Economic impact estimates of the initial lockdowns due to the COVID-19 pandemic showed a significant reduction in economic activities globally. However, the succeeding impacts and their spatiotemporal distribution within countries remain unknown. Studies showed that nighttime light data (NTL) has effectively revealed the spatiotemporal dimensions of the economic effects of COVID-19. Thus, this study used NTL data to determine the medium-term regional monthly economic impacts of the pandemic in the Philippines in terms of the Economic Activity Reduction (EAR) index. We generated a spatial error model, regressing pre-pandemic NTL on mean temperature, maximum rainfall, and built-up area. This model explained 81.6% of the pre-pandemic NTL and was used to estimate the counterfactual NTL. We subtracted the actual from the counterfactual to compute the EAR. Then, the EAR was regressed on regional factors to determine which ones influence the impacts. Results showed uneven distribution of EAR across space and time. The EAR was generally higher in urban regions than in rural ones. Overall, more regions in the south had higher EAR. Temporally, the EAR showed a dynamic pattern, increasing in less urban regions and decreasing in highly urbanized regions. Regional analysis showed that urbanization level, population density, and poverty incidence had a significant positive relationship with the EAR. Beyond the immediate impacts, NTL effectively revealed spatiotemporal dimensions of the economic effects of a long-term global hazard.

中文翻译：

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使用夜间灯光数据估算 COVID-19 大流行期间的中期区域月度经济活动减少量


对 COVID-19 大流行导致的最初封锁的经济影响估计显示，全球经济活动显着减少。然而，由此产生的影响及其在国家内部的时空分布仍然未知。研究表明，夜间灯光数据 （NTL） 有效地揭示了 COVID-19 经济影响的时空维度。因此，本研究使用 NTL 数据来确定大流行对菲律宾的中期区域月度经济影响，即经济活动减少 （EAR） 指数。我们生成了一个空间误差模型，对大流行前 NTL 的平均温度、最大降雨量和建成区进行了回归。该模型解释了大流行前 NTL 的 81.6%，并用于估计反事实 NTL。我们从反事实中减去实际值来计算 EAR。然后，对区域因素进行 EAR 回归，以确定哪些因素会影响影响。结果显示 EAR 在空间和时间上的分布不均匀。城市地区的 EAR 通常高于农村地区。总体而言，南部更多地区的 EAR 较高。从时间上看，EAR 呈现出动态模式，在城市较少的地区增加，在高度城市化的地区减少。区域分析表明，城市化水平、人口密度和贫困发生率与 EAR 呈显著正相关。除了直接影响之外，NTL 还有效地揭示了长期全球灾害的经济影响的时空维度。