Acta Geographica Sinica ›› 2021, Vol. 76 ›› Issue (4): 818-834.doi: 10.11821/dlxb202104004
• Urban Studies • Previous Articles Next Articles
SHENG Kerong(), ZHANG Jie, ZHANG Hongxia
Received:
2019-06-28
Revised:
2020-05-16
Online:
2021-04-25
Published:
2021-06-25
Supported by:
SHENG Kerong, ZHANG Jie, ZHANG Hongxia. Network embedding and urban economic growth in China: A study based on the corporate networks of top 500 public companies[J].Acta Geographica Sinica, 2021, 76(4): 818-834.
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Tab. 1
Partial correlation coefficient between urban network embedding and economic growth
样本类型 | 变量 | ln(perGDP) | |||||
---|---|---|---|---|---|---|---|
核心 | 外围 | 东部 | 中西部 | 较大 | 较小 | ||
全部样本 | ln(degree) | 0.622*** | 0.501*** | 0.503*** | 0.533*** | 0.445*** | 0.433*** |
ln(NlagGDP) | 0.584*** | 0.054 | 0.425*** | 0.094* | 0.459*** | 0.055 | |
2006年样本 | ln(degree) | 0.599*** | 0.434*** | 0.728*** | 0.482*** | 0.412*** | 0.315*** |
ln(NlagGDP) | 0.433*** | -0.039 | 0.115 | -0.034 | 0.189* | -0.013 | |
2016年样本 | ln(degree) | 0.682*** | 0.312*** | 0.404*** | 0.474*** | 0.469*** | 0.421*** |
ln(NlagGDP) | 0.452*** | 0.014 | 0.114 | -0.014 | 0.164** | 0.07 |
Tab. 2
Statistical description of variables
变量含义 | 平均值 | 中位数 | 标准差 | 最大值 | 最小值 | |
---|---|---|---|---|---|---|
ln(perGDP) | 人均实际GDP(元)对数 | 10.24 | 10.28 | 0.70 | 11.87 | 8.04 |
ln(k) | 人均实际资本(元)对数 | 11.38 | 11.44 | 0.93 | 13.57 | 9.01 |
ln(degree) | 度数的对数 | 2.70 | 2.56 | 1.36 | 8.20 | 0.00 |
ln(NlagGDP) | 网络滞后变量(元)对数 | 10.36 | 10.61 | 1.77 | 11.39 | 0.00 |
ln(density) | 人口密度(人/km2)对数 | 8.23 | 8.23 | 0.46 | 10.33 | 6.21 |
ln(SlagGDP) | 空间滞后变量(元)对数 | 10.03 | 10.35 | 1.73 | 11.74 | 0.00 |
manufact | 制造业工业占比(%) | 27.50 | 26.03 | 14.74 | 79.66 | 0.94 |
reseacher | 研发人员占比(%) | 1.95 | 1.55 | 1.47 | 11.17 | 0.00 |
Tab. 3
Regression results from benchmark models
变量 | 模型(1) | 模型(2) | 模型(3) | 模型(4) | 模型(5) | 模型(6) | 模型(7) | 模型(8) |
---|---|---|---|---|---|---|---|---|
全体 | 全体 | 东部 | 中西部 | 核心 | 外围 | 较大 | 较小 | |
ln(k) | 0.726*** (26.65) | 0.727*** (26.68) | 0.603*** (10.57) | 0.774*** (24.15) | 0.541*** (11.37) | 0.754*** (23.45) | 0.550*** (9.55) | 0.771*** (26.37) |
ln(degree) | 0.056*** (4.09) | 0.058*** (4.10) | 0.068*** (3.39) | 0.048*** (2.91) | 0.122*** (4.68) | 0.067** (2.59) | 0.118*** (4.58) | 0.031 (1.44) |
ln(NlagGDP) | -0.005 (-0.69) | 0.168* (1.87) | -0.007 (-0.99) | 0.629*** (4.05) | -0.008 (-1.09) | 0.042** (2.26) | -0.007 (-1.25) | |
ln(density) | 0.062** (2.06) | 0.061** (2.03) | 0.096** (2.21) | 0.033 (0.80) | 0.045 (0.81) | 0.062* (1.76) | 0.021 (0.39) | 0.050 (1.47) |
