Family ownership and bank debt availability for Australian small and medium-sized enterprises

Dong Xiang*, Andrew C. Worthington, and Helen Higgs Department of Accounting, Finance and Economics, Griffith University

Abstract Using the Business Longitudinal Database compiled by the Australian Bureau of Statistics, this paper examines agency costs in small and medium-sized enterprises (SMEs) and its impact on the availability of bank debt. A particular focus is whether family ownership helps alleviate agency costs and thereby facilitates the availability of bank debt. Using univariate and multivariate analysis, we confirm that family ownership indeed appears to ease agency costs in SMEs. However, the concentration of control in family firms can increase agency costs because of the self-control problem and the parental altruism phenomenon, and this is more obvious in medium-sized SMEs. Rather than agency costs, banks in fact appear more concerned about informational issues arising from the size of SMEs. JEL codes: C25, G32, L21. Keywords: Agency costs, Bank lending, Family firms, Small and medium-sized enterprises.

*

Corresponding author: Building N50_1.64, Department of Accounting, Finance and Economics, Griffith University, Nathan QLD 4111, Australia, Tel. +61 (0)7 3735 5311, Fax. +61 (0)7 3735 3719, Email. [email protected].

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1. Introduction Agency costs arise from the separation between ownership and control of a firm. The ideal ownership structure for zero agency costs assumed by Jensen and Meckling (1976) is a single owner who also controls the firm. Apart from this ideal structure, Agency costs problem may vary in a proportion to the degree of inconsistency in the relation between ownership and control of a firm or the degree of ownership dispersion (Ang, 2000). Family firms where family members have a critical share of ownership and are involved in the strategic direction of the firm, 1 is expected to help solve the free-rider problem that exists in widely held firms, easing the classic owner-manager conflict (e.g. Fama and Jensen, 1983; Steijver and Voordeckers, 2009). In addition, family owners are thought to be more interested in firm survival and to focus on further horizons than other categories of shareholders (Lee 2006), thus it is likely to reduce managerial myopia (Anderson & Reed, 2003) and may facilitate long-term relationships with other stakeholders (Sacristan-Navarro et al., 2011 ). Therefore, agency costs are assumed to be minimised by the family ownership as compared to nonfamily firms. However, less dispersion of ownership in a family firm, arguably, may not give rise to low agency costs. First, within family firm, conflicts of interests between minority shareholders and family shareholders may occur identified as agency problem II by Villalonga and Amit (2006). Families may protect their interests with governance systems intended to maximise their own utility at the expense of the other shareholders. For example, families may be oriented to maintaining control of the firms they found or acquire, to making value-reducing acquisitions that benefit the dominant family (Sacristan-Navarro et al., 2011). Second, in family firms the division between business and personal objectives often becomes blurred (Davis and Tagiuri, 1991). Thus, family firms are more vulnerable to self-control problems due to the isolation from the discipline from external markets (Steijvers and Voordeckers, 2009). Lastly and more importantly, agency costs may arise from the parental altruism issue addressed by Schulze et al (2003), in which case parental altruism can create a sense of entitlement among family members by encouraging CEOs (usually a parent and/or head of household of the controlling family) to give the family members incentive to prefer consumption to investment in the form of pecuniary and non-pecuniary benefits at rates that are high relative to their ownership stakes. 1

This is the definition of family firm used in this paper, though there are a few different ways to define a family firm (see Sacristan-Navarro et al 2011).

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Additionally, banks can play a special role in delegated monitoring on behalf of other stakeholders because of heavy reliance of the non-publicly traded firms especially SMEs on bank financing (Iturralde, et al., 2010). Agency costs can become the major concern since the in-alignment of interests between owner and manager can be not only at the expense of shareholders but also at the costs of debt holders. For example, Filatotchev and Mickiewitz (2001) suggest that ownership concentration is associated with a less efficient use of financial resources. Niskanen and Niskanen (2010) find that an increase in managerial ownership decreases loan availability. Mueller and Inderst (2001) suggest that firms with relatively dispersed ownership structures face lower agency costs of debt. Furthermore, Steijvers and Voordeckers (2009) use personal collateral as an instrument of measuring agency costs of family firms and find that a family firm is more likely to pledge personal collateral which may indicate the higher levels of agency costs in family firms. However, there is an issue arising from the use of interest rate or personal collateral as an indicator of the effects of agency costs on loan decisions of banks because these measures easily get entangled with the traditionally major concern of banks such as moral hazard and adverse selection. This issue can even be worse in SMEs because of the nature of informational opaqueness of SMEs. In this paper, we first investigate the argument whether owner-manager style of family SMEs can help alleviate agency costs. Making allowances for the self-control problem and the parental altruism, we employ a dummy variable proxied for control concentration in SME because we assume that highly concentrated control power over a family firm can give rise to or exacerbate the self-control problem and the parental altruism phenomenon. Then, we examine the effects of the agency costs measured by ownership and the control concentration on bank debt availability. To address the entanglement issue mentioned above, we stick our concept of agency costs to the conflicts arsing from the separation between ownership and control of a firm, then intend to clarify whether banks have the major concern over agency costs in SMEs. The remainder of the paper is organized as follows. Section 2 briefly discusses the relevant literature. Section 3 details the hypotheses, Section 4 presents the model specifications, and Section 5 discusses the results. Section 6 provides some concluding remarks.

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2. Related literature and hypothesis development Agency costs arise from an agency relationship under which the interests of the firm’s managers and its shareholders, or the firm and its creditors are not completely aligned. As discussed by Jensen and Meckling (1976), agency costs are the sum of the monitoring costs, bonding costs and residual loss. Concerned about the agency relationship, the principal can establish appropriate incentives for the agent thus incur monitoring costs, or, can pay the agent to expend resources which is called bonding costs. And, “in addition there will be some divergence between the agent’s decisions and those decisions which would maximise the welfare of the principal” (p 308). The cost of the latter divergence is referred to as the residual loss. For example, the manager who owns less than 100 percent of a firm has the incentive to consume perks rather than to maximise the value of the firm to all shareholders. On the other hand, as the number of non-manager shareholders increases, aggregate expenditure on monitoring declines due to free rider issue, thus, the magnitude of ownermanager agency-cost problems can increase (Ang et al., 2000). Since the agency costs arise from the separation between the ownership and the management of a firm, owner-manager involvement in a family business should consolidate the firm, thus reduce the equity agency costs (Fama and Jensen, 1983). For example, the monitoring costs can be reduced because familiarity and the intimate knowledge gained from log association facilitate communication and promote cooperation among family owners and family agents (Schulze et al., 2003). Furthermore, a kinship network characterised by norms of reciprocity, strong social ties, a shared identity and a common history can reduce bonding costs and residual costs as well (Ouchi, 1980). In addition, owner–manager or familycontrolled SMEs may have a desire for control and so exhibit greater aversion to the use of external equity (Mishra and McConaughy, 1999). Therefore, external equity seeking is less likely to be a consideration for older family business owners and owners that have a strong preference for retaining family control (Romano et al., 2000). Therefore, in this sense, the agency costs may also be mitigated for a family SME. Using a data set of the US small businesses, Ang et al. (2000) find that agency costs are higher when an outsider manages the firm, and increase with the number of non-manager shareholders and inversely with the manager’s ownership share. A similar phenomenon is reported by Fleming et al.(2005) in the context of Australian SMEs. However, on account of the parental altruism, there is an argument against the statement that family firms incur lower agency costs as compared to non-family firms. Altruism which [4]

