Unemployment dynamics in Italy: a counterfactual analysis at Covid time Illya Bakurov Nova University of Lisbon, Portugal Fabrizio Culotta University of Genoa, Italy This is a section of ASA 2021 Statistics and Information Systems for Policy Evaluation (DOI: 10.36253/978-88-5518-461-8) by Alessandra Petrucci, Bruno Bertaccini, Luigi Fabbris Firenze University Press Firenze 2021 https://doi.org/10.36253/978-88-5518-461-8.40

Available for academic research purposes

Open Access

Copyright Author(s)

Content licence CC BY 4.0

Metadata licence CC0 1.0

This is original content, published for academic research purposes

 Digital edition XML powered by Booksflow

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.

 Covid-19 Italy Unemployment Dynamics Counterfactual Analysis ARIMA

It is available online at https://doi.org/10.36253/978-88-5518-461-8.40

References 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. Hyndman, R. J., Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts. 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.