The impact of corruption on firms’ access to bank loans: evidence from China

Figures & data

Table 1. Variable definitions.

Variable	Definition
Lend	If a firm has a line of credit or a loan from financial institutions, the value of Lend is 1; otherwise, the value of Lend is 0
Bribery	The percentage of total annual sales paid as informal payments or gifts to public officials. Note that firms are sometimes required to make informal payments or gifts to public officials to ‘get things done’ with regard to customs, taxes, licenses, regulations, services, etc.
Scale	Firm size. A firm’s employees. In the empirical test, the data take logarithm form
Age	Firm age. The number of years a firm has existed since its founding year. In the empirical test, the data take logarithm form
State	The percentage of a firm’s shares held by state shareholders
Growth	The average annual sales growth rate is defined as the three-year average growth rate
Profit	Operating margin is defined as the ratio of total profits to sales revenue over the same period
Quality	If a firm has an internationally recognised quality certification, the value of Quality is 1; otherwise, the value of Quality is 0
Audit	If a firm has its annual financial statements checked and certified by an external auditor, the value of Audit is 1; otherwise, the value of Audit is 0
City size	Market size of the city where a firm is located. Cities are divided into four levels: a population of less than 50,000; 50,000 to 250,000; 250,000 to 1,000,000; and over 1,000,000, with values of 1, 2, 3, and 4, respectively
Business	If the city where a firm is located is an important commercial city, the value of Business is 1; otherwise, the value of Business is 0

Table 2. Summary statistics.

Variable	Min	25th percentile	Median	75th percentile	Max	Mean	Standard deviation
Lend	0.0000	0.0000	0.0000	0.0000	1.0000	0.0253	0.0510
Bribery	0.0000	0.0000	0.0000	0.0000	30.0000	0.2159	1.5909
Scale	0.6931	2.9957	4.0943	4.8675	10.2400	3.8677	1.3514
Age	0.6931	2.1972	2.4849	2.7726	4.8283	2.4148	0.4756
State	0.0000	0.0000	0.0000	0.0000	1.0000	0.0486	0.2151
Growth	−0.1595	0.0153	0.0489	0.1006	1.2240	0.0791	0.1543
Profit	−0.7712	−0.0043	0.0059	0.0652	0.5318	0.0092	0.4914
Quality	0.0000	0.0000	1.0000	1.0000	1.0000	0.5826	0.4933
Audit	0.0000	0.0000	1.0000	1.0000	1.0000	0.6861	0.4642
City size	1.0000	3.0000	3.0000	3.0000	3.0000	2.9736	0.2190
Business	0.0000	1.0000	1.0000	1.0000	1.0000	0.8493	0.3579

Note: Variable definitions are provided in Table 1. The sample size of this paper is 1440. The enterprises surveyed covered 26 industries, such as Food, Tobacco, Textiles, Garments, Leather, Wood, Paper, Recorded media, Chemicals, Basic metals, Electronics, Furniture, etc.

Table 3. The estimation results of corruption’s effects on firms’ access to bank loans.

