• 노동경제논집
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• 제37권1호
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• pp.87-116
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• 2014

노동시장 위기관리 시스템의 일환으로 국내외 경제상황 변동으로 야기되는 고용변화를 사전에 감지하는 단기고용변동의 상시적 예측이 요구된다. 이를 위해 본 논문은 경기선행지수 작성방식을 준용하여 비농림 민간부문 임금근로자 변동을 단기적으로 예측하는 고용선행지수를 개발하였다. 고용선행지수는 고용수준 그 자체보다 고용 동향의 국면 및 전환 시점, 변동 속도등 고용의 변화 방향을 조기 탐지하는 것에 중점을 두어 작성되었다. 개발된 지수에 대해 국면 전환 선행성 평가와 고용수준 변동 예측에 대한 모의실험을 통해 검증하고 활용방안을 제시한다.

• 무역학회지
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• 제46권2호
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• pp.281-299
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• 2021

This paper analyzes the feasibility of using machine learning methods to forecast the employment. The machine learning methods, such as decision tree, artificial neural network, and ensemble models such as random forest and gradient boosting regression tree were used to forecast the employment in Busan regional economy. The following were the main findings of the comparison of their predictive abilities. First, the forecasting power of machine learning methods can predict the employment well. Second, the forecasting values for the employment by decision tree models appeared somewhat differently according to the depth of decision trees. Third, the predictive power of artificial neural network model, however, does not show the high predictive power. Fourth, the ensemble models such as random forest and gradient boosting regression tree model show the higher predictive power. Thus, since the machine learning method can accurately predict the employment, we need to improve the accuracy of forecasting employment with the use of machine learning methods.

• 한국항해항만학회지
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• 제39권2호
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• pp.115-123
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• 2015

This study focused on the alternative to estimate the demand of employment in Kenya logistics. First of all, it investigated the importance and necessity of search about the present circumstance of the country's industry. Next, it reviewed respectively the concept and limitation of several previous models for employment, including Bureau of Labor Statistics, USA; ROA, Netherlands; IER (Institute for Employment Research), UK; and IAB, Germany. In regard to the demand forecasting of employers in logistics, it could anticipate more realistically the future demand by the time-lag approach. According to the findings, if value of output record 733,080 KSH million in 2015 and 970,640 in 2020, compared to 655,222 in 2013, demand on wage employment in logistics industry would be reached up to 95,860 in 2015 and 104,329 in 2020, compared to about 89,600 in 2012. To conclude, this study showed the more rational numbers about the demand forecasting of employment than the previous researches and displayed the systematic approach to estimate industry manpower in logistics.

• 무역학회지
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• 제46권1호
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• pp.169-187
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• 2021

This paper analyzes the feasibility of using machine learning and deep learning methods to forecast the income and employment using the strategic industries as well as investment, export, and exchange rates. The decision tree, artificial neural network, support vector machine, and deep learning models were used to forecast the income and employment in Busan. The following were the main findings of the comparison of their predictive abilities. First, the decision tree models predict the income and employment well. The forecasting values for the income and employment appeared somewhat differently according to the depth of decision trees and several conditions of strategic industries as well as investment, export, and exchange rates. Second, since the artificial neural network models show that the coefficients are somewhat low and RMSE are somewhat high, these models are not good forecasting the income and employment. Third, the support vector machine models show the high predictive power with the high coefficients of determination and low RMSE. Fourth, the deep neural network models show the higher predictive power with appropriate epochs and batch sizes. Thus, since the machine learning and deep learning models can predict the employment well, we need to adopt the machine learning and deep learning models to forecast the income and employment.

• Communications for Statistical Applications and Methods
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• 제26권5호
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• pp.497-506
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• 2019

Forecasting the U.S. employment level is made using machine learning methods of the artificial neural network: deep neural network, long short term memory (LSTM), gated recurrent unit (GRU). We consider the big data of the federal reserve economic data among which 105 important macroeconomic variables chosen by McCracken and Ng (Journal of Business and Economic Statistics, 34, 574-589, 2016) are considered as predictors. We investigate the influence of the two statistical issues of the dimension reduction and time series differencing on the machine learning forecast. An out-of-sample forecast comparison shows that (LSTM, GRU) with differencing performs better than the autoregressive model and the dimension reduction improves long-term forecasts and some short-term forecasts.

• International Journal of Internet, Broadcasting and Communication
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• 제12권4호
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• pp.238-247
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• 2020

This study has a purpose to analyze quantitatively whether ICT industry provides the qualitative indicator of employment to attract excellent human resources. We investigate the relationships of labor market conditions among ICT manufacturing, non-ICT manufacturing, ICT services, non-ICT services. Therefore, the quantitative and qualitative indicators of employment (wages, working hours, admission and turnover, involuntary retirement, and the duration years of job) are analyzed for the ICT industry and IT workers. In order to quantitatively analyze qualitative indicators such as employment status and longevity, we used employment statistics. In order to compensate for the limitations of employment insurance data, the comparison analysis with the survey data of economically active population of the National Statistical Office was conducted. As a result of this research, ICT service industry has to improve the working conditions of employees and establish an ecosystem for a lifelong career base to grow as a specialist, need to pursue an investigation for ICT worker career shift, and promote standard labor contracts. In addition, protection of employees, ICT-related job vision and social respect have to be perused.

