The capital structure choices of family firms

The current issue and full text archive of this journal is available at www.emeraldinsight.com/0307-4358.htm

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254 Received 14 March 2013 Revised 10 August 2013 18 October 2013 Accepted 20 October 2013

The capital structure choices of family firms Evidence from Italian medium-large unlisted firms Pietro Gottardo and Anna Maria Moisello Department of Economics and Business, University of Pavia, Pavia, Italy Abstract Purpose – This paper aims to examine the determinants of capital structure of unlisted firms and how family governance-related factors impact on them. Design/methodology/approach – The authors analyze the relation between a set of capital structure determinants and leverage in a unique dataset of 3,006 family and non-family Italian medium-large firms (26,210 obs.), and a control sample of 2,730 small firms (14,780 obs.), using cross-section and panel procedures during 2001-2010. Findings – Capital structure choices of medium-large family firms are linked to balance-sheet variables not used in previous studies, i.e. net working capital and capital turnover, and are significantly affected by the need to maintain control and influence, a relevant dimension of family socioemotional wealth. Family firms are more levered than non-family firms, but the difference is economically and statistically significant only for medium-large companies. The presence of the family in active management increases leverage, as the family endowment in the firm is higher. Research limitations/implications – This research could be developed through an international comparison to check the influence of country-related regulatory issues and of national cultural aspects on family control and influence. Practical implications – The results can give public authorities important insights in order to facilitate firms funding specially in the current critical economic scenario and provide managers useful suggestions to support financial decisions. Originality/value – To the best of the authors’ knowledge, this is the first paper to explore the financial choices of a large dataset of medium-large private firms in a bank-based economy. Keywords Control, Capital structure, Private family firms Paper type Research paper

Managerial Finance Vol. 40 No. 3, 2014 pp. 254-275 q Emerald Group Publishing Limited 0307-4358 DOI 10.1108/MF-03-2013-0065

1. Introduction In the last decade numerous studies on family firms’ capital structure choices have been produced but the factors that drive these decisions are still elusive. Most theoretical and empirical research focuses on listed companies (Croci et al., 2011; Ampenberger et al., 2013; Setia-Atmaja et al., 2009; McConaughy et al., 2001; Anderson and Reeb, 2003a, b; Antoniou et al., 2008; Ellul, 2010; Mishra and McConaughy, 1999; Hagelin et al., 2006; King and Santor, 2008); relatively few studies analyze private firms and these mostly concern small-to-medium and small family businesses (Molly et al., 2012; 2010; Romano et al., 2001; Koropp et al., 2011; Blanco-Mazagatos et al., 2007; Schulze et al., 2001; Lo`pez-Gracia and Sa`nchez-Andujar, 2007; Coleman and Carsky, 1999). This literature shows that small family firms pursue different financial policies from their non-family peers. Large-scale evidences on private family firms’ capital structure choices are missing (Ampenberger et al., 2012), given the difficulty to obtain reliable data on

private firms. It is therefore desirable to develop studies in this area. We analyze a large dataset of Italian private family and non-family firms over the period 2001-2010 focusing on medium-large firms, for which ownership and management information is available. To the best of our knowledge this paper is the first to analyze financing decisions of medium-large private family firms in a bank-oriented economy. The capital structure, as argued by Antoniou et al. (2008) is heavily influenced by the environment and institutions, corporate governance and tax systems, capital market development and borrower-lender relationship. Italy has an underdeveloped stock market compared to Anglo-Saxon countries, with only 271 listed firms including banks, insurances and other financial holdings, a market capitalization that accounts just for 27 percent of GDP (2010), and a bank-oriented financial system with widespread relationship lending. Medium-large firms cover 51 percent of the Italian industrial firms turnover. Private firms account for 83 percent of the industrial value-added (2010), weighting much more heavily in the Italian economy with respect to peer countries like Germany and France. We share with these countries a very similar industrial structure, while the main differences are a larger proportion of small firms and a greater relevance of unlisted firms. Family firms represent 69 percent of the population of Italian medium-large private firms, confirming the prominent role of families in the Italian industrial structure. We find that medium-large private family and non-family firms show differences in capital structures analogous to those found in previous studies for listed firms. Our empirical evidence indicates that family and non-family firms present different characteristics in terms of leverage, size, firm market share, profitability, ownership concentration and ownership involvement in active management; such differences are significant also at the industry level. The regression analyses, show how capital structure determinants affect leverage in different ways, depending on whether a company is family controlled or not. Our findings point out that family firms financing decisions do not depend only on economic and financial factors but are significantly affected by ownership and governance-related factors, that is the need to maintain the family endowment in the firm retaining control and influence. We find that family firms have higher leverage ratios than non-family firms, but the differences are economically and statistically significant only for medium-large companies. Moreover, the family presence in active management leads to significantly higher levels of leverage, as the family endowment in the firm, in terms of degree of family identification, influence and personal investment in the firm is higher. The reminder of the paper is organized as follows: Section 2 reviews some of the more recent papers focusing on the characteristics of family firms; Section 3 reviews the capital structure literature on family firms; Section 4 describes dataset, sample selection criteria and variables; Section 5 provides descriptive statistics; Section 6 shows the correlation, the regression analysis and the discussion of the results; Section 7 concludes and provides avenues for future research. 2. The theory and the empirical literature on family firms Principal-agent theory assumes that the separation of ownership and control (Fama and Jensen, 1983) causes conflicts of interest and asymmetric information between the owner and whoever exercises control. The latter (agent) tends to behave in his or her own interest rather than the interest of the former (principal), giving rise to a moral hazard (Ho¨lmstrom, 1979) and adverse selection ( Jensen and Meckling, 1976).