	Probit	Tobit	Probit	Tobit	IVProbit	IVTobit	IVProbit	IVTobit
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Bribery	0.0728*** (0.0260)	0.0215*** (0.0070)	0.2369*** (0.0465)	0.0249*** (0.0042)	0.8776*** (0.3051)	0.2368*** (0.0855)	1.9793*** (0.6214)	0.2264*** (0.0750)
Briberysq			−0.0089*** (0.0024)	−0.0009*** (0.0003)			−0.0856*** (0.0266)	−0.0096*** (0.0032)
Scale	0.1692*** (0.0275)	0.0197*** (0.0046)	0.1804*** (0.0355)	0.0194*** (0.0050)	0.1599*** (0.0383)	0.0177*** (0.0048)	0.1609*** (0.0426)	0.0171*** (0.0052)
Age	0.1102 (0.0771)	0.0037 (0.0115)	0.0424 (0.0990)	0.0039 (0.0108)	0.2541** (0.1263)	0.0130 (0.0119)	0.1163 (0.0988)	0.0147 (0.0128)
State	−0.7132*** (0.1633)	−0.1194*** (0.0406)	−0.9281*** (0.3018)	−0.1252*** (0.0427)	−0.6427*** (0.2317)	−0.1485*** (0.0392)	−0.9514*** (0.3031)	−0.1185*** (0.0362)
Growth	0.4851** (0.2215)	0.0114*** (0.0032)	0.5615** (0.2803)	0.0125*** (0.0041)	0.5338** (0.2243)	0.0116*** (0.0039)	0.5109** (0.2316)	0.0103** (0.0045)
Profit	−0.3129 (0.2017)	−0.0083* (0.0048)	−0.3121 (0.2042)	−0.0118 (0.0073)	−0.4147* (0.2263)	−0.0085 (0.0063)	−0.3849 (0.2571)	−0.0121* (0.0068)
Quality	0.2328*** (0.0803)	0.0452*** (0.0132)	0.3572*** (0.1136)	0.0475*** (0.0159)	0.5148*** (0.1317)	0.0853*** (0.0201)	0.5243*** (0.1457)	0.0683*** (0.0177)
Audit	0.2355*** (0.0856)	0.0312* (0.0173)	0.2934** (0.1402)	0.0285* (0.0166)	0.3525*** (0.1086)	0.0433*** (0.0137)	0.3517*** (0.1284)	0.0336** (0.0145)
City size	0.7314*** (0.1430)	0.0915*** (0.0201)	0.7279*** (0.1721)	0.0874*** (0.0216)	0.7117*** (0.2416)	0.0916*** (0.0313)	0.6958*** (0.2830)	0.0857** (0.0361)
Business	−0.7930*** (0.0991)	−0.0936*** (0.0117)	−0.7249*** (0.1103)	−0.0887*** (0.0122)	−0.9470*** (0.1549)	−0.1208*** (0.0233)	−0.9074*** (0.2031)	−0.1092*** (0.0237)
Constant	−3.1826*** (0.3914)	−0.4319*** (0.0605)	−3.0414*** (0.5210)	−0.3516*** (0.0715)	−3.4820*** (0.7597)	−0.3971*** (0.0962)	−3.1649*** (0.0793)	−0.3631*** (0.1132)
City dummy	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Industry dummy	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Wald test					11.96***	10.19***	14.72***	12.41***
AR					14.24***	12.51***	19.03***	16.48***
FAR(P)					0.3351	0.2747	0.1821	0.1577
Pseudo R^2	0.0982	0.2112	0.1124	0.2241
Observations	1440	1440	1440	1440	1436	1436	1436	1436

Note: City dummy stands for city fixed effects. Industry dummy stands for industry fixed effects. AR stands for weak instrumental tests. FAR (P) is the exclusion restriction test. Robust standard errors clustered at the industry level are in parentheses in columns (1) and (2). Robust standard errors are in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

Figure 1. The role of government guarantees in corruption affecting firm financing.

Table 4. The structural path coefficients of corruption.

Path	Path coefficients	S. E.	Z-value	P>|z|
Bribery → Guarantee	0.0126**	0.0052	2.42	0.015
Constant	0.1198***	0.0075	15.89	0.000
Guarantee → Lend	0.1308***	0.0321	4.08	0.000
Bribery →Lend	0.0168**	0.0073	2.31	0.021
Constant	0.2874***	0.0112	25.56	0.000
Variance
e. Guarantee	0.1068	0.0035
e. Lend	0.2100	0.0068
Log likelihood = −5200.3911
Log Determinant of Sigma = 13.4718
Adj_R^2 (Greene) =0.6917
Stability index = 5.71e-10

Note: Robust standard errors are in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

Table 5. The indirect effects and total effects of corruption.

Path	Path coefficients	S. E.	Z-value	P>|z|
Indirect effects
Bribery → Lend	0.0016**	0.0008	2.08	0.037
Total effects
Bribery →Guarantee	0.0126**	0.0052	2.42	0.015
Guarantee → Lend	0.1308***	0.0321	4.08	0.000
Bribery → Lend	0.0185**	0.0073	2.53	0.011

Note: Robust standard errors are in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

Table 6. The estimated results of OLS regression.

