Characteristics and Predictive Modeling of Short-term Impacts of Hurricanes on the US Employment
飓风对美国就业短期影响的特征与预测建模AI 解读 ↓
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飓风对美国就业短期影响的特征与预测建模
- 研究问题
- 飓风后的短期就业变化在地区、行业和风暴类型之间如何不同？总体平均变化是否掩盖集中损失，非线性模型能否预测较大就业冲击？
- 方法与思路
- 作者构建更细时空尺度和更广变量覆盖的开放数据，分析 1990—2020 年飓风相关就业变化，并比较随机森林与多元线性回归的预测表现。
- 材料陈述的贡献
- 摘要称，平均就业变化较小但极端风暴后可出现大幅损失，行业间补偿掩盖部分冲击；在所考察案例中，随机森林明显优于线性回归。
- 阅读关注点
- 应核对就业变化的基期和反事实、地区行业覆盖、不同计量分析为何结论不一致，以及预测划分、极端风暴外推和模型评价指标。
- 阅读边界
- 依据所给 arXiv 预印本摘要，不能确认同行评审或期刊接收。摘要未说明因果识别设计与预测误差数值，所述模型优势仅针对考察案例。
原文摘要
The physical and economic damages of hurricanes can acutely affect employment and the well-being of employees. However, a comprehensive understanding of these impacts remains elusive as many studies focused on narrow subsets of regions or hurricanes. Here we present an open-source dataset that serves interdisciplinary research on hurricane impacts on US employment. Compared to past domain-specific efforts, this dataset has greater spatial-temporal granularity and variable coverage. To demonstrate potential applications of this dataset, we focus on the short-term employment disruptions related to hurricanes during 1990-2020. The observed county-level employment changes in the initial month are small on average, though large employment losses (>30%) can occur after extreme storms. The overall small changes partly result from compensation among different employment sectors, which may obscure large, concentrated employment losses after hurricanes. Additional econometric analyses concur on the post-storm employment losses in hospitality and leisure but disagree on employment changes in the other industries. The dataset also enables data-driven analyses that highlight vulnerabilities such as pronounced employment losses related to Puerto Rico and rainy hurricanes. Furthermore, predictive modeling of short-term employment changes shows promising performance for service-providing industries and high-impact storms. In the examined cases, the nonlinear Random Forests model greatly outperforms the multiple linear regression model. The nonlinear model also suggests that more severe hurricane hazards projected by physical models may cause more extreme losses in US service-providing employment. Finally, we share our dataset and analytical code to facilitate the study and modeling of hurricane impacts in a changing climate.