Private Precaution and Public Restrictions: What Drives Social Distancing and Industry Foot Traffic in the Covid-19 Era?

Christopher J. Cronin, W. Evans
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引用次数: 70

Abstract

We examine the role of state and local policies to encourage social distancing, including stay at home orders, public school closures, and restrictions on restaurants, entertainment, and large social gatherings. Outcomes come from cell phone records and include foot traffic in six industries (essential and nonessential retail, entertainment, hotel, restaurant, and business services) plus the fraction of cell phones that are home all day. Structural break models show mobility series at the national and state levels start to change dramatically in a short window from March 8-14, well before state or local restrictions of note are in place. In difference-in-difference models, declarations of state of emergency reduce foot traffic and increase social distancing. Stay at home restrictions explain a modest fraction of the change in behavior across outcomes. Industry-specific restrictions have large impacts. For example, restrictions on dining in restaurants reduce traffic in restaurants, hotels, and nonessential retail. Private, self-regulating behavior explains more than three-quarters of the decline in foot traffic in most industries. Restrictive regulation explains half the decline in foot traffic in essential retail and 75 percent of the increase in the fraction home all day. In this latter result, public school closings have a substantial effect.
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私人预防和公共限制:是什么推动了Covid-19时代的社会距离和行业人流量?
我们研究了州和地方政策在鼓励保持社会距离方面的作用,包括呆在家里的命令、公立学校停课、限制餐馆、娱乐和大型社交聚会。结果来自手机记录,包括六个行业(必要和非必要的零售、娱乐、酒店、餐馆和商业服务)的客流量,以及整天在家使用手机的比例。结构断裂模型显示,在3月8日至14日的短时间内,国家和州一级的流动性序列开始发生巨大变化,这远远早于州或地方限制措施的实施。在不同的模型中,宣布紧急状态会减少人流量并增加社交距离。在所有结果中,居家限制只解释了行为变化的一小部分。特定行业的限制有很大的影响。例如,在餐馆就餐的限制减少了餐馆、酒店和非必要零售的客流量。在大多数行业,私人的、自我调节的行为可以解释客流量下降的四分之三以上。限制性的规章制度解释了基本零售业客流量下降的一半原因,以及全天在家的人数增加的75%原因。在后一种结果中,公立学校的关闭产生了实质性的影响。
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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