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 ...
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.
