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Unemployment dynamics in Italy: a counterfactual analysis at Covid time

  • Illya Bakurov
  • Fabrizio Culotta

This work performs a counterfactual analysis on unemployment dynamics in Italy during the year 2020. In doing so, ARIMA models are estimated and used to make projections for the 2020 quarters. This exercise is performed at population level and for each gender, age and educational groups. Data are from the Italian Labor Force Survey covering the years 2015-2019 at quarterly frequency. Over the quarters of the year 2020, i.e. a time period covered by the Covid-19 pandemic and related restrictions, actual and counterfactual unemployment dynamics are compared. Overall, this work tries to answer to the following question: what would have happened to unemployment dynamics if Covid-19 pandemic and related restrictions would not arise as they did? Results can be informative to policymakers if the ARIMA projections can represent a reference for the aftermath of the pandemic.

  • Keywords:
  • Covid-19,
  • Italy,
  • Unemployment Dynamics,
  • Counterfactual Analysis,
  • ARIMA,
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Illya Bakurov

Nova University of Lisbon, Portugal - ORCID: 0000-0002-6458-942X

Fabrizio Culotta

University of Genoa, Italy - ORCID: 0000-0002-3910-3088

  1. Kwiatkowski, D., Phillips, P. C., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of econometrics, 54(1-3), 159-178.
  2. Hyndman, R. J., Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
  3. Hyndman, R. J., Athanasopoulos, G., Bergmeir, C., Caceres, G., Chhay, L., O'Hara-Wild, M., ... , Wang, E. (2020). Package ‘forecast’. https://cran. r-project.org/web/packages/forecast/forecast.pdf.
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  • Publication Year: 2021
  • Pages: 215-220
  • Content License: CC BY 4.0
  • © 2021 Author(s)

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  • Publication Year: 2021
  • Content License: CC BY 4.0
  • © 2021 Author(s)

Chapter Information

Chapter Title

Unemployment dynamics in Italy: a counterfactual analysis at Covid time

Authors

Illya Bakurov, Fabrizio Culotta

Language

English

DOI

10.36253/978-88-5518-461-8.40

Peer Reviewed

Publication Year

2021

Copyright Information

© 2021 Author(s)

Content License

CC BY 4.0

Metadata License

CC0 1.0

Table of Contents

Book Title

ASA 2021 Statistics and Information Systems for Policy Evaluation

Book Subtitle

BOOK OF SHORT PAPERS of the on-site conference

Editors

Bruno Bertaccini, Luigi Fabbris, Alessandra Petrucci

Peer Reviewed

Publication Year

2021

Copyright Information

© 2021 Author(s)

Content License

CC BY 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/978-88-5518-461-8

eISBN (pdf)

978-88-5518-461-8

eISBN (xml)

978-88-5518-462-5

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

116

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