IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i13p7078-d580854.html
   My bibliography  Save this article

Socioeconomic Effects of COVID-19 Pandemic: Exploring Uncertainty in the Forecast of the Romanian Unemployment Rate for the Period 2020–2023

Author

Listed:
  • Adriana AnaMaria Davidescu

    (Department of Education, Training and Labour Market, National Scientific Research Institute for Labour and Social Protection, 010643 Bucharest, Romania
    Department of Statistics and Econometrics, Bucharest University of Economic Studies, 010552 Bucharest, Romania)

  • Simona-Andreea Apostu

    (Department of Statistics and Econometrics, Bucharest University of Economic Studies, 010552 Bucharest, Romania
    Institute of National Economy, 050711 Bucharest, Romania)

  • Liviu Adrian Stoica

    (Finance Postdoctoral School of Bucharest University of Economic Studies, 010352 Bucharest, Romania)

Abstract

During the health crisis, it is vital to protect not only the critical sectors of the economy, the assets, technology, and infrastructure, but first and foremost, it is fundamental to protect jobs and workers. The current COVID-19 pandemic has had a strong impact on the labor market from three main perspectives: number of jobs (through unemployment and underemployment), quality of work (through wages, or access to social protection), and through the effects on specific groups, with a higher degree of vulnerability to unfavorable labor market outcomes. The measures aiming to reduce economic activity and social contacts lead to a reduction of labor demand and implicitly to the increase of the unemployment rate. In this context, it becomes even more relevant to be able to monitor the unemployment rate, providing relevant forecasts that include the effects of market shocks. Thus, our paper aims to forecast the unemployment rate for the period 2020–2023 using the Box-Jenkins methodology based on ARIMA models, exploring also the uncertainty based on fan charts. Although the baseline forecast offers valuable information, a good understanding of risks and uncertainties related to this forecast is equally important. The empirical results highlighted an ascending trend for unemployment rate during 2020, followed by a slow and continuous decrease until the end of 2023 with a high probability for the forecast to be above the central projection.

