中国区域经济协同网络演变及成因分析——以2003—2017年中国40470组两两城市对为样本

doi:10.11821/dlyj020200389

摘要/Abstract

摘要：

以2003—2017年中国285个地级及以上城市构成的40470组两两城市对为样本,采用效率增值模型、社会网络分析法以及动态面板模型分析了中国区域经济协同网络演变及成因。结果表明：① 中国区域经济协同网络密度呈现三周期、双缺口变化特征,高强度协同作用偏向东部地区的东向性特征凸显;② 协同网络经历了星型覆盖格局-双区扇形发散格局-单区扇形发散格局-多层嵌套新格局的转变,表现为跨越东中西部地区的远距离协同,跨域性特征突出;③ 东部城市比较优势、经济联系和产业分工的良性循环决定了协同作用的东向性,而远距离协同作用利于整合区域差异化比较优势,降低资源争夺和重复建设风险,促进全域高效产业分工体系和跨域性协同网络的形成。

关键词: 协同网络, 效率增值模型, 社会网络分析法, 动态面板模型, 驱动机制

Abstract:

Based on value-added model, social network analysis and dynamic panel model, this paper conducts a sample survey of 40470 city-city units consisting of 285 prefecture-level cities and above from 2003 to 2017, and discusses the evolution of China's regional economic synergetic network and the reasons for its formation. The results show that: (1) The regional economic synergetic network density has experienced three short cycles and two gaps. The first short cycle refers to the regional economic synergetic network density changing from high density in 2003 to low density in 2009. The other two cycles refer to 2009-2013 and 2013-2017. The gaps refer to the low densities between every two cycles in 2009 and 2013. Among all the synergetic movements, the synergic movements with eastern cities are stronger than the those with central and western cities. That is, there is a significant eastern oriented characteristic with high-intensity synergic movements located in eastern cities. (2) The regional economic synergetic network has transferred from a pentagram coverage pattern to a fan-shaped pattern with the Pearl River delta region and the Yangtze River delta region as the two cores. Then the regional economic synergetic network has changed to a fan-shaped pattern with the Pearl River delta region as the core. Finally, the regional economic synergetic network has formed a multilayer nested pattern. With the evolution of the regional economic synergetic network, there is a significant cross-regional characteristic with geographical distant synergies across the eastern, central and western regions. (3) As for the reasons for the regional economic synergetic network formation, comparative advantage, economic contact and industrial system are the three main driving forces that determine the network evolution. The virtuous circle of the comparative advantage, economic contact and industrial system in eastern cities leads to the characteristic of the east orientation. Cross-regional synergic movements are beneficial to the integration of regional differentiation comparative advantage, the reduction of the risk of resource contention and redundancy construction, and the formation of an efficient industrial division of labor system and cross-regional synergetic network.

Key words: synergetic network, value added model, social network analysis, dynamic panel model, driving mechanism

刘莹, 李琳, 张喜艳. 中国区域经济协同网络演变及成因分析——以2003—2017年中国40470组两两城市对为样本[J]. 地理研究, 2020, 39(12): 2779-2795.

LIU Ying, LI Lin, ZHANG Xiyan. The evolution and forming reasons of the regional economic synergetic network in China: Based on the sample of Chinese 40470 city-city units from 2003 to 2017[J]. GEOGRAPHICAL RESEARCH, 2020, 39(12): 2779-2795.

图/表 10

图1

表1

变量与数据来源"

变量	符号	测算公式	数据来源与处理
协同作用	lnSYN	$SY N ij = [(θ ij' - θ ˉ ij') - (θ i' - θ ˉ n')] + [(θ ij' - θ ˉ ij') - (θ j' - θ ˉ n')]$	参照Liu, et al^[11],投入指标为资本投入和劳动投入,资本存量测算参照柯善咨等^[27],数据来源《中国统计年鉴》和《中国城市统计年鉴》
比较优势	lnADV	$AD V it = GD P it / ∑ i = 1 n GD P it Labo r it / ∑ i = 1 n Labo r it$	参照郑玉雯等^[9],劳动生产率差异可反映区域比较优势,GDP为2001年不变价,Labor为从业人数,数据来源于《中国城市统计年鉴》
经济联系	lnCON	$CO N ij = K ? GD P i × GD P j d ij 2$	参照倪维秋等^[26],用引力模型测度经济联系,两两城市间的距离数据来源于全国公路里程查询网站
产业分工	lnIND	$IN D 12 = ∑ j = 1 n E 1 j / ∑ j = 1 n E 1 j - E 2 j / ∑ j = 1 n E 2 j$	数据处理参照杨汝岱^[27],样本量为《中国工业企业数据库》291.8万家制造业企业（剔除异常值后）,匹配属地得各行业的工业总产值
市场分割	lnMAR	$\| Δ Q ijt k \| = \| ln (p it k p jt k) - ln (p it - 1 k p jt - 1 k) \|$ $MA R ijt = 1 n - 1 ∑ k = 1 n (\| Δ Q ijt k \| - \| Δ Q t k ˉ \|) 2$	参照陆铭等^[28],p为食品、饮料及烟酒等16类商品零售价格指数,数据来源于《中国统计年鉴》
城市规模	lnSCA	用城市总人口表征城市规模,控制城市量级差异对协同作用的影响	数据来源于《中国城市统计年鉴》
交通条件	lnTRA	货运总量和客运总量等权重标准化加权,反映城市客流量和人流量,表征交通条件	数据来源于《中国城市统计年鉴》

