Data and Code for: Non-Representativeness in Population Health Research - Evidence from a COVID-19 Antibody Study

Principal Investigator(s):  View help for Principal Investigator(s) Deniz Dutz, University of Chicago; Michael Greenstone, University of Chicago; Ali Hortacsu, University of Chicago; Santiago Lacouture, University of Chicago; Magne Mogstad, University of Chicago; Azeem M. Shaikh, University of Chicago; Alexander Torgovitsky, University of Chicago; Winnie van Dijk, Yale University

Version:  View help for Version V1

Name File Type Size Last Modified
  code 12/07/2023 11:28:AM
  data 11/20/2023 07:49:PM
  output 11/20/2023 07:49:PM
README.pdf application/pdf 101.2 KB 12/07/2023 06:27:AM

Project Citation: 

Dutz, Deniz, Greenstone, Michael, Hortacsu, Ali, Lacouture, Santiago, Mogstad, Magne, Shaikh, Azeem M., … van Dijk, Winnie. Data and Code for: Non-Representativeness in Population Health Research - Evidence from a COVID-19 Antibody Study. Nashville, TN: American Economic Association [publisher], 2024. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2024-08-12. https://doi.org/10.3886/E194205V1

Project Description

Summary:  View help for Summary
We analyze representativeness in a COVID-19 serological study with randomized participation incentives. We find large participation gaps by race and income when incentives are lower. High incentives increase participation rates for all groups, but increase them more among underrepresented groups. High incentives restore representativeness on race and income, and also on health variables likely to be correlated with seropositivity, such as the uninsured rate, hospitalization rates, and an aggregate COVID-19 risk index.
Funding Sources:  View help for Funding Sources University of Chicago Women's Board; Sloan Foundation; Smith Richardson Foundation; Becker Friedman Institute; Booz Allen Foundation; Flu Lab

Scope of Project

Subject Terms:  View help for Subject Terms survey; representativeness; inequality
JEL Classification:  View help for JEL Classification
      C83 Survey Methods; Sampling Methods
      I14 Health and Inequality
Geographic Coverage:  View help for Geographic Coverage Chicago, IL
Time Period(s):  View help for Time Period(s) 12/1/2020 – 3/31/2021
Universe:  View help for Universe Housing Units in Chicago, IL
Data Type(s):  View help for Data Type(s) aggregate data; survey data

Methodology

Response Rate:  View help for Response Rate 14.17%
Sampling:  View help for Sampling 882 Chicago addresses from United States Postal Service data.
Data Source:  View help for Data Source RECOVER Survey Data, 
2019 American Community Survey 5-year estimates,
Chicago Health Atlas,
City Health Dashboard
Collection Mode(s):  View help for Collection Mode(s) mail questionnaire; mixed mode; web-based survey
Unit(s) of Observation:  View help for Unit(s) of Observation Housing unit (respondent)

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Published Versions

V1 [2024-08-12]

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