IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i23p16274-d994843.html
   My bibliography  Save this article

Measuring Financial Sustainability and Social Adequacy of the Italian NDC Pension System under the COVID-19 Pandemic

Author

Listed:
  • Lorenzo Fratoni

    (Department of Statistics, Sapienza University of Rome, 00161 Rome, Italy
    These authors contributed equally to this work.)

  • Susanna Levantesi

    (Department of Statistics, Sapienza University of Rome, 00161 Rome, Italy
    These authors contributed equally to this work.)

  • Massimiliano Menzietti

    (Department of Economics, Statistics and Finance, University of Calabria, 87036 Rende, Italy
    These authors contributed equally to this work.)

Abstract

The COVID-19 pandemic is presently influencing the financial sustainability and the social adequacy of public pension schemes. In this paper, we measure the effects of COVID-19 on the Italian public pension system by introducing a deterministic shock due to the pandemic in the evolution of the variables mainly involved in the system’s evaluation. These variables, namely the unemployment rate, wage growth rate, inflation rate, and mortality rates, are modeled in a stochastic framework. Our results show that COVID-19 worsens the financial sustainability of the pension system in the short–medium term, while it does not appreciably affect social adequacy in the medium term. The Italian pension system already showed a social adequacy problem before 2020, which the pandemic does not further deteriorate essentially.

Suggested Citation

  • Lorenzo Fratoni & Susanna Levantesi & Massimiliano Menzietti, 2022. "Measuring Financial Sustainability and Social Adequacy of the Italian NDC Pension System under the COVID-19 Pandemic," Sustainability, MDPI, vol. 14(23), pages 1-23, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:16274-:d:994843
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/23/16274/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/23/16274/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Moser, Gabriel & Rumler, Fabio & Scharler, Johann, 2007. "Forecasting Austrian inflation," Economic Modelling, Elsevier, vol. 24(3), pages 470-480, May.
    2. Pierre Devolder & Susanna Levantesi & Massimiliano Menzietti, 2021. "Automatic balance mechanisms for notional defined contribution pension systems guaranteeing social adequacy and financial sustainability: an application to the Italian pension system," Annals of Operations Research, Springer, vol. 299(1), pages 765-795, April.
    3. Terasvirta, Timo & van Dijk, Dick & Medeiros, Marcelo C., 2005. "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination," International Journal of Forecasting, Elsevier, vol. 21(4), pages 755-774.
    4. Kapteyn, Arie & Alessie, Rob & Lusardi, Annamaria, 2005. "Explaining the wealth holdings of different cohorts: Productivity growth and Social Security," European Economic Review, Elsevier, vol. 49(5), pages 1361-1391, July.
    5. Ronald D. Lee & Michael W. Anderson & Shripad Tuljapurkar, 2003. "Stochastic Forecasts of the Social Security Trust Fund," Working Papers wp043, University of Michigan, Michigan Retirement Research Center.
    6. Robert Holzmann, 2017. "The ABCs of nonfinancial defined contribution (NDC) schemes," International Social Security Review, John Wiley & Sons, vol. 70(3), pages 53-77, July.
    7. Liu, Yanxin & Li, Johnny Siu-Hang, 2015. "The age pattern of transitory mortality jumps and its impact on the pricing of catastrophic mortality bonds," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 135-150.
    8. Foroni, Claudia & Marcellino, Massimiliano & Stevanovic, Dalibor, 2022. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," International Journal of Forecasting, Elsevier, vol. 38(2), pages 596-612.
    9. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
    10. Hua Chen & Samuel H. Cox, 2009. "Modeling Mortality With Jumps: Applications to Mortality Securitization," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 76(3), pages 727-751, September.
    11. Hubrich, Kirstin, 2005. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," International Journal of Forecasting, Elsevier, vol. 21(1), pages 119-136.
    12. Cox, Samuel H. & Lin, Yijia & Pedersen, Hal, 2010. "Mortality risk modeling: Applications to insurance securitization," Insurance: Mathematics and Economics, Elsevier, vol. 46(1), pages 242-253, February.
    13. Scott R. Baker & Nicholas Bloom & Steven J. Davis & Stephen J. Terry, 2020. "COVID-Induced Economic Uncertainty," NBER Working Papers 26983, National Bureau of Economic Research, Inc.
    14. Mitchell, Olivia S., 2020. "Building better retirement systems in the wake of the global pandemic," CFS Working Paper Series 644, Center for Financial Studies (CFS).
    15. Emilio Bisetti & Carlo Favero, 2014. "Measuring the Impact of Longevity Risk on Pension Systems: The Case of Italy," North American Actuarial Journal, Taylor & Francis Journals, vol. 18(1), pages 87-103.
    16. Antonello D'Agostino & Luca Gambetti & Domenico Giannone, 2013. "Macroeconomic forecasting and structural change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(1), pages 82-101, January.
    17. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2003. "Macroeconomic forecasting in the Euro area: Country specific versus area-wide information," European Economic Review, Elsevier, vol. 47(1), pages 1-18, February.
    18. Naccarato, Alessia & Falorsi, Stefano & Loriga, Silvia & Pierini, Andrea, 2018. "Combining official and Google Trends data to forecast the Italian youth unemployment rate," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 114-122.
    19. Choi, Sun-Yong, 2021. "Analysis of stock market efficiency during crisis periods in the US stock market: Differences between the global financial crisis and COVID-19 pandemic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    20. repec:taf:jnlbes:v:30:y:2012:i:2:p:181-190 is not listed on IDEAS
    21. David Blake & Les Mayhew, 2006. "On The Sustainability of the UK State Pension System in the Light of Population Ageing and Declining Fertility," Economic Journal, Royal Economic Society, vol. 116(512), pages 286-305, June.
    22. Yinglu Deng & Patrick L. Brockett & Richard D. MacMinn, 2012. "Longevity/Mortality Risk Modeling and Securities Pricing," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 79(3), pages 697-721, September.
    23. Yueqiang Zhao & Manying Bai & Yali Liu & Junzhang Hao, 2017. "Quantitative Analyses of Transition Pension Liabilities and Solvency Sustainability in China," Sustainability, MDPI, vol. 9(12), pages 1-16, December.
    24. Aaron George Grech, 2018. "What Makes Pension Reforms Sustainable?," Sustainability, MDPI, vol. 10(8), pages 1-12, August.
    25. Carter, Lawrence R. & Lee, Ronald D., 1992. "Modeling and forecasting US sex differentials in mortality," International Journal of Forecasting, Elsevier, vol. 8(3), pages 393-411, November.
    26. Michele Belloni & Carlo Maccheroni, 2013. "Actuarial Fairness When Longevity Increases: An Evaluation of the Italian Pension System," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 38(4), pages 638-674, October.
    27. Samuel H. Cox & Yijia Lin & Shaun Wang, 2006. "Multivariate Exponential Tilting and Pricing Implications for Mortality Securitization," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 73(4), pages 719-736, December.
    Full references (including those not matched with items on IDEAS)

