Spatial distribution of poverty in Pakistan: an asset-based approach
Abstract The main objective of this study is to construct a valid and reliable asset index at household level by using NSER-BISP data in order to compute asset poverty for provinces, districts, and tehsils of the Pakistan. An asset index may be better measure than current income or expenditure for g...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-01-01
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| Series: | Future Business Journal |
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| Online Access: | https://doi.org/10.1186/s43093-022-00162-4 |
| _version_ | 1797952471102914560 |
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| author | Kifayat Ullah Muhammad Zubair Chishti |
| author_facet | Kifayat Ullah Muhammad Zubair Chishti |
| author_sort | Kifayat Ullah |
| collection | DOAJ |
| description | Abstract The main objective of this study is to construct a valid and reliable asset index at household level by using NSER-BISP data in order to compute asset poverty for provinces, districts, and tehsils of the Pakistan. An asset index may be better measure than current income or expenditure for gauging household’s long-term capacity for buying goods and services and its potential resilience to economic shocks. The study employs multiple correspondence analysis (MCA) to construct asset index contrary to principal component analysis (PCA), as MCA provides us weights and contributions of each dimension of binary variable separately. The average MCA score is showing the level of asset-based poverty wherein higher values of index are representing higher level of poverty. The findings indicate that incidence of asset-based poverty is differently observed across provinces and within provinces through disaggregation of the MCA score at district and tehsil levels. By and large, the poorest districts of Pakistan are belonging to Baluchistan (i.e., Sherani, Kohlu, Chaghi, and Dera Bugti) and Sindh (i.e., Badin, Umerkot, Tando Muhammad Khan, and Tharparker) provinces; however, districts of Punjab (i.e., Lahore, Rawalpindi, and Gujranwala) province are found relatively lower asset-based poverty. Further, the analysis highlights the prevalence of asset-based poverty at tehsils level as well where again the tehsils of Baluchistan and Sindh provinces are bearing the highest asset-based poverty. Furthermore, the study also contributes by visualizing the prevalence of geographical asset-based poverty at district level for all four provinces of Pakistan by GIS mapping. |
| first_indexed | 2024-04-10T22:47:59Z |
| format | Article |
| id | doaj.art-372ba23bcf79498ea6d5b34f95ba914a |
| institution | Directory Open Access Journal |
| issn | 2314-7210 |
| language | English |
| last_indexed | 2024-04-10T22:47:59Z |
| publishDate | 2023-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Future Business Journal |
| spelling | doaj.art-372ba23bcf79498ea6d5b34f95ba914a2023-01-15T12:13:11ZengSpringerOpenFuture Business Journal2314-72102023-01-019112010.1186/s43093-022-00162-4Spatial distribution of poverty in Pakistan: an asset-based approachKifayat Ullah0Muhammad Zubair Chishti1School of Economics, Quaid I Azam UniversitySchool of Economics, Quaid I Azam UniversityAbstract The main objective of this study is to construct a valid and reliable asset index at household level by using NSER-BISP data in order to compute asset poverty for provinces, districts, and tehsils of the Pakistan. An asset index may be better measure than current income or expenditure for gauging household’s long-term capacity for buying goods and services and its potential resilience to economic shocks. The study employs multiple correspondence analysis (MCA) to construct asset index contrary to principal component analysis (PCA), as MCA provides us weights and contributions of each dimension of binary variable separately. The average MCA score is showing the level of asset-based poverty wherein higher values of index are representing higher level of poverty. The findings indicate that incidence of asset-based poverty is differently observed across provinces and within provinces through disaggregation of the MCA score at district and tehsil levels. By and large, the poorest districts of Pakistan are belonging to Baluchistan (i.e., Sherani, Kohlu, Chaghi, and Dera Bugti) and Sindh (i.e., Badin, Umerkot, Tando Muhammad Khan, and Tharparker) provinces; however, districts of Punjab (i.e., Lahore, Rawalpindi, and Gujranwala) province are found relatively lower asset-based poverty. Further, the analysis highlights the prevalence of asset-based poverty at tehsils level as well where again the tehsils of Baluchistan and Sindh provinces are bearing the highest asset-based poverty. Furthermore, the study also contributes by visualizing the prevalence of geographical asset-based poverty at district level for all four provinces of Pakistan by GIS mapping.https://doi.org/10.1186/s43093-022-00162-4PovertyNSER-BISPMCAPCAPakistan |
| spellingShingle | Kifayat Ullah Muhammad Zubair Chishti Spatial distribution of poverty in Pakistan: an asset-based approach Future Business Journal Poverty NSER-BISP MCA PCA Pakistan |
| title | Spatial distribution of poverty in Pakistan: an asset-based approach |
| title_full | Spatial distribution of poverty in Pakistan: an asset-based approach |
| title_fullStr | Spatial distribution of poverty in Pakistan: an asset-based approach |
| title_full_unstemmed | Spatial distribution of poverty in Pakistan: an asset-based approach |
| title_short | Spatial distribution of poverty in Pakistan: an asset-based approach |
| title_sort | spatial distribution of poverty in pakistan an asset based approach |
| topic | Poverty NSER-BISP MCA PCA Pakistan |
| url | https://doi.org/10.1186/s43093-022-00162-4 |
| work_keys_str_mv | AT kifayatullah spatialdistributionofpovertyinpakistananassetbasedapproach AT muhammadzubairchishti spatialdistributionofpovertyinpakistananassetbasedapproach |