has a positive relation between the welfare of individuals and the others compels parents to be generous to their children (Lunati, 1997). Schulze et al. (2001) suggest that altruism can alter the incentive structure of a family firm so that some of the agency benefits gained are offset by free riding and other agency problems. “Altruism can create a sense of entitlement among family members by encouraging CEOs ( usually a parent and/or head of household of the controlling family) to use the firm’s resources to provide family members with employment, perquisits, and privileges that they would not otherwise receive” (Schulze et al., 2003. P 180). Also, altruism can hamper the CEOs’ ability to monitor and discipline their employed children. Thus, the owner-manager style of family firms does not necessarily minimise the agency costs but can exacerbate them. For a non-family, a fractional ownership at the firm gives insiders incentives to free ride on the outside owner’s equity, whereas, a family member may have incentives to free ride on the controlling owner’s equity because of the altruism phenomenon. In addition, family firms may have self-control problems due to the isolation from the discipline from external markets, which can further exacerbate agency problems ( Schulze et al., 2003) Similar to the separation between ownership and management for a non-family firm, the dispersion of ownership in a family firm also drives a wedge between the interests of those who lead the firm and other family owners. First, one of the assumptions of agency costs that increased inside ownership aligns owner preferences implies that individuals are economically rational wealth maximisers. However, there are arguments that individuals are motive by an idiosyncratic set of preferences i.e. some economic and some non-economic (O’Donoghue and Rabin, 2000; Thaler and Shefrin, 1981). Furthermore, Hamelin (2011) argues that SMEs tend to promote profit stability rather than profit maximization; Vos et al. (2007) suggest that growth is not a significant objective for SMEs. Second, Altruism can hamper the ability of a family firm’s principal own to use internal governance mechanism like monitoring to minimize internal conflicts and to minimize agency costs. Third, because of limited ability to access to finance markets, a family firm can has higher exit costs, thus the internal conflicts between the family owners are not so easily to handle by sell-and-buy as compared to a non-family firm. Last, unlike public firms, which can rely on external governance mechanisms to minimize the adverse effects of these internal conflicts, family firms cannot do so because private ownership isolates them from the discipline that external markets provide. In summary, the potential agency problems can arise from weak internal monitoring mechanisms. Therefore, the following hypothesis is set, [5]

H1 Family firms have low agency costs as compared to non-family firms; however, concentration of control on a family firm can increase agency costs. Among numerous number of literature on limited capacity to get access to finance for SMEs, very few of them investigate the effects of agency costs on the finance availability, especially debt availability. Jensen and Meckling (1976) argue that the owner-manager bears the entire wealth effects of the debt agency costs because of the incentive effects. Furthermore, debt can help mitigate the problems of the equity agency costs by means of reducing the free cash flow, and the debt agency costs are the main reason why a firm does not entirely rely on debt financing on account of tax shields (Jensen, 1986). However, the agency costs of debt especially bankruptcy costs are hard and rare to measure (Steijvers and Voordeckers, 2009), and this issue is further exacerbated by the phenomena of SMEs. Furthermore, the assumptions about the agency costs of debt may not entirely apply to the SMEs. First, SMEs are less likely to have access to formal finance, such as security and equity markets. Thus, in theory, there are no incentive effects which can cause the agency costs of debt. But, there obviously exist monitoring costs and the bankruptcy costs in the SMEs, which can be exacerbated by the issue of opaqueness for a SME. Second, SMEs rely heavily on internal finance and banks are the main source of external finance (Iturralde, et al., 2010); thus, the debtors of a SME are not normally the bondholders but the banks. However, banks may wish to have less exposure to SMEs or desire to charge SMEs higher fees and interest rates as compared with larger firms (Beck, et al., 2009). In addition to high levels of operational costs, SMEs have a severe lack of negotiation power (Dietrich, 2010), because the alternatives bank finance are even more costly (Roberts and Sufi, 2009). Therefore, financial obstacles consistently adversely affect SMEs borrowing externally (Beck, et al. 2005). Third, SMEs depend heavily on internal finance either because of credit rationing (Stiglitz and Weiss, 1981), or

may also because of the unwillingness of SME owners to seek external finance

(Berger et al. 1998). Therefore, the lack of knowledge on the effects of agency costs on finance availability is further exacerbated for SMEs, because agency costs are entangled with the issues such as opaqueness, finance obstacles etc. Using a dataset on the US small businesses, Steijvers and Voordeckers (2009) suggest that family firms have higher agency costs of debt which can be reflected in the pledging of personal collateral. Because of a higher risk of owner-manager opportunism and misuse of firms’ assets for the personal benefits of the family owners; banks

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seem to be more cautious when dealing with family firms. Draw on these perspectives, the following hypothesis is presented, H2 Agency costs have negative effects on the availability of bank lending.

3. Research design Agency costs Following Ang et al.(2000), the expense ratio, which is the ratio of total salary, wages and other payments to total sales, is used to measure agency costs. That is, the expense ratio is a measure of how effectively the firm’s management controls operating costs, including excessive perquisite consumption, and other direct agency costs. Therefore, the difference in the ratios of a firm with a certain ownership or management structure multiplied by the total sales can give the excess agency costs related expense in dollars.

For the purpose of

investigating agency costs in SMEs, two approaches are taken in this paper. First, we compare discretionary operating expenses between family and non-family firms, and firms with single in-charge person and those without using mean t-test. Then a regression model is set with inclusion of control variables.

Ultivariate analysis To investigate whether family firms have lower expenses ratio compared to non-family firms, the sample first is divided into a family firm group and a non-family firm group. Because of separation between ownership and control, non-family group is assumed to have a higher mean of expense ratio compared to that of family firms; thus the differences between the means can be interpreted as agency costs if the differences are statistically significant. Then, the family firm group is further divided into two groups according to whether a family business has a single person who is responsible for the major decisions on business operations. The hypothesis 1 assumes that the group of the family firms that have a single person in charge of the major decisions on business operations should have a higher mean of the expense ratios than the group of the family firms that does have, and the differences between the two means should be statistically significant.

Multivariate analysis A further investigation into agency costs is conducted by using a multivariate model as follows, [7]

COST

f (variables of interest, control variables)

(1)

where COST is expense ratio, which is the ratio of total salary, wages and other payments to total sales.

Variables of interest The variables of interest have two dummy variables. OWNF takes a value of one for a family business and zero otherwise. SING is a proxy for control concentration which takes one if a firm has a single person who is responsible for making the major decisions on business operations and zero otherwise. Control variables Because of operating nature, the expense ratio varies widely across industries. Figure 1 reports the ratio of personnel expense to total sales by two-digit ANZSIC industry. The industry of property and business services shows the highest expense ratio up to 43%. The second highest ratio is 33% in personal and other services. At the other side of the spectrum, the lowest expense ratio appears to be the wholesale trade industry; then the agriculture industry is the second salary-saving industry at a ratio of 19%. Therefore, the large differences in expense ratio across the industries emphasize the impotence of controlling for industry in the agency cost models. IND01, IND02, IND03, IND04, IND05, IND06, IND07, IND08, IND09, IND10, IND11 are a series of dummy variables respectively based on the two-digit Australian and New Zealand Standard Industry Classification (ANZSIC) subdivisions of agriculture, mining, manufacturing, construction, wholesale trade, retail trade, accommodation, transport, communication services, property and business services, cultural and recreation services, and personal services. INSERT FIGURE 1 HERE Following Ang(2000) , size and age are also used as control variables, because SMEs may benefit from scale economies. Firm age, on the one hand, may be related to size; on the other hand, a start-up or early-stage SME can have a different organisational structure as compared to a mature SME. SIZE is the logarithm of sales. The age dummy variables are AGE01, AGE02, and AGE03, which are respectively set to equal one for firms that are aged less than 5 years, 10 years to less than 20 years, and 20 years or more, zero otherwise.

