地理学报 ›› 2019, Vol. 74 ›› Issue (6): 1149-1162.doi: 10.11821/dlxb201906006
收稿日期:
2018-06-22
修回日期:
2019-03-05
出版日期:
2019-06-25
发布日期:
2019-06-20
作者简介:
李博(1981-), 男, 天津人, 博士后, 副教授, 硕士生导师, 主要从事城市与区域经济研究。E-mail: mg2011818@126.com
基金资助:
LI Bo1,2,LI Qihang3,SUN Wei1,4()
Received:
2018-06-22
Revised:
2019-03-05
Published:
2019-06-25
Online:
2019-06-20
Supported by:
摘要:
企业融资难和融资成本高是近年来制约中国实体经济发展的重要因素,但是哪些又是影响企业融资成本高的因素呢?基于京津冀地区商业银行分支机构和工业企业的大数据挖掘,定量分析了企业融资成本与商业银行分支机构的距离、1~5 km半径内商业银行分支机构的数量的相关性,解释了地理因素对企业融资成本的作用机制。研究表明:① 总体上看,与商业银行分支机构的距离越近、1~5 km半径内商业银行分支机构的数量越多,工业企业的融资成本越低。② 与商业银行分支机构的距离、1~5 km半径内商业银行分支机构的数量对国有工业企业和非国有工业企业都有显著影响,其中对非国有工业企业的影响显著性更高。③ 北京和天津的工业企业融资成本与商业银行分支机构的距离无关,与1~5 km半径内商业银行分支机构的数量负相关;河北省的工业企业融资成本与商业银行分支机构的距离正相关,与1~5 km半径内商业银行分支机构的数量负相关。④ 与商业银行分支机构的距离对重工业企业和劳动密集型企业的影响更显著,1~5 km半径内商业银行分支机构的数量对不同行业的影响差异性不大。
李博,李启航,孙威. 地理学视角的京津冀地区工业企业融资成本分析[J]. 地理学报, 2019, 74(6): 1149-1162.
LI Bo,LI Qihang,SUN Wei. Analysis of financing costs of industrial enterprises in Beijing-Tianjin-Hebei region from geographic perspective[J]. Acta Geographica Sinica, 2019, 74(6): 1149-1162.
表1
变量定义和解释
变量类型 | 变量说明 |
---|---|
lfcost_r | 融资成本,以利息支出除以全部负债与应付账款的差,保留正数取自然对数,按上下1%缩尾 |
distance | 与企业最近的3个银行分支机构的距离之平均数的自然对数 |
num_1k | 1 km分支机构数量,即企业周围1 km半径内所有商业银行分支机构的数量 |
num_3k | 3 km分支机构数量,即企业周围3 km半径内所有商业银行分支机构的数量 |
num_5k | 5 km分支机构数量,即企业周围5 km半径内所有商业银行分支机构的数量 |
lfa | 固定资产的自然对数 |
lwc | 营运资金的自然对数 |
lworker | 从业人员的自然对数 |
lev | 杠杆率,资产除以负债 |
dum_gov | 国企虚拟变量,国有股份占实收资本超过50%为1,否则为0 |
dum_for | 外企虚拟变量,外资(含港澳台)股份占实收资本超过25%为1,否则为0 |
dum_qg | 轻工虚拟变量,按照国家统计局轻重工业划分,是则为1,否则为0 |
dum_lab | 劳动密集型行业虚拟变量,整个行业劳动人数与产值之比位于全部行业中位数以上为1,否则为0 |
表2
各变量之间的Pearson相关系数
lfcost_r | Distance | num_1k | num_3k | num_5k | lfa | lev | |
---|---|---|---|---|---|---|---|
distance | 0.086*** | 1 | |||||
num_1k | -0.082*** | -0.507*** | 1 | ||||
num_3k | -0.136*** | -0.274*** | 0.769*** | 1 | |||
num_5k | -0.149*** | -0.213*** | 0.672*** | 0.955*** | 1 | ||
lfa | 0.140*** | -0.0130 | 0.028*** | 0.015* | -0.00300 | 1 | |
lev | -0.216*** | -0.063*** | 0.021*** | 0.037*** | 0.039*** | -0.099*** | 1 |
表3
融资成本的OLS和Tobit回归结果
变量 | 回归(1) | 回归(2) | 回归(3) | 回归(4) | 回归(5) | 回归(6) |
---|---|---|---|---|---|---|
OLS | OLS | OLS | Tobit | Tobit | Tobit | |
distance | 0.020** | 0.018*** | 0.019*** | 0.022*** | 0.019*** | 0.021*** |
(2.69) | (3.26) | (3.32) | (3.17) | (3.07) | (3.34) | |
