Systematic Review of Machine Learning Approaches in Forecasting Economic Growth and Enhancing Decent Work Opportunities: A Comprehensive Analysis

Abstract

A systematic review in a field of machine learning applications for forecasting through a comprehensive analysis aims to resolve the various of methodologies employed in this intersection of machine learning and economics, focusing on predictive modeling within the context of socioeconomic development. The paper provides a comprehensive overview of economic growth, the concept of decent work, and the role of machine learning in advancing Sustainable Development Goal 8 (SDG 8) - “Decent Work and Economic Growth.” SDG 8 is committed to achieving comprehensive and productive employment opportunities for all individuals while ensuring equitable working conditions and fostering inclusive economic development. The significance of supporting technological progress, particularly the utilization of machine learning, to job creation, enhance workplace safety, and promote economic expansion while upholding ethical and equitable considerations. Nevertheless, it simultaneously recognizes the necessity of striking a balance between technological advancement, labor rights and social protections. The path towards a sustainable and complete economy provides substantial work opportunities, eliminating poverty, and addressing the diverse needs of individuals across various societies. The objective of this paper is to review various studies on the application of Machine Learning for forecasting economic growth and enhancing decent work opportunities. The literature review section investigates various studies, contributing to the exploration of research questions presented in this paper. This critical review holds importance for researchers by facilitating comparisons with existing works with a comprehensive understanding of evaluation matrices, dataset characteristics, and machine learning models used in each study, aiding researchers in assessing their own work in relation to current research. Additionally, the review will discuss challenges by researchers in this field and propose potential ways for further research.