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The personal ownership involvement discourages managers (agent) from acting against ownership (principal) interest through the misallocation of resources (Schulze et al., 2002). As a result, Daily and Dollinger (1992) claim that family firms need comparatively lower costs for their control, but Schulze et al. (2001) observe that different types of agency cost weigh on family firms, related to altruistic behavior of the owner-manager towards employed family members, to management entrenchment and to shareholder expropriation. Gomez-Mejia et al. (2007) developed a general “socioemotional wealth model” to explain family firms peculiarities. The model is based on the behavioral agency theory (Wiseman and Gomez-Mejia, 1998; Gomez-Mejia et al., 2000), which integrates elements of prospect theory, behavioral theory of the firm and agency theory. This theory focuses on the concept that firms decisions depend on the perspective of the firm’s dominant principals, which are concerned with the preservation of the endowment built up in the firm. Fundamental to family firm’s principals is the conservation of the socioemotional endowment, which refers to “the stock of affect related value that a family derives from its controlling position in a particular firm” (Berrone et al., 2012). During the last decade the financial economic literature has shown an increasing interest in different aspects of family firms. A first stream of studies focused on the comparison between family and non-family firms’ performance, leading to mixed results (Anderson and Reeb, 2003a, b; Villalonga and Amit, 2006; Barontini and Caprio, 2006; Sacristań-Navarro et al., 2011). The studies on investment choices pointed out that many family firms follow long-term investment strategies in order to perpetuate their business for future family members (Gallo and Vilaseca, 1996; McConaughy and Philipps, 1999) and they pursue business stability rather than rapid growth and risky investment strategies (Harris et al., 1994; Tagiuri and Davis, 1992). Other research has studied family influence on firms’ strategic behavior in general (Miller et al., 2011), or has focused on dividend policy (Gugler, 2003 or Correia da Silva et al., 2004), or on diversification decisions (Anderson and Reeb, 2003b or Gomez-Mejia et al., 2010). There is some evidence that family firms’ corporate decision making pursues very conservative strategies (Klasa, 2007; Bauguess and Stegemoller, 2008; Sraer and Thesmar, 2007; Caprio and Croci, 2011) but not much is known about the nature of their capital structure decisions or whether they differ from those of non-family firms. 3. The capital structure of family firms An important part of the research studied the choices of family firms capital structure by testing the main theories on firms’ capital structure decisions, pecking order theory (POT) and trade-off theory (TOT). According to POT, firms do not pursue any leverage ratio target and they meet their capital needs via the following order of preference: accumulated earnings, short-term borrowing, long-term borrowing and equity issuance (Donaldson, 1961; Myers and Majluf, 1984). TOT, on the other hand, assumes that firms have a leverage ratio target determined by balancing the costs and benefits of debt and equity (Titman, 1984). The international empirical evidence gives mixed results but the POT appears to fit better firm behavior, both in family and non-family businesses (Rajan and Zingales, 1995; Shyam-Sunder and Myers, 1999; Lo`pez-Gracia and Sa`nchez-Andujar, 2007; Frank and Goyal, 2009; Sheikh and Wang, 2011; Graham and Leary, 2011).

Mishra and McConaughy (1999) point out that US family firms present a significantly lower level of debt. Further, they show that this is related to the peculiarities of the founding family rather than to the level of managerial ownership. Anderson and Reeb (2003b) find that US family firms have a higher level of debt than their non-family counterparts and Ellul (2010), analyzing an international dataset of companies from 36 countries, confirms these results. King and Santor (2008) and Setia-Atmaja et al. (2009) find higher debt ratios in Canada and Australia. In Europe, Ampenberger et al. (2013), studying a dataset of listed German companies, and Margaritis and Psillaki (2010), focusing on French firms from the manufacturing industry, find the opposite result. Hagelin et al. (2006) find that, on average, Swedish family-controlled firms do not rely on less debt than non-family firms do. Croci et al. (2011), for a European dataset, come to a different conclusion in finding that family firms have a preference for debt financing and a strong aversion to equity financing consistent with the value-of-control hypothesis. This argument is also supported by the studies conducted by Brav (2009) who shows that private UK firms, unlike public ones, rely almost exclusively on debt financing and concludes that control motives affect the choice of external financing. Furthermore, founding families view the firm as an asset that will be handed down to future generations (Chami, 1999) and this consciousness enhances the control motivation. Family firms’ high debt ratios are also related, as Croci et al. (2011) argue, to their cost of debt (lower than for non-family firms) and cost of equity (higher than for non-family firms). Moreover, long-term orientation facilitates connections and relationships between family businesses and stakeholders, among them, external financiers (Carney, 2005; Haynes et al., 1999). Anderson et al. (2003) find that family ownership reduces the cost of debt financing as a result of lower agency conflicts between equity and debt holders. This means bondholders perceive family ownership as a corporate structure that protects their interests well. In line with these results, Croci et al. (2011) show that family firms have a preference for low-risk investments and that credit markets provide more long-term debt to family firms, indicating that markets view their investment decisions as less risky and that the appeal of debt financing in family firms is related to the adverse selection costs of equity. This is caused by information asymmetries related to agency problems, which might develop vertically between owners and managers, as well as horizontally between owners and family firm shareholders (Villalonga and Amit, 2006). Since family blockholders tend to have low diversification (Anderson and Reeb, 2003b), family firms have to manage the trade off between increasing leverage to retain control and reducing leverage in order to minimize the firm’s risk. Molly et al. (2010), for a sample of small-medium businesses point out the effect of succession on leverage and show that a transfer from the first to the second generation negatively influences the debt ratio of the firm, whereas in successions between later generations this effect is reversed. Moreover, Molly et al. (2012) investigate the intergenerational effect on growth and corporate structure finding that next-generation firms grow slower because they prefer to forego part of their growth rather than risk the loss of family control due to the increased use of debt. Gonza´lez et al. (2013), by studying a sample of private and listed firms belonging to Columbian business groups stress the tradeoff between two distinct motivations that determine the capital structure of family firms: risk aversion pushes firms toward


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lower debt levels, but the need to finance growth without losing control makes family firms to prefer higher debt levels. According to the logic of the socioemotional wealth we expect that family firms would be prone to increase leverage in order to maintain control and family influence, as it is a fundamental dimension of the socioemotional endowment (Gomez-Mejia et al., 2007). Socioemotional wealth takes priority over risk aversion (Berrone et al., 2012). On these bases we hypothesize family businesses to be more levered than non-family firms and we expect this feature to be particularly relevant in the case of medium-large firms, which have already faced the problem of how to finance the growth and manage the trade-off between equity and debt. The empirical findings of Gomez-Mejia et al. (2007), show that a stronger role of the family makes more likely the firm to strive to protect its socioemotional wealth. Families can actively exert control over strategic firm decisions appointing a family member as CEO and/or chairman of the board, Family identification, influence and personal investment in the firm increase, so we expect the firms with family members involved in active management to be more levered than family firms professionally managed. 4. Data and variable definition We analyze a unique dataset extracted from AIDA (Italian Digital Database of Companies), which is the Italian provider of the Bureau Van Dijk European Database, and which constitutes the most complete and reliable financial information source about non-public companies. AIDA database contains detailed accounts following the scheme of the 4th EU Directive for up to ten years, shareholders, anagraphic and industrial sector information and, in the case of medium-large firms, CEO and board composition. We selected non-financial firms with the following characteristics: a registered company office in Italy, active in the year 2010 in the form of limited company (Societa` a Responsabilita` Limitata – SRL), or company limited by shares (Societa` per Azioni – SPA), and revenues of over e70,000,000 in one or more years from 2001 to 2010 inclusive; in total we isolated 3,006 firms. We chose only non-financial companies to avoid the effect of financial sector regulation and peculiarities on firms’ financing decisions and we established the minimum revenues to ensure the availability of the basic data for our analysis. We hand-checked all balance-sheet data from the period 2001-2003 in order to correct any errors resulting from the transition from Lira to Euro. We also checked all ratios for outliers to avoid them distorting the analysis results. We completed the database by entering the data on ownership and governance that were not in the AIDA database, taking them from the Italian Chamber of Commerce Register. We define a firm as being a family firm when the founder, or descendents of his or her family (either by blood or through marriage), is a blockholder, individually or as a group, and we assume an ownership cut-off point of 20 percent. Previous research has used different cut-off points but our choice is consistent with a large body of literature: La Porta et al. (1999), Faccio and Lang (2002), Dahya et al. (2008), Ellul (2010) and Croci et al. (2011). La Porta et al. (1999) and Faccio and Lang (2002) also use 20 percent as a threshold, other authors employ 20 and 25 percent (Andres, 2008; Franks et al., 2010). Consequently, our database of medium-large firms is made up of 2,077 family and 929 non-family firms. In the regression analysis, in order to better check our hypothesis, we use two family-firm definitions: the first is ownership-based,

