Official Chinese data on urban household income are seriously flawed because of significant underreporting of income by respondents and non-participation by the high income groups in official household surveys. We collected urban household income and expenditure data in a way that increased their reliability and the coverage of the wealthy. We utilized the well-known relationship between Engel's coefficient and income level through two different approaches to deduce the true level of household income for each of the seven Chinese income categories (lowest income, low income, lower middle income, middle income, upper middle income, high income, and highest income). We found that the ratio of our estimated income to official income increased from 1.12 for the lowest income group to 3.19 for the highest income group. Total household disposable income in 2008 is RMB 14.0 trillion according to the official data but RMB 23.2 trillion according to our estimate; and 63 percent of the unreported income went to the wealthiest 10 percent of urban households. The income of the wealthiest 10 percent of Chinese households is really 65 times that of the poorest 10 percent instead of the 23 times reported in the official data. The Gini coefficient is clearly much higher than the usually reported figure of 0.47.
In one of the estimations, we had to drop the 76 wealthiest households (1.8 percent of our sample) from the analysis because there were no super-rich in the official data for us to match characteristics with. We therefore still understate the income of the highest income households. As the amount of unreported income indicates the degree of corruption, it is troubling that it grew 91 percent in 2005–08 compared to the 71 percent growth in gross national income. Serious institutional reforms must be enacted if corruption is not to derail economic development and social harmony.
This article is part of a research project of the Chinese Research Society for Economic System Reform. We thank the many individuals and organizations who made this project possible. We also thank the readers of the earlier Wang (2007) study and of earlier drafts of this report for their valuable comments. We are solely responsible for the remaining mistakes in this article.