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Household characteristics and poverty: a logistic regression analysis

Author

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  • Mustafa A. Rahman

    (University of Western Sydney, Australia)

Abstract

Poverty being multi-dimensional in nature is the product of various interactive socioeconomic factors. Some of the factors shaping economic status of the household may be cited as widowhood, disability, illiteracy, ageing, household size, household status, dependency, low wages of the female workers, household responsibilities etc. Theory suggests that the ability of a household to earn a given level of income is to a great extent determined by the characteristics internal to the household. The main purpose of this paper is to identify the factors that explain their relative effect on poverty of the household. Poverty thus captured at micro level is expected to provide insights for polices to alleviate poverty at national level. The standard econometric method of logistic regression technique has been used to determine the extent to which the factors influence the probability of a household being poor. The paper is based on data obtained from a sample survey conducted in Bangladesh during 2008–09.

Suggested Citation

  • Mustafa A. Rahman, 2013. "Household characteristics and poverty: a logistic regression analysis," Journal of Developing Areas, Tennessee State University, College of Business, vol. 47(1), pages 303-317, January-J.
    • Handle: RePEc:jda:journl:vol.47:year:2013:issue1:pp:303-317
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    File URL: http://muse.jhu.edu/journals/journal_of_developing_areas/v047/47.1.rahman.html
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    Citations

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    Cited by:

    1. Nosier, Shereen & Beram, Reham & Mahrous, Mohamed, 2021. "Household Poverty in Egypt: Poverty Profile, Econometric Modeling and Policy Simulations," SocArXiv d8spt, Center for Open Science.
    2. Tran, Tuyen Quang & Thi Nguyen, Hoai Thu & Hoang, Quang Ngoc & Van Nguyen, Dinh, 2022. "The influence of contextual and household factors on multidimensional poverty in rural Vietnam: A multilevel regression analysis," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 390-403.
    3. Carlos García & Zaida Quiroz & Marcos Prates, 2023. "Bayesian spatial quantile modeling applied to the incidence of extreme poverty in Lima–Peru," Computational Statistics, Springer, vol. 38(2), pages 603-621, June.
    4. Chenhong Peng & Lue Fang & Julia Shu-Huah Wang & Yik Wa Law & Yi Zhang & Paul S. F. Yip, 2019. "Determinants of Poverty and Their Variation Across the Poverty Spectrum: Evidence from Hong Kong, a High-Income Society with a High Poverty Level," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 144(1), pages 219-250, July.
    5. Salim Shah & Niranjan Debnath, 2022. "Determinants of Multidimensional Poverty in Rural Tripura, India," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(1), pages 69-95, March.
    6. Frank Adusah‐Poku & Kwame Adjei‐Mantey & Paul A. Kwakwa, 2021. "Are energy‐poor households also poor? Evidence from Ghana," Poverty & Public Policy, John Wiley & Sons, vol. 13(1), pages 32-58, March.

    More about this item

    Keywords

    poverty; illiteracy; labour market participation; household-level data; logistic regression;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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