Application of Latent Class Analysis to Classify Household Poverty in Development Studies: A Case Study Using Ghana’s 2021 Population and Housing Census Data

Application of Latent Class Analysis to Classify Household Poverty in Development Studies: A Case Study Using Ghana’s 2021 Population and Housing Census Data | 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 Application of Latent Class Analysis to Classify Household Poverty in Development Studies: A Case Study Using Ghana’s 2021 Population and Housing Census Data Edward Owusu Manu, Bernard Afriyie Owusu, Sarah Asaah Owusu-Kwankye, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8071543/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 poverty assessments often rely on thresholds which can obscure the nuanced realities of multidimensional deprivation and the heterogeneous experiences of households. This study aims to empirically identify and characterise distinct multidimensional poverty profiles among Ghanaian households beyond widely used pre-defined cut-off points, and to determine the social, economic, and demographic characteristics that predict membership in these identified profiles. The household census data from Ghana’s 2021 Population and Housing Census was used. Latent Class Analysis (LCA) was employed to identify unobserved groups based on patterns of deprivation across various indicators (e.g., access to ICT, refrigerator, water, housing materials, toilet, cooking fuel, light, room density). Multinomial logistic regression was conducted to examine the influence of household characteristics on the likelihood of belonging to each identified latent class. The LCA model revealed three distinct multidimensional poverty profiles – relatively non-deprived" (34.72% of households, class 1), severely deprived (15.55%, class 2), and moderately deprived (49.73%, class 3). Male-headed, youth-headed, and aged-headed households, and rural, northern, and middle zone residence, were significantlly associated with higher risks of being in classes 2 and 3. This study demonstrates the utility of LCA in providing a nuanced, empirically grounded understanding of multidimensional poverty in Ghana. Social Policy Development Economics Poverty Multidimensional Poverty Latent Class Analysis Deprivations 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-8071543","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":542313630,"identity":"78f90489-5e7c-48a3-8e8d-ac5471d94582","order_by":0,"name":"Edward Owusu 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