Other definition of firm size - model (3)
We have used the definitions of small - and large firms presented by the Norwegian portal for dialogue between business/industry sector, citizens and government agencies, Altinn, as a robust-test to substantiate the results. These definitions are from the “Act relating to annual accounts” (Loven om
årsregnskap), as there are stricter accounting requirements for larger firms (Altinn 2016). To be considered as a small firm at least two of the following criteria must be fulfilled:
1.! Revenue less than 70 MNOK 2.! Total assets less than 35 MNOK
3.! Less than 50 employees on average during the financial year.
The results are robust to this definition, as the variable SmallFirm is positive and statistically significant at a 1% level, thus the definitions do not differ
substantially. The findings are elaborated in appendix 13.7.7.
Other definition of number of owners - model (4)
In the hypothesis of number of owners, we have also conducted a test with some changes in the intervals to validate the results. Due to the skewed distribution in our sample of firms having few owners, we have only redefined one dummy variable, N3-5, and dropped the dummy variable N6-50. The new dummy variable is N3-50 which is having more than three owners. N1 is still defined as a firm having one owner, and N2 as the firm having two owners. These variables are not statistically significant, and the coefficients indicates that there is barely any impact whether having one or two owners. Hence, we rely on the first test and find support for the hypothesis that fewer owners have greater impact on firm performance. The results are elaborated in appendix 13.7.8.
Other definition of firm efficiency - model (6)
In this hypothesis we have also used an alternative measurement for efficiency;
revenue generated per employee (= revenue divided by the number of employees in the firm). We have taken the natural logarithm and winsorized at 1th and 99th percentiles. By analysing this efficiency measure, we found that the firms that always are nonfamily firms generate higher revenue per employee than those that always are family firms. The firms that go through a family ownership dilution, generate more revenue per employee after the family control dilution. This is the opposite as expected since family firms had higher asset turnover ratio. The result output in appendix 13.7.9 show that the explanatory variable coefficient is
positive, 0.394, and statistically significant at a 1% level, indicating the same pattern as the original model with ATO.
Regression with all explanatory variables included
Lastly, we have constructed a model that includes all explanatory variables we have used in our sub-hypotheses, in addition to the control variables. This is in order to verify that the combination of all variables in one model predicts the same trend in coefficients and significance level as it does in each of the separated models in the sub-hypotheses. Firstly, we conduct the Hausman test to check whether the model is best suited for random – or fixed effects. The test shows that this model is a fixed effects model. The model output indicates that Debt-Ratio and lnAge remain the control variables with negative coefficient sign. The explanatory variable LongTermDebtRatio, which is not statistically significant in the main model still remain statistically non-significant. All other explanatory variables are statistically significant at least at a 5% level. The model predicts the same coefficients signs for the explanatory variables. Overall, this imply that the models we have used are robust. See appendix 13.7.10.
10.4 Endogeneity
Endogeneity is a problem that occurs when a test variable is correlated with the error term (Wooldridge 2013). If this occurs in our observations and the
necessarily precautions are not taken, there is a risk that the estimated coefficients are not valid and can not be interpreted. If endogeneity occurs, the true effect of the dependent variable on the independent variable or opposite might not have been taken into account (Antonakis et al. 2014). In our main hypothesis the endogeneity problem is: is firm performance for family firms greater because they are family firms, or are the firms family firms because they have greater firm performance?
Prior research indicates that endogeneity should be a concern when searching for the relationship between ownership and performance (Demsetz and Villalonga 2001). According to Palia and Lichtenberg (1999), an approach to minimize the endogeneity problem is to perform lagged independent variables as a robustness test. A lagged variable takes the identical value of a variable that the variable had for one of the earlier time periods (Brooks 2014). In accordance with Langli and Che (2015), we lagged all our control variables and dependent variable with one
year. Since the explanatory variables in our main hypothesis are dummy variables that always are constant, it is not necessarily to lag the dummy variables.
By lagging the variables, the explanatory variables D1, D2, D3 are statistically significant at a 1% level and have positive coefficients. D3 still had the highest coefficient. Hence, now D2 is more positive than D1, which was the opposite result for the random effects model without lagged variables. The results are found in appendix 13.7.6.
11.0 Conclusion and final remarks