Table 7

Multivariate analysis of how CEOs’ and board members’ personal characteristics affect the ESG score of their firm

VariablesCEOsBoard members
ESGit (1)ESGit (2)
CONVICTIONit0.001 (0.35)−0.000 (−0.04)
AGEit−0.018*** (−2.64)−0.001 (−0.15)
SINGLEit−0.004 (−1.17)−0.005* (−1.66)
EDUCATION_LEVELit0.011*** (3.47)0.011*** (3.39)
TOTAL_INCOMEit0.001 (0.67)−0.004** (−2.33)
CHILDREN_UNDER18it−0.000 (−0.73)−0.000 (−0.28)
BUSINESS_DEGit−0.024*** (−5.12)−0.027*** (−5.22)
MALEit0.009*** (2.67)0.011*** (3.26)
SIZEit0.018*** (11.67)0.019*** (12.03)
EQUITYit−0.009** (−2.08)−0.011** (−2.43)
ROAit0.010* (1.89)0.015*** (2.79)
InterceptYesYes
Year FEYesYes
Industry FEYesYes
R-Squared0.0260.026
N120,384120,384
Notes:

This table reports the results from re-estimating Models (1) and (2) by adding a new variable, CHILDREN_UNDER18it. The dependent variable ESGit in both columns is the total ESG score of firm i in year t. Column 1 shows the results for the CEOs and Column 2 shows the results for the board members. CHILDREN_UNDER18it is an indicator variable equal to one if the executive has children under 18 years old and zero otherwise. Year-fixed effects and industry-fixed effects are included to control for systematic variations in ESG performance over time and across various industries.  Appendix 2 provides definitions for all other variables. T-statistics are reported in parentheses. ***, ** and * denote significance at the 1, 5 and 10% levels, respectively. All continuous variables are winsorized to the 1st and 99th percentiles of their distributions

Source: Author’s own work

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