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Capital Structure Determinants in the Context of Listed Family Firms Elisabete S. Vieira ISCA Department, GOVCOPP Unit Research, University of Aveiro, Campus Universitário de Santiago 3810-193 Aveiro, Portugal Tel: + 351 234 380 110 Fax: + 351 234 380 111 e-mail:
[email protected] Abstract - This paper provides an analysis of the capital structure in the context of Portuguese listed family firms, covering the period from 1999 to 2010. Using a panel data approach, we find that family controlled firms differ from non-family firms in several aspects. Family firms use more debt than non-family firms and have a lower proportion of independent directors. In addition, they finance new projects with debt financing and are sensitive to the cost of debt and the crisis periods, whereas non-family firms with higher levels of cash have more debt financing. Overall, we find a negative relationship between profitability and non-debt tax shield and debt and a positive relationship between firms’ age and debt. Key Words: Capital Structure, Family Firms, Panel Data
I.
INTRODUCTION
Since the classic work by Modigliani and Miller [1958], the capital structure is one of the most widely classic topics in the context of finance. Although the vast literature analyzing firms’ capital structure, the studies carried out so far do not explore this topic in the context of family-controlled firms. Moreover, a vast number of studies done so far find evidence that family shareholders are common in public traded firms worldwide, having large equity stakes and executive representation, such as the studies of Claessens et al. [2000] in the East Asia, Faccio and Lang [2002] in the Western Europe and Anderson and Reeb [2003], Villalonga and Amit [2006] and Holderness [2009], in the USA. Gersick et al. [1997] estimated that 65% to 80% of worldwide businesses are family controlled. We believe that family firms behave differently from non family firms in their choice of capital structure for several reasons. By one hand, family firms tend to be more averse to risk than non-family counterparts, avoiding debt in their capital structure [Storey 1994; Mishra and McConaughy, 1999; Zhou, 2012]. On the other hand, family firms’ members are afraid of losing control. The desire to maintain the dominant position in firms, leads families to limit the capital hold by other shareholders [Xin-Ping et al., 2006; Prencipe et al., 2008], which can result in higher levels of debt. Céspedes et al. [2010] and González et al. [2011] state that firms tend to prefer debt to equity when losing control is an issue. Moreover, family firms are more likely to avoid debt-covenant violations [Prencipe et al., 2008] because of debt-covenant motivation. Finally, the long-term perspective and the desire to pass the firm onto succeeding generations [Stein, 1989; James, 1999] can influence the capital structure decisions of family firms. In this context, we analyze the capital structure on the context of non-financial family-controlled Portuguese listed firms on Euronext Lisbon (EL) for the period between 1999
and 2010, using an unbalanced panel data sample of 58 firms, corresponding to 583 observations. From the total sample, 35 firms are family-controlled (about 60% of the firms) and 23 are non-family firms. Our paper makes three main contributions to the literature. First, it offers a number of insights into capital structure policy in the context of family firms. Indeed, only a limited number of studies as been conducted on family firms’ capital structure. Second, we analyse the capital structure of family controlled firms in financial crisis periods, a subject not yet explored. Finally, it is the first study to analyse the capital structure of family firms in the Portuguese market, which tend to exhibit concentrated ownership and family control. Faccio and Lang [2002] find evidence that family firms constitute 60.34% of firms sampled in Portugal and that in about 50% of the familycontrolled firms, the controlling owner is in management. Although this aids the contribution made by our study, it also represents a limitation of our work because of the small size of the sample. The remainder of this paper is organised as follows. Section 2 reviews the related literature and presents our hypotheses. Section 3 describes our methodology and data. Section 4 presents the empirical results. Finally, section 5 concludes the paper. II.
THEORETICAL BACKGROUND AND HYPOTHESES
The issue of capital structure has been widely studied, but without having reached a consensus, given the heterogeneity of the results of numerous scientific studies that have been made in recent decades, which makes that Myers [1984] reference to the "puzzle" of the capital structure remains current today. Modigliani and Miller [1958] addressed the issue of capital structure, based on the assumptions of perfect capital markets. In this context, the authors showed that the firms’ value is independent of their financing decisions. This opinion was
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shared by Grinblatt and Titman [2001], among others. However, the hypothesis of the capital structure irrelevance began to be challenged by several authors, since this model is valid only in the context of perfect capital markets, which is not the case in the real world, because of market imperfections, such as taxes, bankruptcy costs, agency costs, asymmetric information and signalling effect [Jensen and Meckling 1976; Miller, 1977; DeAngelo and Masulis 1980], which cause a change in the firms’ value, according to their financing policy. Some years later, Modigliani and Miller [1963] came to recognize that the presence of tax favours the use of debt capital rather than equity, since the tax benefit provided by debt causes a decrease in the weighted average cost of capital (WACC), and therefore an increase in the firms’ value. Thus, there is an optimal capital structure, which will increase the value of companies, minimizing the WACC. However, Myers [2001] states that the substitution of equity for debt has a limit, because after a certain level, the cost of equity increases as a compensation for a higher level of financial risk assumed by keeping constant the cost of capital. The higher the debt levels, the higher the financial risk level. Consequently, the likelihood of bankruptcy increases. Since family controlled firms are more adverse to risk and to loss of control than their counterparts, they avoid debt in their capital structure [Storey 1994; Mishra and McConaughy, 1999, Zhou, 2012]. In addition, the long-term perspective, the desire to pass the firm onto succeeding generations and the management’s concern for reputation [Stein, 1989; Sonnenfeld and Spence, 1989; James, 1999; Anderson et al., 2003] can reinforce the family firms avoidance for leverage. Moreover, family firms are more likely to avoid debt-covenant violations [Prencipe et al., 2008]. However, the desire to maintain the family dominant position and the firms’ control, families are predisposed to limit the capital hold by new shareholders, leading to higher levels of debt [Mira, 2005; Xin-Ping et al., 2006; Prencipe et al., 2008]. Thus, the expected relationship between ownership and leverage is not consensual. Though, based on the risk aversion assumption, we formulate the first hypothesis: H1: Family controlled firms have lower debt levels than nonfamily controlled firms. Indeed, Sonnenfeld and Spence [1989], Gallo and Vilaseca [1996] and Mishra and McConaughy [1999] find that family controlled firms use less debt. According to Sonnenfeld and Spence [1989], family businesses have lower debt to equity levels, in order to avoid losing everything in the case of loan failure and to avoid damaging their family’s reputation and personal guarantees. In contrast with these conclusions, Pindado and Torre [2008] analyzed a sample of Spanish listed firms for the period between 1990 and 1999, finding that family firms are less concerned with financial risk, but more concerned with maintaining their control over the firm than their counterparts. In addition, Setia-Atmaja et al. [2009] and Setia-Atmaja [2010] found evidence that family controlled firms employ higher debt levels, compared to non-family firms, concluding that family firms use debt as a substitute for independent directors. The authors suggest that family firms do not expropriate minority or
