IDEAS home Printed from https://ideas.repec.org/a/spr/sorede/v33y2022i1d10.1134_s107570072201004x.html
   My bibliography  Save this article

Medium-Term Forecast of Government Spending on the Unemployment Social Protection System in Russia in the Conditions of Economic Recession

Author

Listed:
  • M. E. Baskakova

    (Institute of Economics, Russian Academy of Sciences)

  • V. N. Baskakov

    (International Actuarial Advisory Company)

  • E. A. Yanenko

    (International Actuarial Advisory Company)

Abstract

— A simulation model of the Russian unemployment social protection system is developed and used to study the impact of the economic crisis caused by the COVID-19 pandemic, demographic processes, and the increase in retirement age on federal spending on unemployment benefits. Trends of overall and registered unemployment across various socio-demographic groups in 1992–2020 are analyzed, with special attention paid to periods of economic recession and recovery. The identified patterns are used to develop modeling scenarios. Calculations show that in the unfavorable scenario government spending on unemployment benefits will increase many times. As a possible solution to the problem, it is proposed to convert the unemployment social protection system to insurance principles. The time frame in which reforming the system would be less expensive for the federal budget is defined.

Suggested Citation

  • M. E. Baskakova & V. N. Baskakov & E. A. Yanenko, 2022. "Medium-Term Forecast of Government Spending on the Unemployment Social Protection System in Russia in the Conditions of Economic Recession," Studies on Russian Economic Development, Springer, vol. 33(1), pages 45-54, February.
    • Handle: RePEc:spr:sorede:v:33:y:2022:i:1:d:10.1134_s107570072201004x
    DOI: 10.1134/S107570072201004X
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1134/S107570072201004X
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1134/S107570072201004X?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Amos Golan & Jeffrey M. Perloff, 2004. "Superior Forecasts of the U.S. Unemployment Rate Using a Nonparametric Method," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 433-438, February.
    2. Baghestani, Hamid, 2008. "Federal Reserve versus private information: Who is the best unemployment rate predictor," Journal of Policy Modeling, Elsevier, vol. 30(1), pages 101-110.
    3. Junankar, Pramod N. (Raja), 2011. "The Global Economic Crisis: Long-Term Unemployment in the OECD," IZA Discussion Papers 6057, Institute of Labor Economics (IZA).
    4. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 788-829, September.
    5. Semerikova, Elena & Demidova, Olga, 2016. "Using spatial econometric models for regional unemployment forecasting," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 43, pages 29-51.
    6. Floros, Ch., 2005. "Forecasting the UK Unemployment Rate: Model Comparisons," International Journal of Applied Econometrics and Quantitative Studies, Euro-American Association of Economic Development, vol. 2(4), pages 57-72.
    7. Stepan Zemtsov, 2020. "New technologies, potential unemployment and ‘nescience economy’ during and after the 2020 economic crisis," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(4), pages 723-743, August.
    8. Lancaster, Tony, 1979. "Econometric Methods for the Duration of Unemployment," Econometrica, Econometric Society, vol. 47(4), pages 939-956, July.
    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. Francisco Lasso-Valderrama & Héctor M. Zárate-Solano, 2019. "Forecasting the Colombian Unemployment Rate Using Labour Force Flows," Borradores de Economia 1073, Banco de la Republica de Colombia.
    2. D’Amuri, Francesco & Marcucci, Juri, 2017. "The predictive power of Google searches in forecasting US unemployment," International Journal of Forecasting, Elsevier, vol. 33(4), pages 801-816.
    3. Ullrich Heilemann & Herman Stekler, 2010. "Perspectives on Evaluating Macroeconomic Forecasts," Working Papers 2010-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    4. Regis Barnichon & Christopher J. Nekarda, 2012. "The Ins and Outs of Forecasting Unemployment: Using Labor Force Flows to Forecast the Labor Market," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 43(2 (Fall)), pages 83-131.
    5. João C. Claudio & Katja Heinisch & Oliver Holtemöller, 2020. "Nowcasting East German GDP growth: a MIDAS approach," Empirical Economics, Springer, vol. 58(1), pages 29-54, January.
    6. Douglas Sutherland & Peter Hoeller & Balázs Égert & Oliver Röhn, 2010. "Counter-cyclical Economic Policy," OECD Economics Department Working Papers 760, OECD Publishing.
    7. Iva Gregurec & Martina Tomičić Furjan & Katarina Tomičić-Pupek, 2021. "The Impact of COVID-19 on Sustainable Business Models in SMEs," Sustainability, MDPI, vol. 13(3), pages 1-24, January.
    8. Tomohiro Ando & Ruey S. Tsay, 2009. "Model selection for generalized linear models with factor‐augmented predictors," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(3), pages 207-235, May.
    9. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    10. González-Rivera, Gloria & Sun, Yingying, 2017. "Density forecast evaluation in unstable environments," International Journal of Forecasting, Elsevier, vol. 33(2), pages 416-432.
    11. Jonas D. M. Fisher & Chin Te Liu & Ruilin Zhou, 2002. "When can we forecast inflation?," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 26(Q I), pages 32-44.
    12. Ye, Ya-Fen & Shao, Yuan-Hai & Deng, Nai-Yang & Li, Chun-Na & Hua, Xiang-Yu, 2017. "Robust Lp-norm least squares support vector regression with feature selection," Applied Mathematics and Computation, Elsevier, vol. 305(C), pages 32-52.
    13. Salisu, Afees A. & Ademuyiwa, Idris & Isah, Kazeem O., 2018. "Revisiting the forecasting accuracy of Phillips curve: The role of oil price," Energy Economics, Elsevier, vol. 70(C), pages 334-356.
    14. Aastveit, Knut Are & Jore, Anne Sofie & Ravazzolo, Francesco, 2016. "Identification and real-time forecasting of Norwegian business cycles," International Journal of Forecasting, Elsevier, vol. 32(2), pages 283-292.
    15. Evangelos Salachas & Georgios P. Kouretas & Nikiforos T. Laopodis, 2024. "The term structure of interest rates and economic activity: Evidence from the COVID‐19 pandemic," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(4), pages 1018-1041, July.
    16. Fernando M. Duarte & Carlo Rosa, 2015. "The equity risk premium: a review of models," Economic Policy Review, Federal Reserve Bank of New York, issue 2, pages 39-57.
    17. Faust, Jon & Gupta, Abhishek, 2010. "Posterior Predictive Analysis for Evaluating DSGE Models," MPRA Paper 26721, University Library of Munich, Germany.
    18. Guiso, Luigi & Jappelli, Tullio, 2002. "Private Transfers, Borrowing Constraints and the Timing of Homeownership," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 34(2), pages 315-339, May.
    19. Gloria González‐Rivera & C. Vladimir Rodríguez‐Caballero & Esther Ruiz, 2024. "Expecting the unexpected: Stressed scenarios for economic growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 926-942, August.

    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:sorede:v:33:y:2022:i:1:d:10.1134_s107570072201004x. 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.