Does geographic distance have a significant impact on enterprise financing costs?

doi:10.1007/s11442-019-1699-6

Abstract

Abstract:

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 Beijing-Tianjin-Hebei 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 1-5 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 1-5 km radius, the lower the EFCs. (2) Distance to CBBs and number of CBBs within a 1-5 km radius significantly influence state-owned and non-state-owned enterprises, with the effect on non-state-owned 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 1-5 km radius; the EFCs in Hebei Province are positively correlated with distance to CBBs, and negatively correlated with the number of CBBs within a 1-5 km radius. (4) Distance to CBBs has a more significant impact on enterprises engaged in heavy industry and labor-intensive industries, while there is not much difference between different industries in terms of how the number of CBBs within a 1-5 km radius affects them.

Key words: financial geography, enterprise financing, geographic distance, Beijing-Tianjin-Hebei

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.

Figures/Tables 9

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References 12

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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)

	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

Variables	Regression (1)	Regression (2)	Regression (3)	Regression (4)	Regression (5)	Regression (6)
Variables	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

Variables	Regression (1)	Regression (2)	Regression (3)	Regression (4)	Regression (5)	Regression (6)
Variables	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

Variables	Regression (1)	Regression (2)	Regression (3)	Regression (4)	Regression (5)	Regression (6)
Variables	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