Recovering Income Distribution in the Presence of Interval-Censored Data

Gustavo J. Canavire Bacarreza (), Fernando Rios-Avila () and Flavia Sacco-Capurro ()
Additional contact information
Gustavo J. Canavire Bacarreza: World Bank
Flavia Sacco-Capurro: World Bank
Authors registered in the RePEc Author Service: Gustavo Javier Canavire-Bacarreza

No 15921, IZA Discussion Papers from Institute of Labor Economics (IZA)

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.

Keywords: heteroskedastic interval regression; Monte Carlo simulation; interval-censored data; wages (search for similar items in EconPapers)
JEL-codes: C15 C34 J3 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2023-02
New Economics Papers: this item is included in nep-lma
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Published - published in: Journal of Economic Inequality, 2024, 22, 1039 - 1060

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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 (2022)
Working Paper: Recovering Income Distribution in the Presence of Interval-Censored Data (2022)
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