Weather, Lockdown, and the Pandemic: Evidence from the Philippines

Q3 Multidisciplinary
Marjorie C. Pajaron, Glacer Niño A. Vasquez
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引用次数: 0

Abstract

As the landscape of the COVID-19 pandemic continues to evolve, there is a need to better understand the factors that affected COVID-19 health outcomes using a more appropriate dataset and comprehensive variables. This paper constructs a novel daily provincial panel dataset (N = 14,507) during the nascent and important period of the pandemic (April–September 2020) to analyze both the socioeconomic (lockdowns or ECQ, mobility of individuals, health care capacity, and trends in transmission) and environmental factors (rainfall shocks, temperature in Celsius, average relative humidity, and wind speed) that affect COVID-19 health outcomes. A panel dataset is more apt than the other types of datasets since it addresses both spatial and time variations, as well as the time-invariant unobserved heterogeneity that, if ignored, would have resulted in biased estimates and findings. In addition, using a more complete list of explanatory variables could address omitted variable bias, which leads to proper identification and a more reliable set of findings that could aid the government in formulating optimal, multi-faceted, and timely policies for future health crises. Using fixed effects on panel data, our results, which are robust across the different lag structures and time periods used, are consistent with the existing literature with caveats. First, while ECQ is effective in stemming COVID-19 cases, it is ineffective in reducing COVID-19 deaths. Second, exogenous weather variables have heterogenous effects on COVID-19 health outcomes contingent on the period of analysis and the type of health outcome analyzed. Third, public behavior, which is only partially correlated with public policy (ECQ), matters in curtailing viral transmission. We conjecture that individuals voluntarily avoid infection for their own well-being, resulting in positive externalities, or they stay at home due to weather shocks.
天气、封锁和大流行:来自菲律宾的证据
随着COVID-19大流行形势的不断演变,有必要使用更合适的数据集和综合变量,更好地了解影响COVID-19健康结果的因素。本文在大流行的初期和重要时期(2020年4月至9月)构建了一个新的每日省级面板数据集(N = 14,507),以分析影响COVID-19健康结果的社会经济因素(封锁或ECQ、个人流动性、医疗保健能力和传播趋势)和环境因素(降雨冲击、摄氏度温度、平均相对湿度和风速)。面板数据集比其他类型的数据集更合适,因为它处理空间和时间变化,以及时间不变的未观察到的异质性,如果忽略这些异质性,将导致有偏见的估计和发现。此外,使用更完整的解释变量列表可以解决遗漏的变量偏差,从而导致正确的识别和更可靠的一组调查结果,可以帮助政府制定最佳的,多方面的,及时的政策,以应对未来的健康危机。使用面板数据的固定效应,我们的结果在不同的滞后结构和使用的时间段内都是稳健的,与现有文献一致,但有注意事项。首先,虽然ECQ在遏制COVID-19病例方面有效,但在减少COVID-19死亡方面无效。其次,外生天气变量对COVID-19健康结果的影响具有异质性,这取决于分析的时间和分析的健康结果类型。第三,与公共政策(ECQ)仅部分相关的公共行为在遏制病毒传播方面很重要。我们推测,个人为了自己的福祉而自愿避免感染,从而产生正外部性,或者由于天气冲击而呆在家里。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Philippine Journal of Science
Philippine Journal of Science Multidisciplinary-Multidisciplinary
CiteScore
1.20
自引率
0.00%
发文量
55
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