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A Spatial Statistics Research On China's Manufacturing Agglomeration And Its Influencing Factors

Posted on:2012-04-01Degree:MasterType:Thesis
Country:ChinaCandidate:W P GuFull Text:PDF
GTID:2189330332985302Subject:Statistics
Abstract/Summary:PDF Full Text Request
Manufacturing industries are the most important industries in the national economy. Its development and agglomeration draw much attention from many scholars. To analyze China's manufacturing agglomeration and its influncing factors, this article uses Moran'sⅠindex Model, Spatial Lag Model, Spatial Error Model and Geographical Weighted Regression in the spatial statistics and econometric analysis on China's provincial manufacturing agglomeration, based on China's provincial manufacturing data and some economics data in year 1999 to 2008.In the spatial statistics methods, this article uses Exploratory Spatial Data Analysis, using Moran'sⅠstatisitcs to carry out global space-correlated test for provincial manufacturing agglomeration. The results show that the activities of China's provincial manufacturing have spatial dependence, and have a spatially clustered effect. The following LISA analysis also shows that the activities of China's provincial manufacturing have a local spatial correlation.The result of provincial manufacturing influencing factors analysis shows that:(1) At the beginning, the main factors that influence China's provincial manufacturing agglomeration are agriculture resources gift, natural resources gift, capital gift, the level of urbanlization, industries, openness and fiscal income, while in the end these factors become market demand, transportation, industries, services and fiscal income. (2) The activities of China's provincial manufacturing have spatial correlation. The spatial econometric model is more suitable and its analysis is more acurate. When making a gobal estimation, we find that the Spatial Error Model is a better one. While making a local estimation, we find that the Geographical Weighted Regression Model is the optimum model. It is better than OLS, SLM and SEM, because it is able to reveal the different situation all the factors influence provincial manufacturing agglomeration.In the end, this artcle propose some suggestion for China's manufacturing development and location. For example, central government should use fical-tax policy and investment policy correctly and give more surpport to the less developed regions; central government should also make a rational adjustment of regional manufacturing location and transfer some manufacturing industries to the west region of China intentially. The local government should do more work on the development of fundenmental industries and modern service industries, create a better condition of regional transportation, form a complete, healthy, open, united, competitive modern market and give appropriate support to industry agglomeration.
Keywords/Search Tags:manufacturing industries, industry agglomeration, spatial correlation, Spatial Lag Model, Spatial Error Model, Geographical Weighted Regression Model
PDF Full Text Request
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