• Journal of Korea Port Economic Association
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• v.34 no.4
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• pp.1-16
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• 2018

The standard shift-share analysis decomposes a region's sectoral growth into three components: national, industry-mix, and regional-shift effects. Nevertheless, the three components of the traditional shift-share are not related to the behavior of the regional economies that are neighbors of the region under analysis. We incorporate a spatial structure within this basic formulation, and consider spatial interaction in the decomposition analysis. Daesan Port's export grew steadily at an annualized average rate of 4.0% during 2011-2017, and its rank, in terms of export performance, was 13 in 2010; this rose to 6 in 2016, then declined slightly to 7 in 2017 before reaching 6 as of June, 2018. However, not all ports have a similar growth path. The Onsan Port's share declined from 27.4% in 2011 to 21.0% to 2017, whereas the share of petroleum product exports of Daesan Port increased rapidly, from approximately 8.5% in 2011 to 16.0% in 2017. The standard shift-share analysis shows that petroleum products and basic petrochemicals have a positive regional in dustry-mix effect, but petrochemistry materials and synthetic resins have a negative sign, indicating that the former's exports grow faster than national export, while the increase of the latter's export is slower than national one. The spatial shift-share model indicates that for both petroleum products and basic petrochemicals, Incheon and Ulsan Ports have a positive value for the neighbor-nation regional shift effect and a positive value for the region-neighbor regional shift effect. This paper also shows that Yeosu Port for petroleum products; Ulsan Port for basic petrochemicals; Ulsan, Onsan and Yeosu Ports for petrochemistry materials; and Ulsan, Busan, and Incheon Ports for synthetic resins have a positive value for the neighbor-nation regional shift effect but a negative value for the region-neighbor regional shift effect.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.2 no.4
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• pp.71-102
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• 2007

There were many comprehensive analyses conducted within the existing research activities wherein factors affecting technology progress including investment in R&D vis-${\Box}$-vis their influences act as the determinants of TFP. Note, however, that there were few comprehensive analysis in the industrial research performed regarding the impact of the economy of scale as it affects TFP; most of these research studies dealt with the analysis of the non -parametric Malmquist productivity index or used the stochastic frontier production function models. No comprehensive analysis on the impacts of individual independent variables affecting TFP was performed. Therefore, this study obtained the TFP increase rate of each industry by analyzing the factors of the existing growth accounting equation and comprehensively analyzed the TFP determinants by constructing a comprehensive analysis model considering the investment in R&D and economy of scale (smallness by industry) as the influencers of TFP by industry. First, for the TFP increase rate of the 15 industries as a whole, the annual average increase rate for 1993${\sim}$ 1997 was approximately 3.8% only; during 1999${\sim}$ 2000 following the foreign exchange crisis, however, the annual increase rate rose to approximately 7.8%. By industry, the annual average increase rate of TFP between 1993 and 2000 stood at 11.6%, the highest in the electrical and electronic equipment manufacturing business and IT manufacturing sector. In contrast, a -0.4% increase rate was recorded in the furniture and other product manufacturing sectors. In the case of the service industry, the TFP increase rate was 7.3% in the transportation, warehousing, and communication sectors. This is much higher than the 2.9% posted in the electricity, water, and gas sectors and -3.7% recorded in the wholesale, food, and hotel businesses. The results of the comprehensive analysis conducted on the determinants of TFP showed that the correlations between R&D and TFP in general were positive (+) correlations whose significance has yet to be validated; in the model where the self-employed and unpaid family workers were used as proxy variables indicating the smallness of industry out of the total number of workers, however, significant negative (-) correlations were noted. On the other hand, the estimation factors of variables surrogating the smallness of scale in each industry showed that a consistently high "smallness of scale" in an industry means a decrease in the increase rate of TFP in the same industry.

• Journal of Animal Science and Technology
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• v.45 no.5
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• pp.667-678
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• 2003

The objectives of this study were to investigate the genetic characteristics of body weight by growth periods for Hanwoo. A total of 1,736 records were used for body weight. The data for body weights were collected from 1990 to 2000 in Daekwanryong branch, National Livestock Research Institute(NLRI). Estimates of (co)variance components were obtained by derivative-free Restricted Maximum Likelihood (DF-REML). The results are summarized as follows; The means for the weights were 25.60, 79.31, 98.91, 145.40, 283.26, 392.32, 545.65kg at birth, 3, 4, 6, 12, 18, 24month postpartum, respectively. The effects of calving year-season were significant for the milk yield of cow. Heritability estimates of direct genetic effects for birth weight were 0.54(all), 0.52(female), 0.36(male) in modelⅠ, 0.45(all), 0.41(female), 0.24(male) in modelⅡ, and heritabilities estimates of direct genetic effects for 4 month(weaning) weight were 0.47(all), 0.33(female), 0.28(male) in modelⅠ, 0.38(all), 0.21(female), 0.21(male) in modelⅡ. Heritability estimates for male and female data differed from those for combined data. The estimates became smaller for the body weights at 12 month or later(0.13～0.05). The heritabilities of average daily gain were smaller than those for body weights, but showed that the similar pattern to body weights.

