Journal of Geo-information Science ›› 2023, Vol. 25 ›› Issue (5): 982-998.doi: 10.12082/dqxxkx.2023.220614
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YANG Yu1(), SONG Futie1,*(
), ZHANG Jie2
Received:
2022-08-22
Revised:
2022-10-09
Online:
2023-05-25
Published:
2023-04-27
Contact:
SONG Futie
E-mail:yyu0105@126.com;tsong@ecust.edu.cn
Supported by:
YANG Yu, SONG Futie, ZHANG Jie. Research on the Influence Mechanism of Financial Network Centrality on Urban Economic Growth in China[J].Journal of Geo-information Science, 2023, 25(5): 982-998.DOI:10.12082/dqxxkx.2023.220614
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Tab. 1
Source and description of Variable
变量 | 名称 | 含义 | 数据来源 | 时段 |
---|---|---|---|---|
ln(realGDP) | 城市经济增长 | 实际GDP(亿元)取对数 | 《中国统计年鉴》[ | 2005—2020 |
PageRank | 金融网络中心性 | 金融网络中心性指数 | 启信宝网站( | 2005—2020 |
ln(degree) | 金融网络中心性 | 金融链接强度取对数 | 启信宝网站( | 2005—2020 |
ln(populdensity) | 人口集聚变 | 人口密度(万/平方千米)取对数 | 《中国城市建设年鉴》[ | 2005—2020 |
ln(percapital) | 人均固定资本存量 | 人均固定资本(亿元)取对数 | 《中国城市统计年鉴》[ | 2005—2020 |
ln(teacher) | 人力资本 | 普通高校专任教师数(人)取对数 | 《中国城市统计年鉴》[ | 2005—2020 |
ln(knwlspillover) | 知识溢出变量 | 每万人发明专利申请数量(件)取对数 | 佰腾网( | 2005—2020 |
ln(technology) | 技术进步 | 全市科学技术支出(万元)取对数 | 《中国城市统计年鉴》[ | 2005—2020 |
ln(slagrealGDP) | 空间滞后变量 | 实际GDP(亿元)取对数*地理距离权重矩阵 | 《中国统计年鉴》[ | 2005—2020 |
findeepth | 金融深化率 | 金融机构年末存款余额占 GDP 的比重 | 《中国城市统计年鉴》[ | 2005—2020 |
Tab. 2
Geographical concentration of headquarters in 2005 and 2020
2005年 | 2020年 | 2005年 | 2020年 | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
城市名 | 排名 | 总部/个数 | 占比 | 城市名 | 排名 | 总部/个数 | 占比 | 城市名 | 排名 | 出度 | 占比 | 城市名 | 排名 | 出度 | 占比 | ||
上海 | 1 | 105 | 19.92 | 上海 | 1 | 199 | 23.86 | 北京 | 1 | 68 877 | 74.04 | 北京 | 1 | 119 875 | 56.41 | ||
北京 | 2 | 87 | 16.51 | 北京 | 2 | 156 | 18.71 | 上海 | 2 | 10 342 | 11.12 | 上海 | 2 | 26 605 | 12.52 | ||
深圳 | 3 | 53 | 10.06 | 深圳 | 3 | 100 | 11.99 | 深圳 | 3 | 4853 | 5.22 | 深圳 | 3 | 19 395 | 9.13 | ||
广州 | 4 | 15 | 2.85 | 广州 | 4 | 23 | 2.76 | 广州 | 4 | 841 | 0.90 | 福州 | 4 | 3069 | 1.01 | ||
杭州 | 5 | 14 | 2.66 | 杭州 | 5 | 21 | 2.52 | 西安 | 5 | 641 | 0.69 | 天津 | 5 | 2741 | 1.06 | ||
天津 | 6 | 12 | 2.28 | 南京 | 6 | 16 | 1.92 | 南京 | 6 | 561 | 0.60 | 南京 | 6 | 2474 | 1.29 | ||
南京 | 7 | 10 | 1.90 | 天津 | 7 | 16 | 1.92 | 福州 | 7 | 475 | 0.51 | 杭州 | 7 | 2254 | 1.16 | ||
西安 | 8 | 9 | 1.71 | 成都 | 8 | 10 | 1.20 | 长沙 | 8 | 267 | 0.29 | 武汉 | 8 | 2162 | 0.92 | ||
成都 | 9 | 9 | 1.71 | 重庆 | 9 | 13 | 1.56 | 天津 | 9 | 258 | 0.28 | 广州 | 9 | 2148 | 0.62 | ||
