• Journal of Labour Economics
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• v.37 no.1
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• pp.87-116
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• 2014

Forecasting of short-term employment fluctuations provides a useful tool for policy makers in risk managing the labor market. Following the process of producing the composite leading indicator for macro economy, the paper develops the employment leading indicator(ELI) for the purpose of short-term forecasting non-farm payroll employment in private sectors. ELI focuses on early detecting the point of time and the speed in phase change of employment level.

• Korea Trade Review
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• v.46 no.2
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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.

• Journal of Navigation and Port Research
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• v.39 no.2
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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.

• Korea Trade Review
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• v.46 no.1
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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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• v.26 no.5
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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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• v.12 no.4
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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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.273-282
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• 2021

This study investigates for skills mismatching of the IT service sector in the flows of fast technology changes. It was conducted through an in-depth interview method for professional groups. There were differences in demand for skilled labor by business organizations and educators as providers of skilled labor. A five-point Likert scale was used. The degree of importance of 3.7 average point and the degree of satisfaction of 3.4 average point were responded for the set items in case of matching. In addition, the degree of importance of 3.79 average point and the satisfaction of 3.12 were responded in case of non-majored education students for IT. The skills desired from business organizations included multi-dimensional competencies and soft-skill items. For the reason of skills mismatching, business organizations presented ineffective specifications or divisions of the industrial manpower structure, and educational institutions selected the mismatching of time. Professional groups forecasted that the mismatching gap would expand in the future. To solve the gap, the participated professionals selected an industry-university institute collaboration course and gave an opinion to seek a method to foster manpower in the long-term perspective.

• Journal of Korean Society of Water and Wastewater
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• v.20 no.2
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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.

• The Journal of Information Technology
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• v.8 no.2
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.3
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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.