Bayesian Inference for Parametric Growth Incidence Curves

Edwin Fourrier-Nicolaï and Michel Lubrano

A chapter in Research on Economic Inequality: Poverty, Inequality and Shocks, 2021, vol. 29, pp 31-55 from Emerald Group Publishing Limited

Abstract: The growth incidence curve ofRavallion and Chen (2003)is based on the quantile function. Its distribution-free estimator behaves erratically with usual sample sizes leading to problems in the tails. The authors propose a series of parametric models in a Bayesian framework. A first solution consists in modeling the underlying income distribution using simple densities for which the quantile function has a closed analytical form. This solution is extended by considering a mixture model for the underlying income distribution. However, in this case, the quantile function is semi-explicit and has to be evaluated numerically. The last solution consists in adjusting directly a functional form for the Lorenz curve and deriving its first-order derivative to find the corresponding quantile function. The authors compare these models by Monte Carlo simulations and using UK data from the Family Expenditure Survey. The authors devote a particular attention to the analysis of subgroups.

Keywords: Bayesian inference; growth incidence curve; distributional changes; inequality; mixtures of log-normals; Lorenz curves; C11; D31; I31 (search for similar items in EconPapers)
Date: 2021
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Related works:
Working Paper: Bayesian Inference for Parametric Growth Incidence Curves (2021)
Working Paper: Bayesian Inference for Parametric Growth Incidence Curves (2021)
Working Paper: Bayesian Inference for Parametric Growth Incidence Curves (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:eme:reinzz:s1049-258520210000029003

DOI: 10.1108/S1049-258520210000029003

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