as previously specified, the second based on the presence (family firms) or non presence (non-family firms) of family members in active management, without an ownership threshold. To better highlight the peculiarities of medium-large family firms we also analyze a control dataset of small businesses, extracting from AIDA a random sample of 3,000 non-financial firms with revenues in the range e5-10 millions. On the basis of the available shareholders information we retained 2,730 firms: 2,082 family and 648 non-family businesses. The AIDA database does not contain information on CEO and board composition for this size class. To define a family firm we apply the above classification criterion assuming an ownership cut off point of 50 percent. This choice is consistent with previous studies on small family firms (Molly et al., 2012; Blanco-Mazagatos et al., 2007; Lo`pez-Gracia and Sa`nchez-Andujar, 2007; Coleman and Carsky, 1999). In our descriptive statistics, correlation analysis and regression models, we use a definition of leverage corresponding to book leverage, as it is widely used in existing literature: Book leverageðBLevÞ ¼

Longterm financial debts þ Financial debt in current liabilities Total assets

Various firm characteristics have been found to affect capital structure decisions (Rajan and Zingales, 1995; Lemmon and Zender, 2010; Ellul, 2010; Marchica and Mura, 2010), especially profitability, asset tangibility, size, liquidity and depreciation. We measure profitability with the ratio of operating income to total assets; tangibility is the ratio of net property, plant and equipment to total assets. We use two measures of size: the natural logarithm of sales – Log(Sales) – and the total assets value. Liquidity is determined by the ratio of liquid assets to total assets; depreciation is the ratio of depreciation to total assets. To these variables, used in previous research, we add: firm’s market share, cash flow, net working capital, inventory, capital turnover. To measure a firm’s market share we match firms to industries requiring a non-missing ATECO code in the database and we determine market shares on firms’ revenue basis. In doing this we use all available firms with revenues of over e70,000,000 as of 2010 and exclude firms with missing revenues. Cash flow, is measured with the ratio of operational cash flow to total assets; net working capital is computed as the difference between current assets and current liabilities related to total assets; inventory is the ratio of its value to current assets; and capital turnover is the ratio of net sales to capital employed. In order to describe and analyze firms’ governance features we use family ownership, which measures the percentage of equity owned by the family, and management ownership as the percentage of equity owned by a firm’s management. Moreover, we use a dummy variable, firms foreign owned, which indicates if the firm is under control of a foreign firm. 5. Descriptive statistics Table I provides descriptive statistics for firm-level financial and ownership characteristics for medium-large and small firms samples, for the family and non-family businesses. The last column shows the results of the t-test, which verifies the significance of differences of the two samples’ mean values. The medium-large firms descriptive statistics show a highly significant difference in the financial


22.10 11.39 1.26 4.06 4.94 3.00 12.59 75.26 3.59 3.14 23.72 1.23 95.36 16.65 0 0 6.29

2 1.15 1.50 2.85 * * 1.14 3.17 * * 2.45 * 2 0.79 2.68 * * 2 0.56 2 0.10 2 9.49 * * 0.75

0.26

5.51

t-test 14.08 8.90 0.06 4.98 4.96 2.95 12.28 5.22 14.80 4.41 15.28 1.38 0 – –

Non-family Family firms firms Mean Median Mean Median

26.18 25.52 18.11 14.12 211.50 * * 18.83 14.71 19.06 14.98 18.11 8.90 8.90 8.89 8.89 8.90 11.44 11.31 11.90 11.64 10.90 * * 0.10 0.06 0.09 0.06 0.10 1.74 1.20 2.01 1.31 2.94 * * 5.08 4.23 4.14 3.53 2 3.20 * * 5.80 4.58 5.58 4.46 6.51 5.91 5.08 4.68 4.58 2 1.16 5.78 4.73 5.58 4.67 6.41 3.52 2.95 4.02 3.21 3.77 * * 3.53 2.89 3.45 2.87 3.79 17.25 14.17 16.03 9.20 2 1.74 21.24 16.01 21.41 16.88 20.69 6.72 5.45 6.34 5.50 7.95 158.35 66.79 559.69 104.58 3.59 * * 2.77 2.74 4.19 5.86 0.92 17.64 15.66 17.77 15.95 17.23 5.59 3.27 4.40 2.73 2 0.43 8.20 4.21 8.21 4.09 8.16 26.04 22.74 27.98 24.95 19.83 28.99 26.56 20.07 17.02 211.45 * * 1.47 1.24 1.41 1.21 2 1.71 1.52 1.33 1.52 1.31 1.55 90.99 100 12.46 0 72.95 100 95.65 100 0 54.65 60 3.35 0 – – – – – 16.82 0 1.62 0 – – – – – 9.34 0 59.01 100 2.97 1.15 8.80 5.67 5.54 5.66 5.57 5.70 6.87 6.15 7.79 6.83 2.58 * * 2,077 929 2,730 2,082 648

t-test

All firms Mean Median

Notes: Significant at: *5 and * *1 percent levels; the table reports the mean and median of various firm characteristics for family and non-family firm’s sub-samples and separately for medium-large and small firms; the medium-large firms sample consist of all non-financial firms in the BVD Aida database, covering the time span 2001-2010, extant in 2010, with sales . 70 ml e, the small firms sample represents firms with sales in the range 5-10 ml e; book leverage is the ratio of total financial debt to total assets; firm size is measured as the log of sales, we report also total assets in ml e; market share is the ratio of 2010 firm revenues to industry total revenues; profitability is the ratio of operating income before depreciation to total assets; cash flow, is the ratio of operational cash flow to total assets; depreciation is the ratio of depreciation of physical and intangible assets to total assets; tangibility is the ratio of net property, plant and equipment to total assets; net working capital is the ratio of current assets minus current liabilities to total assets; liquidity is the ratio of liquid assets to total assets; inventory is the ratio of inventory to current assets; family ownership is the sum of the voting rights of all family members; management ownership is the sum of direct and indirect voting rights in % owned by all top executives; firms foreign owned indicates to what extent the firms in the sample are controlled by a foreign firm capital turnover is the ratio of sales to capital employed; tests of the differences in means between family and non-family firms are displayed for the medium-large and small firm’s samples

Book leverage 23.71 Log (sales) 11.58 Firm market share 1.83 Profitability 4.74 Cash flow 5.49 Depreciation 3.67 Tangibility 16.88 Total assets 280.96 Net working capital 3.06 Liquidity 5.55 Inventory 26.22 Capital turnover 1.45 Family ownership 63.36 Management ownership 39.01 NoEx board ownership 12.18 Firms foreign owned 24.44 Cost of debt 7.10 Obs. 3,006

