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Bayesian Inference for Parametric Growth Incidence Curves

Research on Economic Inequality: Poverty, Inequality and Shocks

ISBN: 978-1-80071-558-5, eISBN: 978-1-80071-557-8

Publication date: 2 December 2021

Abstract

The growth incidence curve of Ravallion 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.

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Acknowledgements

Acknowledgments

We would like to thank the editor and two anonymous referees for useful comments and remarks. We should also mention about the nice conversations with Jeff Racine and Karim Abadir. Of course, remaining errors are solely ours.

Citation

Fourrier-Nicolaï, E. and Lubrano, M. (2021), "Bayesian Inference for Parametric Growth Incidence Curves", Bandyopadhyay, S. (Ed.) Research on Economic Inequality: Poverty, Inequality and Shocks (Research on Economic Inequality, Vol. 29), Emerald Publishing Limited, Leeds, pp. 31-55. https://doi.org/10.1108/S1049-258520210000029003

Publisher

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Emerald Publishing Limited