Multidimensional Analysis of Water Poverty and Subjective Well-Being: A Case Study on Local Household Variation in Faisalabad, Pakistan

Nadeem, Abdul Majeed; Cheo, Roland; Huang, Shaoan

doi:10.1007/s11205-017-1652-y

Cited by 20 publications

(6 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nonetheless, WPI from other studies in the last 10 years have ranged from severe water poverty in Egypt (WPI ¼ 39.8 average, 14 indicators, 22 governorates) (Jemmali & Sullivan 2014) to low for the Nepalese Kali Gandaki River Basin (WPI ¼ 49.2, average, 14 indicators, 13 administrative areas) (Manandhar et al 2012). At the village level in Pakistan, with scales similar to this study, WPI scores were in the medium range (WPI ¼ 60.3 average, 22 indicators, 10 villages) (Nadeem et al 2018). All these WPIs contained different sub-components in their calculations that were location specific, so comparisons are more relevant for socio-economic issues and an assessment of the methodology applied.…”

Section: Discussionsupporting

confidence: 71%

“…Most studies on water poverty have been explored in large countries where even catchments were orders of magnitude larger than the sites examined here (Nadeem et al 2018). The current analysis characterizes Small Island Developing States such as those in the Caribbean, so comparisons to other studies on larger regions or countries may be of limited relevance in this context.…”

Section: Discussionmentioning

confidence: 96%

See 1 more Smart Citation

Water Poverty Indices of three rural communities in the southern Caribbean

et al. 2021

View full text Add to dashboard Cite

The Caribbean region experiences significant water supply challenges, especially for 30% of the population who live in rural areas. Improved water resource management in this region would enhance water availability for its population, but this requires reliable quantitative data to guide relevant policy decisions. The Water Poverty Index (WPI) and its components can be valuable sources of such data but there has not been any recent calculation of this index for countries or communities in the Caribbean. In this study, the WPI was determined for three rural communities in the southern Caribbean: Carriacou, Grenada; Nariva, Trinidad and Tobago; and Speightstown, Barbados. Using data collected from 606 surveys of households as well as published statistics, the overall WPI scores were calculated to be 65/100 for Carriacou, 65/100 for Nariva and 70/100 for Speightstown, which all fell in the ‘medium-low’ and ‘low’ water poverty scale respectively. Resource and Use components had the lowest scores and thus require urgent attention. There were no statistically significant differences across the major components, which demonstrates the common water related challenges in these Small Island Developing States, despite geographical diversity. This research will aid in defining national water resource management policies in rural communities of the Caribbean.

show abstract

Section: Discussionsupporting

confidence: 71%

Section: Discussionmentioning

confidence: 96%

Water Poverty Indices of three rural communities in the southern Caribbean

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Several indicators have been proposed over the years to assess the state of water stress. The current study employs the water poverty index (WPI), which is a holistic tool that captures the multi-dimensional aspects of water and has been widely used in the literature (Nadeem et al, 2017;Koirala et al, 2020). Similar to the United Nations Human Development IJSE 49,10 Index (HDI) approach, the construction of WPI was put forward by Sullivan (2001).…”

Section: Introductionmentioning

confidence: 99%

“…Although WPI can be applied at different scales (Sullivan et al, 2009), research measuring household level WPI is scarce (Juran et al, 2017). Given that there can be considerable variation between households' water status (Nadeem et al, 2017), the present study attempts to modify the traditional WPI to account for the micro-level differences. Similarly, to account for the multi-dimensional aspect of measuring poverty, the multi-dimensional poverty index (MPI) has been constructed using the Oxford Poverty and Human Development Initiative (OPHI) methodology.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Examining the interstate variations and interlinkage between water poverty and multidimensional poverty in India: evidence from household-level data

Pandey

Mohapatra

Arora

2022

IJSE

View full text Add to dashboard Cite

PurposeThe purpose of the present study is to examine the inter-relationship between the multi-dimensional poverty and water poverty using household level data for Indian states.Design/methodology/approachA modified water poverty index (MWPI) for both rural and urban households was created using the five components approach and various quantifiable proxies. Principal component analysis (PCA) has been used for the construction of MWPI. Multidimensionality of poverty (MPI) is measured using the Alkire and Foster methodology. Further, the study has utilized correlation and Tobit regression analysis to show the relationship between MWPI and MPI.FindingsThe empirical findings suggest that there is a positive and significant relationship between multidimensional poverty and water poverty, with the extent of relationship being greater in rural areas. The results show that in rural areas all the components of water poverty has significant impact on multidimensional poverty, whereas in urban areas except use component all others have significant impact on multidimensional poverty. Further, components of multidimensional poverty were also found to be significantly impacting water poverty.Practical implicationsThe study suggests that policymakers cannot treat both forms of poverty in isolation. If India aims to reduce poverty, then it needs to pay significant attention to improving water conditions.Originality/valueThis is a pioneering attempt to construct water poverty index at the household level while accounting for micro-level differences for Indian economy. It highlights that water poverty leads to multi-dimensional poverty and vice-versa.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-12-2021-0731.

show abstract

Re-examining the Multidimensional Poverty Index of South Africa

Jackson

2023

Soc Indic Res

View full text Add to dashboard Cite

Poverty remains one of the main socio-economic issues in South Africa and is more prevalent amongst black African females, children aged below 18 years, and rural residents with low levels of education. Many local studies focused on money-metric measures in determining poverty levels but few studies examined factors other than income that are also important to multidimensional non-income welfare. Therefore, this study re-examined the extent of multidimensional poverty in South Africa with the derivation of a Multidimensional Poverty Index (MPI) by considering certain dimensions and indicators that have been ignored in previous studies, such as isolation, vulnerability, voicelessness and powerlessness. Using the General Household Survey (GHS) 2018 data, this study adopted two methods to derive the MPI: Method [A] included the above-mentioned additional dimensions and indicators whereas method [B] only included the indicators from the three commonly considered dimensions (education, health and living standards). Focusing on the results from method [A], the descriptive results indicated deprivations were most profound for unemployed African females living in rural areas in Eastern Cape and Limpopo. These deprivations were the highest in the transport asset, sanitation type, refuse removal frequency, water and receipt of post/mail indicators. Furthermore, the econometric analysis found that unemployed Coloured males residing in rural areas in Eastern Cape, KwaZulu-Natal andGauteng were significantly more likely to be MPI poor.The findings also indicated the overall MPI increased as additional dimensions and indicators are added to the method. The increase mainly emanated from the intensity of poverty, as the headcount values were lower. However, the contributions across personal characteristics, dimensions and indicators varied. Furthermore, the newly added dimension (isolation and vulnerability) suggests relatively high deprivations, as well as the indicator receipt of post or mail, has the second-highest deprivation score. It was also found that the education dimension's contribution to multidimensional poverty significantly decreased as additional dimensions and indicators were added to derive the MPI. Lastly, contrary to previous studies, Gauteng was one of the provinces associated with greater MPI poverty likelihood.

show abstract

Multidimensional Analysis of Water Poverty and Subjective Well-Being: A Case Study on Local Household Variation in Faisalabad, Pakistan

Cited by 20 publications

References 25 publications

Water Poverty Indices of three rural communities in the southern Caribbean

Water Poverty Indices of three rural communities in the southern Caribbean

Examining the interstate variations and interlinkage between water poverty and multidimensional poverty in India: evidence from household-level data

Re-examining the Multidimensional Poverty Index of South Africa

Contact Info

Product

Resources

About