Recovering Income Distribution in the Presence of Interval-Censored Data

Gustavo Javier Canavire Bacarreza, Fernando Rios Avila and Flavia Giannina Sacco Capurro
Authors registered in the RePEc Author Service: Gustavo Javier Canavire-Bacarreza and Fernando Rios-Avila ()

No 10147, Policy Research Working Paper Series from The World Bank

Abstract: 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.

Date: 2022-08-22
New Economics Papers: this item is included in nep-ecm
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Related works:
Journal Article: Recovering income distribution in the presence of interval-censored data (2024)
Working Paper: Recovering Income Distribution in the Presence of Interval-Censored Data (2023)
Working Paper: Recovering income distribution in the presence of interval-censored data (2022)
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