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1826 JOURNAL OF COMPUTERS, VOL. 6, NO. 9, SEPTEMBER 2011 Location Quotient (LQ) to describe China’s manufacturing agglomeration. It is defined as follows: LQ ij = ( Eij Ei ) ( Ekj Ek ) In the formula above, ij E indicate the employment in j industry of i district; Ei indicate the total employment of i kj district; E indicate the employment in j industry of the total district k ; Ek indicate the total employment of the total district k . It is generally believed that the greater the Location Quotient coefficient, the higher the level of the region's industry agglomeration. B. Spatial Statistical Analysis of China’s Manufacturing Agglomeration (a) Spatial distribution of China's manufacturing industry To give a better analysis of the spatial variation process of China’s manufacturing agglomeration, we mapping the spatial percentile chart (three periods: 1999-2001, 2002-2005, 2006-2008) with Geoda095i, based on provincial Location Quotient coefficient (Calculated average for each period). The results is shown in chart 1. (a): 1999-2001 (b): 2002-2005 (c):2006-2008 chart 1: Spatial percentile chart of China’s provincial manufacturing agglomeration (1999-2008, three periods) From chart 1 we can see that: From 1999 to 2001, Shanghai, China’s economic center, got the highest LQ coefficient and rank the first echelon; Beijing, Tianjin © 2011 ACADEMY PUBLISHER rank the second echelon; Liaoning, Hebei, Shandong, Jiangsu, Zhejiang, Fujian, Guangdong and some provinces ( 1of ) central region rank the third echelon; however, the agglomeration of manufacturing industry in Xizang, Yunnan and Hainan are still at a low level. From 2002 to 2005, Shanghai still rank the first echelon; however, instead of Beijing, Guangdong came into the second echelon; both Inner Mongolia and Ningxia move forward to the third echelon; Shanxi, Gansu drop to the fourth echelon. Spatial percentile chart of 2006-2008 hasn’t changed compared to the 2002-2005’s. As can be seen from the above analysis, China's manufacturing industry mainly concentrated in the southeast coastal areas. Manufacturing sector of coastal areas showed an increasing trend, but this trend is gradually slowing down. (b) Spatial autocorrelation analysis of China's manufacturing agglomeration In actual research we often use Moran'I index to test the existence of spatial autocorrelation, which is defined as follows: n n ∑∑Wij( Yi −Y )( Yj −Y ) Mora i= 1 j= 1 n′ s I = n n 2 S ∑∑ i= 1 j= 1 W ij n = ∑ i − In the formula above, i= Y Y n 2 1 2 1 S ( ) Y = ∑ Yi n 1 , n i= 1 , Yi is the value ofi district, n is the number of district, Wij is the Contiguity Based Spatial Weights: if region i and region j ij is adjacent, W =1; otherwise, W ij =0. i = 1, 2, ⋅⋅⋅, n ; j = 1, 2, ⋅⋅⋅, m ; m = n or n ≠ m . Moran’s I rank from -1 to 1. For Moran's I index results, we can use standardized statistic Z to test the existence of spatial autocorrelation between the regions. I −E( I) Z = ( 3) VAR( I) Under the assumption of normal distribution, the expectation and variance of Moran's I can be calculated as follows: 2 2 1 n w1 + nw2 + 3w0 2 E( I) = − , VAR( I) = −E ( I) ( 4) 2 2 n−1 w0 ( n −1) In the formula above, n n n n n 1 2 2 w0 = ∑∑w ij, w1 = ∑∑( wij + wji) , w2 = ∑( wi. + wj.) , i= 1 j= 1 2 i= 1 j= 1 i= 1 wi. wj. and are the sum of row i and column j of the spatial weight matrix respectively. Both mean and variance are theoretical. We can make a significant test of the spatial H autocorrelation based on the statistic Z caculated. 0 : spatial autocorrelation between the regions does not exist. In Geoda095i, we use Monte Carlo method to test the existence of spatial autocorrelation, and the significant ( 2)
