A Multidimensional Modeling Approach to Classify the Poverty Levels of Households
DOI:
https://doi.org/10.5614/j.math.fund.sci.2026.57.3.3Keywords:
environment vulnerability, fuzzy modeling, fuzzy set, multidimensional poverty index, poverty classificationAbstract
The conventional method for poverty measurement is the poverty line, which is based only on household income and expenditure. The poverty line can be used to determine whether a given household is poor or not. However, to capture the true nature of poverty, many factors other than income and expenditure should be considered. In this study, we developed a model for developing countries under a multidimensional framework to classify poverty levels of poor households by incorporating a fuzzy approach. The poverty levels are categorized into three levels and threshold values are determined for each level. In order to generate results, we used relevant statistical data from Sri Lanka. The results of this study indicated that individual education, income, and housing characteristics are the most significant factors in determining the poverty level of individual households in Sri Lanka. Further, the developed model provides a menu of focused factors and a policy tool for policymakers to allocate and distribute subsidies for needy areas.
References
Belhadj, B. & Bouanani, M., Attributes Inequality in Multidimensional Poverty Measures Fuzzy Modeling, Soft Computing, 27(4), pp. 1997-2008, 2023.
Asian Development Bank and World Bank, Climate Risk Country Profile: Sri Lanka, Available at: https://reliefweb.int/report/sri-lanka/climate-risk-country-profile-sri-lanka, 2020. [Accessed: July 13, 2023].
Department of Census and Statistics, Sri Lanka, Multidimensional Poverty Index: Brief, 2022. [Accessed June 18, 2023].
NASA, Extreme Weather, https://science.nasa.gov/climate-change/ extreme-weather/, 2024. [Accessed: May 15, 2024].
Marin, S.R., Glasenapp, S., de Almeida Vieira, C., Diniz, G.M., Porsse, M.D.C.S. & Ottoneli, J., Multidimensional Poverty in Silveira Martins/rs: An Application of Alkire-foster Method (af), Revista de Administrao da UFSM, 11(2), pp. 247-267, 09 2018.
Ravallion, M., Issues in Measuring and Modelling Poverty, The Economic Journal, 106(438), pp. 1328-1343, 1996.
Peiris, H.O.W., Chakraverty, S., Perera, S.S.N. & Ranwala, S.M. W., Novel Fuzzy Linguistic Based Mathematical Model to Assess Risk of Invasive Alien Plant Species, Applied Soft Computing, 59, pp. 326-339, 2017.
Klir, G. & Yuan, B., Fuzzy Sets and Fuzzy Logic, Prentice Hall, 1-12, 1995.
Hasan, M.F. & Sobhan, M.A., Describing Fuzzy Membership Function and Detecting the Outlier by Using Five Number Summary of Data, American Journal of Computational Mathematics, 10(3), pp. 410-424, 2020.
Zimmermann, H.J., Fuzzy Set Theory and Its Applications, Springer Science & Business Media, 2011.
Belhadj, B., New Fuzzy Indices of Poverty by Distinguishing Three Levels of Poverty, Research in Economics, 65(3), pp. 221-231, 2011.
Karasan, A. & Erdogan, M., Creating Proactive Behavior for the Risk Assessment by Considering Expert Evaluation: A Case of Textile Manufacturing Plant, Complex & Intelligent Systems, 7(2), pp. 941-959, 2021.
Murty, R.L.N., Kondamudi, S.G., Suryanarayana, M.V. & Giribabu, P., Application of Buckley?s Fuzzy AHP to Identify the Most Important Factor Affecting the Unorganized Micro-Entrepreneurs? Borrowing Decision, International Journal of Management (IJM), 11(6), pp. 665-674, 2020.
Jayathilaka, R., Joachim, S., Mallikarachchi, V., Perera, N. & Ranawaka, D., Do Chronic Illnesses and Poverty Go Hand in Hand?, PloS one, 15(10), e0241232, 2020.
Deyshappriya, N.R. & Feeny, S., Weighting the Dimensions of the Multidimensional Poverty Index: Findings from Sri Lanka, Social Indicators Research, 156(1), pp. 1-19, 2021.
Griliches, Z. & Mason, W.M., Education, Income, and Ability, Journal of Political Economy, 80(3), Part 2, pp. S74-S103, 1972.
ReliefWeb, Sri Lanka: Extreme Weather ? Operation Update No. 1, DREF No. MDRLK015 ? Sri Lanka, Available at: https://reliefweb.int/report/sri-lanka/sri-lanka-extreme-weather-operation-update-ndeg-1-dref-ndeg-mdrlk015, 2022. [Accessed: July 13, 2023].
Weeraratne, B., Re-Defining Urban Areas in Sri Lanka, Institute of Policy Studies of Sri Lanka, 2016.
Department of Census and Statistics, Sri Lanka, Demographic and Health Survey 2016: Full Report, Available at: http://www.statistics.gov.lk/Health/StaticalInformation, 2016. [Accessed July 15, 2023].
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