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In addition, planning, growth opportunities and profitability are also used as control variables. We distinguish between two forms of business planning, the planning process and the written business plan, both of which influence SME’s performance positively when measured as profitability and sales growth (see Brinckmann et al., 2010; Gibson and Cassar, 2005). Formal and/or informal planning can also act as soft information for the SMEs in that soft information can help alleviate the opaqueness issue in SMEs, thus giving easier access to financial resources. For instance, Romano et al. (2000) suggest that planning is positively associated with debt in Australian family businesses. However, a planning activity may incur extra costs for a SME. The influence of growth opportunities on a firm’s finance behaviour has been widely discussed. This reveals that by reducing debt, firms with growth opportunities may avoid the shareholder–creditor conflict in which the benefits can transferred from shareholders to creditors (Myers, 1977; Jensen and Meckling, 1976). Moreover, debt can act as a mechanism to alleviate agency cost by disciplining managers (Jensen, 1986). Therefore, a firm with growth opportunities may be more likely to exhaust internal funds and require additional funds. This can incur extra costs for a SME. Whether a profitable SME can have higher agency costs is an important assumption based on the nature of agency costs. Thus, PLAN, equals one if the business has the following business activities: written strategic and business plans, budget forecasts, formal networking with other businesses, comparison of performance with other businesses, or export market plans, otherwise zero. GROW also takes a value of if the business has introduced any new or significantly improved operational or organizational processes, otherwise zero. As a proxy for the profitability of the SME, PROF is set at one if the firm has higher profitability compared to its major competitors and zero otherwise.

Bank lending availability To investigate whether banks are concerned about agency costs in SMEs, the following equation is used to test hypothesis 2, DENY

f (variables of interest, control variables)

(2)

where DENY is a dummy variable used to proxy for whether application for additional bank finance is available. DENY is one if the finance is unavailable zero otherwise. The variables of interest are OWNF and SING. In addition to the control variables in equation 1, business [9]

objective dummy variables are also included in equation 2 to control business goals or objectives of SMEs. Business objectives may closely relate to the SMEs’ need and outcomes of external finance. Given SMEs appear to be very different from large firms in terms of business operations (Ang, 1991), not all SMEs aim to seek significant growth. For example, mom-and-dad SMEs do not typically pursue a high-growth strategy (Berger et al., 1998). Instead, the owners of SMEs like these may merely enjoy operating the SME itself (Vos et al., 2007). It is therefore likely that desires for independence and control are keys reasons for differences in SME financial behaviour (Curran, 1986). The six variables are FOCUF, FOCUC, FOCUO, FOCUQ, FOCUI, and FOCUH, which are dummy variables that proxy for the stated business focus of SME when assessing overall business performance (namely, financial, cost, operational, quality, innovation, and human measures) as determined by survey, where the value of the dummy is one for firms in the category and zero otherwise. Since the dependent variable is a binary variable, logistic regression mode is used to estimate equation 2. Data In this paper, we utilize the results of the surveys included in the Business Longitudinal Database, Expended CURF (BLD), conducted by the Australian Bureau of Statistics (ABS). The BLD comprises two independent samples (referred to as panels) drawn from the in-scope Australian SME populations, defined in the survey as all businesses employing less than 200 people. Panel 1 in this paper contains three reference periods of data: 2004–05, 2005–06, and 2006–07 (for ease of discussion, we refer to these by the calendar year at the end of the financial year, i.e. 2005, 2006, and 2007, respectively). However, the scope of the questions included in the 2005 survey was refined for the surveys in the following two years. As a result, we only use the 2005 survey in this paper. The Panel 1 sample includes 2,732 SMEs, as selected from a survey frame created in June 2005 containing a population of 1,563,857 Australian SMEs. The BLD data is collected directly via the ABS Business Characteristics Survey (BCS), with the assistance of the Australian Taxation Office and Australian Customs. Table 1 reports summary statistics of the variables used in this paper. Because of missing data owing to respondent omission or errors in sequencing, the samples in 2005 were XXX observations smaller, respectively. About 24% of SMEs are from the industry of agriculture, forestry, and fishing (see Panel C in Table 1). The second-largest group of SMEs in the sample is in the manufacturing industry (16%) while the third-largest sample group are from

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the wholesale trade industry (10%). About 30% of SMEs are less than five years old, and 23% are in operation for more than 20 years. Of the SMEs in the sample, 41% applied for additional finance in 2005: 38% applied for debt, 9% for equity, and thus 6% applied for both debt and equity (see Panel A in Table 1). The applying rates dramatically fell in 2006 to 19%. However, the availability of finance shows a relatively high level in both 2005 and 2006 at about 88% and 83%, respectively. The average of the cost ratio stays steady over the two-year period. About 16% of the SMEs received financial assistance from Australian government organizations in 2005 and some 14% in 2006. Interestingly, in 2005 only 47% of SMEs regarded financial measures, such as profits, sales growth, and returns on investments, as a major measure of business performance, which dropped further to 40% in 2006. Similarly, cost measures also dropped from 40% in 2005 to 34% in 2006. However, the SMEs appear to pay more attention to quality measures, for example, customer satisfaction and defect rates, which increased from 34% in 2005 to 37% in 2006. By comparison, most SMEs do not consider innovation as a major measure. However, more SMEs introduced new goods, services, or processes during the sample period, from 26% of 2005 to 36% of 2006. Half of the SMEs were involved in planning activities in the business. About 26% of SMEs were considered (by the respondents) to be more profitable compared with their major competitors in 2005 and 32% observed an increase in profitability in 2006 compared with the previous year. In addition, 63% of the sampled SMEs are family businesses and 4% have some degree of foreign ownership.

4. Results and discussions Univariate tests The univariate analysis is used to examine the significance of the differences between the groups with different ownership or management structures, thus test hypothesis 1. The differences in the mean expense ratios are report in Table 2. The mean expense ratio for the Australian SMEs ranges from 22% to 27% for the family and non-family SMEs, which is higher than the ratio reported in Fleming et al (2005). 2And, Ang et al (2000) report a much

2

Fleming et al (2005) used a dataset of Australian SMEs over a period of 1996-1998.

