Measuring Inequality Using Censored Data: A Multiple Imputation Approach

By Stephen P. Jenkins, Richard V. Burkhauser, Shuaizhang Feng, Jeff Larrimore. Social Science Research Network, 2009

67 citations recorded by the source corpus.

To measure income inequality with right censored (topcoded) data, we propose multiple imputation for censored observations using draws from Generalized Beta of the Second Kind distributions to provide partially synthetic datasets analyzed using complete data methods. Estimation and inference uses Reiter’s (Survey Methodology 2003) formulae. Using Current Population Survey (CPS) internal data, we find few statistically significant differences in income inequality for pairs of years between 1995 and 2004. We also show that using CPS public use data with cell mean imputations may lead to incorrect inferences about inequality differences. Multiply-imputed public use data provide an intermediate solution.

Subfields: Economics and Econometrics, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/4538330f38386fb1314188ace6ad0468d0761daf

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Citations in the corpus

Commitment to Equity Assessment (CEQ): Estimating the Incidence of Social Spending, Subsidies and Taxes Handbook (2013, methodology): “A more complex multiple imputation approach is given in Jenkins et al. (2011).”
Statistical Methods for Distributional Analysis (2015, background): “…of distributional analysis, such as inferring inequality trends, researchers have adopted a number of work-rounds such as multiplying top-coded values by a given factor (Lemieux 2006, Autor et al. 2008) or attempting imputations for missing data (Burkhauser et al. 2010, Jenkins et al. 2011).”