DOI QR코드

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글로벌 팬데믹 상황에서의 긴급지원금 예산 배분 정책에 대한 연구

Budget Allocation for Emergency Support Funding System During Global Pandemic

  • 박기군 (부산대학교 산업공학과) ;
  • 김도희 (부산대학교 산업공학과) ;
  • 김슬기 (부산대학교 산업공학과) ;
  • 최지원 (부산대학교 산업공학과) ;
  • 배혜림 (부산대학교 산업공학과)
  • 투고 : 2020.11.20
  • 심사 : 2020.12.23
  • 발행 : 2020.12.31

초록

2020년에 발생한 글로벌 펜데믹 현상은 전 세계에 큰 경제 충격을 주었으며, 그 충격은 특히 유동인구 및 관광산업에 영향을 많이 받는 자영업자들에게 더 크게 작용을 하였다. 이 문제를 해결하기 위해 각 국가에서는 긴급재난지원 정책을 실행하는데, 그 기준과 범위를 선정하는 것에 어려움이 존재하였다. 위 문제를 해결하기 위하여 본 논문에서는 다음의 연구를 진행하였다. 첫째, 글로벌 펜데믹이 지역경제에 미치는 영향을 분석한 후, 그 충격을 직관적으로 설명할 수 있는 지표를 정의하였다. 둘째, 정의된 지표를 활용하여 최적의 예산정책을 지급하는 선형 모형을 수립하였다, 제시된 모형은 정부에서 쉽고 빠르게 고려할 수 있는 경제 충격지표와 최적의 해를 제시한다. 마지막으로 제안된 연구모형의 한계점과 시사점에 대해 소개한다.

The global pandemics occurred in 2020 had a great economic impact on the world, and the impact was especially greater on self-employed people who were heavily affected by the floating population and tourism industry. To solve this problem, each country implemented emergency disaster support policies, and it was difficult to select the criteria and scope. The following research carried out two results. First, after analyzing the impact of global pandemics on the local economy, an economical index was defined that could explain the impact intuitively. Second, we propose linear programming methods to provide optimal budget policy using defined indicators, which present economic shock indicators and optimal years that can be considered quickly and easily by the government. Finally, the limitations and implications of the proposed study model are introduced.

키워드

참고문헌

  1. OECD, OECD GDP falls by 1.8% in the first quarter of 2020 (May 2020).
  2. N. C. Peeri, N. Shrestha, M. S. Rahman, R. Zaki, Z. Tan, S. Bibi, M. Baghbanzadeh, N. Aghamohammadi, W. Zhang, U. Haque, The SARS, MERS and novel coronavirus (COVID-19) epidemics, the newest and biggest global health threats: what lessons have we learned?, International journal of epidemiology (2020).
  3. T. G. Ksiazek, D. Erdman, C. S. Goldsmith, S. R. Zaki, T. Peret, S. Emery, S. Tong, C. Urbani, J. A. Comer, W. Lim, A novel coronavirus associated with severe acute respiratory syndrome, New England journal of medicine 348(20) (2003) 1953-1966. https://doi.org/10.1056/NEJMoa030781
  4. A. Assiri, A. McGeer, T. M. Perl, C. S. Price, A. A. A. Rabeeah, D. A. Cummings, Z. N. Alabdullatif, M. Assad, A. Almulhim, H. Makhdoom, Hospital outbreak of Middle East respiratory syndrome coronavirus, New England Journal of Medicine 369(5) (2013) 407-416. https://doi.org/10.1056/NEJMoa1306742
  5. N. Fernandes, Economic effects of coronavirus outbreak (COVID-19) on the world economy, Availableat SSRN 3557504 (2020).
  6. O. Coibion, Y. Gorodnichenko, M. Weber, Labor markets during the covid-19 crisis: A preliminary view (2020).
  7. A. F. A. Press, Highlights of Trump-signed $2.2T economic relief package (March 2020). https://abcnews.go.com/Business/wireStory/highlights-trump-signed-22t-economic-reliefpacage-69847488
  8. A. Still, H. Long, K. Uhrmacker, Calculate How Much You'll Get from the $1,200 (or More) Coronavirus Checks', Washington Post (2020).
  9. K. NEWS, Japan approves nearly $1 tril. package to cushion coronavirus impact. https://english.kyodonews.net/news/2020/04/
  10. M. Nienaber, Germany's anti-coronavirus stimulus package (March 2020).
  11. https://www.moef.go.kr/nw/nes/detailNesDtaView.do?searchBbsId=MOSFBBS_000000000028&searchNttId=MOSF_000000000036587&menuNo=4010100
  12. 손헌일, 코로나 19 극복을 위한 부산시 정책대응 19, BDI (2020) 1-12.
  13. 노대명, 재난기본소득 논의를 통해 본 한국소득보장제도의 문제점과 향후 과제, 2020(3) (2020) 64-84.
  14. 노대명, 재난기본소득 논의를 통해 본 한국소득보장제도의 문제점과 향후 과제, 2020(3) (2020) 64-84.
  15. H. Jang, A decision support framework for robust R&D budget allocation using machine learning and optimization, Decision Support Systems 121 (04 2019). doi:10.1016/j.dss.2019.03.010.
  16. B.-Y. Heo, M. Kim, won Ho Heo, An algorithm forvalidation of the efficiency of disaster and safety managementbudget investment in South Korea, International Journal of Disaster Risk Reduction 47 (2020) 101566. doi:10.1016/j.ijdrr.2020.101566.
  17. L. Dias, S. Simoes, J. Gouveia, J. Seixas, City energy modelling -Optimising local low carbon transitions with household budget constraints, Energy Strategy Reviews 26 (08 2019). doi:10.1016/j.esr.2019.100387.
  18. T. Shinzato, Minimal Investment Risk of Portfolio Optimization Problem with Budget and Investment Concentration Constraints, Journal of Statistical Mechanics: Theory and Experiment 2017 (05 2016). doi:10.1088/1742-5468/aa56a0.
  19. F. Matarise, Intervention Analysis in Time Series, in: M. Lovric (Ed.), International Encyclopedia of Statistical Science, Springer Berlin Heidelberg, Berlin, Heidelberg, 2011, pp. 682-685. doi:10.1007/978-3-642-04898-2_308.
  20. R. J. Hyndman, G. Athanasopoulos, Forecasting: principles and practice, OTexts: Melbourne, Australia, 2018.
  21. Silva, J., Palma, H. H., Nunez, W. N., Ovallos-Gazabon, D., & Varela, N. (2020, January). Time series decomposition using automatic learning techniques for predictive models. In Journal of Physics: Conference Series (Vol. 1432, No. 1, p. 012096). IOP Publishing. https://doi.org/10.1088/1742-6596/1432/1/012096
  22. V. Prema, K. U. Rao, Time series decomposition model for accurate wind speed forecast, Renewables: Wind, Water, and Solar 2(1) (2015) 18. doi:10.1186/s40807-015-0018-9.
  23. G. Omkar, S. Kumar, Time series decomposition model for traffic flow forecasting in urban midblock sections, 2017, pp. 720-723. doi:10.1109/SmartTechCon.2017.8358465.