As countries around the world are adopting drastic social distancing measures to slow down the spread of the novel coronavirus (Covid-19), the question of an optimal balance between the benefits and costs of these policies has received a lot of attention in popular press and policy discussions. Recent empirical evidence demonstrates a clear impact of the reduction in human mobility on the virus growth rates (Soucy et al, 2020; Fang et al, 2020).The effect of various types of social distancing measures on mobility, especially outside of China, has not been studied systematically to date. This effectiveness of measures implemented in various jurisdictions, such as closures of schools and retail outlets, social distancing rules, personal movement restrictions, shelter-in-place orders and lockdowns to reduce human mobility, is an important policy parameter, since all these measures involve significant economic and labour market costs. The evolving landscape of the pandemic crisis makes it difficult to apply lessons learned from the lockdowns in China to other countries. At the time the lockdown measures were implemented in Wuhan in January 2020, there was relatively little information about the virus, even inside China. When comparable measures were later rolled out in other parts of the world, they were implemented against the background of the damage caused by the Covid-19 pandemic in Northern Italy, Spain, NYC and other virus hotspots. It is likely that the new information about the spread and damage of Covid-19 abroad and domestically has led to significant behavioural adjustments, before and independently of the social distancing measures mandated by the governments. School attendance rates dropping before the official school closure decisions, and firms moving to ‘work from home’ mode are some of the examples of such behavioural responses. The goal of this project is to empirically evaluate the impact of the stringency of government responses to the pandemic on human mobility in affected countries, and to separate it from the impact of other factors, such as accumulation of new information about the virus. Analysing trends in mobility indexes is very important in order to further our understanding about the impact of the Covid-19 pandemic and governments’ responses on economic activity and employment opportunities at the aggregate level (e.g. workplaces mobility trend), as well as in severely affected sectors (e.g. retail and recreation mobility trend).It will allow assessing the global effects of the spread of Covid-19 and anti-Covid-19 policies on economic activity and labour markets as the pandemic unfolded, comparing experiences of different countries, and identifying countries’ characteristics that lead to differential responses. The empirical analysis will focus on estimating how human mobility during the Covid-19 pandemic is affected by two groups of explanatory variables, including policy responses and new information about the spread of the virus. Data from several distinct sources described below will be combined to construct a weekly panel for the percentage changes in the six human mobility indices relative to the baseline and all explanatory variables. Several methodological approaches will be employed to estimate the effects of interest, including OLS regressions, fixed effects regressions, and the grouped fixed effects (GFE) method developed in Bonhomme and Manresa (2015) to explore cross-country heterogeneity in the effects of the Covid-19 policies and information on human mobility. The regression models will also control for the ambient temperature, day of the week, public holidays and other variables that are expected to have an independent effect on human mobility. The GFE approach will allow us to segment countries into several groups with district estimated treatment effects. We will then explore which country characteristics (e.g. population structure, urbanisation, strength of the healthcare system, cultural attitudes and values, etc.) are associated with stronger reductions in human mobility in response to the Covid-19 policies and information. Preliminary results using the two sets of Community Mobility Reports released in early April show that, while the stringency of government measures has a strong negative effect on mobility across different categories of locations, the information availability proxies related to the country specific trajectory of the pandemic have an independent and statistically significant effect on the mobility.
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Silvia Mendolia |
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University of Wollongong |
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