{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T19:01:08Z","timestamp":1778698868882,"version":"3.51.4"},"reference-count":28,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T00:00:00Z","timestamp":1768435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Labor market forecasting relies heavily on economic time-series data, often overlooking the \u201chealth\u2013wealth\u201d 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\u20132024) 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 R2 = 0.800). A leakage-safe stacked Ridge ensemble yielded comparable performance (Test R2 = 0.827), while preserving the interpretability of the underlying tree model used for SHAP analysis.<\/jats:p>","DOI":"10.3390\/computation14010022","type":"journal-article","created":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T11:13:21Z","timestamp":1768475601000},"page":"22","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["The Health-Wealth Gradient in Labor Markets: Integrating Health, Insurance, and Social Metrics to Predict Employment Density"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3665-9310","authenticated-orcid":false,"given":"Dingyuan","family":"Liu","sequence":"first","affiliation":[{"name":"School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9492-1773","authenticated-orcid":false,"given":"Qiannan","family":"Shen","sequence":"additional","affiliation":[{"name":"Graduate School of Art and Science, Boston University, Boston, MA 02215, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5731-7244","authenticated-orcid":false,"given":"Jiaci","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Professional Studies, Columbia University, New York, NY 10027, USA"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,15]]},"reference":[{"key":"ref_1","unstructured":"Averett, S.L. 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