Gender Diversity on the Board - BI Norwegian Business School

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GRA 19001 Master Thesis 5 Statistical tests 5.1 Base-case model Our data set contains repeated observations of the same firm for up to eight years. We use the fixed effects model to handle this panel data setting. Fixed effects allow us to control for unobserved explanatory variables and to analyze change over time. The more time points a firm is observed, the more information the firm provides. Hence, we only study firms that are present for at least three years to ensure that sufficient information is provided. The first step in the fixed effect model is to compute the mean for each company, for each variable, and subtract this mean from all observations. For variables that stay constant over time, the value in each year equals the average value across years. Hence, it cancels in the regression equation. When means are subtracted, we estimate the model using ordinary least-squares (OLS). (Petersen, 2004) Table 5.1 shows results from OLS when estimating the base-case model by the fixed effects approach as described above. To control for other firm characteristics that can influence the dependent variable we control for firm size and firm age in the regression. The base-case model will be presented for large nonlisted firms as well as all nonlisted firms. We limit the attention to individual coefficients with a p-value of 10% or less. 22
GRA 19001 Master Thesis Table 5.1: The relationship between gender diversity and firm characteristics FE-models Expected sign Large firms All firms Coeff. P-value Coeff. P-value R 2 0.017 0.034 Durbin-Watson 1.714 1.561 N 17,762 538,130 Independent variables Past performance - 0.007 0.342 0.000 0.727 Family ownership - -0.017 0.022 0.019 0.000 CEO gender + 0.080 0.000 0.178 0.000 Control variables Firm age 0.093 0.000 0.043 0.000 Firm size 0.003 0.688 0.004 0.005 Table 5.1 shows estimates of the base-case fixed effect regression model estimated with OLS. The dependent variable is percentage of female stockholder elected directors. Every variable is demeaned by subtracting a given firm’s observation in a given year from the overall mean across the years. Only standardized coefficients are presented. In Table 3.A1 in the appendix both standardized and unstandardized coefficients are presented. In our interpretation we focus on standardized coefficient to be able to infer economic significance. When interpreting the coefficients in the base-case model, we find a positive relationship between past performance and gender diversity. However, our result is not statistically significant and the coefficient is not very strong. This is the case both for large firms, as well as all nonlisted firms. Adams and Ferreira (2008) and Bøhren and Strøm (2009) find the contrary, that gender diversity is inversely associated with performance. Neither finds very strong coefficients. Our insignificant findings may indicate that performance do not explain the share of female directors on corporate boards in nonlisted firms. There is a negative relationship between family owners and the share of female stockholder-elected directors in large nonlisted firms. The result shows that a 10% increase in family ownership decreases female stockholder-elected directors by 0.17%. Thus, diversity on the board decreases with the extent of family ownership. The result is statistically significant at the 5% level. This is consistent with Hypothesis 2, that family 23
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