Forecasting youth unemployment in the aftermath of the COVID-19 pandemic: the Italian case

Livio Fenga1 & Semen Son-Turan2,*
1University of Exeter, Exeter, United Kingdom
2Department of Business Administration, MEF University, Istanbul, Turkey
DOI – http://doi.org/10.37502/IJSMR.2022.5105

Abstract

Purpose: This study aims at forecasting NEET unemployment in Italy using a counterfactual scenario, based on an original empirical model, whereby the effects of the COVID-19 pandemic on the NEET rate are factored in and left out.
Methodology: An artificial neural network (ANN) model of the type feed-forward, with a Google Trends-generated variable that represents potentially relevant search queries, is employed to backcast, nowcast, and forecast Italian NEET unemployment for 2019, 2020, 2021, respectively.
Findings: Findings suggest that the Italian NEET unemployment rate will slightly increase in a less than proportional way, absorbing the COVID-19 pandemic’s effects in a relatively short time period.
Research Implications/ Limitation: Several limitations with respect to the limited sample size and the few number of explanatory variables are remedied through the use of an adequate methodology.
Originality: The use of an ANN in youth unemployment studies during a pandemic of the present scale is, to the best of the authors’ knowledge, unprecedented.

Keywords: Artificial neural network, COVID–19, youth unemployment, pandemic, maximum entropy bootstrap.

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