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Deprivation analysis based on Bayesian latent class models

Author

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  • Carla Machado
  • Carlos Daniel Paulino
  • Francisco Nunes

Abstract

This article seeks to measure deprivation among Portuguese households, taking into account four well-being dimensions - housing, durable goods, economic strain and social relationships - with survey data from the European Community Household Panel. We propose a multi-stage approach to a cross-sectional analysis, side-stepping the sparse nature of the contingency tables caused by the large number of variables considered and bringing together partial and overall analyses of deprivation that are based on Bayesian latent class models via Markov Chain Monte Carlo methods. The outcomes demonstrate that there was a substantial improvement on household overall well-being between 1995 and 2001. The dimensions that most contributed to the risk of household deprivation were found to be economic strain and social relationships.

Suggested Citation

  • Carla Machado & Carlos Daniel Paulino & Francisco Nunes, 2009. "Deprivation analysis based on Bayesian latent class models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(8), pages 871-891.
    • Handle: RePEc:taf:japsta:v:36:y:2009:i:8:p:871-891
    DOI: 10.1080/02664760802520769
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    References listed on IDEAS

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    1. Alessio Fusco & Paul Dickes, 2008. "The Rasch Model and Multidimensional Poverty Measurement," Palgrave Macmillan Books, in: Nanak Kakwani & Jacques Silber (ed.), Quantitative Approaches to Multidimensional Poverty Measurement, chapter 3, pages 49-62, Palgrave Macmillan.
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    1. Francesco Dotto & Alessio Farcomeni & Maria Grazia Pittau & Roberto Zelli, 2019. "A dynamic inhomogeneous latent state model for measuring material deprivation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(2), pages 495-516, February.
    2. Alaimo, Leonardo Salvatore & Ivaldi, Enrico & Landi, Stefano & Maggino, Filomena, 2022. "Measuring and evaluating socio-economic inequality in small areas: An application to the urban units of the Municipality of Genoa," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    3. Hector E. Najera Catalan, 2017. "Multiple Deprivation, Severity and Latent Sub-Groups: Advantages of Factor Mixture Modelling for Analysing Material Deprivation," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 131(2), pages 681-700, March.
    4. Tullio, Federico & Bartolucci, Francesco, 2019. "Evaluating time-varying treatment effects in latent Markov models: An application to the effect of remittances on poverty dynamics," MPRA Paper 91459, University Library of Munich, Germany.
    5. Candace A. Moonansingh & Wendell C. Wallace & Isaac Dialsingh, 2019. "From Unidimensional to Multidimensional Measurement of Poverty in Trinidad and Tobago: The Latent Class Analysis of Poverty Measurement as an Alternative to the Financial Deprivation Model," Poverty & Public Policy, John Wiley & Sons, vol. 11(1-2), pages 57-72, July.

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