Bayesian Inference for Parametric Growth Incidence Curves

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.

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