IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i23p15716-d984726.html
   My bibliography  Save this article

Social Behavior and COVID-19: Analysis of the Social Factors behind Compliance with Interventions across the United States

Author

Listed:
  • Morteza Maleki

    (School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA)

  • Mohsen Bahrami

    (Institute for Data, Systems, and Society, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA)

  • Monica Menendez

    (Division of Engineering, New York University Abu Dhabi, Saadiyat Island P.O. Box 129188, United Arab Emirates)

  • Jose Balsa-Barreiro

    (Division of Engineering, New York University Abu Dhabi, Saadiyat Island P.O. Box 129188, United Arab Emirates
    MIT Media Lab, Massachusetts Institute of Technology, 75 Amherst St, Cambridge, MA 02139, USA)

Abstract

Since its emergence, COVID-19 has caused a great impact in health and social terms. Governments and health authorities have attempted to minimize this impact by enforcing different mandates. Recent studies have addressed the relationship between various socioeconomic variables and compliance level to these interventions. However, little attention has been paid to what constitutes people’s response and whether people behave differently when faced with different interventions. Data collected from different sources show very significant regional differences across the United States. In this paper, we attempt to shed light on the fact that a response may be different depending on the health system capacity and each individuals’ social status. For that, we analyze the correlation between different societal (i.e., education, income levels, population density, etc.) and healthcare capacity-related variables (i.e., hospital occupancy rates, percentage of essential workers, etc.) in relation to people’s level of compliance with three main governmental mandates in the United States: mobility restrictions, mask adoption, and vaccine participation. Our aim was to isolate the most influential variables impacting behavior in response to these policies. We found that there was a significant relationship between individuals’ educational levels and political preferences with respect to compliance with each of these mandates.

