Table 7.

Chair–CEO age gap and investment efficiency (alternative measures)

VariablesBiddle modelChen model
(1)(2)(3)(4)(5)(6)(7)(8)
IE_BiddleIE_BiddleIED_BiddleIED_BiddleIE_ChenIE_ChenIED_ChenIED_Chen
GAPS−0.0001 (−0.150)−0.010 (−0.296)−0.000008 (−0.023)−0.044* (−1.657)
GAPU−0.0002** (−2.305)−0.020*** (−3.659)−0.0002** (−2.082)−0.008* (−1.786)
ControlsYesYesYesYesYesYesYesYes
Constant−0.166*** (−13.388)−0.165*** (−13.315)−0.162*** (−13.367)−0.161*** (−13.302)
IndustryYesYesYesYesYesYesYesYes
YearYesYesYesYesYesYesYesYes
Observations19,96119,96115,07915,07919,96119,96117,81417,814
Adjusted R20.13330.13350.12270.1229
Note(s):

This table reports Chair–CEO age dissimilarity and IE (alternative measures). Regression (1), (2), (5) and (6) report the results from the OLS regression of association between Chair–CEO age dissimilarity (GAPS and GAPU) and IE (IE_Biddle and IE_Chen) (Biddle et al.’s model and Chen et al.’s model). Robust t-statistics (in parentheses) are based on standard errors clustered by firm and year. Regression (3), (4), (7) and (8) report the results from the fixed effects Logit regression of association between Chair–CEO age dissimilarity (GAPS and GAPU) and IED (IED_Biddle and IED_Chen) (Biddle et al.’s model and Chen et al.’s model), fixed firm. t-Statistics in parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. All variables are defined in  Appendix

Source(s): Authors’ calculations

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