Journal of Geographical Sciences ›› 2019, Vol. 29 ›› Issue (12): 1965-1980.doi: 10.1007/s11442-019-1699-6
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SUN Wei1,2, LI Qihang3, LI Bo1,4()
Received:
2019-05-30
Accepted:
2019-07-29
Online:
2019-12-25
Published:
2019-12-06
Contact:
LI Bo
E-mail:mg2011818@126.com
About author:
Sun Wei (1975─), Associate Professor, specialized in regional development and spatial planning. E-mail: sunw@igsnrr.ac.cn
Supported by:
SUN Wei, LI Qihang, LI Bo. Does geographic distance have a significant impact on enterprise financing costs?[J].Journal of Geographical Sciences, 2019, 29(12): 1965-1980.
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Table 1
Definition and explanation of variables"
Variable category | Explanation |
---|---|
lfcost_r | Natural logarithm of EFCs obtained by retaining positive values from dividing interest expenses by the difference between total liabilities and accounts payable, winsorized at top and bottom 1% |
distance | Natural logarithm of the mean distance to the three closest CBBs to the enterprise |
num_1k | Number of CBBs within a 1 km radius from the enterprise |
num_3k | Number of CBBs within a 3 km radius from the enterprise |
num_5k | Number of CBBs within a 5 km radius from the enterprise |
lfa | Natural logarithm of fixed assets |
lwc | Natural logarithm of operating capital |
lworker | Natural logarithm of number of employees |
lev | Leverage ratio (assets divided by liabilities) |
dum_gov | SOE dummy variable (has a value of 1 if state-owned shares account for more than 50% of paid-in capital, otherwise has a value of 0) |
dum_for | Foreign enterprise dummy variable (has a value of 1 if foreign capital shares (includes Hong Kong, Macao, and Taiwan) account for more than 25% of paid-in capital, otherwise has a value of 0) |
dum_qg | Light industry dummy variable (has a value of 1 if the industry fits in to the light category as defined by the national bureau of statistics, and a value of 0 if it fits into the heavy category) |
dum_lab | Labor intensive industry dummy variable (has a value of 1 if the ratio between number of workers and output value for industry as a whole is above the median of the same ratio for all industries, otherwise has a value of 0) |
Table 2
Correlation coefficients of variables"
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*** | -0.013 | 0.028*** | 0.015* | -0.003 | 1 | |
lev | -0.216*** | -0.063*** | 0.021*** | 0.037*** | 0.039*** | -0.099*** | 1 |
Table 3
Results of OLS regression and Tobit regression of EFCs and other variables"
Variables | Regression (1) | Regression (2) | Regression (3) | Regression (4) | Regression (5) | Regression (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) | |
City effects | YES | YES | YES | YES | YES | YES |
Industry effects | YES | YES | YES | YES | YES | YES |
City cluster-robust standard errors | 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 |
Adjusted R2 | 0.225 | 0.226 | 0.226 |
Table 4
Results of OLS regression of EFCs (sample divided by provincial unit)"
Variables | Regression (1) | Regression (2) | Regression (3) | Regression (4) | Regression (5) | Regression (6) |
---|---|---|---|---|---|---|
Beijing-Tianjin OLS | Hebei OLS | Beijing-Tianjin OLS | Hebei OLS | Beijing-Tianjin OLS | Hebei 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) | |
City effects | YES | YES | YES | YES | YES | YES |
Industry effects | YES | YES | YES | YES | YES | YES |
City cluster-robust standard errors | 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 |
Adjusted R2 | 0.073 | 0.206 | 0.0748 | 0.207 | 0.075 | 0.208 |
Table 5
Results of OLS regression of EFCs (sample divided according to the light and heavy industry categories)"
Variables | Regression (1) | Regression (2) | Regression (3) | Regression (4) | Regression (5) | Regression (6) |
---|---|---|---|---|---|---|
Light industry OLS | Heavy industry OLS | Light industry OLS | Heavy industry OLS | Light industry OLS | Heavy industry 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) | |
City effects | YES | YES | YES | YES | YES | YES |
Industry effects | YES | YES | YES | YES | YES | YES |
City cluster-robust standard errors | 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 |
Adjusted R2 | 0.268 | 0.181 | 0.269 | 0.182 | 0.269 | 0.182 |
Table 6
Results of OLS regression of EFCs (sample divided according to labor-intensive industry and non-labor intensive industry categories)"
Variables | Regression (1) | Regression (2) | Regression (3) | Regression (4) | Regression (5) | Regression (6) |
---|---|---|---|---|---|---|
Intensive OLS | Non-intensive OLS | Intensive OLS | Non-intensive OLS | Intensive OLS | Non-intensive 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) | |
City effects | YES | YES | YES | YES | YES | YES |
Industry effects | YES | YES | YES | YES | YES | YES |
City cluster-robust standard errors | 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 |
Adjusted R2 | 0.226 | 0.197 | 0.228 | 0.198 | 0.228 | 0.198 |
Table 7
Results of OLS and IV-2SLS regression of EFCs"
Variables | Regression (1) | Regression (2) | Regression (3) | Regression (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 |
Fixed effects | Yes | No | No | Yes |
Cluster-robust | Yes | Yes | Yes | Yes |
N | 14845 | 14845 | 14845 | 14845 |
Adjusted R2 | 0.243 | -0.383 | 0.093 | 0.132 |
Weak instruments test | 12.343 | 8.135 | 9.799 | |
Overidentification test | 3.260 | 3.691 | 5.361 | |
0.196 | 0.158 | 0.069 | ||
Endogeneity test | 5.653 | 3.183 | 1.753 | |
0.019 | 0.078 | 0.215 |
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