Suggested Citation

  • Morteza Maleki & Mohsen Bahrami & Monica Menendez & Jose Balsa-Barreiro, 2022. "Social Behavior and COVID-19: Analysis of the Social Factors behind Compliance with Interventions across the United States," IJERPH, MDPI, vol. 19(23), pages 1-26, November.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:23:p:15716-:d:984726
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/23/15716/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/23/15716/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wu, De-Min, 1973. "Alternative Tests of Independence Between Stochastic Regressors and Disturbances," Econometrica, Econometric Society, vol. 41(4), pages 733-750, July.
    2. Raj Chetty & John N Friedman & Michael Stepner & Opportunity Insights Team & Camille Baker & Harvey Barnhard & Matt Bell & Gregory Bruich & Tina Chelidze & Lucas Chu & Westley Cineus & Sebi Devlin-Fol, 2024. "The Economic Impacts of COVID-19: Evidence from a New Public Database Built Using Private Sector Data," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(2), pages 829-889.
    3. Seth G. Benzell & Avinash Collis & Christos Nicolaides, 2020. "Rationing social contact during the COVID-19 pandemic: Transmission risk and social benefits of US locations," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(26), pages 14642-14644, June.
    4. Lesley Chiou & Catherine Tucker, 2020. "Social Distancing, Internet Access and Inequality," NBER Working Papers 26982, National Bureau of Economic Research, Inc.
    5. José Balsa-Barreiro & Alfredo J. Morales & Rubén C. Lois-González & Ãtila Bueno, 2021. "Mapping Population Dynamics at Local Scales Using Spatial Networks," Complexity, Hindawi, vol. 2021, pages 1-14, May.
    6. Carballosa, Alejandro & Balsa-Barreiro, José & Boullosa, Pablo & Garea, Adrián & Mira, Jorge & Miramontes, Ángel & Muñuzuri, Alberto P., 2022. "Assessing the risk of pandemic outbreaks across municipalities with mathematical descriptors based on age and mobility restrictions," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    7. Nakamura, Alice & Nakamura, Masao, 1981. "On the Relationships among Several Specification Error Tests Presented by Durbin, Wu, and Hausman," Econometrica, Econometric Society, vol. 49(6), pages 1583-1588, November.
    8. Jayson S. Jia & Xin Lu & Yun Yuan & Ge Xu & Jianmin Jia & Nicholas A. Christakis, 2020. "Population flow drives spatio-temporal distribution of COVID-19 in China," Nature, Nature, vol. 582(7812), pages 389-394, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zapata-Moya, Angel R. & Freese, Jeremy & Bracke, Piet, 2023. "Mechanism substitution in preventive innovations: Dissecting the reproduction of health inequalities in the United States," Social Science & Medicine, Elsevier, vol. 337(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Doko Tchatoka, Firmin Sabro, 2012. "Specification Tests with Weak and Invalid Instruments," MPRA Paper 40185, University Library of Munich, Germany.
    2. Grossman, Michael & Joyce, Theodore J, 1990. "Unobservables, Pregnancy Resolutions, and Birth Weight Production Functions in New York City," Journal of Political Economy, University of Chicago Press, vol. 98(5), pages 983-1007, October.
    3. Anindya Ghose & Beibei Li & Meghanath Macha & Chenshuo Sun & Natasha Ying Zhang Foutz, 2020. "Trading Privacy for the Greater Social Good: How Did America React During COVID-19?," Papers 2006.05859, arXiv.org.
    4. Davidson, Russell & MacKinnon, James G., 1989. "Testing for Consistency using Artificial Regressions," Econometric Theory, Cambridge University Press, vol. 5(3), pages 363-384, December.
    5. Doko Tchatoka, Firmin & Dufour, Jean-Marie, 2020. "Exogeneity tests, incomplete models, weak identification and non-Gaussian distributions: Invariance and finite-sample distributional theory," Journal of Econometrics, Elsevier, vol. 218(2), pages 390-418.
    6. Robert Mulligan, 1996. "Export-import endogeneity in the context of the Thirlwall- Hussain model: an application of the Durbin-Wu-Hausman test incorporating a Monte Carlo experiment," Applied Economics Letters, Taylor & Francis Journals, vol. 3(4), pages 275-279.
    7. Allen, David, 2022. "Asset Pricing Tests, Endogeneity issues and Fama-French factors," MPRA Paper 113610, University Library of Munich, Germany.
    8. Christopher F Baum & Mark E. Schaffer & Steven Stillman, 2003. "Instrumental variables and GMM: Estimation and testing," Stata Journal, StataCorp LP, vol. 3(1), pages 1-31, March.
    9. Stefanie Stantcheva, 2022. "Inequalities in the times of a pandemic," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 37(109), pages 5-41.
    10. Kiviet, Jan F., 1985. "Model selection test procedures in a single linear equation of a dynamic simultaneous system and their defects in small samples," Journal of Econometrics, Elsevier, vol. 28(3), pages 327-362, June.
    11. Kiviet, Jan F. & Pleus, Milan, 2017. "The performance of tests on endogeneity of subsets of explanatory variables scanned by simulation," Econometrics and Statistics, Elsevier, vol. 2(C), pages 1-21.
    12. Achilleas Psyllidis & Fábio Duarte & Roos Teeuwen & Arianna Salazar Miranda & Tom Benson & Alessandro Bozzon, 2023. "Cities and infectious diseases: Assessing the exposure of pedestrians to virus transmission along city streets," Urban Studies, Urban Studies Journal Limited, vol. 60(9), pages 1610-1628, July.
    13. Firmin Doko Tchatoka & Jean‐Marie Dufour, 2014. "Identification‐robust inference for endogeneity parameters in linear structural models," Econometrics Journal, Royal Economic Society, vol. 17(1), pages 165-187, February.
    14. Raut, Lakshmi K., 1995. "R & D spillover and productivity growth: Evidence from Indian private firms," Journal of Development Economics, Elsevier, vol. 48(1), pages 1-23, October.
    15. Guo, Zijian & Kang, Hyunseung & Cai, T. Tony & Small, Dylan S., 2018. "Testing endogeneity with high dimensional covariates," Journal of Econometrics, Elsevier, vol. 207(1), pages 175-187.
    16. Nakamura, Alice & Nakamura, Masao, 1998. "Model specification and endogeneity," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 213-237.
    17. Mathijs de Vaan & Saqib Mumtaz & Abhishek Nagaraj & Sameer B. Srivastava, 2021. "Social Learning in the COVID-19 Pandemic: Community Establishments’ Closure Decisions Follow Those of Nearby Chain Establishments," Management Science, INFORMS, vol. 67(7), pages 4446-4454, July.
    18. Firmin Doko Tchatoka & Jean-Marie Dufour, 2016. "Exogeneity tests, weak identification, incomplete models and non-Gaussian distributions: Invariance and finite-sample distributional theory," School of Economics and Public Policy Working Papers 2016-01, University of Adelaide, School of Economics and Public Policy.
    19. Judith Anne Clarke, 2017. "Model Averaging OLS and 2SLS: An Application of the WALS Procedure," Econometrics Working Papers 1701, Department of Economics, University of Victoria.
    20. H. Peter Boswijk & Jean-Pierre Urbain, 1997. "Lagrance-multiplier tersts for weak exogeneity: a synthesis," Econometric Reviews, Taylor & Francis Journals, vol. 16(1), pages 21-38.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:23:p:15716-:d:984726. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.