Measuring income inequality using survey data: the case of China
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The purpose of this paper is to raise awareness of missing data when we evaluate income inequality using survey data. If the income data are not missing completely at random, the calculated income inequalities are more likely to be biased, which may lead to inappropriate conclusions and policy recommendations. To handle the missing data on income, a multiple imputation approach is utilized. In particular, we propose an extended approach to correct the possible sample selection bias in the imputation process. A case study using China’s household survey suggests that extended imputation corrects for biases effectively in the calculation of Gini coefficients and results in gains in efficiency as well.
KeywordsIncome inequality Multiple imputation Missing data China
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- 1.Abdelkrim, A., Duclos, J.-Y.: DASP: distributive analysis Stata package. PEP, World Bank, UNDP and University LavalGoogle Scholar
- 2.Cornia, G. A., Court, J.: Inequality, growth and poverty in the era of liberalization and globalization.UNU world institute for development economics research (2001)Google Scholar
- 6.Frick, J. R., Grabka, M. M.: Missing income data in panel surveys: Incidence, imputation and its impact on the income distribution. Discussion papers, p 376 (2004)Google Scholar
- 14.Royston, P., Carlin, J. B., White, I. R.: Multiple imputation of missing values: New features for mim. Stata J. 9 (2), 252–264 (2009)Google Scholar
- 15.Rubin, D. B.: Multiple imputation in sample surveys — a phenomenological Bayesian approach to nonresponse. in proceedings of the section on survey research methods,American Statistical Association, pp 20–34 (1978)Google Scholar
- 18.Schafer, J. L.: Analysis of incomplete multivariate data.London:Chapman and Hall (1997)Google Scholar
- 19.Schenker, N., Raghunathan, T.E., Chiu, P.-L.,Makuc, D.M., Zhang, G., Cohen, A.J.:Multiple imputation of missing income data in the national health interview survey. J. Am. Stat. Assoc. 101(475), 924–933 (2006)Google Scholar