Urban poverty and vulnerability in global South: An alternative multidimensional framework for measurement and targeting

Rath, Anita

doi:10.1111/rsp3.12511

Cited by 5 publications

(1 citation statement)

References 31 publications

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“…Therefore, lack and shortage of food might result in poor dietary intake, especially in children, thus leading to stunted growth. Similar findings were revealed for Indonesia, Pakistan, and India [ 21 , 22 , 23 , 24 , 25 ]. Poor socio-economic status, unavailability of adequate sanitation and hygienic facilities, parental education, incomplete vaccination, and lack of safe drinking water were the most important covariates that significantly affected childhood stunting.…”

Section: Literature Review On Child Malnutritionsupporting

confidence: 82%

Spatial and Machine Learning Approach to Model Childhood Stunting in Pakistan: Role of Socio-Economic and Environmental Factors

Usman¹,

Kopczewska²

2022

IJERPH

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This study presents the determinants of childhood stunting as the consequence of child malnutrition. We checked two groups of factors—the socio-economic situation and climate vulnerability—using disaggregated sub-regional data in the spatial context. Data related to the percentage of stunted children in Pakistan for 2017 were retrieved from MICS 2017-18 along with other features. We used three quantitative models: ordinary least squares regression (OLS) to examine the linear relationships among the selected features, spatial regression (SDEM) to identify and capture the spatial spillover effect, and the Extreme Gradient Boosting machine learning algorithm (XGBoost) to analyse the importance of spatial lag and generate predictions. The results showed a high degree of spatial clustering in childhood stunting at the sub-regional level. We found that a 1 percentage point (p.p.) increase in multi-dimensional poverty may translate into a 0.18 p.p. increase in childhood stunting. Furthermore, high climate vulnerability and common marriages before age 15 each exacerbated childhood stunting by another 1 p.p. On the contrary, high female literacy and their high exposure to mass media, together with low climate vulnerability, may reduce childhood stunting. Model diagnostics showed that the SDEM outperformed the OLS model, as AICOLS = 766 > AICSDEM = 760. Furthermore, XGBoost generated the most accurate predictions in comparison to OLS and SDEM, having the lowest root-mean-square error (RMSE).

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Section: Literature Review On Child Malnutritionsupporting

confidence: 82%

Spatial and Machine Learning Approach to Model Childhood Stunting in Pakistan: Role of Socio-Economic and Environmental Factors

Usman¹,

Kopczewska²

2022

IJERPH

View full text Add to dashboard Cite

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Multidimensional deprivation across social groups in urban india: a state-level analysis

Negi,

Sahoo,

Patra

2024

J. Soc. Econ. Dev.

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Multi-perspective perception of urban relative poverty and its associated factors analysis: A case study of Wuhan City

Chao,

Gao,

Liu

et al. 2024

Cities

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Urban poverty and vulnerability in global South: An alternative multidimensional framework for measurement and targeting

Abstract: The dynamics of urban poverty and vulnerability may play out differently in the Global South, which has been witnessing large scale urbanization not strictly driven by economic forces.

Cited by 5 publications

References 31 publications

Spatial and Machine Learning Approach to Model Childhood Stunting in Pakistan: Role of Socio-Economic and Environmental Factors

Spatial and Machine Learning Approach to Model Childhood Stunting in Pakistan: Role of Socio-Economic and Environmental Factors

Multidimensional deprivation across social groups in urban india: a state-level analysis

Multi-perspective perception of urban relative poverty and its associated factors analysis: A case study of Wuhan City

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