Standard employment forecasting often absorbs national drift, which can obscure the reasons states persistently differ in employment intensity. This study develops and evaluates a forecasting framework for state employment relativity, defined as the log ratio of a state employment per capita measure to its contemporaneous national counterpart. We construct a state year panel for 2014 to 2024 by integrating administrative labor market aggregates, population weighted health and socioeconomic indicators, and small establishment structure measures that proxy local service capacity. To prevent temporal leakage, we use expanding window rolling origin evaluation and treat 2022 to 2024 as a forward held out period for model comparison and interpretation. Averaged over 2022 to 2024, Light Gradient-Boosting Machine (LightGBM) delivers the strongest forward performance with R 2 = 0.87, RMSE = 0.046, and MAE = 0.036 in log points, while tree bagging methods remain competitive but are less stable in the more volatile 2023 environment. SHapley Additive exPlanations (SHAP) based interpretation, evaluated on both the held out period and rolling origin stability diagnostics, identifies a compact and temporally persistent predictor set centered on education composition, health burden proxies, food environment quality, small business services density, and hires intensity. The results support a forecasting interpretation in which state deviations from national employment intensity reflect nonlinear interactions among human capital composition, health access and morbidity, and local enterprise structure. They also show why interpretability claims are stronger when assessed for temporal robustness rather than only pooled averages.