Parametric versus nonparametric inference on Zenga index of inequality: Issues and evidence from survey data

Recent growing disparities suggests to move from inequality measures based on comparing the incomes of the less fortunate with the overall mean, as the Gini, to the new Zenga index, which instead contrasts the means of the less and the more wealthy subpopulations. After providing a thorough analysis of the theoretical and practical aspects of obtaining parametric and non-parametric confidence intervals for the Zenga inequality measure, we develop a cross-regional study based on the Swiss Income and Consumption Survey, wave 2005. Results show that coverage accuracy and average length of confidence intervals improve when the parametric model offers a good fit to the data.