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

The Health-Wealth Gradient in Labor Markets: Integrating Health, Insurance, and Social Metrics to Predict Employment Density | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Health-Wealth Gradient in Labor Markets: Integrating Health, Insurance, and Social Metrics to Predict Employment Density Dingyuan Liu, Qiannan Shen, Jiaci Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8497932/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version 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. Artificial Intelligence and Machine Learning machine learning employment density social determinants of health XGBoost 14 SHAP analysis Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8497932","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":568207602,"identity":"6b925aac-a956-4983-8c79-bd53a34acb0c","order_by":0,"name":"Dingyuan Liu","email":"","orcid":"https://orcid.org/0009-0009-3665-9310","institution":"University of Pennsylvania","correspondingAuthor":false,"prefix":"","firstName":"Dingyuan","middleName":"","lastName":"Liu","suffix":""},{"id":568207603,"identity":"d51ce6ba-5947-4796-bf6c-f5ca2fec46fb","order_by":1,"name":"Qiannan Shen","email":"","orcid":"https://orcid.org/0009-0007-9492-1773","institution":"Boston University","correspondingAuthor":false,"prefix":"","firstName":"Qiannan","middleName":"","lastName":"Shen","suffix":""},{"id":568207604,"identity":"5bb7b231-d14e-4e39-83c9-fc4051034c7d","order_by":2,"name":"Jiaci Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBACxnbmw49//rNhYGBnYGBmMACJJRDQ0syWZszAlgZST6QWBmYeBWkGtsNQLQxEaGFu5mEwLuA5L8/PzGO6uaDAjoGfPceAgMN4DzyeIXHbcGYzj9ntGQbJDJI9bwhp4Usw4DG4nWBwGKiFxwDonRsEbeExkOBJOAfTUs9gT4wWaZ4DB2BaDjMYSBDUwpZmOLMhGegXtjKgX47zSJx5VoBXi2F78+EHHxvs5PnZm7fdLvhTLcffnrwBv5YGNAEevMpBQJ6gilEwCkbBKBgFAMkhP2M1AOqJAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0003-5731-7244","institution":"Columbia University","correspondingAuthor":true,"prefix":"","firstName":"Jiaci","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-01-02 04:08:43","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8497932/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8497932/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99505106,"identity":"1c6009e9-ba44-4dca-9c5c-fb3725143a5b","added_by":"auto","created_at":"2026-01-05 08:25:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":969691,"visible":true,"origin":"","legend":"","description":"","filename":"MDPIRevision1preprintversion.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8497932/v1_covered_227efb50-4c44-4898-97cd-3c42453a4a74.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eThe Health-Wealth Gradient in Labor Markets: Integrating Health, Insurance, and Social Metrics to Predict Employment Density\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Boston University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"machine learning, employment density, social determinants of health, XGBoost, 14 SHAP analysis","lastPublishedDoi":"10.21203/rs.3.rs-8497932/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8497932/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTraditional 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\u003csup\u003e2\u003c/sup\u003e= 0.800)}. 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