A Method to Estimate the Economic Consequences of COVID-19
- DOI
- 10.2991/978-94-6463-124-1_18How to use a DOI?
- Keywords
- COVID-19; economic consequences; estimation method; years of life lost; value of statistical life year
- Abstract
The study proposes an instructive economic consequences estimation method to calculate the economic losses of COVID-19 on the global economy and some countries in 2020 and 2021. The economic consequences do not refer to the impact, but to the result despite the efforts of governments to control it. There are two parts: the actual economic consequences are reflected in GDP falling short of expectations, and the potential economic consequences are reflected in the value of the years of life lost. According to calculations, COVID-19 caused $3.162 trillion in actual losses and $2.005 trillion in potential losses on a global scale in 2020 and 2021, totaling $5.168 trillion. Combining the two losses, COVID-19 caused $6.319 trillion in global losses in 2020, but in 2021 due to economic recovery and so on, the global economy was backfilled with $1.151 trillion. The estimation method can be generalized to other epidemics. Several schemes are also proposed that can help improve estimation accuracy.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Wenrui Zhang PY - 2023 DA - 2023/03/29 TI - A Method to Estimate the Economic Consequences of COVID-19 BT - Proceedings of the 2022 3rd International Conference on Big Data Economy and Information Management (BDEIM 2022) PB - Atlantis Press SP - 149 EP - 155 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-124-1_18 DO - 10.2991/978-94-6463-124-1_18 ID - Zhang2023 ER -