International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies


:: International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

ISSN 2228-9860
eISSN 1906-9642


Vol.11(13) (2020)


    Rifat Mahmood, Ijaz Hussain (Department of Economics, Gomal University, Dera Islami Khan, Khyber Pakhtunkhwa, PAKISTAN).

    Disciplinary: Economic Sciences, Poverty and Wellbeing Studies, Socio-Economic Study, South Asia Study.

    ➤ FullText

    DOI: 10.14456/ITJEMAST.2020.264

    Keywords: Poverty indices; MPI; Multidimensional poor; Pakistan's provinces; Khyber Pakhtunkhwa; Punjab; Sindh; Baluchistan; Poverty dimension; Poverty indicators; PSLM; Poverty measurement; Household poverty; Headcount ratio; Poverty assessment; Average poverty.

    Poverty has multidimensional aspects. That is, it does not only capture the lack of consumption expenditure in terms of food poverty, but it also considers education, sanitation, housing, health, and other aspects as well. Thus, the assessment of poverty also considers the incorporation of all aspects during its assessment phase. Therefore, the new methodology of poverty assessment captures these deficiencies/deprivations that people face. The paper estimates the multidimensional poverty index (MPI), by considering five dimensions i.e. the quality of housing, health facility, education, basic needs, and living standards with 11 indicators. The theme of this paper is to consider all dimensions right at the time of poverty assessment, not to consider after the poverty assessment (as effects of poverty). This will clearly explain the multidimensional aspects of poverty assessment. This study will adopt a methodology used by Alkire and Foster (2007). This study used PSLM (Pakistan Social and Living Standard Measurement Survey, Round VII, 2013-14), which is micro-level data covered eighteen thousand households at the provincial level collected by the Pakistan Bureau of Statistics. This study finds that the nation-wise headcount ratio is 87% and MPI is 35%. The provinces with the highest MPI are seen in Sindh and Balochistan by 40% and minimum MPI are noticed in the provinces Punjab by 31% followed by Kyber Pakhtunwa by 34%.

    Paper ID: 11A13R

    Cite this article:

    Mahmood, R., and Hussain, I. (2020). ESTIMATION OF PROVINCIAL MULTIDIMENSIONAL POVERTY INDEX (MPI) OF PAKISTAN. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 11(13), 11A13R, 1-10.


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