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higher expense-to-sales range for the US SMEs from 47% to 52%. The large difference is mainly due to different definitions for the expenses. In this paper, according to the ABS, expenses are total salary, wages and other payments, whereas the expenses in Ang et al (2000) is defined as total expenses less cost of goods sold, interest expense and managerial compensation. Agency costs are the sum of the monitoring costs, bonding costs and residual loss. Therefore, both definitions can underestimate total agency costs because the ratios under the definitions cannot fully measure indirect agency costs such as the distortion of operating decisions due to agency problems (Ang et al 2000). TABLE 2 HERE The difference in the mean ratio between family and non-family SMEs is 5%. Since a F test for the null hypothesis that the two groups have the same variances are rejected at a p value of less than 1%, unequal t-test is conducted, which show that the difference is statistically significant at a p value of less that 1%. That is, on average, a family firm can save up to 5% of expense ratio equal to XXXX Australian dollar (AUD) as compared to a nonfamily firm. As discussed above,

size

can affect SME expenses either arising from

difference in operating structure or because of the effects of scale of economies. Making allowance for size effects, the sample is categorized into three groups according the number of employees, micro business group (less than 5 employees), small business group (employees between 5 and 20) and medium business group (employees more than 20 and less than 200). In Table 2, for the micro and small businesses, the difference in mean is 5%; comparatively, the medium-sized businesses appear to have a much lower difference in mean at only 2%. That is, the expense savings due to ownership are lower for medium-sized businesses as compared to small and micro businesses up to 3% equal to XXX AUD. All the differences in mean across the three groups are significant at a p value of less than 1%. Thus, drawing on the t-test results, the family ownership appears to significantly alleviate agency costs, which is consistent with the previous literature (e.g. Ang 2000. Fama and Jensen 1983) TABLE 3 HERE However, do all the family firms have lower expense ratios in spite of other effects such as atrium or self-control as mentioned above? Table 3 gives the comparison results among the different categories when control concentration is taken into account. Panel A presents the differences in mean for all sampling SMEs and t-test for the differences. The SMEs with a single person responsible for the major decisions on business operations have a relative

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higher expense ratio by compared to the SMEs without. However, the differences are only statically significant for the medium-sized SMEs at a p value of 10% by t-test. Furthermore, when ownership is allowed for, differences in mean and relevant t-test are reported in Panel B and C respectively. First, Panel C shows none of non-family SMEs with a in-charge person has a significantly higher expense ratio; however, for the family SMEs, medium-sized firms with an in-charge person has a much higher expense ratio by compared to their counterparts without an in-charge person up to 7% which is converted to XXXX AUD. This difference is significant at a p value of 5%. This can give evidence of effects of parental atrium or selfcontrol. As compared to large firms, SMEs normally have a rather simple control structure because of their size, especially for smaller SMEs. For small or micro businesses, whether or not there is a person who is responsible for the major decisions on business operating can be hard to clarify. For example, a family who owns a dairy farm or a small grocery shop with 1 or 2 casual employees answered that there is an in-charge person in the survey because the husband is usually supposed to make the major decisions. However, in this case, the husband may usually discuss with his wife about decisions on running the business because this discussing procedure is part of life rather than a business decision-making procedure, which may depend on the character of the couple. Therefore, control concentration or its proxy, whether or not there was an in-charge person, hardly make significant difference in terms of the effects of parental atrium and self-control problem on agency costs. By comparison, Medium-sized family firms may have a small board; The small board may consist of a principal owner who is usually the CEO or the founder and minority shareholders who could be members of the nuclear and/or extended family and often but not always employed by the firm. In this case, the firm has a single person to make the major decisions, that is, the CEO. As mentioned, CEOs are most likely the founder and patriarch in a family; thus, parental altruism can play an important role in the decision-making of a family firm. Altruism can compel the controlling owner to compromise her or his first-best options because of considering the needs of her or his family members in the firm. Over time, the economic incentive to do what can maximize the utility of the family members can blur the controlling owner’s perception of what is best for the firm. Furthermore, conflicts also can arise between the family members and the controlling owner, and between the family members because the family members in the firm may consider more their own benefits rather than the benefits of the family-owned firm. And, family-member employees of family business, like their [13]

counterparts in public firms, bear just only a fraction of the risk associated with an investment decision but ,unlike their counterparts, are entitled to have much more share of the benefits due to their family status (2003). More seriously, unlike a large public firm, which have a chain of external supervising mechanisms, for example, shareholders, bondholders, creditors, and analysts who can monitor the firm relying on the relatively transparent information in the markets to minimise the adverse effects of this internal conflicts, family SMEs inhere the nature of informational opaqueness of SMEs, thus effectively isolates them from the discipline that external market provide. In addition, a small board in a SME can help little alleviate the issue of lack discipline because a controlling owner tends to appoint the directors who are the family members or the friends of the family (Gersick et al., 1997). In summary, a family firm with a single in-charge person could have a control structure such as a small board or a similar structure like this for the larger SMEs. Thus, agency costs can be higher in these circumstances due to the effects of altruism and self- control problem. However, the variation in the agency costs in SMEs may be determined by some factors other than ownership and control structure. For this purpose, a regression model is used to investigate the effects of variables of interest while the other variables under control.

Multivariate analysis Making allowances for the potentially influential factors such as industry, size, age etc. in determining SMEs’ expenses, multivariate regression is employed to clarify the effects of ownership and control structure on agency costs. Since the dependent avertable is expense ratio censored between 0 and 1, a censored regression model i.e. Tobit model is used to estimate the determinants of agency costs (Green, 1999). The results are reported in Table 4. Model A suggests that ownership dummy is significant at a p value of 1%. A family owned SME has a lower expense ratio at a marginal value of 3% equal to XXXAUD. This is perfectly consistent with the results of univariate analysis in Table 3. Furthermore, among the control variables, size, planning and most of industry dummies are statistically significant; however, profitability, age and growth opportunities do have a significant effect on agency costs. Negative effects of size on expense ratio suggests that larger firms can benefit from the scale of economies or other perquisite savings from the size at a marginal value of 5%. As expectations, planning can incur extra costs for SMEs. That is, a SME with a business plan have an additional cost of 2% as compared to a SME without a business plan.

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TABLE 4 HERE The effects of industry dummies in the multivariate regression models are also highly consistent with the results of univariate analysis. In Table 4, out of 11 industry dummy variables, 9 variables show a significant effect on agency costs at a p value of less than 5%. Note that, the industry of personal and other services acts as the reference in the series of the industry dummy set. As discussed above, the industry of personal and other services has the second-highest expense ratio among the 12 industries at 33%. Thus, all the other industries except the industry of Property & Business Services have a negative coefficient value. Among them, the industry of Agriculture, Forestry and Fishing and the industry of Wholesale Trade have the largest savings in expense ratio at a marginal ratio of 13%. Then, by comparison to the reference industry, the industry of retail Trade can reduce expense by 10%; the industry of Accommodation, Cafes and Restaurants and the industry of Communication Services can both save expenses by 7%. At the other end of the spectrum, the industry of property and Business Services has the highest expense ratio at a marginal value up to 9% equal to XXX AUD. Then, Model B shows that the control concentration does not have a significant effect on the agency costs given that family ownership is not under control. Thus, Model C is used to investigate whether the control concentration affect agency costs while family ownership is under control and how the control concentration and family ownership can interact. The results of Model C in Table 4 show that family ownership is still negatively associated with agency costs proxied by expense ratio; in addition, the interacting item between family ownership and the control concentration has a significant effect on agency costs at a p value of 5%. That is, a family SME that demonstrates a control concentration can incur higher agency costs up to 4% as compared to the family firm that without a control concentration. This is also highly consistent with the results of univariate analysis as discussed above. TABLE 5 HERE Similarly, in Table 5, we break down the sample into three groups according to employee number. Since the industry dummies present very similar effects as in Table 4, the results of the industry dummy variables are not reported in Table 5.

