Big Data Analysis of Income Inequality and Distribution in the United States Using IRS and Census Microdata

Big Data Analysis of Income Inequality and Distribution in the United States Using IRS and Census Microdata | 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 Big Data Analysis of Income Inequality and Distribution in the United States Using IRS and Census Microdata Francis Ekow Bediako This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8882668/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 examines income inequality and distribution across U.S. states and demographic groups using linked Internal Revenue Service administrative records combined with Census microdata. By integrating large-scale administrative and survey-based datasets, the analysis overcomes longstanding limitations in traditional inequality measurement, particularly those related to top-income under coverage and reporting bias. The methodological framework emphasizes big data integration, geospatial analysis, and micro–macro reconciliation to ensure consistency between individual-level income records and aggregate national accounts. State-level and demographic disaggregation enable a more precise assessment of spatial and population-based inequality patterns that are often obscured in conventional survey estimates. The findings reveal substantial discrepancies between inequality measures derived from survey data and those based on administrative tax records, with surveys systematically underestimating income concentration at the upper tail of the distribution. High-income earners are shown to be significantly underrepresented in traditional datasets, leading to downward-biased estimates of income shares and inequality trends. Geospatial analysis further highlights pronounced heterogeneity in income concentration across states, as well as persistent disparities across demographic groups. The study concludes that administrative big data sources provide a more granular, accurate, and policy-relevant understanding of income inequality in the United States. By leveraging linked administrative and census records, the approach enhances empirical precision and offers stronger foundations for evaluating distributional outcomes, fiscal policy, and regional economic inequality. Economic Theory Income inequality Big data analytics IRS microdata Census data Administrative records Income distribution United States 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-8882668","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591478176,"identity":"c288664f-a9dd-4efc-b173-350948b5697c","order_by":0,"name":"Francis Ekow Bediako","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0005-0401-6792","institution":"St. Cloud State University, United state","correspondingAuthor":true,"prefix":"","firstName":"Francis","middleName":"Ekow","lastName":"Bediako","suffix":""}],"badges":[],"createdAt":"2026-02-14 21:54:36","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-8882668/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8882668/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102834582,"identity":"6af4b32a-51f4-4919-a459-94fe69944b61","added_by":"auto","created_at":"2026-02-17 10:42:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":530358,"visible":true,"origin":"","legend":"","description":"","filename":"NanaManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8882668/v1_covered_cff16422-363d-480e-a790-89b7830b8fa1.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eBig Data Analysis of Income Inequality and Distribution in the United States Using IRS and Census Microdata\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"St. Cloud State UniversitySt. Cloud State 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":"Income inequality; Big data analytics; IRS microdata; Census data; Administrative records; Income distribution; United States","lastPublishedDoi":"10.21203/rs.3.rs-8882668/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8882668/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines income inequality and distribution across U.S. states and demographic groups using linked Internal Revenue Service administrative records combined with Census microdata. 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