Inference for the measurement of poverty in the presence of a stochastic weighting variable
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DOI: 10.1007/s10888-006-9045-5
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References listed on IDEAS
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Citations
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Cited by:
- Edwin Fourrier-Nicolaï & Michel Lubrano, 2020.
"Bayesian inference for TIP curves: an application to child poverty in Germany,"
The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 18(1), pages 91-111, March.
- 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, France.
- Edwin Fourrier-Nicolai & Michel Lubrano, 2017. "Bayesian Inference for TIP curves: An Application to Child Poverty in Germany," Working Papers halshs-01494354, HAL.
- Edwin Fourrier-Nicolai & Michel Lubrano, 2020. "Bayesian inference for TIP curves: an application to child poverty in Germany," Post-Print hal-02477216, HAL.
- Tim Goedemé, 2013. "How much Confidence can we have in EU-SILC? Complex Sample Designs and the Standard Error of the Europe 2020 Poverty Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 110(1), pages 89-110, January.
- Ilaria Benedetti & Gianni Betti & Federico Crescenzi, 2020. "Measuring Child Poverty and Its Uncertainty: A Case Study of 33 European Countries," Sustainability, MDPI, vol. 12(19), pages 1-12, October.
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More about this item
Keywords
Decomposable poverty measure; Stochastic dominance; Statistical inference; C40; I32;All these keywords.
JEL classification:
- C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
- I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
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