For the SMEs with less than 5

employees, Family ownership present a consistently negative effect on agency costs across all four models ( see Panel A). However, interestingly, when the interacting effects between family ownership and control concentration is under control, control concentration shows a

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negative effect on the expense ratio. The interacting item itself has a positive effect on the expense ratio. This is a little puzzling. An interpretation for this could be that micro family SMEs with higher control concentration can incur extra costs but this is not the case for micro non-family SMEs. For the control variables, sale, plan, and industry variables all have a similar effect as in Table 4. Younger micro SMEs appear to can reduce expense ratio by 3%. Small businesses with employees between 5 and 20 in Panel B have the similar results of family ownership and control variables. However, older small business with employees between 5 and 20 can have some savings in expense ratio by 3% or 4%. Control concentration does not have significant effects among this group. The effects of control concentration on agency costs for medium-sized businesses showed in Panel B and Panel C of Table 5 are also consistent with the results of univariate analysis in Table 3. By comparison, medium –sized firm do not show an expense saving unless the interacting effects between family ownership and control concentration are under control. That is, when the effects of control concentration are controlled in Model D, family ownership shows a negative effect on agency costs at a marginal value up to 5%. Put differently, without controlling on the significantly positive effects of control concentration in family firms on agency costs, Medium sized firms cannot see an ownership saving in Model A and C. This is highly consistent with the results of univariate analysis in Table 5. The consistency in the results of multivariate analysis and univariate analysis gives the evidence of the effects of parental atruism and self-control on agency costs among the family SMEs. That is, family ownership can alleviate agency costs; however, concentrated control structure can dampen this effect, especially for the medium-sized firms. Thus, hypothesis 1 is confirmed.

Bank lending availability Since the dependent variable is the rejection of bank debt application in Equation 2, Table 6 gives the results of the determinants of bank debt unavailability. Because of missing data, the sample of bank debt unavailability further drops to 630, of which 176 are micro SMEs with less than 5 employees, 206 are SMEs with employees between 5 and 20, then 248 are medium-sized firms with employees between 20 and 200. All models are significant under a log likelihood test at 10% of a p value except for the micro business group whose p value is 15.13%. For the whole sample, both family ownership and control concentration do show a [16]

significant effect on bank debt unavailability; however, the interacting item between them appears to have a significant positive effect. This is interpreted as that banks have concern about the family firms with a concentrating control structure. For those kinds of family firms, the rejection of applying for debt to banks can increase up to 11%. This appears to be consistent with the results of agency costs models that the family firms with a concentrating control structure can incur a higher level of agency costs. However, the insignificance of the effects of ownership on bank debt unavailability shows that bank debt rejection and agency costs are not consistently positively related. TABLE 6 HERE In terms of control variables, SALE shows a significant negative effect on the bank debt rejection. That is, smaller firms are more likely to get rejection at a marginal value of 4%. This is because smaller firms have more seriously informational problems thus are treated as higher risky debtors, which is consistent with previous literature (e.g.). Government assistance can help a SME get finance at a marginal value of up to 9% . Interestingly, business objectives are also show a significant effect on the debt availability. SMEs whose focus is on innovation measures such as process innovations are more likely to get rejected when applying for finance by a range from 7% to 10%. And the micro businesses are more like to be negatively affected by this focus. As discussed above, the innovation focus can treated as higher risk by creditors (e.g.). However, a focus on human resources such as job satisfaction and skills development can firm get finance by 6%. Furthermore, the sample is also broken up into three size groups. Among the three groups, the variables of interest i.e. family ownership, control concentration and interacting item between them show a significant effect on bank debt unavailability only for the micro business group i.e. the SMEs with less than 5 employees. The family ownership and control concentration which alleviate agency costs can help reduce the possibility of getting rejection of applying for bank finance by 26%; the interacting item between them which increases agency costs has a positive effect on bank debt unavailability. This is consistent with the results of agency costs showed I Panel A of Table 5. However, for the medium-sized firms (with employees between 20 and 200), the three variables of interest that have significant effects on agency costs in Panel C of Table 5, do not have any significant effects on bank debt availability in Table 6. Similarly, the family ownership which can significantly reduce agency costs also does not have a significant effect on bank debt unavailability. That is, the consistency between the factors in agency costs and those in bank debt unavailability is just [17]

among smaller business. However, as mentioned, the results of the micro business fimrs are not significant at a p value of 10% under a likelihood ratio test. Therefore, banks seem to have more concern about informational issues arising from the size of SMEs rather than the agency costs issues. That is, there is no relationship between bank debt unavailability and agency costs. Consequently, Hypothesis 2 cannot be confirmed.3 With respect to control variables, SALE is no longer significant within each size group; government assistance only has significant effects for medium-sized firms; for the firms with employees less than 20, the focus on quality measures such as customer satisfactions appears to increase the rejection rate, whereas the focus on human resources such as job satisfaction can reduce the rejection rate, which is only significant for the micro business group and the medium-sized firm group. In addition, a plan can reduce the debt rejection for the firms with employee between 5-20.

Robust tests As shown above, family ownership has significant negative effects on the expense ratio measured by the ratio of personnel costs and other payments to total sales. However, family businesses may benefit from the savings of business operations, such as transport spending, rent, and so on if the family business is home-based. Thus, we introduce another dummy variable to control this effect (see Table 7). In Model A, a home-based dummy controls for the effects of these sorts of savings. Family ownership and the interacting item between family ownership and control concentration are still significant. The marginal effects in Model A of Table7 are similar to those in Table 5 at a range between 3% and 5%. In addition, the home-based dummy is also significant at a p value of slightly more than 1%, which can reduce the expense ratio by 3%. Model C then allows the interaction between family ownership and home-based dummy. The home-based dummy and the interaction dummy are no longer significant. This means family and home-based SMEs cannot obtain further savings. However, the family ownership and its interaction with the control concentration are still significant. This implies that family ownership can reduce the agency costs; however, the control concentration also can increase the agency costs for the family SMEs, which confirms hypothesis 1. 3

We cannot say hypothesis 2 is rejected, because we are using agency costs of equity not agency costs of debt in this paper. As discussed in this paper, agency costs of debt is very hard to measure especially for SMEs because it is hard to disentangle agency costs of debt from the issue of the informational opaqueness.

[18]

TABLE 7 HERE

5. Concluding remarks This paper provided empirical evidence relating to agency costs and its relevance to bank debt availability. The two questions that this paper attempts to answer are whether a family ownership helps alleviate agency costs and whether the major financing source of SMEs, banks, are concerned about agency costs. To address the first question, using bother univariate and multivariate analysis, we first confirm that family ownership appears to help ease agency costs problem. A family owned SME can have a lower expense ratio at a marginal value ranged from 3% to 5% as compared to a non-family owned SME. However, this reduction in agency costs is more obvious among small businesses i.e. the SMEs with less than 20 employees. For the medium-sized businesses, the family ownership does not have a significant effect on agency costs through both univariate analysis and multivariate analysis. Then, the variable of control concentration is used to examine the effects of the self-control problem and the parental altruism phenomenon in family SME. The significant positive effects of the variable of control concentration on agency costs give evidence of the existence of the self-control problem and the parental altruism phenomenon among family firms. In contrast to the effects of family ownership, this increase in agency costs arising from the self-control problem and the parental altruism phenomenon is more obvious and significant among medium-sized firms. This is also give the reason why the family ownership does not show a significant effect among the medium-sized family firms. With respect to control variables, size, planning and most of industry dummies are statistically significant; however, profitability, age and growth opportunities do not have a significant effect on agency costs. Negative effects of size on expense ratio suggest that larger firms can benefit from the scale of economies or other perquisite savings from the size. As expectations, planning can incur extra costs for SMEs. Nine out of 11 industry variables show a significant effect on agency costs at a p value of less than 5% given that the industry of personal and other services acts as the reference in the series of the industry dummy set. . The industry of property and business services shows the highest expense ratio; the industry of personal and other services has the second-highest expense ratio among the 12 industries. .