outside shareholders, but might adopt higher debt levels in order to improve monitoring. May [1995] finds that CEOs that have more firm-specific human capital act to reduce firm risk by using less debt. More of the intangible value may be related to firm-specific human capital [Mishra and McConaughy 1999]. The Trade-off theory combines the benefits and disadvantages of debt. By one hand, the debt is advantageous because of its tax benefit; on the other hand, high debt levels can induce the presence of bankruptcy costs, since the probability of incurring bankruptcy increases with the degree of indebtedness of firms [Myers 1977]. For low levels of debt, the probability of bankruptcy is irrelevant. However, from higher levels of indebtedness, agency and bankruptcy costs become significant, and the tax benefits of debt are covered by bankruptcy costs [Baxter 1967; Kraus and Litzenberger 1973]. Thus, in accordance with the trade-off hypothesis, there is an optimal debt ratio, reached at the point where the incremental costs of failure equals the tax relief marginal increase in the debt. Graham and Harvey [2001] and Bancel and Mittoo [2004] results show that firms have a target for the value of its debt ratio, finding evidence supporting the trade-off hypothesis for the U.S. market and 16 European countries, respectively. While the trade-off theory suggests an optimal level of debt that maximizes firm value and minimizes the cost of capital by balancing the tax benefits and the costs of bankruptcy of high borrowing levels, the Pecking Order approach does not advocate an optimal capital structure, but a hierarchy of funding sources according to its cost. The Pecking Order approach [Myers 1984; Myers and Majluf 1984] argues that companies follow a hierarchical sequence in the selection of their funding sources, in order to minimize the costs of financing. Initially, companies prefer to use internal financing, using only new debt when internal funds are not enough. In this case, firms start by issuing debt, leaving a last resort to issuing new shares. According to Rajan and Zingales [1995], companies with high levels of earnings have low levels of debt capital because they are able to use internal financing, not needing to rely on external resources. On the other hand, unprofitable companies whose cash flows are insufficient to meet its investments, tend to issue new debt, since, within the external financing alternatives, this is the one closest to the top of hierarchies. In this context, it is expected that the higher the firms profitability, the lower the need for external borrowing. There is a significant number of studies that found a negative relationship between profitability and debt, such as Norton [1990], Rajan and Zingales [1995], Mao [2003], Flannery and Rangan [2006], Serrasqueiro and Nunes [2008], Rocca et al. [2009], Couto and Ferreira [2010] and Vieira and Novo [2010]. However, some studies question the Pecking Order approach [Brennan and Kraus 1987; Constantinides and Grundy 1989; Barclay and Smith, 1995]. Romano et al. [2000] analyse the capital structure decisions by small and medium family business, based on a questionnaire send to Australian family businesses. Their results reveal that small family firms’ debt is significantly related with firm size,
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family control, business planning and business objectives. In addition, they find that the older the business owners, the lower the preference for equity. According the authors, the Pecking Order hypothesis provides useful explanations for family business financing decisions. In the context of the Pecking Order approach, having more cash (cash and marketable securities) reduces the need to borrow. Consequently, we expect a negative relationship between debt and cash. However, because family firms are more averse to risk, more specifically, to the risk of financial distress [Zhou, 2012], they tend to reinforce this relationship. Ward [1987] state that family firms tend to use internal funds for additional growth. Thus, we formulate the following hypothesis: H2: The negative relationship between debt level and cash is stronger for family controlled firms than for non-family controlled firms. The relations between managers, shareholders and creditors can cause conflicts which impacts the firms’ value, resulting in agency costs [Jensen and Meckling 1976; Diamond 1989; Harris and Raviv 1991; Ang 1991]. Jensen and Meckling [1976] suggest that the use of debt reduces agency costs by reducing the cash flows available for the implementation of strategies for managers’ selfish, reconciling shareholders and managers interests and increasing the firms’ value. Anderson et al. [2003] analyzed the debt policy of family firms based on a sample of 252 American firms for the period between 1993 and 1998. Consistent with the hypothesis that founding-family ownership in publicly traded firms reduces the agency costs of debt, they find that family firms enjoy a lower cost of debt than non-family firms. Their results are consistent with the idea that family firms have incentive structures that result in fewer agency conflicts between equity and debt. Another factor influencing the capital structure deals with the existence of information asymmetry [Ross 1977; Leland and Pyle 1977; Keasey and Watson 1996]. According to them, firms can use the financing decisions to signal the market about firms’ expected future cash flows. Thus, the share price of companies depends on the market interpretation about the signals sent by corporate managers. Investors tend to see new debt issue as a good signal and associate lower levels of indebtedness with greater financial difficulties. However, empirical studies have found no evidence of capital structure signalling [Norton 1990; Barclay and Smith 1995] or found only weak evidence for this effect [Graham and Harvey 2001]. Considering a different approach, several studies have focused on identifying the determinants of firms’ capital structure. Some of the factors commonly recognized as determinants for the financing policy are the current debt level, asset tangibility, firm size and growth opportunities. Sogorb-Mira and Lopez-Gracia [2003], Serrasqueiro and Nunes [2008], Bhaird and Lucey [2009] and Vieira and Novo [2010] found a positive relationship between firm size and debt, which is consensual since larger size firms are less likely to incur in financial distress costs because of their higher capacity to take advantage of debt tax benefits.
The principle that the higher the age of firms, the lower their debt ratio, given the higher capacity of older firms to generate internal financing funds, was evidenced by several authors [Vilabella and Silvosa 1997; Gama 2000; Sogorb-Mira and Lopez-Gracia 2003; Vos and Shen 2007; Bhaird and Lucey 2009]. Companies that have higher levels of fixed assets will have greater ease in obtaining credit, since fixed assets serve as collateral, making debt less risky [Titman and Wessels 1988]. Indeed, a positive relationship between tangibility of assets and debt level was evidenced by Rajan and Zingales [1995], Chittenden et al. [1996], Jorge and Armada [2001], SogorbMira and Lopez-Gracia [2003] and Couto and Ferreira [2010]. In this context, we expect that debt level is positively related to fixed assets. Family firms’ long term perspective might be associated, ceteris paribus, with a higher level of fixed assets than non-family counterparts. In addition, we suppose that family members are more likely to use their personal assets as collateral, at least, in the Portuguese context. Consequently, we expect that debt level is positively related to fixed assets, and that this relation is reinforced in the case of family firms, formulating the following hypothesis: H3: The positive relationship between debt level and fixed assets is stronger for family controlled firms than for nonfamily controlled firms. According to the Pecking Order assumptions [Myers 1984; Myers and Majluf 1984], it is expected a positive relationship between growth opportunities and firms debt, in order to finance new projects. However, the same theory suggests a negative relationship between profitability and debt. Because firms prefer internal finance over external finance (debt and equity), the higher the firms’ profitability, the higher the level of internal finance and, consequently, the lower the debt level. At the same time, there is large empirical evidence that family firms outperform non-family firms [Stein, 1989; James, 1999; Anderson and Reeb, 2003; Sraer and Thesmar, 2007; Scholes et al., 2012]. Moreover, family firms tend to use internal funds for additional growth [Ward, 1987]. Considering the evidence that family firms have higher levels of profitability than non-family firms and the assumptions of the Pecking Order approach, we expect that firms increase debt levels with growth opportunities, but in a lower level in the case of family firms, because they generate more internal funds. Consequently, we formulate the fourth hypothesis: H4: The positive relationship between debt level and growth opportunities is weaker for family controlled firms than for non-family controlled firms. Firms with more growth opportunities will have higher market-to-book equity ratios [Smith and Watts, 1992]. III.
PROPOSED METHODS
In this section, we start to describe the adopted methodology in order to analyse the family firms’ capital structure and test the formulated hypotheses. Subsequently, we present the data.
