ROE as a performance measure in performance-vested stock option contracts in China

Frontiers of Business Research in China

Table 6 Use of a Two-stage Procedure to Control for Potential Endogeneity

Panel A: Full sample
	Coeff.	p value
Intercept	7.92	<0.01
DILUTION	−0.44	0.87
ROE	−0.49	0.86
ROEDILUTION*	0.14	0.96
DEVIATION	2.50	0.02
MB	−0.07	0.51
RET	0.56	0.01
ROA	−0.11	0.97
ISSUESIZE	7.59	0.01
POTSHR	−0.07	0.94
FCF	13.19	<0.01
SOE	−0.36	0.40
Year Effect	YES
Industry Effect	YES
Pseudo R²	0.478
N	214
Panel B: Firms with a high level of access to bank loans
	Coeff.	p value
Intercept	25.27	<0.01
DILUTION	−24.37	<0.01
ROE	−21.81	0.01
ROEDILUTION*	26.89	<0.01
DEVIATION	0.03	0.99
MB	0.09	0.69
RET	0.28	0.21
ROA	−1.34	0.77
ISSUESIZE	19.55	<0.01
POTSHR	1.69	0.37
FCF	24.21	<0.01
SOE	−0.97	0.21
Year Effect	YES
Industry Effect	YES
Pseudo R²	0.650
N	107
Panel C: Firms with a low level of access to bank loans
	Coeff.	p value
Intercept	9.09	<0.01
DILUTION	−1.74	0.57
ROE	1.02	0.76
ROEDILUTION*	1.08	0.78
DEVIATION	3.95	0.11
MB	0.24	0.26
RET	1.15	0.01
ROA	−19.48	0.08
ISSUESIZE	−1.92	0.70
POTSHR	−0.37	0.80
FCF	14.37	<0.01
SOE	0.00	1.00
Year Effect	YES
Industry Effect	YES
Pseudo R²	0.637
N	107

This table presents estimation results from our main model-model (1) after controlling for potential endogeneity, where the dependent variable is DEBTFINANCING. We obtain the predicted value of ROE from a probit estimation (Table 5); the second stage probit regression is estimated using this predicted value of ROE. p values are based on two-tailed tests