2.2.1 Firm characteristics
Firm size is considered as an inverse proxy of bankruptcy costs. The TOT predicts a positive relationship between firm size and leverage, because size is assumed as a proxy for earnings volatility and larger firms are generally more diversified and show less volatility (Fama and French
2002). Less volatile earnings reduce indirect bankruptcy costs such that firms can take on more debt. The POT also predicts a positive relationship between firm size and leverage, because more diversification and less volatile earnings mitigate problems of asymmetric information. This decreases the costs of debt compared with other sources of finance. Several empirical studies find a positive relationship for both large firms and SMEs (Van Dijk
1997; De Jong
1999; Fama and French
2002; Michaelas et al.
1999; Cassar and Holmes
2003; Sogorb-Mira
2005; Hall et al.
2004). Our first empirical proposition (or hypothesis) based on the TOT and POT is:
The effect of firm size on short-term debt has been empirically verified by several authors. Michaelas et al. (
1999) and Hall et al. (
2004) report a negative effect, even though the effect on total leverage is positive. Sogorb-Mira (
2005) finds similar effects for total debt but no significant effects of firm size on short-term debt. Ortiz-Molina and Penas (
2006) find that size increases the maturity of lines of credit. The high business risk and informational opacity increase if firms are smaller. Small firms then have to rely more on short-term debt. We therefore formulate the following two propositions based upon previous empirical work:
The firm’s
asset structure is a second factor determining capital structure. Asset tangibility is expected to be positively correlated with debt, as it provides collateral. Collateral reduces agency problems with debtholders and reduces bankruptcy costs and credit risk. Therefore, the TOT predicts a positive relationship between collateral and leverage. Collateral also mitigates information asymmetry problems such that also the POT implies a positive correspondence. De Jong (
1999) confirms the positive relationship between tangible assets and leverage, whereas Titman and Wessels (
1988) report a negative, though not statistically significant, relationship. The information asymmetry argument is particularly relevant for SMEs, as they are more opaque than large firms. Small firms often do not have to provide (audited) financial statements or do not issue traded securities. For these reasons, collateralized lending is important for SMEs. Michaelas et al. (
1999) and Sogorb-Mira (
2005) find a positive effect of tangible assets on leverage for SMEs. Hall et al. (
2004) report a small positive relationship for Dutch SMEs. Therefore, our proposition regarding asset structure is:
Collateral may affect short-term and long-term debt differently. Previous work documents a negative relationship for short-term debt and a positive one for long-term debt (Van der Wijst and Thurik
1993; Michaelas et al.
1999; Hall et al.
2004; Sogorb-Mira
2005). Ortiz-Molina and Penas (
2006) argue that collateral and maturity are substitutes in reducing agency problems. We therefore supplement proposition 2 with:
Liquidity is a second dimension of a firm’s asset structure. Illiquid firms are restricted in attracting debt, as bankruptcy costs are high. The TOT then predicts a positive relationship between liquidity and leverage. We employ “net debtors” as a proxy for liquidity. It is particularly relevant for SMEs because small firms generally put less pressure on collecting payments from customers. Late payments are often financed by trade credit. In the pecking order, trade credit may be on top of the preference list. Suppliers grant trade credit as they may have superior information compared with banks regarding their customers’ liquidity. This alleviates problems of asymmetric information (Berger and Udell
2006). Of course, firms cannot delay late payments to creditors beyond a certain point. It can therefore be expected that short-term debt increases if a firm suffers from late payments. Michaelas et al. (
1999) report positive coefficients of net debtors on short-term and long-term debt, although the effect on long-term debt was negligible. These results give rise to the next propositions:
Profitability is another determinant of capital structure. The free cash flow theory of Jensen (
1986) states that more debt disciplines the manager if profits increase. A positive relationship between debt and profitability is then expected. The POT predicts the opposite effect of profitability on leverage. Retained earnings are on top of the preference list to finance investments, so higher profits reduce the necessity to raise debt. Studies using large-company data find a negative relationship between debt and profitability (Titman and Wessels
1988; Van Dijk
1997; Fama and French
2002). The POT also applies to SMEs, whereas agency conflicts between managers and shareholders should be less relevant (see also Ang
1992). Studies on SMEs also find a negative impact of profitability on debt (Van der Wijst and Thurik
1993; Michaelas et al.
