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Measuring inequality using censored data: a multiple‐imputation approach to estimation and inference
Author(s) -
Jenkins Stephen P.,
Burkhauser Richard V.,
Feng Shuaizhang,
Larrimore Jeff
Publication year - 2011
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/j.1467-985x.2010.00655.x
Subject(s) - imputation (statistics) , inference , statistics , econometrics , current population survey , parametric statistics , point estimation , mathematics , population , data set , grouped data , inequality , computer science , missing data , demography , artificial intelligence , sociology , mathematical analysis
Summary. To measure income inequality with right‐censored (top‐coded) data, we propose multiple‐imputation methods for estimation and inference. Censored observations are multiply imputed using draws from a flexible parametric model fitted to the censored distribution, yielding a partially synthetic data set from which point and variance estimates can be derived using complete‐data methods and appropriate combination formulae. The methods are illustrated using US Current Population Survey data and the generalized beta of the second kind distribution as the imputation model. With Current Population Survey internal data, we find few statistically significant differences in income inequality for pairs of years between 1995 and 2004. We also show that using Current Population Survey public use data with cell mean imputations may lead to incorrect inferences. Multiply‐imputed public use data provide an intermediate solution.