Suggested Citation

  • Adriana AnaMaria Davidescu & Simona-Andreea Apostu & Liviu Adrian Stoica, 2021. "Socioeconomic Effects of COVID-19 Pandemic: Exploring Uncertainty in the Forecast of the Romanian Unemployment Rate for the Period 2020–2023," Sustainability, MDPI, vol. 13(13), pages 1-22, June.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:13:p:7078-:d:580854
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/13/7078/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/13/7078/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Oscar Claveria, 2019. "Forecasting the unemployment rate using the degree of agreement in consumer unemployment expectations," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 53(1), pages 1-10, December.
    2. Dick van Dijk & Timo Terasvirta & Philip Hans Franses, 2002. "Smooth Transition Autoregressive Models — A Survey Of Recent Developments," Econometric Reviews, Taylor & Francis Journals, vol. 21(1), pages 1-47.
    3. repec:pri:indrel:dsp01dr26xx382 is not listed on IDEAS
    4. Schanne, N. & Wapler, R. & Weyh, A., 2010. "Regional unemployment forecasts with spatial interdependencies," International Journal of Forecasting, Elsevier, vol. 26(4), pages 908-926, October.
    5. Gil-Alana, Luis A, 2001. "A Fractionally Integrated Exponential Model for UK Unemployment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(5), pages 329-340, August.
    6. Olivier J. Blanchard & Daniel Leigh, 2013. "Growth Forecast Errors and Fiscal Multipliers," American Economic Review, American Economic Association, vol. 103(3), pages 117-120, May.
    7. Tanujit Chakraborty & Ashis Kumar Chakraborty & Munmun Biswas & Sayak Banerjee & Shramana Bhattacharya, 2021. "Unemployment Rate Forecasting: A Hybrid Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 183-201, January.
    8. repec:iab:iabjlr:v:53:i:1:p:art.3 is not listed on IDEAS
    9. Koop, Gary & Potter, Simon M, 1999. "Dynamic Asymmetries in U.S. Unemployment," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(3), pages 298-312, July.
    10. Edlund, Per-Olov & Karlsson, Sune, 1993. "Forecasting the Swedish unemployment rate VAR vs. transfer function modelling," International Journal of Forecasting, Elsevier, vol. 9(1), pages 61-76, April.
    11. D. A. Peel & A. E. H. Speight, 2000. "Threshold nonlinearities in unemployment rates: further evidence for the UK and G3 economies," Applied Economics, Taylor & Francis Journals, vol. 32(6), pages 705-715.
    12. Geraint Johnes, 1999. "Forecasting unemployment," Applied Economics Letters, Taylor & Francis Journals, vol. 6(9), pages 605-607.
    13. World Commission on Environment and Development,, 1987. "Our Common Future," OUP Catalogue, Oxford University Press, number 9780192820808.
    14. Proietti, Tommaso, 2003. "Forecasting the US unemployment rate," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 451-476, March.
    15. Sylvain Leduc & Zheng Liu, 2020. "The Uncertainty Channel of the Coronavirus," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, vol. 2020(07), pages 1-05, March.
    16. Nagao, Shintaro & Takeda, Fumiko & Tanaka, Riku, 2019. "Nowcasting of the U.S. unemployment rate using Google Trends," Finance Research Letters, Elsevier, vol. 30(C), pages 103-109.
    17. Laura Brown & Saeed Moshiri, 2004. "Unemployment variation over the business cycles: a comparison of forecasting models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(7), pages 497-511.
    18. Chen, Chun-I, 2008. "Application of the novel nonlinear grey Bernoulli model for forecasting unemployment rate," Chaos, Solitons & Fractals, Elsevier, vol. 37(1), pages 278-287.
    19. Nikolaos Dritsakis & Paraskevi Klazoglou, 2018. "Forecasting Unemployment Rates in USA using Box-Jenkins Methodology," International Journal of Economics and Financial Issues, Econjournals, vol. 8(1), pages 9-20.
    20. Skalin, Joakim & Teräsvirta, Timo, 2002. "Modeling Asymmetries And Moving Equilibria In Unemployment Rates," Macroeconomic Dynamics, Cambridge University Press, vol. 6(2), pages 202-241, April.
    21. Orley Ashenfelter & David Card, 1982. "Time Series Representations of Economic Variables and Alternative Models of the Labour Market," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 49(5), pages 761-782.
    22. Oscar Claveria, 2019. "Forecasting the unemployment rate using the degree of agreement in consumer unemployment expectations," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 53(1), pages 1-10, December.
    23. Aurel MARIN, 2013. "The impact of unemployment on the development of macroregions," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(Special I), pages 247-257, December.
    24. Vicente, María Rosalía & López-Menéndez, Ana J. & Pérez, Rigoberto, 2015. "Forecasting unemployment with internet search data: Does it help to improve predictions when job destruction is skyrocketing?," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 132-139.
    25. James Wong & Albert Chan & Y. H. Chiang, 2005. "Time series forecasts of the construction labour market in Hong Kong: the Box-Jenkins approach," Construction Management and Economics, Taylor & Francis Journals, vol. 23(9), pages 979-991.
    26. George Atsalakis & Camelia Ioana Ucenic & Christos Skiadas, 2008. "Forecasting Unemployment Rate Using a Neural Network with Fuzzy Inference System," Working Papers 0823, University of Crete, Department of Economics.
    27. Hansen Bruce E., 1997. "Inference in TAR Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 2(1), pages 1-16, April.
    28. Zameelah Rifkha Khan Jaffur & Noor-Ul-Hacq Sookia & Preethee Nunkoo Gonpot & Boopendra Seetanah, 2017. "Out-of-sample forecasting of the Canadian unemployment rates using univariate models," Applied Economics Letters, Taylor & Francis Journals, vol. 24(15), pages 1097-1101, September.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Dominika Gajdosikova & Katarina Valaskova & Tomas Kliestik & Veronika Machova, 2022. "COVID-19 Pandemic and Its Impact on Challenges in the Construction Sector: A Case Study of Slovak Enterprises," Mathematics, MDPI, vol. 10(17), pages 1-20, September.
    2. Constantin Anghelache & Mădălina-Gabriela Anghel & Ștefan Virgil Iacob & Mirela Panait & Irina Gabriela Rădulescu & Alina Gabriela Brezoi & Adrian Miron, 2022. "The Effects of Health Crisis on Economic Growth, Health and Movement of Population," Sustainability, MDPI, vol. 14(8), pages 1-22, April.
    3. Federico Benjamín Galacho-Jiménez & David Carruana-Herrera & Julián Molina & José Damián Ruiz-Sinoga, 2022. "Tempo-Spatial Modelling of the Spread of COVID-19 in Urban Spaces," IJERPH, MDPI, vol. 19(15), pages 1-17, August.
    4. Valentin Marian ANTOHI, 2021. "The Paradigm of Financing the Health Services from the Hospital Healthcare under the Impact of the COVID-19 Pandemic," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 3, pages 22-28.
    5. Federico Benjamín Galacho-Jiménez & David Carruana-Herrera & Julián Molina & José Damián Ruiz-Sinoga, 2022. "Evidence of the Relationship between Social Vulnerability and the Spread of COVID-19 in Urban Spaces," IJERPH, MDPI, vol. 19(9), pages 1-22, April.

