<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The study profiled and compared household multidimensional poverty status and its determinants among urban and rural households in the Eastern Cape Province of South Africa, using information from a sample population of 3033 households interviewed from the Province during the General Household Survey conducted by the Statistics South Africa in 2014. Analytical techniques utilized include the recent multidimensional poverty index (MPI), descriptive statistics and Tobit regression. Findings reveal that multiple deprivations are found mostly in the rural area of the province; the multidimensional headcount is highest in the rural area, though the intensity of multidimensional poverty is almost similar in both geographical locations. The standard of living dimension is also the largest contribution to MPI in both locations. MPI has significant links with education attainment of household head, access to electricity and asset stock in both geographical locations, but is influenced by the gender of head, agriculture engagement and household monthly income in rural areas only. In order to improve households’ multidimensional poverty status in both urban and rural locations, there is the need to take into account some significant variables such as education of head, increase electricity subsidy coverage during winter period, asset accumulation and increase in households’ participation in agricultural activities, especially those residing in rural areas.</p></abstract>

The study profiled and compared household multidimensional poverty status and its determinants among urban and rural households in the Eastern Cape Province of South Africa, using information from a sample population of 3033 households interviewed from the Province during the General Household Survey conducted by the Statistics South Africa in 2014. Analytical techniques utilized include the recent multidimensional poverty index (MPI), descriptive statistics and Tobit regression. Findings reveal that multiple deprivations are found mostly in the rural area of the province; the multidimensional headcount is highest in the rural area, though the intensity of multidimensional poverty is almost similar in both geographical locations. The standard of living dimension is also the largest contribution to MPI in both locations. MPI has significant links with education attainment of household head, access to electricity and asset stock in both geographical locations, but is influenced by the gender of head, agriculture engagement and household monthly income in rural areas only. In order to improve households’ multidimensional poverty status in both urban and rural locations, there is the need to take into account some significant variables such as education of head, increase electricity subsidy coverage during winter period, asset accumulation and increase in households’ participation in agricultural activities, especially those residing in rural areas.

Multidimensional Poverty Analysis of Urban and Rural Households in South Africa

Studia Universitatis Babes-Bolyai Oeconomica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Multidimensional Poverty Analysis of Urban and Rural Households in South Africa"}

The study profiled and compared household multidimensional poverty status and its determinants among urban and rural households in the Eastern Cape Province of...

Multidimensional Poverty Analysis of Urban and Rural Households in South Africa

Publié en ligne: 26 mai 2018

Pages: 3 - 19

DOI: https://doi.org/10.2478/subboec-2018-0001

Mots clés
Multidimensional Poverty Index, Urban, Rural, Tobit Regression

© 2018 Ebenezer Toyin Megbowon, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Multidimensional Poverty Analysis of Urban and Rural Households in South Africa

Publié en ligne: 26 mai 2018

Pages: 3 - 19

DOI: https://doi.org/10.2478/subboec-2018-0001

Mots clésMultidimensional Poverty Index, Urban, Rural, Tobit Regression

© 2018 Ebenezer Toyin Megbowon, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
Multidimensional Poverty Index, Urban, Rural, Tobit Regression