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Research On Regional Differences In Consumption Structure Of China

Posted on:2016-05-06Degree:MasterType:Thesis
Country:ChinaCandidate:W ZhangFull Text:PDF
GTID:2309330470964549Subject:Applied Economics
Abstract/Summary:PDF Full Text Request
Nowadays no matter the country or the community is concentrate on growth and development of economics, but actually initial and ultimate purposes of economic growth and development are improving people’s living standard, and enhancing people’s sense of happiness. Moreover, the most significant part of measuring and reflecting people’s living standard and quality is resident consumption. With the development of our nation’s economic standard, our consumption level is far less than other nations which has same economic standard, our consumption standard is obviously inefficient. The lower consumption level not only affects our economic growth rate, but also reflects our present situation of our lower resident consumption structure. Residents of our country also obtain less interests from our nation’s economic development, which can be seen from our country’s lower resident consumption structure. Therefore it has profound and significant meaning to research into consumption level and consumption structure. Additionally, due to China’s large population and vast territory, the economic development of each region is unbalanced,situations of resident consumption in various regions are quite different as well.Conducting research about regional division of our nation’s resident consumption is able to regard consumption differences of those regions as rules of consumption demands of different development stages, and it can provide guide for our economic direction change. Based on above reasons, this dissertation mainly concentrates on researching consumption structures of various regions in China and obtaining conclusions to offer certain suggestions to relative policy makers.Our research data are collected through relevant yearbooks which are published by China National Bureau of Statistics, which includes data of 31 provinces of China,such as average cash consumption expenditures of rural and urban areas, average income, rural and urban population, average consumption of eight categories of consumer goods in each region, etc. The reasons for dividing China into 10 regions are:(1). Since it is the research about China’s 31 provinces’ resident consumption data,the amount of statistics is large, if 31 provinces perform comparisons in time and cross-sectional dimensions simultaneously, it would trigger expatiatory contents and many regional similarities and regularities would be ignored.(2). For division ofregions, it is general to put provinces together, which is according to the economic zone or the economic level, and thus the cultural similarities effect on consumption and the geographical indivisibilities are ignored. The research method of the paper starts with descriptive statistics and empirical study to describe and present consumption differences of regions in China. In this dissertation, I carry out descriptive analysis of consumption structure of each region and eight categories of one-way spending in the time and cross-sectional dimensions is processed, and consumption differences in different regions are shown from the most direct point of absolute differences, figures, etc. Additionally, the autonomous expenditure differences in various regions are studied and compared, which use the panel data model in terms of data features and research purposes.Main research findings are:(1) In China, provinces of coastal regions have higher spending on transportation communications due to economic and geographic factors.(2) The proportion of consumption expenditure on clothing of each region shows the order of decreasing from west to east, which is mainly influenced by geographical factors.(3) The consumption structure of high-income regions in China tends to be an enjoyable and developmental type of consumption.
Keywords/Search Tags:regional differences, consumption structure, Panel Data
PDF Full Text Request
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