Table 6

Identification strategies

Lead DVFixed effectsSystem GMMPropensity score matching (PSM)
Pre-match
probit
Post-Match
probit
Pooled OLS
Variables(1)(2)(3)(4)(5)(6)
ROAROAt+1CGCI_UW_DUMMYROA
ROAt0.925*** (0.190)
CGCI_UW0.042*** (0.016)0.036*** (0.013)0.229*** (0.073)0.049*** (0.016)
DTA−0.121*** (0.012)−0.190*** (0.014)−0.033 (0.109)0.073 (0.182)0.296 (0.249)−0.164*** (0.015)
Constant−0.304** (0.149)0.006 (0.162)1.607* (0.862)−4.337** (1.936)0.013 (0.026)−0.348* (0.182)
All controlsYesYesYesYesYesYes
Observations1,6621,8382,3141,7001,3571,694
IndustryNoYesYesYesYesYes
YearNoYesYesYesYesYes
Adjusted R20.2780.3290.519
Pseudo R20.0430.0230.409
F-statistics81.2560.2579.3735.59
AR1 (p-value)0.000
Sargan (p-value)0.021
Hansen-J0.302

Notes:

This table presents results for the relationship between corporate governance compliance, profitability and capital structure using different identification strategies; Model 1 replaces the standard proxy of firm profitability (ROA) with a one-year leading value (ROA)t+1 to address the issue of reverse causality; Model 2 presents the results using firm fixed-effects to consider the issue of omitted variables-bias; Model 3 presents results for the relationship between CG compliance and FP with CS mediation using a system GMM estimation; Models 4 and 5 report the results of pre- and post-match probit regressions where the dependent variable CGCI_UW_DUMMY is coded 1 if CGCI_UW is greater than the industry-year average of CGCI_UW and 0 otherwise; Model 6 presents results using matched samples; the standard errors are reported in parentheses; ***, ** and * denote significance at the 1, 5 and 10% levels, respectively

Source: Authors’ own work

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