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Local mortality estimates during the COVID-19 pandemic in Italy

Author

Listed:
  • Augusto Cerqua

    (Sapienza Università di Roma)

  • Roberta Di Stefano

    (Sapienza Università di Roma)

  • Marco Letta

    (Sapienza Università di Roma)

  • Sara Miccoli

    (Sapienza Università di Roma)

Abstract

Estimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The ‘official’ approach adopted by public institutions to estimate the ‘excess mortality’ during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving prediction accuracy of local mortality in ‘ordinary’ years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and June 2020. Such estimates allow us to provide insights about the demographic evolution of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.

Suggested Citation

  • Augusto Cerqua & Roberta Di Stefano & Marco Letta & Sara Miccoli, 2020. "Local mortality estimates during the COVID-19 pandemic in Italy," Discussion Paper series in Regional Science & Economic Geography 2020-06, Gran Sasso Science Institute, Social Sciences, revised Oct 2020.
  • Handle: RePEc:ahy:wpaper:wp6
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    Cited by:

    1. Resce, Giuliano, 2022. "Political and Non-Political Officials in Local Government," Economics & Statistics Discussion Papers esdp22079, University of Molise, Department of Economics.
    2. Resce, Giuliano & Vaquero-Piñeiro, Cristina, 2022. "Predicting agri-food quality across space: A Machine Learning model for the acknowledgment of Geographical Indications," Food Policy, Elsevier, vol. 112(C).
    3. Resce, Giuliano, 2022. "The impact of political and non-political officials on the financial management of local governments," Journal of Policy Modeling, Elsevier, vol. 44(5), pages 943-962.
    4. Andrea Ascani & Alessandra Faggian & Sandro Montresor & Alessandro Palma, 2021. "Moving (within and across) spatial labour markets in times of COVID-19: evidence from Facebook mobility data on Italian labour market areas," Discussion Paper series in Regional Science & Economic Geography 2021-01, Gran Sasso Science Institute, Social Sciences, revised Jan 2021.
    5. Gert Bijnens & Shyngys Karimov & Jozef Konings, 2023. "Does Automatic Wage Indexation Destroy Jobs? A Machine Learning Approach," De Economist, Springer, vol. 171(1), pages 85-117, March.
    6. Ascani, Andrea & Faggian, Alessandra & Montresor, Sandro & Palma, Alessandro, 2021. "Mobility in times of pandemics: Evidence on the spread of COVID19 in Italy's labour market areas," Structural Change and Economic Dynamics, Elsevier, vol. 58(C), pages 444-454.
    7. Federico Crudu & Roberta Di Stefano & Giovanni Mellace & Silvia Tiezzi, 2022. "The Gray Zone," Department of Economics University of Siena 874, Department of Economics, University of Siena.
    8. Di Stefano, Roberta & Resce, Giuliano, "undated". "The Determinants of Missed Funding: Predicting the Paradox of Increased Need and Reduced Allocation," Economics & Statistics Discussion Papers esdp23092, University of Molise, Department of Economics.
    9. Delogu, Marco & Lagravinese, Raffaele & Paolini, Dimitri & Resce, Giuliano, 2024. "Predicting dropout from higher education: Evidence from Italy," Economic Modelling, Elsevier, vol. 130(C).
    10. Cerqua, Augusto & Letta, Marco, 2020. "Local economies amidst the COVID-19 crisis in Italy: a tale of diverging trajectories," MPRA Paper 104404, University Library of Munich, Germany.
    11. 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.
    12. Resce, Giuliano & Vaquero-Piñeiro, Cristina, 2023. "Taste of home: Birth town bias in Geographical Indications," Economics & Statistics Discussion Papers esdp23089, University of Molise, Department of Economics.
    13. Caravaggio, Nicola & Resce, Giuliano, 2023. "Enhancing Healthcare Cost Forecasting: A Machine Learning Model for Resource Allocation in Heterogeneous Regions," Economics & Statistics Discussion Papers esdp23090, University of Molise, Department of Economics.
    14. Roy Cerqueti & Raffaella Coppier & Alessandro Girardi & Marco Ventura, 2022. "The sooner the better: lives saved by the lockdown during the COVID-19 outbreak. The case of Italy [Using synthetic controls: Feasibility, data requirements, and methodological aspects]," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 46-70.
    15. Pietro Perotti & Paola Bertuccio & Stefano Cacitti & Silvia Deandrea & Lorenza Boschetti & Simona Dalle Carbonare & Stefano Marguati & Simona Migliazza & Eleonora Porzio & Simona Riboli & Ennio Cadum , 2022. "Impact of the COVID-19 Pandemic on Total and Cause-Specific Mortality in Pavia, Northern Italy," IJERPH, MDPI, vol. 19(11), pages 1-10, May.
    16. 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.
    17. Cerqua, Augusto & Letta, Marco, 2022. "Local inequalities of the COVID-19 crisis," Regional Science and Urban Economics, Elsevier, vol. 92(C).
    18. Augusto Cerqua & Roberta Di Stefano & Marco Letta & Sara Miccoli, 2021. "Was there a COVID-19 harvesting effect in Northern Italy?," Papers 2103.01812, arXiv.org, revised Mar 2021.

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    More about this item

    Keywords

    COVID-19; coronavirus; local mortality; Italy; machine learning; counterfactual building;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • I10 - Health, Education, and Welfare - - Health - - - General
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts

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