A Multidimensional Poverty Index for Latin America

Santos, María Emma; Villatoro, Pablo

doi:10.1111/roiw.12275

Cited by 125 publications

(115 citation statements)

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“…At the regional level, World Bank () and later Santos et al. () have recently proposed a new multidimensional poverty index for Latin America, estimating it for 17 countries.…”

Section: Introductionmentioning

confidence: 99%

Job quality and poverty in Latin America

Brummund

Mann²,

Rodríguez‐Castelán³

2018

Review Development Economics

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Labor market dynamics have played a significant role in the remarkable social gains experienced in the recent past across Latin America. Assessing quality of employment, beyond the perspective of income, to include other fundamental aspects of jobssuch as whether jobs are secure, provide benefits, or are satisfyingcan shed light on the sustainability of these achievements. This is particularly pertinent given the region's current economic slowdown. Using harmonized data for 15 countries in Latin America, this paper connects the role of job quality with the recent process of inclusive growth across the region, and particularly with how individuals worked their way out of poverty. The paper first proposes a multi-dimensional measure for job quality, and then uses this measure to compare job quality across countries and over time. The paper also studies correlates of job quality and the relationship between job quality and poverty. One main finding is that job quality across the region began to increase in 2003. The best predictors of job quality are age, gender, firm size, formal employment, and union membership. Benefits and job satisfaction are the dimensions of job quality that are best correlated with not living in poverty.

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“…At the regional level, World Bank () and later Santos et al. () have recently proposed a new multidimensional poverty index for Latin America, estimating it for 17 countries.…”

Section: Introductionmentioning

confidence: 99%

Job quality and poverty in Latin America

Brummund

Mann²,

Rodríguez‐Castelán³

2018

Review Development Economics

View full text Add to dashboard Cite

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“…This has meant that most policy applications incorporating a multiplicatively defined measure of poverty either change these indicators, insofar as even the dimensions, when they try to identify or measure multidimensional poverty in their specific contexts (Dhongde & Haveman, (2015) for USA; Santos, Villatoro, Mancero, & Gerstenfeld, (2015) for Latin American countries; Ura, Alkire, & Zangmo, (2012) for Bhutan; Rippin (2012b) for Germany; Coneval (2009) for Mexico etc.). The question that this analysis tries to address is whether these variables are enough and if not, are there other indicators that are better or equally good proxies for poverty and deprivation measurement?…”

Section: Introductionmentioning

confidence: 99%

Regional Perspectives on the Multidimensional Poverty Index

Pasha

2017

World Development

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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. Terms of use: Documents in AbstractThe Multidimensional Poverty Index (MPI) is not the first attempt to examine poverty along multiple definitions. Vis-a-vis the existing work and other presently available measures, this method has greater advantage in terms of international comparability and reporting. However, the methodology of the Multidimensional Poverty Index (MPI) has been under strong scrutiny since its inception. One of the reasons for these critiques lies in the variation in the MPI country ranks and scores based on different indicators and a different weighing scheme. This paper analyzes the consequences of a different weighting scheme within the MPI, using a more data driven approach rather than a normative or equal weighting scheme. It attempts to assess this alternative weighting via its impact on the scores and relative ranking of various countries. Moreover, it attempts to resolve the differences in the definition of poverty that might emerge upon changing indicators, and thereby evaluate how this affects the construction of the MPI. An analysis covering 22 countries, using the Demographic and Health Survey data, is carried out to quantitatively evaluate the weights assigned to each of the indicators, using the technique of Principal Component Analysis (PCA) and Multiple Correspondence Analysis (MCA). A more detailed country-level analysis is carried out for India, wherein additional indicators based on the data are made available and therefore an alternative multidimensional construct is possible. The analysis shows that equal weighting of the three dimensions cannot be statistically justified and that in trying to capture a more multidimensional view of poverty and well-being, there might actually not be so much multidimensionality in the MPI.

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“…There seems also to be a U-shaped relationship between the household size to which the individual belongs and the probability that they will be deemed poor. The estimates also make clear that ceteris paribus, the individuals from rural areas really have a higher probability of being poor, mainly monetary poor, than those from urban areas, a finding that has been highlighted by the regional and global empirical evidence as well (see, for instance, Battiston et al, 2013;ECLAC, 2013;Alkire and Santos, 2014;Santos and Villatoro, 2016), and that warrants special attention. The probability of being considered as poor seems also to be much larger among individuals living outside the capital, Managua, and it is the highest for individuals living in the Central and Atlantic rural areas, which has also been suggested by Altamirano andDamiano (2017, p. 1051).…”

Section: Determinants Of the Monetary And Multidimensional Povertymentioning

confidence: 81%