• 수산해양교육연구
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• 제15권2호
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• pp.166-175
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• 2003

In order to offer fundamental data for the standard of comfilation of the budget on the compensation money for the reduction of fishing baot and index of investigation for computation on the average earnings of reduction of fishing boat, the fluctuation of actual outputs, expenses, earnings and the difference by type of coastal and off-shore fishery was investigated and analyzed. The results are as follows : 1. The average ouput money by large powered purse seine fishery was much with about 3,510 million won, but the average output money by off-shore gill nets fishery was little with about 8.4 million won. 2. The average catch by large powered purse seine fishery was many with about 296,000 M/T, but the average catch by eastern sea danish seine fishery was few with 4,600 M/T. 3. The average expense by large powered purse seine fishery was much with about 3,360 million won, but the average expense by diving fishery was little with 6.3 million won. 4. The average earning by large powered purse seine fishery was much with about 240million won, the average earning by offshore long line fishery was little with 18 million won. 5. The average earning rate by diving fishery was much with 31.62%, but the average earning rate by large powered purse seine fishery was little with 7.30%.

• Journal of Animal Science and Technology
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• 제53권4호
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• pp.319-324
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• 2011

본 연구는 연간수득상금형질을 이용한 국내 씨수말 평가시스템을 분석하여 주파기록과의 관계를 구명하고자 수행되었다. 씨수말 당평균 자마수와 출주수는 각각 34두와 221회였다. 반면에 평균 2세 자마수와 출주수는 각각 9두와 25회였으며, 2세 자마들이 벌어들인 상금은 연간 수득상금의 8.3%였다. 연간수득상금에 대한 자마수와 출주수간의 단순상관계수는 각각 0.922와 0.934, 그리고 자마수와 출주수간의 단순상관계수는 0.985로 매우 높게 나타났다. 씨수말의 검정경력 첫해의 평균 자마수는 6두, 출주수는 17회로 매우 낮았으며, 검정경력에 따른 자마수와 연간수득상금은 매우 밀접한 관계를 나타내었다. 검정경력 4년차에서 40두 이상의 자마수를 확보하는 것으로 나타났으며, 자마수가 증가할수록 평균수득상금지수는 일괄적으로 증가하였다. 자마수가 30두 이하의 씨수말들의 평균수득상금지수는 1.00 보다 낮았으며, 자마수가 10두 이하인 경우에는 0.06에서 0.13의 범위를 나타내어 자마수는 씨수말의 순위 결정에 매우 큰 영향을 미치고 있었다. 주파기록에 대한 육종가와 출주당 수득상금의 상관계수는 다른 형질들에 비하여 가장 높은 -0.524에서 -0.633의 범위를 나타내었다.

• The Journal of Asian Finance, Economics and Business
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• 제7권10호
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• pp.571-579
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• 2020

This paper examines how human capital and other economic variables, such as private investment, economic growth, government investment, inflation, and unemployment influence inequality in Indonesia's provinces. We apply panel data model with fixed effect estimation for the data of 34 provinces from the period 2013 to 2019. We develop a new index for human capital using the education index approach. The results show that human capital has a negative and significant effect on income inequality. An increase in human capital is related to an increase in knowledge and competence due to the longer average school year and expectations of the school year. Human capital has increased the possibility of a person being accepted into the job market and earning a higher income; hence, it lowers income inequality. We also find that inflation leads to a higher gap of income distribution. A further implication of this situation is that the rise in inflation causes an increase in low-income people, and as a consequence, makes their lives worse off. This paper will be beneficial for policy-makers for whom human capital, which is measured using an education index, is an important factor that significantly affects income inequality, in addition to other economic factors.

• 산업경영시스템학회지
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• 제40권4호
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• pp.221-229
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• 2017

In the international businesses human resource elements acquired in different countries might have different values in varied industries due to the different quality of education and experiences in the original countries. Using selection models to evaluate expected values in earnings equation of human resource elements such as education and experiences etc. acquired in sending countries, system equations are expanded to examine also the values of science and engineering degrees in technology jobs with selectivity bias correction. This paper used the US census survey data of 2015 on earnings, academic degrees, occupations etc. The US has long maintained the policy of accepting more STEM workers than any other countries and helped maintaining own technological leadership. Assuming per capita GDP gap between the sending country and the US downgrades immigrant human resource quality, it rarely affects occupational selection but depresses earnings on average by two or more years' worth of education. Immigrant quality index in the sense of GDP gap appears to be a valid tool to assess the expected earnings of the worker with. Engineering degrees increase significantly the probability of selecting not only engineering jobs but also general management jobs, as well as increasing the expected earning additionally over nine years'worth of education. Getting a technology job is additionally worth about four years of education. Economics and business degrees are worth additionally almost six years of education but humanities degrees depress expected earnings. Since years after immigration does not very fast enhance earnings capacity, education level and English language ability might be more useful criteria to expect better future earnings by.