长沙 | 10 | 8 | 1.52 | 福州 | 10 | 9 | 1.08 | 成都 | 10 | 248 | 0.27 | 西安 | 10 | 1965 | 0.63 | ||
总和 | - | 322 | 61.12 | 总和 | - | 563 | 67.51 | 总和 | - | 87 363 | 93.91 | 总和 | - | 182 688 | 84.75 |
Tab. 3
Geographical concentration of branches in 2005 and 2020
2005年 | 2020年 | 2005年 | 2020年 | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
城市名 | 排名 | 分支/个 | 占比 | 城市名 | 排名 | 分支/个 | 占比 | 城市名 | 排名 | 入度 | 占比 | 城市名 | 排名 | 入度 | 占比 | ||
上海 | 1 | 2542 | 3.38 | 上海 | 1 | 4854 | 3.06 | 上海 | 1 | 3119 | 3.35 | 上海 | 1 | 6376 | 3.00 | ||
北京 | 2 | 2208 | 2.94 | 北京 | 2 | 4715 | 2.97 | 北京 | 2 | 2679 | 2.88 | 北京 | 2 | 6046 | 2.85 | ||
广州 | 3 | 1668 | 2.22 | 重庆 | 3 | 2859 | 1.80 | 重庆 | 3 | 2016 | 2.17 | 重庆 | 3 | 4357 | 1.69 | ||
重庆 | 4 | 1666 | 2.22 | 广州 | 4 | 3384 | 2.13 | 广州 | 4 | 1919 | 2.06 | 深圳 | 4 | 3762 | 2.05 | ||
天津 | 5 | 1470 | 1.95 | 深圳 | 5 | 2739 | 1.72 | 天津 | 5 | 1690 | 1.82 | 成都 | 5 | 3697 | 1.56 | ||
成都 | 6 | 1198 | 1.59 | 成都 | 6 | 2827 | 1.78 | 深圳 | 6 | 1509 | 1.62 | 广州 | 6 | 3590 | 1.74 | ||
深圳 | 7 | 1179 | 1.57 | 天津 | 7 | 2828 | 1.78 | 成都 | 7 | 1423 | 1.53 | 天津 | 7 | 3315 | 1.77 | ||
苏州 | 8 | 1106 | 1.47 | 杭州 | 8 | 2074 | 1. 31 | 苏州 | 8 | 1273 | 1.37 | 杭州 | 8 | 2901 | 1.19 | ||
杭州 | 9 | 1061 | 1.41 | 武汉 | 9 | 2257 | 1.42 | 杭州 | 9 | 1252 | 1.35 | 武汉 | 9 | 2676 | 1.37 | ||
武汉 | 10 | 1032 | 1.37 | 苏州 | 10 | 2127 | 1.34 | 武汉 | 10 | 1210 | 1.30 | 西安 | 10 | 2578 | 1.26 | ||
总和 | - | 15 130 | 20.12 | 总和 | - | 30 664 | 19.31 | 总和 | - | 18 090 | 19.44 | 总和 | - | 39 298 | 18.48 |
Tab. 4
Global spatial autocorrelation results
年份 | Moran's I | Z | P值 |
---|---|---|---|
2005 | 0.35 | 7.29 | 0.001 |
2006 | 0.35 | 8.28 | 0.009 |
2007 | 0.34 | 8.12 | 0.032 |
2008 | 0.34 | 9.99 | 0.034 |
2009 | 0.32 | 8.32 | 0.044 |
2010 | 0.30 | 10.44 | 0.002 |
2011 | 0.32 | 10.44 | 0.002 |
2012 | 0.27 | 10.32 | 0.023 |
2013 | 0.27 | 11.86 | 0.015 |
2014 | 0.27 | 11.10 | 0.003 |
2015 | 0.21 | 11.99 | 0.004 |
2016 | 0.24 | 12.79 | 0.006 |
2017 | 0.19 | 12.60 | 0.000 |
2018 | 0.19 | 13.19 | 0.004 |
2019 | 0.18 | 13.19 | 0.000 |
2020 | 0.18 | 13.79 | 0.002 |
Tab. 5
Benchmark regression and robustness test results
变量 | 模型1 | 模型2 | 模型3 | 模型4 | 模型5 | ||
---|---|---|---|---|---|---|---|
基准回归 | 滞后1期 | 滞后2期 | 工具变量分析 | 更换核心解释变量 | |||
PageRank | 0.039*** | 0.310*** | |||||
(0.007) | (0.032) | ||||||
lag1PageRank | 0.033* | ||||||
(0.019) | |||||||
lag2PageRank | 0.032** | ||||||