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A. Methodology In order to understand the determinants of firms financing, we consider as dependent variable the debt level (DEBT). We calculate DEBT as the ratio of the book value of total liabilities to the sum of the book value of liabilities and equity. We need to distinguish between family firms (FF) and nonfamily firms (NFF). There are several definitions for family firms in the literature. Guided by La Porta et al. [2000] and Setia-Atmaja et al. [2009], we define family firms (FAMILY) as those in which the founding family or family member controlled 20% per cent or more equity, and was involved in the top management of the firm. The classification of FF and NFF was corroborated in the firm’s annual report. FAMILY is a dummy variable that takes value 1 if a firm is familiar and zero otherwise. Fixed Assets (FA) are computed as the ratio of fixed assets to the total assets.
We expect a negative relationship between age and debt, given the higher capacity of older firms to generate internal financing funds [Vilabella and Silvosa 1997; Gama 2000; Sogorb-Mira and Lopez-Gracia 2003; Vos and Shen 2007; Bhaird and Lucey 2009]. We consider firm age (AGE) as the natural logarithm of the difference between incorporation year and a fiscal year. The board independence (BOARD) is measured as the proportion of independent directors on the board. According to Anderson and Reeb [2003] and Setia-Atmaja [2010], independent directors are the individuals whose only business relationship to the firm is their directorship, identified in firm’s annual reports. To analyse the relationship between DEBT (dependent variable) and all the independent variables, we employ the following regression model: DEBT α β1 FAi,t x FAMILYi,t
Cash (CASH) is calculated as the sum of cash and marketable securities to the total assets.
β 2 CASHi,t x FAMILYi,t β 3 MBi,t x FAMILYi,t β 4 RISKi,t β 5 PROFi,t β 6 NDTAXi,t β 7 AGE i,t
We use the market-to-book ratio (MB) as a proxy for the growth opportunities, calculated by dividing the market price per share by the book value per share. In addition, we control for operating risk, non-debt tax shields, profitability [Titman and Wessels 1988; Prowse 1990], firm size [e.g., Romano et al. 2000], age [Vos and Shen 2007; Bhaird and Lucey 2009] and board independence. If operating risk is high, managers may reduce their total risk exposure by reducing financial leverage [Jensen et al. 1992]. Thus, we expect a negative relationship between debt and operating risk. Following Mishra and McConaughy [1999], we measure operating risk (RISK) as the standard deviation (calculated over the past three years) of operating income before depreciation to annual sales. According to the pecking order theory, profitable firms generate more internal resources, having lower needs for external financing. Thus, it is expected a negative relation between debt and profitability. Profitability (PROF) is measured as net income scaled by sales. As the non-debt tax shield increases, it decreases the probability a firm take full advantage of the deductibility of interest expenses. Consequently, firms with high non-debt tax shield are associated with low levels of debt. We compute nondebt tax shield (NDTAX) as the ratio of operating income less interest expense less taxes to corporate tax rate, and scaled the result by sales [Prowse 1990]. Some authors find evidence of a positive relationship between firm size and financial structure. Like Chittenden et al. [1996] and Romano et al. [2000] and based on the pecking order approach, we expect a positive relation between debt and firm size, since the costs associated with external finance are higher for small firms than for the larger ones. However, Renfrew et al. [1984] noted that small firms are less likely to use debt. Firm Size (FS) is measured as the natural logarithm of the book value of total assets of a firm.
(1)
β 8 BOARDi,t β 9 FS β10 INDUSTRYi,t β11 YEAR i,t ε i,t
We include industry dummy variables in order to consider any variation in the dependent variable due to industry differences. Bradley et al. [1984] and Harris and Raviv [1991], for example, noted that capital structure varies with the industry sector. Finally, we consider year dummy variables to remove any secular effects among the independent variable. The other variables as already been specified. If FF are more averse to debt, the sign of the interaction term on the 1 will be positive and significant, and the interaction terms on 2 and 3 will be significant and negative. In addition, we extend regression (1), exploring new independent variables that may explain the firms’ debt. We introduce the operating return on assets (OROA), calculated as the operating earnings divided by total assets, the sales growth (SG), considering the change in log sales, the employment (EMPLOY), computed as the natural logarithm of the number of employees in the firm and the cost of debt (COST), calculated as the interest expenses scaled by debt. Because last years have been characterised by financial and economic crisis, we consider a dummy variable to identify the market crisis, in order to analyse whether debt policy differs according the market conditions. We consider that the period when financial crisis really strikes the financial market is the 2008-2010 period. Thus, this variable will take value 1 for the 2008, 2009 and 2010 years, and zero otherwise. Consequently, we can express our new regression model in the following way:
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In what concerns the debt level, and contrary to the expected, our results show that FF use more debt than the counterparts (a mean of 71.5% and 67.2%, respectively). Consequently, it does not support the first hypothesis. Although this result is contrary to the ones of Sonnenfeld and Spence [1989], Gallo and Vilaseca [1996] and Mishra and McConaughy [1999], it is consistent with the evidence of Pindado and Torre [2008], Setia-Atmaja et al. [2009] and Setia-Atmaja [2010].
DEBT α β1 FAi,t x FAMILYi,t β 2 CASHi,t x FAMILYi,t β 3 MBi,t x FAMILYi,t β 4 RISKi,t β 5 PROFi,t β 6 NDTAXi,t β 7 AGE i,t β 8 BOARDi,t β 9 FS β10 OROA i,t β11 SG i,t β12 EMPLOY β13 COST β14 CRISIS β15 INDUSTRYi,t β16 YEAR i,t ε i,t (2)
We employ a panel data methodology, using the pooled ordinary least squares (OLS), the fixed effects model (FEM), and the random effects model (REM). Subsequently, we use the F-statistic and the Hausman [1978] test to choose the most appropriate model. We present the standard errors corrected for heteroscedasticity and covariance, based on the White’s [1980] heteroscedasticity consistent standard errors method. B. Data Our sample consists on Portuguese non-financial listed FF and NFF on EL for the period between 1999 and 2010, using an unbalanced panel data sample. Data were obtained from SABI, a private database provided by Bureau van Dijk and complemented with firm´s annual reports. Table 1 reports the number firms and observations related to FF and NFF. As we can see, the sample is constituted by 58 firms, corresponding to 583 observations. From the total sample, 35 firms are family-controlled (about 60% of the firms) and 23 are non-family firms. This percentage is similar to the one found by Faccio and Lang [2002] for Portugal (60.34%). Looking for the number of observations, almost 65% of the observations are related to family firms. These results are consistent with the evidence that family shareholders are common in public traded firms’ worldwide [Gersick et al. 1997; Claessens et al. 2000; Faccio and Lang 2002; Anderson and Reeb 2003; Villalonga and Amit 2006; Holderness 2009]. IV.
RESULTS
Table 2 reports the descriptive statistics on the variables used in the subsequent analysis for FF and NFF, as well as the differences in mean variables between FF and NFF. In addition, we consider the sales (SALES), calculated as the natural logarithm of the sales, the current liabilities (LIAB), computed as the ratio between current liabilities and total debt, the equity ratio (EQ), calculated as equity divided by the total assets, and the financial debt (FINDEB), computed as the ratio between financial debt (bank loans and bonds) and total debt, in order to compare family and non-family firms. Looking for the univariate results, we can see that the variables that are significantly distinct between FF and NFF are the DEBT, FA, CASH, RISK, NDTAX, AGE, BOARD, FS, EMPLOY, SALES, EQ and FINDEB. FF are more indebted, presenting lower levels of equity, have more cash, have higher amounts of non-debt tax shield, are older and bigger (in what concerns assets, sales, and number of employees). On the other hand, they have lower levels of fixed assets, are less riskier and present lower levels of board independence.