num_1k | -0.004 | -0.005 | ||||
(-1.17) | (-1.31) | |||||
num_3k | -0.002*** | -0.003*** | ||||
(-4.01) | (-4.58) | |||||
num_5k | -0.001*** | -0.001*** | ||||
(-3.69) | (-5.08) | |||||
lfa | 0.110*** | 0.107*** | 0.106*** | 0.176*** | 0.171*** | 0.170*** |
(5.90) | (5.73) | (5.80) | (13.54) | (13.11) | (13.01) | |
lwc | -0.081 | -0.076 | -0.075 | -0.055*** | -0.047*** | -0.046*** |
(-1.54) | (-1.48) | (-1.47) | (-3.50) | (-3.00) | (-2.91) | |
lworker | 0.009 | 0.012 | 0.013 | 0.004 | 0.008 | 0.009 |
(0.32) | (0.42) | (0.43) | (0.19) | (0.40) | (0.43) | |
lev | -1.034* | -1.025* | -1.024* | -0.855*** | -0.843*** | -0.840*** |
(-2.00) | (-2.00) | (-2.00) | (-12.34) | (-12.16) | (-12.13) | |
dum_gov | 0.175* | 0.182** | 0.184** | 0.287*** | 0.299*** | 0.301*** |
(2.08) | (2.21) | (2.22) | (6.16) | (6.40) | (6.45) | |
dum_for | -0.065 | -0.069 | -0.070 | -0.084** | -0.090** | -0.092** |
(-1.10) | (-1.23) | (-1.26) | (-2.14) | (-2.30) | (-2.35) | |
城市效应 行业效应 | YES | YES | YES | YES | YES | YES |
YES | YES | YES | YES | YES | YES | |
城市聚类稳健标准误 | YES | YES | YES | - | - | - |
_cons | 2.042** | 2.014** | 1.999** | 0.938*** | 0.902*** | 0.879*** |
(2.97) | (2.95) | (2.94) | (4.03) | (3.89) | (3.79) | |
N | 11780 | 11780 | 11780 | 15987 | 15987 | 15987 |
调整R2 | 0.225 | 0.226 | 0.226 |
表4
按省份企业融资成本的OLS回归结果
变量 | 回归(1) | 回归(2) | 回归(3) | 回归(4) | 回归(5) | 回归(6) |
---|---|---|---|---|---|---|
京津OLS | 河北OLS | 京津OLS | 河北OLS | 京津OLS | 河北OLS | |
distance | 0.00537 | 0.0216 | 0.00526 | 0.0192** | 0.00643 | 0.0213** |
(2.188) | (1.147) | (1.903) | (2.323) | (3.250) | (2.659) | |
num_1k | -0.00535*** | -0.00738 | ||||
(-78.62) | (-0.417) | |||||
num_3k | -0.00149** | -0.00462*** | ||||
(-20.96) | (-4.863) | |||||
num_5k | -0.000590 | -0.00225*** | ||||
(-4.994) | (-3.506) | |||||
lfa | 0.0666* | 0.0755** | 0.0644* | 0.0712** | 0.0645* | 0.0695** |
(9.409) | (2.634) | (10.28) | (2.531) | (11.84) | (2.539) | |
lwc | -0.0867 | -0.218*** | -0.0835 | -0.210*** | -0.0836 | -0.208*** |
(-1.801) | (-9.332) | (-1.770) | (-9.123) | (-1.810) | (-9.167) | |
lworker | -0.00807 | 0.0489 | -0.00624 | 0.0563 | -0.00594 | 0.0569 |
(-0.474) | (1.156) | (-0.389) | (1.390) | (-0.384) | (1.428) | |
lev | -0.828 | -2.463*** | -0.825 | -2.433*** | -0.825 | -2.424*** |
(-3.177) | (-13.02) | (-3.244) | (-13.17) | (-3.243) | (-13.47) | |
