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Covid-19 and mobility: determinant or consequence?

Author

Listed:
  • Hippolyte d'Albis

    (PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Emmanuelle Augeraud-Véron

    (BSE - Bordeaux Sciences Economiques - UB - Université de Bordeaux - CNRS - Centre National de la Recherche Scientifique)

  • Dramane Coulibaly

    (GATE Lyon Saint-Étienne - Groupe d'Analyse et de Théorie Economique Lyon - Saint-Etienne - UL2 - Université Lumière - Lyon 2 - UJM - Université Jean Monnet - Saint-Étienne - CNRS - Centre National de la Recherche Scientifique)

  • Rodolphe Desbordes

    (SKEMA Business School - SKEMA Business School)

Abstract

This paper disentangles the relationship between COVID-19 propagation and mobility. In a theoretical model allowing mobility to be endogenously determined by the COVID-19 prevalence rate, we show that an exogenous epidemic shock has an immediate effect on mobility whereas an exogenous mobility shock influences epidemic variables with a delay. In the long run, exogenous disease contagiousness and mobility jointly shape epidemiological outcomes. The short-run theoretical result allows us to recover, empirically, the causal impacts of mobility and COVID-19 hospitalisations on each other in France. We find that hospitalisations are highly sensitive to mobility whereas mobility is little influenced by hospitalisations. In France, it seems therefore that voluntary social distancing would not have been effective to control the epidemic, in the absence of social distancing mandates.

Suggested Citation

  • Hippolyte d'Albis & Emmanuelle Augeraud-Véron & Dramane Coulibaly & Rodolphe Desbordes, 2023. "Covid-19 and mobility: determinant or consequence?," Post-Print halshs-04331269, HAL.
    • Handle: RePEc:hal:journl:halshs-04331269
    DOI: 10.1007/s00199-023-01510-3
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-04331269
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    References listed on IDEAS

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    1. Giorgio Fabbri & Salvatore Federico & Davide Fiaschi & Fausto Gozzi, 2024. "Mobility decisions, economic dynamics and epidemic," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 77(1), pages 495-531, February.
    2. Jinyong Hahn & Guido Kuersteiner, 2002. "Asymptotically Unbiased Inference for a Dynamic Panel Model with Fixed Effects when Both "n" and "T" Are Large," Econometrica, Econometric Society, vol. 70(4), pages 1639-1657, July.
    3. Abel Brodeur & David Gray & Anik Islam & Suraiya Bhuiyan, 2021. "A literature review of the economics of COVID‐19," Journal of Economic Surveys, Wiley Blackwell, vol. 35(4), pages 1007-1044, September.
    4. Fabio Milani, 2021. "COVID-19 outbreak, social response, and early economic effects: a global VAR analysis of cross-country interdependencies," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 223-252, January.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    COVID-19; Epidemic models; Mobility;
    All these keywords.

    JEL classification:

    • I1 - Health, Education, and Welfare - - Health
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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