We suggest some possible reasons for this evidence. First, it might be explained by the lower level of risk of FF, which allows for higher levels of debt, compensating the lower levels of fixed assets. Second, older business owners tend to present lower levels of preference for equity [Romano et al. 2000], and, in our sample, FF are indeed older. Third, as FF are bigger, they can use higher levels of debt, since the costs associated with external finance are higher for small firms than for the bigger ones [Chittenden et al. 1996; Romano et al. 2000]. Fourth, it can suggest that family firms are less concerned with financial risk, but more concerned with maintaining their dominant position and control over the firm than their counterparts [Mira, 2005; Xin-Ping et al., 2006; Prencipe et al., 2008; Pindado and Torre 2008]. Fifth, family firms might use debt as a substitute for independent directors [Setia-Atmaja et al. 2009; Setia-Atmaja 2010]. Finally, we find evidence that FF use more financial debt (bank loans and bonds) that non-family firms. On average, the ratio from financial debt to assets is about 0.449 for FF and 0.393 for NFF. Indeed, family firms work, on average, with more banks than non-family firms, which can facilitate the access to credit. We want to emphasize, however, that although FF present a higher mean of debt level, the minimum value is significantly lower than the value for the NFF. The maximum value for the debt ratio (2.287), as well as the minimum value of EQ (1.287) are associated to the last year event of a firm that in the meantime filed for insolvency and is no more listed in the EL. The mean proportion of independent directors is significantly lower for FF (39.2%) than for NFF (50.8%), being the result consistent with the perspective that family members dominate the board of directors [Anderson and Reeb 2004; Setia-Atmaja 2010] and that family shareholders are common in public traded firms [Claessens et al. 2000; Faccio and Lang 2002; Villalonga and Amit 2006; Holderness 2009]. Although family firms do not differ from their counterparts in what concerns the current liabilities (LIAB), we can see that, on average, approximately 62% of the debt is comprised by current liabilities. Table 3 reports the Pearson correlations among the independent variables for FF (Panel A) and NFF (Panel B). For the FF, the higher levels of correlation are between PROF and NDTAX and FA and FS, both presenting positive values. In respect to NFF, the higher correlations are between RISK and BOARD (positive) as well as with RISK and NDTAX (negative). In general, although we have some significant correlations, the coefficients are all bellow 50%, except one, which presents a value of about 72%). Consequently, it does not appear to be sufficiently large to cause concern about multicollinearity problems. Gujarati
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[2003] suggests that the multicollinearity could be a serious problem when the correlation coefficient is in excess of 0.8.
In line with the results of Table 4, more profitable firms use less debt financing and older firms use more debt.
In order to analyse the determinants of capital structure, we estimate model (1). Table 4 reports the regression (1) results, considering the OLS, the FEM and the REM. According to the Hausman statistic and the F test, the efficient estimation is the FEM, so, we will analyse the results based on this model.
In what concerns the new independent variables, the OROA result (another profitability measure) is consistent with PROF result, being its statistical significance even higher. It seems that OROA is more accurate to measure profitability because it is unaffected by the changes in capital structure [Sraer and Thesmar 2007].
The estimated parameters 1, 2 and 3 are not statistically significant, not permitting to distinguish between FF and NFF in what concerns to CASH, FA and MB. Consequently, we find no evidence to support the hypotheses 2, 3 and 4. The variables that explain firms’ debt are PROF, NDTAX and AGE. The coefficient on PROF, as expected, is negative, suggesting that the more profitable the firm, the lower the needs for external financing, which gives support to the pecking order theory [Myers 1984; Myers and Majluf 1984] and is consistent with the results of several authors, such as Mao [2003], Flannery and Rangan [2006], Serrasqueiro and Nunes [2008], Rocca et al. [2009], Couto and Ferreira [2010] and Vieira and Novo [2010]. The negative and significant value of the NDTAX coefficient indicates that firms with high non-debt tax shield are associated with low levels of debt, which is consistent with the evidence presented by Prowse [1990], Gama [2000] and Vieira and Novo [2010]. The coefficient on AGE is positive, showing a positive relationship between firms’ age and debt. We expected a negative signal, based on the assumption that older firms present higher capacity of to generate internal financing funds [Sogorb-Mira and Lopez-Gracia 2003; Vos and Shen 2007; Bhaird and Lucey 2009]. According to our results, it seems not to be the case or, another possible reason might be associated with the evidence that older business owners tend to present lower levels of preference for equity [Romano et al. 2000]. The results of regression (2) model are shown in Table 5, considering the OLS, the FEM and the REM. According to the Hausman statistic and the F test, the efficient estimation is the FEM, so, we will analyse the results based on this model. Globally, we can see that this model explains better the dependent variable than the last one (Table 4), which can be seen by the adjusted R2, which increases from about 70% to 72%. In addition, almost the variables we include are statistically significant. The coefficient 3 is statistically significant and positive, which is consistent with the positive relationship between debt level and growth opportunities, as formulated in hypothesis four. However, this relationship is stronger for FF than NFF, contrary to the expected. Indeed, the degree of relationship is consistent with the confirmation that FF have more debt than NFF (Table 2). The independent variables that are statistically significant, explaining the firms’ debt level are PROF, OROA, EMPLOY, COST (with a negative signal) and AGE and CRISIS, with a positive signal.
The higher (lower) the number of employees, the lower (higher) is the debt level. This finding suggests that FF are firms with less employment, because they present higher levels of debt (Table 2). The results show a negative relationship between the cost of debt and the financing level, and a positive relation between crisis periods and the debt, which is in agreement with the expected results. When the debt cost is higher, firms seem to avoid this means of financing, which supports the pecking order theory [Myers, 1984 Myers and Majluf 1984], but when firms are confronted with financial problems, they have necessity to face them with debt capital. A. Robustness tests For robustness reasons, we analyse whether our results are robust to several alternative specifications. First, we recalculate the descriptive statistics considering the 1999-2006 period, since crisis years might be driving the results. The results (not reported) are quite similar to the ones reported in Table 2. However, we notice that, comparing the results from Table 2 with the ones for the period 1999-2006, family firms’ present lower maximum values for DEBT (1.584) and RISK (6.634). Concerning the mean differences between FF and NFF, the only variables that are no more significantly distinct are the CASH and FS. However, considering the global period of the sample, they were only marginally significant, at the 10% level. Next, we re-estimate equation (1) considering different measures for some of the variables. For example, following Renfrew et al. [1984], we measure FS as the natural logarithm of the book value of total assets of a firm. Instead of calculating the PROF as the return on sales, we consider the return on equity, measured as net income scaled by equity. The results were quite similar, so our main conclusions remain the same. In addition, we run the model regressions (1) and (2), adding a variable to measure the equity concentration (Herfindahl index). The estimated coefficient is not statistically significant in both regression, and the results for model regression (1) and (2) are similar to the results presented in Table 4 and Table 5, respectively. Thus, our conclusions remain the same. Finally, and because in global terms the variables that are statistically significant are the control ones, we run the model regression (2), considering the FF and NFF in separated regressions. The results for the best model (the REM and the FEM, in the sub-sample of FF and NFF, respectively) are shown in Table 6.
