• Korean Journal of Health Education and Promotion
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• v.33 no.5
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• pp.13-21
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• 2016

Objectives: The present study aims to examine associations between job demands and problem drinking, smoking, and practice of regular exercise among middle-aged and older male employees. Methods: Analyses were based on 239 employees aged 50+ and participated the 1st(2006) and the 4th(2012) waves of the Korean Longitudinal Study of Ageing(KLoSA). Panel logit regression analyses were performed to explore longitudinal associations between physical and cognitive job demands and the health behaviors when effects of demographic characteristics and objective job conditions were controlled. Results: Results suggested that first, workers who reported greater cognitive job demands were less likely to engage in problem drinking over the 6-year-period. Second, increased physical demands of the job were associated with greater odds of smoking, while physical demands predicted a reduced likelihood of practicing regular exercise. Conclusions: Results from the present analyses emphasize job demands could lead workers to problem health behaviors and suggest areas for health promotion efforts at the workplace that are sensitive to the needs of aging workers.

• Korean Journal of Child Studies
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• v.30 no.5
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• pp.103-117
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• 2009

This study examined patterns of delinquent behavior trajectory from late childhood to early adolescence and examined relationships between patterns of trajectory and individual, family, and school factors. Youth delinquent behavior trajectories were examined by mixed growth models using data from the 2nd to 5th year surveys of the Seoul Panel Study of Children. Relationships between patterns and effect factors were examined by multinominal logit models. Four patterns emerged: non-delinquency (80%); rapidly accelerating delinquency (3.3%); decelerating delinquency (6.0%); and moderately accelerating (10.7%) groups. Contacts with a delinquent peer group had persistent effects on more serious delinquent behavior trajectories. Increased levels of self-esteem and school achievement prevented increase in delinquent behaviors; close relationships with parents and parental supervision caused decrease in delinquent behaviors.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.232-241
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• 2019

This study examines one of the conventional and controversial issues in modern finance. Specifically, this study identifies financial determinants of corporate R&D intensity for firms belonging to Korean Chaebols. Empirical estimation procedures are applied to derive more robust results of each hypothesis test. Static panel data, Tobit regression and stepwise regression models are employed to obtain significant financial factors of R&D expenditures, while logit, probit and complementary log-log regression models are used to detect financial differences between Chaebol firms and their counterparts not classified as Chaebols. Study results found the level of R&D intensity in the prior fiscal year, market-value based leverage ratio and firm size empirically showed their significance to account for corporate R&D intensity in the first hypothesis test, whereas the majority of explanatory variables had important power on a relative basis. Assuming that the current circumstances in the domestic capital market may necessitate gradual changes of Korean Chaebols in terms of their socio-economic function, the results of this study are expected to contribute to identifying financial antecedents that can be beneficial to attain optimal level of corporate R&D expenditures for Chaebol firms on a virtuous cycle.

• Environmental and Resource Economics Review
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• v.22 no.1
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• pp.3-29
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• 2013

The purpose of this study is to estimate consumer preferences on hybrid cars and electric cars by employing a choice experiment reflecting the various market conditions, such as different projected market shares of green vehicles and $CO_2$ emission regulations. Depending on different market scenarios, we examine as to which attribute and individual characteristic affect the preferences of potential consumers on green vehicles and further, forecast the potential market shares of green cars. The primary results, estimated by a conditional logit and panel probit models, indicate that sales price, fuel cost, maximum speed, emission of air pollutants, fuel economy, and distance between fuel stations can significantly affect consumer's choice of environment-friendly cars. The second finding is that the unique features of electric cars might better appeal to consumers as the market conditions for electric cars are improved. Third, education, age, and gender can significantly affect individual preferences. Finally, as the market conditions become more favorable toward green cars, the forecasted market shares of hybrid and electric vehicles will increase up to 67% and 14%.

• Journal of Animal Science and Technology
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• v.58 no.1
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• pp.1.1-1.6
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• 2016

Background: Genotype imputation is an important process of predicting unknown genotypes, which uses reference population with dense genotypes to predict missing genotypes for both human and animal genetic variations at a low cost. Machine learning methods specially boosting methods have been used in genetic studies to explore the underlying genetic profile of disease and build models capable of predicting missing values of a marker. Methods: In this study strategies and factors affecting the imputation accuracy of parent-offspring trios compared from lower-density SNP panels (5 K) to high density (10 K) SNP panel using three different Boosting methods namely TotalBoost (TB), LogitBoost (LB) and AdaBoost (AB). The methods employed using simulated data to impute the un-typed SNPs in parent-offspring trios. Four different datasets of G1 (100 trios with 5 k SNPs), G2 (100 trios with 10 k SNPs), G3 (500 trios with 5 k SNPs), and G4 (500 trio with 10 k SNPs) were simulated. In four datasets all parents were genotyped completely, and offspring genotyped with a lower density panel. Results: Comparison of the three methods for imputation showed that the LB outperformed AB and TB for imputation accuracy. The time of computation were different between methods. The AB was the fastest algorithm. The higher SNP densities resulted the increase of the accuracy of imputation. Larger trios (i.e. 500) was better for performance of LB and TB. Conclusions: The conclusion is that the three methods do well in terms of imputation accuracy also the dense chip is recommended for imputation of parent-offspring trios.

• Environmental and Resource Economics Review
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• v.14 no.2
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• pp.337-379
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• 2005

This choice experiments study values the non-market benefits of water quality improvements in Paldang Reservoir and Han River, located in Korea. A fractional factorial orthogonal design was used to produce four different choice sets per respondent, before employing choice examples to screen out irrational responses. The panel mixed logit model (with normal distributions for the attributes) fit the data best, indicating that allowing for both heterogeneous preferences across households and correlation between repeated choices may represent actual choice behaviors best of all the estimated models. The significant standard deviations of the random attributes suggest that the taste for each attribute may vary considerably in the population. The annual benefits to the Seoul Metropolitan area for a small (large) enhancement of the clarity of water, a gradual removal of unpleasant waters, and a gradual improvement in biodiversity, were estimated to be some 1.5 trillion (1.7 trillion) Won, 2 trillion Won, and 1.7 trillion Won, respectively, with 1.8~2.6 trillion Won for at least two of them occurring together. The study also discusses potential biases germane to choice experiments studies of this type.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.248-258
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• 2021

The study analyzed the financial determinants of corporate R&D intensity that require more attention from academics and practitioners in the Korean capital market. Domestic small and medium enterprises (SMEs) may face with developing substitutes by making more R&D investments in scale and scope, given the unprecedented economic conditions such as the limitation of importing core components and materials from other nation(s). KOSDAQ-listed SMEs were selected as sample data, whose R&D expenditures may be less than those of large firms during the post-global financial turmoil period (2010~2018). Static panel data model was applied, along with Tobit and stepwise regression models, for examining the validity of results. Logit, probit, and complementary log-log regressions were also employed for a relative analysis. R&D expenditures in the prior year, the interaction effect between the previous R&D intensity and high-tech sector, firm size, and growth rate were significant to determine R&D intensity. Moreover, a majority of explanatory variables were found to change between the years 2011 and 2018, while time-lagged effects between the R&D intensity and growth rate exist. Results of the study are expected to be used for future research to detect optimal levels of R&D expenditures for the value maximization of SMEs.