산업경영시스템학회지 (Journal of Korean Society of Industrial and Systems Engineering)

  • 제47권1호
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  • Pages.41-50
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  • 2024
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  • 2005-0461(pISSN)
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  • 2287-7975(eISSN)
한국산업경영시스템학회 (Society of Korea Industrial and System Engineering)
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설명 가능한 인공지능을 이용한 지역별 출산율 차이 요인 분석

Analysis of Regional Fertility Gap Factors Using Explainable Artificial Intelligence

  • Dongwoo Lee (Department of Industrial Engineering, Hanyang University) ;
  • Mi Kyung Kim (Department of Industrial Engineering, Hanyang University) ;
  • Jungyoon Yoon (Department of Industrial Engineering, Hanyang University) ;
  • Dongwon Ryu (Department of Industrial Engineering, Hanyang University) ;
  • Jae Wook Song (Department of Industrial Engineering, Hanyang University)
  • 투고 : 2024.02.28
  • 심사 : 2024.03.05
  • 발행 : 2024.03.31
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초록

Korea is facing a significant problem with historically low fertility rates, which is becoming a major social issue affecting the economy, labor force, and national security. This study analyzes the factors contributing to the regional gap in fertility rates and derives policy implications. The government and local authorities are implementing a range of policies to address the issue of low fertility. To establish an effective strategy, it is essential to identify the primary factors that contribute to regional disparities. This study identifies these factors and explores policy implications through machine learning and explainable artificial intelligence. The study also examines the influence of media and public opinion on childbirth in Korea by incorporating news and online community sentiment, as well as sentiment fear indices, as independent variables. To establish the relationship between regional fertility rates and factors, the study employs four machine learning models: multiple linear regression, XGBoost, Random Forest, and Support Vector Regression. Support Vector Regression, XGBoost, and Random Forest significantly outperform linear regression, highlighting the importance of machine learning models in explaining non-linear relationships with numerous variables. A factor analysis using SHAP is then conducted. The unemployment rate, Regional Gross Domestic Product per Capita, Women's Participation in Economic Activities, Number of Crimes Committed, Average Age of First Marriage, and Private Education Expenses significantly impact regional fertility rates. However, the degree of impact of the factors affecting fertility may vary by region, suggesting the need for policies tailored to the characteristics of each region, not just an overall ranking of factors.

키워드

과제정보

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2021S1A5C2A03088191)

참고문헌

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