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Comparing the results of FF and NFF, we can see that they differ in what respects to FA, CASH, MB, COST and CRISIS. In what concerns the FF, firms with more fixed assets uses less debt, which is contrary to the expected results as this type of assets can be used as collateral, and with the conclusions of Rajan and Zingales [1995], Sogorb-Mira and Lopez-Gracia [2003] and Couto and Ferreira [2010]. In addition, this evidence does not give support for hypothesis 3. The MB is positive and statistically significant. Firms with more growth opportunities will have higher market-to-book equity ratios [Smith and Watts 1992]. So, the result suggests that FF finance new projects with debt financing. Our results give support for hypothesis four concerning the relationship between growth opportunities and debt. However, the relationship is stronger for FF than for NFF, contrary to the expected, as we have already concluded. The evidence of a negative relationship between the cost of debt and the financing level, and a positive relation between crisis periods and the debt shown on Table 5 is due to FF, because these variables’ coefficients are only significant in the FF regression results (Table 6). In respect to NFF, firms with higher levels of cash have more debt financing, which contradicts the assumption that companies prefer to use internal financing, using only new debt when internal funds are not enough [Myers 1984; Myers and Majluf 1984]. One possible reason for this evidence might be associated with more liquidity and solvability, which facilitates lending by credit institutions. PROF, AGE and OROA are all statistically significant, and consistent with previous results (Table 5), and already commented. V.
CONCLUSION
This study investigates the capital structure in Portuguese listed and family-controlled firms, using an unbalanced panel data for the period 1999 - 2010. Overall, the results show that family controlled firms use more debt than non-family firms and have a lower proportion of independent directors, which is consistent with the perspective that family members dominate the board of directors and that family shareholders are common in public traded firms. The regression results show a negative relationship between profitability and debt, which gives support to the pecking order theory and is consistent with the results of several authors, such as Mao [2003], Flannery and Rangan [2006], Serrasqueiro and Nunes [2008], Rocca et al. [2009], Couto and Ferreira [2010] and Vieira and Novo [2010]. Moreover, we find that firms with high non-debt tax shield are associated with low levels of debt, which is consistent with the evidence presented by Prowse [1990], Gama [2000] and Vieira and Novo [2010]. Finally, the evidence of a positive relationship between firms’ age and debt might suggest that older business owners tend to present lower levels of preference for equity [Romano et al., 2000].
The robustness results suggest that FF and NFF differ in what respects to fixed assets, cash, growth opportunities, the cost of debt and the crisis effect on debt financing. Although family controlled firms with more fixed assets uses less debt and finance new projects with debt financing, nonfamily firms with higher levels of cash have more debt financing. Finally, family firms are sensitive to the cost of debt and the crisis periods. When the debt cost is higher, firms seem to avoid this means of financing, which supports the pecking order theory [Myers 1984; Myers and Majluf 1984], but when firms are confronted with financial problems, they have necessity to face them with debt capital. For policy makers, this finding could serve to justify a decision in what concerns the financing system policy. In addition, the research results can also be useful for the decision making process of family firms’ financial managers concerning the capital structure policy. Although studying a Portuguese sample aids the contribution made by our study because it is a market not yet explored in this context, it also represents a research limitation because of the small size of the sample, resulting from the small size of the Portuguese capital market. In future research, it will be interesting to explore why family owned firms in Portugal use more debt than non-family firms, as well as to analyse whether capital structure decisions in family firms are influenced by behavioural reasons. REFERENCES R. C. Anderson, and D. M. Reeb, “Board composition: Balancing family influence in S&P 500 firms”, Administrative Science Quarterly, vol. 49, pp. 209-237, 2004. [2] R. C. Anderson, and D. M. Reeb, “Founding family ownership and firm performance: Evidence from the S&P 500”, Journal of Finance, vol. 58 (3), pp. 1301-1328, 2003. [3] R. C. Anderson, S. A. Mansi, and D. M. Reeb, “Founding family ownership and the agency cost of debt”, Journal of Financial Economics, vol. 68, pp. 263-285, 2003. [4] J. S. Ang, “Small business uniqueness and the theory of financial management”, Journal of Small Business Finance, vol. 1 (1), pp. 1-13, 1991. [5] F. Bancel, and U. Mittoo, “Cross-country determinants of capital structure choice: a survey of European firms”, Financial Management, vol. 33 (4), pp. 103-132, 2004. [6] M. J. Barclay, and C. W. Smith, “The maturity structure of corporate debt”, Journal of Finance, vol. 50, pp. 609-631, 1995. [7] N. Baxter, “Leverage, risk of ruin and the cost of capital”, The Journal of Finance, vol. 22, pp. 395-403, 1967. [8] C. M. Bhaird, and B. Lucey, “Determinants of capital structure in Irish SMEs”, Small Business Economics, vol. 35 (5), pp. 357-375, 2009. [9] M., Bradley, G., Jarrell, and E. H., Kim, “On the existence of an optimal capital structure: Theory and evidence”, Journal of Finance, vol. 39, pp. 857–878, 1984. [10] M. J. Brennan, and A. Kraus, “Efficient financing under asymmetric information”, Journal of Finance, vol. 42 (50), pp. 1225-1243, 1987. [11] J. Céspedes, M. González, and C., Molina, “Ownership and capital structure in Latin America”, Journal of Business Research, vol. 63 (3), pp. 248–254, 2010. [12] F. Chittenden, G. Hall, and P. Hutchinson, “Small growth, access to capital markets and financial structure: review of issues and an empirical investigation”, Small Business Economics, vol. 8, pp. 59-67, 1996. [1]
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[38] H. E. Leland, and D. H. Pyle, “Informational asymmetries, financial structure, and financial intermediation” Journal of Finance, vol. 32 (2), pp. 371-387, 1977. [39] C. X. Mao, “Interaction of debt agency problems and optimal capital structure: theory and evidence”, Journal of Financial and Quantitative Analysis, vol. 38, pp. 399-423, 2003. [40] D. May, “Do managerial motives influence firm risk reduction strategies?”, Journal of Finance, vol. 50 (4), pp. 1291-1308, 1995. [41] M. H. Miller, “Debt and taxes”, Journal of Finance, vol. 32 (2), pp. 261275, 1977. [42] F. Mira, “How SME uniqueness affects capital structure: Evidence from a 1994-1998 Spanish panel data”, Small Business Economics, vol. 25 (5), pp. 447-457, 2005. [43] C. S. Mishra, and D. L. McConaughy, “Founding family control and capital structure: the risk of loss of control and the aversion to debt”, Entrepreneurship theory and practice, pp. 53-64, 1999. [44] F. Modigliani, and M. H. Miller, “Corporate income taxes and the cost of capital: A correction”, The American Economic Review, vol. 53 (3), pp. 437-447, 1963. [45] F. Modigliani, and M. H. Miller, “The cost of capital, corporation finance, and the theory of investment”, The American Economic Review, vol. 48 (3), pp. 261-297, 1958. [46] S. C. Myers, “Capital structure”, Journal of Economic Perspectives, vol. 15 (2), pp. 81-102, 2001. [47] S. C. Myers, “The capital structure puzzle”, Journal of Finance, vol. 39 (3), pp. 575-592, 1984. [48] S. C. Myers, “Determinants of corporate borrowing”, Journal of Financial Economics, vol. 5, pp. 147-175, 1977. [49] S. C. Myers, and N. S. Majluf, “Corporate financing and investments decisions: when firms have information that inventors do not have”, Journal of Financial Economics, vol. 13, pp. 187-221, 1984. [50] E. Norton, “Similarities and differences in small and large corporation beliefs about capital structure policy”, Small Business Economics, vol. 2, pp. 229-245, 1990. [51] J. Pindado and C. Torre, “Financial decisions as determinants of ownership structure: Evidence from Spanish family controlled firms”, Managerial Finance, vol. 34 (12), pp. 868-885, 2008. [52] A. Prencipe, G. Markarian, and L. Pozza, “Earnings management in family-firms: Evidence from R&D cost capitalization in Italy”, Family Business Review, vol. 21 (1), pp. 71-88, 2008. [53] S. Prowse, “Institutional investment patterns and corporate financial behaviour in the United States and Japan”, Journal of Financial Economics, vol. 27, pp. 43-66, 1990. [54] R. G. Rajan, and L. Zingales, “What do we know about capital structure? Some evidence from international data”, Journal of Finance, vol. 50, pp. 1421-1460, 1995. [55] K. M. Renfrew, W. J. Sheehan, and W. C. Dunlop, “Financing and Growth of Small Businesses In Australia”, Canberra: Bureau of Industry Economics,1984. [56] M. Rocca, T. Rocca, and A. Cariola, “Capital Structure Decisions during a Firm’s Life Cycle”, Small Business Economics, vol. 37 (1), pp. 107130, 2009. [57] C. A. Romano, G. A. Tanewski, and K. X. Smyrnios, “Capital structure decision making: A model for family business”, Journal of Business Venturing, vol. 16, pp. 285-310, 2000. [58] S. A. Ross, “The determination of financial structure: The incentivesignalling approach”, The Bell Journal of Economics, vol. 8 (1), pp. 2340, 1977. [59] L. Scholes, N. Wilson, M. Wright, and H. Noke, “Listed family firms: Industrial and geographical context, governance and performance”, Working paper available at http://ssrn.com/abstract=2002906, 2012. [60] Z. Serrasqueiro, and P. M. Nunes, “Determinants of capital structure: Comparison of empirical evidence from the use of different estimators”, International Journal of Applied Economics, vol. 5(1), pp. 14-29, 2008. [61] L. S. Setia-Atmaja, “Dividend and debt policies of family controlled firms: the impact of board independence”, International Journal of Managerial Finance, vol. 6 (2), pp. 128-142, 2010.
