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

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  • Germán Caruso
  • Walter Sosa-Escudero
  • Marcela Svarc

Abstract

type="main"> We approach the problems of measuring the dimensionality of welfare and that of identifying the multidimensionally poor, by first finding the poor using the original space of attributes, and then reducing the welfare space. The starting point is the notion that the “poor” constitutes a group of individuals that are essentially different from the “non-poor” in a truly multidimensional framework. Once this group has been identified through a clustering procedure, we propose reducing the dimension of the original welfare space using recent blinding methods for variable selection. We implement our approach to the case of Latin America based on the Gallup World Poll, which contains ample information on many dimensions of welfare.

Suggested Citation

  • Germán Caruso & Walter Sosa-Escudero & Marcela Svarc, 2015. "Deprivation and the Dimensionality of Welfare: A Variable-Selection Cluster-Analysis Approach," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 61(4), pages 702-722, December.
    • Handle: RePEc:bla:revinw:v:61:y:2015:i:4:p:702-722
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    1. Leonardo Gasparini & Pablo Glüzmann, 2012. "Estimating Income Poverty and Inequality from the GallupWorld Poll: The case of Latin America and the Caribbean," Journal of Income Distribution, Ad libros publications inc., vol. 21(1), pages 3-27, March.
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    3. Keely, Louise C. & Tan, Chih Ming, 2008. "Understanding preferences for income redistribution," Journal of Public Economics, Elsevier, vol. 92(5-6), pages 944-961, June.
    4. Alkire, Sabina & Foster, James, 2011. "Counting and multidimensional poverty measurement," Journal of Public Economics, Elsevier, vol. 95(7-8), pages 476-487, August.
    5. Ravallion, Martin & Lokshin, Michael, 2002. "Self-rated economic welfare in Russia," European Economic Review, Elsevier, vol. 46(8), pages 1453-1473, September.
    6. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    7. Fraiman, Ricardo & Justel, Ana & Svarc, Marcela, 2008. "Selection of Variables for Cluster Analysis and Classification Rules," Journal of the American Statistical Association, American Statistical Association, vol. 103(483), pages 1294-1303.
    8. Giovanni Ferro Luzzi & Yves Flückiger & Sylvain Weber, 2008. "A Cluster Analysis of Multidimensional Poverty in Switzerland," Palgrave Macmillan Books, in: Nanak Kakwani & Jacques Silber (ed.), Quantitative Approaches to Multidimensional Poverty Measurement, chapter 4, pages 63-79, Palgrave Macmillan.
    9. Tadesse, Mahlet G. & Sha, Naijun & Vannucci, Marina, 2005. "Bayesian Variable Selection in Clustering High-Dimensional Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 602-617, June.
    10. Raftery, Adrian E. & Dean, Nema, 2006. "Variable Selection for Model-Based Clustering," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 168-178, March.
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    Cited by:

    1. Rolf Aaberge & Andrea Brandolini, 2014. "Multidimensional poverty and inequality," Discussion Papers 792, Statistics Norway, Research Department.
    2. Caruso, Germán & Miller, Sebastian, 2015. "Long run effects and intergenerational transmission of natural disasters: A case study on the 1970 Ancash Earthquake," Journal of Development Economics, Elsevier, vol. 117(C), pages 134-150.
    3. Mario Lucchini & Christine Butti & Sara Della Bella & Angela Lisi, 2018. "The application of a topological clustering technique to capture forms and dynamics of deprivation in contemporary Switzerland," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(1), pages 227-248, January.
    4. Sebastián J. Miller & Germán Caruso, 2014. "Quake'n and Shake'n...Forever! Long-Run Effects of Natural Disasters: A Case Study on the 1970 Ancash Earthquake," IDB Publications (Working Papers) 86774, Inter-American Development Bank.
    5. Caruso, Germán & Scartascini, Carlos & Tommasi, Mariano, 2015. "Are we all playing the same game? The economic effects of constitutions depend on the degree of institutionalization," European Journal of Political Economy, Elsevier, vol. 38(C), pages 212-228.
    6. María Edo & Walter Sosa Escudero & Marcela Svarc, 2021. "A multidimensional approach to measuring the middle class," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 19(1), pages 139-162, March.
    7. Wendy Brau, 2022. "How multidimensional is welfare? A sparse principal components analysis," Young Researchers Working Papers 5, Universidad de San Andres, Departamento de Economia, revised Oct 2022.
    8. Agustín Alvarez & Marcela Svarc, 2021. "A variable selection procedure for depth measures," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(2), pages 247-271, June.
    9. Mariano Tommasi & Germán Caruso & Carlos Scartascini, 2014. "Are We Playing the Same Game? The Economic Effects of Constitutions Depend on the Degree of Institutionalization," Working Papers 116, Universidad de San Andres, Departamento de Economia, revised Dec 2014.

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    More about this item

    JEL classification:

    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other

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