Deprivation analysis based on Bayesian latent class models

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.

Keywords:

• poverty
• deprivation
• Bayesian latent class model
• label-switching
• MCMC method
• Portugal

Acknowledgements

The authors are grateful for the financial support received from the Fundação para a Ciência e Tecnologia through the Centre for Mathematics and Applications, IST and the Research Unit on Complexity and Economics (UECE), ISEG. The authors also thank two referees for their constructive comments.

Notes

In 23, a survey of the literature on deprivation measurement is provided. Recently, other authors have added to this body of literature, such as 9 13 15 18 20 21 26 36 37 and others.

The measurement of income poverty refers to households’ disposable income adjusted by an equivalence scale. The poverty risk is defined, by Eurostat, as the share of persons with an equivalised disposable income below the at-risk-of-poverty threshold (60% of the national median equivalised disposable income) 10.

We eliminated 28 and 54 households with incomplete data in 1995 and 2001, respectively. They represent only a small proportion of the sampled households.

The individuals making up a household are those who share the same space, expenses and budget, regardless of whether they are related or not.

The steps of this algorithm were implemented through MS Excel.

After obtaining the necessary posterior simulated samples, the calculation of the ln CPO values was done via MS Excel.