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Understanding How Socioeconomic Inequalities Drive Inequalities in COVID-19 Infections

59 Pages Posted: 30 Aug 2021

See all articles by Rachid Laajaj

Rachid Laajaj

Universidad de los Andes, Chile

Duncan Webb

Paris School of Economics (PSE)

Danilo Aristizabal

Universidad de los Andes, Colombia

Eduardo Behrentz

Universidad de los Andes, Colombia

Raquel Bernal

Universidad de los Andes, Colombia - Department of Economics

Giancarlo Buitrago Gutierrez

National University of Colombia

Zulma Cucunuba

Imperial College London

Fernando de la Hoz

National University of Colombia

Alejandro Gaviria Uribe

Universidad de los Andes, Colombia - Department of Economics

Luis Jorge Hernández Florez

Universidad de los Andes, Colombia

Camilo De Los Rios

Inter-American Development Bank

Andrea Ramirez Varela

Universidad de los Andes, Colombia - School of Medicine

Silvia Restrepo

Universidad de los Andes, Colombia

Norbert Schady

Inter-American Development Bank (IDB)

Martha Vives Florez

Universidad de los Andes, Colombia

More...

Abstract

Background: Across the world, the COVID-19 pandemic has disproportionately affected economically disadvantaged groups. This differential impact has numerous possible explanations, each with significantly different policy implications. We examine, for the first time in a low- or middle-income country, which mechanisms best explain the disproportionate impact of the virus on the poor.

Methods: We use primary data from the CoVIDA project, including the results of 59,770 RT-PCR tests in Bogota´, targeted on a mostly asymptomatic adult population June 2020 to March 3rd, 2021. This is combined with administrative data that covers all reported cases in Bogota´. We estimate a number of parameters that are likely to drive inequality in COVID-19 infection rates across socioeconomic groups, then use these estimates in an individual-level branching process model of the epidemic. We use counterfactual scenarios to estimate the relative importance of different channels for explaining inequality in infection rates.

Findings: Total infections and inequalities in infections are largely driven by inequalities in the ability to work remotely and in within-home secondary attack rates. Inequalities in isolation behavior are less important but non-negligible, while access to testing and contract-tracing plays practically no role. Interventions that mitigate transmission are found to be more effective when targeted on socioeconomically disadvantaged groups.

Interpretation: Socioeconomically disadvantaged groups are particularly vulnerable to COVID-19 infections, and this appears to be driven by the need to work out of home, and higher transmission within home. Policies that can successfully reduce these channels of transmission among the poor are likely to have large benefits.

Funding: The authors acknowledge generous support from the Interamerican Development Bank, the Development Bank of Latin America (CAF), the University of Los Andes and the Universidad Nacional de Colombia. Duncan Webb gratefully acknowledges support from the ED 465 at University Paris 1, and the EUR project ANR-17-EURE-0001.

Declaration of Interest: The authors have no competing interests to declare.

Ethical Approval: Ethics approval was obtained from the ethics committee of Universidad de los Andes (Act number 1278 of 2020). The ethics committee approved that the participants would receive the information via telephone and give their verbal consent, in order to comply with physical distancing and limit the restriction for a study is part of a public health surveillance strategy implemented jointly with the Health Secretary of Bogota.

Suggested Citation

Laajaj, Rachid and Webb, Duncan and Aristizabal, Danilo and Behrentz, Eduardo and Bernal, Raquel and Buitrago Gutierrez, Giancarlo and Cucunuba, Zulma and de la Hoz, Fernando and Gaviria Uribe, Alejandro and Hernández Florez, Luis Jorge and De Los Rios, Camilo and Ramirez Varela, Andrea and Restrepo, Silvia and Schady, Norbert and Vives Florez, Martha, Understanding How Socioeconomic Inequalities Drive Inequalities in COVID-19 Infections. Available at SSRN: https://ssrn.com/abstract=3912528 or http://dx.doi.org/10.2139/ssrn.3912528

Rachid Laajaj (Contact Author)

Universidad de los Andes, Chile ( email )

Mons. Álvaro del Portillo
Las Condes
Santiago, 12.455
Chile

Duncan Webb

Paris School of Economics (PSE) ( email )

48 Boulevard Jourdan
Paris, 75014 75014
France

Danilo Aristizabal

Universidad de los Andes, Colombia ( email )

Carrera Primera # 18A-12
Bogota, DC D.C. 110311
Colombia

Eduardo Behrentz

Universidad de los Andes, Colombia ( email )

Carrera Primera # 18A-12
Bogota, DC D.C. 110311
Colombia

Raquel Bernal

Universidad de los Andes, Colombia - Department of Economics ( email )

Carrera 1a No. 18A-10
Santafe de Bogota, AA4976
Colombia

Giancarlo Buitrago Gutierrez

National University of Colombia ( email )

Carrera 30 45-03
Bogota, None
Colombia

Zulma Cucunuba

Imperial College London

South Kensington Campus
Exhibition Road
London, SW7 2AZ
United Kingdom

Fernando De la Hoz

National University of Colombia ( email )

Carrera 30 45-03
Bogota, None
Colombia

Alejandro Gaviria Uribe

Universidad de los Andes, Colombia - Department of Economics ( email )

Carrera 1a No. 18A-10
Santafe de Bogota, AA4976
Colombia

Luis Jorge Hernández Florez

Universidad de los Andes, Colombia ( email )

Carrera Primera # 18A-12
Bogota, DC D.C. 110311
Colombia

Camilo De Los Rios

Inter-American Development Bank

Andrea Ramirez Varela

Universidad de los Andes, Colombia - School of Medicine ( email )

Cra 7 #116-05
Bogotá, 11001000
Colombia

Silvia Restrepo

Universidad de los Andes, Colombia ( email )

Carrera Primera # 18A-12
Bogota, DC D.C. 110311
Colombia

Norbert Schady

Inter-American Development Bank (IDB) ( email )

1300 New York Avenue NW
Washington, DC 20577
United States

Martha Vives Florez

Universidad de los Andes, Colombia ( email )

Carrera Primera # 18A-12
Bogota, DC D.C. 110311
Colombia

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