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Activity Number: 389 - Novel Data Collection Strategies in Business and Economics
Type: Contributed
Date/Time: Wednesday, August 10, 2022 : 8:30 AM to 10:20 AM
Sponsor: Business and Economic Statistics Section
Abstract #320995
Title: Spatial Analysis of Multidimensional Poverty in Pakistan: Does Income and Poverty Score of Neighboring Regions Matter?
Author(s): Brian William Sloboda* and Sami Ullah Khan
Companies: University of Maryland, Global Campus and Gomal University
Keywords: spatial statistics; spatial autocorrelations; Multidimensional poverty; Living standards; Pakistan
Abstract:

The eradication of multidimensional poverty remains a stumbling block. This paper examines a multidimensional poverty (MDP) index that has been constructed by adopting the Alkire-Foster methodology and GIS to i) measure the multidimensional poverty in all districts of Pakistan, ii) recognize the spatial patterns, iii) examine the impact of per-capita income on MDP and, iv) assess the spatial impact of neighbors’ poverty scores on MDP. The findings indicated that the magnitude of both headcount and MDP varies across provinces and was found to be higher and lower in Balochistan and Punjab, respectively. The spatial results revealed that in terms of both MDP and headcount poverty, the majority of Punjab districts were significantly clustered in low and very low quantiles (low-low quadrants) whereas, districts from Balochistan were concentrated in high and very high quantiles (high-high quadrants). The spatial analysis showed that per capita income reduced poverty. The poverty score of neighboring districts has significantly influenced the incidence of district Based on the spatial results, Balochistan is considered as the highly poverty-stricken that needs government intervention


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