Las normas generales para la limitación de derechos

Data analysis went through different stages before being imported and used in the data analysis programme. Firstly, data collected from financial statements was entered manually in the MS Excel sheet, itemising both dependent and independent variables as

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proposed above. During this stage, the data was calculated based on the proxies that are used as parameters. Since the data was ready for the regression, the next step was to move to the data analysis econometric software (STATA).

The next procedure was a multicollinearity test for some sets of explanatory variables. The variables were tested to ensure that there is no multicollinearity among the independent variables that would affect the significance of the regression results. This procedure examines an exact linear relationship in the observation between the means of the response variables and the value of explanatory variables (Van Horne, 2001). The aim of this test is to analyse whether there is a correlation between independent variables. The way to detect the problem of multicollinearity in this study is by using the tolerance values and/or the variance inflation factor (VIF) (Hair et al., 1998). A variable whose mean VIF values are greater than 10 could be considered as a linear combination of other independent variables. Those variables will not include a predictor variable in a model if they have a VIF value of more than 10 or a mean VIF greater than 10. Hence, the main estimation method applied in this analysis uses panel regression.

Moreover, to correct the possibility of heterokedasticity, the pooled model estimated using the procedure of robust standard error named the White-Hubber standard error correction process (Schmid, 2013; Patersen, 2009). These tests ensure that the coefficients of the independent variables are not biased as the result of incorrect standards errors. The reason to apply this test is because if the analysis involves time series data, there is a higher probability that there exists heterokedasticity in the error terms. Heterokedasticity specifies that the variance of the error terms is not constant as the dependent variables change. To correct the heterokedasticity, this study followed a correction technique proposed by White (1980). Furthermore, the regressions are tested for the overall significance of the model by the F test and its probability value (p-value), for individual variables partially using T test and p-values, as well.

T-test was used to test the hypotheses that independent variables: family control and influence, renewal of family bonds through dynastic succession and binding social ties have relationships with leverage. In this test, the null hypothesis assumes there is no relationship between independent variables and leverage of listed family firms in Indonesia. The alternative hypothesis assumes that there is significant relationship between independent variables and leverage of listed family firms in Indonesia Stock

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exchange (IDX). The level of significance will be expressed using p-value is more than 0.05 then the null hypothesis is true since this means that there is no statistically significant relationship between independent variables and financial leverage of listed family firms in Indonesia.

Similarity, if the p-value is less than 0.05 percent then the alternative hypothesis is considered true since this means that there is significant relationship between the variables. Coefficient of adjusted determinant determination (adjusted R-squared) was used to provide a measurement of how well the observed result was explained by the model, as proportion of total variation of outcomes explain by the model.

Notwithstanding the identification of the parameters and their influence on literature related to the capital structure of family firms, there is no single model that fits perfectly with such research as this. Nevertheless, most studies have looked for the impact of ownership concentration, family control on debt level or regressed debt levels against the firm’s characteristics as determinants of capital structure (Santos et al., 2014; Schmid, 2013; Ampeberger et al., 2013; Croci et al., 2011). A pooled cross section estimation is conducted that involves observations over a five year time period for 160 different Indonesian family firms. The panel estimation approaches that are employed in this study to examine the fixed effects model approach and random effects model. These instruments allow the investigation of dynamics by a time order and reveal unobserved heterogeneity. A fixed effect model controls for the effects of time-invariant variables with time in-variant effects. There are omitted variables that are correlated with the variables in the model under family influence and control; the fixed-effects model may provide a means of controlling any omitted variable bias (Schmid, 2013).

On the contrary, random effects models address the possibility of a spurious relationship between the dependent and independent variables (Setia-Atmaja et al., 2009). The spurious relationship may because the exclusion of unmeasured explanatory variables still affects a firm’s behaviour. Therefore, the Hausman test will be conducted to decide the preferred estimation model of this research. Rejection implies that the fixed effects model is more reasonable or preferred than the random effects model. The model used is a panel regression of a firm’s leverage against: a) the family’s influence and control, b) family succession, c) board independence and d) the firm’s characteristics as control variables.

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The hypotheses are tested by pooling the data using the primary specification model above.

Another alternative specification test is considering alternative measures of leverage. In the primary analysis, this research uses the ratio of long term debt to total assets as a leverage measure. Croci et al. (2011) and Johnson (2003) used short term debt as a proxy for leverage to examine the role of short term debt maturity in mitigating the debt overhang problem for high growth firms. The researchers found that the short-term debt maturity alleviates the negative impact of growth opportunities on leverage. This outcome follows asset substitution theory that short-term maturity debt alleviates the agency costs of debt (Leland and Toft, 1996; Diamond, 1991; Barnea et al., 1981).