Table I. Descriptive statistics All firms Mean Median

Small firms

260

Non-family Family firms firms Mean Median Mean Median

Medium-large firms

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structure of family and non-family firms. The data suggest an higher book leverage for family firms, with a mean of 26.18. For non-family firms, the mean book leverage is 18.11; for all firms the mean value is 23.71. This result shows a significant difference in the financial choices of private family and non-family firms and confirms the result of empirical studies carried out by Anderson and Reeb (2003b), Ellul (2010), King and Santor (2008) and Setia-Atmaja et al. (2009) showing that firms with entrenched managers select less risky investments and use more debt finance; it is also consistent with theoretical papers (Fulghieri and Suominen, 2012) that argue that poor corporate governance (entrenched firms) can lead to greater debt financing. Both the size measures adopted, sales and total assets, present lower values for the family firms. They present a sales mean of 11.44 while, for non-family firms, the figure is 11.90; for the entire panel it is 11.58. The total assets mean is e158.35 million for family firms, e559.69 million for non-family firms and e280.96 million for all firms. These results, statistically significant at the 1 percent level, taking into account the higher book leverage, may indicate that family firms are growing with an annual median growth rate of 8.4 percent (4.5 percent for non-family firms) but this growth is conditioned by the need to retain control. The market share of family firms is, on average, lower (1.74) than non-family firms’ (2.01) although the median values are quite close: 1.20 for family and 1.31 for non-family firms. Even if family firms present, on average, a lower firm market share compared to the non-family control sample, they have a higher profitability level: the family mean value is 5.08 and non-family is 4.14. The higher level of profitability could be due to a higher capital turnover ratio: 1.47 for family firms and 1.41 for non-family ones, but this difference in the means is not statistically significant. The results, in terms of profitability, are in contrast with the evidence described by Ellul (2010), on an international dataset of listed companies, but are in line with those of Barontini and Caprio (2006) on a dataset of publicly-traded corporations from 11 countries in continental Europe. Andres (2008) finds, for a dataset of German companies, that performance is higher for family firms than non-family when the family has board representation. Anderson et al. (2003) observe that, when family members serve as CEOs, performance is better than with outside CEOs. The high degree of family involvement in active management in our dataset is likely to positively affect profitability levels. As we can see, family firms’ ownership has a high degree of involvement in the management as executive board ownership mean value is 54.65, while non-executive board member ownership mean value is 16.82. On the contrary, non-family firms present a very low degree of management entrenchment as executive board ownership mean value is 3.35 versus a non-executive board ownership of 1.62 and more than half of these firms do not have owners involved in the management or on the non-executive board. Family ownership is very concentrated; in fact we observe a mean value of 90.99. Family firms, unlike non-family ones, are characterized by a strictly national ownership: only 9.34 percent are foreign owned (versus a value of 59.01 percent for non-family firms). From the assets characteristics we observe that tangibility shows a higher mean value for family (17.25) than for the non-family firms (16.03) (Anderson et al., 2010). We observe lower levels of depreciation for family (3.52) than for non-family firms (4.02). This effect is likely to be due to the weight of land and buildings, not subject to depreciation, on the aggregate fixed assets or to a conservative plant and machinery management with longer depreciation periods. However, we do not have the fixed asset


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data in disaggregated form to check these hypotheses. A higher liquidity ratio characterizes the family firms, which reach 5.59 versus a value of 4.40 for non-family, but, for the former, a higher level of current debt limits the ability to cope with monetary liabilities, as the net working capital is 2.77 for family firms and 4.19 for non-family. Moreover, inventory presents a higher weight on working capital for family firms, as the mean value is 28.99 versus a value of 20.07 for non-family ones. This result could be due to basic inventory control systems related to the conservative behavior of family firms and the high level of family involvement in management: on the one hand it could imply the employment of non-professional family members or make the company less attractive for professional, highly-qualified managers, as Schulze et al. (2001) study demonstrate. Coherent with Anderson et al. (2003), the cost of debt is lower in family firms (with a mean of 6.87) than in non-family ones (with a mean of 7.79), as credit markets perceive the former less risky than the latter (Croci et al., 2011). These statistics indicate that medium-large family and non-family firms have different characteristics both from the economic and financial magnitudes point of view and from that of governance and that they are statistically significant at the 1 percent level for financial structure (leverage), profitability, size (log(sales) and total assets), firm market share, inventory, depreciation and cost of debt. Analyzing the small firms statistics we see that the main differences between family and non-family firms tend to persist. Nevertheless, small family businesses present a different balance sheet structure in terms of higher liquidity, tangibility and net working capital. As we expected, we observe that small family are more levered than non-family ones, even if the difference is not statistically significant – this result is in line with the findings of Coleman and Carsky (1999) – but at the same time we see that small firms are much less levered than their medium-large counterparts. Small firms are almost all domestic regardless of family ownership. The characteristics of medium-large firms could be related to industry peculiarities and the results could be affected by the distribution across industries of family and non-family firms’ and industries weight in the database. As Cohen and Yagil (2010) suggest, industries may have specific financial needs caused by different operating conditions and also an imitation effect may occur within them; so, in Table II, we show the descriptive statistics computing the variables’ mean value for each industry. As Italian ATECO codes up to four digits are coherent with European standards, we base our classification on four-digit ATECO codes and we assign firms to 27 industries. Single-industry family firms, on average, tend to maintain the characteristics observed in the entire dataset. Family firms presents a higher book leverage in 85 percent of industries, they are smaller in 92 percent, more profitable in 69 percent, have a higher level of depreciation in 69 percent and a higher inventory in 77 percent. Family firms’ net working capital mean value is higher in 42 percent of industries but the median value confirms the tendency observed at the entire dataset level, in fact family firms’ net working capital median value is higher in 65 percent of industries. 6. The determinants of family firm capital structure. Correlation, regression analysis and discussion of the results Table III summarizes the correlation analysis results. Book leverage shows a significant relation with both economic-financial and governance related factors.

19.08 35.7 31.80 24.88 26.27 21.13 19.99 25.18 21.10 25.86 16.64 24.30 27.40 19.89 22.42 21.27 28.46 30.80 27.99 32.96 17.20 22.32 24.75 27.73 27.39 31.43 19.66

Apparel Food Paper Chemicals Wholesale and retail trade Construction Printing, publishing Computer and electronics Household and other elect. Coal, oil and gas Farmaceutical Beverage Rubber Commun., web and data processing Building materials and glass Machinery and computer equip. Metal industries Wood and furniture Transport. equip. and related services Leather and leather product Motion, theatre and entert. Lodging and eating drinking services Utilities Textile Transportation and related services Miscellaneous manufact. Other business services

11.64 11.48 11.73 11.52 11.45 11.00 11.64 11.25 11.51 12.57 11.97 11.40 11.42 11.38 11.46 11.40 11.44 11.30 11.51 11.49 11.82 11.28 11.37 11.43 11.45 11.45 11.57

LnS 8.18 4.47 3.20 4.86 5.48 3.08 3.66 5.26 7.33 5.71 9.30 9.48 5.17 3.53 5.46 6.13 5.40 3.68 3.41 6.40 0.77 4.82 4.29 4.00 4.97 5.31 2.86

