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Gold Science and Technology ›› 2021, Vol. 29 ›› Issue (6): 874-883.doi: 10.11872/j.issn.1005-2518.2021.06.074

• Mining Technology and Mine Management • Previous Articles     Next Articles

Can Commercial Credit Financing Promote the High Quality Development of Mining Enterprises——Based on the Moderating Effect of Enterprise Scale

Minggui ZHENG1,2(),Biying YOU1(),Ping WU1   

  1. 1.Research Center of Mining Trade & Investment, Jiangxi University of Science and Technology, Ganzhou 341000, Jiangxi, China
    2.School of Management, University of Science and Technology of China, Hefei 230026, Anhui, China
  • Received:2021-06-09 Revised:2021-07-07 Online:2021-12-31 Published:2022-03-07
  • Contact: Biying YOU E-mail:mgz268@sina.com;1149037752@qq.com

Abstract:

As the main body of material production of national economy,mining enterprises provide huge material wealth for economic construction.In the new era from scale speed type to quality efficiency type,the original extensive production mode has been difficult to meet the high-quality development requirements of the existing economy. The problems of imperfect self governance mechanism and shortage of funds lead to the lack of self-development ability. As an external financing channel,commercial credit financing can provide financial support for enterprises and has an important impact on the high-quality development of enterprises.Taking the data of mining enterprises from 2014 to 2018 in China as samples,this paper uses total factor productivity to measure the high-quality development of enterprises. It focuses on the empirical study on the relationship between commercial credit financing and high-quality development of mining enterprises,and examines the moderating role of enterprise scale in the relationship between them.Firstly,based on the input-output perspective,the net fixed assets and the total number of employees are selected as input variables,the total output value of the enterprise is selected as output variables,and the DEA-Malmquist index method is used to calculate the total factor productivity,so as to measure the high-quality development level of the enterprise.The results show that the total factor productivity of Chinese mining enterprises varies greatly and is generally low from 2014 to 2018,with an overall average of 3.014;The overall degree of commercial credit financing used by Chinese mining enterprises is not high,and the difference is obvious,of which the maximum value is 1.680 and the minimum value is 0.015.The empirical results show that:(1) commercial credit financing has a negative impact on the high-quality development of mining enterprises;(2) enterprise scale plays a significant role in weakening the negative impact of commercial credit financing on the high-quality development of mining enterprises;(3) further research shows that there is property right heterogeneity in the negative impact of commercial credit financing on the high-quality development of mining enterprises,which is more significant in non-state-owned enterprises.According to the empirical results,this paper puts forward targeted suggestions to improve the high-quality development level of Chinese mining enterprises,such as reducing commercial credit financing,expanding financing channels,maintaining capital liquidity to reduce repayment risk,and differentiated management.

Key words: commercial credit financing, enterprise scale, heterogeneity of property rights, mining enterprises, total factor productivity

CLC Number: 

  • F426.1

Table 1

Variable definition table"

变量类型变量名称变量符号变量描述
被解释变量企业高质量发展TFP以全要素生产率衡量,用DEA-Malmquist指数计算得出
解释变量商业信用融资TC应付账款/总资产
调节变量企业规模Size资产总额的自然对数
控制变量企业存续时间Age企业成立年数的自然对数
企业成长能力Growth营业收入增长率
产权性质State国有为0,非国有为1
企业类型虚拟变量Type涉及6种类型,设置5个虚拟变量
年份虚拟变量Year根据证监会行业分类标准(2021),涉及5个年度,设置4个虚拟变量

Table 2

Descriptive statistical results of variables"

变量单位均值标准差最大值最小值
TFP3.0145.64036.1800.027
TC%0.1550.2571.6800.015
Size亿元11.3412.24019.4502.708
Age2.4710. 5914.7360.693
Growth%0.7372.67919.055-0.889

Table 3

Correlated analysis results of variables"

变量TFPTCSizeAgeGrowthStateYearId
TFP1.000
TC-0.138***1.000
Size-0.244***0.059***1.000
Age-0.109***-0.0080.329***1.000
Growth0.0040.005-0.015*-0.1031***1.000
State-0.106***0.0060.541***0.356***-0.06***1.000
Year-0.125***-0.034*0.286***0.183***-0.177***0.0241.000
Type0.027-0.002-0.264***-0.170***-0.021-0.189***0.00051.000

Table 4

Results of model regression"

变量模型(1)模型(2)
TC

-0.118***

(-7.01)

-0.169***

(-6.33)

Size

-0.814***

(-9.62)

-0.822***

(-9.72)

TC × Size-

0.112**

(2.45)

Age

-0.328*

(-1.21)

-0.312*

(-1.16)

Growth

0.179**

(2.53)

0.183**

(2.59)

State

0.552*

(1.27)

0.529*

(1.22)

Year控制控制
Type控制控制
Cons

14.56***

(14.11)

14.451***

(14.00)

Wald chi2332.36***338.89***

Table 5

Results of robustness test"

变量模型(1)模型(2)
TC

-0.138***

(-7.36)

-0 .199***

(-6.45)

Size

-0 .821***

(-9.71)

-0.818***

(-9.69)

TC × Size-

0 .113***

(2.40)

Age

-0 .309*

(-1.15)

-0 .304*

(-1.13)

Growth

0.185**

(2.61)

0.185**

(2.61)

State

0.554*

(1.27)

0.544*

(1.25)

Year控制控制
Type控制控制
Cons

14.354***

(13.89)

14.257***

(13.61)

Wald chi2337.97***338.05***

Table 6

Endogenetic test"

变量模型(1)模型(2)
L.TC

-0.268**

(-0.86)

-1.474**

(-0.67)

Size

-0 .638***

(-7.78)

-0.654***

(-7.49)

L.TC × Size-

0 .111*

(0.55)

Age

0 .228*

(0.88)

0 .235*

(0.91)

Growth

0.234**

(2.13)

0.236*

(2.15)

State

0.417*

(1.03)

0.414*

(1.03)

Year控制控制
Type控制控制
Cons

8.364***

(7.16)

8.516***

(7.09)

Wald chi2114.91***115.22***

Table 7

Heterogeneity analysis"

变量非国有矿业企业国有矿业企业
TC

-0.121***

(-6.71)

-0.164***

(-5.12)

-0.053

(-0.95)

-0.313

(-0.59)

Size

-0.992***

(-10.21)

-0.995***

(-10.24)

-0.242*

(-1.68)

-0.219*

(-1.51)

TC × Size-

0.012*

(1.63)

-

0.017

(0.49)

Age

-0.275

(-0.79)

-0.272

(-0.78)

-0.393*

(-1.13)

-0.401*

(-1.15)

Growth

0.057*

(1.43)

0.057*

(1.42)

0.004

(0.09)

0.005

(0.11)

Year控制控制控制控制
Type控制控制控制控制
Cons

15.638***

(12.90)

15.742***

(12.98)

7.214***

(3.57)

7.404***

(3.60)

Wald chi2291.16***294.0422.6022.82
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