2020
DOI: 10.3386/w27374
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Socioeconomic Network Heterogeneity and Pandemic Policy Response

Abstract: and to Alexei Pozdnukhov at Replica for the ongoing cooperation. Alex Weinberg provided outstanding research assistance throughout this project. We also thank Stephen Eubank and the University of Virginia Biocomplexity Institute, Tim Bresnahan, Matt Jackson, and Mike Whinston for their insightful comments. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. At least one co-author has disclosed a financial relationship of pote… Show more

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Cited by 39 publications
(6 citation statements)
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“…There is some evidence that socioeconomic conditions, such as income, could be related to a higher level of exposure and risk of death by Covid-19, 3 , 8 although most of it is based on aggregated data. There is also a group of studies 9 , 10 , 11 that claims the importance of taking occupation into account in public policy to deal with the pandemic.…”
Section: Discussionmentioning
confidence: 99%
“…There is some evidence that socioeconomic conditions, such as income, could be related to a higher level of exposure and risk of death by Covid-19, 3 , 8 although most of it is based on aggregated data. There is also a group of studies 9 , 10 , 11 that claims the importance of taking occupation into account in public policy to deal with the pandemic.…”
Section: Discussionmentioning
confidence: 99%
“…The natural next step is to provide a model in which individual infection rate is endogenously Pareto-distributed. We believe SIR models with social networks along the line of Pastor-Satorras and Vespignani (2001), Moreno et al (2002), Castellano and Pastor-Satorras (2010), May and Lloyd (2001), Zhang et al (2013), Gutin et al (2020), and Akbarpour et al (2020) are promising avenue to generate endogenous power law in individual infection rates.…”
Section: Theorymentioning
confidence: 97%
“…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%