Unveiling the Dynamics of Household Poverty: Empirical Insights from a Developing Country.
This study examines the determinants of poverty across all provinces using 2019–20 data from the Pakistan Social and Living Standards Measurement (PSLM) survey. The results from binary logistic regression analysis reveal several significant findings; household size, the square of the household head'...
| Publicado en: | Journal of Poverty Vol. 30; no. 1; pp. 52 - 70 |
|---|---|
| Autores principales: | , , |
| Formato: | Conference Paper/Materials |
| Publicado: |
Taylor & Francis Ltd
Jan2026
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=190647734&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 190647734 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 10875549 41T jtl: Journal of Poverty issn: 10875549 maglogo: N pubinfo: dt: Jan2026 vid: 30 iid: 1 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 190647734 10.1080/10875549.2024.2338168 ppf: 52 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P size: 541KB tig: atl: Unveiling the Dynamics of Household Poverty: Empirical Insights from a Developing Country. aug: au: Khan, Irum Hasan, Hamid Rehman, Hafiz Abdur affil: International Institute of Islamic Economics, International Islamic University, Islamabad, Pakistan School of Economics, International Islamic University, Islamabad, Pakistan su: Poverty Poverty reduction Policy analysis Living conditions Logistic regression analysis Poverty rate sug: subj: Poverty Poverty reduction Policy analysis Living conditions Logistic regression analysis Poverty rate keyword: Logit model Pakistan Per Capita Income poverty alleviation PSLM Sustainable Development ab: This study examines the determinants of poverty across all provinces using 2019–20 data from the Pakistan Social and Living Standards Measurement (PSLM) survey. The results from binary logistic regression analysis reveal several significant findings; household size, the square of the household head's age, marital status and province-specific factors are positively associated with a household head's classification as poor. Conversely, the age and gender of the household head, educational levels of the household head, household characteristics, and assets exhibit negative correlations with poverty. The findings will offer valuable insights for policymakers to formulate targeted poverty reduction strategies toward potential solutions. pubtype: Academic Journal doctype: Conference Paper/Materials src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of Journal of Poverty is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: Journal of Poverty holder: Taylor & Francis Ltd dt: @attributes: year: 2026 holdings: @attributes: islocal: N |
|---|