dum_gov | -0.0590 | -0.103 | -0.0610 | -0.102 | -0.0623 | -0.102 |
(-0.691) | (-0.930) | (-0.731) | (-0.926) | (-0.756) | (-0.918) | |
城市效应 行业效应 | YES | YES | YES | YES | YES | YES |
YES | YES | YES | YES | YES | YES | |
城市聚类稳健标准误 | YES | YES | YES | YES | YES | YES |
_cons | 2.358 | 4.775*** | 2.350 | 4.699*** | 2.328 | 4.663*** |
(2.087) | (11.56) | (2.053) | (12.44) | (2.039) | (12.10) | |
N | 3752 | 5533 | 3752 | 5533 | 3752 | 5533 |
调整R2 | 0.073 | 0.206 | 0.0748 | 0.207 | 0.075 | 0.208 |
表5
按轻重行业分企业融资成本的OLS回归结果
变量 | 回归(1) | 回归(2) | 回归(3) | 回归(4) | 回归(5) | 回归(6) |
---|---|---|---|---|---|---|
轻工OLS | 重工OLS | 轻工OLS | 重工OLS | 轻工OLS | 重工OLS | |
distance | 0.0146 | 0.0224* | 0.00781 | 0.0244** | 0.00936 | 0.0260** |
(1.134) | (2.053) | (1.135) | (2.835) | (1.344) | (2.988) | |
num_1k | 0.00157 | -0.00819 | ||||
(0.159) | (-1.516) | |||||
num_3k | -0.00268*** | -0.00211*** | ||||
(-5.250) | (-3.742) | |||||
num_5k | 0.00109*** | -0.000850*** | ||||
(-4.154) | (-3.578) | |||||
lfa | 0.0728** | 0.0839*** | 0.0677* | 0.0815*** | 0.0672* | 0.0814*** |
(2.247) | (5.488) | (2.098) | (5.628) | (2.106) | (5.739) | |
lwc | -0.186*** | -0.180*** | -0.175*** | -0.176*** | -0.175*** | -0.176*** |
(-3.660) | (-5.151) | (-3.530) | (-5.143) | (-3.625) | (-5.178) | |
lworker | 0.0368 | 0.0229 | 0.0415 | 0.0255 | 0.0410 | 0.0254 |
(1.186) | (0.566) | (1.313) | (0.632) | (1.298) | (0.635) | |
lev | -2.266*** | -1.853*** | -2.252*** | -1.842*** | -2.253*** | -1.842*** |
(-5.049) | (-5.830) | (-5.084) | (-5.869) | (-5.114) | (-5.872) | |
dum_gov | -0.0859 | -0.0855 | -0.0882 | -0.0861 | -0.0877 | -0.0870 |
(-1.130) | (-1.167) | (-1.209) | (-1.196) | (-1.228) | (-1.216) | |
城市效应 行业效应 | YES | YES | YES | YES | YES | YES |
YES | YES | YES | YES | YES | YES | |
城市聚类稳健标准误 | YES | YES | YES | YES | YES | YES |
_cons | 4.283*** | 4.052*** | 4.270*** | 3.998*** | 4.262*** | 3.982*** |
(8.526) | (7.664) | (8.215) | (7.940) | (8.325) | (7.928) | |
N | 2317 | 6968 | 2317 | 6968 | 2317 | 6968 |
调整R2 | 0.268 | 0.181 | 0.269 | 0.182 | 0.269 | 0.182 |
表6
按劳动密集型与非劳动密集型行业区分企业融资成本的OLS回归结果
变量 | 回归(1) | 回归(2) | 回归(3) | 回归(4) | 回归(5) | 回归(6) |
---|---|---|---|---|---|---|
劳密OLS | 非劳密OLS | 劳密OLS | 非劳密OLS | 劳密OLS | 非劳密OLS | |
distance | 0.0235** | 0.0155 | 0.0239*** | 0.0152 | 0.0259*** | 0.0163 |
(2.900) | (1.337) | (4.335) | (1.550) | (4.416) | (1.666) | |