1999; Sogorb-Mira
2005). Therefore, our next proposition is:
Profitability may differentially impact short-term and long-term debt. Michaelas et al. (
1999) find a larger effect of profitability on long-term debt compared with short-term debt. They argue that SMEs prefer short-term financing and that long-term debt will be reduced if internal funding is available. On the other hand, short-term debt can be amortized more easily and carries higher interest rates. This suggests a stronger influence on short-term debt, which is validated by several SME studies (Van der Wijst and Thurik
1993; Cassar and Holmes
2003; Sogorb-Mira
2005). Therefore, proposition P4 is supplemented as follows:
Agency problems between managers and debtholders are particularly relevant for firms with
growth opportunities. Myers (
1977), for example, argues that managers underinvest because equity holders may not earn a profit on some projects with positive net present value (NPV) if interest payments are high. The TOT predicts a negative relationship between growth opportunities and leverage. Myers (
1977), however, models that short-term debt could overcome the underinvestment problem and therefore is positively affected by growth opportunities. According to the POT, growth opportunities and leverage are expected to be positively related. Firms with growth opportunities are more likely to raise new funds than are firms without growth possibilities (De Jong
1999). Growth opportunities for larger or publicly listed firms are proxied by research and development (R&D) expenses, the market-to-book ratio or intangible assets. Titman and Wessels (
1988), Fama and French (
2002), and Graham and Harvey (
2001) report a negative relationship between their proxies of growth opportunities and leverage. Another explanation for a negative link is that assets needed for future growth are poor collateral. Studies on SMEs find evidence for a positive relationship of leverage with growth opportunities. Growth opportunities in these studies are proxied by intangible assets or growth in sales or assets. Sogorb-Mira (
2005) reports a stronger positive effect on long-term debt, but a negative impact on short-term debt. Michaelas et al. (
1999) find a positive impact on short-term debt.
We also briefly discuss expected impacts from taxation. Modigliani and Miller (
1963) argue that firms prefer debt financing because of the tax shield, so a positive relationship between the tax rate and leverage can be expected. Studies focusing on SMEs, however, find a negative relationship for SMEs and argue that the tax status of a company is not informative. Sogorb-Mira (
2005) show that SME managers choose other instruments to lower their tax payments, whereas Jordan et al. (
1998) claim that taxes lower retained earnings. The total tax burden of a firm is not solely determined by the tax rate but by taxable income as well. Some authors argue that this is even more important than testing the tax rate itself (Van Dijk
1997). Interest payments reduce taxable income, but other items can do the same. These nondebt tax shields could substitute for the tax shield of debt (Titman and Wessels
1988). Hence, a negative relationship with debt ratio is expected. In the empirical section below, we also test for tax effects, but we do not formulate an explicit proposition.
2.2.2 Industry characteristics
We now turn to formulating explicit propositions on industry effects. We first focus on
inter-industry effects. The TOT posits that firms target an optimal leverage ratio, and this optimal leverage may differ across industries. This can be captured by industry fixed effects. The POT, in contrast, does not deliver a clear prediction with respect to industry fixed effects. However, to the extent that there are unobservable factors that are correlated within an industry, then also industry fixed effects could be significant (see also Cole
2008). Finally, the TOT and the POT could also be of differential importance across industries. For example, the degree to which propositions P1–P5, particularly propositions P4 and P5, apply may be different. The empirical investigation of inter-industry effects deals with the question of the extent to which capital structure variation between firms is explained by industry characteristics compared with firm characteristics. Balakrishnan and Fox (
1993), for example, find that 52% of capital structure variation is explained by firm effects and 11% by inter-industry differences. MacKay and Phillips (
2005) report similar percentages for firm and inter-industry effects. Michaelas et al. (
1999) use industry fixed effects to test whether industry effects have an influence on SME capital structure. They find significant industry fixed effects, but the impacts are primarily on short-term debt. We therefore formulate the following two propositions:
Next to heterogeneity across industries, leverage could also exhibit
intra-industry heterogeneity. This may be driven, for example, by industry competition, the degree of agency conflicts, and the heterogeneity in employed technology. The degree of competition, for example, determines whether a firm is close to the optimal degree of leverage within an industry. In particular, in industries with low competition, firms face less pressure to be close to the optimal target, whereas in industries with high competition, firms can only survive by choosing the optimal degree of leverage in order to minimize costs (Leibenstein
1966; MacKay and Phillips
2005). Agency conflicts resulting from conflicting objectives between shareholders and managers may determine firms’ capital structures; for example, managers could choose too low debt ratios in order to protect their human capital (Fama
1980) or to avoid pressure from interest payments (Jensen
1986). Managers may take on too much leverage in order to signal their quality or to decrease takeover attempts (e.g., Harris and Raviv
1991 or Stulz
1988). We then expect that, in industries without agency conflicts, there should be less leverage dispersion. Finally, Maksimovic and Zechner (
1991) model that industries with more technological dispersion exhibit more capital structure dispersion. We do not formulate a hypothesis on intra-industry effects, as our dataset only contains limited information on competition, technological dispersion, or agency problems within an industry.