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

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.