num_1k | -0.00831 | -0.00389 | ||||
(-1.509) | (-0.781) | |||||
num_3k | -0.00275*** | -0.00162** | ||||
(-4.603) | (-2.808) | |||||
num_5k | -0.00107*** | -0.000660** | ||||
(-3.733) | (-2.778) | |||||
lfa | 0.0712** | 0.0876*** | 0.0674** | 0.0850*** | 0.0671** | 0.0850*** |
(2.625) | (3.570) | (2.508) | (3.556) | (2.522) | (3.588) | |
lwc | -0.177*** | -0.181*** | -0.170*** | -0.177*** | -0.171*** | -0.177*** |
(-4.226) | (-5.142) | (-4.144) | (-5.133) | (-4.205) | (-5.197) | |
lworker | 0.0312 | 0.0162 | 0.0343 | 0.0188 | 0.0339 | 0.0188 |
(0.875) | (0.496) | (0.952) | (0.583) | (0.953) | (0.587) | |
lev | -2.106*** | -1.838*** | -2.091*** | -1.830*** | -2.091*** | -1.829*** |
(-5.469) | (-5.420) | (-5.501) | (-5.458) | (-5.515) | (-5.470) | |
dum_gov | -0.105 | -0.0680 | -0.105 | -0.0699 | -0.106 | -0.0700 |
(-1.533) | (-0.981) | (-1.562) | (-1.033) | (-1.607) | (-1.039) | |
城市效应 行业效应 | YES | YES | YES | YES | YES | YES |
YES | YES | YES | YES | YES | YES | |
城市聚类稳健标准误 | YES | YES | YES | YES | YES | YES |
_cons | 3.128*** | 3.965*** | 3.078*** | 3.937*** | 3.066*** | 3.925*** |
(4.747) | (7.532) | (4.770) | (7.686) | (4.754) | (7.681) | |
N | 4201 | 5084 | 4201 | 5084 | 4201 | 5084 |
调整R2 | 0.226 | 0.197 | 0.228 | 0.198 | 0.228 | 0.198 |
表7
OLS和IV-2SLS回归结果对比
变量 | 回归(1) | 回归(2) | 回归(3) | 回归(4) |
---|---|---|---|---|
OLS | IV | IV | IV | |
ldis | 0.074*** | 0.698*** | 0.374** | 0.377** |
(6.67) | (3.78) | (2.56) | (2.36) | |
lfa | 0.105*** | 0.069*** | 0.071*** | |
(9.57) | (2.65) | (2.94) | ||
lwc | -0.091** | -0.038 | -0.027 | |
(-2.30) | (-0.83) | (-0.64) | ||
lworker | 0.039 | 0.076* | 0.055* | |
(1.43) | (1.74) | (1.75) | ||
lev | -0.951** | -0.837*** | -0.755*** | |
(-2.95) | (-2.93) | (-3.05) | ||
dum_gov | 0.438*** | 0.435*** | 0.389*** | |
(6.77) | (4.46) | (4.59) | ||
dum_for | -0.019 | -0.044 | -0.049** | |
(-0.43) | (-1.64) | (-2.18) | ||
_cons | 0.828 | -4.352*** | -2.343* | -2.043 |
(1.50) | (-3.33) | (-1.84) | (-1.45) | |
IV | No | Yes | Yes | Yes |
固定效应 | Yes | No | No | Yes |
聚类稳健 | Yes | Yes | Yes | Yes |
N | 14845 | 14845 | 14845 | 14845 |
调整R2 | 0.243 | -0.383 | 0.093 | 0.132 |
弱工具检验 | 12.343 | 8.135 | 9.799 | |
过度识别检验 | 3.260 | 3.691 | 5.361 | |
0.196 | 0.158 | 0.069 | ||
内生性检验 | 5.653 | 3.183 | 1.753 | |
0.019 | 0.078 | 0.215 |
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