Intergenerational Transmission of Income Gap for Chinese Urban and Rural Households

Xu Xiaohong

School of Economics, Anhui University, Hefei, China

Abstract:  An accurate understanding of the intergenerational transmission of income gap is the foundation for theoretical research and policy formulation to address this issue. This paper has employed the method of two sample instrumental variables to effectively integrate CHIP data and CFPS data and correct the temporal income bias, life-cycle bias and coresidence bias, which are common problems in existing studies, and investigated the tendencies of intergenerational transmission of income gap for China’s urban and rural households between 2002 and 2012. Results of empirical study indicate that the intergenerational transmission of income gap for China’s urban and rural households has been on the decline yet the level of intergenerational transmission is greater for urban residents than for rural residents. This level of  intergenerational transmission of income gap in China is at a medium international level lower than that of countries like the United States, Brazil and Japan and higher than that of Sweden and Chinese Taiwan. Further analysis of the intergenerational mobility of various income groups suggests the following: the intergenerational solidification of the bottom and top income groups of urban residents has significantly improved, which is the source for the reduction of intergenerational transmission of income gap. Rural residents of bottom income group are vulnerable to falling into the trap of intergenerational transmission of low income. In order to mitigate the intergenerational transmission of income gap, efforts must be made to improve educational allowance policy and increase the opportunities for children from poor and underprivileged families to receive education and to eliminate the divide of labor markets to create equal job opportunities for each and every worker.
Keywords: intergenerational income elasticity, method of two sample instrumental variables, measurement biases, income gap

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