IDEAS home Printed from https://ideas.repec.org/a/eee/ecmode/v120y2023ics0264999323000032.html
   My bibliography  Save this article

Does noncompliance with COVID-19 regulations impact the depressive symptoms of others?

Author

Listed:
  • Oyenubi, Adeola
  • Kollamparambil, Umakrishnan

Abstract

Before vaccines became commonly available, compliance with nonpharmaceutical only preventive measures offered protection against COVID-19 infection. Compliance is therefore expected to have physical health implications for the individual and others. Moreover, in the context of the highly contagious coronavirus, perceived noncompliance can increase the subjective risk assessment of contracting the virus and, as a result, increase psychological distress. However, the implications of (public) noncompliance on the psychological health of others have not been sufficiently explored in the literature. Examining this is of utmost importance in light of the pandemic's elevated prevalence of depressive symptoms across countries. Using nationally representative data from South Africa, we explore the relationship between depressive symptoms and perceived noncompliance. We examine this relationship using a double machine learning approach while controlling for observable selection. Our result shows that the perception that neighbors are noncompliant is correlated with self-reported depressive symptoms. Therefore, in the context of a highly infectious virus, noncompliance has detrimental effects on the wellbeing of others.

Suggested Citation

  • Oyenubi, Adeola & Kollamparambil, Umakrishnan, 2023. "Does noncompliance with COVID-19 regulations impact the depressive symptoms of others?," Economic Modelling, Elsevier, vol. 120(C).
    • Handle: RePEc:eee:ecmode:v:120:y:2023:i:c:s0264999323000032
    DOI: 10.1016/j.econmod.2023.106191
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0264999323000032
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econmod.2023.106191?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. Michael C. Knaus, 2021. "A double machine learning approach to estimate the effects of musical practice on student’s skills," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 282-300, January.
    3. Blayac, Thierry & Dubois, Dimitri & Duchêne, Sébastien & Nguyen-Van, Phu & Ventelou, Bruno & Willinger, Marc, 2022. "What drives the acceptability of restrictive health policies: An experimental assessment of individual preferences for anti-COVID 19 strategies," Economic Modelling, Elsevier, vol. 116(C).
    4. Hainmueller, Jens, 2012. "Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies," Political Analysis, Cambridge University Press, vol. 20(1), pages 25-46, January.
    5. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
    6. Cattaneo, Matias D., 2010. "Efficient semiparametric estimation of multi-valued treatment effects under ignorability," Journal of Econometrics, Elsevier, vol. 155(2), pages 138-154, April.
    7. Dorrit Posel & Adeola Oyenubi & Umakrishnan Kollamparambil, 2021. "Job loss and mental health during the COVID-19 lockdown: Evidence from South Africa," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-15, March.
    8. Peter T. Leeson & Louis Rouanet, 2021. "Externality and COVID‐19," Southern Economic Journal, John Wiley & Sons, vol. 87(4), pages 1107-1118, April.
    9. Emily Oster, 2019. "Unobservable Selection and Coefficient Stability: Theory and Evidence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(2), pages 187-204, April.
    10. Farrell, Max H., 2015. "Robust inference on average treatment effects with possibly more covariates than observations," Journal of Econometrics, Elsevier, vol. 189(1), pages 1-23.
    11. Kosuke Imai & Marc Ratkovic, 2014. "Covariate balancing propensity score," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 243-263, January.
    12. Keyes, C.L.M. & Myers, J.M. & Kendler, K.S., 2010. "The structure of the genetic and environmental influences on mental well-being," American Journal of Public Health, American Public Health Association, vol. 100(12), pages 2379-2384.
    13. Lin, Tian & Harris, Elizabeth A. & Heemskerk, Amber & Van Bavel, Jay J. & Ebner, Natalie C., 2021. "A multi-national test on self-reported compliance with COVID-19 public health measures: The role of individual age and gender demographics and countries’ developmental status," Social Science & Medicine, Elsevier, vol. 286(C).
    14. Hugo Bodory & Martin Huber & Lukáš Lafférs, 2022. "Evaluating (weighted) dynamic treatment effects by double machine learning [Identification of causal effects using instrumental variables]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 628-648.
    15. Adeola Oyenubi, 2020. "A note on Covariate Balancing Propensity Score and Instrument-like variables," Economics Bulletin, AccessEcon, vol. 40(1), pages 202-209.
    16. Abi Adams-Prassl & Teodora Boneva & Marta Golin & Christopher Rauh, 2022. "The impact of the coronavirus lockdown on mental health: evidence from the United States," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 37(109), pages 139-155.