表1

图2

图3

图4

表2

图5

表3

表4

表5

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排序	点度中心度				接近中心度				中介中心度
排序	辐射节点	2003年	2017年	均值	通达节点	2003年	2017年	均值	枢纽节点	2003年	2017年	均值
1	深圳市	97.18	93.66	95.80	佛山市	24.91	32.24	25.13	深圳市	1.32	1.52	1.32
2	广州市	95.42	96.83	95.38	广州市	24.78	33.06	25.08	广州市	1.06	1.59	1.24
3	上海市	96.48	93.66	95.05	深圳市	24.89	32.72	25.05	佛山市	1.35	1.19	1.18
4	佛山市	97.54	89.09	95.00	天津市	24.72	33.06	25.04	上海市	1.31	1.37	1.11
5	天津市	94.37	96.83	92.56	上海市	24.85	32.72	25.00	东莞市	1.06	0.41	1.08
6	北京市	88.73	80.99	89.67	北京市	24.38	31.42	24.77	天津市	0.98	1.49	0.99
7	青岛市	84.51	90.85	88.92	青岛市	24.13	32.42	24.68	北京市	0.84	0.84	0.86
8	苏州市	92.25	83.10	88.54	苏州市	24.59	31.63	24.59	青岛市	0.72	1.34	0.85
9	东莞市	81.34	58.80	84.53	淄博市	24.25	31.35	24.40	苏州市	0.99	0.95	0.83
10	淄博市	86.62	80.28	84.08	唐山市	24.09	30.21	24.34	大庆市	0.81	0.55	0.73
11	唐山市	83.80	68.31	82.23	东莞市	23.95	29.37	24.28	淄博市	0.87	0.95	0.68
12	大庆市	85.56	68.31	82.00	大庆市	24.19	30.21	24.22	唐山市	0.82	0.46	0.67
13	无锡市	90.14	86.62	80.02	无锡市	24.46	31.98	24.14	无锡市	0.93	1.00	0.59
14	济南市	82.04	82.04	79.88	济南市	23.99	31.52	24.09	常州市	0.83	1.26	0.57
15	常州市	80.99	86.62	75.77	常州市	23.93	31.98	24.00	济南市	0.66	0.94	0.56
16	杭州市	90.49	72.89	75.49	宁波市	24.27	31.87	23.90	宁波市	0.83	1.08	0.53

变量	样本量	LLC检验	HT检验	结论
lnSYN	445170	-190.000^***	-200.000^***	平稳
lnADV	445170	-170.000^***	-130.000^***	平稳
lnCON	445170	-85.250^***	-79.908^***	平稳
lnIND	445170	-270.000^***	-240.000^***	平稳
lnMAR	445170	-250.000^***	-610.000^***	平稳
lnSCA	445170	-340.000^***	-87.969^***	平稳
lnTRA	445170	-73.150^***	-260.000^***	平稳

原假设	统计量	P值	结论
lnADV不是lnSYN的Granger原因	595.746	0.000	拒绝原假设
lnSYN不是lnADV的Granger原因	223.982	0.000	拒绝原假设
lnCON不是lnSYN的Granger原因	3.907	0.048	拒绝原假设
lnSYN不是lnCON的Granger原因	728.704	0.000	拒绝原假设
lnIND不是lnSYN的Granger原因	39.158	0.000	拒绝原假设
lnSYN不是lnIND的Granger原因	118.354	0.000	拒绝原假设
lnADV不是lnCON的Granger原因	367.616	0.000	拒绝原假设
lnCON不是lnADV的Granger原因	24.753	0.000	拒绝原假设
lnADV不是lnIND的Granger原因	66.996	0.000	拒绝原假设
lnIND不是lnADV的Granger原因	0.323	0.570	接受原假设
lnCON不是lnIND的Granger原因	1292.486	0.000	拒绝原假设
lnIND不是lnCON的Granger原因	2.708	0.100	接受原假设

变量	全国	东部内部	中部内部	西部内部	东中之间	东西之间	中西之间
L1.lnSYN	0.529^*** (46.00)	0.791^*** (55.39)	0.361^*** (18.58)	0.439^*** (22.37)	0.513^*** (25.81)	0.559^*** (26.34)	0.496^*** (28.52)
lnADV	0.051^*** (26.00)	0.030^*** (12.14)	0.026^*** (4.96)	0.046^*** (6.03)	0.059^*** (17.11)	0.034^*** (7.08)	0.082^*** (14.37)
lnCON	0.004^*** (9.69)	0.002^*** (4.37)	0.010^*** (10.33)	0.007^*** (4.55)	0.008^*** (10.56)	0.003^*** (3.39)	0.007^*** (6.64)
lnIND	-0.007^*** (-3.25)	0.010^*** (3.71)	0.015^** (2.41)	-0.042^*** (-3.98)	0.011^*** (2.68)	-0.041^*** (-6.68)	-0.044^*** (-5.92)
lnADV×lnCON	0.016^*** (33.56)	0.012^*** (14.49)	0.017^*** (13.04)	0.017^*** (9.31)	0.016^*** (17.97)	0.014^*** (15.25)	0.021^*** (16.38)
lnADV×lnIND	0.040^*** (7.26)	0.013 (1.33)	0.020^* (1.73)	0.099^*** (5.76)	0.024^** (2.41)	0.085^*** (6.13)	0.022 (1.54)
lnCON×lnIND	-0.006^*** (-9.90)	-0.012^*** (-11.39)	0.002 (1.13)	-0.014^*** (-5.53)	-0.008^*** (-5.27)	-0.013^*** (-8.39)	-0.008^*** (-4.12)
常数项	-0.267^*** (-25.18)	0.028^* (1.84)	-0.279^*** (-10.53)	-0.296^*** (-10.38)	-0.114^*** (-5.76)	-0.249^*** (-12.39)	-0.338^*** (-14.69)
样本量	404700	50500	49500	34860	101000	84840	84000
F值	7064.95	5661.63	1028.85	727.11	2214.83	3004.95	1923.44