Socioeconomic Disparities in the Effects of Pollution on Spread of Covid-19: Evidence from US Counties

  1. Osvaldo Allen
  2. Ava Brown
  3. Ersong Wang

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Abstract

This paper explores disparities in the effect of pollution on confirmed cases of Covid-19 based on counties’ socioeconomic and demographic characteristics. Using daily data on all US counties over the year 2020 and applying a rich panel data fixed effect model, we document that: 1) there are discernible social and demographic disparities in the spread of Covid-19. Blacks, low educated and poorer people are at higher risks of being infected by the new disease. 2) The criteria pollutants including Ozone, CO, PM10, and PM2.5 have the potential to accelerate the outbreak of the novel corona virus. 3) The disadvantaged population is more vulnerable to the effects of pollution on the spread of corona virus. Specifically, the effects of pollution on confirmed cases become larger for blacks, low educated, and counties with lower average wages in 2019. The results suggest that welfare programs during a global pandemic should be differentially distributed among families with different socioeconomic status since the effects of these programs in reducing the spread of the pandemic is different among subpopulations. This paper is the first study to evaluate the differential effects of pollution on the spread of novel corona virus across different subpopulations based on their socioeconomic status.

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  1. Version published to 10.22610/jebs.v12i6(j).3131
  2. ScreenIT

    SciScore for 10.1101/2021.01.06.21249303: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  3. Version published to 10.1101/2021.01.06.21249303v1 on medRxiv