The literature indicates the problems in the data to calculate the Gini coefficient of Chinese residents' income. Although many studies have tried to overcome the problem by decomposing the nationwide Gini ratio into urban and rural ones, the final results have been underestimated as a result of...

Using a case study about rural Tianjin and Shandong provinces, we try to explain what mechanism affects income inequality in rural areas, especially how rural dual structural transformation leads to the income inequality "inverted-U" Curve in some developed areas in the People's Republic of...

This study proposes a new approach to analyse the effects of an overlap term on the calculation of the overall Gini coefficient and estimates China's Gini ratios since the adoption of the economic reform and open-door policies. A decomposition of the Chinese Gini coefficient for 1978–2010...

This study contributes to the literature on inequality of opportunity (IOp) in China by covering a longer and more recent span of time, employing better measures of given characteristics, and analyzing IOp for household income per capita with comparisons to individual income. Furthermore, we...

By using the five waves of the China Household Income Project surveys conducted during 1988-2013, this paper investigates long-term changes in income inequality and poverty in China. Income inequality rose before 2007 and then fell by a small amount. The main reason for the rise in income...

Using two large samples for 1988 and 1995 we decompose the Gini coefficient of household income according to type of income with the purpose of analyzing reasons for the rapid increase of inequality. The results show that the change in relative size of money income and its changed profile are...

There are two main types of data sources of income distributions in China: household survey data and grouped data. Household survey data are typically available for isolated years and individual provinces. In comparison, aggregate or grouped data are typically available more frequently and...