    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. Tanujit Chakraborty & Ashis Kumar Chakraborty & Munmun Biswas & Sayak Banerjee & Shramana Bhattacharya, 2021. "Unemployment Rate Forecasting: A Hybrid Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 183-201, January.
    2. Elena Olmedo, 2014. "Forecasting Spanish Unemployment Using Near Neighbour and Neural Net Techniques," Computational Economics, Springer;Society for Computational Economics, vol. 43(2), pages 183-197, February.
    3. Muneeb Ahmad & Yousaf Ali Khan & Chonghui Jiang & Syed Jawad Haider Kazmi & Syed Zaheer Abbas, 2023. "The impact of COVID‐19 on unemployment rate: An intelligent based unemployment rate prediction in selected countries of Europe," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 528-543, January.
    4. Phi-Hung Nguyen & Jung-Fa Tsai & Ihsan Erdem Kayral & Ming-Hua Lin, 2021. "Unemployment Rates Forecasting with Grey-Based Models in the Post-COVID-19 Period: A Case Study from Vietnam," Sustainability, MDPI, vol. 13(14), pages 1-27, July.
    5. 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.
    6. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    7. Bårdsen Gunnar & Hurn Stanley & McHugh Zöe, 2012. "Asymmetric Unemployment Rate Dynamics in Australia," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(1), pages 1-22, January.
    8. Singh, Tarlok, 2014. "On the regime-switching and asymmetric dynamics of economic growth in the OECD countries," Research in Economics, Elsevier, vol. 68(2), pages 169-192.
    9. Ginger M. Davis & Katherine B. Ensor, 2007. "Multivariate Time‐Series Analysis With Categorical and Continuous Variables in an Lstr Model," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(6), pages 867-885, November.
    10. Kurmaş Akdoğan, 2017. "Unemployment hysteresis and structural change in Europe," Empirical Economics, Springer, vol. 53(4), pages 1415-1440, December.
    11. Alejandro López-Vera & Andrés D. Pinchao-Rosero & Norberto Rodríguez-Niño, 2018. "Non-Linear Fiscal Multipliers for Public Expenditure and Tax Revenue in Colombia," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 36(85), pages 48-64, April.
    12. Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 8, pages 413-457, Elsevier.
    13. Matthews, Kent & Minford, Patrick & Naraidoo, Ruthira, 2008. "Vicious and virtuous circles -- The political economy of unemployment in interwar UK and USA," European Journal of Political Economy, Elsevier, vol. 24(3), pages 605-614, September.
    14. Olmedo, Elena, 2011. "Is there chaos in the Spanish labour market?," Chaos, Solitons & Fractals, Elsevier, vol. 44(12), pages 1045-1053.
    15. Milas, Costas & Rothman, Philip, 2008. "Out-of-sample forecasting of unemployment rates with pooled STVECM forecasts," International Journal of Forecasting, Elsevier, vol. 24(1), pages 101-121.
    16. Mihaela, Simionescu, 2020. "Improving unemployment rate forecasts at regional level in Romania using Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    17. Donayre, Luiggi & Panovska, Irina, 2016. "Nonlinearities in the U.S. wage Phillips curve," Journal of Macroeconomics, Elsevier, vol. 48(C), pages 19-43.
    18. Mihai Mutascu & Scott W. Hegerty, 2023. "Predicting the contribution of artificial intelligence to unemployment rates: an artificial neural network approach," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(2), pages 400-416, June.
    19. Kurmaş Akdoğan, 2015. "Unemployment Hysteresis and Structural Change in Europe," EY International Congress on Economics II (EYC2015), November 5-6, 2015, Ankara, Turkey 266, Ekonomik Yaklasim Association.
    20. Simionescu, Mihaela & Cifuentes-Faura, Javier, 2022. "Can unemployment forecasts based on Google Trends help government design better policies? An investigation based on Spain and Portugal," Journal of Policy Modeling, Elsevier, vol. 44(1), pages 1-21.

    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:13:y:2021:i:13:p:7078-:d:580854. 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.