Loan guarantee, management earnings forecasts and cost of debt: evidence from Chinese manufacturing firms

Figures & data

Table 1. Descriptive table.

Variable	Observations	Mean	Std. dev	Min	Max
COD	7,200	0.0960	0.1390	0.0160	1.5510
LG	7,200	0.0520	0.0870	0.0000	1.3440
MEF	7,200	0.5820	0.4930	0.0000	1.0000
N-analyst	7,200	5.6750	7.9760	0.0000	39.0000
Lev	7,200	0.4320	0.2100	0.0070	3.2620
ROE	7,200	0.0810	0.0710	0.0000	0.4000
SOE	7,200	0.6540	0.4760	0.0000	1.0000
B4AQ	7,200	0.0430	0.2030	0.0000	1.0000
Tobin’s Q	7,200	1.9240	1.6330	0.0000	13.5970
FA	7,200	9.2410	6.4530	0.0000	29.0000
CFO	7,200	0.0490	0.0760	−1.6860	0.4080
Grow	7,200	0.0720	0.2310	−2.5790	0.9930
FS	7,200	22.0240	1.1140	18.7600	27.1040

Table 2. Correlation analysis.

Vari	1. COD	2. LG	3. MEF	4. N-Analyst	5. LEV	6. ROE	7. SOE	8.B4AQ	9. T.Q	10. FA	11. CFO	12. Grow	13. FS
1	1
2	−0.0631***	1
3	−0.0259*	0.1231***	1
4	−0.0461***	0.0132	0.0680***	1
5	−0.4592***	0.0297*	−0.0667***	−0.1251***	1
6	0.2496***	−0.0887***	−0.1489***	0.0358**	−0.1604***	1
7	−0.0720***	−0.0109	−0.1000***	−0.0394**	0.1419***	−0.0217	1
8	0.0067	−0.0448***	−0.0557*	−0.0194	0.0616***	0.1036***	0.0533***	1
9	0.2968***	0.0823***	0.0305**	0.1074***	−0.4296***	0.2501***	−0.0958***	−0.0686***	1
10	−0.1597***	0.0236	−0.2243***	−0.0785***	0.3442***	−0.0392*	0.0656***	0.0944***	−0.0591***	1
11	0.2050***	−0.0291*	−0.0500***	0.0079	−0.1525***	0.3899***	−0.0231	0.0713***	0.1482***	0.0673***	1
12	−0.0047	−0.0468***	−0.0374**	0.0238	−0.1120***	0.3077***	−0.0389**	0.0019	0.0846***	−0.1520***	0.0558***	1
13	−0.1762*	−0.1143***	−0.1391***	−0.0295*	0.3857***	0.0826***	0.1589***	0.3471***	−0.3883***	0.3045***	0.0924***	−0.001	1

*, **, *** presents level of significance at 10%, 5% and 1%, respectively.

Table 3. Results of baseline regression analysis.

Variables	Model-1	Model-2
	COD	COD
LG	−0.04800**	−0.07979***
	(0.02007)	(0.02552)
MEF	–	−0.01284**
	–	(0.00513)
LG*MEF	–	0.02823**
	–	(0.01099)
N-Analyst	−0.00380***	−0.00212***
	(0.0012)	(0.00058)
Lev	−0.22260***	−0.09619***
	(0.01476)	(0.01425)
ROE	0.16161***	0.14783***
	(0.03352)	(0.03844)
SOE	−0.03893**	−0.03378**
	(0.01673)	(0.01609)
B4AQ	0.04436**	0.05019**
	(0.01887)	(0.01956)
T.Q	−0.00225	−0.00427**
	(0.00156)	(0.00174)
FA	−0.00784	−0.00529
	(0.00483)	(0.00641)
CFO	0.04581*	0.04265
	(0.02491)	(0.02605)
Grow	−0.02414***	−0.01779**
	(0.00751)	(0.00767)
FS	−0.02937***	−0.04499***
	(0.00552)	(0.00610)
Constant	0.84888***	1.15487***
	(0.12515)	(0.13982)
Industry FE	Yes	Yes
Year FE	Yes	Yes
R-squared	0.108	0.076
Hausman Chi^2	166.050	187.920
Observations	7,200	7,200

Notes: The cost of debt (COD) is the dependent variable. The loan guarantee (LG) is the independent variable. Management’s Earnings Forecast (MEF) is the only variable used for adjustment. *, **, ***present degree of significance at 10%, 5%, and 1%, respectively, for the t-statistics values presented, which are reported as (White, 1980) robust standard errors. Table 1 provides definitions of the variables.

Table 4. Result of mean difference test.

	SOE firms = 1		NSOE firms = 0
	N	Treated	N	Control	Mean difference (= Treated – Control)	t-Statistics
COD	4680	0.089	2520	0.110	−0.021	4.676***
LG	4680	0.031	2520	0.029	−0.002	0.708*
MEF	4680	0.546	2520	0.650	−0.104	6.510***
LG*MEF	4680	0.021	2520	0.027	−0.006	2.253**
N-Analyst	4680	5.447	2520	6.107	−0.661	2.554**
Lev	4680	0.454	2520	0.391	0.062	−9.283***
ROE	4680	0.080	2520	0.083	−0.003	1.405***
B4AQ	4680	0.024	2520	0.028	−0.004	−3.457***
T.Q	4680	1.810	2520	2.139	−0.329	6.235***
FA	4680	9.549	2520	8.658	0.890	−4.260***
CFO	4680	0.048	2520	0.051	−0.004	1.495
Grow	4680	0.066	2520	0.084	−0.019	2.522**
FS	4680	22.152	2520	21.780	0.372	−10.421***

Notes: the cost of debt (COD) is the dependent variable. The loan guarantee (LG) is the independent variable. Management’s Earnings Forecast (MEF) is the control variable, while the other variables serve as the adjustment. *, **, ***present degree of significance at 10%, 5%, and 1%, respectively, for the t-statistics values presented, which are reported as (White, 1980) robust standard errors. Table 1 provides definitions of the variables.