    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. Blake, David & El Karoui, Nicole & Loisel, Stéphane & MacMinn, Richard, 2018. "Longevity risk and capital markets: The 2015–16 update," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 157-173.
    2. David Blake & Marco Morales & Enrico Biffis & Yijia Lin & Andreas Milidonis, 2017. "Special Edition: Longevity 10 – The Tenth International Longevity Risk and Capital Markets Solutions Conference," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 84(S1), pages 515-532, April.
    3. Blake, David & Cairns, Andrew J.G., 2021. "Longevity risk and capital markets: The 2019-20 update," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 395-439.
    4. Rui Zhou & Johnny Siu-Hang Li & Ken Seng Tan, 2013. "Pricing Standardized Mortality Securitizations: A Two-Population Model With Transitory Jump Effects," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 80(3), pages 733-774, September.
    5. Liu, Yanxin & Li, Johnny Siu-Hang, 2015. "The age pattern of transitory mortality jumps and its impact on the pricing of catastrophic mortality bonds," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 135-150.
    6. Bahl, Raj Kumari & Sabanis, Sotirios, 2021. "Model-independent price bounds for Catastrophic Mortality Bonds," Insurance: Mathematics and Economics, Elsevier, vol. 96(C), pages 276-291.
    7. Raj Kumari Bahl & Sotirios Sabanis, 2016. "Model-Independent Price Bounds for Catastrophic Mortality Bonds," Papers 1607.07108, arXiv.org, revised Dec 2020.
    8. Chen, Fen-Ying & Yang, Sharon S. & Huang, Hong-Chih, 2022. "Modeling pandemic mortality risk and its application to mortality-linked security pricing," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 341-363.
    9. Wang, Zihe & Li, Johnny Siu-Hang, 2016. "A DCC-GARCH multi-population mortality model and its applications to pricing catastrophic mortality bonds," Finance Research Letters, Elsevier, vol. 16(C), pages 103-111.
    10. Longo, Luigi & Riccaboni, Massimo & Rungi, Armando, 2022. "A neural network ensemble approach for GDP forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    11. Colin Bermingham & Antonello D’Agostino, 2014. "Understanding and forecasting aggregate and disaggregate price dynamics," Empirical Economics, Springer, vol. 46(2), pages 765-788, March.
    12. Patrick L. Brockett & Shuo-li Chuang & Yinglu Deng & Richard D. MacMinn, 2013. "Incorporating Longevity Risk and Medical Information Into Life Settlement Pricing," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 80(3), pages 799-826, September.
    13. Enrico Biffis & David Blake & Lorenzo Pitotti & Ariel Sun, 2016. "The Cost of Counterparty Risk and Collateralization in Longevity Swaps," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 83(2), pages 387-419, June.
    14. Sandra Eickmeier & Christina Ziegler, 2008. "How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 237-265.
    15. Huang, Yu-Lieh & Tsai, Jeffrey Tzuhao & Yang, Sharon S. & Cheng, Hung-Wen, 2014. "Price bounds of mortality-linked security in incomplete insurance market," Insurance: Mathematics and Economics, Elsevier, vol. 55(C), pages 30-39.
    16. O. De Bandt & E. Michaux & C. Bruneau & A. Flageollet, 2007. "Forecasting inflation using economic indicators: the case of France," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(1), pages 1-22.
    17. Mitchell, Daniel & Brockett, Patrick & Mendoza-Arriaga, Rafael & Muthuraman, Kumar, 2013. "Modeling and forecasting mortality rates," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 275-285.
    18. Hendry, David F. & Hubrich, Kirstin, 2011. "Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(2), pages 216-227.
    19. Duarte, Claudia & Rua, Antonio, 2007. "Forecasting inflation through a bottom-up approach: How bottom is bottom?," Economic Modelling, Elsevier, vol. 24(6), pages 941-953, November.
    20. Marie Angèle Cathleen Alijean & Jason Narsoo, 2018. "Evaluation of the Kou-Modified Lee-Carter Model in Mortality Forecasting: Evidence from French Male Mortality Data," Risks, MDPI, vol. 6(4), pages 1-26, October.

    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:gam:jsusta:v:14:y:2022:i:23:p:16274-:d:994843. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.