Labor market forecasting relies heavily on economic time-series data, often overlooking the “health–wealth” gradient that links population health to workforce participation. This study develops a machine learning framework integrating non-traditional health and social metrics to predict state-level employment density. Methods: We constructed a multi-source longitudinal dataset (2014–2024) by aggregating county-level Quarterly Census of Employment and Wages (QCEW) data with County Health Rankings to the state level. Using a time-aware split to evaluate performance across the COVID-19 structural break, we compared LASSO, Random Forest, and regularized XGBoost models, employing SHAP values for interpretability. Results: The tuned, regularized XGBoost model achieved strong out-of-sample performance (Test $R^2$ = 0.800). A leakage-safe stacked Ridge ensemble yielded comparable performance (Test $R^2$ = 0.827), while preserving the interpretability of the underlying tree model used for SHAP analysis.

Traditional labor market forecasting relies heavily on economic time-series data, often overlooking the "health-wealth" gradient that links population health to workforce participation. This study develops a machine learning framework integrating non-traditional health and social metrics to predict state-level employment density. Methods: We constructed a multi-source longitudinal dataset (2014–2024) combining Quarterly Census of Employment and Wages (QCEW) data with County Health Rankings. Using a time-aware split to evaluate performance across the COVID-19 structural break, we compared LASSO, Random Forest, and regularized XGBoost models, employing SHAP values for interpretability.Results: The tuned, regularized XGBoost model achieved strong out-of-sample performance (Test R ² = 0.800)}. A leakage-safe stacked Ridge ensemble yielded comparable performance (Test R ² = 0.827), while preserving the interpretability of the underlying tree model used for SHAP analysis.