Labor market forecasting in unprecedented times: A machine learning approach

The COVID-19 pandemic ushered in unprecedented social and economic conditions, alongside unexpected policy responses, challenging the effectiveness of traditional labor market forecasting approaches. This article presents a novel approach that integrates macroeconomic variables, traditional labor market metrics, and Google search data to develop a machine learning-based indicator for the Colombian labor market. We employ support vector machine for regression and neural networks models to forecast monthly employment and unemployment rates, explicitly focusing on the third wave of COVID-19 in the first half of 2021. Our study’s findings reveal that the proposed models outperform the autoregressive benchmark regarding forecast accuracy, demonstrating a rapid adaptation to labor market shifts.

• Autores: Orozco Castañeda, Johanna Marcela; Sierra Suárez, Lya Paola; Vidal Alejandro, Pavel
• Tipo de recurso: Article of investigation
• Fecha de publicación: 2024
• Institución: Universidad de Antioquia
• Repositorio: Repositorio UdeA
• Idioma: eng
• OAI Identifier: oai:bibliotecadigital.udea.edu.co:10495/46924
• Acceso en línea: https://hdl.handle.net/10495/46924
• Palabra clave: COVID-19 Pandemic, 2020-2023; Predicción; Forecasting; Aprendizaje Automático; Machine learning; https://id.nlm.nih.gov/mesh/D014478; COVID-19; Desempleo; Unemployment; http://id.loc.gov/authorities/subjects/sh2020008759; https://id.nlm.nih.gov/mesh/D005544; https://id.nlm.nih.gov/mesh/D000069550; https://id.nlm.nih.gov/mesh/D000086382
• Rights: openAccess
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/