Analysis Of The Influence Of The Tertiary Industry On The Income Disparity Between Rural And Urban Residents

Since reform and opening up, China has been maintaining a relatively high rate of economic growth when its per capita GDP is growing and income disparity is expanding rapidly. Although the country has carried out a series of policies to stop the trend, yet most of the policies could not reverse the situation. At present, the academic world mainly takes Gini coefficient as the measurement of income gap, but the concealed Gini data makes such measurement rather difficult. In addition, because China’s urban-rural income gap contributes about80%to the overall income disparity, the measurement of urban-rural income disparity could to some extent represent overall income disparity.Kuznets has proposed the inverted U hypothesis about the relationship between economic growth and income distribution, which explains that the income gap would widen at the early stage of economic development, while this gap would narrow after full development of the economy. Assuming the correctness of the Kuznets model in China, we could infer from the current expanding income disparity and conclude that China is experiencing a period of income widening. According to the laws of economic development, the tertiary industry takes an apparent larger proportion in developed countries than in developing countries; therefore there is a huge potential for the development of the tertiary industry in China. However, how the development of this industry will influence the income distribution is still hard to predict. Many domestic papers have involved the factors affecting income disparity, for example, industrial upgrading, education level, urban-rural income gap and the financial industry etc., but few discussed the relationship of the tertiary industry and income disparity. The accommodation of employment of the tertiary industry and the rise in efficiency by the information and financial industry are very likely to narrow and widen the gap at the same time. Therefore, this issue is worthy of discussion.As an economically developed region in China’s coastal areas, the income distribution variation in China’s Yangtze River Delta region is probably what the less developed regions is experiencing or will experience. Therefore, the utilization of the data in this region is somehow enlightening. This paper directly uses the Kuznets inverted U curve model and takes the added value of the tertiary industry as the main independent variable while at the same time taking into account other influencing factors, to observe the influence of the tertiary industry on income disparity.This thesis includes five parts:The first chapter is the introduction in which it gives the research background, purpose, hypothesis, methodology and limitations. The second chapter is literature review, in which it gives the definitions, relative research findings, and classical models for the thesis. The third chapter is data description, in which it explains in detail the sources of the data, calculation method and data features etc. and based on which it proposes relative hypotheses. The fourth chapter is empirical testing, in which the thesis uses pooled OLS regression model to test the data of the Yangtze River Delta region from2000-2010and makes brief analysis and predictions. The fifth chapter is policy recommendations, where it gives out policy suggestions for urban-rural income disparity in response to the empirical result, data processing features by the combination of relative research findings.This thesis mainly has the following conclusions:First, the Kuznets model exists at least in the Yangtze River Delta region, and income disparity did widen from2000-2010. This trend is probably to slow down due to political reasons;Second, the restructuring and the upgrading of the secondary industry can create more job opportunities thus narrowing income gap; while the development of the tertiary industry would widen the gap at least in the short run, owing to the polarization and aggregation effect of information and financial industries;Third, other influencing factors like urbanization, can narrow the gap via industrialization, while education and insurance which can obviously create equal opportunities could not be ignored either. And these all demands policy support.