Journal of Geographical Sciences >
Does geographic distance have a significant impact on enterprise financing costs?
Sun Wei (1975─), Associate Professor, specialized in regional development and spatial planning. Email: sunw@igsnrr.ac.cn 
Received date: 20190530
Accepted date: 20190729
Online published: 20191206
Supported by
Strategic Priority Research Program of the Chinese Academy of Sciences, No(XDA19040401)
Youth Fund for Humanities and Social Sciences of the Ministry of Education of China, No(16YJCZH040)
Youth Fund for Humanities and Social Sciences of the Ministry of Education of China, No(14YJCZH078)
National Natural Science Foundation of China, No(41571117)
National Natural Science Foundation of China, No(41871117)
Social Science Foundation of Beijing, No(14CSB010)
Shandong Taishan Scholar Youth Expert Support Program
Copyright
As information technology has been applied more broadly and transportation infrastructure has improved, persistent debate has existed as to the question of whether geographic distance influences enterprise financing costs (EFCs). Through mining big data regarding industrial enterprises and commercial bank branches (CBBs) in the BeijingTianjinHebei region, this paper conducts quantitative analysis of correlation between the EFCs and their distance to CBBs as well as the number of CBBs within a 15 km radius, and investigates how geographic factors affect EFCs. The results indicate the following: (1) In overall terms, the shorter the distance to CBBs and the greater the number of CBBs within a 15 km radius, the lower the EFCs. (2) Distance to CBBs and number of CBBs within a 15 km radius significantly influence stateowned and nonstateowned enterprises, with the effect on nonstateowned enterprises being more pronounced. (3) The EFCs in Beijing and Tianjin are not correlated with distance to CBBs, and negatively correlated to the number of CBBs within a 15 km radius; the EFCs in Hebei Province are positively correlated with distance to CBBs, and negatively correlated with the number of CBBs within a 15 km radius. (4) Distance to CBBs has a more significant impact on enterprises engaged in heavy industry and laborintensive industries, while there is not much difference between different industries in terms of how the number of CBBs within a 15 km radius affects them.
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 . DOI: 10.1007/s1144201916996
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 stateowned shares account for more than 50% of paidin 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 paidin 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) 
Figure 1 Kernel density estimation map for industrial enterprises (a) and banks (b) in the BeijingTianjinHebei region in 2013 
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 
Note: The *, **, and *** symbols represent 10%, 5%, and 1% levels of significance, respectively 
Figure 2 Kernel density estimation of financing cost logarithms with the full sample (a) and with zero values eliminated (b) 
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 clusterrobust 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 R^{2}  0.225  0.226  0.226 
Note: Numbers in brackets are the t values of regression coefficients. The *, **, and *** symbols represent 10%, 5%, and 1% significance levels, respectively. 
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) 

BeijingTianjin OLS  Hebei OLS  BeijingTianjin OLS  Hebei OLS  BeijingTianjin 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 clusterrobust 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 R^{2}  0.073  0.206  0.0748  0.207  0.075  0.208 
Note: Numbers in brackets are the t values of regression coefficients. The *, **, and *** symbols represent 10%, 5%, and 1% significance levels, respectively. 
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 clusterrobust 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 R^{2}  0.268  0.181  0.269  0.182  0.269  0.182 
Note: Numbers in brackets are the t values of regression coefficients. The *, **, and *** symbols represent 10%, 5%, and 1% significance levels, respectively. 
Table 6 Results of OLS regression of EFCs (sample divided according to laborintensive industry and nonlabor intensive industry categories) 
Variables  Regression (1)  Regression (2)  Regression (3)  Regression (4)  Regression (5)  Regression (6) 

Intensive OLS  Nonintensive OLS  Intensive OLS  Nonintensive OLS  Intensive OLS  Nonintensive 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 clusterrobust 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 R^{2}  0.226  0.197  0.228  0.198  0.228  0.198 
Note: Numbers in brackets are the t values of regression coefficients. The *, **, and *** symbols represent 10%, 5%, and 1% significance levels, respectively. 
Table 7 Results of OLS and IV2SLS 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 
Clusterrobust  Yes  Yes  Yes  Yes 
N  14845  14845  14845  14845 
Adjusted R^{2}  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 
Note: Numbers in brackets are the t values of regression coefficients. The *, **, and *** symbols represent 10%, 5%, and 1% significance levels, respectively. 
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