(0.019) | |||||||
ln(degree) | 0.333*** | ||||||
(0.022) | |||||||
ln(populdensity) | 0.051*** | 0.045*** | 0.046*** | 0.056*** | 0.047** | ||
(0.014) | (0.014) | (0.014) | (0.013) | (0.013) | |||
ln(percapital) | 0.037*** | 0.025*** | 0.043*** | 0.126*** | 0.034*** | ||
(0.004) | (0.004) | (0.004) | (0.007) | (0.004) | |||
ln(teacher) | 0.034*** | 0.031*** | 0.033*** | 0.006 | 0.027*** | ||
(0.007) | (0.007) | (0.007) | (0.007) | (0.007) | |||
ln(slagrealGDP) | 0.899*** | 0.776*** | 0.903*** | 0.397*** | 0.721*** | ||
(0.008) | (0.008) | (0.008) | (0.007) | (0.006) | |||
N | 4176 | 4176 | 4176 | 4176 | 4176 | ||
R2 | 0.923 | 0.934 | 0.925 | 0.955 | 0.912 | ||
Durbin | 0.000 | ||||||
Wu-Hausman | 0.000 | ||||||
固定效应 | 控制 | 控制 | 控制 | 控制 | 控制 |
Tab. 6
Results of mediating effect test
变量 | 模型1 | 模型2 | 模型3 |
---|---|---|---|
ln(knwlspillover) | ln(realGDP) | ln(realGDP) | |
PageRank | 0.121*** | 0.019*** | |
(0.023) | (0.004) | ||
ln(knwlspillover) | 0.071*** | 0.071*** | |
(0.004) | (0.004) | ||
N | 4176 | 4176 | 4176 |
R2 | 0.873 | 0.918 | 0.900 |
固定效应 | 控制 | 控制 | 控制 |
变量 | 模型4 | 模型5 | 模型6 |
ln(technology) | ln(realGDP) | ln(realGDP) | |
PageRank | 0.221*** | 0.017*** | |
(0.035) | (0.005) | ||
ln(technology) | 0.041*** | 0.042*** | |
(0.002) | (0.002) | ||
N | 4176 | 4176 | 4176 |
R2 | 0.661 | 0.920 | 0.921 |
固定效应 | 控制 | 控制 | 控制 |
变量 | 模型7 | 模型8 | 模型9 |
findeepth | ln(realGDP) | ln(realGDP) | |
PageRank | 0.154*** | 0.015*** | |
(0.031) | (0.003) | ||
findeepth | 0.091*** | 0.090*** | |
(0.006) | (0.006) | ||
N | 4176 | 4176 | 4176 |
R2 | 0.503 | 0.912 | 0.924 |
固定效应 | 控制 | 控制 | 控制 |
Tab. 7
Results of heterogeneity analysis
变量 | PageRank指数作为核心解释变量 | 金融链接强度作为核心解释变量 | ||
---|---|---|---|---|
模型1:核心 | 模型2:外围 | 模型3:核心 | 模型4:外围 | |
PageRank | 0.067** | 0.014** | ||
(0.025) | (0.007) | |||
ln(degree) | 0.562*** | 0.277*** | ||
(0.045) | (0.027) | |||
ln(populdensity) | 0.089*** | 0.043*** | 0.069** | 0.034** |
(0.031) | (0.015) | (0.027) | (0.015) | |
ln(percapital) | 0.069*** | 0.038*** | 0.037*** | 0.029*** |
(0.009) | (0.005) | (0.008) | (0.005) | |
ln(teacher) | 0.046** | 0.035*** | 0.048*** | 0.020*** |
(0.019) | (0.008) | (0.017) | (0.007) | |
ln(slagrealGDP) | 0.900*** | 0.899*** | 0.493*** | 0.744*** |
(0.016) | (0.010) | (0.036) | (0.018) | |
N | 832 | 3344 | 832 | 3344 |
R2 | 0.931 | 0.922 | 0.944 | 0.925 |
固定效应 | 控制 | 控制 | 控制 | 控制 |
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