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[62] L. S. Setia-Atmaja, G. A. Tanewski, and M. Skully, “The role of dividends, debt and board structure in the governance of familycontrolled firms”, Journal of Business Finance & Accounting, vol. 36 (7&8), pp. 863-898, 2009. [63] C. Smith, and R. Watts, “The investment opportunity set and corporate financing, dividend, and compensation policies”, Journal of Financial Economics, vol. 32, pp. 263-292, 1992. [64] F. Sogorb-Mira, and J. López-Gracia, “Pecking Order versus trade-off: An empirical approach to the Small and Medium Enterprise Capital Structure”, SSRN Working Paper, 2003. [65] J. A., Sonnenfeld, and P. L. Spence, “The parting patriarch of a family firm”, Family Business Review, vol. 2 (4), pp. 355–375, 1989. [66] D. Sraer, and D. Thesmar, “Performance and behaviour of family firms: evidence from the French stock market”, Journal of European Economic Association, vol. 5 (4), pp. 709-751, 2007. [67] J. Stein, “Efficient capital markets, inefficient firms: A model of myopic corporate behaviour”, Quarterly Journal of Economics, November, pp. 655-69, 1989. [68] D. J. Storey, “Understanding the Small Business Sector”, London: Routledge, 1994. [69] S. Titman, and R. Wessels, “The determinants of capital structure choice”, Journal of Finance, vol. 43 (1), pp. 1-19, 1988. [70] E. Vieira, and J. Novo, “A Estrutura de Capital das PME: Evidência no Mercado Português”, Revista Estudos do ISCA, n.º 2, disponível online. ISSN: 1646-4850, 2010. [71] L. B. Vilabella, and A. R. Silvosa, “Un modelo de síntesis de los factores que determinan la estructura de capital óptima de las PYMES”, Revista Europea de Dirección y Economía de la Empresa, vol. 6 (1), pp. 107-124, 1977. [72] B. Villalonga, and R. Amit, “How do family ownership, control, and management, affect firm value?”, Journal of Financial Economics, vol. 80 (2), pp. 385-417, 2006. [73] E. Vos, and Y. Shen, “The happy story told by small business capital structure”, Working paper available at http://ssrn.com/abstract=1000293, 2007. [74] X. Xin-Ping, Z. Zhen-Song, and Z. Y. Ming-gui, “Voting Power, Bankruptcy Risk and Radical Debt Financing Behavior of Family Firms: Empirical Evidences from China. Working paper available at: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=4105121&url=ht tp%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumb er%3D4105121, 2006. [75] J. L. Ward, “Keeping the Family Business Healthy”, San Francisco: Jossey-Bass, 1987. [76] H. White, “A Heteroscedasticity-consistent covariance matrix estimator and a direct test for heteroscedasticity”, Econometrica, vol. 48 (4), pp. 149-170, 1980. [77] H. Zhou, “Are family firms better performers during financial crisis?”, Working paper available at http://ssrn.com/abstract=1990250, 2012.
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TABLE I.
SAMPLE. THIS TABLE PROVIDES THE SAMPLE NUMBER OF FIRMS AND OBSERVATIONS, DIVIDED BY FAMILY AND NON-FAMILY FIRMS
Firms
%
Observations
%
Family firms
35
60,3%
377
64,7%
Non-family firms
23
39,7%
206
35,3%
58
100%
583
100%
TABLE II.
DESCRIPTIVE STATISTICS
This table shows the descriptive statistics for FF and NFF, including means, medians, standard deviations (SD), maximum and minimum values, as well as the differences in mean variables between FF and NFF. The significance levels for means differences are based on a two-tailed t-test. FF are those in which the founding family or family member controlled 20% per cent or more equity, and was involved in the top management of the firm. FAMILY is a dummy variable that takes value 1 if a firm is familiar and zero otherwise. DEBT is the ratio of the book value of total liabilities to the sum of the book value of liabilities and equity. FA is computed as the ratio of fixed assets to the total assets. CASH is calculated as the sum of cash and marketable securities to the total assets. The MB is calculated by dividing the market price per share by the book value per share. RISK is the standard deviation of operating income before depreciation to annual sales. PROF is measured as net income scaled by sales. NDTAX is the ratio of operating income less interest expense less taxes to corporate tax rate, scaled the result by sales. BOARD is measured as the proportion of independent directors on the board. FS is measured as the natural logarithm of the book value of total assets of a firm. AGE is the natural logarithm of the difference between incorporation year and a fiscal year. SALES is the natural logarithm of the sales. LIAB is the ratio between current liabilities and total deb. EQ is the equity divided by the total assets. FINDEB is the ratio between financial debt (bank loans and bonds) and total assets. FF
NFF
Mean
Median
Minimum
Maximum
SD
DEBT
0.715
0.728
0.007
2.287
0.224
FA
0.568
0.588
0.106
0.989
CASH
0.060
0.038
0.000
MB
2.535
1.068
Mean
t
Mean
Median
Minimum
Maximum
SD
Differences
DEBT
0.672
0.693
0.046
1.630
0.270
DEBT
0.0427
1.936
*
0.192
FA
0.618
0.628
0.128
0.983
0.217
FA
-0.0500
-2.771
***
0.601
0.074
CASH
0.047
0.022
0.000
0.371
0.075
CASH
0.0123
1.901
*
-14.094
78.391
7.338
MB
1.911
1.144
-28.141
39.739
5.702
MB
0.6242
1.138
RISK
0.665
0.266
0.001
27.071
1.673
RISK
1.292
0.490
0.005
18.963
2.641
RISK
-0.6270
-3.086
PROF
0.012
0.025
-3.579
8.877
0.664
PROF
-0.308
0.035
-16.932
18.529
2.799
PROF
0.3207
1.619
***
NDTAX
0.059
0.024
-3.544
8.267
0.546
NDTAX
-0.297
0.010
-11.031
1.083
1.302
NDTAX
0.3559
3.746
***
AGE
3.372
3.497
0.000
5.094
0.812
AGE
2.929
3.238
0.000
4.263
0.949
AGE
0.4434
5.670
***
BOARD
0.392
0.333
0.000
0.857
0.200
BOARD
0.508
0.400
0.124
1.000
0.270
BOARD
-0.1161
-5.411
***
FS
19.583
19.704
12.506
22.837
1.987
FS
19.255
18.642
15.490
24.424
2.363
FS
0.3282
1.693
*
EMPLOY
7.466
7.440
4.718
10.874
1.536
EMPLOY
6.118
6.191
1.099
10.237
2.263
EMPLOY
1.3485
7.646
***
SALES
19.093
19.382
11.513
22.886
2.091
SALES
18.245
18.281
9.711
23.437
3.215
SALES
0.8485
3.421
***
LIAB
0.615
0.618
0.001
1.000
0.231
LIAB
0.615
0.610
0.003
1.000
0.237
LIAB
0.0001
-0.007
EQ
0.285
0.272
-1.287
0.993
0.224
EQ
0.328
0.307
-0.630
0.954
0.270
EQ
-0.0427
-1.936
*
0.455
0,000
1.292
0.205
FINDEB
0.393
0.399
0.000
0.869
0.203
FINDEB
0.0556
3.160
***
FINDEB
0.449
***,* Significantly different from zero at the 1%, 10% level
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TABLE III.
