Modeling Career Mobility and Attrition Using Markov Chains and Survival Analysis

Modeling Career Mobility and Attrition Using Markov Chains and Survival Analysis | 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 Modeling Career Mobility and Attrition Using Markov Chains and Survival Analysis Mohamed Yasser BOUNNITE This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6398455/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 This study presents a stochastic modeling framework for analyzing career progression and employee retention through three complementary methodologies: discrete-time Markov chains, survival analysis, and multivariate logistic regression. Using longitudinal human resources data from a multinational technology corporation, we model transitions between four hierarchical states: junior, confirmed, manager, and exit; while addressing data censoring through robust imputation techniques. The transition matrix estimation incorporates bootstrap confidence intervals and regularization to ensure statistical reliability. Key findings reveal substantial inertia in managerial roles, with a 75% probability of remaining unchanged between periods. A critical salary threshold emerges at 6,500 euros monthly, beyond which attrition risk decreases by 60%. Survival analysis identifies elevated exit probabilities between two and five years of tenure, suggesting strategic intervention windows. While promotions reduce attrition odds non-linearly, most effectively at junior levels, their protective effect diminishes by 34% for managerial positions. Multivariate models confirm job satisfaction and promotion history as dominant retention predictors, with respective odds ratios of 0.73 and 0.66. The framework demonstrates strong predictive validity (adjusted R-squared = 0.85) and conforms to normality assumptions (Shapiro-Wilk p-value = 0.15). Methodological constraints include potential survivorship bias in tenure data and discrete time intervals masking shortterm transitions. Practical applications enable organizations to simulate policy impacts, optimize retention budgets, and design personalized career pathways. These contributions advance both academic research in organizational behavior and evidence-based human capital management strategies. Full computational implementations are provided for reproducibility and practical adaptation. Applied Statistics Markov Chains Career Mobility Regularized Estimation Survival Analysis Human Resources Analytics 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-6398455","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":451480184,"identity":"a747b9cc-eca2-4516-8799-cea6e0faaec6","order_by":0,"name":"Mohamed Yasser BOUNNITE","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYFACHhjJfADESCBeCw8DWwJpWoAUjwFxWnQbeA8+5t1xR8aevefzZ94d9/IY2A8f3YBPi9kBvmRj3jPPeHh4zm6T5j1TXMzAk5Z2A78WHjNp3rbDPDwSuduYedsSEhskeMyI1CL/5vFnErVI8DBIE6flMF+y4VyQljNpZpJzzyQkthH0y/Hegw/eth22Z28//PjD2x0Jif3sh4/h1cLAjMxhbGBgYMOrHAOAtIyCUTAKRsEoQAcA2w5E4E4bjMcAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0002-6221-9270","institution":"SUP'RH Business School \u0026 AI","correspondingAuthor":true,"prefix":"","firstName":"Mohamed","middleName":"Yasser","lastName":"BOUNNITE","suffix":""}],"badges":[],"createdAt":"2025-04-08 02:42:39","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-6398455/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6398455/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82015693,"identity":"6f459e8d-32d5-4163-9153-ebf4bbbb4301","added_by":"auto","created_at":"2025-05-06 03:28:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":954159,"visible":true,"origin":"","legend":"","description":"","filename":"JournalRH.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6398455/v1_covered_9c8fdf15-d65a-4242-959f-45d59951bd56.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cu\u003eModeling\u003c/u\u003e Career Mobility and Attrition Using Markov Chains and Survival Analysis\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"SUP'RH Business School \u0026 AI","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":"Markov Chains, Career Mobility, Regularized Estimation, Survival Analysis, Human Resources Analytics","lastPublishedDoi":"10.21203/rs.3.rs-6398455/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6398455/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents a stochastic modeling framework for analyzing career progression and employee retention through three complementary methodologies: discrete-time Markov chains, survival analysis, and multivariate logistic regression. 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