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A dynamic inhomogeneous latent state model for measuring material deprivation

Author

Listed:
  • Francesco Dotto
  • Alessio Farcomeni
  • Maria Grazia Pittau
  • Roberto Zelli

Abstract

Material deprivation can be used to assess poverty in a society. The status of poverty is not directly observable, but it can be measured with error for instance through a list of deprivation items. Given two unobservable classes, corresponding to poor and not poor, we develop a time inhomogeneous latent Markov model which enables us to classify households according to their current and intertemporal poverty status, and to identify transitions between classes that may occur year by year. Households are grouped by estimating their posterior probability of belonging to the latent status of poverty. We then estimate an optimal weighting scheme, associated with the list of items, to obtain an optimal deprivation score. Our score is arguably better at predicting the poverty status than simple item counting (equal weighting). We use the longitudinal component of the European Union statistics Survey on Income and Living Conditions for evaluating poverty patterns over the period 2010–2013 in Greece, Italy and the UK.

Suggested Citation

  • 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.
  • Handle: RePEc:bla:jorssa:v:182:y:2019:i:2:p:495-516
    DOI: 10.1111/rssa.12408
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    References listed on IDEAS

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    1. Walter Bossert & Satya R. Chakravarty & Conchita D’Ambrosio, 2019. "Poverty and Time," Themes in Economics, in: Satya R. Chakravarty (ed.), Poverty, Social Exclusion and Stochastic Dominance, pages 63-82, Springer.
      • BOSSERT, Walter & CHAKRAVARTY, Satya R. & D’AMBROSIO, Conchita, 2008. "Poverty and Time," Cahiers de recherche 05-2008, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
      • Walter Bossert & Satya R. Chakravarty & Conchita D’Ambrosio, 2008. "Poverty and Time," Working Papers 87, ECINEQ, Society for the Study of Economic Inequality.
      • Walter Bossert & Satya R. Chakravarty & Conchita D'Ambrosio, 2010. "Poverty and Time," WIDER Working Paper Series wp-2010-074, World Institute for Development Economic Research (UNU-WIDER).
      • BOSSERT, Walter & CHAKRAVARTY, Satya R. & D’AMBROSIO, Conchita, 2008. "Poverty and Time," Cahiers de recherche 2008-05, Universite de Montreal, Departement de sciences economiques.
    2. Fotis Papadopoulos & Panos Tsakloglou, 2015. "Chronic material deprivation and long-term poverty in Europe in the pre-crisis period," ImPRovE Working Papers 15/16, Herman Deleeck Centre for Social Policy, University of Antwerp.
    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. Francesco Lagona & Antonello Maruotti & Fabio Padovano, 2015. "Multilevel multivariate modelling of legislative count data, with a hidden Markov chain," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(3), pages 705-723, June.
    5. Scott S. L., 2002. "Bayesian Methods for Hidden Markov Models: Recursive Computing in the 21st Century," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 337-351, March.
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    Cited by:

    1. Alessio Farcomeni & Monia Ranalli & Sara Viviani, 2021. "Dimension reduction for longitudinal multivariate data by optimizing class separation of projected latent Markov models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 462-480, June.
    2. Fulvia Pennoni & Ewa Genge, 2020. "Analysing the course of public trust via hidden Markov models: a focus on the Polish society," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 399-425, June.
    3. 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).
    4. Gordon Anderson & Alessio Farcomeni & Maria Grazia Pittau & Roberto Zelli, 2019. "Rectangular latent Markov models for time‐specific clustering, with an analysis of the wellbeing of nations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(3), pages 603-621, April.
    5. Francesco Bartolucci & Alessio Farcomeni, 2022. "A hidden Markov space–time model for mapping the dynamics of global access to food," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 246-266, January.

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