Recovering Income Distribution in the Presence of Interval-Censored Data - Gustavo J. Canavire Bacarreza; Fernando Rios-Avila; Flavia Sacco-Capurro (Institute of Labor Economics (IZA), 2023)
This paper proposes a method to analyze interval-censored data, using multiple imputation based on a heteroskedastic interval regression approach. The proposed model aims to obtain a synthetic data set that can be used for standard analysis, including standard linear regression, quantile regression, or poverty and inequality estimation. The paper presents two applications to show the performance of the method. First, it runs a Monte Carlo simulation to show the method's performance under the assumption of multiplicative heteroskedasticity, with and without conditional normality. Second, it uses the proposed methodology to analyze labor income data in Grenada for 2013–20, where the salary data are interval-censored according to the salary intervals prespecified in the survey questionnaire. The results obtained are consistent across both exercises.
Keywords: C15; C34; J3; 330; interval-censored data; Monte Carlo simulation; heteroskedastic interval regression; wages