ln(SlagGDP) | 0.011** (2.33) | 0.011** (2.31) | 0.026** (2.41) | 0.004 (0.84) | 0.026*** (3.18) | 0.009 (1.61) | 0.015 (1.58) | 0.010** (2.20) |
manufact | 0.332*** (4.27) | 0.334*** (4.29) | 0.292* (1.98) | 0.093 (0.88) | -0.076 (-0.43) | 0.240*** (2.66) | 0.558** (2.50) | 0.322*** (3.86) |
reseacher | 0.144 (1.50) | 0.142** (1.48) | -0.169 (-0.99) | 0.134 (1.52) | 0.224 (1.33) | 0.065 (0.74) | 0.029 (0.14) | 0.172 (1.60) |
time | -0.121*** (-5.58) | -0.120*** (-5.48) | -0.116** (-2.49) | -0.164*** (-5.87) | -0.271*** (-4.75) | -0.147*** (-5.72) | -0.026 (-0.76) | -0.146*** (-5.85) |
样本数量 | 513 | 513 | 193 | 314 | 137 | 370 | 115 | 398 |
max vif | 3.28 | 3.29 | 5.71 | 3.60 | 7.46 | 3.07 | 4.04 | 3.24 |
mean vif | 2.06 | 2.00 | 2.94 | 2.07 | 3.75 | 1.94 | 2.64 | 1.94 |
R2 | 0.852 | 0.853 | 0.767 | 0.873 | 0.868 | 0.834 | 0.889 | 0.839 |
Tab. 4
Robust estimation results from two-stage least squares method
变量 | 模型(9) | 模型(10) | 模型(11) | 模型(12) | 模型(13) | 模型(14) | 模型(15) | 模型(16) |
---|---|---|---|---|---|---|---|---|
全体 | 全体 | 东部 | 中西部 | 核心 | 外围 | 较大 | 较小 | |
ln(k) | 0.709*** (26.72) | 0.712*** (26.84) | 0.579*** (11.00) | 0.755*** (24.13) | 0.538*** (11.87) | 0.741*** (24.30) | 0.534*** (9.68) | 0.753*** (26.77) |
ln(degree) | 0.074*** (5.56) | 0.077*** (5.58) | 0.079*** (3.50) | 0.055*** (2.83) | 0.128*** (4.92) | 0.102*** (4.00) | 0.134*** (4.99) | 0.064*** (3.04) |
ln(NlagGDP) | -0.008 (-1.20) | 0.117 (1.35) | -0.008 (-1.20) | 0.578*** (3.88) | -0.015* (-1.90) | 0.039** (2.23) | -0.014** (-2.17) | |
ln(density) | 0.054* (1.83) | 0.053* (1.79) | 0.092** (2.07) | 0.013 (0.32) | 0.023 (0.42) | 0.055 (1.59) | 0.012 (0.23) | 0.049 (1.44) |
ln(SlagGDP) | 0.010** (2.07) | 0.009** (2.04) | 0.025** (2.33) | 0.005 (1.10) | 0.026*** (3.32) | 0.009 (1.40) | 0.014 (1.61) | 0.009* (1.89) |
manufact | 0.323*** (4.22) | 0.327*** (4.27) | 0.395*** (3.00) | 0.306*** (3.15) | 0.207 (1.12) | 0.317*** (3.78) | 0.576*** (2.70) | 0.307*** (3.78) |
reseacher | 0.102 (1.07) | 0.101 (1.05) | 0.260 (1.03) | 0.178 (1.60) | 0.069 (0.39) | 0.085 (0.78) | -0.003 (-0.01) | 0.145 (1.36) |
time | -0.119*** (-5.53) | -0.117*** (-5.40) | -0.089* (-1.84) | -0.143*** (-5.36) | -0.235*** (-4.29) | -0.145*** (-5.64) | -0.021 (-0.65) | -0.147*** (-5.87) |
样本数量 | 513 | 513 | 195 | 314 | 139 | 374 | 115 | 398 |
Durbin | 0.003 | 0.003 | 0.023 | 0.039 | 0.045 | 0.016 | 0.048 | 0.013 |
Wu-Hausman | 0.003 | 0.003 | 0.019 | 0.036 | 0.030 | 0.017 | 0.028 | 0.013 |
R2 | 0.851 | 0.852 | 0.768 | 0.873 | 0.862 | 0.834 | 0.888 | 0.838 |
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