This paper argues that the conventional approach of data averaging is problematic for exploring the growth–inequality nexus. It introduces the polynomial inverse lag (PIL) framework so that the impacts of inequality on investment, education, and ultimately on growth can be measured at...

We describe a new method of facilitating inequality and poverty analysis of grouped distributional data by allowing individual income observations to be reconstructed from any feasible grouping pattern. In contrast to earlier methods, our procedure ensures that the characteristics of the...

This paper analyzes the evolution of poverty in China from the late 1980s to the late 1990s, employing a version of Shapley decomposition tailored to unit-record household survey data. The changes in poverty trends are attributed to two proximate causes— income growth and shifts in income...

This paper examines the distributional impact of globalization on the poor in urban China. Employing the kernel density estimation technique, we recovered from irregularly grouped household survey data the income distribution for 29 Chinese provinces for 1988-2001. Panels of the income shares of...

How significantly inter-industry wage differentials contribute to rising income inequality is an essential policy issue for transitional economies. Using regression-based inequality decomposition, this paper finds that inter-industrial wage differentials contributed increasingly to income...