Prof 3.90 3.62 5.21 4.36 3.08 1.59 5.07 3.82 3.61 4.28 4.83 3.72 5.54 4.02 4.73 3.45 4.26 3.64 2.60 3.17 10.64 3.54 2.69 4.89 3.94 2.63 3.49

18.01 23.15 25.37 2.47 2.67 1.54 23.92 5.85 12.27 5.75 14.72 0.19 0.10 29.17 4.96 9.79 6.26 22.18 3.75 8.80 214.15 216.63 26.17 10.97 210.99 8.84 20.92

Family firms Depr NWC 29.30 29.97 25.15 22.39 26.33 47.13 10.79 21.77 26.00 17.62 26.21 22.98 23.34 5.83 28.02 31.99 32.28 32.03 49.11 35.94 1.87 8.67 10.30 35.37 5.31 28.01 29.07

Inv 51 153 22 73 526 131 23 24 53 13 27 18 41 18 56 140 225 38 173 31 9 18 55 36 98 16 9

n 22.30 24.96 15.10 19.20 18.08 19.51 20.98 16.71 18.80 28.92 23.65 30.31 24.12 11.01 13.34 16.59 19.89 5.53 18.49 26.98 15.58 11.04 17.45 – 13.89 31.05 7.80

BLev 11.75 11.88 11.96 11.71 11.79 11.53 12.22 11.84 11.88 13.42 12.12 12.05 11.71 12.00 11.78 11.64 11.66 11.35 12.32 11.19 12.06 11.70 12.28 – 12.06 11.51 11.55

LnS 23.68 23.45 18.78 21.74 19.88 21.88 12.19 24.77 24.71 23.40 25.81 20.67 21.62 4.35 28.97 31.83 32.72 18.49 31.48 20.95 0.50 4.12 4.55 – 4.03 33.52 26.97

3.05 6.02 5.46 5.22 2.84 2.66 3.62 5.62 3.70 5.04 4.77 4.92 5.56 7.43 5.12 3.81 4.10 2.61 4.16 4.37 16.08 4.79 3.25 – 3.56 4.06 2.58

5.08 21.40 7.16 6.65 8.16 23.18 3.92 12.34 10.98 29.45 6.33 214.22 25.10 6.19 3.98 7.02 4.66 19.16 2.58 21.43 217.15 23.58 26.99 – 5.64 21.85 25.34

0.46 21.09 4.54 5.07 5.05 3.85 4.90 3.86 4.57 1.55 8.36 1.97 4.56 4.46 6.85 4.00 2.09 5.83 2.00 4.39 22.29 3.87 3.00 – 2.82 2.36 7.11

Inv

Non-family firms Prof Depr NWC 5 19 10 57 228 42 12 32 42 13 29 10 26 33 15 72 42 3 59 4 5 9 93 0 53 4 12

n

Notes: The table reports the industrial distribution and the mean of various firm characteristics for medium-large family and non-family firm’s sub-samples; the sample consist of all non-financial firms in the BVD Aida database, covering the time span 2001-2010, extant in 2010, with sales . 70 ml e; book leverage (BLev) is the ratio of total financial debt to total assets; firm size (LnS) is measured as the log of sales; profitability (Prof) is the ratio of operating income before depreciation to total assets; Depr is the ratio of depreciation of physical and intangible assets to total assets; net working capital (NWC) is the ratio of current assets minus current liabilities to total assets; inventory (Inv) is the ratio of inventory to current assets; n represents the number of family or non-family firms in each of the 27 two-digit industries listed in the table

BLev

Sector


Table II. Industrial distribution of medium-large firms

Table III. Pearson’s correlation matrix, medium-large firms

2 0.078 (0.001)

2 0.126 (0.001) 0.078 (0.001) 20.176 (0.001) 0.013 (0.48) 0.028 (0.12)

Prof 20.087 (0.001) 0.076 (0.001) 0.144 (0.001) 20.084 (0.001)

Depr 0.133 (0.001) 0.008 (0.64) 0.024 (0.18) 20.011 (0.56) 0.401 (0.001)

Tang 20.462 (0.001) 20.041 (0.025) 20.021 (0.25) 0.344 (0.001) 20.154 (0.001) 20.359 (0.001)

NWC 20.284 (0.001) 20.097 (0.001) 0.046 (0.012) 0.178 (0.001) 20.036 (0.048) 20.140 (0.001) 0.222 (0.001)

Liq 0.100 (0.001) 20.086 (0.001) 20.069 (0.001) 20.037 (0.040) 20.104 (0.001) 20.020 (0.26) 0.017 (0.34) 20.118 (0.001)

Inv 2 0.071 (0.001) 0.078 (0.001) 2 0.003 (0.88) 0.144 (0.001) 2 0.179 (0.001) 2 0.241 (0.001) 0.165 (0.001) 0.125 (0.001) 2 0.070 (0.001)

CTR

0.174 (0.001) 2 0.227 (0.001) 2 0.054 (0.003) 0.058 (0.002) 2 0.055 (0.003) 0.051 (0.006) 2 0.013 (0.49) 0.017 (0.36) 0.185 (0.001) 0.042 (0.022)

FOwn

0.156 (0.001) 2 0.198 (0.001) 2 0.073 (0.001) 0.084 (0.001) 2 0.068 (0.001) 0.026 (0.15) 0.017 (0.34) 0.041 (0.026) 0.171 (0.001) 0.041 (0.026)

MOwn

Notes: The table reports Pearson correlations and p-values for the full sample of medium-large firms; the sample consist of all non-financial firms in the BVD Aida database, covering the time span 2001-2010, extant in 2010, with sales . 70 ml e, it comprises 3,006 firms; book leverage (BLev) is the ratio of total financial debt to total assets; firm size is measured as the log of sales (LnS); market share (FMS) is the ratio of firm revenues to industry total revenues; profitability (Prof) is the ratio of operating income before depreciation to total assets; depreciation (Depr) is the ratio of depreciation of physical and intangible assets to total assets; tangibility (Tang) is the ratio of net property, plant and equipment to total assets; net working capital (NWC) is the ratio of current assets minus current liabilities to total assets; liquidity (Liq) is the ratio of liquid assets to total assets; inventory (Inv) is the ratio of inventory to current assets; capital turnover (CTR) is the ratio of sales to capital employed; family ownership (FOwn) is the sum of the voting rights of all family members; management ownership (MOwn) is the sum of direct and indirect voting rights in % owned by all top executives; p-value in parentheses