[19]

At the other side of the spectrum, the lowest expense ratio appears to be the wholesale trade industry; then the agriculture industry is the second salary-saving industry at a ratio of 19%. However, there is no relationship between bank debt availability and agency costs. The consistency between the factors in agency costs and those in bank debt availability is just among smaller business. Size shows a significant negative effect on the bank debt rejection. That is, smaller firms are more likely to get rejection. Therefore, banks seem to have more concern about informational issues arising from the size of SMEs rather than the agency costs issues, because smaller firms have more seriously informational problems, thus, are treated as higher risky debtors. Government assistance can help SME get finance; and, interestingly, business objectives are also show a significant effect on the debt availability.

[20]

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Table 1. Variable definitions and summary statistics Varia bles

Definition FINA ADEBT COST FOCUF FOCUC FOCUO FOCUQ FOCUI FOCUH PLAN

GROW OWNF PROF SALE SIZE01 SIZE02 SIZE03 AGE01 AGE02 AGE03 ASSI IND01 IND02 IND03 IND04

Equals one if the firm applied for additional finance in 2005 or 2006; zero otherwise. Equals one if the debt is available in 2005; zero otherwise. Ratio of personnel costs to total sales in 2005. Equals one if the business focus is on financial measures; zero otherwise. Equals one if the business focus is on cost measures; zero otherwise. Equals one if the business focus is on operational measures; zero otherwise. Equals one if the business focus is on quality measures; zero otherwise. Equals one if the business focus is on innovation measures; zero otherwise. Equals one if the business focus is human resource measures; zero otherwise. Equals one if the business has the following business activities: written strategic and business plans, budget forecasts, formal networking with other businesses, comparison of performance with other businesses, export market plans; zero otherwise. Dummy, if the business has introduced any new or significantly improved operational or organizational processes; zero otherwise. Equals one if the firm is a family business; zero otherwise. Equals one if the firm was considered more profitable than its major competitors in 2005; zero otherwise. Or equals one if firm profitability increased compared to the previous year in 2006, zero otherwise. Total sales in thousands of dollars Equals one if the business had no employees; zero otherwise. Equals one if the business had 1–5 employees; zero otherwise. Equals one if the business had 6–20 employees; zero otherwise. Equals one if the business had been in operation for less than 5 years; zero otherwise. Equals one if the business had been in operation for a period of 10 to 20 years; zero otherwise. Equals one if the business had been in operation for a period of more than 20 years; zero otherwise. Equals one if the business received any financial assistance from government organizations; zero otherwise. Equals one if the firm is in agriculture industry; zero otherwise. Equals one if the firm is in mining industry; zero otherwise. Equals one if the firm is in manufacturing industry; zero otherwise. Equals one if the firm is in construction industry; zero otherwise.

Mean 2005 0.41 0.89 0.24 0.47 0.40 0.29 0.34 0.21 0.21 0.50

Std. dev. 2005 0.49 0.32 0.18 0.50 0.49 0.45 0.47 0.41 0.41 0.50

0.26

0.44

0.63 0.12

0.48 0.33

2678.30 0.189 0.299 0.272 0.30 0.26 0.23 0.16 0.24 0.04 0.16 0.05

8707.82 0.392 0.458 0.445 0.46 0.44 0.42 0.35 0.42 0.20 0.37 0.22

IND05 IND06 IND07 IND08 IND09 IND10 IND11

Equals one if the firm is in wholesale trade industry; zero otherwise. Equals one if the firm is in retail trade industry; zero otherwise. Equals one if the firm is in accommodation and restaurant industry; zero otherwise. Equals one if the firm is in transport industry; zero otherwise. Equals one if the firm is in communication services industry; zero otherwise. Equals one if the firm is in property industry; zero otherwise. Equals one if the firm is in cultural and recreational services industry; zero otherwise.

0.10 0.06 0.07 0.06 0.05 0.06 0.05

0.30 0.25 0.25 0.24 0.21 0.23 0.22

Table 2 Agency costs, ownership, and management structure

Family firms (Panel A)

Non-family firms(Panel A)

Differences

F-test for variance

T-test for difference in means

Number

of

Ratio mean

St.dev

firms All firms

Less than 5

5-20

20-200

1027

380

342

305

0.22

0.19

0.23

0.25

0.18

0.17

0.17

0.19

Number of

Ratio

firms

mean

579

0.27

0.17

215

192

0.24

0.28

0.27

Notes: Asterisks indicate significance: *** p < 0.01, ** p < 0.05, * p < 0.10.

St.dev

0.19

0.19

0.19

0.19

0.05

0.05

0.05

0.02

1.14***

4.65***

(0.00)

(0.00)

1.17***

3.05***

(0.00)

(0.00)

1.28***

3.19***

(0.00)

(0.00)

1.04***

1.23

(0.00)

(0.23)

Table 3 Agency costs, management structure Single in-charge person( Panel B)

Number of firms

Ratio mean

Non-single in-charge person (Panel B)

St.dev

Number

of

Ratio mean

St.dev

0.23

0.18

Difference

F-test for

T-test for differences in means

0.01

1.02***

0.86

(0.00)

(0.39)

1.31***

0.08

(0.00)

(0.93)

1.11***

0.71

(0.00)

(0.48)

1.21***

1.75*

(0.00)

(0.08)

1.06***

2.14**

(0.00)

(0.03)

1.23***

0.51

firms Panel A All firms

Less than 5

5-20

20-200

899

337

317

245

0.24

0.21

0.25

0.25

0.18

0.17

0.18

0.20

707

214

240

252

0.21

0.24

0.23

0.19

0.17

0.18

0.00

0.01

0.02

Panel B All family firms

Less than 5

578

219

0.23

0.19

0.18

0.16

450

161

0.21

0.18

0.17

0.18

0.02

-0.01

5-20

199

20-200

160

0.23

0.29

0.16

0.20

143

145

0.22

0.22

0.17

0.17

0.01

0.07

(0.00)

(0.61)

1.10***

0.27

(0.00)

(0.79)

1.53***

3.26***

(0.00)

(0.00)

1.02***

1.22

(0.00)

(0.22)

1.47**

1.39

(0.05)

(0.17)

1.43**

0.94

(0.05)

(0.35)

1.08

1.08

(>0.10)

(0.28)

Panel C All

non-family

321

0.26

0.19

257

0.28

0.19

-0.02

firms Less than 5

5-20

20-200

118

118

85

0.23

0.29

0.26

0.18

0.20

0.19

53

97

107

0.27

0.26

0.29

Notes: Asterisks indicate significance: *** p < 0.01, ** p < 0.05, * p < 0.10.