• Journal of Animal Science and Technology
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• v.53 no.4
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• pp.325-332
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• 2011

This research was conducted to analyze the effects of raising farm on the heritability and breeding values of Hanwoo cows for their carcass traits, including cold carcass weight (CWT), back-fat thickness (BFT), eye-muscle area (EMA) and marbling score (MAR). The carcass data and pedigree data were collected from steers raised on Hanwoo farms in Pyeongchang-gun, Gangwon-do, South Korea. Three analytical models were applied for the estimation of heritabilities and breeding values. The first model (model 1) included slaughter house-year-month combination as fixed effects and age at slaughter was fitted as linear and quadratic covariates. The second model (model 2) was similar to model 1, but raising farm was additionally included as random effect. The third model (model 3) was similar to model 1 but farm effects were additionally included as fixed effect. The comparisons between the model 1 and the models including farm effect (model 2 and model 3) revealed that heritability estimates from model 2 or model 3 were smaller to those from model 1 for all carcass traits. Especially, obvious decrease of heritability was observed in CWT where heritability was 0.23 from model 1, 0.15 from model 2 and 0.18 from model 3. The maximum log likelihood of the model 2 and 3 were higher than those of model 1 for all traits. In model 2 that raising farm was included as a random effect, the ratio of farm variance to the total phenotypic variance were ranged from 4% (EMA) to 18% (CWT). Top 10% and bottom 10% of female cows were selected based on the breeding values from model 1, and the Spearman's rank correlation coefficients among models were estimated for each trait within selected group. The correlation coefficients were ranged from 0.57 to 0.95 in top 10% group and from 0.68 to 0.95 in bottom 10% group. These results show that the discrepancies in the rankings of breeding values can be based on the models applied. In conclusion, the results obtained in this study suggest that the herd effect or farm effect should be included in the analytical model when breeding values are estimated with the purpose of improvement of carcass traits of Hanwoo breeding cows.

• Journal of Animal Science and Technology
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• v.52 no.5
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• pp.349-356
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• 2010

This study was conducted to estimate heritabilities, repeatabilities and rank correlation coefficients among breeding values for litter size and sex ratio of Yorkshire and Landrace pigs using various single trait animal models. The analyses were carried out the data comprising 26,390 litters of Yorkshire and 26,173 litters of Landrace collected from the year 1998 to 2008 at a private swine breeding farm located in central part of Korea. Five different analytical models were used for genetic parameter estimation. Model 1 was most simple basic model fitted with year-month contemporary group fixed effect, random additive genetic effect and random residual effect. Model 2 was similar to the model 1 but permanent maternal environmental effect added as random effect, and model 3 was similar with the model 2 but linear and quadratic effects of sow age were added as fixed covariate effect. Model 4 was similar as model 2 except that the parity was added as fixed effect and model 5 was similar to model 3 or model 4 but covariate of sow age was nested within parity effect. The results obtained in this study are summarized as follows: The means and standard error of total number of pigs born per litter (TNB) and number of pigs born alive per litter (NBA) were $11.35{\pm}0.02$ and $10.04{\pm}0.02$ for Yorkshire, $10.97{\pm}0.02$ and $9.98{\pm}0.02$ for Landrace, respectively. The sex ratio (percentage of female per litter) was $45.75{\pm}0.11%$ and $45.75{\pm}0.11%$ for Yorkshire and Landrace, respectively. The heritability estimates of TNB (0.243) and NBA (0.192) from model 1 tended to be higher than those from any other models in both breeds. Differences in heritability and repeatability for TNB were not large among models 3, 4 and 5 and same tendency of negligible differences among estimates by models 3, 4 and 5 were observed for NBA, where heritability and repeatability ranged from 0.096 to 0.099 and from 0.188 to 0.193, respectively, in Yorkshire; and ranged from 0.092 to 0.098 and from 0.193 and 0.196, respectively, in Landrace. The heritability estimates for sex ratio were close to zero which was ranged from 0.002 to 0.003 for TNB and from 0.001 to 0.003 for NBA over the models applied. The rank correlation coefficients of breeding values by model 1 with those from other models (model 2, 3, 4 and 5), and breeding values by model 2 with those from other models (model 1, 3, 4 and 5) were highly positive but lower than the coefficients among breeding values by model 3, model 4 and model 5 which were high of 0.99, approximately, for TNB and NBA of both breeds.