550 millimeters. 4. Effects to be influenced upon percolation by transpiration of rice plants. 1) The stronger absorbtive action is, the more the amount of percolation decreases, because absorbtive action of rice plant roots influence upon percolation(Table 21, Table 22) 2) In case of planting of rice plants, there are several entirely different changes in the amount of percolation in the forenoon, at night and in the afternoon during the growing season, that is, is the morning and at night, the amount of percolation increases gradually after transplantation to the peak in the end of July or the early part of August (wast or soil temperature is the highest), and it decreases gradually after that, neverthless, in the afternoon, it decreases gradually after transplantation to be at the minimum in the middle of August, and it increases gradually after that. 3) In spite of the increasing amount of transpiration, the amount of daytime percolation decreases gadually after transplantation and appears to suddenly decrease about head swelling dates or heading-flowering period, but it begins to increase suddenly at the end of August again. 4) Changs of amount of percolation during all growing periods show some variable phenomena, that is, amount of percolation decreases after the end of July, and it increases in end August again, also it decreases after that once more. This phenomena may be influenced complexly from water or soil temperature(night time and forenoon) as absorbtive action of rice plant roots. 5) Correlation between the amount of daytime percolation and the amount of transpiration shows high negative, amount of night percolation is influenced by water or soil temperature, but there is little no influence by transpiration. It is estimated that the amount of a daily percolation is more influenced by of other causes than transpiration. 6) Correlation between the amount of night percoe, lation and water or soil temp tureshows high positive, but there is not any correlation between the amount of forenoon percolation or afternoon percolation and water of soil temperature. 7) There is high positive correlation which is r=+0.8382 between the amount of daily percolation of planting pot of rice plant and amount and amount of daily percolation of non-planting pot. 8) The total amount of percolation through all growin. periods of rice plants may be influenced more from specific permeability of soil, water of soil temperature, and otheres than transpiration of rice plants.