Asset indexes and the measurement of poverty, inequality and welfare in Southeast Asia

Deutsch, Joseph; Silber, Jacques; Wan, Guanghua; Zhao, Mengxue

doi:10.1016/j.asieco.2020.101220

Cited by 20 publications

(16 citation statements)

References 29 publications

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“… Ayala and Navarro (2007) used a latent class model assigning households to different classes, showing that a vector of observed variables –having hot running water, heating, a leaky roof, damp walls or floor, rot in window frames and floors and overcrowding– and the correlations among such variables could be explained by a single latent variable. Other authors have used the latent trait model under item response theory (IRT) to measure poverty ranking assets according to the prevalence of ownership of durables, including housing equipment ( Deutsch et al, 2020 ). Martínez and Navarro (2016) used IRT to analyze a set of indicators of material deprivation, including some of the most common housing indicators.…”

Section: A Fuzzy Approach For the Measurement Of Housing Deprivationmentioning

confidence: 99%

COVID-19 lockdown and housing deprivation across European countries

Cañón

Bárcena‐Martín

Cantó

et al. 2022

Social Science & Medicine

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Section: A Fuzzy Approach For the Measurement Of Housing Deprivationmentioning

confidence: 99%

COVID-19 lockdown and housing deprivation across European countries

Cañón

Bárcena‐Martín

Cantó

et al. 2022

Social Science & Medicine

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“…This approach uses absolute poverty and income inequality variables. Deutsch et al (2020) researched poverty and inequality in the Southeast Asian region. This research is only descriptive so it is lacking in analyzing the relationship between the two variables.…”

Section: Literature Reviewmentioning

confidence: 99%

The Relationship Between Absolute Poverty Income Inequality in Rural and Urban Areas in Indonesia: The Granger Causality Approach

Mustika

Haryadi²,

Junaidi³

et al. 2022

JEP

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This study aims to determine the relationship between poverty and inequality in rural and urban areas by using the Granger causality test approach. This research method uses the Granger causality test analysis tool with rural and urban areas as objects in all provinces in Indonesia with use data from thirty four province and period year from 2014 until 2020. The results of the Granger causality test for rural areas show that there is a realtionship unidirectional relationship between poverty and inequality, the relationship shows significant inequality that causes poverty in rural areas, that meaning in rural area absolute poverty happen because inequality income distribution, while the results of causality tests for urban areas show that there is no relationship at all between poverty and inequality in the urban areas. that meaning In urban areas no causality between absolute poverty and inequality income distribution, this result shows that poverty and inequality in urban areas do not contain a direct one way or two-way relationship.

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“…Most studies have primarily investigated the impacts of transport disadvantage (Carroll et al, 2021 ; Oviedo & Sabogal, 2020 ), housing affordability (Edwards, 2016 ; Gallent, 2016 ), and social inequality and poverty (Deutsch et al, 2020 ; Lai & Taylor-Robinson, 2018 ; Moro et al, 2021 ), respectively, rather than using a simple composite vulnerability index to assess the overall vulnerability of individuals at an aggregated level.…”

Section: Introductionmentioning

confidence: 99%

Development of a composite regional vulnerability index and its relationship with the impacts of the COVID-19 pandemic

et al. 2023

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The interactions between vulnerability and human activities have largely been regarded in terms of the level of risk they pose, both internally and externally, for certain groups of disadvantaged individuals and regions/areas. However, to date, very few studies have attempted to develop a comprehensive composite regional vulnerability index, in relation to travel, housing, and social deprivation, which can be used to measure vulnerability at an aggregated level in the social sciences. Therefore, this research aims to develop a composite regional vulnerability index with which to examine the combined issues of travel, housing and socio-economic vulnerability (THASV index). It also explores the index’s relationship with the impacts of the COVID-19 pandemic, reflecting both social and spatial inequality, using Greater London as a case study, with data analysed at the level of Middle Layer Super Output Areas (MSOAs). The findings show that most of the areas with high levels of composite vulnerability are distributed in Outer London, particularly in suburban areas. In addition, it is also found that there is a spatial correlation between the THASV index and the risk of COVID-19 deaths, which further exacerbates the potential implications of social deprivation and spatial inequality. Moreover, the results of the multiscale geographically weighted regression (MGWR) show that the travel and socio-economic indicators in a neighbouring district and the related vulnerability indices are strongly associated with the risk of dying from COVID-19. In terms of policy implications, the findings can be used to inform sustainable city planning and urban development strategies designed to resolve urban socio-spatial inequalities and the potential related impacts of COVID-19, as well as guiding future policy evaluation of urban structural patterns in relation to vulnerable areas.

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Asset indexes and the measurement of poverty, inequality and welfare in Southeast Asia

Cited by 20 publications

References 29 publications

COVID-19 lockdown and housing deprivation across European countries

COVID-19 lockdown and housing deprivation across European countries

The Relationship Between Absolute Poverty Income Inequality in Rural and Urban Areas in Indonesia: The Granger Causality Approach

Development of a composite regional vulnerability index and its relationship with the impacts of the COVID-19 pandemic

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