Deprivation and the Dimensionality of Welfare: A Variable‐Selection Cluster‐Analysis Approach

Caruso, Germán Daniel; Sosa-Escudero, Walter; Svarc, Marcela

doi:10.1111/roiw.12127

Cited by 14 publications

(7 citation statements)

References 26 publications

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“…Ferro Luzzi et al (2008),Pisati et al (2010),Whelan et al (2010) Lucchini and Assi (2013) andCaruso et al (2014) for an application of cluster analysis to identify population subgroups homogeneous by well-being or deprivation level, andHirschberg et al (1991) for an analogous comparison across countries;Asselin and Anh (2008) andCoromaldi and Zoli (2012) for an application of multiple correspondence analysis and non-linear principal component analysis, respectively. (2012) apply methods developed in efficiency analysis to aggregate the various attributes of well-being.…”

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confidence: 99%

Multidimensional Poverty and Inequality

Aaberge

Brandolini

2015

Handbook of Income Distribution

252

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This paper examines different approaches to the measurement of multidimensional inequality and poverty. First, it outlines three aspects preliminary to any multidimensional study: the selection of the relevant dimensions; the indicators used to measure them; and the procedures for their weighting. It then considers the counting approach and the axiomatic treatment in poverty measurement. Finally, it reviews the axiomatic approach to inequality analysis. The paper provides a selective review of a rapidly growing theoretical literature with the twofold aim of highlighting areas for future research and offering some guidance on how to use multidimensional methods in empirical and policy-oriented applications.

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confidence: 99%

Multidimensional Poverty and Inequality

Aaberge

Brandolini

2015

Handbook of Income Distribution

252

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“…This approach includes clustering and factor analysis techniques, which are better strategies than the synthetic index approach, as they not only assess a full set of items covering different aspect of life but also produce a higher-dimensional profile, especially when data from a database are used [19]. In addition, clustering may facilitate the creation of internally homogeneous but externally heterogeneous poverty groups, which is valuable for comparisons and for identifying both the factors that influence these groups and the appropriate alleviation program [24], [25] . Nevertheless, identifying and classifying poverty based on a multidimensional theory and advanced statistical tools is only the first step.…”

Section: A Multidimensional Approach To the Study Of Poverty And An Assessment Of Poverty Alleviation Programsmentioning

confidence: 99%

Poverty Groups Identification and Assessment of Poverty Alleviation Programs in Rural China

Zhen¹,

Mengxian²

2020

Proceedings of the 6th International Conference on Humanities and Social Science Research (ICHSSR2020)

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This study analyzed the outcomes of 194 individuals living in rural poverty in Guizhou Province using data obtained from a poverty alleviation information system together with several machine-learning tools. First, four dimensions were abstracted from a multiple factor analysis: family background and work condition, self-development, regional context, and health status and labor capacity. Second, five heterogeneous groups were identified through k-medoids clustering based on the above dimensions. The results showed that the most significant differences among poverty groups were related to health status and household size. The effectiveness of poverty alleviation programs for the different poverty groups was then evaluated. By employing association rule mining, we showed that education assistance is effective in poverty groups in relatively 'good' condition but not in others, whereas the 'Minimum Living Standard' program failed in all groups, particularly when it was the sole program. Thus, our study indicated that additional targeted alleviation programs should be implemented to address the needs of different poverty groups, especially in terms of education assistance, medical care, and medical prevention programs.

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“…multidimensional wealth. This is a binary variable calculated using a cluster analysis approach following Caruso, Sosa-Escudero and Svarc (2014) that includes information about the assets available for each household so as to identify the financial status of households.…”

Section: Peruvian National Censusesmentioning

confidence: 99%

Quake'n and Shake'n... Forever! Long-Run Effects of Natural Disasters: A Case Study on the 1970 Ancash Earthquake

Miller

Caruso

2014

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the Inter-American Development Bank, its Board of Directors, or the countries they represent. Terms of use: Documents inThe unauthorized commercial use of Bank documents is prohibited and may be punishable under the Bank's policies and/or applicable laws.Copyright © Inter-American Development Bank. This working paper may be reproduced for any non-commercial purpose. It may also be reproduced in any academic journal indexed by the American Economic Association's EconLit, with previous consent by the Inter-American Development Bank (IDB), provided that the IDB is credited and that the author(s) receive no income from the publication.Cataloging-in-Publication data provided by the Inter-American Development Bank Felipe Herrera Library Caruso, Germán .Quake'n and shake'n… forever! long-run effects of natural disasters: a case study on the 1970 This study estimates the effects of the 1970 Ancash earthquake on human capital accumulation on the affected and subsequent generation, 37 years after the shock, using the Peruvian censuses of 1993 and 2007. The main finding is that males affected by the earthquake in utero completed on average 0.5 years less schooling while females affected by the earthquake completed 0.8 years less schooling. Surprisingly, those whose mothers were affected at birth by the earthquake have 0.4 less years of education, while those whose fathers were affected by the earthquake at birth have no effects on their education. The evaluation of other outcomes also suggests that the level of welfare of the affected individuals has been negatively impacted in the long run. The present investigation supports previous literature on shocks in early childhood, providing evidence of the existence of intergenerational transmission of shocks.JEL classifications: D31, I32, C49

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Deprivation and the Dimensionality of Welfare: A Variable‐Selection Cluster‐Analysis Approach

Cited by 14 publications

References 26 publications

Multidimensional Poverty and Inequality

Multidimensional Poverty and Inequality

Poverty Groups Identification and Assessment of Poverty Alleviation Programs in Rural China

Quake'n and Shake'n... Forever! Long-Run Effects of Natural Disasters: A Case Study on the 1970 Ancash Earthquake

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