0.22

0.17

0.19

-0.04

-0.03

-0.03

Table 4 Determinants of agency costs for all firms Dependent variable

Expense ratio (A)

Expense ratio (B)

Expense ratio (C)

Expense ratio (D)

Coef

Sed.err

P value

Coef

Sed.err

P value

Coef

Sed.err

P value

Coef

Sed.err

P value

Intercept

0.67***

0.04

0.00

0.63***

0.04

0.00

0.67***

0.04

0.00

0.69***

0.05

0.00

OWNF

-0.04***

0.01

0.00

-0.04***

0.01

0.00

-0.06***

0.01

0.00

0.01

0.08

0.02

0.01

0.01

0.00

0.01

0.02

Panel A All firms

(-0.03)

(-0.03)

SING

0.00

0.01

0.59

0.00

(-0.06) 0.01

0.68

-0.03* (-0.02)

OWNF×SING

0.04** (0.04)

SALE

-0.05***

0.01

0.00

(-0.05) PLAN

0.02**

-0.05***

0.01

0.00

(-0.05) 0.01

0.02

(0.02)

0.02**

-0.06***

0.01

0.00

(-0.05) 0.01

0.01

(0.02)

0.02**

-0.06*** (-0.05)

0.01

0.01

(0.02)

0.02** (0.02)

PROF

-0.00

0.01

0.88

-0.00

0.01

0.91

-0.00

0.01

0.88

0.00

0.01

0.92

GROW

0.00

0.01

0.77

0.00

0.01

0.79

0.00

0.01

0.79

0.00

0.01

0.83

AGE01

-0.01

0.01

0.33

-0.01

0.01

0.44

-0.01

0.01

0.34

-0.01

0.01

0.33

AGE02

0.00

0.01

0.91

-0.00

0.01

0.81

0.00

0.01

0.91

0.00

0.01

0.91

AGE03

-0.00

0.01

0.69

-0.01

0.01

0.34

-0.00

0.01

0.69

-0.00

0.01

0.72

IND01

-0.14***

0.02

0.00

-0.15***

0.02

0.00

-0.14***

0.02

0.00

-0.14***

0.02

0.00

(-0.13) IND02

-0.05**

(-0.13) 0.03

0.05

(-0.05) IND03

-0.06***

-0.06**

(-0.13) 0.03

0.04

(-0.05) 0.02

0.00

(-0.06)

-0.07***

-0.05*

(-0.13) 0.03

0.05

(-0.05) 0.02

0.00

(-0.06)

-0.07***

-0.06**

0.03

0.04

0.02

0.00

(-0.05) 0.02

0.00

(-0.06)

-0.07*** (-0.06)

IND04

-0.03

0.02

0.31

-0.03

0.02

0.30

-0.02

0.02

0.31

-0.02

0.02

0.31

IND05

-0.14***

0.02

0.00

-0.14***

0.02

0.00

-0.14***

0.02

0.00

-0.14***

0.02

0.00

0.02

0.00

0.02

0.00

0.02

0.00

0.03

0.00

0.02

0.00

0.02

0.10

(-0.13) IND06

-0.11***

(-0.13) 0.02

0.00

(-0.10) IND07

-0.07***

-0.06**

0.02

0.00

-0.07***

0.02

0.01

0.10***

-0.08***

-0.06***

0.03

0.00

-0.07**

0.02

0.00

0.00

(0.09)

0.11***

0.02

0.00

0.13

-0.03

0.00

-0.07***

-0.06**

0.03

0.01

-0.07***

0.02

0.00

0.00

0.10***

0.02

0.01

0.17

-0.03

-0.06*** (-0.05)

0.03

0.00

-0.08*** (-0.07)

0.02

0.00

(0.09) 0.02

-0.08*** (-0.07)

(-0.07) 0.02

-0.11*** (-0.10)

(-0.05)

(0.10) 0.02

0.02

(-0.07)

(-0.06) 0.02

-0.11***

(-0.13)

(-0.10)

(-0.06)

(-0.07) IND10

0.00

(-0.07)

(-0.05) IND09

0.02

(-0.10)

(-0.07) IND08

-0.11***

(-0.13)

0.10*** (0.09)

IND11

-0.04

0.02

0.13

-0.04

Obs.

1616

1616

1616

1616

Likelihood

604.90

597.43

604.98

608.03

Notes: Asterisks indicate significance: *** p < 0.01, ** p < 0.05, * p < 0.10, marginal effects in parentheses for significant coefficients only.

Table 5 Determinants of agency costs for different size groups Dependent variable

Number

Expense ratio (A)

Expense ratio (B)

Expense ratio (C)

Expense ratio (D)

