Predicting skill demand shifts over time: exploring demographic changes and future workforce trends using machine learning

Abstract

Understanding the differences in skill requirements between age groups is crucial for strategic workforce planning and education. This study examines how demographic trends and machine learning can predict shifts in skill demand across generations. Focusing on Kenya, from 2020 to 2024, it analyzes job postings, industry trends, job functions, and required skills This study fills a gap in African research by using advanced machine learning to analyze generational skill demands, a perspective often overlooked in previous studies. This is significant as it fills a gap in existing research and provides critical insights for the African job market. In this study, we used KMeans clustering, hierarchical clustering, and DBSCAN to predict future patterns in skill demand by examining past job market data and demographic trends. We used the silhouette score and the elbow method to assess the models' performance. KMeans silhouette score = 0.49, Agglomerative Clustering Silhouette Score = 0.44, and Silhouette Score for DBSCAN = -0.24. A KMeans model of 4 centroids, as indicated by the elbow curve, was used to predict skill demand shifts. The model revealed changes in skills over time, with visible clusters showing previous and current market requirements. There is a notable demand for digital skills, data-related skills, and accounting, with remote skills being highly sought after. This study has several potential applications. Educational institutions can tailor curricula to future skill demands, while employers align workforce plans for upcoming transitions. Policymakers and industry leaders can address skill gaps, fostering a resilient job market. This study provides a data-driven approach to workforce dynamics, promoting adaptability in the labor ecosystem. In an increasingly dynamic job landscape, combining demographic insights with machine learning developments holds significant potential for informed decision-making. Keywords: skill demand shifts, demographic changes, generational trends, machine learning, predictive analytics, workforce planning, education alignment, job market dynamics.