Frontiers of Business Research in China

Table 12 ESOPs and crash risk: DID model

From: Employee stock ownership plan and stock price crash risk

Variable	NCSKEW (1)	DUVOL (2)
ESOP_i,t-1 × After_i,t	−0.177^c	−0.143^c
ESOP_i,t-1 × After_i,t	(−3.05)	(−3.00)
ESOP _i,t-1	−0.005	−0.016
ESOP _i,t-1	(−0.32)	(−1.13)
After _i,t	0.093^b	0.087^c
After _i,t	(2.39)	(2.73)
Dturn _i,t-1	−0.019^c	− 0.01^a
Dturn _i,t-1	(−2.86)	(−1.69)
Hret _i,t-1	0.318^c	0.22^c
Hret _i,t-1	(9.97)	(7.97)
Sigma _i,t-1	−3.959^b	−2.447^a
Sigma _i,t-1	(−2.40)	(−1.78)
MB _i,t-1	0.013^c	0.012^c
MB _i,t-1	(3.78)	(4.23)
Size _i,t-1	0.036^c	0.052^c
Size _i,t-1	(3.10)	(5.46)
Lev _i,t-1	0.046	−0.054
Lev _i,t-1	(0.81)	(−1.16)
ROA _i,t-1	0.006	0.002
ROA _i,t-1	(1.25)	(0.62)
DA _i,t-1	−0.116	−0.075
DA _i,t-1	(−1.35)	(−1.06)
NCSKEW _i,t-1	0.079^c
NCSKEW _i,t-1	(5.02)
DUVOL _i,t-1		0.066^c
DUVOL _i,t-1		(3.81)
Intercept	−1.362^c	−1.689^c
Intercept	(−4.90)	(−7.37)
Fix_ind	Yes	Yes
Fix_time	No	No
Adj-R ²	0.0487	0.0664
No. of obs.	5517	5517

Notes. This table reports the influence of ESOPs on crash risk using a DID model. The empirical test equation is:
\( {CrashRisk}_{i,t}={\alpha}_0+{\alpha}_1\times {ESOP}_{i,t-1}\times {After}_{i,t}+{\alpha}_2\times {ESOP}_{i,t-1}+{\alpha}_3\times {After}_{i,t}+{\alpha}_4\times Control\kern0.3em {Variables}_{t-1}+{\varepsilon}_{i,t} \)
The t-statistics, reported in parentheses, are based on standard errors clustered by both firm and time. Time and industry fixed effects are included. Here ^a, ^b, and ^c indicate statistical significance at the 10%, 5%, and 1% levels, respectively

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