JOURNAL OF COMPUTERS, VOL. 6, NO. 9, SEPTEMBER 2011 1827 level is determined by the p value of the statistic Z. If p < α , 0 H is denied; otherwise, Table 1: Moran’s I value of China’s manufacturing agglomeration (1999-2008) Year Moran’s I Mean Standard dev iation p value 1999 0.3191 -0.0333 0.1073 0.0060 2000 0.3216 -0.0330 0.1087 0.0058 2001 0.3342 -0.0333 0.1072 0.0042 2002 0.3063 -0.0337 0.1081 0.0062 2003 0.3147 -0.0321 0.1094 0.0053 2004 0.3294 -0.0337 0.1097 0.0042 2005 0.3421 -0.0345 0.1112 0.0031 2006 0.3572 -0.0335 0.1131 0.0031 2007 0.3377 -0.0332 0.1100 0.0032 2008 0.3496 -0.0324 0.1090 0.0032 From table 1 we can make a conclusion that there was a significant positive spatial autocorrelation between China’s provincial manufacturing industries. This indicates that China's manufacturing industry did not distribute randomly, and the spatial distribution of manufacturing industry showed a clear concentration trend over the last decade: the provinces that have similar LQ coefficient tend to concentrate geographically. III. METHOD AND MODEL A. Research Methods (a) Spatial Lag Model Spatial Lag Model (SLM) is mainly used to discuss whether there is a spillover effect of variables in a region. The model is expressed as follows: y = ρ Wy + Xβ + ε In the formula above, y is a dependent variable; X is a n × k matrix of exogenous explanatory variables; ρ is a spatial regression coefficient, reflecting the effect of spatial dependence in observations; W is a n× n matrix of spatial weight; Wy is a spatial lagged dependent variable; ε is a random error vector. Parameter β reveals the effect the explanatory variable X has to dependent variable y . Spatial lagged dependent variable Wy is a exogenous variable reflecting how spatial distance influence the act of regions. The act of regions is strongly affected by the cultural environment and the transfer cost related to spatial distance. (b) Spatial Error Model Spatial Error Model (SEM) is expressed as follows: y = Xβ + ε, ε = λWε + µ In the formula above, ε is a random error vector; λ is a n × 1 spatial error matrix of dependent variable vector; µ is a random error vector in normal distribution. Parameter β reveals the effect the explanatory variable X has to dependent variable y . Parameter λ © 2011 ACADEMY PUBLISHER 0 H is accepted. reflects the effect of spatial dependence in observations. The spatial dependence in random error term measures how the error impact of dependent variable of neighboring areas influence the observations in this region. (c) Estimation method Considering the endogeneity of explanatory variables in spatial regression model, coefficient estimates will be biased or invalid if we use OLS method to estimate the coefficient in Spatial Lag Model and Spatial Error Model. Instead, we can use other methods to estimate, such as Instrumental Variable method, Maximum Likelihood method, Generalized Least Squares method and Generalized Method of Moments. Anselin (1998) recommended Maximum Likelihood method for estimating the coefficient in SLM and SEM. (d) ( A 5) choice between SLM, SEM and spatial autocorrelation test Anselin and Florax (1995) proposed the following criterion: We can determine that Spatial Lag Model would be more appropriate if (a) LMLAG is more significant than LMERR statistically; (b) R-LMLAG is significant but R-LMERR is not significant. In the Opposite, Spatial Error Model would be better if (a) LMERR is more significant than LMLAG; (b) R-LMERR is significant but R-LMLAG isn’t. Besides R-squared, some common criterion includes Log likelihood, Likelihood Ratio, Akaike Information Criterion (AIC) and Schwartz Criterion (SC). The higher the Log likelihood, the lower the AIC and SC, the better the model. These indicators can also be used to compare the regression effect between OLS, SLM and SEM. B. Econometric Model In this ( article, 6) we proposes the main factors that affecting industrial agglomeration from four perspectives: comparative advantage, new economic geography, knowledge spillovers and the role of government. Considering measurability of indicators and availability of data, we proposes the following twelve indicators in table 2.
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A Modified Technique for Analysis o
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