Which Workers Bear the Burden of Social Distancing Policies?

Mongey, Simon; Pilossoph, Laura; Weinberg, Alex

doi:10.2139/ssrn.3586077

Cited by 141 publications

(196 citation statements)

References 2 publications

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“…A related set of papers provides evidence of differences in the effects of the pandemic by socioeconomic status. Mongey et al ( 2020 ) analyze the characteristics of workers in jobs that are most likely to be affected by social distancing measures. They demonstrate that these people are more economically vulnerable and live in areas that engage in less social distancing.…”

Section: Literature Reviewmentioning

confidence: 99%

Socio-demographic factors associated with self-protecting behavior during the Covid-19 pandemic

et al. 2021

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Given the role of human behavior in the spread of disease, it is vital to understand what drives people to engage in or refrain from health-related behaviors during a pandemic. This paper examines factors associated with the adoption of self-protective health behaviors, such as social distancing and mask wearing, at the start of the Covid-19 pandemic in the USA. These behaviors not only reduce an individual’s own risk of infection but also limit the spread of disease to others. Despite these dual benefits, universal adoption of these behaviors is not assured. We focus on the role of socioeconomic differences in explaining behavior, relying on data collected in April 2020 during the early stages of the Covid-19 pandemic. The data include information on income, gender and race along with unique variables relevant to the current pandemic, such as work arrangements and housing quality. We find that higher income is associated with larger changes in self-protective behaviors. These gradients are partially explained by the fact that people with less income are more likely to report circumstances that make adopting self-protective behaviors more difficult, such as an inability to tele-work. Both in the USA and elsewhere, policies that assume universal compliance with self-protective measures—or that otherwise do not account for socioeconomic differences in the costs of doing so—are unlikely to be effective or sustainable.

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Section: Literature Reviewmentioning

confidence: 99%

Socio-demographic factors associated with self-protecting behavior during the Covid-19 pandemic

et al. 2021

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“…Boeri et al [70] have proposed a classification of home workers in three types: Type 1 (complete WFH), Type 2 (WFH and limited mobility but no face-toface interaction) and Type 3 (WFH, mobility and limited interaction). Dingel and Neiman [71] and Mongey et al [72] have used the US O � Net survey to classify work activities in terms of their potential for WFH. Overall they estimate that complete WFH can be done for 30-35% of work positions, while WFH with limited mobility may achieve approximately 50% of workers.…”

Section: Remote Workingmentioning

confidence: 99%

Rapid detection of fast innovation under the pressure of COVID-19

et al. 2020

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Covid-19 has rapidly redefined the agenda of technological research and development both for academics and practitioners. If the medical scientific publication system has promptly reacted to this new situation, other domains, particularly in new technologies, struggle to map what is happening in their contexts. The pandemic has created the need for a rapid detection of technological convergence phenomena, but at the same time it has made clear that this task is impossible on the basis of traditional patent and publication indicators. This paper presents a novel methodology to perform a rapid detection of the fast technological convergence phenomenon that is occurring under the pressure of the Covid-19 pandemic. The fast detection has been performed thanks to the use of a novel source: the online blogging platform Medium. We demonstrate that the hybrid structure of this social journalism platform allows a rapid detection of innovation phenomena, unlike other traditional sources. The technological convergence phenomenon has been modelled through a network-based approach, analysing the differences of networks computed during two time periods (pre and post COVID-19). The results led us to discuss the repurposing of technologies regarding “Remote Control”, “Remote Working”, “Health” and “Remote Learning”.

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“…These studies document significant increases in unemployment and estimate job loss numbers of up to 20 million. Mongey et al. (2020) indicate that workers in “low-ability-to-work-from-home” sectors experienced greater losses in short-term employment and are also more likely to be economically vulnerable.…”

Section: Introductionmentioning

confidence: 99%

Coronavirus infections and deaths by poverty status: The effects of social distancing

Jung

Manley

Shrestha

2021

Journal of Economic Behavior & Organization

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We study the spread of COVID-19 infections and deaths by county poverty level in the US. In the beginning of the pandemic, counties with either very low poverty levels or very high poverty levels reported the highest numbers of cases. A U-shaped relationship prevails for counties with high population density while among counties with low population density, only poorer counties report high incidence rates of COVID-19. Second, we discuss the pattern of infections spreading from higher to lower income counties. Third, we show that stay-at-home mandates caused significantly higher reductions in mobility in high income counties that experienced adverse weather shocks than counties that did not. These effects are not present in counties with high poverty rates. Using weather shocks in combination with stay-at-home mandates as an instrument for social distancing, we find that measures taken to promote social distancing helped curb infections in high income counties but not in low income counties. These results have important policy implications for containing the spread of infectious diseases in the future.

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Which Workers Bear the Burden of Social Distancing Policies?

Cited by 141 publications

References 2 publications

Socio-demographic factors associated with self-protecting behavior during the Covid-19 pandemic

Socio-demographic factors associated with self-protecting behavior during the Covid-19 pandemic

Rapid detection of fast innovation under the pressure of COVID-19

Coronavirus infections and deaths by poverty status: The effects of social distancing

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