CTR

Inv

Liq

NWC

Tang

Depr

Prof

FMS

LnS

BLev

FMS

264

LnS

MF 40,3

Book leverage is negatively and significantly related with size, firm market share, profitability, depreciation, net working capital, liquidity, and capital turnover ratio and is positively related to tangibility, inventory, family ownership and management ownership. This is to say that, on the one hand, as size, the ability to meet monetary commitments and generate internal resources for financing increase, leverage decreases. On the other hand, as inventory grows, short-term debt and, consequently, leverage increases. The findings of the correlation analysis conducted on the entire dataset are coherent with POT predictions (Romano et al., 2001). Focusing on family firms, we observe that the correlation analysis on governance related factors shows that leverage tends to increase as family ownership grows and this result is consistent with Gonza´lez et al. (2012) findings for a sample of large firms. Moreover, we observe that, consistently with King and Santor (2008), family ownership is positively related with leverage and profitability consistently with the descriptive statistics which show that family firms are more profitable and, as we expected, more levered than non-family ones. Family firms’ financing decisions cannot be explained purely by POT because they are also affected by governance-related motivations (Romano et al., 2001). These findings also confirm those of Ellul (2010), who interprets family firms’ higher leverage as a means, for the controlling family, to maintain control of the firm’s management and those of Gomez-Mejia et al. (2007) which show that family firms exhibit a stronger preference to retain owner control of the organization than non-family firms. To test our hypothesis we carry out a cross-sectional analysis. We run a model specification for family firms selected on an ownership basis, family firms identified according to family members’ presence in active management, non-family firms defined through ownership criterion and non-family firms identified as those with no family members in active management. In the regression analysis we use variables commonly used in previous research on firms’ capital structure, that is their size, growth, profitability, tangibility and liquidity. As these variables explain only a small fraction of leverage variability, we add inventory, capital turnover, family in active management, and net working capital. Table IV shows the cross-sectional analysis results with heteroskedasticity robust standard errors as the White test reported in the table always rejects the hypothesis of homoskedasticity of the residuals. Consistent with Rajan and Zingales’ (1995)results on German listed companies, we observe that size, for medium-large non-family firms have a significant negative relation with leverage, for family firms the negative coefficient is not significant. Conversely, for small family firms we find a positive relation, significant only for the family firms (Blanco-Mazagatos et al., 2007; Lo`pez-Gracia and Sa`nchez-Andujar, 2007; Gallo and Villaseca, 1996; Romano et al., 2001). The relation between profitability and leverage is positive for medium-large firms, as for Croci et al. (2011) sample of listed firms, but never significant. The coefficient is negative and significant only when the model specification does not include net working capital[1]. On the contrary small family firms present a significant negative relationship, confirming the results of Lo`pez-Gracia and Sa`nchez-Andujar (2007) and Molly et al. (2010), respectively, for small and small to medium firms. A firm’s market share also presents a negative relation with leverage and it is stronger in family firms, but this results holds only for medium-large firms. For family firms tangibility is negatively related with leverage and the significant negative


Table IV. Cross-sectional analysis of family ownership and capital structure

0.391 20.009 21.136 0.030 20.072 20.638 0.005 0.006 0.048 20.361 2,053 129.40 0.31

(6.31) (2 1.64) (2 7.05) (0.64) (2 3.00) (2 11.21) (0.42) (1.55) (6.16) (2 21.48)

(4.38) (0.61) (25.35) (0.74) (23.00) (211.98) (20.27) (2.83)

20.337 (216.51) 1,521 88.73 0.32

0.299 0.004 21.101 0.052 20.081 20.799 20.003 0.013

FMngt¼ 1 0.400 2 0.016 2 0.343 0.024 2 0.047 2 0.281 0.103 2 0.008 0.060 2 0.301 920 221.51 0.27

(6.72) (23.28) (21.35) (0.26) (21.12) (25.09) (3.07) (21.18) (1.55) (24.78)

(8.61) (2 4.12) (2 4.14) (1.01) (2 0.92) (2 4.49) (3.96) (2 1.04)

20.332 (2 6.82) 1,484 257.20 0.28

0.419 20.016 20.747 0.062 20.030 20.245 0.098 20.005

Non-family firms Own , 20 FMngt¼ 0

(24.01) (5.59) (0.84) (22.49) (23.28) (28.73) (8.18) (216.98)

(2 0.96) (1.74) (2 1.04) (1.40) (2 0.54) (2 5.07) (4.37) (2 8.51) 20.329 (2 9.49) 648 38.56 0.34

20.419 0.085 25.215 0.083 20.025 20.321 0.159 20.064

Small firms Non-family firms

2 0.292 (213.59) 2,082 108.51 0.37

2 0.981 0.153 2.725 2 0.153 2 0.078 2 0.314 0.143 2 0.086

Family firms

Notes: The medium-large firms sample consist of all non-financial firms in the BVD Aida database, covering the time span 2001-2010, extant in 2010, with sales . 70 ml e, e, the small firms sample represents firms with sales in the range 5-10 ml e; the table presents parameter estimates from crosssectional regressions of book leverage; the t-statistics in parentheses are computed using standard errors robust for heteroskedasticity, as the White test statistics shows that the null hypothesis of homoskedasticity is rejected at the standard 1 percent level; family firms are defined in two ways: (1) familyowned firms in which one or more family members are individually or as a group blockholders, with a minimum threshold ownership of 20 percent; (2) family controlled and managed firms whose CEO and eventually some other executive director is a member of the blockholding family; book leverage (BLev) is the ratio of total financial debt to total assets; firm size is measured as the log of sales (LnS); market share (FMS) is the ratio of firm revenues to industry total revenues; profitability (Prof) is the ratio of operating income before depreciation to total assets; tangibility (Tang) is the ratio of net property, plant and equipment to total assets; liquidity (Liq) is the ratio of liquid assets to total assets; inventory (Inv) is the ratio of inventory to current assets; capital turnover (CTR) is the ratio of sales to capital employed; FMngt is a dummy that in family firms takes value one if one or more shareholders in active management are family members and zero otherwise, in non-family firms it takes value one if a shareholder is in active management and zero otherwise; net working capital (NWC) is the ratio of current assets minus current liabilities to total assets; t statistics corrected for heteroskedasticity in parentheses

Interc LnS FMS Prof Tang Liq Inv CTR FMngt NWC Obs. White test Adj. R 2

Own $ 20

Medium-large firms

266

Model

Family firms

MF 40,3

correlation between tangibility and net working capital accounts for this effect. Unsurprisingly we find a negative relation with liquidity (Anderson and Reeb, 2003b). Inventory has some explanatory power only in the case of non-family firms and for small family firms for which the growth of the inventory results in an increase in leverage. Moreover, the leverage of medium-large firms shows a negligible positive relationship with capital turnover while small firms show a strong negative relation between capital turnover and leverage which is consistent with the assumption that the faster the investment turns the lower the need for funding; the sensibility of leverage to capital turnover is small, as we can see from the coefficient. A model specification that takes into account the net working capital, improves dramatically the explanatory power of the model. An increase in net working capital of 10 percent is associated with a reduction in leverage of 3.6 percent for family firms and of 3 percent for non-family ones. This demonstrates that investing in working capital components is a key factor, linked to the choice of capital structure and debt. These differences between family and non-family medium-large firms, the positive relationship that exists between management ownership and leverage (Ellul, 2010), which is higher for family firms and the family firms’ characteristics (tangibility, depreciation, profitability and inventory) that emerge through the descriptive statistics, reaffirm that POT alone cannot fully explain family firms’ financial choices that are affected by governance-related factors arising from the particular interaction between family and business, and which have an influence on operational management and motivation to control. A cross-sectional analysis implies a loss of information on the temporal dynamics of the variables under consideration. Therefore, we carry out a panel data analysis in order to verify the cross-sectional results taking into account the time dimension of our dataset. To test whether a fixed effects or random effects model is appropriate we use a Hausman test. The results of the specification test in Table V – panel A indicates that the hypothesis of random effects is rejected at the standard 1 percent level. To obtain better estimates than traditional ordinary least squares we perform pooling regressions with adjusted standard errors, i.e. standard errors adjusted to account for possible correlation within a cluster. In our procedure the clusters are the firms. The resulting standard errors are unbiased and produce correct confidence intervals whether the firm effect is temporary or permanent (Petersen, 2009). To deal with the presence of a industry effect we should control for this parametrically and we choose to include sector dummies in the model. The results in Table V are substantially the same we obtained with the cross-sectional analysis with heteroskedasticity robust standard errors. In summary, the results show that the traditional financial explanatory variables are relevant in explaining the capital structure choices of both family and non-family firms and the same result holds for some of the new financial variables we introduced, namely capital turnover and net working capital. However, family and non-family firms are also different, the model explanatory power is greater for family firms as can be seen from the coefficients and statistical significance of market share (Gallo and Villaseca, 1996), profitability, liquidity and net working capital and the model R 2. Finally, for both family and non-family medium-large firms, an active ownership presence in management has a sizeable effect on the capital structure choices, and this finding is statistically stronger in the case of family members servicing as CEOs. This result confirms the hypothesis that firms with family


