The traditional approach to poverty measurement utilises only monetary variables as indicators of individuals’ intensity of the state of deprivation, causing measurement errors of the phenomenon under investigation. Moreover, when adopted in a longitudinal context, this approach tends to overestimate transition poverty. Since poverty is not directly observable, a latent definition can be adopted: in such a conception is possible to use Markov chain models in their latent acceptation. This chapter proposes to use Latent class Markov models which allow taking into account more observed (manifest) variables. We define those variables via monetary and non-monetary fuzzy indicators.
Marano, G., Betti, G., Gagliardi, F. (2015). Latent Class Markov Models for Measuring Longitudinal Fuzzy Poverty. In B.E. Carpita M. (a cura di), Advances in Latent Variables: Methods, models and applications (pp. 73-81). New York : Springer [10.1007/10104_2014_4].
Latent Class Markov Models for Measuring Longitudinal Fuzzy Poverty
Betti Gianni
Membro del Collaboration Group
;Gagliardi FrancescaMembro del Collaboration Group
2015-01-01
Abstract
The traditional approach to poverty measurement utilises only monetary variables as indicators of individuals’ intensity of the state of deprivation, causing measurement errors of the phenomenon under investigation. Moreover, when adopted in a longitudinal context, this approach tends to overestimate transition poverty. Since poverty is not directly observable, a latent definition can be adopted: in such a conception is possible to use Markov chain models in their latent acceptation. This chapter proposes to use Latent class Markov models which allow taking into account more observed (manifest) variables. We define those variables via monetary and non-monetary fuzzy indicators.File | Dimensione | Formato | |
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https://hdl.handle.net/11365/1042711