Laissez-Faire, Social Networks, and Race in a Pandemic

Abstract

We study the effects of race, network centrality, and policies that tolerate some level of virus spread (laissez-faire) on COVID-19 deaths in nursing homes in the United States. Our analysis uses unique data on nursing home networks and calibration-based estimates of states' preferences for health relative to short-term economic gains. Our findings suggest that laissez-faire policies increase deaths. Nursing homes with a larger share of Black residents experience more deaths, but they are less vulnerable to laissez-faire policies, especially when not central in social networks. Our findings highlight significant interactions between COVID-19 policies, race, and network structure among US seniors.

Optimal interventions in networks during a pandemic

“…Although we only apply our model to the COVID-19 pandemic, we believe that our theory has implications for other infections that spread through physical contacts. In line with Pongou and Serrano ( 2013 ), Chang et al ( 2021 ), Fajgelbaum et al ( 2021 ), Debnam Guzman et al ( 2022 ), and Pongou et al ( 2022 ), our study also contributes to the growing literature investigating the importance of network structure in the distributional effects of virus spread.…”

Vaccine and inclusion

“…The issue of inclusion and global coordination in allocating scarce resources such as vaccines is timely. Studies show that fragmented and segregated communities have been hit hardest by the COVID‐19 pandemic (DebnamGuzman et al, 2022), and restrictive policies—policies that favor population health over economic gains—benefited minority groups less in the United States (see, e.g., Pongou et al, 2022a). As such, the issue of inclusion has received the attention of several academics and policymakers during the current COVID‐19 pandemic.…”

Profits, Pandemics, and Lockdown Effectiveness: Theory and Evidence from Nursing Home Networks

“…in two important respects. First, the model in Pongou et al (2022aPongou et al ( , 2022b does not incorporate lockdown effectiveness; it implicitly assumes that the lockdown completely reduces the contagion (i.e., θ = 1). 6 Second, in contrast to Pongou et al (2022aPongou et al ( , 2022b, we split the population into two sectors based on one key variable-nursing home ownership.…”

“…First, the model in Pongou et al (2022aPongou et al ( , 2022b does not incorporate lockdown effectiveness; it implicitly assumes that the lockdown completely reduces the contagion (i.e., θ = 1). 6 Second, in contrast to Pongou et al (2022aPongou et al ( , 2022b, we split the population into two sectors based on one key variable-nursing home ownership. In this regard, our work is related to recent studies that investigate the role of individual characteristics such as age on pandemic fatalities in epidemiological models (see, e.g., Gollier (2020), Acemoglu et al (2021), and the references therein).…”

“…The epidemiological framework we use to model the planning problem is a continuous-time individual-based meanfield model, which belongs to the class of theoretical approaches for epidemic modeling on undirected heterogeneous networks. Pastor-Satorras, Castellano, Van Mieghem, and Vespignani (2015) provide a review of these epidemiological models, and Boucekkine, Carvajal, Chakraborty, and Goenka (2021), Fajgelbaum et al (2021), Debnam Guzman, Mabeu, and Pongou (2022, Berger et al (2022), Eichenbaum et al (2022), Pongou et al (2022a), Fernández-Villaverde and Jones (2022), Pongou et al (2022b), Nganmeni, Pongou, Tchantcho, andTondji (2022), Shi (2022), and the references therein highlight the recent economic contributions to the COVID-19 pandemic. 10 Another contribution by 8 We retrieved data on U.S. governor approval for mandated COVID-19 policies on April 29, 2022, from the COVID STATES PROJECT accessible at https://lazerlab.shinyapps.io/Behaviors During COVID/.…”