Table V. Pooled regressions with Hausman and Chow tests, clustered standard errors FMngt¼ 1 Own , 20

Medium large firms Non-family FMngt¼ 0

20.236 (2 0.71) 0.064 (1.72) (continued)

(2 0.20) (1.52) (2 2.23) (0.48) (2 0.71) (2 6.10) (3.41) (2 8.26)

2 0.675 (2 4.16) 0.120 (6.53)

20.053 0.046 211.597 0.017 20.032 20.263 0.143 20.073

Non-family

20.310 (2 10.85) Yes 3,618 0.35 17.78 [0.0001]

(2 2.91) (6.10) (2 0.46) (2 3.99) (2 7.17) (2 9.75) (7.60) (2 16.41)

Small firms

2 0.367 (2 20.32) Yes 11,162 0.38 15.42 [0.0001]

2 0.383 0.091 2 2.104 2 0.157 2 0.162 2 0.296 0.146 2 0.094

Family

268

Panel A: pooled results with Hausman test for fixed or random effects Interc. 0.238 (6.22) 0.256 (5.78) 0.244 (4.91) 0.279 (7.17) LnS 0.001 (0.40) 0.05 (1.28) 2 0.02 (20.44) 2 0.004 (21.26) FMS 2 1.141 (25.35) 2 1.079 (24.13) 2 0.587 (21.88) 2 0.863 (23.88) Prof 2 0.202 (25.48) 2 0.228 (24.91) 2 0.194 (25.48) 2 0.177 (25.69) Tang 2 0.015 (20.57) 2 0.059 (22.24) 0.009 (0.28) 0.036 (1.24) Liq 2 0.512 (214.10) 2 0.609 (213.33) 2 0.173 (24.30) 2 0.203 (26.04) Inv 0.040 (1.68) 0.012 (0.43) 0.035 (0.86) 0.067 (2.11) CTR 2 0.014 (23.30) 2 0.07 (21.18) 2 0.023 (23.90) 2 0.024 (25.12) FMngt 0.052 (6.38) 0.098 (3.13) NWC 2 0.203 (211.92) 2 0.222 (210.99) 2 0.163 (27.26) 2 0.173 (29.51) Dindustry Yes Yes Yes Yes Obs. 18,418 13,560 7,792 12,650 R2 0.21 0.24 0.13 0.14 Hausman test 21.09 [0.0001] 21.08 [0.0001] 15.06 [0.0001] 16.22 [0.0001] Panel B: pooled results with Chow tests for intercept and slope effects (2007 financial crisis) Interc. 0.187 (4.58) 0.224 (4.66) 0.206 (4.02) 0.231 (5.69) LnS 0.004 (1.12) 0.006 (1.57) 2 0.001 (20.37) 2 0.002 (20.63)

Family Own $ 20

MF 40,3

2 0.996 (24.66) 2 0.160 (23.61) 0.035 (1.16) 2 0.563 (213.82) 0.047 (1.87) 2 0.013 (22.85) 0.053 (6.25) 2 0.184 (29.84) Yes 0.158 (3.91) Yes 10.84 [0.0001] 18,418 0.21 2 0.211 (29.63) Yes 0.103 (2.24) Yes 5.09 [0.0001] 13,560 0.24

2 0.898 (23.38) 2 0.206 (23.60) 2 0.034 (21.06) 2 0.645 (212.95) 0.007 (0.23) 2 0.06 (21.24)

FMngt¼ 1

2 0.153 (27.76) Yes 0.175 (3.98) Yes 12.34 [0.0001] 12,650 0.14

(23.67) (24.03) (2.57) (26.04) (2.98) (23.97)

FMngt¼ 0 2 0.792 2 0.143 0.082 2 0.242 0.096 2 0.020

Non-family 2 0.505 (21.62) 2 0.168 (24.29) 0.037 (1.08) 2 0.185 (23.77) 0.069 (1.68) 2 0.017 (22.73) 0.107 (3.27) 2 0.153 (26.37) Yes 0.123 (2.17) Yes 6.65 [0.0001] 7,792 0.13

Own , 20

Medium large firms

(2 0.32) (2 4.68) (2 1.70) (2 8.80) (6.75) (2 15.69)

20.276 (2 8.92) Yes 0.603 (1.90) Yes 3.85 [0.0001] 3,618 0.36

(2 2.49) (2 0.46) (0.58) (2 6.15) (4.15) (2 8.48)

Non-family 213.467 20.025 0.030 20.285 0.225 20.074

Small firms

2 0.323 (2 17.07) Yes 1.065 (5.45) Yes 24.93 [0.0001] 11,162 0.40

2 1.420 2 0.195 2 0.044 2 0.274 0.175 2 0.091

Family

Notes: The medium-large firms sample consist of all non-financial firms in the BVD Aida database, covering the time span 2001-2010, extant in 2010, with sales . 70 ml e, e, the small firms sample represents firms with sales in the range 5-10 ml e; the table presents parameter estimates from pooling regressions of book leverage; the t-statistics from clustered standard errors (firms) are in parentheses; family firms are defined in two ways: (1) familyowned firms in which one or more family members are individually or as a group blockholders, with a minimum threshold ownership of 20 percent; (2) family controlled and managed firms whose CEO and eventually some other executive director is a member of the blockholding family; book leverage (BLev) is the ratio of total financial debt to total assets; firm size is measured as the log of sales (LnS); market share (FMS) is the ratio of firm revenues to industry total revenues; profitability (Prof) is the ratio of operating income before depreciation to total assets; tangibility (Tang) is the ratio of net property, plant and equipment to total assets; liquidity (Liq) is the ratio of liquid assets to total assets; inventory (Inv) is the ratio of inventory to current assets; capital turnover (CTR) is the ratio of sales to capital employed; FMngt is a dummy that in family firms takes value one if one or more shareholders in active management are family members and zero otherwise, in non-family firms it takes value one if a shareholder is in active management and zero otherwise; net working capital (NWC) is the ratio of current assets minus current liabilities to total assets; to salvage space the coefficients for the industry dummies and the slope interaction variables are not shown; the Hausman and Chow reported statistics tests, respectively, for fixed vs random effects and for a structural break triggered by the 2007 financial crisis; t statistics in parentheses, p-values in brackets

FMS Prof Tang Liq Inv CTR FMngt NWC Dindustry D2007-10 Dslope Chow test Obs. R2

Family Own $ 20


Table V.

