Bank loan information and information asymmetry in the stock market: evidence from China

Financial Innovation

Table 6 Heckman’s two-stage model results

Variables	The second stage results
	PIN
	1	2	3	4	5	6
Intercept	0.3965***	0.4035***	0.3938***	0.3930***	0.3913***	0.3925***
Intercept	(5.74)	(5.84)	(5.70)	(5.69)	(5.67)	(5.68)
Loan size	\(-\) 0.0028***
Loan size	(\(-\) 3.07)
Tbank		\(-\) 0.0047***
Tbank		(\(-\) 2.64)
OL			0.0084**
OL			(2.03)
OL rate				0.0261***
OL rate				(2.63)
OL Tbank					0.0209***
OL Tbank					(2.70)
OL Nbank						0.0048*
OL Nbank						(1.96)
Lambda	\(-\) 0.0056	0.0054	0.0055	\(-\) 0.0042	\(-\) 0.0042	\(-\) 0.0041
Lambda	(\(-\) 1.11)	(1.36)	(1.38)	(\(-\) 0.84)	(\(-\) 0.83)	(\(-\) 0.81)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Year \(\times\) industry-fixed effect	Yes	Yes	Yes	Yes	Yes	Yes
Firm-fixed effect	Yes	Yes	Yes	Yes	Yes	Yes
Adjusted \(R^2\)	0.0805	0.0805	0.0804	0.0758	0.0758	0.0757
Obs.	27025	27025	27025	27025	27025	27025

This table reports the results of the second stage of the Heckman (1979) two-step procedure that considers the potential selection bias. The dependent variable in the first-stage probit regression is a dummy variable that equals 1 if a firm has at least one outstanding loan in a given month and equals 0 otherwise. In the second stage, we estimate Eq. (2) including an additional control variable equal to the inverse Mills ratio obtained from the first stage. The t-statistics reported are based on standard errors clustered by firm. Symbols *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively