Advanced Industrial SCIence (산업과 과학)

Convergence Society for SMB (중소기업융합학회)
권호 표지
DOI QR코드

DOI QR Code

Analysis of Economic Indicators and Depression using Panel Data: based on data from 2018 to 2022

패널 데이터를 활용한 경제적 지표와 우울증 분석: 2018년부터 2022년 데이터를 기반으로

  • Sung-Min Woo (Dept. of Computer Engineering, Seowon University) ;
  • Bong-Hyun Kim (Dept. of Computer Engineering, Seowon University)
  • 우성민 (서원대학교 컴퓨터공학과) ;
  • 김봉현 (서원대학교 컴퓨터공학과)
  • Received : 2024.08.14
  • Accepted : 2024.09.20
  • Published : 2024.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

This study aims to analyze the impact of economic indicators (economic growth rate, employment rate, inflation) on individuals' mental health, particularly the occurrence of depression, and to clarify the correlation between economic stability and mental health. Data on economic indicators and depression were collected from public data portals and national statistics, and then refined and analyzed using Python and Pandas. Data visualization was performed using Seaborn and Matplotlib. The results showed a strong correlation between economic instability and increased depression rates, with a tendency for the number of depression cases to rise during periods of inflation and declines in economic growth. Additionally, certain age groups and genders exhibited higher depression rates, with social isolation and economic difficulties identified as major contributing factors. This study contributes to mental health policy development, and further research considering various social factors is needed.

본 연구는 경제적 지표(경제 성장률, 취업률, 물가)가 개인의 정신 건강, 특히 우울증 발생에 미치는 영향을 분석하고, 이를 통해 경제적 안정성과 정신 건강의 상관관계를 규명하는 것을 목적으로 한다. 공공데이터포털과 국가통계 포털에서 경제 지표와 우울증 데이터를 수집하고, Python과 Pandas를 활용하여 데이터를 정제 및 분석하였다. Seaborn과 Matplotlib을 사용해 데이터의 시각화를 수행했다. 연구 결과, 경제적 불안정성은 우울증 발생률 증가와 높은 상관관계를 보였으며, 특히 물가 상승과 경제 성장률 감소 시 우울증 환자 수가 증가하는 경향을 확인했다. 또한, 특정 연령대와 성별에서 우울증 발생률이 높게 나타났으며, 이는 사회적 고립과 경제적 어려움 등이 주요 원인으로 작용함을 발견했다. 본 연구는 정신 건강 정책 수립에 기여할 수 있으며, 향후 다양한 사회적 요인을 고려한 추가 연구가 필요하다.

Keywords

References

  1. Shin, T. H., Lee, D. H., & Kim, S. H. (2024). A study on the depression screening and wearable device integration technology to predict depression risk scales. The Society of Convergence Knowledge Transactions, 12(1), 65-75. DOI : 10.22716/SCKT.2024.12.1.007.
  2. Lee, J. L., Lee, D. H., & Kim, S. H. (2012). Object relations of depressive disorder patients. Korea Journal of Counseling, 13(2), 585-609. DOI : 10.15703/KJC.13.2.201204.585.
  3. Nam, J. J., & Cho, M. J. (1997). Approaches to mental health promotion: Focusing on depression status analysis. Health and Welfare Policy Forum, 5, 59-66. DOI : 10.23062/1997.05.9.
  4. Kwan, H. K. (2009). External family support moderating the effects of economic pressure: Applying family stress model. Family and Culture, 21(2), 111-131. DOI : 10.21478/FAMILY.21.2.200906.005.
  5. Lee, G. S. (1983). An analysis of economic determinants of migration. KDI Journal of Economic Policy, 5(2), 30-44. DOI : 10.23895/KDIJEP.1983.5.2.30.
  6. Choi, S. M. (2020). Exploration of self-sufficiency paths in low-income youth: Relationships between emotional self-sufficiency, career preparation behavior, and economic self-sufficiency. Korean Journal of Social Welfare Studies, 51(1), 83-106. DOI : 10.16999/KASWS.2020.51.1.83.
  7. Yang, H.-C., & Kim, C. G. (2019). An implementation of Python web crawler using thread. Proceedings of the Korea Information Processing Society Conference, 70-72. DOI : 10.3745/PKIPS.Y2019M10A.70.
  8. Kim, H. K., & Hwang, W. Y. (2020). Proposal for improving data processing performance using Python. The Journal of Korea Institute of Information, Electronics, and Communication Technology, 13(4), 306-311. DOI : 10.17661/JKIIECT.2020.13.4.306.
  9. Kalaiarasi, K., & Sindhuja, N. (2024). Optimization of stock management system with deficiencies through fuzzy rationale with signed distance method in Seaborn programming tool. Journal of Applied Mathematics & Informatics, 42(2), 379-390. DOI : 10.14317/JAMI.2024.379.
  10. Kim, J. S., Song, T. M., & Kwon, E. J. (2014). A study on using social big data for expanding analytical knowledge - Domestic big data supply-demand expectation. Knowledge Management Review, 15(3), 169-188. DOI : 10.15813/KMR.2014.15.3.008.
  11. Lee, W. S. (2024). Operation plan of big data prediction model using cut-off-voting classifier in administrative big data environment. The Journal of the Convergence on Culture Technology, 10(3), 145-154. DOI : 10.17703/JCCT.2024.10.3.145.
  12. Jung, Y. K., Suk, M. G., & Kim, C. J. (2014). A study on the success factors of big data through an analysis of introduction effect of big data. Journal of Digital Convergence, 12(11), 241-248. DOI : 10.14400/JDC.2014.12.11.241.
  13. Byung, T. C. (2021). Validation and analysis of human behavior database data for robot learning. Journal of Knowledge Information Technology and Systems, 16(5), 995-1001. DOI : 10.34163/JKITS.2021.16.5.010.
  14. Suh, Y. Y. (2017). Data analytics for social risk forecasting and assessment of new technology. Journal of the Korean Society of Safety, 32(3), 83-89. DOI : 10.14346/JKOSOS.2017.32.3.83.
  15. Ha, R., Kim, K., Park, S., & Sim, H. (2020). The effect of debt on depression in young adults. Health and Social Welfare Review, 40(4), 295-331. DOI : 10.15709/HSWR.2020.40.4.295.