PEARSON CORRELATION MATRIX
This table presents the Pearson correlations among independent variables for FF (Panel A) and NFF (Panel B). FF are those in which the founding family or family member controlled 20% per cent or more equity, and was involved in the top management of the firm. FAMILY is a dummy variable that takes value 1 if a firm is familiar and zero otherwise. DEBT is the ratio of the book value of total liabilities to the sum of the book value of liabilities and equity. FA is computed as the ratio of fixed assets to the total assets. CASH is calculated as the sum of cash and marketable securities to the total assets. The MB is calculated by dividing the market price per share by the book value per share. RISK is the standard deviation of operating income before depreciation to annual sales. PROF is measured as net income scaled by sales. NDTAX is the ratio of operating income less interest expense less taxes to corporate tax rate, scaled the result by sales. BOARD is measured as the proportion of independent directors on the board. FS is measured as the natural logarithm of the book value of total assets of a firm. AGE is the natural logarithm of the difference between incorporation year and a fiscal year. Panel A – Family firms DEBT DEBT FA
FA
CASH
MB
RISK
PROF
-0.0923
BOARD
FS
1
-0.1343 -0.2468
1
MB
0.0067 -0.0543
0.0178
1
0.0715 -0.0026
0.0072
1
0.0748
0.0254
0.0360
1
0.0060 -0.0387
RISK
0.0439
PROF
-0.2058
0.0007
NDTAX -0.2294 -0.0607 -0.0042
0.7244
1
0.0589 -0.0226
0.0568
1
0.1126 -0.0748 -0.0013
-0.1329
0.1760
1
0.0280 -0.1752
-0.1885
0.1884 -0.1671 -0.1003 -0.1725
BOARD -0.1177 -0.1049 -0.1691 FS
AGE
1
CASH
AGE
NDTAX
0.1126
0.3305 -0.0543 -0.0997
0.0454
0.0911
1
Panel B – Non-family firms DEBT DEBT FA
FA
CASH
-0.1851
-0.2981 -0.3873
1
-0.0909 -0.0375
0.0140
NDTAX AGE
-0.0261
NDTAX
AGE
BOARD
FS
1
0.1346 -0.0960 -0.0039
1
0.0965 -0.1917
0.0696
0.0042 -0.3891
1
0.2314 -0.1881
0.1240 -0.0010 -0.3970
0.3570
1
0.2595 -0.2150 -0.1470 -0.0468
0.0033
-0.2523
0.0631
BOARD -0.0540 -0.0913 -0.1034 FS
PROF
1
MB PROF
RISK
1
CASH RISK
MB
0.2571
0.1684 -0.1004
0.0201
0.4337 -0.3368
0.0592 -0.2623
0.2144
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1
-0.3200 -0.2928
1
0.3113 -0.3306
-0.2741
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TABLE IV.
REGRESSION (1) RESULTS
This table shows the regression (1) results. FF are those in which the founding family or family member controlled 20% per cent or more equity, and was involved in the top management of the firm. FAMILY is a dummy variable that takes value 1 if a firm is familiar and zero otherwise. DEBT is the ratio of the book value of total liabilities to the sum of the book value of liabilities and equity. FA is computed as the ratio of fixed assets to the total assets. CASH is calculated as the sum of cash and marketable securities to the total assets. The MB is calculated by dividing the market price per share by the book value per share. RISK is the standard deviation of operating income before depreciation to annual sales. PROF is measured as net income scaled by sales. NDTAX is the ratio of operating income less interest expense less taxes to corporate tax rate, scaled the result by sales. BOARD is measured as the proportion of independent directors on the board. FS is measured as the natural logarithm of the book value of total assets of a firm. AGE is the natural logarithm of the difference between incorporation year and a fiscal year. The table presents the results estimated using pooled OLS, FEM and REM. The numbers in parentheses are the t-statistics corrected for heteroscedasticity using the White (1980) method. It reports the F test, a test for the equality of sets of coefficients, and the Hausman (1978) test, a test with H0: random effects are consistent and efficient, versus H1: random effects are inconsistent, in order to choose the most appropriate model for each particular sample.
DEBT α β1 FAi,t x FAMILYi,t β 2 CASH i,t x FAMILYi,t β 3 MBi,t x FAMILYi,t β 4 RISK i,t β 5 PROFi,t β 6 NDTAXi,t β 7 AGEi,t β 8 BOARDi,t β 9 FS β10 INDUSTRYi,t β11 YEAR i,t ε i,t OLS Coefficient t-value
FEM Coefficient t-value
REM Coefficient t-value
constant
0.2788
2.322
**
0.4543
1.466
0.1742
0.749
FA x FAMILY
-0.2314
-3.406 ***
-0.0199
-0.226
-0.0621
-0.764
CASH x FAMILY
-0.5181
-3.004 ***
0.0309
0.205
-0.0125
-0.085
MB x FAMILY
0.0006
0.371
0.0021
1.483
0.0025
1.932
RISK
0.0062
1.250
0.0005
0.147
0.0004
0.114
PROF
-0.0060
-0.953
-0.0190
-2.997 ***
-0.0183
-3.026 ***
*
NDTAX
0.0052
0.423
-0.0223
-2.344
**
-0.0195
-2.067
**
AGE
-0.0050
-0.406
0.0902
3.632 ***
0.0479
2.460
**
BOARD
-0.0701
-1.506
-0.739
0.0214
4.203 ***
-1.122 -0.139
-0.0924
FS
-0.0812 -0.0024
0.0199
1.882
Industry dummy Year dummy
Yes Yes
N
583
583
583
Adjusted R
0.093
0.697
0.241
F-test
18.099
2
Hausman test
*
*** 18.811
**
***, **, *: Significantly different from zero at the 1%, 5%, 10% level
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- Financing -
SCI
Journal of Economy, Business and Financing
SCIENTIFIC PUBLICATION
www.sci-pub.com
TABLE V.
