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Excess Mortality as a Predictor of Mortality Crises: The Case of COVID-19 in Italy

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  • Ceriani, Lidia
  • Verme, Paolo

Abstract

The paper provides initial evidence that excess mortality rates by locality can be used as a statistically reliable predictor of looming mortality crises. Using recently published daily deaths figures for 7,357 Italian municipalities, we estimate the growth in daily mortality rates between the period 2015-2019 and 2020 by province. All provinces that experienced a major mortality shock in mid-March 2020 had increases in mortality rates of 100% or above already in mid-February 2020. This increase was particularly strong for males and older people, two recognizable features of COVID-19. Using panel data models, we find a strong positive and significant association between overall deaths and COVID-19 related deaths, and between early increases in mortality rates in February 2020 for any cause and the March 2020 outbreak in COVID-19 deaths. We conclude that the growth in mortality rates can potentially be used as a statistically reliable predictor of mortality crises, including COVID- 19 crises.

Suggested Citation

  • Ceriani, Lidia & Verme, Paolo, 2020. "Excess Mortality as a Predictor of Mortality Crises: The Case of COVID-19 in Italy," GLO Discussion Paper Series 618, Global Labor Organization (GLO).
    • Handle: RePEc:zbw:glodps:618
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    File URL: https://www.econstor.eu/bitstream/10419/222516/1/GLO-DP-0618.pdf
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    1. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
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    Cited by:

    1. Augusto Cerqua & Roberta Di Stefano & Marco Letta & Sara Miccoli, 2021. "Local mortality estimates during the COVID-19 pandemic in Italy," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(4), pages 1189-1217, October.
    2. Domenico Depalo, 2021. "True COVID-19 mortality rates from administrative data," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 253-274, January.

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