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True COVID-19 mortality rates from administrative data

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  • Domenico Depalo

    (Banca d’Italia, Economics and Statistics Department)

Abstract

In this paper, I use administrative data to estimate the number of deaths, the number of infections, and mortality rates from COVID-19 in Lombardia, the hot spot of the disease in Italy and Europe. The information will assist policy makers in reaching correct decisions and the public in adopting appropriate behaviors. As the available data suffer from sample selection bias, I use partial identification to derive the above quantities. Partial identification combines assumptions with the data to deliver a set of admissible values or bounds. Stronger assumptions yield stronger conclusions but decrease the credibility of the inference. Therefore, I start with assumptions that are always satisfied, then I impose increasingly more restrictive assumptions. Using my preferred bounds, during March 2020 in Lombardia, there were between 10,000 and 18,500 more deaths than in previous years. The narrowest bounds of mortality rates from COVID-19 are between 0.1 and 7.5%, much smaller than the 17.5% discussed in earlier reports. This finding suggests that the case of Lombardia may not be as special as some argue.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:jopoec:v:34:y:2021:i:1:d:10.1007_s00148-020-00801-6
    DOI: 10.1007/s00148-020-00801-6
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Chris Sampson’s journal round-up for 23rd November 2020
      by Chris Sampson in The Academic Health Economists' Blog on 2020-11-23 12:00:14

    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Health > Measurement

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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. Ainaa, Carmen & Brunetti, Irene & Mussida, Chiara & Scicchitano, Sergio, 2021. "Who lost the most? Distributive effects of COVID-19 pandemic," GLO Discussion Paper Series 829, Global Labor Organization (GLO).
    3. Isaure Delaporte & Julia Escobar & Werner Peña, 2021. "The distributional consequences of social distancing on poverty and labour income inequality in Latin America and the Caribbean," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(4), pages 1385-1443, October.
    4. Guccio, Calogero, 2021. "Measuring resilience and fatality rate during the first wave of COVID-19 pandemic in Northern Italy: a note," EconStor Preprints 231374, ZBW - Leibniz Information Centre for Economics.
    5. Daniel L. Millimet & Christopher F. Parmeter, 2022. "COVID‐19 severity: A new approach to quantifying global cases and deaths," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1178-1215, July.
    6. Luca Bonacini & Giovanni Gallo & Fabrizio Patriarca, 2021. "Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 275-301, January.
    7. Schettino, Francesco & Scicchitano, Sergio & Suppa, Domenico, 2024. "COVID 19 and Wage Polarization: A task based approach," GLO Discussion Paper Series 1398, Global Labor Organization (GLO).
    8. Burzyński, Michał & Machado, Joël & Aalto, Atte & Beine, Michel & Goncalves, Jorge & Haas, Tom & Kemp, Françoise & Magni, Stefano & Mombaerts, Laurent & Picard, Pierre & Proverbio, Daniele & Skupin, A, 2021. "COVID-19 crisis management in Luxembourg: Insights from an epidemionomic approach," Economics & Human Biology, Elsevier, vol. 43(C).
    9. Anna Godøy & Maja Weemes Grøtting & Rannveig Kaldager Hart, 2022. "Reopening schools in a context of low COVID-19 contagion: consequences for teachers, students and their parents," Journal of Population Economics, Springer;European Society for Population Economics, vol. 35(3), pages 935-961, July.
    10. Mauro Caselli & Andrea Fracasso & Sergio Scicchitano, 2022. "From the lockdown to the new normal: individual mobility and local labor market characteristics following the COVID-19 pandemic in Italy," Journal of Population Economics, Springer;European Society for Population Economics, vol. 35(4), pages 1517-1550, October.
    11. Annie Tubadji, 2021. "Culture and mental health resilience in times of COVID-19," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(4), pages 1219-1259, October.
    12. Luca Bonacini & Giovanni Gallo & Sergio Scicchitano, 2021. "Working from home and income inequality: risks of a ‘new normal’ with COVID-19," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 303-360, January.
    13. YAMAMURA, Eiji & Tsutsui, Yoshiro, 2020. "Impact of closing schools on mental health during the COVID-19 pandemic: Evidence using panel data from Japan," MPRA Paper 105023, University Library of Munich, Germany.
    14. Carmen Aina & Irene Brunetti & Chiara Mussida & Sergio Scicchitano, 2023. "Distributional effects of COVID-19," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 13(1), pages 221-256, March.
    15. Eiji Yamamura & Yoshiro Tsustsui, 2021. "School closures and mental health during the COVID-19 pandemic in Japan," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(4), pages 1261-1298, October.

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    More about this item

    Keywords

    COVID-19; Mortality; Bounds;
    All these keywords.

    JEL classification:

    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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