AI Summary & Key Takeaways
AI InsightFindings- Empirical results indicate that XGBoost substantially outperforms LightGBM, achieving a lower RMSE (2,304.76) and a higher R² (0.9325), compared to LightGBM’s RMSE of 6,017.03 and R² of 0.5398.
📌 3 Key Takeaways
Abstract
Purpose- Rapid advances in industrial automation, artificial intelligence, and digital production technologies are transforming labour market structures worldwide, intensifying concerns related to job displacement, occupational vulnerability, and regional inequality. This study aims to forecast technology-induced labour market dynamics using an interpretable and policy-relevant machine-learning framework. Methodology- The study develops an interpretable predictive modelling framework based on a large-scale, harmonized panel dataset comprising 68,882 occupation–region–year observations spanning the period 2010–2023. The dataset integrates labour-force microdata, task-based automation risk indicators, occupational characteristics, and macroeconomic control variables across multiple economies. Two state-of-the-art gradient-boosting algorithms—XGBoost and LightGBM—are trained and evaluated using temporally consistent cross- validation. Model performance is assessed using Root Mean Squared Error (RMSE) and the coefficient of determination (R²), while model interpretability is achieved through SHapley Additive exPlanations (SHAP). Findings- Empirical results indicate that XGBoost substantially outperforms LightGBM, achieving a lower RMSE (2,304.76) and a higher R² (0.9325), compared to LightGBM’s RMSE of 6,017.03 and R² of 0.5398. These results demonstrate XGBoost’s superior ability to capture nonlinear relationships and heterogeneous automation effects across occupations and regions. Conclusion- SHAP-based interpretability analysis identifies task repetitiveness, physical proximity, and cognitive complexity as the most influential drivers of automation-related labour market vulnerability. Scenario-based simulations further reveal that targeted policy interventions—such as reskilling programmes and workforce transition support—can significantly reduce projected job displacement, particularly among mid-risk occupations. Overall, the findings confirm that interpretable gradient-boosting models provide a robust and policy-relevant tool for forecasting automation-driven labour market dynamics and supporting evidence-based workforce planning in economies undergoing rapid technological transformation.