Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
권호 표지
DOI QR코드

DOI QR Code

Finding factors on employment by adult life cycle using decision tree model

의사결정나무모형을 사용한 성인 생애주기별 취업 영향요인 분석

  • Kwak, Minjung (Department of Data Information, Pyeongtaek University) ;
  • Rhee, Sung-Suk (Department of Business Administration, Seowon University)
  • Received : 2016.10.11
  • Accepted : 2016.11.21
  • Published : 2016.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

Due to global economy recession with low oil price, Korea has stepped into a stage of sluggish development, and the unemployment has become a major issue. Hence, we study various risk factors influencing on unemployment using the Korean labor and income panel data of 2014. We first divide the adult life cycle into three categories, such as young adult, adult, and old adult. Then we consider demographic variables, occupational variables and health related variables as risk factors. The decision tree models have shown that education and gender are the most important factors respectively in young adult group and in adult group. Gender, health status, and education are influential factors in old adult group.

세계적으로 유가하락과 더불어 경제가 침체되면서 우리나라도 저성장의 기조를 보이고 있고, 노동 시장에서는 취업난이 가중되고 있으므로 취업영향 요인을 파악하여 적절한 취업 정책을 수립하는 것이 절실한 현실이다. 따라서 본 연구에서는 제17차년도 노동패널자료를 사용하여 취업에 영향을 미치는 요인을 파악하고자 한다. 성인 생애주기는 청년기, 중장년기와 노년기로 구분하였으며 취업에 영향을 미치는 요인으로 인구통계학적 변수, 직업관련 변수 그리고 건강관련 변수를 고려하였다. 의사결정나무분석을 사용하여 분석한 결과 청년기에는 학력이 가장 중요한 요인이었으며, 중장년기에는 가장 중요한 요인이 성별이었고, 남성의 경우 건강상태, 여성의 경우, 직업훈련경험, 연령, 건강상태의 순으로 나타났다. 노년기에도 성별이 가장 중요한 요인이었고 그 다음으로 건강상태, 학력 등의 순으로 나타났다.

Keywords

References

  1. Breiman, L., Friedman, J., Stone, C. and Olshen, R. A. (1993). Classification and regression trees, CRC Press, New York.
  2. Cho, J. S. (2010). The influence analysis of admission variables on academic achievements. Journal of the Korean Data & Information Science Society, 21, 729-736.
  3. Cho, J. S. (2014). Analysis of employee's characteristic using data visualization. Journal of the Korean Data & Information Science Society, 25, 727-736. https://doi.org/10.7465/jkdi.2014.25.4.727
  4. Choi, K. L. and Kim, B. (2013). A study on the relationship between job transition and personal attributes using multiple logit model. Journal of the Korean Data Analysis Society, 15, 799-811.
  5. Choi, Y. J., Kwak, M. J. and Yoon, M. (2015). The diffusion and policy options of the diagnostic imaging technologies in Korea. Journal of the Korean Data & Information Science Society, 26, 179-185. https://doi.org/10.7465/jkdi.2015.26.1.179
  6. Jung, I. H. (2012). A study on self-esteem and influencing factors of adults by life cycle: comparison of young, middle-aged and elderly. Korean Review of Crisis and Emergency Management, 8, 235-250.
  7. Jung, J. H. and Min, D. K. (2013). The study of foreign exchange trading revenue model using decision tree and gradient boosting. Journal of the Korean Data & Information Science Society, 24, 161-170. https://doi.org/10.7465/jkdi.2013.24.1.161
  8. Lee, S. (2004). A study on the cause of unemployment for women: Based on reservation wage and market wage. Korean Journal of Labour Economics, 27, 135-164.
  9. Lee, Y. S., Dong, S. O., Jung, Y. A., Kang, C. and Kim, K. K. (2010). Background of successful Employment after graduation. Journal of the Korean Data Analysis Society, 12, 1523-1533.
  10. Oh, S. R. (2008). A study about the predictor variables of employment of persons with disabilities. Social Welfare Policy, 34, 255-275.
  11. Sung, J. M. and Ahn, J. Y. (2006). What makes the older work for satisfactory? Journal of Labor Policy, 6, 39-74.
  12. Ryu, G. R. (2012). Analysis of labor market activation policy and its employment outcomes: The effects of employment and tailored social service provision. Korean Social Policy Review, 19, 149-183. https://doi.org/10.17000/kspr.19.3.201209.149
  13. Yum, D. W. (2008) The determinants of the older's decision to work: Focusing on 1st Korean retirement and income study. Journal of Labor Policy, 8, 17-38.

Cited by

  1. 딥러닝분석과 기술적 분석 지표를 이용한 한국 코스피주가지수 방향성 예측 vol.28, pp.2, 2017, https://doi.org/10.7465/jkdi.2017.28.2.287