Latent class Markov models for addressing measurement problems in poverty dynamics
AbstractThe 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 paper 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.
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Bibliographic InfoPaper provided by Department of Economics, University of Siena in its series Department of Economics University of Siena with number 695.
Date of creation: Apr 2014
Date of revision:
Poverty dynamics; Measurement errors; LCMM;
Find related papers by JEL classification:
- I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2014-04-18 (All new papers)
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