Variable	Coefficients	S.E.
Bribery	0.0106**	0.0047
Briberysq	−0.0003**	0.0001
Pscore	0.1308**	0.0571
Bribery × Pscore	0.0445*	0.0246
Constant	−0.0091	0.0301

Note: Robust standard errors are in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

Figure 2. The estimation diagram of dose-response function and treatment effect.

Table 7. The effects of corruption on firms’ bank loan intensity.

Corruption level	Treatment level	S. E.
0.01	0.0510***	0.0106
0.02	0.0418**	0.0155
0.03	−0.0219*	0.0122
0.05	−0.0755***	0.0218
0.10	−0.1273***	0.0319

Note: Robust standard errors are in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

Table 8. Results of robustness test.

	IVProbit	IVTobit	IVProbit	IVTobit	IVTobit	IVTobit
	(1)	(2)	(3)	(4)	(5)	(6)
Bribery					0.0751*** (0.0196)	0.0377** (0.0136)
Briberysq					−0.0035*** (0.0010)	−0.0020** (0.0008)
Corruption	1.7548*** (0.4355)	0.1733*** (0.0539)	0.8301*** (0.1847)	0.0919*** (0.0214)
Corruptionsq	−0.7924*** (0.2161)	−0.0352*** (0.0085)	−0.2152*** (0.0711)	−0.0096*** (0.0026)
Law	0.3945*** (0.1310)	0.0546*** (0.0165)	0.2320*** (0.0666)	0.0410*** (0.0113)
Law_Corruption	−0.0814** (0.0308)	−0.0130*** (0.0042)	−0.0625*** (0.0213)	−0.0092*** (0.0027)
Scale	0.1136** (0.0574)	0.0108 (0.0070)	0.1443*** (0.0412)	0.0143*** (0.0053)	0.0186*** (0.0051)	0.0212** (0.0085)
Age	0.2004 (0.1342)	0.0190 (0.0153)	0.1142 (0.0961)	0.0104 (0.0119)	−0.0024 (0.0122)	0.0237 (0.0240)
State	−0.0922 (0.0897)	−0.0189 (0.0473)	−0.4037 (0.2813)	−0.0611 (0.0358)	−0.7042 (19.786)	0.0912* (0.0553)
Growth	0.7637*** (0.2433)	0.0214** (0.0110)	0.8315*** (0.2462)	0.0164** (0.0087)	0.0121* (0.0067)	0.0486 (0.0585)
Profit	−0.4635 (0.3711)	−0.0114 (0.0099)	−0.4934 (0.3253)	−0.0091 (0.0067)	−0.0084 (0.0065)	−0.1418* (0.0082)
Quality	0.3571*** (0.1255)	0.0491*** (0.0166)	0.3529*** (0.1156)	0.0507*** (0.0151)	0.0447*** (0.0141)	0.0588*** (0.0215)
Audit	0.3357** (0.1415)	0.0354** (0.0172)	0.2863** (0.1164)	0.0305** (0.0151)	0.0173 (0.0126)	0.0730*** (0.0243)
City size	0.5794* (0.3106)	0.0718* (0.0420)	0.7093*** (0.2491)	0.0825** (0.0344)	0.0751** (0.0305)	0.4173 (17.155)
Business	−0.9843*** (0.1971)	−0.1358*** (0.0293)	−0.9051*** (0.1436)	−0.1265*** (0.0201)	−0.0859*** (0.0163)	−0.0948*** (0.0310)
Constant	−4.0541*** (0.9649)	−0.4762*** (0.1109)	−3.3274*** (0.7915)	−0.5430*** (0.1268)	−0.3217*** (0.0961)	−1.0128 (51.440)
City dummy	Yes	Yes	Yes	Yes	Yes	Yes
Industry dummy	Yes	Yes	Yes	Yes	Yes	Yes
Wald test	17.20***	13.58***	15.43***	9.82***	9.91***	8.72***
AR	21.83***	18.47***	19.10***	13.57***	12.18***	11.07***
FAR(p)	0.2795	0.2327	0.1841	0.1027	0.0996	0.0819
Observations	1384	1384	1384	1384	946	494

Note: City dummy stands for city fixed effects. Industry dummy stands for industry fixed effects. Robust standard errors are in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.