IDEAS home Printed from https://ideas.repec.org/a/spr/jopoec/v34y2021i1d10.1007_s00148-020-00801-6.html
   My bibliography  Save this article

True COVID-19 mortality rates from administrative data

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00148-020-00801-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s00148-020-00801-6?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 look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Manski, Charles F, 1990. "Nonparametric Bounds on Treatment Effects," American Economic Review, American Economic Association, vol. 80(2), pages 319-323, May.
    2. Manski, Charles F. & Molinari, Francesca, 2021. "Estimating the COVID-19 infection rate: Anatomy of an inference problem," Journal of Econometrics, Elsevier, vol. 220(1), pages 181-192.
    3. Bonacini, Luca & Gallo, Giovanni & Patriarca, Fabrizio, 2020. "Drawing policy suggestions to fight Covid-19 from hardly reliable data. A machine-learning contribution on lockdowns analysis," GLO Discussion Paper Series 534, Global Labor Organization (GLO).
    4. Klaus F. Zimmermann & Gokhan Karabulut & Mehmet Huseyin Bilgin & Asli Cansin Doker, 2020. "Inter‐country distancing, globalisation and the coronavirus pandemic," The World Economy, Wiley Blackwell, vol. 43(6), pages 1484-1498, June.
    5. Yun Qiu & Xi Chen & Wei Shi, 2020. "Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(4), pages 1127-1172, October.
    6. 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.
    7. 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).
    8. 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.
    9. Charles F. Manski & Aleksey Tetenov, 2020. "Statistical Decision Properties of Imprecise Trials Assessing COVID-19 Drugs," Papers 2006.00343, arXiv.org.
    10. Goodman-Bacon, Andrew & Marcus, Jan, 2020. "Using Difference-in-Differences to Identify Causal Effects of COVID-19 Policies," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 14(2), pages 153-158.
    11. Charles F. Manski & John V. Pepper, 2018. "How Do Right-to-Carry Laws Affect Crime Rates? Coping with Ambiguity Using Bounded-Variation Assumptions," The Review of Economics and Statistics, MIT Press, vol. 100(2), pages 232-244, May.
    12. Smriti Mallapaty, 2020. "What the cruise-ship outbreaks reveal about COVID-19," Nature, Nature, vol. 580(7801), pages 18-18, April.
    13. Alberto Abadie, 2005. "Semiparametric Difference-in-Differences Estimators," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(1), pages 1-19.
    14. Charles F. Manski, 2011. "Policy Analysis with Incredible Certitude," Economic Journal, Royal Economic Society, vol. 121(554), pages 261-289, August.
    15. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    16. Andrew Goodman-Bacon & Jan Marcus, 2020. "Difference-in-Differences to Identify Causal Effects of COVID-19 Policies," Discussion Papers of DIW Berlin 1870, DIW Berlin, German Institute for Economic Research.
    17. Heckman, James J, 1996. "Randomization as an Instrumental Variable: Notes," The Review of Economics and Statistics, MIT Press, vol. 78(2), pages 336-341, May.
    18. Bhattacharya, Jay & Shaikh, Azeem M. & Vytlacil, Edward, 2012. "Treatment effect bounds: An application to Swan–Ganz catheterization," Journal of Econometrics, Elsevier, vol. 168(2), pages 223-243.
    19. Charles F. Manski, 2020. "Bounding the Predictive Values of COVID-19 Antibody Tests," NBER Working Papers 27226, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Citations

    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

    Citations

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


    Cited by:

    1. 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).
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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).
    7. 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.
    8. 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.
    9. 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).
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.

    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. Nicholas W. Papageorge & Matthew V. Zahn & Michèle Belot & Eline Broek-Altenburg & Syngjoo Choi & Julian C. Jamison & Egon Tripodi, 2021. "Socio-demographic factors associated with self-protecting behavior during the Covid-19 pandemic," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(2), pages 691-738, April.
    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. 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.
    4. 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.
    5. Callaway, Brantly & Li, Tong, 2023. "Policy evaluation during a pandemic," Journal of Econometrics, Elsevier, vol. 236(1).
    6. Fischer, Kai & Reade, J. James & Schmal, W. Benedikt, 2022. "What cannot be cured must be endured: The long-lasting effect of a COVID-19 infection on workplace productivity," Labour Economics, Elsevier, vol. 79(C).
    7. 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.
    8. Depalo, Domenico, 2023. "Should the Daylight Saving Time be abolished? Evidence from work accidents in Italy," Economic Modelling, Elsevier, vol. 128(C).
    9. Black, Dan A. & Joo, Joonhwi & LaLonde, Robert & Smith, Jeffrey A. & Taylor, Evan J., 2022. "Simple Tests for Selection: Learning More from Instrumental Variables," Labour Economics, Elsevier, vol. 79(C).
    10. Tut, Daniel, 2022. "Investment, Q and epidemic diseases," Finance Research Letters, Elsevier, vol. 47(PB).
    11. Mauro Caselli & Andrea Fracasso & Sergio Scicchitano, 2020. "From the lockdown to the new normal: An analysis of the limitations to individual mobility in Italy following the Covid-19 crisis," Discussion Paper series in Regional Science & Economic Geography 2020-07, Gran Sasso Science Institute, Social Sciences, revised Oct 2020.
    12. Yuehao Bai & Azeem M. Shaikh & Max Tabord-Meehan, 2024. "A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances," Papers 2405.03910, arXiv.org.
    13. 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).
    14. Marco Caliendo & Daniel Graeber & Alexander S. Kritikos & Johannes Seebauer, 2023. "Pandemic Depression: COVID-19 and the Mental Health of the Self-Employed," Entrepreneurship Theory and Practice, , vol. 47(3), pages 788-830, May.
    15. 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.
    16. Jay Bhattacharya & Adam Isen, 2008. "On Inferring Demand for Health Care in the Presence of Anchoring, Acquiescence, and Selection Biases," NBER Working Papers 13865, National Bureau of Economic Research, Inc.
    17. Manski, Charles F., 2013. "Public Policy in an Uncertain World: Analysis and Decisions," Economics Books, Harvard University Press, number 9780674066892, Spring.
    18. Dion Bongaerts & Francesco Mazzola & Wolf Wagner, 2021. "Closed for business: The mortality impact of business closures during the Covid-19 pandemic," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-17, May.
    19. John Mullahy, 2020. "Discovering Treatment Effectiveness via Median Treatment Effects—Applications to COVID-19 Clinical Trials," NBER Working Papers 27895, National Bureau of Economic Research, Inc.
    20. Chakraborty, Tanika & Mukherjee, Anirban, 2022. "Economic geography of contagion: A study on Covid-19 outbreak in India," GLO Discussion Paper Series 1028, Global Labor Organization (GLO).

    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

    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:spr:jopoec:v:34:y:2021:i:1:d:10.1007_s00148-020-00801-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.