PDF(Pubmed)
Abstract：
Economic loss estimation is critical for policymakers to craft policies that balance economic and health concerns during pandemic emergencies. However, this task is time-consuming and resource-intensive, posing challenges during emergencies.
To address this, we proposed using electricity consumption (EC) and nighttime lights (NTL) datasets to estimate the total, commercial, and industrial economic losses from COVID-19 lockdowns in the Philippines. Regression models were employed to establish the relationship of GDP with EC and NTL. Then, models using basic statistics and weather data were developed to estimate the counterfactual EC and NTL, from which counterfactual GDP was derived. The difference between the actual and the counterfactual GDP from 2020 to 2021 yielded economic loss.
This paper highlights three findings. First, the regression model results established that models based on EC (adj-R2 ≥ 0.978) were better at explaining GDP than models using NTL (adj-R2 ≥ 0.663); however, combining both EC and NTL improved the prediction (adj-R2 ≥ 0.979). Second, counterfactual EC and NTL could be estimated using models based on statistics and weather data explaining more than 81% of the pre-pandemic values. Last, the estimated total loss amounted to 2.9 trillion PhP in 2020 and 3.2 trillion PhP in 2021. More than two-thirds of the losses were in the commercial sector as it responded to both policies and the COVID-19 case surge. In contrast, the industrial sector was affected primarily by the lockdown implementation.
This method allowed monitoring of economic losses resulting from long-term and large-scale hazards such as the COVID-19 pandemic. These findings can serve as empirical evidence for advocating targeted strategies that balance public health and the economy during pandemic scenarios.
To address this, we proposed using electricity consumption (EC) and nighttime lights (NTL) datasets to estimate the total, commercial, and industrial economic losses from COVID-19 lockdowns in the Philippines. Regression models were employed to establish the relationship of GDP with EC and NTL. Then, models using basic statistics and weather data were developed to estimate the counterfactual EC and NTL, from which counterfactual GDP was derived. The difference between the actual and the counterfactual GDP from 2020 to 2021 yielded economic loss.
This paper highlights three findings. First, the regression model results established that models based on EC (adj-R2 ≥ 0.978) were better at explaining GDP than models using NTL (adj-R2 ≥ 0.663); however, combining both EC and NTL improved the prediction (adj-R2 ≥ 0.979). Second, counterfactual EC and NTL could be estimated using models based on statistics and weather data explaining more than 81% of the pre-pandemic values. Last, the estimated total loss amounted to 2.9 trillion PhP in 2020 and 3.2 trillion PhP in 2021. More than two-thirds of the losses were in the commercial sector as it responded to both policies and the COVID-19 case surge. In contrast, the industrial sector was affected primarily by the lockdown implementation.
This method allowed monitoring of economic losses resulting from long-term and large-scale hazards such as the COVID-19 pandemic. These findings can serve as empirical evidence for advocating targeted strategies that balance public health and the economy during pandemic scenarios.
摘要：
经济损失估计对于决策者制定在大流行紧急情况下平衡经济和健康问题的政策至关重要。然而,这项任务既耗时又资源密集，在紧急情况下面临挑战。
■为了解决这个问题，我们建议使用用电量(EC)和夜间灯光(NTL)数据集来估计总量，商业,以及菲律宾COVID-19封锁造成的工业经济损失。采用回归模型建立GDP与EC和NTL的关系。然后，使用基本统计数据和天气数据开发了模型来估计反事实EC和NTL，从中得出反事实GDP。从2020年到2021年，实际GDP和反事实GDP之间的差异产生了经济损失。
■本文重点介绍了三个发现。首先,回归模型结果表明，基于EC(adj-R2≥0.978)的模型比使用NTL(adj-R2≥0.663)的模型更好地解释GDP；然而，结合EC和NTL改善了预测(adj-R2≥0.979)。第二,可以使用基于统计数据和天气数据的模型来估计反事实EC和NTL，这些数据解释了大流行前值的81%以上。最后,预计2020年总损失为2.9万亿PhP，2021年为3.2万亿PhP。超过三分之二的损失发生在商业部门，因为它对政策和COVID-19病例激增做出了反应。相比之下,工业部门主要受到封锁实施的影响。
■这种方法可以监测长期和大规模危害（如COVID-19大流行）造成的经济损失。这些发现可以作为经验证据，用于倡导在大流行情景中平衡公共卫生和经济的针对性策略。
■为了解决这个问题，我们建议使用用电量(EC)和夜间灯光(NTL)数据集来估计总量，商业,以及菲律宾COVID-19封锁造成的工业经济损失。采用回归模型建立GDP与EC和NTL的关系。然后，使用基本统计数据和天气数据开发了模型来估计反事实EC和NTL，从中得出反事实GDP。从2020年到2021年，实际GDP和反事实GDP之间的差异产生了经济损失。
■本文重点介绍了三个发现。首先,回归模型结果表明，基于EC(adj-R2≥0.978)的模型比使用NTL(adj-R2≥0.663)的模型更好地解释GDP；然而，结合EC和NTL改善了预测(adj-R2≥0.979)。第二,可以使用基于统计数据和天气数据的模型来估计反事实EC和NTL，这些数据解释了大流行前值的81%以上。最后,预计2020年总损失为2.9万亿PhP，2021年为3.2万亿PhP。超过三分之二的损失发生在商业部门，因为它对政策和COVID-19病例激增做出了反应。相比之下,工业部门主要受到封锁实施的影响。
■这种方法可以监测长期和大规模危害（如COVID-19大流行）造成的经济损失。这些发现可以作为经验证据，用于倡导在大流行情景中平衡公共卫生和经济的针对性策略。
