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Recovering income distribution in the presence of interval-censored data

Author

Listed:
  • Gustavo Javier Canavire-Bacarreza

    (The World Bank)

  • Fernando Rios-Avila

    (Levy Economics Institute)

Abstract

We propose a method to analyze interval-censored data, using a multiple imputation based on a heteroskedastic interval regression approach. The proposed model aims to obtain a synthetic dataset that can be used for standard analysis, including standard linear regression, quantile regression, or poverty and inequality estimation. We present two applications to show the performance of our method. First, we run a Monte Carlo simulation to show the method's performance under the assumption of multiplicative heteroskedasticity, with and without conditional normality. Second, we use the proposed methodology to analyze labor income data in Grenada for 2013–2020, 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.

Suggested Citation

  • Gustavo Javier Canavire-Bacarreza & Fernando Rios-Avila, 2022. "Recovering income distribution in the presence of interval-censored data," 2022 Stata Conference 19, Stata Users Group.
    • Handle: RePEc:boc:usug22:19
    as

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    References listed on IDEAS

    as
    1. Li‐Pang Chen, 2022. "Introduction to data science: Data analysis and prediction algorithms with R," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(2), pages 733-734, April.
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    3. Xiuqing Zhou & Yanqin Feng & Xiuli Du, 2017. "Quantile regression for interval censored data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(8), pages 3848-3863, April.
    4. Aldi Hagenaars & Klaas de Vos, 1988. "The Definition and Measurement of Poverty," Journal of Human Resources, University of Wisconsin Press, vol. 23(2), pages 211-221.
    5. Sergio Firpo & Nicole M. Fortin & Thomas Lemieux, 2009. "Unconditional Quantile Regressions," Econometrica, Econometric Society, vol. 77(3), pages 953-973, May.
    6. Fernando Rios-Avila, 2020. "Recentered influence functions (RIFs) in Stata: RIF regression and RIF decomposition," Stata Journal, StataCorp LP, vol. 20(1), pages 51-94, March.
    7. McDonald, James & Stoddard, Olga & Walton, Daniel, 2018. "On using interval response data in experimental economics," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 72(C), pages 9-16.
    8. Zachary Parolin & Christoper Wimer, 2020. "Forecasting Estimates of Poverty During the COVID-19 Crisis," Poverty and Social Policy Brief 2046, Center on Poverty and Social Policy, Columbia University.
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    More about this item

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • J3 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs

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