Bayesian inference for TIP curves: an application to child poverty in Germany

Edwin Fourrier-Nicolaï and Michel Lubrano

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Abstract: TIP curves are cumulative poverty gap curves used for representing the three different aspects of poverty: incidence, intensity and inequality. The paper provides Bayesian inference for TIP curves, linking their expression to a parametric representation of the income distribution using a mixture of log-normal densities. We treat specifically the question of zero-inflated income data and survey weights, which are two important issues in survey analysis. The advantage of the Bayesian approach is that it takes into account all the information contained in the sample and that it provides small sample credible intervals and tests for TIP dominance. We apply our methodology to evaluate the evolution of child poverty in Germany after 2002, providing thus an update the portrait of child poverty in Germany given in Corak et al. (Rev. Income Wealth 54(4), 547–571, 2008).

Keywords: poverty; inequality; zero-inflatedmodel; mixture model; survey weights; Bayesian inference (search for similar items in EconPapers)
Date: 2020-03
New Economics Papers: this item is included in nep-eur
Note: View the original document on HAL open archive server: https://amu.hal.science/hal-02477216
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Citations: View citations in EconPapers (6)

Published in Journal of Economic Inequality, 2020, 18 (1), pp.91-111. ⟨10.1007/s10888-019-09426-6⟩

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Journal Article: Bayesian inference for TIP curves: an application to child poverty in Germany (2020)
Working Paper: Bayesian Inference for TIP curves: An Application to Child Poverty in Germany (2017)
Working Paper: Bayesian Inference for TIP curves: An Application to Child Poverty in Germany (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02477216

DOI: 10.1007/s10888-019-09426-6

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