REGRESSION (2) RESULTS
This table shows the regression (2) results. FF are those in which the founding family or family member controlled 20% per cent or more equity, and was involved in the top management of the firm. FAMILY is a dummy variable that takes value 1 if a firm is familiar and zero otherwise. DEBT is the ratio of the book value of total liabilities to the sum of the book value of liabilities and equity. FA is computed as the ratio of fixed assets to the total assets. CASH is calculated as the sum of cash and marketable securities to the total assets. The MB is calculated by dividing the market price per share by the book value per share. RISK is the standard deviation of operating income before depreciation to annual sales. PROF is measured as net income scaled by sales. NDTAX is the ratio of operating income less interest expense less taxes to corporate tax rate, scaled the result by sales. BOARD is measured as the proportion of independent directors on the board. FS is measured as the natural logarithm of the book value of total assets of a firm. AGE is the natural logarithm of the difference between incorporation year and a fiscal year. OROA is the operating earnings divided by total assets. SG is the change in log sales. EMPLOY is the natural logarithm of the number of employees in the firm. COST is the interest expenses scaled by debt. CRISIS is a dummy variable equal to one in 2008, 2009 and 2010 years, and zero otherwise. The table presents the results estimated using pooled OLS, FEM and REM. The numbers in parentheses are the tstatistics corrected for heteroscedasticity using the White (1980) method. It reports the F test, a test for the equality of sets of coefficients, and the Hausman (1978) test, a test with H0: random effects are consistent and efficient, versus H1: random effects are inconsistent, in order to choose the most appropriate model for each particular sample. DEBT α β 1 FA i, t x FAMILYi, t β 2 CASH i, t x FAMILYi, t β 3 MBi, t x FAMILYi, t β 4 RISK i, t β 5 PROFi, t β 6 NDTAXi, t β 7 AGEi, t β 8 BOARDi, t β 9 FS β 10 OROAi, t β 11 SG i, t β 12 EMPLOY β 13 COST β 14 CRISIS β 15 INDUSTRYi, t β 16 YEAR i, t ε i, t
constant
OLS FEM Coefficient t-value Coefficient t-value 0.9461 6.432 *** 0.3726 1.168
FA x FAMILY
-0.0371
-1.069
CASH x FAMILY
-0.2558
-1.701
MB x FAMILY
0.0010
REM Coefficient t-value 0.4229 1.836
-0.1226
-1.381
-0.0829
-1.384
0.0006
0.004
0.0019
0.014
0.638
0.0029
2.106
0.0031
2.440
*
**
*
**
RISK
0.0059
1.226
-0.0023
-0.640
-0.0021
-0.589
PROF
-0.0038
-0.638
-0.0193
-2.951 ***
-0.0180
-2.929 ***
NDTAX
0.0105
0.805
-0.0143
-1.478
-0.0114
-1.171
AGE
0.0044
0.380
0.0763
2.780 ***
0.0387
1.964
*
BOARD
-0.1164
-2.471
**
-0.1565
-2.149
-0.1967
-1.827
*
FS
-0.0260
-2.810 ***
0.0223
1.091
0.0115
0.820
OROA
-0.2157
-4.751 ***
-0.2697
-4.535 ***
-0.2576
-4.697 ***
SG
0.1655
0.361
-0.0333
-0.105
-0.0286
-0.090
**
EMPLOY
0.0487
4.609 ***
-0.0420
-1.717
*
0.0091
0.566
COST
-1.1430
-7.555 ***
-0.2904
-2.021
**
-0.3965
-2.818 ***
CRISIS
0.0821
3.555 ***
0.0307
1.749
*
0.0434
2.602 ***
Industry dummy Year dummy N Adjusted R2 F-test Hausman test
Yes Yes 583 0.191 16.706
583 0.717
583 0.242
*** 38.536
***
***, **, *: Significantly different from zero at the 1%, 5%, 10% level
- 24 -
- Financing -
SCI
Issn:1339-3723,volume 2, issue 1, 2014
SCIENTIFIC PUBLICATION
www.sci-pub.com
TABLE VI.
REGRESSION (2) RESULTS FOR SUB-SAMPLES OF FF AND NFF
This table shows the regression (2) results for the sub-samples of FF and NFF, considering the best model. FF are those in which the founding family or family member controlled 20% per cent or more equity, and was involved in the top management of the firm. FAMILY is a dummy variable that takes value 1 if a firm is familiar and zero otherwise. DEBT is the ratio of the book value of total liabilities to the sum of the book value of liabilities and equity. FA is computed as the ratio of fixed assets to the total assets. CASH is calculated as the sum of cash and marketable securities to the total assets. The MB is calculated by dividing the market price per share by the book value per share. RISK is the standard deviation of operating income before depreciation to annual sales. PROF is measured as net income scaled by sales. NDTAX is the ratio of operating income less interest expense less taxes to corporate tax rate, scaled the result by sales. BOARD is measured as the proportion of independent directors on the board. FS is measured as the natural logarithm of the book value of total assets of a firm. AGE is the natural logarithm of the difference between incorporation year and a fiscal year. OROA is the operating earnings divided by total assets. SG is the change in log sales. EMPLOY is the natural logarithm of the number of employees in the firm. COST is the interest expenses scaled by debt. CRISIS is a dummy variable equal to one in 2008, 2009 and 2010 years, and zero otherwise. The table presents the results estimated using pooled OLS, FEM and REM. The numbers in parentheses are the t-statistics corrected for heteroscedasticity using the White (1980) method. It reports the F test, a test for the equality of sets of coefficients, and the Hausman (1978) test, a test with H 0: random effects are consistent and efficient, versus H1: random effects are inconsistent, in order to choose the most appropriate model for each particular sample. DEBT α β 1 FA i, t x FAMILYi, t β 2 CASH i, t x FAMILYi, t β 3 MBi, t x FAMILYi, t β 4 RISK i, t β 5 PROFi, t β 6 NDTAXi, t β 7 AGEi, t β 8 BOARDi, t β 9 FS β 10 OROAi, t β 11 SG i, t β 12 EMPLOY β 13 COST β 14 CRISIS β 15 INDUSTRYi, t β 16 YEAR i, t ε i, t
FF - REM Constant
Coefficient 0.3530
t-value 1.358
FA
-0.1306
-1.668
CASH
-0.1095
-0.801
MB
0.0030
2.398
RISK
-0.0002
-0.031
NFF - FEM
* ** **
Coefficient -0.1624
t-value -0.229
0.0186
0.175
0.6923
2.353
0.0030
1.430
-0.0055
-1.049
-0.0128
-1.705
-0.0006
-0.046
0.1023
2.554
-0.0506 0.0489
-0.465 1.157
-0.6916
-5.091
PROF
-0.0422
-2.312
NDTAX
-0.0385
-1.394
AGE
0.0623
2.455
BOARD
-0.2147
-1.604
FS
0.0236
1.380
OROA
-0.1570
-2.543
SG
-0.7944
-1.621
0.0405
0.086
EMPLOY
-0.0171
-0.813
-0.0680
-1.346
**
**
COST
-0.4130
-2.998
***
-0.2929
-0.326
CRISIS
0.0456
2.367
**
0.0058
0.182
Industry dummy
Yes Yes
Year dummy N
377
206
Adjusted R
0.696
0.777
F-test
10.231
Hausman test
16.180
2
***
13.654
***
173.5
***
**
* **
***
***, **, *: Significantly different from zero at the 1%, 5%, 10% level
- 25 -
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