Rethinking Dimensions: The South African Multidimensional Poverty Index

Ntsalaze, Lungile; Ikhide, Sylvanus

doi:10.1007/s11205-016-1473-4

Cited by 16 publications

(9 citation statements)

References 59 publications

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“…Other studies have also used composite indices approach to develop national and provincial indices of multiple deprivation (Klaseen, 2000;Noble et al, 2006;Noble, Barnes, Wright, and Roberts, 2010). More recently, Finn et al, 2013;Woolard et al, 2013;Stat SA, 2014;Alkire and Santos, 2014;Ntsalaze and Ikhide, 2016;Mushongera et al, 2017;Frame et al, 2016;Rogan 2016;Pasha, 2016;OPHI, 2015;OPHI, 2017) have considered multidimensional poverty vis-a-vis gender dimension, youth dimension and cash grants at the national level using the Alkire and Foster (2011) technique and presenting mostly descriptive inferences. The Alkire and Foster MPI methodology has many advantages, which include its decompostional ability of helping to know how much each indicator and each dimension contributes to overall poverty and its ability to allow poverty comparisons across countries and regions of the world, as well as within-country comparisons between regions, ethnic groups, rural and urban areas, and other key household and community characteristics .…”

Section: Literature Reviewmentioning

confidence: 99%

Multidimensional Poverty Analysis of Urban and Rural Households in South Africa

Megbowon

2018

Studia Universitatis Babeș-Bolyai Oeconomica

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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. JEL classification: I32

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Section: Literature Reviewmentioning

confidence: 99%

Multidimensional Poverty Analysis of Urban and Rural Households in South Africa

Megbowon

2018

Studia Universitatis Babeș-Bolyai Oeconomica

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“…Klasen (2000) This chapter aims to apply Ntsalaze and Ikhide's (2016b) framework and present multidimensional poverty rates for South Africa in 2012, using household-level panel data from the National Income Dynamics Study (NIDS) Survey, and compare its racial and spatial distributions across rural and urban areas. In the multidimensional setting, the main questions are: who is considered to be poor and how can an aggregate poverty measure be obtained?…”

Section: Construction Of the South Africanmentioning

confidence: 99%

“…Other recent studies considered the multidimensional nature of poverty (see Burger et al, 2004;Adams et al, 2015;Bhorat & Van der Westhuizen, 2013;Finn et al, 2013) (2016) focused on the forms of deprivation faced by the current generation of young people in South Africa, and found that education and economic opportunities contributed the highest shares to the overall Youth MPI score. The next section implements the SAMPI-OI as proposed by Ntsalaze and Ikhide (2016b) to determine the multidimensional poverty index and the related poverty rates.…”

Section: Multidimensional Poverty Measurement In South Africamentioning

confidence: 99%

The threshold effects of household indebtedness on multidimensional poverty

Ntsalaze

Ikhide

2017

IJSE

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DeclarationBy submitting this dissertation electronically, I declare that the entirety of the work contained therein is my own, original work, that I am the sole author thereof (save to the extent explicitly otherwise stated), that reproduction and publication thereof by Stellenbosch University will not infringe any third party rights and that I have not previously in its entirety or in part submitted it for obtaining any qualification.This dissertation includes two published and one accepted but not yet published original papers in peer-reviewed international journals. The fourth paper representing chapter eight is ready for submission for peer review. The development and writing of the papers (published and unpublished) were the principal responsibility of myself and, for each of the cases where this is not the case, a declaration is included in the dissertation indicating the nature and extent of the contributions of co-authors. AbstractGlobal household indebtedness has reached unprecedented levels over the past few decades.The household sector has had to cope with significant losses in income and wealth as well as the burden of debt service since the beginning of the financial crisis. Research focus on this phenomenon, together with its social implications, has grown. This study uses the National Income Dynamics Study (NIDS) data to empirically investigate the effects of household debt on multidimensional poverty. This was achieved through four independent research papers meant to address different angles of the subject. Chapter 5 provides a snapshot of the prevalence of over-indebtedness, using various international indicators and the National Credit Regulator (NCR) indicator, and describes which households are over-indebted. A total of eight percent of South African households are overindebted, and 61.4 percent of those households are found in the lowest income category (R0 -R2 000), spending more than 45 percent of their household income on debt repayments, which is beyond levels that are considered sustainable. The alarming revelation is that, according to the unsecured debt indicator, 15.2 percent of households are over-indebted, while 11 percent of households are driven below the relative income poverty line after making debt repayments. Stellenbosch University https://scholar.sun.ac.za 3The racial distribution indicates that households headed by Africans are overrepresented (79%).Most over-indebted households are found amongst those who own their places of residence (78.6%), do not receive government grants (71.7%), are male (53.8%), and have an unemployed household head (53.5%).Chapter 6 examines the presence of thresholds in the debt-poverty nexus at micro level, i.e. the tipping point above which debt is associated with more multidimensional poverty. By applying the Generalised Additive Model (GAM) using regression splines, the study finds the existence of critical tipping points between household debt service-to-income ratio and multidimensional poverty along with other explanatory variables (age...

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“…Los Rios and Los Rios (2010), in Chile, similarly found improvements in the coherence of the poverty definition by adding new dimensions including shame which increased the MPI poverty incidence by around 11 per cent, (dis-)empowerment by about 19 per cent and violence by 21 per cent. Working in South Africa, Ntsalaze and Ikhide (2018) did not include psycho-social dimensions but discovered that over-indebtedness and unemployment, both shown in other studies to be major sources of stress, needed to be added to the MPI to enhance its validity. Also in South Africa, Fransman and Yu (2019) note the association of poverty with physical and social isolation and with feelings of vulnerability, powerlessness and helplessness but fail to measure them, while (Bessell, 2015;Wisor et al, 2015) As the list of dimensions has expanded, longstanding debates have been reignited about whether low income is integral to the very experience of poverty or merely a convenient indicator of other deprivations intrinsic to poverty (Ringen, 1988).…”

Section: Poverty: On the Idea Of Multidimensionalitymentioning

confidence: 99%