    17. Johannes C. Buggle & Steven Nafziger, 2021. "The Slow Road from Serfdom: Labor Coercion and Long-Run Development in the Former Russian Empire," The Review of Economics and Statistics, MIT Press, vol. 103(1), pages 1-17, March.
    18. Mohanty, Aatishya & Sharma, Swati, 2022. "COVID-19 regulations, culture, and the environment," Economic Modelling, Elsevier, vol. 113(C).
    19. Ihsaan Bassier & Joshua Budlender & Rocco Zizzamia & Ronak Jain, 2023. "The labour market and poverty impacts of COVID‐19 in South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 91(4), pages 419-445, December.
    20. Glynn, Adam N. & Quinn, Kevin M., 2010. "An Introduction to the Augmented Inverse Propensity Weighted Estimator," Political Analysis, Cambridge University Press, vol. 18(1), pages 36-56, January.
    21. Andreas Diekmann, 2022. "Emergence of and compliance with new social norms: The example of the COVID crisis in Germany," Rationality and Society, , vol. 34(2), pages 129-154, May.
    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. Di Novi, Cinzia & Paruolo, Paolo & Verzillo, Stefano, 2023. "Does labour protection influence mental-health responses to employment shocks? Evidence on older workers in Europe," Economic Modelling, Elsevier, vol. 126(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. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    2. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org.
    3. Michael C. Knaus, 2021. "A double machine learning approach to estimate the effects of musical practice on student’s skills," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 282-300, January.
    4. Chunrong Ai & Oliver Linton & Kaiji Motegi & Zheng Zhang, 2021. "A unified framework for efficient estimation of general treatment models," Quantitative Economics, Econometric Society, vol. 12(3), pages 779-816, July.
    5. Phillip Heiler & Michael C. Knaus, 2021. "Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments," Papers 2110.01427, arXiv.org, revised Aug 2023.
    6. Dongcheng Zhang & Kunpeng Zhang, 2020. "Weighting-Based Treatment Effect Estimation via Distribution Learning," Papers 2012.13805, arXiv.org, revised May 2023.
    7. Sallin, Aurelién, 2021. "Estimating returns to special education: combining machine learning and text analysis to address confounding," Economics Working Paper Series 2109, University of St. Gallen, School of Economics and Political Science.
    8. Huber, Martin, 2019. "An introduction to flexible methods for policy evaluation," FSES Working Papers 504, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    9. Aur'elien Sallin, 2021. "Estimating returns to special education: combining machine learning and text analysis to address confounding," Papers 2110.08807, arXiv.org, revised Feb 2022.
    10. Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
    11. Sant’Anna, Pedro H.C. & Zhao, Jun, 2020. "Doubly robust difference-in-differences estimators," Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
    12. Black, Dan A. & Grogger, Jeffrey & Kirchmaier, Tom & Sanders, Koen, 2023. "Criminal charges, risk assessment and violent recidivism in cases of domestic abuse," LSE Research Online Documents on Economics 121374, London School of Economics and Political Science, LSE Library.
    13. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Jun 2024.
    14. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
    15. Dingke Tang & Dehan Kong & Wenliang Pan & Linbo Wang, 2023. "Ultra‐high dimensional variable selection for doubly robust causal inference," Biometrics, The International Biometric Society, vol. 79(2), pages 903-914, June.
    16. Frölich, Markus & Huber, Martin & Wiesenfarth, Manuel, 2017. "The finite sample performance of semi- and non-parametric estimators for treatment effects and policy evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 91-102.
    17. Phillip Heiler, 2022. "Heterogeneous Treatment Effect Bounds under Sample Selection with an Application to the Effects of Social Media on Political Polarization," Papers 2209.04329, arXiv.org, revised Jul 2024.
    18. Simon Calmar Andersen & Louise Beuchert & Phillip Heiler & Helena Skyt Nielsen, 2023. "A Guide to Impact Evaluation under Sample Selection and Missing Data: Teacher's Aides and Adolescent Mental Health," Papers 2308.04963, arXiv.org.
    19. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    20. Stojčić, Nebojša & Dabić, Marina & Kraus, Sascha, 2024. "Customisation and co-creation revisited: Do user types and engagement strategies matter for product innovation success?," Technovation, Elsevier, vol. 134(C).

    More about this item

    Keywords

    Mental health; Causal inference; Double machine learning; Negative externality; South Africa;
    All these keywords.

    JEL classification:

    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • I20 - Health, Education, and Welfare - - Education - - - General

    Statistics

    Access and download statistics

    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:eee:ecmode:v:120:y:2023:i:c:s0264999323000032. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30411 .

    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.