Work

The Health-Wealth Gradient in Labor Markets: Integrating Health, Insurance, and Social Metrics to Predict Employment Density

Year: 2026

Type: article

Abstract: 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.

Source: Computation

DOI: 10.3390/computation14010022

Authors: Dingyuan Liu, Qiannan Shen, Jiaci Liu

Institutions: University of Pennsylvania, Boston University, Columbia University

Language: English

Topic: Regional Economic and Spatial Analysis

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDGs: Decent work and economic growth, Reduced inequalities, Good health and well-being

Open Access status: gold

Estimated APC paid: $2,172