Evaluating predictors of medium-term job growth using machine learning

Rishabh Sinha

MPRA Paper from University Library of Munich, Germany

Abstract: Using data from 116 countries spanning 2005-2023, this paper provides a global comparative assessment of the factors predicting medium-term job growth. I assemble 25 macroeconomic, demographic, health, and institutional variables and employ tuned machine-learning models to evaluate their predictive power. Gradient Boosting Regression delivers the strongest performance, and Shapley decompositions reveal three main results. First, GDP growth is not the dominant predictor of employment expansion, and its predictive strength has weakened over time, except during global recoveries. Second, past job growth is one of the most consistent predictors across income groups, indicating strong labor-market momentum, especially following global macroeconomic instability. Third, some structural forces, such as urbanization and quality of governance, contribute to job growth. But their effects shift with global macroeconomic conditions and a country's level of development. Overall, the evidence underscores the role of these contextual factors, suggesting that economic growth is often insufficient to sustain job creation.

Keywords: Job growth; Economic growth; Medium-term forecasts of job creation; Machine learning; SHAP; Structural determinants of job growth (search for similar items in EconPapers)
JEL-codes: J21 O15 O47 (search for similar items in EconPapers)
Date: 2025-11
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