Coef

Sed.err

P value

Coef

Sed.err

P value

Coef

Sed.err

P value

Coef

Sed.err

P value

Intercept

0.94***

0.08

0.00

0.94***

0.08

0.00

0.95***

0.08

0.00

0.99***

0.09

0.00

OWNF

-0.03**

0.02

0.03

-0.03**

0.02

0.03

-0.07***

0.03

0.00

0.03

0.04

0.03

0.06

0.02

0.00

0.01

0.07

Panel A less than 5

554

-0.03

-0.03

SING

-0.01

0.01

0.58

-0.01

-0.06 0.01

0.41

-0.06** -0.04

OWNF×SING

0.06* 0.05

SALE

-0.12***

0.02

0.00

-0.10 PLAN

0.03* 0.02

-0.12***

0.02

0.00

-0.11 0.02

0.06

0.03* 0.02

-0.12***

0.02

0.00

-0.11 0.02

0.08

0.03* 0.02

-0.12*** -0.11

0.02

0.07

0.03* 0.02

PROF

-0.00

0.02

0.94

-0.00

0.02

0.97

-0.01

0.01

0.92

0.00

0.02

0.97

GROW

0.02

0.02

0.33

0.01

0.02

0.39

0.01

0.02

0.31

0.02

0.02

0.34

AGE01

-0.03*

0.02

0.08

-0.03

0.02

0.10

-0.03*

0.02

0.07

-0.03*

0.02

0.06

-0.03

-0.03

-0.03

AGE02

-0.02

0.02

0.39

-0.02

0.02

0.33

-0.02

0.02

0.37

-0.02

0.02

0.36

AGE03

-0.02

0.02

0.30

-0.03

0.02

0.19

-0.02

0.02

0.29

-0.02

0.02

0.28

Intercept

1.20***

0.08

0.00

1.17***

0.08

0.00

1.21***

0.08

0.00

1.19***

0.08

0.00

OWNF

-0.03***

0.01

0.00

-0.03***

0.01

0.00

-0.03*

0.02

0.09

0.01

0.02

0.71

-0.01

0.03

0.67

-0.14***

0.01

0.00

0.01

0.92

Panel B 5-20

559

-0.03

-0.03

SING

0.00

0.01

0.89

0.00

-0.02 0.01

0.95

OWNF×SING

SALE

-0.14***

0.01

0.00

-0.13 PLAN

0.00

-0.14***

0.01

0.00

-0.13 0.01

0.94

0.00

-0.14***

0.01

0.00

-0.13 0.01

0.85

0.00

-0.13 0.01

0.93

0.00

PROF

-0.00

0.01

0.89

-0.00

0.01

0.89

-0.00

0.01

0.89

-0.00

0.01

0.88

GROW

-0.04***

0.01

0.00

-0.04***

0.01

0.00

-0.04***

0.01

0.00

-0.04***

0.01

0.00

-0.04

-0.04

-0.04

-0.04

AGE01

-0.01

0.02

0.55

-0.01

0.02

0.58

-0.01

0.02

0.54

-0.01

0.02

0.55

AGE02

-0.02

0.02

0.32

-0.02

0.02

0.17

-0.02

0.02

0.32

-0.02

0.02

0.32

AGE03

-0.03*

0.02

0.06

-0.04**

0.02

0.02

-0.03*

0.02

0.06

-0.04*

0.01

0.06

-0.03

Panel C 20-200 Intercept OWNF

-0.04

-0.03

-0.03

498 1.25***

0.09

0.00

-0.02

0.02

0.13

1.19***

0.09

0.00

1.23***

0.09

0.00

1.25***

0.09

0.00

-0.02

0.02

0.12

-0.06***

0.02

0.00

-0.02

0.02

0.49

0.07**

0.03

0.02

0.01

0.00

-0.05 SING

0.03*

0.014

0.06

0.02

0.02*

0.01

0.06

0.02

OWNF×SING

0.06 SALE

-0.14*** -0.13

0.01

0.00

-0.14*** -0.13

0.01

0.00

-0.14*** -0.13

0.01

0.00

-0.14*** -0.13

PLAN

-0.01

0.02

0.55

-0.01

0.02

0.58

-0.01

0.02

0.60

-0.01

0.02

0.44

PROF

-0.00

0.02

0.83

-0.00

0.02

0.76

-0.01

0.02

0.74

-0.00

0.02

0.86

GROW

0.03*

0.02

0.06

0.03*

0.02

0.07

0.03*

0.02

0.07

0.03*

0.02

0.08

0.03

0.02

0.02

0.02

AGE01

0.01

0.02

0.54

0.02

0.02

0.40

0.01

0.02

0.44

0.01

0.02

0.47

AGE02

0.00

0.02

0.96

-0.01

0.02

0.82

-0.01

0.02

0.93

-0.00

0.02

0.91

AGE03

-0.01

0.02

0.48

-0.02

0.02

0.36

-0.01

0.02

0.52

-0.01

0.02

0.52

Notes: Asterisks indicate significance: *** p < 0.01, ** p < 0.05, * p < 0.10, marginal effects in parentheses for significant coefficients only.

Table 6 Determinants of bank debt unavailability Dependent variable

All firms

Coef

With 5 employees

Sed.err Coef

Intercept

Sed.err

1.19

3.33

-0.46

2.83

3.47***

1.04

-0.61

1.42

0.49

0.89

-0.06

1.35

-1.15

1.28

0.89

1.64

1.54

1.40

0.48

SING

-0.65

0.50

-0.26 3.50***

OWNF×SING

1.27** 0.11

0.61

-0.26 4.22*** 0.31

0.18

0.33

-0.24

0.32

Sed.err Coef

3.18

-0.70

0.09

Sed.err Coef

With 20-200 employees

4.46

OWNF

FOCUF

With 5-20 employees

1.18

1.40 0.80

1.61

0.88

-0.38

0.65

-0.06

0.82

-0.18

0.82

0.57

0.61

FOCUC -0.25

0.33

-1.39

0.90

-1.20

0.81

0.36

0.63

0.50

0.32

0.84

-0.67

0.63

0.79

1.15* 0.09

0.60

0.78

1.56** 0.08 1.41* 0.07 -1.21

0.74

0.35

2.20*** 0.16 1.38* 0.10 -0.94

0.89

0.60

0.28

0.76

0.65

0.19

0.71

1.08* -0.08 0.04

0.54

-1.24

0.84

-0.48

0.79

0.01

0.54

-0.70

0.71

0.75

0.79

0.82

0.54

-1.40* -0.07 -0.74

0.49

-0.43

0.41

FOCUO FOCUQ

0.92*** 0.08 -0.75**

FOCUI

0.33

0.84

FOCUH -0.06 0.09

GROW PROF

-0.20

0.32

PLAN

-0.18

SALE

0.16

AGE01

-0.41*** -0.04 0.34

0.37

0.51

0.84

0.27

0.98

0.19

0.83

AGE02

0.11

0.40

0.61

1.08

-0.05

0.92

0.14

0.71

AGE03

-0.06

0.43

1.10

0.06

0.95

-0.00

0.71

-1.01**

0.45

0.90

-0.53

0.89

1.50** -0.11

0.78

ASSI

0.31

-0.83

0.37 -0.10

-0.09 Obs. Likelihood ratio test R-squared

630 40.67*** 0.11

176 22.92 0.21

Notes: Asterisks indicate significance: *** p < 0.01, in parentheses for significant coefficients only.

206 30.89** 0.30 **

248 26.32* 0.21

p < 0.05, * p < 0.10, marginal effects

Table 7. Impact of agency costs Dependent variable Intercept OWNF

Cost ratio (A) Coef. 0.73*** -0.06*** -0.05 -0.02

SING

HOME

-0.03** -0.03

HOME OWNF OWNF×SING

0.04** 0.03 0.02

Cost ratio ( B)

Std. err.

P value

Coef.

Std. err.

P value

0.04 0.01

0.00 0.00

0.04 0.01

0.00 0.00

0.01

0.11

0.72*** -0.05*** -0.05 -0.02* -0.02

0.01

0.07

0.01

0.01

-0.01

0.02

0.64

0.01

-0.02 0.04***

0.02 0.02

0.24 0.00

0.04 0.02** 0.01

0.01

0.02

0.01 0.01 0.01

0.98 0.83 0.00

0.01

0.27

0.02 0.01

0.02

0.01 0.01 0.01

0.95 0.81 0.00

0.01

0.27

0.00 0.00 -0.06*** -0.06 -0.01

PLAN PROF GROW SALE AGE01

0.00 0.00 -0.06*** -0.06 0.01

AGE02

0.00

0.01

0.98

-0.00

0.01

0.99

AGE03

-0.00

0.01

0.71

-0.00

0.01

0.73

-0.13***

0.01

0.00

-0.13***

0.01

0.00

0.02

0.08

0.02

0.08

0.02

0.00

0.01

0.00

0.02 0.02

0.62 0.00

0.02 0.02

0.58 0.00

0.02

0.00

0.02

0.00

0.02

0.00

0.02

0.02

0.02

0.00

0.02

0.00

0.02

0.11

IND01 IND02 IND03 IND04 IND05 IND06

-0.11 -0.04* -0.04 -0.06*** -0.06 -0.01 -0.13*** -0.12 -0.10*** -0.09

IND07

-0.07***

-0.09

0.02

0.00

-0.07 IND08 IND09 IND10 IND11 Obs

-0.05** -0.04 -0.07*** -0.06 0.10*** 0.09 -0.03

-0.11 -0.04* -0.04 -0.06*** -0.06 -0.01 -0.13*** -0.12 -0.10*** -0.07*** -0.07

0.02

0.02

0.02

0.00

0.02

0.00

0.02

0.11

Notes: Asterisks indicate significance: *** p < 0.01, in parentheses for significant coefficients only.

-0.05** -0.04 -0.07*** -0.06 0.10*** 0.09 -0.03 **

p < 0.05, * p < 0.10, marginal effects

Personal&Other Services Cultural&Recreational Services Property&Business Services Communication Services Transport Accommodation&Restaurants Retail Trade Wholesale Trade Construction Manufacturing Mining Agriculture 0

0.05

0.1

0.15

0.2

0.25

Figure 1. Expense-to-sales ratio by two-digit ANZSIC industry

0.3

0.35

0.4

0.45