• 한국산학기술학회논문지
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• 제22권2호
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• pp.273-282
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• 2021

본 연구는 급격한 기술 변화의 흐름 속에서 IT서비스업 분야의 스킬 미스매칭에 대해 살펴보는 것을 목적으로 한다. 전문가 집단에 대한 심층 인터뷰 방식으로 연구가 진행되었다. 숙련노동의 수요자인 기업이 바라보는 IT 산업의 현황과 숙련노동의 공급과 관련된 교육자들이 바라보는 시각에 일부 차이가 있었다. 숙련에 대한 분석 도구로 중요도와 만족도에 대해 5점 척도를 사용하였다. 매칭이 이루어진 경우 주어진 항목들에 대한 중요도의 평균은 3.7점이었고, 만족도의 평균은 3.4점으로, 채용한 인력에 대해 어느 정도의 차이가 존재한다는 것을 알 수 있었다. 한편 비전공자 교육생의 숙련도 수준에 대해서는 중요도의 평균이 3.79점이었으나, 만족도의 평균은 3.12점이었다. 학력, 학점, 자격증, 수상경력, 어학능력에 대해서는 중요도를 낮게 평가하는 공통점이 있었다. 전공역량의 중요도에 대해서는 매칭 전 집단에 대한 만족도보다는 매칭이 이뤄진 집단에서의 만족도가 평균 대비 높게 나타났다. 기업이 원하는 숙련은 다차원적인 역량을 포함하며, 주로 soft-skill 항목들과 관련이 높았다. IT 서비스업에 속한 기업들은 숙련 미스매치의 원인으로 산업의 인력구조가 세분화되어 있지 않은 것을 이유로 들었고, 교육기관에서는 시간의 불일치를 꼽았다. 전문가 집단들은 모두 향후 미스매치 갭이 확대될 것으로 보고 있었다. 숙련의 공급은 기술이 변해가는 속도를 따라가지 못할 것이라는 전망이었다. 갭의 해결 방안으로 전문가 모두 산학연 과정을 뽑았으며, 장기적인 안목에서 인력을 육성할 방안을 모색해야 한다고 했다.

• 상하수도학회지
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• 제20권2호
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• pp.187-196
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• 2006

Forecasting the long-term water demand is important in the plan of water supply system because the location and capacity of water facilities are decided according to it. To forecast the long-term water demand, the existing method based on lpcd and population has been usually used. But, these days the trend among the variation of water demand has been disappeared, so expressing other variation of it is needed to forecast correct water demand. To accomplish it, we introduced the System Dynamics method to consider total connections of water demand factor. Firstly, the factors connected with water demand were divided into three sectors(water demand, industry, and population sectors), and the connections of factors were set with multiple regression model. And it was compared to existing method. The results are as followings. The correlation efficients are 0.330 in existing model and 0.960 in SD model and MAE are 3.96% in existing model and 1.68% in SD model. So, it is proved that SD model is superior to the existing model. To forecast the long-term water demand, scenarios were made with variations of employment condition, economic condition and consumer price indexes and forecasted water demands in 2012. After all scenarios were performed, the results showed that it was not needed to increase the water supply ability in Seoul.

• 정보학연구
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• 제8권2호
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• pp.103-120
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• 2005

As IT industries importance for economic growth, export and the promotion of employment increases, forecasting and analysing development direction in the IT industry & the meaning of the national economy is more important than ever. This study will contribute to policy making in IT industry through improving comprehension of IT and understanding development trend of the new fields of IT industry. Moreover, It will be helpful to formulating the various support programs for the IT industry. In this situation, this study has importance in the side of taking a triangular position in policy direction based on the future of IT industry.

• 산업경영시스템학회지
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• 제30권3호
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• pp.109-116
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• 2007

Entrepreneurship is considered as the main leadership creating enterprises and employment. However, in Korea empirical studies linking Korean entrepreneurial performances with her characteristics are rarely in existence. Current study focuses on Korean entrepreneurs in manufacturing, construction and other technologically intensive (MCOT henceforth) industries compared to entrepreneurs in service and other technologically less intensive (SOT henceforth) industries and to professional/technical wage workers and examines effects of human capital, demographic, and risk-taking characteristics on earnings. Education premium is higher for entrepreneurs in MCOT industries than for professional/technical workers, even though science and engineering diploma pays better in the latter, and that concentration in college causes more selection into the latter occupational family. In terms of education premium and effects of other characteristics SOT industry entrepreneurship and self-employment appear to be lower grade occupational families, even though there appear to be significant comparative advantages working in their selection.