Data and Code for: Laissez-faire, Social Networks, and Race in a Pandemic

Principal Investigator(s): View help for Principal Investigator(s) Roland Pongou, University of Ottawa; Guy Tchuente, University of Kent; Jean-Baptiste Tondji, The University of Texas Rio Grande Valley

Version: View help for Version V1

Name	File Type	Size	Last Modified
Crossection_NursingHdata.dta	application/x-stata-dta	13.6 MB	01/10/2022 08:58:AM
Estimation.xlsx	application/vnd.openxmlformats-officedocument.spreadsheetml.sheet	16.4 KB	01/09/2022 03:45:PM
Laissez_faire_Covid_19.do	text/plain	3.8 KB	01/11/2022 12:31:PM
ReadMe.pdf	application/pdf	131.8 KB	01/10/2022 04:04:PM

Project Citation:

Pongou, Roland, Tchuente, Guy, and Tondji, Jean-Baptiste. Data and Code for: Laissez-faire, Social Networks, and Race in a Pandemic. Nashville, TN: American Economic Association [publisher], 2022. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2022-04-07. https://doi.org/10.3886/E159041V1

Project Description

Summary: We use unique data on nursing home networks in the United States and estimation of state-level preference for prioritizing short-term economic gains over health to analyze the effects of race, laissez-faire (more tolerance to the virus spread) policies, and network centrality on COVID-19 deaths in nursing homes. 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 U.S. seniors.

Funding Sources: Government of Ontario in Canada (ERA grant 400201-190299-2001); SSHRC's Partnership Engage Grants COVID-19 Special Initiative (PEG 231377-190299-2001); SSHRC's Insight Grant (231415-190299-2001)

Scope of Project

Subject Terms: Race; Nursing homes; COVID-19; Laissez-faire policies; Network centrality

JEL Classification:
      D85 Network Formation and Analysis: Theory
      E61 Policy Objectives; Policy Designs and Consistency; Policy Coordination
      H12 Crisis Management
      I18 Health: Government Policy; Regulation; Public Health
      J15 Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination

Geographic Coverage: U.S. nursing homes; States in the United States

Time Period(s): 3/13/2020 – 8/16/2020

Collection Date(s): 3/13/2020 – 8/16/2020

Universe: The dataset on U.S. nursing home networks was constructed by the ``Protect Nursing Homes" project members. They used device-level geolocation data for 501,503 smartphones in at least one of the 15,307 nursing homes in the 11 weeks following the nationwide restriction on nursing home visits on March 13, 2020. We complement the dataset with the estimated values of COVID-19 tolerable infection incidence for each U.S state.

Data Type(s): administrative records data; geographic information system (GIS) data; survey data

Collection Notes: The final dataset was constructed using restricted-access data on the U.S. nursing homes from Yale University’s “Protect Nursing Homes Project” and other publicly available sources.

Methodology

Data Source: The data on U.S. nursing homes are publicly available and originated from Chen, Chevalier, and Long (2021)’s replication package. The conditions to access the data are available on the project webpage. The data on the measures of tolerable infection incidence in each U.S. state are collected from Pongou, Tchuente, and Tondji (2021).

Collection Mode(s): web scraping

Unit(s) of Observation: Staffs and contracted who visited U.S. nursing facilities; nursing homes; each state in the U.S.

Geographic Unit: A state in the United States.

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Published Versions

V1 [2022-04-07]

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