MF 40,3

270

members involved in active management are more levered than family firms professionally managed. This finding is in contrast with the agency theory for which the presence of the family in the board would bring a substitution effect, i.e. more direct monitoring of management by family board members implies a reduced need to use debt to prevent managerial opportunistic behavior (Gonza´lez et al., 2013). Our result is consistent with the socioemotional framework, which contradicts some basic agency theory prediction (Berrone et al., 2012). Gomez-Mejia et al. (2007) suggests a relation between the role of the family in the business and the desire to protect its socioemotional wealth by means of the preservation of control. The presence of the family in active management signals the family commitment to the business and makes stronger the sense of self-identification with the firm which is a relevant dimension of the socioemotional wealth framework (Gomez-Mejia et al., 2007; Berrone et al., 2012). We observe that the balance sheet variables present a higher explanatory power for small family firms; the model specification, in this case, does not include the presence of the family in active management because this data is unavailable in AIDA, but nonetheless it explains 38 percent of the time series and cross-sectional variability. Moreover, small family firms’ leverage has a strong relation with size, but it is not affected by the firm’ market share. Given that our dataset covers the period 2001-2010 an important issue is to analyse whether the recent financial crisis influenced substantially the leverage decisions of family and non-family firms. To verify this hypothesis we introduce in the model an intercept dummy that takes value one for the period 2007-2010 and interaction variables to account for differential relationships with the dependent variables in the same period. The results for the Chow F tests show that we can reject the null hypothesis that the intercept and slope dummy variables are jointly zero. The conclusion is that the estimated model has different parameter values before and after the financial crisis, another interesting observation we can draw form the results is that the effect of the crisis is stronger for family than non-family firms, but the medium-large family firms were in better conditions to cope with this structural change with respect to the small family firms. 7. Conclusions The present study focuses on the determinants of capital structure choices of non-financial medium-large private family firms and how family governance-related factors impact on them. Family firms present a higher leverage level than non-family firms, their ownership is strictly domestic and, consistent with the main theories explaining family firms’ behavior, and they are more profitable although their firm market share is lower. Family firms are smaller in size and characterized by a higher level of tangibility and higher turnover of the capital employed. These overall characteristics tend to persist at the industry level. We show that capital structure choices of medium-large family firms are linked to governance characteristics and to variables related to the balance sheet structure that have not been used in previous studies, such as net working capital and capital turnover. Our findings highlight that family firms avoid opening their capital to non-family shareholders, as “family control and influence” is an important dimension of the family socioemotional wealth. So family firms mitigate risk aversion and finance their growth using debt but at the same time family firms with leading market share positions in their industry recur less to

debt financing maybe as a result of their increased capability to produce cash flow. Leverage tends to increase with family ownership and family involvement in management. Family firms are more levered than the non-family counterpart and the differences are economically and statistically more significant for medium-large than small firms. This suggests the small size of family firms could not be the result of debt aversion but the consequence of constraints on credit availability. This is supported by the evidence of an impact of the recent financial crisis on family firms, stronger for small businesses. Our results, based on a large dataset, can give public authorities an important insight in order to facilitate firms funding specially in the current critical economic scenario, relaxing the regulation to permit also to private firms to issue bonds or promoting the development of credit funds as a viable alternative to bank financing. An international comparison could be the next step in developing this research, in order to check if the results are influenced by country-related regulatory aspects, and national cultural aspects, which could affect the control dimension of the socioemotional wealth. Moreover, further studies should be developed on the impact of the other dimensions – in the first the renewal of family bonds to the firm – on capital structure choices of private firms. An interesting area of research could be the integration of the socioemotional wealth dimensions with the law and finance approach to take into account the level of protection of investors and the rights of shareholders and creditors, to develop a comprehensive model of family firms’ financial decisions. The main difficulty for this research agenda remains the availability of data and information on private firms. Note 1. The results for this model specification are available on request.

References Ampenberger, M., Bennedsen, M. and Zhu, H. (2012), “The capital structure of family firms”, in Cumming, D. (Ed.), Oxford Handbook of Entrepreneurial Finance, Oxford University Press, Oxford, pp. 167-191. Ampenberger, M., Schmid, T., Achleitner, A.K. and Kaserer, C. (2013), “Capital structure decisions in family firms: empirical evidence from a bank-based economy”, Review of Managerial Sciences, Vol. 7 No. 3, pp. 247-275. Anderson, R.C. and Reeb, D.M. (2003a), “Founding-family ownership and firm performance: evidence from the S&P 500”, Journal of Finance, Vol. 58 No. 3, pp. 1301-1328. Anderson, R.C. and Reeb, D.M. (2003b), “Founding-family ownership, corporate diversification and firm leverage”, Journal of Law & Economics, Vol. 46 No. 2, pp. 653-680. Anderson, R.C., Duru, A. and Reeb, D.M. (2010), “Family preferences and investment policy: evidence from R&D spending and capital expenditures”, working paper, Kogod School of Business, American University, Washington, DC. Anderson, R.C., Mansi, A.M. and Reeb, D.M. (2003), “Founding family ownership and the agency cost of debt”, Journal of Financial Economics, Vol. 68 No. 2, pp. 263-285. Andres, C. (2008), “Large shareholders and firm performance: an empirical examination of founding-family ownership”, Journal of Corporate Finance, Vol. 14 No. 4, pp. 431-445.


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Villalonga, B. and Amit, R. (2006), “How do family ownership, control and management affect firm value?”, Journal of Financial Economics, Vol. 80 No. 2, pp. 385-417. Wiseman, R.M. and Gomez-Mejia, L.R. (1998), “A behavioral agency model of managerial risk taking”, Academy of Management Review, Vol. 23 No. 1, pp. 133-153. About the authors Pietro Gottardo graduated in economics at the University of Pavia (Italy). He achieved the Doctorate in “Financial Markets” at the University of Bergamo (Italy). He is an Associate Professor in “Corporate Finance”. Main research interests are capital structure, trading and financial markets, risk management. current teaching: “Corporate Finance”, “Financial Modeling”, at the Department of Economics and Management of the University of Pavia. Anna Maria Moisello graduated in economics at the University of Pavia (Italy). She achieved the Doctorate in “Business and Administration” at the “Bocconi University” of Milan (Italy). She is a Researcher in “Business and Administration”; main research interests in management accounting and control. Current teaching: “Business and Administration”, “Management Control” at the Department of Economics and Management of the University of Pavia. Anna Maria Moisello is the corresponding author and can be contacted at: [email protected]

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