Poverty status probability: a new approach to measuring poverty and the progress of the poor
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References listed on IDEAS
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CitationsCitations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
- Edwin Fourrier-Nicolai & Michel Lubrano, 2017.
"Bayesian Inference for TIP curves: An Application to Child Poverty in Germany,"
AMSE Working Papers
1710, Aix-Marseille School of Economics, Marseille, France.
- Edwin Fourrier-Nicolai & Michel Lubrano, 2017. "Bayesian Inference for TIP curves: An Application to Child Poverty in Germany," Working Papers halshs-01494354, HAL.
- Gordon Anderson & Alessio Farcomeni & Maria Grazia Pittau & Roberto Zelli, 2018. "Multidimensional Nation Wellbeing, More Equal yet More Polarized: An Analysis of the Progress of Human Development since 1990," Working Papers tecipa-602, University of Toronto, Department of Economics.
More about this item
KeywordsPoverty frontiers; Mixture models; Gibrat’s law; C14; I32; O1;
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
- O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development
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