Table 5. Results of Heckman’s (1979) two-step selection bias method analysis results.

Variables	1st Step	2nd Step
		Model-1	Model-2
	LGD	COD	COD
LG	–	−0.03984**	−0.07768***
	–	(0.0197)	(0.02269)
MEF	–	–	−0.0901**
	–	–	(0.0439)
LG*MEF	–	–	0.03883**
	–	–	(0.01946)
N-Analyst	0.00513**	−0.00237***	−0.00261***
	(0.00258)	(0.00032)	(0.0003)
Lev	1.23414***	0.36392***	0.42859***
	(0.12456)	(0.05132)	(0.04784)
ROE	−2.50934***	−1.19869***	−1.22411***
	(0.34817)	(0.11883)	(0.11122)
SOE	−0.07209*	−0.07449***	−0.04860***
	(0.04344)	(0.01668)	(0.00711)
B4AQ	−0.27480**	−0.11445***	−0.13511***
	(0.11111)	(0.02281)	(0.01806)
T.Q	0.05685***	0.02687***	0.02898***
	(0.01547)	(0.00288)	(0.00258)
FA	0.02177***	0.00449	0.01169***
	(0.00354)	(0.00485)	(0.00107)
CFO	−0.49461*	−0.22461***	−0.22523***
	(0.2972)	(0.03335)	(0.03181)
Grow	0.31655***	0.15479***	0.15670***
	(0.09341)	(0.01673)	(0.01557)
FS	−0.00859	−0.02285***	−0.01627***
	(0.02349)	(0.00544)	(0.00293)
Instrument variable
PEF	0.0240***	–	–
	(0.00517)	–	–
IMR	–	0.77668***	0.84781***
	–	(0.0652)	(0.05995)
Constant	−0.80732***	−0.41643**	−0.67128***
	(0.20535)	(0.16233)	(0.10868)
LR chi2(10)	313.97
Pseudo R2	0.057
R-squared	–	0.14242	0.1418
Observations	7,200	7,200	7,200

*, **, *** presents degree of significance at 10%, 5%, and 1%, respectively. Appendix A describes the entire variables mentioned above.

Table 6. Results of 2-stage least square (2SLS) analysis.

Variables	Model-1	Model-2
	COD	COD
LG	−0.0352**	−0.05699**
	(0.01206)	(0.01751)
MEF	–	0.0996**
	–	(0.0388)
LG*MEF	–	0.06103**
	–	(0.02045)
N-Analyst	−0.00348***	−0.00202***
	(0.0011)	(0.00057)
Lev	−0.2098***	−0.07339***
	(0.00675)	(0.00624)
ROE	0.17441***	0.17063***
	(0.02551)	(0.02943)
SOE	−0.02613**	−0.01098**
	(0.00872)	(0.00808)
B4AQ	0.05716**	0.07299**
	(0.01086)	(0.01155)
T.Q	0.01055	0.01853
	(0.00645)	(0.00627)
FA	0.00496	−0.00249
	(0.00318)	(0.0016)
CFO	0.05861*	0.06545
	(0.0169)	(0.01804)
Grow	−0.01134***	0.00691**
	(0.0005)	(0.00034)
FS	−0.01657***	−0.02219***
	(0.00249)	(0.00191)
Constant	0.86168***	1.17767***
	(0.11714)	(0.13181)
WI-Test	32.145	30.426
Sargan stats	0.5908	0.6065
Obs.	7,200	7,200
R-sq.	0.192	0.994

*, **, ***presents degree of significance at 10%, 5%, and 1%, respectively. Appendix. A describes the entire variables mentioned above.

Table

Variable	Definition
COD	Interest expenditure as a percentage of total liabilities (including current and future) is the formula used to determine a company’s cost of debt (COD).
LG	The end-of-year guarantee balance as a percentage of total assets
LGD	When a company has a loan guarantee at the end of the year, the value is 1; when it does not, it is 0.
MEF	If a company has issued a prognosis for year t, the MEF value is 1; otherwise, it is 0.
N-Analyst	N-Analyst is the metric of finance analysts, defined as the total number of people who make earnings predictions for companies in a given year.
LEV	Total liabilities divided by total assets is a company’s leverage (LEV).
ROE	Return on equity (ROE) is calculated by dividing a company’s net income by the market value of its shareholders’ equity.
FS	The natural logarithm of the company’s total resources at the completion of the firm-year t is known as firm size (FS).
SOE	State-owned enterprises (SOE) are computed with a t value of 1 if the company is initially possessed or managed immediately by the government and a t value of 0 alternatively.
B4AQ	If the company is inspected by one of the Big4 auditors in year t, then the Big4 audit quality (B4AQ) is 1; otherwise, it is 0.
FA	Firm age (FA) is the period since a particular company’s initial public offering (IPO).
CFO	Cash Flow from Operations (CFO) is the end-of-year ratio of operating capital flow to cash holdings.
Grow	Revenue growth (Grow) is defined as the percentage increase in sales over a specific time period, and is found by dividing total sales by the difference between the previous sales period and the current one.
Tobin’s Q	T.Q. is calculated by multiplying the stock market price by the book price of debt times the firm’s net revenues.
Ind.FE	The industry dummy variable is used to adjust for the fixed impacts of the industry.
Yr. FE	The year dummy variable is used to adjust for seasonal and other year-specific factors.

White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838. https://doi.org/10.2307/1912934

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Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica, 47(1), 153–161. https://doi.org/10.2307/1912352

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