Socioeconomic Network Heterogeneity and Pandemic Policy Response

Akbarpour, Mohammad; Cook, Cody; Marzuoli, Aude; Mongey, Simon; Nagaraj, Abhishek; Saccarola, Matteo; Tebaldi, Pietro; Vasserman, Shoshana; Yang, Hanbin

doi:10.2139/ssrn.3623111

Cited by 21 publications

(26 citation statements)

References 10 publications

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“…52 The matrix is based on data provided by Replica, which uses anonymized cellphone GPS data to simulate a "synthetic population" that "closely approximates both age and industry distributions from the Census ACS, as well as granular groundtruth data on mobility patterns from a variety of different sources" (Akbarpour et al, 2020).…”

Section: Assessing Treatment Effects Through An Epidemiological Modelmentioning

confidence: 99%

Misinformation During a Pandemic

Bursztyn¹,

Rao²,

Roth³

et al. 2020

239

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Media outlets often present diverging, even conflicting, perspectives on reality-not only informing, but potentially misinforming audiences. We study the extent to which misinformation broadcast on mass media at the early stages of the coronavirus pandemic influenced health outcomes. We first document large differences in content between the two most popular cable news shows in the US, both on the same network, and in the adoption of preventative behaviors among viewers of these shows. Through both a selection-on-observables strategy and an instrumental variable approach, we find that areas with greater exposure to the show downplaying the threat of COVID-19 experienced a greater number of cases and deaths. We assess magnitudes through an epidemiological model highlighting the role of externalities and provide evidence that contemporaneous information exposure is a key underlying mechanism.

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Section: Assessing Treatment Effects Through An Epidemiological Modelmentioning

confidence: 99%

Misinformation During a Pandemic

Bursztyn¹,

Rao²,

Roth³

et al. 2020

239

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show abstract

“…Finally, on the theory side, several papers have studied the macroeconomic and distributional consequences of the pandemic using our data on occupational characterizations as inputs. For example, Akbarpour et al (2020) use a heterogeneous agent model to evaluate targeted social distancing policies, and discipline their model using our data on an industry's WFH ability. Similarly, Kaplan et al (2020) develop a macro-SIR ("susceptible, infected, recovered") model with occupational heterogeneity disciplined by our estimates.…”

Section: Introductionmentioning

confidence: 99%

Which workers bear the burden of social distancing?

2021

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Using data from O ∗ NET, we construct two measures of an occupation’s potential exposure to social distancing measures: (i) the ability to conduct that job from home and (ii) the degree of physical proximity to others the job requires. After validating these measures with comparable measures from ATUS as well as realized work-from-home rates during the pandemic, we employ the measures to study the characteristics of workers in these types of jobs. Our results show that workers in low-work-from-home and high-physical-proximity jobs are more economically vulnerable across various measures constructed from the CPS and PSID: they are less educated, of lower income, have fewer liquid assets relative to income, and are more likely renters. Consistent with the idea that high physical proximity or low work-from-home occupations were more exposed to the Coronavirus shock, we show that the types of workers predicted to be employed in them experienced greater declines in employment during the pandemic. We conclude by comparing the aggregate employment losses in these occupations to their employment losses in the 2008 recession, and find evidence that these occupations were disproportionately exposed to the pandemic shock, and not just comprised of more cyclically sensitive workers. Supplementary Information The online version contains supplementary material available at 10.1007/s10888-021-09487-6.

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“…Baqaee et al (2020) andAkbarpour et al (2020) both use a network framework to analyze the economic and epidemiological effects of containment and re-opening policies. Baqaee et al (2020) focus on aggregate (US) outcomes, while Akbarpour et al (2020) focus, as…”

mentioning

confidence: 99%

Pandemic Control in Econ-Epi Networks

Fogli

Perri

Ponder

et al. 2020

Preprint

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We develop an ECON-EPI network model to evaluate policies designed to improve health and economic outcomes during a pandemic. Relative to the standard epidemiological SIR set-up, we explicitly model social contacts among individuals and allow for heterogeneity in their number and stability. In addition, we embed the network in a structural economic model describing how contacts generate economic activity. We calibrate it to the New York metro area during the 2020 COVID-19 crisis and show three main results. First, the ECON-EPI network implies patterns of infections that better match the data compared to the standard SIR. The switching during the early phase of the pandemic from unstable to stable contacts is crucial for this result. Second, the model suggests the design of smart policies that reduce infections and at the same time boost economic activity. Third, the model shows that reopening sectors characterized by numerous and unstable contacts (such as large events or schools) too early leads to fast growth of infections.

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Socioeconomic Network Heterogeneity and Pandemic Policy Response

Cited by 21 publications

References 10 publications

Misinformation During a Pandemic

Misinformation During a Pandemic

Which workers bear the burden of social distancing?

Pandemic Control in Econ-Epi Networks

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