The dynamics of regional convergence include spatial and temporal dimensions. Spatial Markov chain can be used to explore how regions evolve by considering both individual regions and their geographic neighbors. Based...The dynamics of regional convergence include spatial and temporal dimensions. Spatial Markov chain can be used to explore how regions evolve by considering both individual regions and their geographic neighbors. Based on per capita GDP data set of 77 counties from 1978 to 2000, this paper attempts to investigate the spatial-temporal dynamics of regional convergence in Jiangsu. First, traditional Markov matrix for five per capita GDP classes is constructed for later comparison. Moreover, each region’s spatial lag is derived by averaging all its neighbors’ per capita GDP data. Conditioning on per capita GDP class of its spatial lag at the beginning of each year, spatial Markov transition probabilities of each region are calculated accordingly. Quantitatively, for a poor region, the probability of moving upward is 3.3% if it is surrounded by its poor neighbors, and even increases to 18.4% if it is surrounded by its rich neighbors, but it goes down to 6.2% on average if ignoring regional context. For a rich region, the probability of moving down ward is 1.2% if it is surrounded by its rich neighbors, but increases to 3.0% if it is surrounded by its poor neighbors, and averages 1.5% irrespective of regional context. Spatial analysis of regional GDP class transitions indicates those 10 upward moves of both regions and their neighbors are unexceptionally located in the southern Jiangsu, while downward moves of regions or their neighbors are almost in the northern Jiangsu. These empirical results provide a spatial explanation to the "convergence clubs" detected by traditional Markov chain.展开更多
Agglomeration economies are the important factors for the regional development. However, the common indicators to measure them, such as Gini Coefficients neglect the spatial ingredient of data, leading to a-spatial es...Agglomeration economies are the important factors for the regional development. However, the common indicators to measure them, such as Gini Coefficients neglect the spatial ingredient of data, leading to a-spatial estimates. In order to assess spatial neighbor effects of agglomeration economies, this study makes the new attempts by applying a series of techniques of spatial autocorrelation analysis, specifically, measuring the economies of urbanization and localization at the county level in the secondary and tertiary industries of Jiangsu Province in 1999 and 2002. The conclusions in this study reveal that on the whole, the localization effects on the economies of the secondary industry might be stronger than urbanization effects for that period, and highly agglomerative economies were limited within the southern Jiangsu and parts of middle along the Changjiang (Yangtze) River. Moreover, the tertiary industry has been strong urbanization rather than localization economies in the whole Jiangsu. Unlike the secondary industry, the tertiary industry held the high levels of agglomeration economies can be also found in the poor northern Jiangsu, and then the spatial clusters of trade and services might be basically seen in each of urban districts in 13 cities. All in all, spatial autocorrelation analysis is a better method to test agglomeration economies.展开更多
基金Under the auspices ofthe National Natural Science Foundation of China (No .40301038)
文摘The dynamics of regional convergence include spatial and temporal dimensions. Spatial Markov chain can be used to explore how regions evolve by considering both individual regions and their geographic neighbors. Based on per capita GDP data set of 77 counties from 1978 to 2000, this paper attempts to investigate the spatial-temporal dynamics of regional convergence in Jiangsu. First, traditional Markov matrix for five per capita GDP classes is constructed for later comparison. Moreover, each region’s spatial lag is derived by averaging all its neighbors’ per capita GDP data. Conditioning on per capita GDP class of its spatial lag at the beginning of each year, spatial Markov transition probabilities of each region are calculated accordingly. Quantitatively, for a poor region, the probability of moving upward is 3.3% if it is surrounded by its poor neighbors, and even increases to 18.4% if it is surrounded by its rich neighbors, but it goes down to 6.2% on average if ignoring regional context. For a rich region, the probability of moving down ward is 1.2% if it is surrounded by its rich neighbors, but increases to 3.0% if it is surrounded by its poor neighbors, and averages 1.5% irrespective of regional context. Spatial analysis of regional GDP class transitions indicates those 10 upward moves of both regions and their neighbors are unexceptionally located in the southern Jiangsu, while downward moves of regions or their neighbors are almost in the northern Jiangsu. These empirical results provide a spatial explanation to the "convergence clubs" detected by traditional Markov chain.
基金Under the auspicesoftheNationalNatural Science FoundationofChina(No.40271040)
文摘Agglomeration economies are the important factors for the regional development. However, the common indicators to measure them, such as Gini Coefficients neglect the spatial ingredient of data, leading to a-spatial estimates. In order to assess spatial neighbor effects of agglomeration economies, this study makes the new attempts by applying a series of techniques of spatial autocorrelation analysis, specifically, measuring the economies of urbanization and localization at the county level in the secondary and tertiary industries of Jiangsu Province in 1999 and 2002. The conclusions in this study reveal that on the whole, the localization effects on the economies of the secondary industry might be stronger than urbanization effects for that period, and highly agglomerative economies were limited within the southern Jiangsu and parts of middle along the Changjiang (Yangtze) River. Moreover, the tertiary industry has been strong urbanization rather than localization economies in the whole Jiangsu. Unlike the secondary industry, the tertiary industry held the high levels of agglomeration economies can be also found in the poor northern Jiangsu, and then the spatial clusters of trade and services might be basically seen in each of urban districts in 13 cities. All in all, spatial autocorrelation analysis is a better method to test agglomeration economies.
